diff --git a/CHANGELOG b/CHANGELOG
index 0d4062fcdee16e40e47a1d7804a638e453548842..7b6e2cda2b9abd775c471e3ab7e0e81625592e9d 100755
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -1,3 +1,10 @@
+2.1.0
+    2020/03/05
+    - Rotated nested grid
+    - GFAS hourly working
+    - Compression level added to the configuration file
+    - Added regional emission inventories (Is mandatory to add 'coverage' column with 'global' or 'regional' in the 'cross_table' file)
+
 2.0.3
     2020/02/07
     - Corrected bug on grid calculation for some versions of numpy.
diff --git a/conf/EI_configuration.csv b/conf/EI_configuration.csv
index 34a0ac3dd3047e08686571f2f6c9fcf26f31b233..fade8b454a1e4e45582235615aaa225dbabb770f 100755
--- a/conf/EI_configuration.csv
+++ b/conf/EI_configuration.csv
@@ -1,98 +1,126 @@
-ei;sector;ref_year;active;factor_mask;regrid_mask;pollutants;path;frequency;source_type;p_vertical;p_month;p_week;p_hour;p_speciation;p_day
-GFASv12;;2015;1;;;co,nox_no,pm25,oc,bc,so2,ch3oh,c2h5oh,c3h8,c2h4,c3h6,c5h8,terpenes,hialkenes,hialkanes,ch2o,c2h4o,c3h6o,nh3,c2h6s,c2h6,c7h8,c6h6,c8h10,c4h8,c5h10,c6h12,c8h16,c4h10,c5h12,c6h14,c7h16;<data_path>/ecmwf/gfas/daily_mean;daily;point;method=sovief,approach=uniform;;;H001;E001;
-HTAPv2;energy;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;V001;;W002;H002;E002;
-HTAPv2;industry;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;V002;;W003;H004;E003;
-HTAPv2;residential;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;;;W003;H003;E004;
-HTAPv2;transport;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/monthly_mean;monthly;area;;;W005;weekday=H006, saturday=H009, sunday=H010;E005;
-HTAPv2;agriculture;2010;1;;;nh3;<data_path>/jrc/htapv2/monthly_mean;monthly;area;;;W001;H007;E006;
-HTAPv2;air_lto;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;V003;M001;W001;H001;E007;
-HTAPv2;air_cds;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;V004;M001;W001;H001;E007;
-HTAPv2;air_crs;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;V005;M001;W001;H001;E007;
-HTAPv2;ships;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/yearly_mean;yearly;area;;M001;W001;H001;E008;
-wiedinmyer;;2010;1;;;bc,c2h2,c2h4,c3h6,c6h6,ch2o,ch3cooh,ch3oh,co,hcl,nh3,nox_no,oc,pm10,pm25,so2;<data_path>/ucar/wiedinmyer/yearly_mean;yearly;area;;M001;W001;H008;E009;
-TNO_MACC-III;snap1;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;V001;M002;W002;H002;E010;
-TNO_MACC-III;snap2;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M003;W003;H003;E011;
-TNO_MACC-III;snap34;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;V002;M004;W003;H004;E012;
-TNO_MACC-III;snap5;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc12,voc13,voc14,voc15;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M005;W001;H001;E013;
-TNO_MACC-III;snap6;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc06,voc14,voc15,voc17,voc18,voc19,voc20,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M006;W004;H005;E014;
-TNO_MACC-III;snap71;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E015;
-TNO_MACC-III;snap72;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E016;
-TNO_MACC-III;snap73;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc07,voc08,voc12,voc17,voc21,voc22;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E017;
-TNO_MACC-III;snap74;2011;0;;;voc03,voc04,voc05,voc06,voc12,voc13,voc14,voc15;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E018;
-TNO_MACC-III;snap75;2011;0;;;pm10,pm25;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E019;
-TNO_MACC-III;snap8;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M008;W001;H001;E020;
-TNO_MACC-III;snap9;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M001;W001;H001;E021;
-TNO_MACC-III;snap10;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;;M009;W001;H007;E022;
-ECLIPSEv5a;agriculture;2010;0;;;pm10,pm25,nh3;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;;W001;H007;E023;
-ECLIPSEv5a;flaring;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/yearly_mean;yearly;area;V006;M001;W001;H001;E024;
-ECLIPSEv5a;energy;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;V001;;W002;H002;E025;
-ECLIPSEv5a;industry;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;V002;;W003;H004;E026;
-ECLIPSEv5a;transport;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;;W005;weekday=H006, saturday=H009, sunday=H010;E027;
-ECLIPSEv5a;residential;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;;W003;H003;E028;
-ECLIPSEv5a;waste;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;;W001;H001;E029;
-ECLIPSEv5.a;solvent;2010;0;;;nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;M006;W004;H005;E030;
-ECLIPSEv5a;agricultural_waste;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;;;W001;H007;E031;
-ECLIPSEv5a;ships;2010;0;;;co,nox_no2,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/yearly_mean;yearly;area;;M001;W001;H001;E032;
-EDGARv432_AP;ags;2010;0;;;nox_no2,nh3,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H007;E033;
-EDGARv432_AP;awb;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H007;E034;
-EDGARv432_AP;che;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E035;
-EDGARv432_AP;ene;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V001;;W002;H002;E036;
-EDGARv432_AP;foo_pap;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E037;
-EDGARv432_AP;fff;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V006;;W001;H001;E038;
-EDGARv432_AP;ind;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E039;
-EDGARv432_AP;iro;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E040;
-EDGARv432_AP;mnm;2010;0;;;nox_no2,nh3,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H007;E041;
-EDGARv432_AP;neu;2010;0;;;pm10;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E042;
-EDGARv432_AP;nfe;2010;0;;;nox_no2,co,so2,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E043;
-EDGARv432_AP;nmm;2010;0;;;co,so2,nh3,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V002;;W003;H004;E044;
-EDGARv432_AP;pro;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E045;
-EDGARv432_AP;pru_sol;2010;0;;;nh3,nmvoc,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W004;H005;E046;
-EDGARv432_AP;rco;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W003;H003;E047;
-EDGARv432_AP;ref_trf;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V006;;W001;H001;E048;
-EDGARv432_AP;swd_inc;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V006;;W001;H001;E049;
-EDGARv432_AP;swd_ldf;2010;0;;;nh3,nmvoc,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E050;
-EDGARv432_AP;tnr_aviation_cds;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V004;;W001;H001;E051;
-EDGARv432_AP;tnr_aviation_crs;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V005;;W001;H001;E052;
-EDGARv432_AP;tnr_aviation_lto;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;V003;;W001;H001;E053;
-EDGARv432_AP;tnr_other;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E054;
-EDGARv432_AP;tnr_ship;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E055;
-EDGARv432_AP;tro;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W005;weekday=H006, saturday=H009, sunday=H010;E056;
-EDGARv432_AP;wwt;2010;0;;;nh3,nmvoc;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;;;W001;H001;E057;
-EDGARv432_VOC;awb;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H007;E058;
-EDGARv432_VOC;ene;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V001;;W002;H002;E059;
-EDGARv432_VOC;fff;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc8,voc10,voc12,voc13,voc14,voc15,voc17,voc18,voc19,voc20;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V006;;W001;H001;E060;
-EDGARv432_VOC;ind;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V002;;W003;H004;E061;
-EDGARv432_VOC;ppa;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E062;
-EDGARv432_VOC;pro;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E063;
-EDGARv432_VOC;rco;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W003;H003;E064;
-EDGARv432_VOC;ref;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc12,voc13,voc14,voc15,voc16,voc17,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V006;;W003;H004;E065;
-EDGARv432_VOC;swd;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc17,voc18,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E066;
-EDGARv432_VOC;tnr_aviation_cds;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V004;;W001;H001;E067;
-EDGARv432_VOC;tnr_aviation_crs;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V005;;W001;H001;E068;
-EDGARv432_VOC;tnr_aviation_lto;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;V003;;W001;H001;E069;
-EDGARv432_VOC;tnr_other;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E070;
-EDGARv432_VOC;tnr_ship;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E071;
-EDGARv432_VOC;trf;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W001;H001;E072;
-EDGARv432_VOC;tro;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;;;W005;weekday=H006, saturday=H009, sunday=H010;E073;
-EMEP;a_publicpower;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;V001;M002;W002;H002;E074;
-EMEP;b_industry;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;V002;M004;W003;H004;E075;
-EMEP;c_otherstationarycomb;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M003;W003;H003;E076;
-EMEP;d_fugitive;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M005;W001;H001;E077;
-EMEP;e_solvents;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M006;W004;H005;E078;
-EMEP;f_roadtransport;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M007;W005;weekday=H006, saturday=H009, sunday=H010;E079;
-EMEP;g_shipping;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M001;W001;H001;E080;
-EMEP;h_aviation;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;V003;M001;W001;H001;E081;
-EMEP;i_offroad;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M001;W001;H001;E082;
-EMEP;j_waste;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M001;W001;H001;E083;
-EMEP;k_agrilivestock;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M009;W001;H007;E084;
-EMEP;l_agriother;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;;M009;W001;H007;E085;
-carn;;2015;1;;;so2;<data_path>/mtu/carnetal/yearly_mean;yearly;point;;M001;W001;H001;E086;
-CEDS;agriculture;2014;0;;;nox_no2,nh3;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W001;H007;E087;
-CEDS;energy;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;V001;;W002;H002;E088;
-CEDS;industrial;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;V002;;W003;H004;E089;
-CEDS;transportation;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W005;weekday=H006, saturday=H009, sunday=H010;E090;
-CEDS;residential;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W003;H003;E091;
-CEDS;solvent;2014;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W004;H005;E092;
-CEDS;waste;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W001;H001;E093;
-CEDS;shipping;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W001;H001;E094;
-CEDS;air;2014;0;;;co,nox_no2,oc,bc,so2,nh3,nmvoc;<data_path>/jgcri/ceds/monthly_mean;monthly;area;;;W001;H001;E095;
\ No newline at end of file
+ei;sector;ref_year;active;factor_mask;regrid_mask;pollutants;path;frequency;source_type;coverage;p_vertical;p_month;p_week;p_day;p_hour;p_speciation;comment
+GFASv12;;2015;1;;;co,nox_no,pm25,oc,bc,so2,ch3oh,c2h5oh,c3h8,c2h4,c3h6,c5h8,terpenes,hialkenes,hialkanes,ch2o,c2h4o,c3h6o,nh3,c2h6s,c2h6,c7h8,c6h6,c8h10,c4h8,c5h10,c6h12,c8h16,c4h10,c5h12,c6h14,c7h16;<data_path>/ecmwf/gfas/daily_mean;daily;point;global;method=sovief,approach=uniform;;;;H001;E001;
+HTAPv2;energy;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;global;V001;;W002;;H002;E002;
+HTAPv2;industry;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;global;V002;;W003;;H004;E003;
+HTAPv2;residential;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/monthly_mean;monthly;area;global;;;W003;;H003;E004;
+HTAPv2;transport;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,nh3,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E005;
+HTAPv2;agriculture;2010;1;;;nh3;<data_path>/jrc/htapv2/monthly_mean;monthly;area;global;;;W001;;H007;E006;
+HTAPv2;air_lto;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;global;V003;M001;W001;;H001;E007;
+HTAPv2;air_cds;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;global;V004;M001;W001;;H001;E007;
+HTAPv2;air_crs;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/jrc/htapv2/yearly_mean;yearly;area;global;V005;M001;W001;;H001;E007;
+HTAPv2;ships;2010;1;;;co,nox_no2,pm10,pm25,oc,bc,so2,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/htapv2/yearly_mean;yearly;area;global;;M001;W001;;H001;E008;
+wiedinmyer;;2010;1;;;bc,c2h2,c2h4,c3h6,c6h6,ch2o,ch3cooh,ch3oh,co,hcl,nh3,nox_no,oc,pm10,pm25,so2;<data_path>/ucar/wiedinmyer/yearly_mean;yearly;area;global;;M001;W001;;H008;E009;
+TNO_MACC-III;snap1;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;V001;M002;W002;;H002;E010;
+TNO_MACC-III;snap2;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M003;W003;;H003;E011;
+TNO_MACC-III;snap34;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;V002;M004;W003;;H004;E012;
+TNO_MACC-III;snap5;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc12,voc13,voc14,voc15;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M005;W001;;H001;E013;
+TNO_MACC-III;snap6;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc06,voc14,voc15,voc17,voc18,voc19,voc20,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M006;W004;;H005;E014;
+TNO_MACC-III;snap71;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E015;
+TNO_MACC-III;snap72;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E016;
+TNO_MACC-III;snap73;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc02,voc03,voc07,voc08,voc12,voc17,voc21,voc22;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E017;
+TNO_MACC-III;snap74;2011;0;;;voc03,voc04,voc05,voc06,voc12,voc13,voc14,voc15;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E018;
+TNO_MACC-III;snap75;2011;0;;;pm10,pm25;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E019;
+TNO_MACC-III;snap8;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M008;W001;;H001;E020;
+TNO_MACC-III;snap9;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M001;W001;;H001;E021;
+TNO_MACC-III;snap10;2011;0;;;co,nox_no2,so2,nh3,pm10,pm25;<data_path>/tno/tno_macc_iii/yearly_mean/;yearly;area;regional;;M009;W001;;H007;E022;
+ECLIPSEv5a;agriculture;2010;0;;;pm10,pm25,nh3;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;;W001;;H007;E023;
+ECLIPSEv5a;flaring;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/yearly_mean;yearly;area;global;V006;M001;W001;;H001;E024;
+ECLIPSEv5a;energy;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;V001;;W002;;H002;E025;
+ECLIPSEv5a;industry;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;V002;;W003;;H004;E026;
+ECLIPSEv5a;transport;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E027;
+ECLIPSEv5a;residential;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;;W003;;H003;E028;
+ECLIPSEv5a;waste;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;;W001;;H001;E029;
+ECLIPSEv5a;solvent;2010;0;;;nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;M006;W004;;H005;E030;
+ECLIPSEv5a;agricultural_waste;2010;0;;;co,nox_no2,nh3,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/monthly_mean;monthly;area;global;;;W001;;H007;E031;
+ECLIPSEv5a;ships;2010;0;;;co,nox_no2,pm10,pm25,oc,bc,so2,nmvoc;<data_path>/iiasa/eclipsev5a/yearly_mean;yearly;area;global;;M001;W001;;H001;E032;
+EDGARv432_AP;ags;2010;0;;;nox_no2,nh3,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H007;E033;
+EDGARv432_AP;awb;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H007;E034;
+EDGARv432_AP;che;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E035;
+EDGARv432_AP;ene;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V001;;W002;;H002;E036;
+EDGARv432_AP;foo_pap;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E037;
+EDGARv432_AP;fff;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V006;;W001;;H001;E038;
+EDGARv432_AP;ind;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E039;
+EDGARv432_AP;iro;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E040;
+EDGARv432_AP;mnm;2010;0;;;nox_no2,nh3,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H007;E041;
+EDGARv432_AP;neu;2010;0;;;pm10;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E042;
+EDGARv432_AP;nfe;2010;0;;;nox_no2,co,so2,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E043;
+EDGARv432_AP;nmm;2010;0;;;co,so2,nh3,nmvoc,pm10,bc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V002;;W003;;H004;E044;
+EDGARv432_AP;pro;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E045;
+EDGARv432_AP;pru_sol;2010;0;;;nh3,nmvoc,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W004;;H005;E046;
+EDGARv432_AP;rco;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W003;;H003;E047;
+EDGARv432_AP;ref_trf;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V006;;W001;;H001;E048;
+EDGARv432_AP;swd_inc;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V006;;W001;;H001;E049;
+EDGARv432_AP;swd_ldf;2010;0;;;nh3,nmvoc,pm10,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E050;
+EDGARv432_AP;tnr_aviation_cds;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V004;;W001;;H001;E051;
+EDGARv432_AP;tnr_aviation_crs;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V005;;W001;;H001;E052;
+EDGARv432_AP;tnr_aviation_lto;2010;0;;;nox_no2,co,so2,nmvoc,pm10,bc,oc,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;V003;;W001;;H001;E053;
+EDGARv432_AP;tnr_other;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E054;
+EDGARv432_AP;tnr_ship;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E055;
+EDGARv432_AP;tro;2010;0;;;nox_no2,co,so2,nh3,nmvoc,pm10,bc,oc,pm25_bio,pm25_fossil;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E056;
+EDGARv432_AP;wwt;2010;0;;;nh3,nmvoc;<data_path>/jrc/edgarv432_ap/monthly_mean;monthly;area;global;;;W001;;H001;E057;
+EDGARv432_VOC;awb;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H007;E058;
+EDGARv432_VOC;ene;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V001;;W002;;H002;E059;
+EDGARv432_VOC;fff;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc8,voc10,voc12,voc13,voc14,voc15,voc17,voc18,voc19,voc20;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V006;;W001;;H001;E060;
+EDGARv432_VOC;ind;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V002;;W003;;H004;E061;
+EDGARv432_VOC;ppa;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E062;
+EDGARv432_VOC;pro;2010;0;;;voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E063;
+EDGARv432_VOC;rco;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W003;;H003;E064;
+EDGARv432_VOC;ref;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc12,voc13,voc14,voc15,voc16,voc17,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V006;;W003;;H004;E065;
+EDGARv432_VOC;swd;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc17,voc18,voc19,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E066;
+EDGARv432_VOC;tnr_aviation_cds;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V004;;W001;;H001;E067;
+EDGARv432_VOC;tnr_aviation_crs;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V005;;W001;;H001;E068;
+EDGARv432_VOC;tnr_aviation_lto;2010;0;;;voc1,voc2,voc3,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;V003;;W001;;H001;E069;
+EDGARv432_VOC;tnr_other;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E070;
+EDGARv432_VOC;tnr_ship;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E071;
+EDGARv432_VOC;trf;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W001;;H001;E072;
+EDGARv432_VOC;tro;2010;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc10,voc11,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jrc/edgarv432_voc/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E073;
+EMEP;a_publicpower;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;V001;M002;W002;;H002;E074;
+EMEP;b_industry;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;V002;M004;W003;;H004;E075;
+EMEP;c_otherstationarycomb;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M003;W003;;H003;E076;
+EMEP;d_fugitive;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M005;W001;;H001;E077;
+EMEP;e_solvents;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M006;W004;;H005;E078;
+EMEP;f_roadtransport;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E079;
+EMEP;g_shipping;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M001;W001;;H001;E080;
+EMEP;h_aviation;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;V003;M001;W001;;H001;E081;
+EMEP;i_offroad;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M001;W001;;H001;E082;
+EMEP;j_waste;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M001;W001;;H001;E083;
+EMEP;k_agrilivestock;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M009;W001;;H007;E084;
+EMEP;l_agriother;2015;0;;;co,nox_no2,pm10,pm25,so2,nmvoc,nh3;<data_path>/ceip/emepv18/yearly_mean;yearly;area;regional;;M009;W001;;H007;E085;
+carn;;2015;1;;;so2;<data_path>/mtu/carnetal/yearly_mean;yearly;point;global;;M001;W001;;H001;E086;
+CEDS;agriculture;2014;0;;;nox_no2,nh3;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W001;;H007;E087;
+CEDS;energy;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;V001;;W002;;H002;E088;
+CEDS;industrial;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;V002;;W003;;H004;E089;
+CEDS;transportation;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E090;
+CEDS;residential;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W003;;H003;E091;
+CEDS;solvent;2014;0;;;voc1,voc2,voc3,voc4,voc5,voc6,voc7,voc8,voc9,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W004;;H005;E092;
+CEDS;waste;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W001;;H001;E093;
+CEDS;shipping;2014;0;;;co,nox_no2,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W001;;H001;E094;
+CEDS;air;2014;0;;;co,nox_no2,oc,bc,so2,nh3,nmvoc;<data_path>/jgcri/ceds/monthly_mean;monthly;area;global;;;W001;;H001;E095;
+CAMS-GLOB-ANT_v21;agriculture;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H007;E114;
+CAMS-GLOB-ANT_v21;energy;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;V001;;W002;;H002;E115;
+CAMS-GLOB-ANT_v21;fugitive_fuel;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H001;E116;
+CAMS-GLOB-ANT_v21;industry;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;V002;;W003;;H004;E117;
+CAMS-GLOB-ANT_v21;livestock;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;nox_no,nh3;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H007;E118;
+CAMS-GLOB-ANT_v21;residential;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W003;;H003;E119;
+CAMS-GLOB-ANT_v21;solvents;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H001;E120;
+CAMS-GLOB-ANT_v21;waste;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H001;E121;
+CAMS-GLOB-ANT_v21;non_road_transport;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W001;;H001;E122;
+CAMS-GLOB-ANT_v21;road_transport;2015;0;;+ NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no,oc,bc,so2,nh3,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_glob_antv21/monthly_mean;monthly;area;global;;;W005;;weekday=H006, saturday=H009, sunday=H010;E123;
+CAMS-GLOB-SHIP_v11;;2015;0;;;co,nox_no,oc,ec,so4,ash,so2,nmvoc;<data_path>/ecmwf/cams_glob_shipv11/monthly_mean;monthly;area;global;;;W001;;H001;E111;
+CAMS-GLOB-SOIL_v11;total;2015;0;;;nox_no;<data_path>/ecmwf/cams_glob_soilv11/monthly_mean;monthly;area;global;;;W001;;H007;E112;
+CAMS-GLOB-OCEAN_v11;;2015;0;;;c2h6s;<data_path>/ecmwf/cams_glob_oceanv11/monthly_mean;monthly;area;global;;;W001;;H001;E113;
+CAMS-REG-AP_v221;gnfr_A;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;V001;M002;W002;;H002;E096;
+CAMS-REG-AP_v221;gnfr_B;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;V002;M004;W003;;H004;E097;
+CAMS-REG-AP_v221;gnfr_C;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M003;W003;;H003;E098;
+CAMS-REG-AP_v221;gnfr_D;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc19,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M005;W001;;H001;E099;
+CAMS-REG-AP_v221;gnfr_E;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,voc01,voc06,voc14,voc15,voc17,voc18,voc19,voc20,voc23;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M006;W004;;H005;E100;
+CAMS-REG-AP_v221;gnfr_F1;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E101;
+CAMS-REG-AP_v221;gnfr_F2;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E102;
+CAMS-REG-AP_v221;gnfr_F3;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc02,voc03,voc07,voc08,voc12,voc17,voc21,voc22;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E103;
+CAMS-REG-AP_v221;gnfr_F4;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;pm10,pm25,oc,ec,voc03,voc04,voc05,voc06,voc12,voc13,voc14,voc15;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M007;W005;;weekday=H006, saturday=H009, sunday=H010;E104;
+CAMS-REG-AP_v221;gnfr_G;2015;0;;+ AUT,CHE,FIN,FRA,ITA,ROU;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M001;W001;;H001;E105;
+CAMS-REG-AP_v221;gnfr_H;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,so4,voc01,voc02,voc03,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc17,voc21,voc22,voc23;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;V003;M001;W001;;H001;E106;
+CAMS-REG-AP_v221;gnfr_I;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,so4,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M001;W001;;H001;E107;
+CAMS-REG-AP_v221;gnfr_J;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;V007;M001;W001;;H001;E108;
+CAMS-REG-AP_v221;gnfr_K;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;nh3,pm10,pm25,oc;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M010;W001;;H007;E109;
+CAMS-REG-AP_v221;gnfr_L;2015;0;;- NPL,THA,IND,TJK,CHN,MNG,AFG,ARE,ARM,AZE,BEN,BFA,BHR,CAF,CIV,CMR,COD,COG,DJI,DZA,EGY,ERI,ESH,ETH,GAB,GEO,GHA,GIN,GMB,GNB,GNQ,IRN,IRQ,ISR,JOR,KAZ,KEN,KWT,LBN,LBR,LBY,MAR,MLI,MRT,NER,NGA,OMN,PAK,PSE,QAT,RUS,SAU,SDN,SEN,SLE,SOM,SSD,SYR,TCD,TGO,TKM,TUN,UGA,UZB,YEM;co,nox_no2,so2,nh3,pm10,pm25,oc,ec,voc01,voc02,voc03,voc04,voc05,voc06,voc07,voc08,voc09,voc12,voc13,voc14,voc15,voc16,voc17,voc18,voc19,voc20,voc21,voc22,voc23,voc24;<data_path>/ecmwf/cams_reg_apv221/yearly_mean;yearly;area;regional;;M011;W001;;H007;E110;
\ No newline at end of file
diff --git a/conf/hermes.conf b/conf/hermes.conf
index 7a125976b8999b572c72dcc012ebf68037b17ee4..7b64440880dd1e1fdce9f603d1130956a3c2c895 100755
--- a/conf/hermes.conf
+++ b/conf/hermes.conf
@@ -12,7 +12,9 @@ output_timestep_type = hourly
 output_timestep_num = 24
 output_timestep_freq = 1
 first_time = 0
-erase_auxiliary_files = 1
+erase_auxiliary_files = 0
+# Compression level from 0 to 9; 0 corresponds to no compression and 9 to maximum compression
+compression_level = 4
 
 [DOMAIN]
 
@@ -22,12 +24,8 @@ output_model = MONARCH
 # output_model = WRF_CHEM
 output_attributes = <input_dir>/data/global_attributes.csv
 
-# ***** domain_type=[global, regular, lcc, rotated, mercator] *****
+# domain_type=[lcc, rotated, mercator, regular, rotated_nested]
 domain_type = global
-# domain_type = regular
-# domain_type = lcc
-# domain_type = rotated
-# domain_type = mercator
 vertical_description = <input_dir>/data/profiles/vertical/Benchmark_15layers_vertical_description.csv
 auxiliary_files_path = <input_dir>/data/auxiliar_files/<domain_type>_<resolution>
 
@@ -52,6 +50,14 @@ auxiliary_files_path = <input_dir>/data/auxiliar_files/<domain_type>_<resolution
     #inc_rlat = 0.1
     #inc_rlon = 0.1
 
+# if domain_type == rotated_nested:
+    #parent_grid_path = /scratch/Earth/ctena/temporal_coords.nc
+    #parent_ratio = 4
+    #i_parent_start = 266
+    #j_parent_start = 164
+    #n_rlat = 212
+    #n_rlon = 196
+
 # if domain_type == lcc:
     #lat_1 = 37.0
     #lat_2 = 43.0
@@ -59,7 +65,7 @@ auxiliary_files_path = <input_dir>/data/auxiliar_files/<domain_type>_<resolution
     #lat_0 = 40.0
     #nx = 478
     #ny = 398
-    #inc_x = 12000
+    #inc_x = 12000gqqq
     #inc_y = 12000
     #x_0 = -2131849.000
     #y_0 = -2073137.875
@@ -86,7 +92,8 @@ p_month = <input_dir>/data/profiles/temporal/TemporalProfile_Monthly.csv
 p_week = <input_dir>/data/profiles/temporal/TemporalProfile_Weekly.csv
 p_day = <input_dir>/data/profiles/temporal/TemporalProfile_Daily.csv
 p_hour = <input_dir>/data/profiles/temporal/TemporalProfile_Hourly.csv
-p_speciation = <input_dir>/data/profiles/speciation/Speciation_profile_cb05_aero5_MONARCH.csv
+p_speciation = <input_dir>/data/profiles/speciation/Speciation_profile_cb05_aero5_Benchmark.csv
 
 molecular_weights = <input_dir>/data/profiles/speciation/MolecularWeights.csv
 world_info = <input_dir>/data/profiles/temporal/tz_world_country_iso3166.csv
+countries_shapefile = <input_dir>/data/gadm_country_mask/gadm_country_ISO3166.shp
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.cpg b/data/gadm_country_mask/gadm_country_ISO3166.cpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ad133c048f2189041151425a73485649e6c32c0
--- /dev/null
+++ b/data/gadm_country_mask/gadm_country_ISO3166.cpg
@@ -0,0 +1 @@
+UTF-8
\ No newline at end of file
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.dbf b/data/gadm_country_mask/gadm_country_ISO3166.dbf
new file mode 100644
index 0000000000000000000000000000000000000000..7f148fda4066eaefe8d0abb76845f107d2ebd606
Binary files /dev/null and b/data/gadm_country_mask/gadm_country_ISO3166.dbf differ
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.prj b/data/gadm_country_mask/gadm_country_ISO3166.prj
new file mode 100644
index 0000000000000000000000000000000000000000..f45cbadf0074d8b7b2669559a93bc50bb95f82d4
--- /dev/null
+++ b/data/gadm_country_mask/gadm_country_ISO3166.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.sbn b/data/gadm_country_mask/gadm_country_ISO3166.sbn
new file mode 100644
index 0000000000000000000000000000000000000000..34e86bf48a892ff880cac24094dc84e8e5fa0df8
Binary files /dev/null and b/data/gadm_country_mask/gadm_country_ISO3166.sbn differ
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.sbx b/data/gadm_country_mask/gadm_country_ISO3166.sbx
new file mode 100644
index 0000000000000000000000000000000000000000..aee1e8bfd73b24c631c46b2f7b3a28eeeebac3e0
Binary files /dev/null and b/data/gadm_country_mask/gadm_country_ISO3166.sbx differ
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.shp b/data/gadm_country_mask/gadm_country_ISO3166.shp
new file mode 100644
index 0000000000000000000000000000000000000000..bb1870bd025862fae48ea52a0b42e56612a58bdb
Binary files /dev/null and b/data/gadm_country_mask/gadm_country_ISO3166.shp differ
diff --git a/data/gadm_country_mask/gadm_country_ISO3166.shx b/data/gadm_country_mask/gadm_country_ISO3166.shx
new file mode 100644
index 0000000000000000000000000000000000000000..acf26c7515f965291a4a5bdcefb69d123b83e195
Binary files /dev/null and b/data/gadm_country_mask/gadm_country_ISO3166.shx differ
diff --git a/data/profiles/speciation/MolecularWeights.csv b/data/profiles/speciation/MolecularWeights.csv
index 1a43266a6dd480fc13f34f54e3efa018e8dceacb..d1090eb6a09aed8c2fb82a10a80ceab0a9b25480 100755
--- a/data/profiles/speciation/MolecularWeights.csv
+++ b/data/profiles/speciation/MolecularWeights.csv
@@ -2,6 +2,7 @@ Specie,MW
 nox_no,30.01
 nox_no2,46.01
 co,28.01
+co2,44.01
 so2,64.06
 nh3,17.03
 pm10,1.0
@@ -66,4 +67,6 @@ voc22,68.8
 voc23,75.3
 voc24,59.1
 voc25,86.9
-nmvoc,1.0
\ No newline at end of file
+nmvoc,1.0
+tpm,1.0
+ch4,16.04
\ No newline at end of file
diff --git a/data/profiles/speciation/Speciation_profile_cb05_aero5_Benchmark.csv b/data/profiles/speciation/Speciation_profile_cb05_aero5_Benchmark.csv
new file mode 100755
index 0000000000000000000000000000000000000000..9fb2e8bac821d75c1136d64afdb39f7acf05f4c7
--- /dev/null
+++ b/data/profiles/speciation/Speciation_profile_cb05_aero5_Benchmark.csv
@@ -0,0 +1,123 @@
+ID;NO;NO2;HONO;CO;SO2;NH3;ALD2;ALDX;BENZENE;ETH;ETHA;ETOH;FORM;IOLE;ISOP;MEOH;OLE;PAR;SESQ;TERP;TOL;XYL;DMS;HCL;SULF;POC;PEC;PNO3;PSO4;PMFINE;PMC
+units;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;mol.s-1.m-2;kg.s-1.m-2;kg.s-1.m-2;kg.s-1.m-2;kg.s-1.m-2;kg.s-1.m-2;kg.s-1.m-2
+short_description;nitrogen_monoxide;nitrogen_dioxide;nitrous_acid;carbon_monoxide;sulfur_dioxide;ammonia;acetaldehyde;higher_aldehydes;benzene;ethene;ethane;ethanol;formaldehyde;internal_olefin_carbon_bond;isoprene;methanol;terminal_olefin_carbon_bond;paraffin_carbon_bond;sesquiterpenes;terpene;toluene;xylene;dimethyl_sulfide;hydrogen_chloride;sulfuric_acid;primary_organic_carbon;primary_elemental_carbon;primary_nitrate_fine;primary_sulfate_fine;primary_others_fine;pm_coarse
+E001;0.72*nox_no;0.18*nox_no;0.1*nox_no;co;so2;nh3;c2h4o;0;c6h6;c2h4;c2h6;c2h5oh;0;0.5*hialkenes;c5h8;ch3oh;c8h16+c5h10+c3h6+c4h8+c6h12+0.5*hialkanes;4*c4h10+6*c6h14+5*hialkanes+6*c8h16+3*c5h10+c3h6+3*c3h6o+2*c4h8+7*c7h16+4*c6h12+hialkenes+5*c5h12+1.5*c3h8;0;terpenes;ch2o+c7h8;c8h10;c2h6s;0;0;oc;5.9*bc;0;0;3.3*pm25-3*oc-5.9*bc;0
+E002;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;(pm25-oc-bc)*0.155;(pm25-oc-bc)*0.845;pm10-pm25
+E003;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;(pm25-oc-bc)*0.089;(pm25-oc-bc)*0.911;pm10-pm25
+E004;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;(pm25-oc-bc)*0.047;(pm25-oc-bc)*0.953;pm10-pm25
+E005;0.823*nox_no2;0.16*nox_no2;0.017*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;(pm25-oc-bc)*0.059;(pm25-oc-bc)*0.941;pm10-pm25
+E006;0;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0
+E007;0.9*nox_no2;0.1*nox_no2;0;co;so2;0;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+5*voc05+7.5*voc06+voc08+voc09+voc13+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;oc;bc;0;0;pm25-oc-bc;pm10-pm25
+E008;0.9*nox_no2;0.1*nox_no2;0;co;so2;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;(pm25-oc-bc)*0.537;(pm25-oc-bc)*0.463;pm10-pm25
+E009;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0;0;c6h6;c2h4;0;0;0;0;0;ch3oh;c3h6;c2h2+c3h6+ch3cooh;0;0;ch2o;0;0;hcl;0;oc;bc;0;0;pm25-oc-bc;pm10-pm25
+E010;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.02*pm25;0.01*pm25;0;0.15*pm25;0.82*pm25;pm10-pm25
+E011;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.35*pm25;0.18*pm25;0;0.02*pm25;0.45*pm25;pm10-pm25
+E012;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.03*pm25;0.01*pm25;0;0.1*pm25;0.86*pm25;pm10-pm25
+E013;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;voc13;0;voc02;0;0;0.666*voc12;0;0;0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc13;0;0;voc14;voc15;0;0;0;0;0;0;0;1*pm25;pm10-pm25
+E014;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0.01*voc18+0.3*voc19;0;0;0;0.5*voc01;0;0;0;0.5*voc01;0;7.5*voc06+2.2*voc17+4.11*voc18+4*voc19+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;0;0;0;0;0;0
+E015;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.58*pm25;0.21*pm25;0;0.01*pm25;0.21*pm25;pm10-pm25
+E016;0.7*nox_no2;0.283*nox_no2;0.017*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;0.288*pm25;0.675*pm25;0;0.01*pm25;0.037*pm25;pm10-pm25
+E017;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;0;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+voc08+2.2*voc17+1.875*voc22;0;0;0.2*voc17;voc17;0;0;0;0.58*pm25;0.21*pm25;0;0.01*pm25;0.21*pm25;pm10-pm25
+E018;0;0;0;0;0;0;0;0;voc13;0;0;0;0;0.666*voc12;0;0;0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc13;0;0;voc14;voc15;0;0;0;0;0;0;0;0;0
+E019;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0.2*pm25;0.06*pm25;0;0;0.74*pm25;pm10-pm25
+E020;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.31*pm25;0.41*pm25;0;0.03*pm25;0.25*pm25;pm10-pm25
+E021;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0.31*pm25;0.2*pm25;0;0;0.49*pm25;pm10-pm25
+E022;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0.48*pm25;0.15*pm25;0;0;0.37*pm25;pm10-pm25
+E023;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;pm25;pm10-pm25
+E024;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;oc;bc;0;0;pm25-oc-bc;pm10-pm25
+E025;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.000008*nmvoc;0;0;0.000858*nmvoc;0.004177*nmvoc;0;0.018548*nmvoc;0;0;0;0.00104*nmvoc;0.011594*nmvoc;0;0;0;0.000893*nmvoc;0;0;0;oc;bc;0;(pm25-oc-bc)*0.155;(pm25-oc-bc)*0.845;pm10-pm25
+E026;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.000013*nmvoc;0;0;0.003224*nmvoc;0.001662*nmvoc;0.000659*nmvoc;0.008038*nmvoc;0;0;0.000175*nmvoc;0.000496*nmvoc;0.025751*nmvoc;0;0;0;0.001726*nmvoc;0;0;0;oc;bc;0;(pm25-oc-bc)*0.089;(pm25-oc-bc)*0.911;pm10-pm25
+E027;0.823*nox_no2;0.16*nox_no2;0.017*nox_no2;co;so2;nh3;0.000592*nmvoc;0;0;0.002423*nmvoc;0.001607*nmvoc;0;0.000899*nmvoc;0;0;0;0.002589*nmvoc;0.028079*nmvoc;0;0;0;0.003302*nmvoc;0;0;0;oc;bc;0;(pm25-oc-bc)*0.059;(pm25-oc-bc)*0.941;pm10-pm25
+E028;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.000047*nmvoc;0;0;0.002914*nmvoc;0.004187*nmvoc;0.000849*nmvoc;0.00186*nmvoc;0;0;0;0.002559*nmvoc;0.029992*nmvoc;0;0;0;0.001755*nmvoc;0;0;0;oc;bc;0;(pm25-oc-bc)*0.047;(pm25-oc-bc)*0.953;pm10-pm25
+E029;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0.001867*nmvoc;0.008553*nmvoc;0.000529*nmvoc;0.005349*nmvoc;0;0.000169*nmvoc;0.000133*nmvoc;0.000435*nmvoc;0.025797*nmvoc;0;0;0;0.000742*nmvoc;0;0;0;oc;bc;0;0;pm25-oc-bc;pm10-pm25
+E030;0;0;0;0;0;0;0;0;0;0.000087*nmvoc;0.000148*nmvoc;0.003563*nmvoc;0.000004*nmvoc;0;0.000001*nmvoc;0.001904*nmvoc;0.000161*nmvoc;0.036006*nmvoc;0;0;0;0.001686*nmvoc;0;0;0;0;0;0;0;0;0
+E031;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.002093*nmvoc;0.000525*nmvoc;0.000105*nmvoc;0.002929*nmvoc;0.001894*nmvoc;0.000023*nmvoc;0.002826*nmvoc;0.000116*nmvoc;0.000043*nmvoc;0.003664*nmvoc;0.003078*nmvoc;0.010261*nmvoc;0;0.000006*nmvoc;0.00013*nmvoc;0.000006*nmvoc;0;0;0;oc;bc;0;0;pm25-oc-bc;pm10-pm25
+E032;0.9*nox_no2;0.1*nox_no2;0;co;so2;0;0;0;0.002496*nmvoc;0.004456*nmvoc;0;0;0.001693*nmvoc;0;0;0;0.001111*nmvoc;0.030672*nmvoc;0;0;0.001132*nmvoc;0.000762*nmvoc;0;0;0;oc;bc;0;(pm25-oc-bc)*0.537;(pm25-oc-bc)*0.463;pm10-pm25
+E033;nox_no2;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;pm25_fossil;pm10-pm25_fossil
+E034;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;oc;bc;0;0;pm25_bio;pm10-pm25_bio
+E035;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;bc;0;0;pm25_fossil-bc;pm10-pm25_fossil
+E036;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;oc;bc;0;(pm25_fossil+pm25_bio-oc-bc)*0.155;(pm25_fossil+pm25_bio-oc-bc)*0.845;pm10-pm25_fossil-pm25_bio
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+E100;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0.01*voc18+0.3*voc19;0;0;0;0.5*voc01;0;0;0;0.5*voc01;0;7.5*voc06+2.2*voc17+4.11*voc18+4*voc19+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;0;0;0;0;pm25;pm10-pm25
+E101;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
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+E103;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;0;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+voc08+2.2*voc17+1.875*voc22;0;0;0.2*voc17;voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
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+E106;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+5*voc05+7.5*voc06+voc08+voc09+voc13+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;oc;0;0;so4;pm25-oc-so4;pm10-pm25
+E107;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E108;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;0;pm25-oc-ec;pm10-pm25
+E109;0;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;oc;0;0;0;pm25-oc;pm10-pm25
+E110;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;0;pm25-oc-ec;pm10-pm25
+E114;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E115;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;5.6*(oc+bc)-oc-bc;3.4*(oc+bc)
+E116;0.9*nox_no;0.1*nox_no;0;co;so2;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E117;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;3.1*(oc+bc)-oc-bc;0.6*(oc+bc)
+E118;nox_no;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0
+E119;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;1.7*(oc+bc)-oc-bc;1.5*(oc+bc)
+E120;0;0;0;0;0;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0;0;0;0;0;0
+E121;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E122;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;3.4*(oc+bc)-oc-bc;0
+E123;0.7*nox_no;0.283*nox_no;0.017*nox_no;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;1.2*(oc+bc)-oc-bc;0
\ No newline at end of file
diff --git a/data/profiles/speciation/Speciation_profile_cb05_aero5_CMAQ.csv b/data/profiles/speciation/Speciation_profile_cb05_aero5_CMAQ.csv
index d667082d115bd9a1d77f07139e6f719751fe3ddb..a124558eb9263f1cf1dd34b50e0dea14fe64802f 100755
--- a/data/profiles/speciation/Speciation_profile_cb05_aero5_CMAQ.csv
+++ b/data/profiles/speciation/Speciation_profile_cb05_aero5_CMAQ.csv
@@ -96,3 +96,28 @@ E092;0;0;0;0;0;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;
 E093;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;voc10;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
 E094;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;voc10;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
 E095;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.001084*nmvoc;0.000756*nmvoc;0.000248*nmvoc;0.00622*nmvoc;0.000293*nmvoc;0;0.005437*nmvoc;0.000086*nmvoc;0;0;0.003296*nmvoc;0.011816*nmvoc;0;0;0.000192*nmvoc;0.000124*nmvoc;0;0;0;oc;bc;0;0;0;0
+E096;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E097;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E098;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E099;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E100;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0;0.01*voc18+0.3*voc19;0;0;0;0.5*voc01;0;0;0;0.5*voc01;0;7.5*voc06+2.2*voc17+4.11*voc18+4*voc19+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;0;0;0;0;pm25;pm10-pm25
+E101;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E102;0.7*nox_no2;0.283*nox_no2;0.017*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E103;0.95*nox_no2;0.042*nox_no2;0.008*nox_no2;co;so2;nh3;0.625*voc22;0.375*voc22;0;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+voc08+2.2*voc17+1.875*voc22;0;0;0.2*voc17;voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E104;0;0;0;0;0;0;0;0;voc13;0;0;0;0;0.666*voc12;0;0;0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc13;0;0;voc14;voc15;0;0;0;oc;ec;0;0;pm25-oc-ec;pm10-pm25
+E105;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E106;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+5*voc05+7.5*voc06+voc08+voc09+voc13+2.2*voc17+1.875*voc22+4*voc23;0;0;voc14+0.2*voc17;voc15+voc17;0;0;0;oc;0;0;so4;pm25-oc-so4;pm10-pm25
+E107;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.375*voc22;voc13;voc07;voc02;0;voc21;0.666*voc12;0;0;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;so4;pm25-oc-ec-so4;pm10-pm25
+E108;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;0;pm25-oc-ec;pm10-pm25
+E109;0;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;oc;0;0;0;pm25-oc;pm10-pm25
+E110;0.9*nox_no2;0.1*nox_no2;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;ec;0;0;pm25-oc-ec;pm10-pm25
+E114;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E115;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;5.6*(oc+bc)-oc-bc;3.4*(oc+bc)
+E116;0.9*nox_no;0.1*nox_no;0;co;so2;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E117;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;3.1*(oc+bc)-oc-bc;0.6*(oc+bc)
+E118;nox_no;0;0;0;0;nh3;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0
+E119;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;1.7*(oc+bc)-oc-bc;1.5*(oc+bc)
+E120;0;0;0;0;0;0;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;0;0;0;0;0;0
+E121;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;0;0
+E122;0.9*nox_no;0.1*nox_no;0;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;3.4*(oc+bc)-oc-bc;0
+E123;0.7*nox_no;0.283*nox_no;0.017*nox_no;co;so2;nh3;0.625*voc22;0.01*voc18+0.3*voc19+0.375*voc22;voc13;voc07;voc02;0.5*voc01;voc21;0.666*voc12;0;0.5*voc01;voc08+0.333*voc12;1.5*voc03+4*voc04+5*voc05+7.5*voc06+voc08+voc09+voc13+voc16+2.2*voc17+4.11*voc18+4*voc19+1.875*voc22+4*voc23+voc24;0;0;voc14+0.2*voc17;voc15+voc16+voc17;0;0;0;oc;bc;0;0;1.2*(oc+bc)-oc-bc;0
\ No newline at end of file
diff --git a/hermesv3_gr/config/config.py b/hermesv3_gr/config/config.py
index 6cb466a4a97006fd91c7e376e83ba8a49735b877..aa613e1ac651db00bbc16178826a76c46de8bf21 100755
--- a/hermesv3_gr/config/config.py
+++ b/hermesv3_gr/config/config.py
@@ -75,11 +75,14 @@ class Config(ArgParser):
 
         p.add_argument('--output_model', required=True, help='Name of the output model.',
                        choices=['MONARCH', 'CMAQ', 'WRF_CHEM'])
+        p.add_argument('--compression_level', required=False, type=int, choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+                       help='Compression level of the NetCDF output (0 for no compressed output).', default=4)
+
         p.add_argument('--output_attributes', required=False,
                        help='Path to the file that contains the global attributes.')
 
         p.add_argument('--domain_type', required=True, help='Type of domain to simulate.',
-                       choices=['global', 'lcc', 'rotated', 'mercator', 'regular'])
+                       choices=['global', 'lcc', 'rotated', 'mercator', 'regular', 'rotated_nested'])
         p.add_argument('--auxiliary_files_path', required=True,
                        help='Path to the directory where the necessary auxiliary files will be created if them are ' +
                             'not created yet.')
@@ -117,6 +120,20 @@ class Config(ArgParser):
                        help='Longitudinal grid resolution (rotated degrees). Corresponds to the DLMD parameter  ' +
                             'in NMMB-MONARCH.', type=float)
 
+        # Rotated_nested options
+        p.add_argument('--parent_grid_path', required=False, type=str,
+                       help='Path to the netCDF that contains the grid definition.')
+        p.add_argument('--parent_ratio', required=False, type=int,
+                       help='Ratio between the parent and the nested domain.')
+        p.add_argument('--i_parent_start', required=False, type=int,
+                       help='Location of the I to start the nested.')
+        p.add_argument('--j_parent_start', required=False, type=int,
+                       help='Location of the J to start the nested.')
+        p.add_argument('--n_rlat', required=False, type=int,
+                       help='Number of rotated latitude points.')
+        p.add_argument('--n_rlon', required=False, type=int,
+                       help='Number of rotated longitude points.')
+
         # Lambert conformal conic arguments
         p.add_argument('--lat_1', required=False,
                        help='Standard parallel 1 (in deg). Corresponds to the P_ALP parameter of the GRIDDESC file.',
@@ -167,6 +184,8 @@ class Config(ArgParser):
                        help='Path to the file that contains the molecular weights of the input pollutants.')
         p.add_argument('--world_info', required=True,
                        help='Path to the file that contains the world information like timezones, ISO codes, ...')
+        p.add_argument('--countries_shapefile', required=False,
+                       help='...')
 
         arguments, unknown = p.parse_known_args()
         if len(unknown) > 0:
@@ -184,6 +203,9 @@ class Config(ArgParser):
                 elif arguments.domain_type == 'rotated':
                     arguments.__dict__[item] = arguments.__dict__[item].replace('<resolution>', '{1}_{2}'.format(
                         item, arguments.inc_rlat, arguments.inc_rlon))
+                elif arguments.domain_type == 'rotated_nested':
+                    arguments.__dict__[item] = arguments.__dict__[item].replace('<resolution>', '{1}_{2}'.format(
+                        item, arguments.n_rlat, arguments.n_rlon))
                 elif arguments.domain_type == 'lcc' or arguments.domain_type == 'mercator':
                     arguments.__dict__[item] = arguments.__dict__[item].replace('<resolution>', '{1}_{2}'.format(
                         item, arguments.inc_x, arguments.inc_y))
diff --git a/hermesv3_gr/config/settings.py b/hermesv3_gr/config/settings.py
index 8a6966b3743a8dadd6fbe295d1852f74149ed8ac..c114f84b782db1d2e0f6cf1fc07a4b88aa556055 100755
--- a/hermesv3_gr/config/settings.py
+++ b/hermesv3_gr/config/settings.py
@@ -30,11 +30,6 @@ precision = np.float64
 global writing_serial
 writing_serial = True
 
-global compressed_netcdf
-compressed_netcdf = True
-
-if not writing_serial:
-    compressed_netcdf = False
 
 global icomm
 global comm
diff --git a/hermesv3_gr/hermes.py b/hermesv3_gr/hermes.py
index 1df2970d2e8cb67a377177a046b381952e077637..d7faef45fbfa25cd100d12e1a607064edbc403fa 100755
--- a/hermesv3_gr/hermes.py
+++ b/hermesv3_gr/hermes.py
@@ -34,7 +34,7 @@ class HermesGr(object):
     Interface class for HERMESv3_GR.
     """
     def __init__(self, config, new_date=None, comm=None):
-        from hermesv3_gr.modules.grids.grid import Grid
+        from hermesv3_gr.modules.grids.grid import select_grid
         from hermesv3_gr.modules.temporal.temporal import TemporalDistribution
         from hermesv3_gr.modules.writing.writer import Writer
         global full_time
@@ -65,18 +65,11 @@ class HermesGr(object):
 
         self.levels = VerticalDistribution.get_vertical_output_profile(self.options.vertical_description)
 
-        self.grid = Grid.select_grid(
-            self.comm,
-            self.options.domain_type, self.options.vertical_description,  self.options.output_timestep_num,
-            self.options.auxiliary_files_path, self.options.inc_lat, self.options.inc_lon, self.options.lat_orig,
-            self.options.lon_orig, self.options.n_lat, self.options.n_lon, self.options.centre_lat,
-            self.options.centre_lon, self.options.west_boundary, self.options.south_boundary, self.options.inc_rlat,
-            self.options.inc_rlon, self.options.lat_1, self.options.lat_2, self.options.lon_0, self.options.lat_0,
-            self.options.nx, self.options.ny, self.options.inc_x, self.options.inc_y, self.options.x_0,
-            self.options.y_0, self.options.lat_ts)
+        self.grid = select_grid(self.comm, self.options)
 
-        self.emission_list = EmissionInventory.make_emission_list(self.options, self.grid, self.levels,
-                                                                  self.options.start_date)
+        self.emission_list = EmissionInventory.make_emission_list(self.comm, self.options, self.grid, self.levels,
+                                                                  self.options.start_date,
+                                                                  self.options.countries_shapefile)
 
         self.delta_hours = TemporalDistribution.calculate_delta_hours(
             self.options.start_date, self.options.output_timestep_type, self.options.output_timestep_num,
@@ -85,8 +78,7 @@ class HermesGr(object):
         self.writer = Writer.get_writer(
             self.options.output_model, self.config.get_output_name(self.options.start_date), self.grid,
             self.levels, self.options.start_date, self.delta_hours, self.options.output_attributes,
-            compress=settings.compressed_netcdf,
-            parallel=not settings.writing_serial)
+            self.options.compression_level, parallel=not settings.writing_serial)
 
         settings.write_log('End of HERMESv3 initialization.')
         settings.write_time('HERMES', 'Init', timeit.default_timer() - st_time, level=1)
diff --git a/hermesv3_gr/modules/emision_inventories/emission_inventory.py b/hermesv3_gr/modules/emision_inventories/emission_inventory.py
index 167042479a4a6f79db5b1c727b89eec59dfd8e35..6519782b52d8ecc1a3e494bad955b42d0ca3fb3f 100755
--- a/hermesv3_gr/modules/emision_inventories/emission_inventory.py
+++ b/hermesv3_gr/modules/emision_inventories/emission_inventory.py
@@ -74,23 +74,27 @@ class EmissionInventory(object):
     :param p_speciation: ID of the speciation profile to use.
     :type p_speciation: str
     """
-    def __init__(self, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
-                 input_frequency, vertical_output_profile, reference_year=2010, factors=None, regrid_mask=None,
-                 p_vertical=None, p_month=None, p_week=None, p_day=None, p_hour=None, p_speciation=None):
+    def __init__(self, comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
+                 input_frequency, vertical_output_profile, reference_year=2010, coverage='global', factors=None,
+                 regrid_mask=None, p_vertical=None, p_month=None, p_week=None, p_day=None, p_hour=None,
+                 p_speciation=None, countries_shapefile=None):
         from hermesv3_gr.modules.masking.masking import Masking
 
         st_time = timeit.default_timer()
         settings.write_log('\t\tCreating area source emission inventory.', level=3)
 
         # Emission Inventory parameters
+        self.comm = comm
         self.source_type = source_type
         self.date = current_date
         self.inventory_name = inventory_name
         self.sector = sector
         self.reference_year = reference_year
+        self.coverage = coverage
         self.inputs_path = inputs_path
         self.input_frequency = input_frequency
         self.grid = grid
+        self.auxiliary_files_path = options.auxiliary_files_path
 
         # Profiles
         p_vertical = self.get_profile(p_vertical)
@@ -104,12 +108,13 @@ class EmissionInventory(object):
         self.masking = Masking(
             options.world_info, factors, regrid_mask, grid,
             world_mask_file=os.path.join(os.path.dirname(options.auxiliary_files_path),
-                                         '{0}_WorldMask.nc'.format(inventory_name)))
+                                         '{0}_WorldMask.nc'.format(inventory_name)),
+            countries_shapefile=countries_shapefile)
 
         self.input_pollutants = pollutants
         self.pollutant_dicts = self.create_pollutants_dicts(pollutants)
 
-        self.masking.check_regrid_mask(self.pollutant_dicts[0]['path'])
+        self.masking.check_regrid_mask(self.comm, self.pollutant_dicts[0]['path'])
 
         # Creating Regrid Object
         # It will also create the WoldMasks necessaries
@@ -118,7 +123,7 @@ class EmissionInventory(object):
                 self.pollutant_dicts,
                 os.path.join(options.auxiliary_files_path,
                              "Weight_Matrix_{0}_{1}.nc".format(self.inventory_name, settings.size)),
-                grid, masking=self.masking)
+                grid, self.coverage, masking=self.masking)
         if not options.first_time:
             # Creating Vertical Object
             if p_vertical is not None:
@@ -171,7 +176,7 @@ class EmissionInventory(object):
         for pollutant_name in pollutants:
             pollutant_list.append(
                 {'name': pollutant_name,
-                 'path': self.get_input_path(pollutant_name),
+                 'path': self.get_input_path(pollutant=pollutant_name),
                  'Dataset': "{0}_{1}".format(self.inventory_name, self.sector)}
             )
         return pollutant_list
@@ -258,14 +263,17 @@ class EmissionInventory(object):
         st_time = timeit.default_timer()
 
         settings.write_log("\tRegridding", level=2)
-        regridded_emissions = self.regrid.start_regridding()
+        if self.coverage == 'global':
+            regridded_emissions = self.regrid.start_regridding()
+        else:
+            regridded_emissions = self.regrid.start_esmpy_regridding()
         for emission in regridded_emissions:
             dict_aux = {'name': emission['name'], 'data': emission['data'], 'units': 'm'}
             self.emissions.append(dict_aux)
         settings.write_time('EmissionInventory', 'do_regrid', timeit.default_timer() - st_time, level=2)
 
     @staticmethod
-    def make_emission_list(options, grid, vertical_output_profile, date):
+    def make_emission_list(comm, options, grid, vertical_levels, date, countries_shapefile):
         """
         Extract the information of the cross table to read all the needed emissions.
 
@@ -275,8 +283,8 @@ class EmissionInventory(object):
         :param grid: Grid to use.
         :type grid: Grid
 
-        :param vertical_output_profile: Path to eht file that contains the vertical profile.
-        :type vertical_output_profile: str
+        :param vertical_levels: Verical levels
+        :type vertical_levels: list
 
         :param date: Date to simulate.
         :type date: datetime.datetime
@@ -289,6 +297,7 @@ class EmissionInventory(object):
         from .point_gfas_emission_inventory import PointGfasEmissionInventory
         from .gfas_emission_inventory import GfasEmissionInventory
         from .point_source_emission_inventory import PointSourceEmissionInventory
+        from .point_gfas_hourly_emission_inventory import PointGfasHourlyEmissionInventory
 
         st_time = timeit.default_timer()
         settings.write_log('Loading emissions')
@@ -296,7 +305,7 @@ class EmissionInventory(object):
         path = options.cross_table
         df = pd.read_csv(path, sep=';', index_col=False)
         for column in ['ei', 'sector', 'ref_year', 'active', 'factor_mask', 'regrid_mask', 'pollutants', 'path',
-                       'frequency', 'source_type', 'p_vertical', 'p_month', 'p_week', 'p_day', 'p_hour',
+                       'frequency', 'source_type', 'coverage', 'p_vertical', 'p_month', 'p_week', 'p_day', 'p_hour',
                        'p_speciation']:
             df_cols = list(df.columns.values)
             if column not in df_cols:
@@ -345,51 +354,59 @@ class EmissionInventory(object):
             if emission_inventory.source_type == 'area':
                 if emission_inventory.ei == 'GFASv12':
                     emission_inventory_list.append(
-                        GfasEmissionInventory(options, grid, date, emission_inventory.ei,
-                                              emission_inventory.source_type, emission_inventory.sector, pollutants,
-                                              emission_inventory_path,
-                                              emission_inventory.frequency, vertical_output_profile,
-                                              reference_year=emission_inventory.ref_year,
-                                              factors=emission_inventory.factor_mask,
-                                              regrid_mask=emission_inventory.regrid_mask,
-                                              p_vertical=emission_inventory.p_vertical,
-                                              p_month=p_month,
-                                              p_week=p_week,
-                                              p_day=p_day,
-                                              p_hour=p_hour,
-                                              p_speciation=emission_inventory.p_speciation))
+                        GfasEmissionInventory(
+                            comm, options, grid, date, emission_inventory.ei, emission_inventory.source_type,
+                            emission_inventory.sector, pollutants, emission_inventory_path,
+                            emission_inventory.frequency, vertical_levels,
+                            reference_year=emission_inventory.ref_year,
+                            coverage=emission_inventory.coverage,
+                            factors=emission_inventory.factor_mask,
+                            regrid_mask=emission_inventory.regrid_mask,
+                            p_vertical=emission_inventory.p_vertical, p_month=p_month, p_week=p_week, p_day=p_day,
+                            p_hour=p_hour, p_speciation=emission_inventory.p_speciation,
+                            countries_shapefile=countries_shapefile))
                 else:
                     emission_inventory_list.append(
-                        EmissionInventory(options, grid, date, emission_inventory.ei, emission_inventory.source_type,
-                                          emission_inventory.sector, pollutants,
-                                          emission_inventory_path,
-                                          emission_inventory.frequency, vertical_output_profile,
-                                          reference_year=emission_inventory.ref_year,
-                                          factors=emission_inventory.factor_mask,
-                                          regrid_mask=emission_inventory.regrid_mask,
-                                          p_vertical=emission_inventory.p_vertical,
-                                          p_month=p_month,
-                                          p_week=p_week,
-                                          p_day=p_day,
-                                          p_hour=p_hour,
-                                          p_speciation=emission_inventory.p_speciation))
+                        EmissionInventory(
+                            comm, options, grid, date, emission_inventory.ei, emission_inventory.source_type,
+                            emission_inventory.sector, pollutants, emission_inventory_path,
+                            emission_inventory.frequency, vertical_levels,
+                            reference_year=emission_inventory.ref_year,
+                            coverage=emission_inventory.coverage,
+                            factors=emission_inventory.factor_mask,
+                            regrid_mask=emission_inventory.regrid_mask,
+                            p_vertical=emission_inventory.p_vertical, p_month=p_month, p_week=p_week, p_day=p_day,
+                            p_hour=p_hour, p_speciation=emission_inventory.p_speciation,
+                            countries_shapefile=countries_shapefile))
             elif emission_inventory.source_type == 'point':
                 if emission_inventory.ei == 'GFASv12':
-                    emission_inventory_list.append(
-                        PointGfasEmissionInventory(
-                            options, grid, date, emission_inventory.ei, emission_inventory.source_type,
-                            emission_inventory.sector, pollutants, emission_inventory_path,
-                            emission_inventory.frequency, vertical_output_profile,
-                            reference_year=emission_inventory.ref_year, factors=emission_inventory.factor_mask,
-                            regrid_mask=emission_inventory.regrid_mask, p_vertical=emission_inventory.p_vertical,
-                            p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour,
-                            p_speciation=emission_inventory.p_speciation))
+                    if emission_inventory.frequency == 'daily':
+                        emission_inventory_list.append(
+                            PointGfasEmissionInventory(
+                                comm, options, grid, date, emission_inventory.ei, emission_inventory.source_type,
+                                emission_inventory.sector, pollutants, emission_inventory_path,
+                                emission_inventory.frequency, vertical_levels,
+                                reference_year=emission_inventory.ref_year, factors=emission_inventory.factor_mask,
+                                regrid_mask=emission_inventory.regrid_mask, p_vertical=emission_inventory.p_vertical,
+                                p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour,
+                                p_speciation=emission_inventory.p_speciation))
+                    elif emission_inventory.frequency == 'hourly':
+                        emission_inventory_list.append(
+                            PointGfasHourlyEmissionInventory(
+                                comm, options, grid, date, emission_inventory.ei, emission_inventory.source_type,
+                                emission_inventory.sector, pollutants, emission_inventory_path,
+                                emission_inventory.frequency, vertical_levels,
+                                reference_year=emission_inventory.ref_year, factors=emission_inventory.factor_mask,
+                                regrid_mask=emission_inventory.regrid_mask, p_vertical=emission_inventory.p_vertical,
+                                p_speciation=emission_inventory.p_speciation))
+                    else:
+                        raise NotImplementedError("ERROR: {0} frequency are not implemented, use 'daily' or 'hourly.")
                 else:
                     emission_inventory_list.append(
-                        PointSourceEmissionInventory(options, grid, date, emission_inventory.ei,
+                        PointSourceEmissionInventory(comm, options, grid, date, emission_inventory.ei,
                                                      emission_inventory.source_type, emission_inventory.sector,
                                                      pollutants, emission_inventory_path,
-                                                     emission_inventory.frequency, vertical_output_profile,
+                                                     emission_inventory.frequency, vertical_levels,
                                                      reference_year=emission_inventory.ref_year,
                                                      factors=emission_inventory.factor_mask,
                                                      regrid_mask=emission_inventory.regrid_mask,
@@ -398,7 +415,8 @@ class EmissionInventory(object):
                                                      p_week=p_week,
                                                      p_day=p_day,
                                                      p_hour=p_hour,
-                                                     p_speciation=emission_inventory.p_speciation))
+                                                     p_speciation=emission_inventory.p_speciation,
+                                                     countries_shapefile=countries_shapefile))
             else:
                 settings.write_log('ERROR: Check the .err file to get more info.')
                 if settings.rank == 0:
diff --git a/hermesv3_gr/modules/emision_inventories/gfas_emission_inventory.py b/hermesv3_gr/modules/emision_inventories/gfas_emission_inventory.py
index c06261561fa526b69de98473bb325f2aaec0914d..fe2f35d4cb115516f8122e1355b47b3eb08a23b0 100755
--- a/hermesv3_gr/modules/emision_inventories/gfas_emission_inventory.py
+++ b/hermesv3_gr/modules/emision_inventories/gfas_emission_inventory.py
@@ -68,20 +68,21 @@ class GfasEmissionInventory(EmissionInventory):
     :type p_speciation: str
     """
 
-    def __init__(self, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
+    def __init__(self, comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
                  frequency, vertical_output_profile,
-                 reference_year=2010, factors=None, regrid_mask=None, p_vertical=None, p_month=None, p_week=None,
-                 p_day=None, p_hour=None, p_speciation=None):
+                 reference_year=2010, coverage='global', factors=None, regrid_mask=None, p_vertical=None, p_month=None,
+                 p_week=None, p_day=None, p_hour=None, p_speciation=None):
         from hermesv3_gr.modules.vertical.vertical_gfas import GfasVerticalDistribution
 
         st_time = timeit.default_timer()
         settings.write_log('\t\tCreating GFAS emission inventory.', level=3)
 
         super(GfasEmissionInventory, self).__init__(
-            options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
+            comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
             vertical_output_profile,
-            reference_year=reference_year, factors=factors, regrid_mask=regrid_mask, p_vertical=None,
-            p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour, p_speciation=p_speciation)
+            reference_year=reference_year, coverage=coverage, factors=factors, regrid_mask=regrid_mask,
+            p_vertical=None, p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour, p_speciation=p_speciation,
+            countries_shapefile=countries_shapefile)
         if not options.first_time:
             self.approach = self.get_approach(p_vertical)
             self.method = self.get_method(p_vertical)
diff --git a/hermesv3_gr/modules/emision_inventories/point_gfas_emission_inventory.py b/hermesv3_gr/modules/emision_inventories/point_gfas_emission_inventory.py
index 2cc879f895192dbe96b663798bca363ea4446fbb..820390e03801ef9c950d1f7d30fff4632e95c963 100755
--- a/hermesv3_gr/modules/emision_inventories/point_gfas_emission_inventory.py
+++ b/hermesv3_gr/modules/emision_inventories/point_gfas_emission_inventory.py
@@ -70,19 +70,20 @@ class PointGfasEmissionInventory(EmissionInventory):
     :type p_speciation: str
     """
 
-    def __init__(self, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
+    def __init__(self, comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
                  frequency, vertical_output_profile,
                  reference_year=2010, factors=None, regrid_mask=None, p_vertical=None, p_month=None, p_week=None,
-                 p_day=None, p_hour=None, p_speciation=None):
+                 p_day=None, p_hour=None, p_speciation=None, countries_shapefile=None):
         from hermesv3_gr.modules.vertical.vertical_gfas import GfasVerticalDistribution
 
         st_time = timeit.default_timer()
         settings.write_log('\t\tCreating GFAS emission inventory as point source.', level=3)
         super(PointGfasEmissionInventory, self).__init__(
-            options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
+            comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
             vertical_output_profile,
             reference_year=reference_year, factors=factors, regrid_mask=regrid_mask, p_vertical=None,
-            p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour, p_speciation=p_speciation)
+            p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour, p_speciation=p_speciation,
+            countries_shapefile=countries_shapefile)
 
         # self.approach = self.get_approach(p_vertical)
         self.method = self.get_method(p_vertical)
@@ -203,26 +204,26 @@ class PointGfasEmissionInventory(EmissionInventory):
 
         return return_value
 
-    def do_vertical_allocation(self, values):
-        """
-        Allocates the fire emissions on their top level.
-
-        :param values: 2D array with the fire emissions
-        :type values: numpy.array
-
-        :return: Emissions already allocated on the top altitude of each fire.
-        :rtype: numpy.array
-        """
-        print('do_vertical_allocation')
-        sys.exit()
-        st_time = timeit.default_timer()
-
-        return_value = self.vertical.do_vertical_interpolation_allocation(values, self.altitude)
-
-        settings.write_time('PointGfasEmissionInventory', 'do_vertical_allocation', timeit.default_timer() - st_time,
-                            level=3)
-
-        return return_value
+    # def do_vertical_allocation(self, values):
+    #     """
+    #     Allocates the fire emissions on their top level.
+    #
+    #     :param values: 2D array with the fire emissions
+    #     :type values: numpy.array
+    #
+    #     :return: Emissions already allocated on the top altitude of each fire.
+    #     :rtype: numpy.array
+    #     """
+    #     print('do_vertical_allocation')
+    #     sys.exit()
+    #     st_time = timeit.default_timer()
+    #
+    #     return_value = self.vertical.do_vertical_interpolation_allocation(values, self.altitude)
+    #
+    #     settings.write_time('PointGfasEmissionInventory', 'do_vertical_allocation', timeit.default_timer() - st_time,
+    #                         level=3)
+    #
+    #     return return_value
 
     def do_regrid(self):
         import pandas as pd
@@ -261,7 +262,7 @@ class PointGfasEmissionInventory(EmissionInventory):
         gdf['geometry'] = [Point(xy) for xy in zip(lons.flatten()[gdf['src_index']],
                                                    lats.flatten()[gdf['src_index']])]
         gdf['src_area'] = netcdf.variables['cell_area'][:].flatten()[gdf['src_index']]
-        grid_shp = self.grid.to_shapefile(full_grid=False)
+        grid_shp = self.grid.to_shapefile(full_grid=False).reset_index()
 
         temp_coords = Dataset(os.path.join(self.grid.temporal_path, 'temporal_coords.nc'), mode='r')
 
@@ -294,71 +295,71 @@ class PointGfasEmissionInventory(EmissionInventory):
 
         return True
 
-    def do_regrid_balanced_NOT_USED(self):
-        import pandas as pd
-        import geopandas as gpd
-        from shapely.geometry import Point
-        import numpy as np
-        from netCDF4 import Dataset
-
-        st_time = timeit.default_timer()
-        settings.write_log("\tAllocating GFAS as point sources on grid:", level=2)
-
-        netcdf = Dataset(self.pollutant_dicts[0]['path'], mode='r')
-        if settings.rank == 0:
-            gdf = gpd.GeoDataFrame(crs={'init': 'epsg:4326'})
-
-            first_var = netcdf.variables[self.input_pollutants[0]][:]
-
-            gdf['src_index'] = np.where(first_var.flatten() > 0)[0]
-
-            gdf['altitude'] = self.vertical.altitude.flatten()[gdf['src_index']]
-
-            lat_1d = netcdf.variables['lat'][:]
-            lon_1d = netcdf.variables['lon'][:]
-            lon_1d[lon_1d > 180] -= 360
-            # 1D to 2D
-            lats = np.array([lat_1d] * len(lon_1d)).T
-            lons = np.array([lon_1d] * len(lat_1d))
-
-            gdf['geometry'] = [Point(xy) for xy in zip(lons.flatten()[gdf['src_index']],
-                                                       lats.flatten()[gdf['src_index']])]
-            gdf['src_area'] = netcdf.variables['cell_area'][:].flatten()[gdf['src_index']]
-            grid_shp = self.grid.to_shapefile()
-
-            temp_coords = Dataset(os.path.join(self.grid.temporal_path, 'temporal_coords.nc'), mode='r')
-            cell_area = temp_coords.variables['cell_area'][:].flatten()
-            grid_shp['dst_area'] = cell_area[grid_shp['FID']]
-            # grid_shp['dst_area'] = grid_shp.to_crs({'init': 'epsg:3857'}).area
-            # print grid_shp.crs['units']
-            # sys.exit()
-
-            gdf = gpd.sjoin(gdf.to_crs(grid_shp.crs), grid_shp, how='inner')
-            print(gdf)
-            gdf = np.array_split(gdf, settings.size)
-        else:
-            gdf = None
-
-        gdf = settings.comm.scatter(gdf, root=0)
-
-        for num, pollutant in enumerate(self.pollutant_dicts):
-            settings.write_log('\t\tPollutant {0} ({1}/{2})'.format(
-                pollutant['name'], num + 1, len(self.pollutant_dicts)), level=3)
-            print(('\t\tPollutant {0} ({1}/{2})'.format(pollutant['name'], num + 1, len(self.pollutant_dicts))))
-            aux = netcdf.variables[pollutant['name']][:].flatten()[gdf['src_index']]
-
-            gdf[pollutant['name']] = (aux / gdf['dst_area'].values) * netcdf.variables['cell_area'][:].flatten()[
-                gdf['src_index']]
-        # print netcdf.variables['bc'][:].sum()
-
-        netcdf.close()
-
-        settings.write_time('PointGfasEmissionInventory', 'do_regrid', timeit.default_timer() - st_time, level=2)
-        print('regrid done')
-        del gdf['src_index'], gdf['index_right']
-        self.emissions = gdf
-
-        return True
+    # def do_regrid_balanced_NOT_USED(self):
+    #     import pandas as pd
+    #     import geopandas as gpd
+    #     from shapely.geometry import Point
+    #     import numpy as np
+    #     from netCDF4 import Dataset
+    #
+    #     st_time = timeit.default_timer()
+    #     settings.write_log("\tAllocating GFAS as point sources on grid:", level=2)
+    #
+    #     netcdf = Dataset(self.pollutant_dicts[0]['path'], mode='r')
+    #     if settings.rank == 0:
+    #         gdf = gpd.GeoDataFrame(crs={'init': 'epsg:4326'})
+    #
+    #         first_var = netcdf.variables[self.input_pollutants[0]][:]
+    #
+    #         gdf['src_index'] = np.where(first_var.flatten() > 0)[0]
+    #
+    #         gdf['altitude'] = self.vertical.altitude.flatten()[gdf['src_index']]
+    #
+    #         lat_1d = netcdf.variables['lat'][:]
+    #         lon_1d = netcdf.variables['lon'][:]
+    #         lon_1d[lon_1d > 180] -= 360
+    #         # 1D to 2D
+    #         lats = np.array([lat_1d] * len(lon_1d)).T
+    #         lons = np.array([lon_1d] * len(lat_1d))
+    #
+    #         gdf['geometry'] = [Point(xy) for xy in zip(lons.flatten()[gdf['src_index']],
+    #                                                    lats.flatten()[gdf['src_index']])]
+    #         gdf['src_area'] = netcdf.variables['cell_area'][:].flatten()[gdf['src_index']]
+    #         grid_shp = self.grid.to_shapefile()
+    #
+    #         temp_coords = Dataset(os.path.join(self.grid.temporal_path, 'temporal_coords.nc'), mode='r')
+    #         cell_area = temp_coords.variables['cell_area'][:].flatten()
+    #         grid_shp['dst_area'] = cell_area[grid_shp['FID']]
+    #         # grid_shp['dst_area'] = grid_shp.to_crs({'init': 'epsg:3857'}).area
+    #         # print grid_shp.crs['units']
+    #         # sys.exit()
+    #
+    #         gdf = gpd.sjoin(gdf.to_crs(grid_shp.crs), grid_shp, how='inner')
+    #         print(gdf)
+    #         gdf = np.array_split(gdf, settings.size)
+    #     else:
+    #         gdf = None
+    #
+    #     gdf = settings.comm.scatter(gdf, root=0)
+    #
+    #     for num, pollutant in enumerate(self.pollutant_dicts):
+    #         settings.write_log('\t\tPollutant {0} ({1}/{2})'.format(
+    #             pollutant['name'], num + 1, len(self.pollutant_dicts)), level=3)
+    #         print(('\t\tPollutant {0} ({1}/{2})'.format(pollutant['name'], num + 1, len(self.pollutant_dicts))))
+    #         aux = netcdf.variables[pollutant['name']][:].flatten()[gdf['src_index']]
+    #
+    #         gdf[pollutant['name']] = (aux / gdf['dst_area'].values) * netcdf.variables['cell_area'][:].flatten()[
+    #             gdf['src_index']]
+    #     # print netcdf.variables['bc'][:].sum()
+    #
+    #     netcdf.close()
+    #
+    #     settings.write_time('PointGfasEmissionInventory', 'do_regrid', timeit.default_timer() - st_time, level=2)
+    #     print('regrid done')
+    #     del gdf['src_index'], gdf['index_right']
+    #     self.emissions = gdf
+    #
+    #     return True
 
     def calculate_altitudes(self, vertical_description_path):
         """
diff --git a/hermesv3_gr/modules/emision_inventories/point_gfas_hourly_emission_inventory.py b/hermesv3_gr/modules/emision_inventories/point_gfas_hourly_emission_inventory.py
new file mode 100755
index 0000000000000000000000000000000000000000..a6b53cf0bd260b20347f119ba127b14a4e6776fe
--- /dev/null
+++ b/hermesv3_gr/modules/emision_inventories/point_gfas_hourly_emission_inventory.py
@@ -0,0 +1,463 @@
+#!/usr/bin/env python
+
+# Copyright 2018 Earth Sciences Department, BSC-CNS
+#
+# This file is part of HERMESv3_GR.
+#
+# HERMESv3_GR is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# HERMESv3_GR is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with HERMESv3_GR. If not, see <http://www.gnu.org/licenses/>.
+
+import sys
+import os
+import timeit
+import warnings
+from datetime import datetime
+import pandas as pd
+import hermesv3_gr.config.settings as settings
+from .emission_inventory import EmissionInventory
+from hermesv3_gr.modules.temporal.temporal import TemporalDistribution
+from hermesv3_gr.modules.vertical.vertical_gfas_hourly import GfasHourlyVerticalDistribution
+from hermesv3_gr.tools.mpi_tools import *
+# import mpi4py
+# mpi4py.rc.recv_mprobe = False
+
+BUFFER_SIZE = 2**25
+
+
+class PointGfasHourlyEmissionInventory(EmissionInventory):
+    """
+    Class that defines the content and the methodology for the GFAS hourly emission inventories
+
+    :param current_date: Date to simulate.
+    :type current_date: datetime.datetime
+
+    :param inventory_name: Name of the inventory to use.
+    :type inventory_name: str
+
+    :param sector: Name of the sector of the inventory to use.
+    :type sector: str
+
+    :param pollutants: List of the pollutant name to take into account.
+    :type pollutants: list of str
+
+    :param frequency: Frequency of the inputs. [yearly, monthly, daily]
+    :type frequency: str
+
+    :param reference_year: year of reference of the information of the dataset.
+    :type reference_year: int
+
+    :param factors: NOT IMPLEMENTED YET
+    :type factors: NOT IMPLEMENTED YET
+
+    :param regrid_mask: NOT IMPLEMENTED YET
+    :type regrid_mask: NOT IMPLEMENTED YET
+
+    :param p_vertical: ID of the vertical profile to use.
+    :type p_vertical: str
+
+    :param p_speciation: ID of the speciation profile to use.
+    :type p_speciation: str
+    """
+
+    def __init__(self, comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
+                 frequency, vertical_output_profile,
+                 reference_year=2010, factors=None, regrid_mask=None, p_vertical=None, p_speciation=None):
+
+        st_time = timeit.default_timer()
+        settings.write_log('\t\tCreating GFAS emission inventory as point source.', level=3)
+        super(PointGfasHourlyEmissionInventory, self).__init__(
+            comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
+            vertical_output_profile,
+            reference_year=reference_year, factors=factors, regrid_mask=regrid_mask, p_vertical=None,
+            p_month=None, p_week=None, p_day=None, p_hour=None, p_speciation=p_speciation)
+
+        self.method = self.get_method(p_vertical)
+        self.altitude_name = self.get_altitude_name()
+
+        self.vertical = GfasHourlyVerticalDistribution(vertical_output_profile, self.get_approach(p_vertical))
+
+        self.date_array = TemporalDistribution.caclulate_date_array(
+            options.start_date, options.output_timestep_type, options.output_timestep_num, options.output_timestep_freq)
+
+        self.grid_shp = self.grid.to_shapefile(full_grid=False)
+        self.cell_id_by_proc = self.get_cell_ids()
+        self.coordinates = self.get_coordinates()
+        self.fid_distribution = self.get_fids()
+
+        self.temporal = None
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'Init', timeit.default_timer() - st_time, level=3)
+
+    def __str__(self):
+        string = "PointGfasHourlyEmissionInventory:\n"
+        string += "\t self.method = {0}\n".format(self.method)
+        string += "\t self.vertical = {0}\n".format(self.vertical)
+
+        return string
+
+    @ staticmethod
+    def get_approach(p_vertical):
+        """
+        Extract the given approach value.
+
+        :return: Approach value
+        :rtype: str
+        """
+        import re
+
+        st_time = timeit.default_timer()
+
+        return_value = None
+        aux_list = re.split(', |,| , | ,', p_vertical)
+        for element in aux_list:
+            aux_value = re.split('=| =|= | = ', element)
+            if aux_value[0] == 'approach':
+                return_value = aux_value[1]
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'get_approach', timeit.default_timer() - st_time,
+                            level=3)
+
+        return return_value
+
+    def get_coordinates(self):
+        from netCDF4 import Dataset
+        import geopandas as gpd
+        import pandas as pd
+        from shapely import wkt
+        from hermesv3_gr.tools.netcdf_tools import get_grid_area
+
+        # Getting 2D coordinates
+        coordinates_path = os.path.join(
+            self.auxiliary_files_path, 'gfas', 'coordinates_s{0}_n{1}.csv'.format(settings.size, settings.rank))
+
+        if not os.path.exists(coordinates_path):
+            if settings.rank == 0:
+                if not os.path.exists(os.path.dirname(coordinates_path)):
+                    os.makedirs(os.path.dirname(coordinates_path))
+
+                coordinates_shapefile = os.path.join(
+                    self.auxiliary_files_path, 'gfas', 'coordinates.shp'.format(settings.size, settings.rank))
+                if not os.path.exists(coordinates_shapefile):
+                    src_area = get_grid_area(self.get_input_path(date=self.date_array[0]))
+                    netcdf = Dataset(self.get_input_path(date=self.date_array[0]), mode='r')
+                    lats = netcdf.variables['latitude'][:]
+                    lons = netcdf.variables['longitude'][:]
+                    netcdf.close()
+
+                    lons[lons > 180] = lons[lons > 180] - 360.0
+
+                    len_lats = len(lats)
+                    len_lons = len(lons)
+
+                    lats = np.array([lats] * len_lons).T.flatten()
+                    lons = np.array([lons] * len_lats).flatten()
+
+                    coordinates = gpd.GeoDataFrame(lats, columns=['lats'], crs={'init': 'epsg:4326'})
+                    coordinates['lons'] = lons
+                    coordinates.index.name = 'FID'
+
+                    coordinates['geometry'] = \
+                        'POINT (' + coordinates['lons'].astype(str) + ' ' + coordinates['lats'].astype(str) + ')'
+                    coordinates.drop(columns=['lats', 'lons'], inplace=True)
+                    coordinates['src_area'] = src_area.flatten()
+                    coordinates['geometry'] = coordinates['geometry'].apply(wkt.loads)
+                    coordinates.reset_index(inplace=True)
+                    coordinates.to_file(coordinates_shapefile)
+                else:
+                    coordinates = gpd.read_file(coordinates_shapefile)
+                coordinates = gpd.sjoin(coordinates,
+                                        self.grid.to_shapefile(full_grid=True).to_crs(coordinates.crs),
+                                        how='inner', op='within')
+
+                coordinates.rename(columns={'FID_left': 'FID'}, inplace=True)
+
+                coordinates = coordinates[['FID', 'src_area', 'Cell_ID']]
+                for proc in range(settings.size):
+                    if proc > 0:
+                        settings.comm.send(coordinates[coordinates['Cell_ID'].isin(self.cell_id_by_proc[proc])],
+                                           dest=proc)
+                coordinates = coordinates[coordinates['Cell_ID'].isin(self.cell_id_by_proc[0])]
+
+            else:
+                coordinates = settings.comm.recv(source=0)
+
+            # coordinates = coordinates[['FID', 'Cell_ID']]
+            temp_coords = Dataset(os.path.join(self.grid.temporal_path, 'temporal_coords.nc'), mode='r')
+
+            cell_area = pd.DataFrame(temp_coords.variables['cell_area'][:].flatten(), columns=['dst_area'])
+            temp_coords.close()
+            cell_area['Cell_ID'] = cell_area.index
+
+            coordinates = pd.merge(coordinates, cell_area, on='Cell_ID')
+            # print(coordinates)
+            # exit()
+            coordinates.to_csv(coordinates_path)
+        else:
+            coordinates = pd.read_csv(coordinates_path)
+            coordinates = coordinates[['FID', 'src_area', 'Cell_ID', 'dst_area']]
+        coordinates.set_index('FID', inplace=True)
+
+        return coordinates
+
+    def get_input_path(self, date=None, pollutant=None, extension='nc'):
+        """
+        Completes the path of the NetCDF that contains the needed information of the given pollutant.
+
+        :param date: Date of the NetCDF.
+        :type date: datetime
+
+        :param pollutant: Name of the pollutant of the NetCDF.
+        :type pollutant: str
+
+        :param extension: Extension of the input file.
+        :type: str
+
+        :return: Full path of the needed NetCDF.
+        :rtype: str
+        """
+        st_time = timeit.default_timer()
+
+        if date is None:
+            date = self.date
+
+        # TODO to change path
+        file_path = os.path.join(self.inputs_path, 'multivar', 'ftp',
+                                 '{0}'.format(date.strftime('%Y%m%d')),
+                                 'ga_{0}.{1}'.format(date.strftime('%Y%m%d_%H'), extension))
+
+        # Checking input file
+        if not os.path.exists(file_path):
+            settings.write_log('ERROR: Check the .err file to get more info.')
+            if settings.rank == 0:
+                raise IOError('ERROR: File {0} not found.'.format(file_path))
+            sys.exit(1)
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'get_input_path', timeit.default_timer() - st_time,
+                            level=3)
+
+        return file_path
+
+    def get_altitude_name(self):
+        st_time = timeit.default_timer()
+
+        if self.method == 'sovief':
+            alt_var = 'apt'
+        elif self.method == 'prm':
+            alt_var = 'mami'
+        else:
+            alt_var = None
+            print("ERROR: Only 'sovief' and 'prm' methods are accepted.")
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'get_altitude_name', timeit.default_timer() - st_time,
+                            level=3)
+        return alt_var
+
+    @ staticmethod
+    def get_method(p_vertical):
+        """
+        Extract the given method value.
+
+        :return: Method value
+        :rtype: str
+        """
+        import re
+
+        st_time = timeit.default_timer()
+
+        return_value = None
+        aux_list = re.split(', |,| , | ,', p_vertical)
+        for element in aux_list:
+            aux_value = re.split('=| =|= | = ', element)
+            if aux_value[0] == 'method':
+                return_value = aux_value[1]
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'get_method', timeit.default_timer() - st_time, level=3)
+
+        return return_value
+
+    def get_cell_ids(self):
+        cell_id_by_proc = {}
+        for proc in range(settings.size):
+            if proc == settings.rank:
+                aux_fid = self.grid_shp['Cell_ID'].values
+            else:
+                aux_fid = None
+            cell_id_by_proc[proc] = bcast_array(settings.comm, aux_fid, master=proc)
+
+        return cell_id_by_proc
+
+    def get_fids(self):
+        fid_by_proc = {}
+        for proc in range(settings.size):
+            if proc == settings.rank:
+                aux_fid = list(np.unique(self.coordinates.index.get_level_values('FID').values))
+            else:
+                aux_fid = None
+            fid_by_proc[proc] = bcast_array(settings.comm, aux_fid, master=proc)
+
+        return fid_by_proc
+
+    def do_regrid(self):
+
+        import numpy as np
+        from netCDF4 import Dataset
+
+        st_time = timeit.default_timer()
+        settings.write_log("\tAllocating GFAS as point sources on grid:", level=2)
+
+        # # Masking
+        # if self.masking.regrid_mask is not None:
+        #     gdf = gdf.loc[gdf['src_index'].isin(np.where(self.masking.regrid_mask.flatten() > 0)[0]), ]
+
+        distribution = None
+        # st_time = timeit.default_timer()
+        shapefile_list = []
+
+        # One NetCDF for time step (hour)
+        for tstep, date in enumerate(self.date_array):
+            # print(date, self.get_input_path(date=date))
+            shapefile_list_hour = []
+            netcdf = Dataset(self.get_input_path(date=date), mode='r')
+            for pollutant in self.input_pollutants:
+                if distribution is None:
+                    distribution = get_balanced_distribution(
+                        settings.size, netcdf.variables[pollutant].shape)[settings.rank]
+                var = netcdf.variables[pollutant][0, distribution['y_min']: distribution['y_max'], :].flatten()
+
+                var_index = np.where(var > 0)[0]
+                shapefile_list_hour.append(
+                    pd.DataFrame(var[var_index], columns=[pollutant],
+                                 index=pd.MultiIndex.from_product([(var_index + distribution['fid_min']).astype(int),
+                                                                   [tstep]], names=['FID', 'tstep'])))
+
+            shapefile = pd.concat(shapefile_list_hour, axis=1, sort=False)
+
+            # shapefile = shapefile[sorted(sum(fid_distribution.values(), []))]
+
+            altitude_var = netcdf.variables[self.altitude_name][:].flatten()
+            netcdf.close()
+
+            shapefile['altitude'] = altitude_var[shapefile.index.get_level_values('FID')]
+
+            shapefile_list.append(shapefile)
+        shapefile = pd.concat(shapefile_list, sort=False)
+
+        # Filtering shapefile with the involved cells
+        shapefile = shapefile[shapefile.index.get_level_values('FID').isin(
+            sorted(sum(self.fid_distribution.values(), [])))]
+
+        shapefile = balance_dataframe(settings.comm, shapefile)
+        shapefile.fillna(0.0, inplace=True)
+
+        # print(timeit.default_timer() - st_time)
+        # shapefile = shapefile.reset_index().set_index(['FID', 'tstep'])
+
+        self.emissions = shapefile
+
+        return True
+
+    def distribute(self, emissions):
+        emissions_list = []
+        # print('distributing')
+        for proc in range(settings.size):
+            partial_emis = emissions[emissions['FID'].isin(sorted(np.unique(self.fid_distribution[proc])))].copy()
+            if settings.rank < proc:
+                settings.comm.send(partial_emis, dest=proc)
+                recv_partial_emis = settings.comm.recv(source=proc)
+            elif settings.rank > proc:
+                recv_partial_emis = settings.comm.recv(source=proc)
+                settings.comm.send(partial_emis, dest=proc)
+            else:
+                recv_partial_emis = partial_emis
+            emissions_list.append(recv_partial_emis)
+
+        emissions = pd.concat(emissions_list)
+
+        emissions = pd.merge(emissions, self.coordinates.reset_index(), on='FID')
+
+        for pollutant in self.input_pollutants:
+            emissions[pollutant] = emissions[pollutant] * (emissions['src_area'] / emissions['dst_area'])
+        emissions.drop(columns=['FID', 'src_area', 'dst_area'], inplace=True)
+
+        # Replacing Cell_ID by FID to be able to write
+        emissions = pd.merge(emissions, self.grid_shp[['FID', 'Cell_ID']], on='Cell_ID')
+        emissions.drop(columns=['Cell_ID'], inplace=True)
+
+        emissions.rename(columns={'Cell_ID': 'FID'}, inplace=True)
+        emissions = emissions.groupby(['FID', 'tstep', 'layer']).sum()
+
+        return emissions
+
+    def calculate_altitudes(self, vertical_description_path):
+        """
+        Calculate the number layer to allocate the point source.
+
+        :param vertical_description_path: Path to the file that contains the vertical description
+        :type vertical_description_path: str
+
+        :return: True
+        :rtype: bool
+        """
+        import pandas as pd
+
+        st_time = timeit.default_timer()
+        settings.write_log("\t\tCalculating vertical allocation.", level=3)
+        df = pd.read_csv(vertical_description_path, sep=',')
+        if self.vertical.approach == 'surface':
+            self.emissions['altitude'] = 0
+
+        self.emissions['layer'] = None
+        for i, line in df.iterrows():
+            layer = line['Ilayer'] - 1
+            self.emissions.loc[self.emissions['altitude'] <= line['height_magl'], 'layer'] = layer
+            self.emissions.loc[self.emissions['altitude'] <= line['height_magl'], 'altitude'] = None
+
+        # Fires with higher altitudes than the max layer limit goes to the last layer
+        if len(self.emissions[~self.emissions['altitude'].isna()]) > 0:
+            self.emissions.loc[~self.emissions['altitude'].isna(), 'layer'] = layer
+            warnings.warn('WARNING: One or more fires have an altitude of fire emission injection higher than the top' +
+                          ' layer of the model defined in the {0} file'.format(vertical_description_path))
+
+            # print(self.emissions.loc[~self.emissions['altitude'].isna()])
+        del self.emissions['altitude']
+
+        self.emissions = self.emissions.groupby(['FID', 'tstep', 'layer']).sum()
+        self.emissions.reset_index(inplace=True)
+
+        self.emissions = self.vertical.distribute_vertically(self.emissions, self.input_pollutants)
+
+        settings.write_time('PointGfasHourlyEmissionInventory', 'calculate_altitudes', timeit.default_timer() - st_time,
+                            level=2)
+        return True
+
+    def point_source_by_cell(self):
+        """
+        Sums the different emissions that are allocated in the same cell and layer.
+
+        :return: None
+        """
+        emissions = self.distribute(self.emissions).reset_index()
+        self.location = emissions.loc[:, ['FID', 'tstep', 'layer']]
+
+        self.emissions = []
+        for input_pollutant in self.input_pollutants:
+            dict_aux = {
+                'name': input_pollutant,
+                'units': '...',
+                'data': emissions.loc[:, input_pollutant].values
+            }
+            self.emissions.append(dict_aux)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/hermesv3_gr/modules/emision_inventories/point_source_emission_inventory.py b/hermesv3_gr/modules/emision_inventories/point_source_emission_inventory.py
index 5eab7dc7526bf7e03cb94d2bb151f2795fcd383a..b213e5f35baf28e302301623ada8e5336dffbd21 100755
--- a/hermesv3_gr/modules/emision_inventories/point_source_emission_inventory.py
+++ b/hermesv3_gr/modules/emision_inventories/point_source_emission_inventory.py
@@ -73,18 +73,19 @@ class PointSourceEmissionInventory(EmissionInventory):
     :type p_speciation: str
     """
 
-    def __init__(self, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
+    def __init__(self, comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path,
                  frequency, vertical_output_profile, reference_year=2010, factors=None, regrid_mask=None,
-                 p_vertical=None, p_month=None, p_week=None, p_day=None, p_hour=None, p_speciation=None):
+                 p_vertical=None, p_month=None, p_week=None, p_day=None, p_hour=None, p_speciation=None,
+                 countries_shapefile=None):
 
         st_time = timeit.default_timer()
         settings.write_log('\t\tCreating point source emission inventory.', level=3)
 
         super(PointSourceEmissionInventory, self).__init__(
-            options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
+            comm, options, grid, current_date, inventory_name, source_type, sector, pollutants, inputs_path, frequency,
             vertical_output_profile, reference_year=reference_year, factors=factors, regrid_mask=regrid_mask,
             p_vertical=p_vertical, p_month=p_month, p_week=p_week, p_day=p_day, p_hour=p_hour,
-            p_speciation=p_speciation)
+            p_speciation=p_speciation, countries_shapefile=countries_shapefile)
 
         self.crs = {'init': 'epsg:4326'}
         self.location = None
diff --git a/hermesv3_gr/modules/grids/grid.py b/hermesv3_gr/modules/grids/grid.py
index 79ebcb8d08424c1ff8b621cdc8e085dbc6cc8572..a4784dd934b72e56da7e6cf279cea97477b2b059 100755
--- a/hermesv3_gr/modules/grids/grid.py
+++ b/hermesv3_gr/modules/grids/grid.py
@@ -26,6 +26,61 @@ import ESMF
 import hermesv3_gr.config.settings as settings
 
 
+def select_grid(comm, arguments):
+
+    st_time = timeit.default_timer()
+    settings.write_log('Selecting grid', level=1)
+
+    # Creating a different object depending on the grid type
+    if arguments.domain_type == 'global':
+        from hermesv3_gr.modules.grids.grid_global import GlobalGrid
+        grid = GlobalGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                          arguments.auxiliary_files_path, arguments.inc_lat, arguments.inc_lon, comm=comm)
+
+    elif arguments.domain_type == 'regular':
+        from hermesv3_gr.modules.grids.grid_latlon import LatLonGrid
+        grid = LatLonGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                          arguments.auxiliary_files_path, arguments.inc_lat, arguments.inc_lon,
+                          arguments.lat_orig, arguments.lon_orig, arguments.n_lat, arguments.n_lon, comm=comm)
+
+    elif arguments.domain_type == 'rotated':
+        from hermesv3_gr.modules.grids.grid_rotated import RotatedGrid
+        grid = RotatedGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                           arguments.auxiliary_files_path, arguments.centre_lat, arguments.centre_lon,
+                           arguments.west_boundary, arguments.south_boundary, arguments.inc_rlat, arguments.inc_rlon,
+                           comm=comm)
+
+    elif arguments.domain_type == 'rotated_nested':
+        from hermesv3_gr.modules.grids.grid_rotated_nested import RotatedNestedGrid
+        grid = RotatedNestedGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                                 arguments.auxiliary_files_path, arguments.parent_grid_path, arguments.parent_ratio,
+                                 arguments.i_parent_start, arguments.j_parent_start, arguments.n_rlat, arguments.n_rlon,
+                                 comm=comm)
+
+    elif arguments.domain_type == 'lcc':
+        from hermesv3_gr.modules.grids.grid_lcc import LccGrid
+        grid = LccGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                       arguments.auxiliary_files_path, arguments.lat_1, arguments.lat_2, arguments.lon_0,
+                       arguments.lat_0, arguments.nx, arguments.ny, arguments.inc_x, arguments.inc_y, arguments.x_0,
+                       arguments.y_0, comm=comm)
+
+    elif arguments.domain_type == 'mercator':
+        from hermesv3_gr.modules.grids.grid_mercator import MercatorGrid
+        grid = MercatorGrid(arguments.domain_type, arguments.vertical_description, arguments.output_timestep_num,
+                            arguments.auxiliary_files_path, arguments.lat_ts, arguments.lon_0, arguments.nx,
+                            arguments.ny, arguments.inc_x, arguments.inc_y, arguments.x_0, arguments.y_0, comm=comm)
+    else:
+        settings.write_log('ERROR: Check the .err file to get more info.')
+        if settings.rank == 0:
+            raise NotImplementedError("The grid type {0} is not implemented.".format(arguments.domain_type)
+                                      + " Use 'global', 'regular, 'rotated', 'lcc' or 'mercator.")
+        sys.exit(1)
+
+    settings.write_time('Grid', 'select_grid', timeit.default_timer() - st_time, level=3)
+
+    return grid
+
+
 class Grid(object):
     """
     Grid object that contains the information of the output grid.
@@ -61,6 +116,7 @@ class Grid(object):
         self.coords_netcdf_file = os.path.join(temporal_path, 'temporal_coords.nc')
         self.temporal_path = temporal_path
         self.shapefile_path = None
+        self.border_shapefile_path = None
 
         # self.esmf_grid = None
         self.x_lower_bound = None
@@ -110,125 +166,6 @@ class Grid(object):
         settings.write_time('Grid', 'create_esmf_grid_from_file', timeit.default_timer() - st_time, level=3)
         return grid
 
-    @staticmethod
-    def select_grid(comm, grid_type, vertical_description_path, timestep_num, temporal_path, inc_lat, inc_lon, lat_orig,
-                    lon_orig, n_lat, n_lon, centre_lat, centre_lon, west_boundary, south_boundary, inc_rlat, inc_rlon,
-                    lat_1, lat_2, lon_0, lat_0, nx, ny, inc_x, inc_y, x_0, y_0, lat_ts):
-        # TODO describe better the rotated parameters
-        """
-        Create a Grid object depending on the grid type.
-
-        :param grid_type: type of grid to create [global, rotated, lcc, mercator]
-        :type grid_type: str
-
-        :param vertical_description_path: Path to the file that contains the vertical description.
-        :type vertical_description_path: str
-
-        :param timestep_num: Number of timesteps.
-        :type timestep_num: int
-
-        :param temporal_path: Path to the temporal folder.
-        :type temporal_path: str
-
-        :param inc_lat: [global] Increment between latitude centroids (degrees).
-        :type inc_lat: float
-
-        :param inc_lon: [global] Increment between longitude centroids (degrees).
-        :type inc_lon: float
-
-        :param centre_lat: [rotated]
-        :type centre_lat: float
-
-        :param centre_lon: [rotated]
-        :type centre_lon: float
-
-        :param west_boundary: [rotated]
-        :type west_boundary: float
-
-        :param south_boundary: [rotated]
-        :type south_boundary: float
-
-        :param inc_rlat: [rotated] Increment between rotated latitude centroids (degrees).
-        :type inc_rlat: float
-
-        :param inc_rlon: [rotated] Increment between rotated longitude centroids (degrees).
-        :type inc_rlon: float
-
-        :param lat_ts: [mercator]
-        :type lat_ts: float
-
-        :param lat_1: [lcc] Value of the Lat1 for the LCC grid type.
-        :type lat_1: float
-
-        :param lat_2: [lcc] Value of the Lat2 for the LCC grid type.
-        :type lat_2: float
-
-        :param lon_0: [lcc, mercator] Value of the Lon0 for the LCC grid type.
-        :type lon_0: float
-
-        :param lat_0: [lcc] Value of the Lat0 for the LCC grid type.
-        :type lat_0: float
-
-        :param nx: [lcc, mercator] Number of cells on the x dimension.
-        :type nx: int
-
-        :param ny: [lcc, mercator] Number of cells on the y dimension.
-        :type ny: int
-
-        :param inc_x: [lcc, mercator] Increment between x dimensions cell centroids (metres).
-        :type inc_x: int
-
-        :param inc_y: [lcc, mercator] Increment between y dimensions cell centroids (metres).
-        :type inc_y: int
-
-        :param x_0: [lcc, mercator] Value of the X0 for the LCC grid type.
-        :type x_0: float
-
-        :param y_0: [lcc, mercator] Value of the Y0 for the LCC grid type.
-        :type y_0: float
-
-        :return: Grid object. It will return a GlobalGrid, RotatedGrid or LccGrid depending on the type.
-        :rtype: Grid
-        """
-
-        st_time = timeit.default_timer()
-        settings.write_log('Selecting grid', level=1)
-
-        # Creating a different object depending on the grid type
-        if grid_type == 'global':
-            from hermesv3_gr.modules.grids.grid_global import GlobalGrid
-            grid = GlobalGrid(grid_type, vertical_description_path, timestep_num, temporal_path, inc_lat, inc_lon,
-                              comm=comm)
-
-        elif grid_type == 'regular':
-            from hermesv3_gr.modules.grids.grid_latlon import LatLonGrid
-            grid = LatLonGrid(grid_type, vertical_description_path, timestep_num, temporal_path, inc_lat, inc_lon,
-                              lat_orig, lon_orig, n_lat, n_lon, comm=comm)
-        elif grid_type == 'rotated':
-            from hermesv3_gr.modules.grids.grid_rotated import RotatedGrid
-            grid = RotatedGrid(grid_type, vertical_description_path, timestep_num, temporal_path,
-                               centre_lat, centre_lon, west_boundary, south_boundary, inc_rlat, inc_rlon, comm=comm)
-
-        elif grid_type == 'lcc':
-            from hermesv3_gr.modules.grids.grid_lcc import LccGrid
-            grid = LccGrid(grid_type, vertical_description_path, timestep_num, temporal_path, lat_1, lat_2, lon_0,
-                           lat_0, nx, ny, inc_x, inc_y, x_0, y_0, comm=comm)
-
-        elif grid_type == 'mercator':
-            from hermesv3_gr.modules.grids.grid_mercator import MercatorGrid
-            grid = MercatorGrid(grid_type, vertical_description_path, timestep_num, temporal_path, lat_ts, lon_0,
-                                nx, ny, inc_x, inc_y, x_0, y_0, comm=comm)
-        else:
-            settings.write_log('ERROR: Check the .err file to get more info.')
-            if settings.rank == 0:
-                raise NotImplementedError("The grid type {0} is not implemented.".format(grid_type)
-                                          + " Use 'global', 'regular, 'rotated', 'lcc' or 'mercator.")
-            sys.exit(1)
-
-        settings.write_time('Grid', 'select_grid', timeit.default_timer() - st_time, level=3)
-
-        return grid
-
     @staticmethod
     def set_vertical_levels(vertical_description_path):
         """
@@ -492,23 +429,45 @@ class Grid(object):
             list_points = df.values
             del df['p1'], df['p2'], df['p3'], df['p4']
 
+            # Calculating Cell ID
+            index = np.array(range(self.full_shape[2] * self.full_shape[3]))
+            index = index.reshape((self.full_shape[2], self.full_shape[3]))
+            index = index[self.x_lower_bound:self.x_upper_bound, self.y_lower_bound:self.y_upper_bound]
+            df['Cell_ID'] = index.flatten()
             # List of polygons from the list of points
             geometry = [Polygon(list(points)) for points in list_points]
 
             gdf = gpd.GeoDataFrame(df, crs={'init': 'epsg:4326'}, geometry=geometry)
             gdf = gdf.to_crs(self.crs)
 
-            gdf['FID'] = gdf.index
+            gdf.index.name = 'FID'
 
-            gdf.to_file(self.shapefile_path)
+            gdf.reset_index().to_file(self.shapefile_path)
         else:
             settings.write_log('\t\tGrid shapefile already done. Lets try to read it.', level=3)
             gdf = gpd.read_file(self.shapefile_path)
+            gdf.set_index('FID')
 
         settings.write_time('Grid', 'to_shapefile', timeit.default_timer() - st_time, level=1)
 
         return gdf
 
+    def get_border(self):
+        from geopandas import GeoDataFrame
+        from hermesv3_gr.tools.mpi_tools import bcast_array
+
+        st_time = timeit.default_timer()
+        # settings.write_log('\t\tGetting grid shapefile', level=3)
+
+        if settings.rank == 0:
+            grid = self.to_shapefile(full_grid=True)
+            border = GeoDataFrame(geometry=[grid.unary_union], crs=grid.crs)
+        else:
+            border = None
+
+        border = bcast_array(settings.comm, border)
+        return border
+
     def chech_coords_file(self):
         """
         Checks if the auxiliary coordinates file is created well.
diff --git a/hermesv3_gr/modules/grids/grid_mercator.py b/hermesv3_gr/modules/grids/grid_mercator.py
index e956d17f676349bf90e5c4248a44cde76717f11b..171443973b94e58eff015cc34c500028210f2280 100755
--- a/hermesv3_gr/modules/grids/grid_mercator.py
+++ b/hermesv3_gr/modules/grids/grid_mercator.py
@@ -68,12 +68,12 @@ class MercatorGrid(Grid):
     """
 
     def __init__(self, grid_type, vertical_description_path, timestep_num, temporal_path, lat_ts, lon_0,
-                 nx, ny, inc_x, inc_y, x_0, y_0, earth_radius=6370000.000):
+                 nx, ny, inc_x, inc_y, x_0, y_0, earth_radius=6370000.000, comm=None):
         st_time = timeit.default_timer()
         settings.write_log('\tCreating Mercator grid.', level=2)
 
         # Initialises with parent class
-        super(MercatorGrid, self).__init__(grid_type, vertical_description_path, temporal_path)
+        super(MercatorGrid, self).__init__(grid_type, vertical_description_path, temporal_path, comm)
 
         # Setting parameters
         self.lat_ts = lat_ts
diff --git a/hermesv3_gr/modules/grids/grid_rotated_nested.py b/hermesv3_gr/modules/grids/grid_rotated_nested.py
new file mode 100755
index 0000000000000000000000000000000000000000..1ae474f2533082027ab0dfaec4186539f3f8db98
--- /dev/null
+++ b/hermesv3_gr/modules/grids/grid_rotated_nested.py
@@ -0,0 +1,253 @@
+#!/usr/bin/env python
+
+# Copyright 2018 Earth Sciences Department, BSC-CNS
+#
+# This file is part of HERMESv3_GR.
+#
+# HERMESv3_GR is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# HERMESv3_GR is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with HERMESv3_GR. If not, see <http://www.gnu.org/licenses/>.
+
+
+import sys
+import os
+import timeit
+import hermesv3_gr.config.settings as settings
+from .grid import Grid
+
+
+class RotatedNestedGrid(Grid):
+    # TODO Rotated options description
+    """
+    :param grid_type: Type of the output grid [global, rotated, lcc, mercator].
+    :type grid_type: str
+
+    :param vertical_description_path: Path to the file that contains the vertical description.
+    :type vertical_description_path: str
+
+
+    :param timestep_num: Number of timesteps.
+    :type timestep_num: int
+    """
+
+    def __init__(self, grid_type, vertical_description_path, timestep_num, temporal_path, parent_grid_path,
+                 parent_ratio, i_parent_start, j_parent_start, n_rlat, n_rlon, comm=None):
+        st_time = timeit.default_timer()
+        settings.write_log('\tCreating Rotated grid.', level=2)
+
+        # Initialises with parent class
+        super(RotatedNestedGrid, self).__init__(grid_type, vertical_description_path, temporal_path, comm)
+
+        # Setting parameters
+        attributes = {'parent_grid_path': parent_grid_path, 'parent_ratio': parent_ratio,
+                      'i_parent_start': i_parent_start, 'j_parent_start': j_parent_start,
+                      'n_rlat': n_rlat, 'n_rlon': n_rlon, 'crs': {'init': 'epsg:4326'}}
+        attributes = self.get_parent_attributes(attributes)
+
+        self.new_pole_longitude_degrees = attributes['parent']['new_pole_longitude_degrees']
+        self.new_pole_latitude_degrees = attributes['parent']['new_pole_latitude_degrees']
+        self.rlat_1st = attributes['1st_rlat']
+        self.rlon_1st = attributes['1st_rlon']
+        self.inc_rlat = attributes['inc_rlat']
+        self.inc_rlon = attributes['inc_rlon']
+        self.n_rlat = attributes['n_rlat']
+        self.n_rlon = attributes['n_rlon']
+
+        # Rotated coordinates
+        self.rlat = None
+        self.rlon = None
+
+        # Create coordinates
+        self.crs = {'init': 'epsg:4326'}
+        self.create_coords()
+
+        if not os.path.exists(self.coords_netcdf_file):
+            if settings.rank == 0:
+                self.write_coords_netcdf()
+            settings.comm.Barrier()
+
+        self.calculate_bounds()
+
+        self.shape = (timestep_num, len(self.vertical_description), self.x_upper_bound-self.x_lower_bound,
+                      self.y_upper_bound-self.y_lower_bound)
+
+        total_area = self.get_cell_area()
+        self.full_shape = (timestep_num, len(self.vertical_description), total_area.shape[-2], total_area.shape[-1])
+        self.cell_area = total_area[self.x_lower_bound:self.x_upper_bound, self.y_lower_bound:self.y_upper_bound]
+
+        settings.write_time('RotatedNestedGrid', 'Init', timeit.default_timer() - st_time, level=1)
+
+    @staticmethod
+    def get_parent_attributes(attributes):
+        from netCDF4 import Dataset
+
+        netcdf = Dataset(attributes['parent_grid_path'], mode='r')
+
+        rlat = netcdf.variables['rlat'][:]
+        attributes['inc_rlat'] = (rlat[1] - rlat[0]) / attributes['parent_ratio']
+        # j_parent_start start the index at 1 so we must - 1
+        attributes['1st_rlat'] = rlat[int(attributes['j_parent_start']) - 1]
+
+        rlon = netcdf.variables['rlon'][:]
+        attributes['inc_rlon'] = (rlon[1] - rlon[0]) / attributes['parent_ratio']
+        # i_parent_start start the index at 1 so we must - 1
+        attributes['1st_rlon'] = rlon[attributes['i_parent_start'] - 1]
+
+        rotated_pole = netcdf.variables['rotated_pole']
+        attributes['parent'] = {'new_pole_longitude_degrees': rotated_pole.grid_north_pole_longitude,
+                                'new_pole_latitude_degrees': 90 - rotated_pole.grid_north_pole_latitude}
+
+        return attributes
+
+    def create_coords(self):
+        """
+        Create the coordinates for a rotated domain.
+        """
+        from hermesv3_gr.tools.coordinates_tools import create_regular_rotated
+        import numpy as np
+
+        st_time = timeit.default_timer()
+        settings.write_log('\t\tCreating rotated coordinates.', level=3)
+
+        # Create rotated coordinates
+        (self.rlat, self.rlon, br_lats_single, br_lons_single) = create_regular_rotated(
+            self.rlat_1st, self.rlon_1st, self.inc_rlat, self.inc_rlon, self.n_rlat, self.n_rlon)
+        if len(self.rlon)//2 < settings.size:
+            settings.write_log('ERROR: Check the .err file to get more info.')
+            if settings.rank == 0:
+                raise AttributeError("ERROR: Maximum number of processors exceeded. " +
+                                     "It has to be less or equal than {0}.".format(len(self.rlon)//2))
+            sys.exit(1)
+        # 1D to 2D
+        c_lats = np.array([self.rlat] * len(self.rlon)).T
+        c_lons = np.array([self.rlon] * len(self.rlat))
+
+        # Create rotated boundary coordinates
+        b_lats = super(RotatedNestedGrid, self).create_bounds(c_lats, self.inc_rlat, number_vertices=4, inverse=True)
+        b_lons = super(RotatedNestedGrid, self).create_bounds(c_lons, self.inc_rlon, number_vertices=4)
+
+        # Rotated to Lat-Lon
+        self.boundary_longitudes, self.boundary_latitudes = self.rotated2latlon(b_lons, b_lats)
+        self.center_longitudes, self.center_latitudes = self.rotated2latlon(c_lons, c_lats)
+
+        settings.write_time('RotatedNestedGrid', 'create_coords', timeit.default_timer() - st_time, level=2)
+
+    def rotated2latlon(self, lon_deg, lat_deg, lon_min=-180):
+        """
+        Calculate the unrotated coordinates using the rotated ones.
+
+        :param lon_deg: Rotated longitude coordinate.
+        :type lon_deg: numpy.array
+
+        :param lat_deg: Rotated latitude coordinate.
+        :type lat_deg: numpy.array
+
+        :param lon_min: Minimum value for the longitudes: -180 (-180 to 180) or 0 (0 to 360)
+        :type lon_min: float
+
+        :return: Unrotated coordinates. Longitudes, Latitudes
+        :rtype: tuple(numpy.array, numpy.array)
+        """
+        import numpy as np
+        import math
+
+        st_time = timeit.default_timer()
+        settings.write_log('\t\t\tTransforming rotated coordinates to latitude, longitude coordinates.', level=3)
+
+        # TODO Document this function
+        degrees_to_radians = math.pi / 180.
+        # radians_to_degrees = 180. / math.pi
+
+        # Positive east to negative east
+        # self.new_pole_longitude_degrees -= 180
+
+        tph0 = self.new_pole_latitude_degrees * degrees_to_radians
+        tlm = lon_deg * degrees_to_radians
+        tph = lat_deg * degrees_to_radians
+        tlm0d = self.new_pole_longitude_degrees
+        ctph0 = np.cos(tph0)
+        stph0 = np.sin(tph0)
+
+        stlm = np.sin(tlm)
+        ctlm = np.cos(tlm)
+        stph = np.sin(tph)
+        ctph = np.cos(tph)
+
+        # Latitude
+        sph = (ctph0 * stph) + (stph0 * ctph * ctlm)
+        # if sph > 1.:
+        #     sph = 1.
+        # if sph < -1.:
+        #     sph = -1.
+        # print type(sph)
+        sph[sph > 1.] = 1.
+        sph[sph < -1.] = -1.
+
+        aph = np.arcsin(sph)
+        aphd = aph / degrees_to_radians
+
+        # Longitude
+        anum = ctph * stlm
+        denom = (ctlm * ctph - stph0 * sph) / ctph0
+        relm = np.arctan2(anum, denom) - math.pi
+        almd = relm / degrees_to_radians + tlm0d
+
+        # if almd < min_lon:
+        #     almd += 360
+        # elif almd > max_lon:
+        #     almd -= 360
+        almd[almd > (lon_min + 360)] -= 360
+        almd[almd < lon_min] += 360
+
+        settings.write_time('RotatedNestedGrid', 'rotated2latlon', timeit.default_timer() - st_time, level=3)
+
+        return almd, aphd
+
+    def write_coords_netcdf(self):
+        """
+        Writes the temporal file with the coordinates of the output needed to generate the weight matrix.
+        If it is already well created it will only add the cell_area parameter.
+        """
+        from hermesv3_gr.modules.writing.writer import Writer
+
+        st_time = timeit.default_timer()
+        settings.write_log('\tWriting {0} file.'.format(self.coords_netcdf_file), level=3)
+
+        if not self.chech_coords_file():
+            # Writes an auxiliary empty NetCDF only with the coordinates and an empty variable.
+            Writer.write_netcdf(self.coords_netcdf_file, self.center_latitudes, self.center_longitudes,
+                                [{'name': 'var_aux', 'units': '', 'data': 0}],
+                                boundary_latitudes=self.boundary_latitudes,
+                                boundary_longitudes=self.boundary_longitudes,
+                                roated=True, rotated_lats=self.rlat, rotated_lons=self.rlon,
+                                north_pole_lat=self.new_pole_latitude_degrees,
+                                north_pole_lon=self.new_pole_longitude_degrees)
+
+            # Calculate the cell area of the auxiliary NetCDF file
+            self.cell_area = self.get_cell_area()
+
+            # Re-writes the NetCDF adding the cell area
+            Writer.write_netcdf(self.coords_netcdf_file, self.center_latitudes, self.center_longitudes,
+                                [{'name': 'var_aux', 'units': '', 'data': 0}],
+                                boundary_latitudes=self.boundary_latitudes,
+                                boundary_longitudes=self.boundary_longitudes, cell_area=self.cell_area,
+                                roated=True, rotated_lats=self.rlat, rotated_lons=self.rlon,
+                                north_pole_lat=self.new_pole_latitude_degrees,
+                                north_pole_lon=self.new_pole_longitude_degrees)
+        else:
+            self.cell_area = self.get_cell_area()
+
+        settings.write_time('RotatedNestedGrid', 'write_coords_netcdf', timeit.default_timer() - st_time, level=3)
+
+
+if __name__ == '__main__':
+    pass
diff --git a/hermesv3_gr/modules/masking/masking.py b/hermesv3_gr/modules/masking/masking.py
index 90670cae1cc825744927c0805bc3b6417b5d3acb..4983a1e1d60f85e06a559672eff9a7a88b5ab0dc 100755
--- a/hermesv3_gr/modules/masking/masking.py
+++ b/hermesv3_gr/modules/masking/masking.py
@@ -44,12 +44,14 @@ class Masking(object):
     :type world_mask_file: str
     """
 
-    def __init__(self, world_info, factors_mask_values, regrid_mask_values, grid, world_mask_file=None):
+    def __init__(self, world_info, factors_mask_values, regrid_mask_values, grid, world_mask_file=None,
+                 countries_shapefile=None):
         from timezonefinder import TimezoneFinder
 
         st_time = timeit.default_timer()
         settings.write_log('\t\tCreating mask.', level=2)
 
+        self.countries_shapefile = countries_shapefile
         self.adding = None
         self.world_info = world_info
         self.country_codes = self.get_country_codes()
@@ -85,46 +87,41 @@ class Masking(object):
         settings.write_time('Masking', 'get_country_codes', timeit.default_timer() - st_time, level=3)
         return countries_dict
 
-    def create_country_iso(self, in_nc):
+    def create_country_iso(self, comm, netcdf_path):
         import numpy as np
-        from hermesv3_gr.tools.netcdf_tools import extract_vars
+        from hermesv3_gr.tools.netcdf_tools import netcdf2shp, extract_vars, gather_shapefile
         from hermesv3_gr.modules.writing.writer import Writer
+        import geopandas as gpd
 
         st_time = timeit.default_timer()
         settings.write_log('\t\t\tCreating {0} file.'.format(self.world_mask_file), level=2)
         # output_path = os.path.join(output_dir, 'iso.nc')
+        shapefile = netcdf2shp(comm, netcdf_path)
 
-        lat_o, lon_o = extract_vars(in_nc, ['lat', 'lon'])
-        lon = np.array([lon_o['data']] * len(lat_o['data']))
-        lat = np.array([lat_o['data']] * len(lon_o['data'])).T
-
-        dst_var = []
-        num = 0
-        aux_list = list(zip(lat.flatten(), lon.flatten()))
-        points = np.array(aux_list[:])
-
-        points_list = np.array_split(points, settings.size)
-
-        for lat_aux, lon_aux in points_list[settings.rank]:
-            num += 1
-
-            settings.write_log("\t\t\t\tlat:{0}, lon:{1} ({2}/{3})".format(
-                lat_aux, lon_aux, num, len(points_list[settings.rank])), level=3)
+        if comm.Get_rank() == 0:
+            countries = gpd.read_file(self.countries_shapefile)
+            countries = countries.to_crs(shapefile.crs)
+        else:
+            countries = None
+        countries = comm.bcast(countries, root=0)
 
-            tz = self.find_timezone(lat_aux, lon_aux)
-            tz_id = self.get_iso_code_from_tz(tz)
-            dst_var.append(tz_id)
-        dst_var = np.array(dst_var)
-        dst_var = settings.comm.gather(dst_var, root=0)
+        shapefile = gpd.sjoin(shapefile, countries[['M49', 'geometry']], how='left', op='intersects')
+        shapefile = shapefile[~shapefile.index.duplicated(keep='first')]
+        shapefile.fillna(0, inplace=True)
+        shapefile = gather_shapefile(comm, shapefile)
 
         if settings.rank == 0:
-            dst_var = np.concatenate(dst_var)
-            dst_var = dst_var.reshape((1,) + lat.shape)
+            lat_o, lon_o = extract_vars(netcdf_path, ['lat', 'lon'])
+            lon = np.array([lon_o['data']] * len(lat_o['data']))
+            lat = np.array([lat_o['data']] * len(lon_o['data'])).T
+
+            values = shapefile['M49'].astype(int).values
             data = [{
                 'name': 'timezone_id',
                 'units': '',
-                'data': dst_var,
+                'data': values.reshape((1, lat.shape[0], lat.shape[1])),
             }]
+
             Writer.write_netcdf(self.world_mask_file, lat, lon, data, regular_latlon=True)
         settings.comm.Barrier()
 
@@ -223,15 +220,15 @@ class Masking(object):
 
         return code_list
 
-    def check_regrid_mask(self, input_file):
+    def check_regrid_mask(self, comm, input_file):
 
         if self.regrid_mask_values is not None:
             if not os.path.exists(self.world_mask_file):
-                self.create_country_iso(input_file)
+                self.create_country_iso(comm, input_file)
             self.regrid_mask = self.custom_regrid_mask()
         if self.factors_mask_values is not None:
             if not os.path.exists(self.world_mask_file):
-                self.create_country_iso(input_file)
+                self.create_country_iso(comm, input_file)
             self.scale_mask = self.custom_scale_mask()
 
     def custom_regrid_mask(self):
diff --git a/hermesv3_gr/modules/regrid/regrid.py b/hermesv3_gr/modules/regrid/regrid.py
index 1302a9d810ea088085bed065bb9b371b98721e4c..a7f0379b27dfe1756048369e61987671c1d4db1c 100755
--- a/hermesv3_gr/modules/regrid/regrid.py
+++ b/hermesv3_gr/modules/regrid/regrid.py
@@ -25,11 +25,12 @@ import hermesv3_gr.config.settings as settings
 
 class Regrid(object):
     # TODO Documentation
-    def __init__(self, pollutant_dicts, weight_matrix_file, grid, masking=None):
+    def __init__(self, pollutant_dicts, weight_matrix_file, grid, src_coverage, masking=None):
         st_time = timeit.default_timer()
         settings.write_log('\t\t\tInitializing Regrid.', level=3)
 
         self.grid = grid
+        self.src_coverage = src_coverage
         self.pollutant_dicts = pollutant_dicts
         self.weight_matrix_file = weight_matrix_file
         self.masking = masking
diff --git a/hermesv3_gr/modules/regrid/regrid_conservative.py b/hermesv3_gr/modules/regrid/regrid_conservative.py
index d75850a5a214516c9fb20eed9096c9924f9aec6a..1108697a69eb6f858cf40e8612751650e8917ae5 100755
--- a/hermesv3_gr/modules/regrid/regrid_conservative.py
+++ b/hermesv3_gr/modules/regrid/regrid_conservative.py
@@ -28,22 +28,24 @@ from .regrid import Regrid
 
 class ConservativeRegrid(Regrid):
     # TODO Documentation
-    def __init__(self, pollutant_dicts, weight_matrix_file, grid, masking=None):
+    def __init__(self, pollutant_dicts, weight_matrix_file, grid, src_coverage, masking=None):
         st_time = timeit.default_timer()
         settings.write_log('\t\tInitializing Conservative regrid.', level=2)
 
-        super(ConservativeRegrid, self).__init__(pollutant_dicts, weight_matrix_file, grid, masking=masking)
+        super(ConservativeRegrid, self).__init__(
+            pollutant_dicts, weight_matrix_file, grid, src_coverage, masking=masking)
 
         settings.write_time('ConservativeRegrid', 'Init', timeit.default_timer() - st_time, level=2)
 
-    def create_weight_matrix(self):
+    def create_weight_matrix(self,):
         """
         Calls to ESMF_RegridWeightGen to generate the weight matrix.
         """
 
         st_time = timeit.default_timer()
 
-        src_grid = self.grid.create_esmf_grid_from_file(self.pollutant_dicts[0]['path'])
+        src_grid = self.grid.create_esmf_grid_from_file(self.pollutant_dicts[0]['path'],
+                                                        sphere=self.src_coverage == 'global')
         src_field = ESMF.Field(src_grid, name='my input field')
         src_field.read(filename=self.pollutant_dicts[0]['path'], variable=self.pollutant_dicts[0]['name'],
                        timeslice=0)
@@ -52,7 +54,8 @@ class ConservativeRegrid(Regrid):
 
         dst_field = ESMF.Field(dst_grid, name='my outut field')
 
-        ESMF.Regrid(src_field, dst_field, filename=self.weight_matrix_file, regrid_method=ESMF.RegridMethod.CONSERVE,)
+        ESMF.Regrid(src_field, dst_field, filename=self.weight_matrix_file, regrid_method=ESMF.RegridMethod.CONSERVE,
+                    unmapped_action=1)
 
         settings.write_time('ConservativeRegrid', 'create_weight_matrix', timeit.default_timer() - st_time, level=1)
 
@@ -109,6 +112,53 @@ class ConservativeRegrid(Regrid):
         settings.write_time('ConservativeRegrid', 'start_regridding', timeit.default_timer() - st_time, level=3)
         return dst_field_list
 
+    def start_esmpy_regridding(self, gfas=False, vertical=None):
+
+        st_time = timeit.default_timer()
+
+        regrid = None
+
+        src_grid = self.grid.create_esmf_grid_from_file(self.pollutant_dicts[0]['path'],
+                                                        sphere=self.src_coverage == 'global')
+
+        x_lower = src_grid.lower_bounds[ESMF.StaggerLoc.CENTER][1]
+        x_upper = src_grid.upper_bounds[ESMF.StaggerLoc.CENTER][1]
+        y_lower = src_grid.lower_bounds[ESMF.StaggerLoc.CENTER][0]
+        y_upper = src_grid.upper_bounds[ESMF.StaggerLoc.CENTER][0]
+
+        dst_grid = self.grid.create_esmf_grid_from_file(self.grid.coords_netcdf_file,
+                                                        sphere=self.grid.grid_type == 'global')
+
+        dst_field_list = []
+        num = 1
+        for pollutant_single_dict in self.pollutant_dicts:
+            settings.write_log('\t\tPollutant {0} ({1}/{2})'.format(
+                pollutant_single_dict['name'], num, len(self.pollutant_dicts)), level=3)
+            num += 1
+
+            src_field = ESMF.Field(src_grid, name='my input field')
+            src_field.read(filename=pollutant_single_dict['path'], variable=pollutant_single_dict['name'], timeslice=0)
+
+            if self.masking.regrid_mask is not None:
+                src_field.data[:] = self.masking.regrid_mask[0, x_lower:x_upper, y_lower:y_upper].T * src_field.data[:]
+            if self.masking.scale_mask is not None:
+                src_field.data[:] = self.masking.scale_mask[0, x_lower:x_upper, y_lower:y_upper].T * src_field.data[:]
+
+            dst_field = ESMF.Field(dst_grid, name=pollutant_single_dict['name'])
+            if regrid is None:
+                # os.remove(self.weight_matrix_file)
+                # regrid = ESMF.Regrid(src_field, dst_field, filename=self.weight_matrix_file,
+                #                      regrid_method=ESMF.RegridMethod.CONSERVE,
+                #                      extrap_method=ESMF.ExtrapMethod.NEAREST_STOD)
+                regrid = ESMF.RegridFromFile(src_field, dst_field, self.weight_matrix_file)
+            dst_field = regrid(src_field, dst_field)
+
+            dst_field_list.append({'data': dst_field.data.T, 'name': pollutant_single_dict['name']})
+
+        settings.write_time('ConservativeRegrid', 'start_esmpy_regridding', timeit.default_timer() - st_time, level=3)
+
+        return dst_field_list
+
     def read_weight_matrix(self):
         from netCDF4 import Dataset
         dict_aux = {}
diff --git a/hermesv3_gr/modules/temporal/temporal.py b/hermesv3_gr/modules/temporal/temporal.py
index e1630aadbda8a4ada642ade11eb196687b09a944..256246fb3c411d7a51e159dd065cab12da61a813 100755
--- a/hermesv3_gr/modules/temporal/temporal.py
+++ b/hermesv3_gr/modules/temporal/temporal.py
@@ -122,7 +122,8 @@ class TemporalDistribution(object):
 
         settings.write_time('TemporalDistribution', 'Init', timeit.default_timer() - st_time, level=2)
 
-    def caclulate_date_array(self, st_date, time_step_type, time_step_num, time_step_freq):
+    @staticmethod
+    def caclulate_date_array(st_date, time_step_type, time_step_num, time_step_freq):
         from datetime import timedelta
         from calendar import monthrange, isleap
 
diff --git a/hermesv3_gr/modules/vertical/vertical_gfas_hourly.py b/hermesv3_gr/modules/vertical/vertical_gfas_hourly.py
new file mode 100755
index 0000000000000000000000000000000000000000..b650de4a83c935774743e65e5bc452971ddf9bbf
--- /dev/null
+++ b/hermesv3_gr/modules/vertical/vertical_gfas_hourly.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python
+
+# Copyright 2018 Earth Sciences Department, BSC-CNS
+#
+# This file is part of HERMESv3_GR.
+#
+# HERMESv3_GR is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# HERMESv3_GR is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with HERMESv3_GR. If not, see <http://www.gnu.org/licenses/>.
+
+
+import timeit
+import hermesv3_gr.config.settings as settings
+from .vertical import VerticalDistribution
+
+
+class GfasHourlyVerticalDistribution(VerticalDistribution):
+    """
+    Class that contains all the needed information to vertically distribute the fire emissions.
+
+    :param vertical_output_profile: Path to the file that contains the vertical description of the desired output.
+    :type vertical_output_profile: str
+
+    :param approach: Approach to take into account.
+    :type approach: str
+    """
+    def __init__(self, vertical_output_profile, approach):
+        st_time = timeit.default_timer()
+
+        self.output_heights = vertical_output_profile
+        self.approach = approach
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'Init', timeit.default_timer() - st_time, level=3)
+
+    def __str__(self):
+
+        string = "GFAS Hourly Vertical distribution:\n"
+
+        string += "\t self.approach = {0}\n".format(self.approach)
+
+        string += "\t self.output_heights = {0}\n".format(self.output_heights)
+
+        return string
+
+    @staticmethod
+    def calculate_widths(heights_list):
+        """
+        Calculate the width of each vertical level.
+
+        :param heights_list: List of the top altitude in meters of each level.
+        :type heights_list: list
+
+        :return: List with the width of each vertical level.
+        :rtype: list
+        """
+        st_time = timeit.default_timer()
+
+        widths = []
+        for i in range(len(heights_list)):
+            if i == 0:
+                widths.append(heights_list[i])
+            else:
+                widths.append(heights_list[i] - heights_list[i - 1])
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'calculate_widths',
+                            timeit.default_timer() - st_time, level=3)
+        return widths
+
+    def get_weights(self, heights_list):
+        """
+        Calculate the proportion (%) of emission to put on each layer.
+
+        :param heights_list: List with the width of each vertical level.
+        :type heights_list: list
+
+        :return: List of the weight to apply to each layer.
+        :rtype: list
+        """
+        st_time = timeit.default_timer()
+
+        weights = []
+        width_list = self.calculate_widths(heights_list)
+        if self.approach == 'uniform':
+            max_percent = 1.
+        elif self.approach == '50_top':
+            max_percent = 0.5
+            width_list = width_list[0:-1]
+        else:
+            max_percent = 1.
+
+        for width in width_list:
+            weights.append((width * max_percent) / sum(width_list))
+        if self.approach == '50_top':
+            if len(heights_list) == 1:
+                weights.append(1.)
+            else:
+                weights.append(0.5)
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'get_weights', timeit.default_timer() - st_time, level=3)
+        return weights
+
+    def apply_approach(self, top_fires):
+        """
+        Scatters the emissions vertically.
+
+        :param top_fires: 4D array (time, level, latitude, longitude) with all the emission on each top layer.
+        :type top_fires: numpy.array
+
+        :return: 4D array (time, level, latitude, longitude) with all the emission distributed on all the involved
+                layers.
+        :rtype: numpy.array
+        """
+        import numpy as np
+
+        st_time = timeit.default_timer()
+
+        fires = np.zeros(top_fires.shape)
+        for i in range(len(self.output_heights)):
+            if top_fires[i].sum() != 0:
+                weight_list = self.get_weights(list(self.output_heights[0: i + 1]))
+                for i_weight in range(len(weight_list)):
+                    fires[i_weight] += top_fires[i] * weight_list[i_weight]
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'apply_approach',
+                            timeit.default_timer() - st_time, level=3)
+        return fires
+
+    def do_vertical_interpolation_allocation(self, values, altitude):
+        """
+        Allocates the fire emissions on their top level.
+
+        :param values: 2D array with the fire emissions
+        :type values: numpy.array
+
+        :param altitude: 2D array with the altitude of the fires.
+        :type altitude: numpy.array
+
+        :return: Emissions already allocated on the top altitude of each fire.
+        :rtype: numpy.array
+        """
+        import numpy as np
+
+        st_time = timeit.default_timer()
+
+        fire_list = []
+        aux_var = values
+        for height in self.output_heights:
+            aux_data = np.zeros(aux_var.shape)
+            ma = np.ma.masked_less_equal(altitude, height)
+            aux_data[ma.mask] += aux_var[ma.mask]
+            aux_var -= aux_data
+            fire_list.append(aux_data)
+        fire_list = np.array(fire_list).reshape((len(fire_list), values.shape[1], values.shape[2]))
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'do_vertical_interpolation_allocation',
+                            timeit.default_timer() - st_time, level=3)
+        return fire_list
+
+    def do_vertical_interpolation(self, values):
+        """
+        Manages all the process to do the vertical distribution.
+
+        :param values: Emissions to be vertically distributed.
+        :type values: numpy.array
+
+        :return: Emissions already vertically distributed.
+        :rtype: numpy.array
+        """
+        st_time = timeit.default_timer()
+
+        fire_list = self.apply_approach(values)
+
+        settings.write_time('GfasHourlyVerticalDistribution', 'do_vertical_interpolation',
+                            timeit.default_timer() - st_time, level=3)
+        return fire_list
+
+    def calculate_weight_layer_dict(self, layer):
+        weight_layer_dict = {x: None for x in range(layer + 1)}
+        if self.approach == '50_top':
+            weight_layer_dict[layer] = 0.5
+            to_distribute = 0.5
+            layer = layer - 1
+        elif self.approach == 'uniform':
+            to_distribute = 1.
+
+        total_width = self.output_heights[layer]
+
+        previous_height = 0
+        for i, height in enumerate(self.output_heights[0:layer + 1]):
+            partial_width = height - previous_height
+            weight_layer_dict[i] = to_distribute * partial_width / total_width
+
+            previous_height = height
+
+        return weight_layer_dict
+
+    def distribute_vertically(self, emissions_df, input_pollutant_list):
+        import pandas as pd
+
+        vert_emissions = []
+        for layer, emis in emissions_df.groupby('layer'):
+            if layer == 0:
+                vert_emissions.append(emis)
+            else:
+                weight_layer_dict = self.calculate_weight_layer_dict(layer)
+                for layer, weight in weight_layer_dict.items():
+                    aux_emis = emis.copy()
+                    aux_emis.loc[:, 'layer'] = layer
+                    aux_emis.loc[:, input_pollutant_list] = aux_emis[input_pollutant_list].multiply(weight)
+
+                    vert_emissions.append(aux_emis)
+        if len(vert_emissions) == 0:
+            vert_emissions = emissions_df
+        else:
+            vert_emissions = pd.concat(vert_emissions)
+
+        vert_emissions = vert_emissions.groupby(['FID', 'tstep', 'layer']).sum()
+        vert_emissions.reset_index(inplace=True)
+        return vert_emissions
+
+
+if __name__ == '__main__':
+    pass
diff --git a/hermesv3_gr/modules/writing/writer.py b/hermesv3_gr/modules/writing/writer.py
index 9fb1d1a968225a1497f02d8246a851faebbd7ec1..69f3f314712562d8a717e626e6b1f1f0beca2889 100755
--- a/hermesv3_gr/modules/writing/writer.py
+++ b/hermesv3_gr/modules/writing/writer.py
@@ -58,11 +58,21 @@ class Writer(object):
     :type parallel. bool
     """
 
-    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compress=True, parallel=False):
+    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compression_level, parallel=False):
 
         self.path = path
         self.grid = grid
-        self.compress = compress
+
+        if compression_level > 0:
+            if parallel:
+                settings.write_log("WARNING: Parallel write doesn't allow compression. Setting it to no compression")
+                self.compress = False
+            else:
+                self.compress = True
+                self.compression_level = compression_level
+        else:
+            self.compress = False
+
         self.parallel = parallel
 
         self.variables_attributes = None
@@ -225,9 +235,15 @@ class Writer(object):
                                 else:
                                     aux_data = emission['data']
                         elif ei.source_type == 'point':
-                            aux_data = np.zeros((shape[1], shape[2] * shape[3]))
-                            aux_data[ei.location['layer'], ei.location['FID']] = emission['data']
-                            aux_data = aux_data.reshape((shape[1], shape[2], shape[3]))
+                            if 'tstep' in ei.location.columns:
+                                aux_data = np.zeros((shape[0], shape[1], shape[2] * shape[3]))
+                                aux_data[ei.location['tstep'], ei.location['layer'],
+                                         ei.location['FID']] = emission['data']
+                                aux_data = aux_data.reshape(shape)
+                            else:
+                                aux_data = np.zeros((shape[1], shape[2] * shape[3]))
+                                aux_data[ei.location['layer'], ei.location['FID']] = emission['data']
+                                aux_data = aux_data.reshape((shape[1], shape[2], shape[3]))
                         else:
                             aux_data = None
 
@@ -241,7 +257,10 @@ class Writer(object):
                         if ei.temporal_factors is not None:
                             data += aux_data[np.newaxis, :, :, :] * ei.temporal_factors[:, np.newaxis, :, :]
                         else:
-                            data += aux_data[np.newaxis, :, :, :]
+                            if len(aux_data.shape) == 4:
+                                data += aux_data
+                            else:
+                                data += aux_data[np.newaxis, :, :, :]
                         settings.write_time('TemporalDistribution', 'calculate_data_by_var',
                                             timeit.default_timer() - temporal_time, level=2)
         # Unit changes
@@ -307,7 +326,7 @@ class Writer(object):
         return new_list
 
     @staticmethod
-    def get_writer(output_model, path, grid, levels, date, hours, global_attributes_path, compress, parallel):
+    def get_writer(output_model, path, grid, levels, date, hours, global_attributes_path, compression_level, parallel):
         """
         Choose between the different writers depending on the desired output model.
 
@@ -332,8 +351,8 @@ class Writer(object):
         :param global_attributes_path: Path to the file that contains the static global attributes.
         :type global_attributes_path: str
 
-        :param compress: Indicates if you want to compress the netCDF variable data.
-        :type compress: bool
+        :param compression_level: Indicates if you want to compress the netCDF variable data.
+        :type compression_level: bool
 
         :param parallel: Indicates if you want to write in parallel mode.
         :type parallel. bool
@@ -347,11 +366,11 @@ class Writer(object):
 
         settings.write_log('Selecting writing output type for {0}.'.format(output_model))
         if output_model.lower() == 'monarch':
-            return WriterMonarch(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+            return WriterMonarch(path, grid, levels, date, hours, global_attributes_path, compression_level, parallel)
         elif output_model.lower() == 'cmaq':
-            return WriterCmaq(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+            return WriterCmaq(path, grid, levels, date, hours, global_attributes_path, compression_level, parallel)
         elif output_model.lower() == 'wrf_chem':
-            return WriterWrfChem(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+            return WriterWrfChem(path, grid, levels, date, hours, global_attributes_path, compression_level, parallel)
         else:
             settings.write_log('ERROR: Check the .err file to get more info.')
             if settings.rank == 0:
@@ -454,14 +473,14 @@ class Writer(object):
         # ===== Variables =====
         # Time
         if date is None:
-            time = netcdf.createVariable('time', 'd', ('time',), zlib=True)
+            time = netcdf.createVariable('time', 'd', ('time',), zlib=False)
             time.units = "months since 2000-01-01 00:00:00"
             time.standard_name = "time"
             time.calendar = "gregorian"
             time.long_name = "time"
             time[:] = [0.]
         else:
-            time = netcdf.createVariable('time', 'd', ('time',), zlib=True)
+            time = netcdf.createVariable('time', 'd', ('time',), zlib=False)
             # print u.offset_by_time(encode_time(date.year, date.month, date.day, date.hour, date.minute, date.second))
             # Unit('hour since 1970-01-01 00:00:00.0000000 UTC')
             time.units = str(Unit('hours').offset_by_time(
@@ -472,7 +491,7 @@ class Writer(object):
             time[:] = hours
 
         # Latitude
-        lats = netcdf.createVariable('lat', 'f', lat_dim, zlib=True)
+        lats = netcdf.createVariable('lat', 'f', lat_dim, zlib=False)
         lats.units = "degrees_north"
         lats.axis = "Y"
         lats.long_name = "latitude coordinate"
@@ -481,12 +500,12 @@ class Writer(object):
 
         if boundary_latitudes is not None:
             lats.bounds = "lat_bnds"
-            lat_bnds = netcdf.createVariable('lat_bnds', 'f', lat_dim + ('nv',), zlib=True)
+            lat_bnds = netcdf.createVariable('lat_bnds', 'f', lat_dim + ('nv',), zlib=False)
             # print lat_bnds[:].shape, boundary_latitudes.shape
             lat_bnds[:] = boundary_latitudes
 
         # Longitude
-        lons = netcdf.createVariable('lon', 'f', lon_dim, zlib=True)
+        lons = netcdf.createVariable('lon', 'f', lon_dim, zlib=False)
 
         lons.units = "degrees_east"
         lons.axis = "X"
@@ -496,32 +515,32 @@ class Writer(object):
         lons[:] = center_longitudes
         if boundary_longitudes is not None:
             lons.bounds = "lon_bnds"
-            lon_bnds = netcdf.createVariable('lon_bnds', 'f', lon_dim + ('nv',), zlib=True)
+            lon_bnds = netcdf.createVariable('lon_bnds', 'f', lon_dim + ('nv',), zlib=False)
             # print lon_bnds[:].shape, boundary_longitudes.shape
             lon_bnds[:] = boundary_longitudes
 
         if roated:
             # Rotated Latitude
-            rlat = netcdf.createVariable('rlat', 'f', ('rlat',), zlib=True)
+            rlat = netcdf.createVariable('rlat', 'f', ('rlat',), zlib=False)
             rlat.long_name = "latitude in rotated pole grid"
             rlat.units = Unit("degrees").symbol
             rlat.standard_name = "grid_latitude"
             rlat[:] = rotated_lats
 
             # Rotated Longitude
-            rlon = netcdf.createVariable('rlon', 'f', ('rlon',), zlib=True)
+            rlon = netcdf.createVariable('rlon', 'f', ('rlon',), zlib=False)
             rlon.long_name = "longitude in rotated pole grid"
             rlon.units = Unit("degrees").symbol
             rlon.standard_name = "grid_longitude"
             rlon[:] = rotated_lons
         if lcc or mercator:
-            x_var = netcdf.createVariable('x', 'd', ('x',), zlib=True)
+            x_var = netcdf.createVariable('x', 'd', ('x',), zlib=False)
             x_var.units = Unit("km").symbol
             x_var.long_name = "x coordinate of projection"
             x_var.standard_name = "projection_x_coordinate"
             x_var[:] = lcc_x
 
-            y_var = netcdf.createVariable('y', 'd', ('y',), zlib=True)
+            y_var = netcdf.createVariable('y', 'd', ('y',), zlib=False)
             y_var.units = Unit("km").symbol
             y_var.long_name = "y coordinate of projection"
             y_var.standard_name = "projection_y_coordinate"
@@ -531,18 +550,18 @@ class Writer(object):
         # Levels
         if levels is not None:
             var_dim = ('lev',) + var_dim
-            lev = netcdf.createVariable('lev', 'f', ('lev',), zlib=True)
+            lev = netcdf.createVariable('lev', 'f', ('lev',), zlib=False)
             lev.units = Unit("m").symbol
             lev.positive = 'up'
             lev[:] = levels
 
         # All variables
         if len(data_list) is 0:
-            var = netcdf.createVariable('aux_var', 'f', ('time',) + var_dim, zlib=True)
+            var = netcdf.createVariable('aux_var', 'f', ('time',) + var_dim, zlib=False)
             var[:] = 0
         for variable in data_list:
             # print ('time',) + var_dim
-            var = netcdf.createVariable(variable['name'], 'f', ('time',) + var_dim, zlib=True)
+            var = netcdf.createVariable(variable['name'], 'f', ('time',) + var_dim, zlib=False)
             var.units = Unit(variable['units']).symbol
             if 'long_name' in variable:
                 var.long_name = str(variable['long_name'])
@@ -577,7 +596,7 @@ class Writer(object):
             # Rotated pole
             mapping = netcdf.createVariable('rotated_pole', 'c')
             mapping.grid_mapping_name = 'rotated_latitude_longitude'
-            mapping.grid_north_pole_latitude = north_pole_lat
+            mapping.grid_north_pole_latitude = 90 - north_pole_lat
             mapping.grid_north_pole_longitude = north_pole_lon
         elif lcc:
             # CRS
diff --git a/hermesv3_gr/modules/writing/writer_cmaq.py b/hermesv3_gr/modules/writing/writer_cmaq.py
index 71242c10677390be8f9aae06821640ff920c3e09..f9e46882793ea5331985994330e8bbea2338765a 100755
--- a/hermesv3_gr/modules/writing/writer_cmaq.py
+++ b/hermesv3_gr/modules/writing/writer_cmaq.py
@@ -49,15 +49,16 @@ class WriterCmaq(Writer):
    :param global_attributes_path: Path to the file that contains the static global attributes.
    :type global_attributes_path: str
 
-   :param compress: Indicates if you want to compress the netCDF variable data.
-   :type compress: bool
+   :param compression_level: Indicates if you want to compress the netCDF variable data.
+   :type compression_level: bool
 
    :param parallel: Indicates if you want to write in parallel mode.
    :type parallel. bool
    """
 
-    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compress=True, parallel=False):
-        super(WriterCmaq, self).__init__(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compression_level=True, parallel=False):
+        super(WriterCmaq, self).__init__(path, grid, levels, date, hours, global_attributes_path, compression_level,
+                                         parallel)
 
         self.global_attributes_order = [
             'IOAPI_VERSION', 'EXEC_ID', 'FTYPE', 'CDATE', 'CTIME', 'WDATE', 'WTIME', 'SDATE', 'STIME', 'TSTEP', 'NTHIK',
@@ -434,7 +435,12 @@ class WriterCmaq(Writer):
         # data_list, var_list = self.change_variable_attributes(self.variables_attributes)
         for var_name in self.variables_attributes.keys():
             index += 1
-            var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',), zlib=self.compress)
+            if self.compress:
+                var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',), zlib=self.compress,
+                                            complevel=self.compression_level)
+            else:
+                var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',))
+
             var.setncatts(self.variables_attributes[var_name])
             settings.write_log("\t\t\t'{0}' variable created with size: {1}".format(var_name, var[:].shape) +
                                "\n\t\t\t\t'{0}' variable will be filled later.".format(var_name), level=3)
@@ -567,8 +573,12 @@ class WriterCmaq(Writer):
                             data = 0
                 st_time = timeit.default_timer()
                 index += 1
+                if self.compress:
+                    var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',), zlib=True,
+                                                complevel=self.compression_level)
+                else:
+                    var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',))
 
-                var = netcdf.createVariable(var_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL',), zlib=self.compress)
                 var.setncatts(self.variables_attributes[var_name])
                 # var.units = variable['units']
                 # var.long_name = str(variable['long_name'])
diff --git a/hermesv3_gr/modules/writing/writer_monarch.py b/hermesv3_gr/modules/writing/writer_monarch.py
index 1bd675827d67b5d5ed7c6b63818dd000968bf497..6a087f4da26682fc9ddf889ddf6149991cfa23db 100755
--- a/hermesv3_gr/modules/writing/writer_monarch.py
+++ b/hermesv3_gr/modules/writing/writer_monarch.py
@@ -49,15 +49,16 @@ class WriterMonarch(Writer):
    :param global_attributes_path: Path to the file that contains the static global attributes.
    :type global_attributes_path: str
 
-   :param compress: Indicates if you want to compress the netCDF variable data.
-   :type compress: bool
+   :param compression_level: Indicates if you want to compress the netCDF variable data.
+   :type compression_level: bool
 
    :param parallel: Indicates if you want to write in parallel mode.
    :type parallel. bool
    """
 
-    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compress=True, parallel=False):
-        super(WriterMonarch, self).__init__(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compression_level=True, parallel=False):
+        super(WriterMonarch, self).__init__(path, grid, levels, date, hours, global_attributes_path, compression_level,
+                                            parallel)
 
         # self.global_attributes = {
         #     'nom_attribut': 'value_attribut'
@@ -129,7 +130,7 @@ class WriterMonarch(Writer):
         LambertConformalConic = False
         if self.grid.grid_type == 'global' or self.grid.grid_type == 'regular':
             RegularLatLon = True
-        elif self.grid.grid_type == 'rotated':
+        elif self.grid.grid_type in ['rotated', 'rotated_nested']:
             Rotated = True
         elif self.grid.grid_type == 'lcc':
             LambertConformalConic = True
@@ -430,7 +431,7 @@ class WriterMonarch(Writer):
 
             if self.grid.grid_type == 'global' or self.grid.grid_type == 'regular':
                 regular_latlon = True
-            elif self.grid.grid_type == 'rotated':
+            elif self.grid.grid_type in ['rotated', 'rotated_nested']:
                 rotated = True
             elif self.grid.grid_type == 'lcc':
                 lcc = True
@@ -693,7 +694,11 @@ class WriterMonarch(Writer):
                         except (UnboundLocalError, TypeError, IndexError):
                             data = 0
                 index += 1
-                var = netcdf.createVariable(var_name, 'f', ('time',) + var_dim, zlib=self.compress)
+                if self.compress:
+                    var = netcdf.createVariable(var_name, 'f', ('time',) + var_dim, zlib=self.compress,
+                                                complevel=self.compression_level)
+                else:
+                    var = netcdf.createVariable(var_name, 'f', ('time',) + var_dim)
 
                 var.units = Unit(self.variables_attributes[var_name]['units']).symbol
 
diff --git a/hermesv3_gr/modules/writing/writer_wrf_chem.py b/hermesv3_gr/modules/writing/writer_wrf_chem.py
index 5f27d7998d5c8fc4e7491dbcd77c483fdf33ecc2..30df6befc04e310e21dd94505986af6f6af712c7 100755
--- a/hermesv3_gr/modules/writing/writer_wrf_chem.py
+++ b/hermesv3_gr/modules/writing/writer_wrf_chem.py
@@ -49,15 +49,16 @@ class WriterWrfChem(Writer):
    :param global_attributes_path: Path to the file that contains the static global attributes.
    :type global_attributes_path: str
 
-   :param compress: Indicates if you want to compress the netCDF variable data.
-   :type compress: bool
+   :param compression_level: Indicates if you want to compress the netCDF variable data.
+   :type compression_level: bool
 
    :param parallel: Indicates if you want to write in parallel mode.
    :type parallel. bool
    """
 
-    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compress=True, parallel=False):
-        super(WriterWrfChem, self).__init__(path, grid, levels, date, hours, global_attributes_path, compress, parallel)
+    def __init__(self, path, grid, levels, date, hours, global_attributes_path, compression_level=True, parallel=False):
+        super(WriterWrfChem, self).__init__(path, grid, levels, date, hours, global_attributes_path, compression_level,
+                                            parallel)
 
         self.global_attributes_order = [
             'TITLE', 'START_DATE', 'WEST-EAST_GRID_DIMENSION', 'SOUTH-NORTH_GRID_DIMENSION',
@@ -308,11 +309,6 @@ class WriterWrfChem(Writer):
         return aux_times
 
     def create_parallel_netcdf(self):
-        # TODO Documentation
-        """
-
-        :return:
-        """
         st_time = timeit.default_timer()
         settings.write_log("\tCreating parallel NetCDF file.", level=2)
         netcdf = Dataset(self.path, mode='w', format="NETCDF4")
@@ -342,8 +338,12 @@ class WriterWrfChem(Writer):
         index = 0
         for var_name in self.variables_attributes.keys():
             index += 1
-            var = netcdf.createVariable(var_name, 'f', ('Time', 'emissions_zdim', 'south_north', 'west_east',),
-                                        zlib=self.compress)
+            if self.compress:
+                var = netcdf.createVariable(var_name, 'f', ('Time', 'emissions_zdim', 'south_north', 'west_east',),
+                                            zlib=self.compress, complevel=self.compression_level)
+            else:
+                var = netcdf.createVariable(var_name, 'f', ('Time', 'emissions_zdim', 'south_north', 'west_east',))
+
             var.setncatts(self.variables_attributes[var_name])
             settings.write_log("\t\t\t'{0}' variable created with size: {1}".format(var_name, var[:].shape) +
                                "\n\t\t\t\t'{0}' variable will be filled later.".format(var_name), level=3)
diff --git a/hermesv3_gr/tools/mpi_tools.py b/hermesv3_gr/tools/mpi_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..430530266531ddb3f131a5c555f08e151799873b
--- /dev/null
+++ b/hermesv3_gr/tools/mpi_tools.py
@@ -0,0 +1,78 @@
+import numpy as np
+
+
+def scatter_array(comm, data, master=0):
+    if comm.Get_size() == 1:
+        data = data
+    else:
+        if comm.Get_rank() == master:
+            data = np.array_split(data, comm.Get_size())
+        else:
+            data = None
+        data = comm.scatter(data, root=master)
+
+    return data
+
+
+def bcast_array(comm, data, master=0):
+    if comm.Get_size() == 1:
+        data = data
+    else:
+        data = comm.bcast(data, root=master)
+
+    return data
+
+
+def balance_dataframe(comm, data, master=0):
+    import pandas as pd
+    data = comm.gather(data, root=master)
+    if comm.Get_rank() == master:
+        data = pd.concat(data)
+        data = np.array_split(data, comm.Get_size())
+    else:
+        data = None
+
+    data = comm.scatter(data, root=master)
+
+    return data
+
+
+def get_balanced_distribution(processors, shape):
+    while len(shape) < 4:
+        shape = (0,) + shape
+
+    fid_dist = {}
+    total_rows = shape[2]
+
+    procs_rows = total_rows // processors
+    procs_rows_extended = total_rows-(procs_rows*processors)
+
+    rows_sum = 0
+    for proc in range(processors):
+        if proc < procs_rows_extended:
+            aux_rows = procs_rows + 1
+        else:
+            aux_rows = procs_rows
+
+        total_rows -= aux_rows
+        if total_rows < 0:
+            rows = total_rows + aux_rows
+        else:
+            rows = aux_rows
+
+        min_fid = proc * aux_rows * shape[3]
+        max_fid = (proc + 1) * aux_rows * shape[3]
+
+        fid_dist[proc] = {
+            'y_min': rows_sum,
+            'y_max': rows_sum + rows,
+            'x_min': 0,
+            'x_max': shape[3],
+            'fid_min': min_fid,
+            'fid_max': max_fid,
+            'shape': (shape[0], shape[1], rows, shape[3]),
+        }
+
+        rows_sum += rows
+
+    return fid_dist
diff --git a/hermesv3_gr/tools/netcdf_tools.py b/hermesv3_gr/tools/netcdf_tools.py
index 9e0fa356af80f61245069cf47bd80f75ec002705..3be6772ea41a5422968314941ef343dfade65f86 100755
--- a/hermesv3_gr/tools/netcdf_tools.py
+++ b/hermesv3_gr/tools/netcdf_tools.py
@@ -673,5 +673,88 @@ def calculate_displacements(counts):
     return new_list
 
 
+def netcdf2shp(comm, netcdf_path):
+    from netCDF4 import Dataset
+    import numpy as np
+    import pandas as pd
+    import geopandas as gpd
+    from shapely import wkt
+
+    if comm.Get_rank() == 0:
+        nc_in = Dataset(netcdf_path, mode='r')
+        lat_bnds = nc_in.variables['lat_bnds'][:]
+        lon_bnds = nc_in.variables['lon_bnds'][:]
+        nc_in.close()
+
+        lat_bnds_0 = np.array([lat_bnds[:, 0]] * lon_bnds.shape[0]).T.flatten()
+        lat_bnds_1 = np.array([lat_bnds[:, 1]] * lon_bnds.shape[0]).T.flatten()
+        lon_bnds_0 = np.array([lon_bnds[:, 0]] * lat_bnds.shape[0]).flatten()
+        lon_bnds_1 = np.array([lon_bnds[:, 1]] * lat_bnds.shape[0]).flatten()
+
+        coordinates = pd.DataFrame(lat_bnds_0, columns=['lat_bnds_0'])
+        coordinates.index.name = 'FID'
+        coordinates['lat_bnds_1'] = lat_bnds_1
+        coordinates['lon_bnds_0'] = lon_bnds_0
+        coordinates['lon_bnds_1'] = lon_bnds_1
+    else:
+        coordinates = None
+    coordinates = split_shapefile(comm, coordinates)
+    coordinates['Coordinates'] = \
+        'POLYGON ((' + coordinates['lon_bnds_0'].astype(str) + ' ' + coordinates['lat_bnds_0'].astype(str) + ', ' + \
+        coordinates['lon_bnds_0'].astype(str) + ' ' + coordinates['lat_bnds_1'].astype(str) + ', ' + \
+        coordinates['lon_bnds_1'].astype(str) + ' ' + coordinates['lat_bnds_1'].astype(str) + ', ' + \
+        coordinates['lon_bnds_1'].astype(str) + ' ' + coordinates['lat_bnds_0'].astype(str) + ', ' + \
+        coordinates['lon_bnds_0'].astype(str) + ' ' + coordinates['lat_bnds_0'].astype(str) + '))'
+
+    coordinates['Coordinates'] = coordinates['Coordinates'].apply(wkt.loads)
+
+    shapefile = gpd.GeoDataFrame(coordinates, geometry='Coordinates', crs={'init': 'epsg:4326'})
+    return shapefile
+
+
+def split_shapefile(comm, data, rank=0):
+    import numpy as np
+
+    if comm.Get_size() == 1:
+        data = data
+    else:
+        if comm.Get_rank() == rank:
+            data = np.array_split(data, comm.Get_size())
+        else:
+            data = None
+        data = comm.scatter(data, root=rank)
+
+    return data
+
+
+def gather_shapefile(comm, data, rank=0):
+    import pandas as pd
+
+    if comm.Get_size() == 1:
+        data = data
+    else:
+        data = comm.gather(data, root=rank)
+        if comm.Get_rank() == rank:
+            data = pd.concat(data)
+        else:
+            data = None
+    return data
+
+
+def write_shapefile_parallel(comm, data, path, rank=0):
+    import os
+    import pandas as pd
+    data = comm.gather(data, root=rank)
+    if comm.Get_rank() == rank:
+        if not os.path.exists(os.path.dirname(path)):
+            os.makedirs(os.path.dirname(path))
+        data = pd.concat(data)
+        data.to_file(path)
+
+    comm.Barrier()
+
+    return True
+
+
 if __name__ == '__main__':
     pass
diff --git a/hermesv3_gr/tools/sample_files.py b/hermesv3_gr/tools/sample_files.py
index 17c7eac88ce876a1fa9b35511ccc8c7991a71ef0..11a588af76b20a16f343db96d9d668556246395d 100755
--- a/hermesv3_gr/tools/sample_files.py
+++ b/hermesv3_gr/tools/sample_files.py
@@ -44,6 +44,8 @@ def make_profiles_file_list():
             {'profiles': [{
                 'speciation': [
                     os.path.join(main_dir, 'data', 'profiles', 'speciation', 'MolecularWeights.csv'),
+                    os.path.join(main_dir, 'data', 'profiles', 'speciation',
+                                 'Speciation_profile_cb05_aero5_Benchmark.csv'),
                     os.path.join(main_dir, 'data', 'profiles', 'speciation', 'Speciation_profile_cb05_aero5_CMAQ.csv'),
                     os.path.join(main_dir, 'data', 'profiles', 'speciation',
                                  'Speciation_profile_cb05_aero5_MONARCH.csv'),
@@ -56,6 +58,7 @@ def make_profiles_file_list():
                 ]},
                 {'temporal': [
                     os.path.join(main_dir, 'data', 'profiles', 'temporal', 'TemporalProfile_Daily.csv'),
+                    os.path.join(main_dir, 'data', 'profiles', 'temporal', 'TemporalProfile_Weekly.csv'),
                     os.path.join(main_dir, 'data', 'profiles', 'temporal', 'TemporalProfile_Hourly.csv'),
                     os.path.join(main_dir, 'data', 'profiles', 'temporal', 'TemporalProfile_Monthly.csv'),
                     os.path.join(main_dir, 'data', 'profiles', 'temporal', 'tz_world_country_iso3166.csv'),
diff --git a/preproc/uchile_preproc.py b/preproc/uchile_preproc.py
new file mode 100755
index 0000000000000000000000000000000000000000..5ce0ce7a7fc25a45fee36325582fde54dd98be8a
--- /dev/null
+++ b/preproc/uchile_preproc.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+
+# Copyright 2018 Earth Sciences Department, BSC-CNS
+#
+# This file is part of HERMESv3_GR.
+#
+# HERMESv3_GR is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# HERMESv3_GR is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with HERMESv3_GR. If not, see <http://www.gnu.org/licenses/>.
+
+
+import os
+import timeit
+from netCDF4 import Dataset
+
+# ============== README ======================
+"""
+downloading website: ¿¿¿???
+reference: ¿¿¿???
+Besides citing HERMESv3_GR, users must also acknowledge the use of the corresponding emission inventories in their works
+"""
+
+# ============== CONFIGURATION PARAMETERS ======================
+INPUT_PATH = '/esarchive/recon/uchile/cr2_dgf/original_files/emis_<sector>_v20190928/<year>/<in_poll>-<year>.nc'
+OUTPUT_PATH = '/esarchive/recon/uchile/cr2_dgf/'
+LIST_POLLUTANTS = ['bc', 'ch4', 'co', 'co2', 'nox_no2', 'pm25', 'nmvoc']
+SECTOR_LIST = {'TRA': 'road_transport'}
+
+YEAR_LIST = [2013, 2014, 2015, 2016, 2017]
+# YEAR_LIST = [2015]
+# ==============================================================
+
+
+def get_pollutant_dict(sector):
+    if sector == 'TRA':
+        pollutant_dict = {
+            'bc': {'file': 'BC', 'var': 'BC'},
+            'ch4': {'file': 'CH4', 'var': 'CH4'},
+            'co': {'file': 'CO-', 'var': 'CO'},
+            'co2': {'file': 'CO2', 'var': 'CO2'},
+            'nox_no2': {'file': 'NOx', 'var': 'NOx'},
+            'pm25': {'file': 'PM25', 'var': 'PM25'},
+            'nmvoc': {'file': 'VOC', 'var': 'VOC'},
+        }
+
+    return pollutant_dict
+
+
+def do_transformation():
+    from hermesv3_gr.tools.netcdf_tools import write_netcdf, get_grid_area
+    from hermesv3_gr.tools.coordinates_tools import create_bounds
+    import calendar
+    import numpy as np
+    from datetime import datetime
+
+    lats = lons = blats = blons = cell_area = None
+
+    for sector in SECTOR_LIST.keys():
+        pollutant_dict = get_pollutant_dict(sector)
+        for year in YEAR_LIST:
+            for pollutant in pollutant_dict.keys():
+                out_file_name = os.path.join(OUTPUT_PATH, 'yearly_mean',
+                                             '{0}_{1}'.format(pollutant, SECTOR_LIST[sector]),
+                                             "{0}_{1}.nc".format(pollutant, year))
+                file_name = INPUT_PATH.replace('<year>', str(year)).replace('<sector>', sector).replace(
+                    '<in_poll>', pollutant_dict[pollutant]['file'])
+
+                netcdf = Dataset(file_name, mode='r')
+                if lats is None:
+                    lat_nc = netcdf.variables['lat'][:]
+                    lon_nc = netcdf.variables['lon'][:]
+                    lat_inc = round(lat_nc[1] - lat_nc[0], 5)
+                    lon_inc = round(lon_nc[1] - lon_nc[0], 5)
+
+                    lats = np.linspace(lat_nc[0], lat_nc[0] + (lat_inc * (len(lat_nc) - 1)), len(lat_nc),
+                                       dtype=np.float)
+                    lons = np.linspace(lon_nc[0], lon_nc[0] + (lon_inc * (len(lon_nc) - 1)), len(lon_nc),
+                                       dtype=np.float)
+                    del lat_nc, lon_nc
+                    blats = create_bounds(lats)
+                    blons = create_bounds(lons)
+                    cell_area = get_grid_area(file_name)
+
+                var = netcdf.variables[pollutant_dict[pollutant]['var']][:]
+
+                if calendar.isleap(year):
+                    days = 366
+                else:
+                    days = 365
+
+                # from ton/yr to kg/m2.s
+                var = (var / cell_area) * (1000 / (days * 24 * 60 * 60))
+
+                data = [{'name': pollutant, 'units': 'kg.m-2.s-1', 'data': var.reshape((1,) + var.shape)}]
+
+                if not os.path.exists(os.path.dirname(out_file_name)):
+                    os.makedirs(os.path.dirname(out_file_name))
+                print(out_file_name)
+                write_netcdf(out_file_name, lats, lons, data, date=datetime(year=year, month=1, day=1),
+                             boundary_latitudes=blats, boundary_longitudes=blons, cell_area=cell_area,
+                             regular_latlon=True)
+
+
+if __name__ == '__main__':
+    starting_time = timeit.default_timer()
+
+    do_transformation()
+
+    print('Time(s):', timeit.default_timer() - starting_time)
diff --git a/setup.py b/setup.py
index fab8b0c5e32abe719073e5acdfd5a51cc280945a..1461d7dcfd3252de05e14097a7044292185484d6 100755
--- a/setup.py
+++ b/setup.py
@@ -80,7 +80,8 @@ setup(
                 ('data', ['data/global_attributes.csv', ]),
                 ('data/profiles', []),
                 ('data/profiles/speciation', [
-                          'data/profiles/speciation/MolecularWeights.csv', 
+                          'data/profiles/speciation/MolecularWeights.csv',
+                          'data/profiles/speciation/Speciation_profile_cb05_aero5_Benchmark.csv',
                           'data/profiles/speciation/Speciation_profile_cb05_aero5_CMAQ.csv',
                           'data/profiles/speciation/Speciation_profile_cb05_aero5_MONARCH.csv',
                           'data/profiles/speciation/Speciation_profile_cb05_aero6_CMAQ.csv',
@@ -88,6 +89,7 @@ setup(
                           'data/profiles/speciation/Speciation_profile_radm2_madesorgam_WRF_CHEM.csv', ]),
                 ('data/profiles/temporal', [
                           'data/profiles/temporal/TemporalProfile_Daily.csv',
+                          'data/profiles/temporal/TemporalProfile_Weekly.csv',
                           'data/profiles/temporal/TemporalProfile_Hourly.csv', 
                           'data/profiles/temporal/TemporalProfile_Monthly.csv',
                           'data/profiles/temporal/tz_world_country_iso3166.csv', ]),