diff --git a/preproc/uchile_inema_preproc.py b/preproc/uchile_inema_preproc.py
new file mode 100755
index 0000000000000000000000000000000000000000..a57068bb2adc52795096997a82e736d86ecc6791
--- /dev/null
+++ b/preproc/uchile_inema_preproc.py
@@ -0,0 +1,120 @@
+#!/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/inema_v1/original_files/<year>/<sector>/inema_<pollutant>_<subsector>_0.01x0.01_<year>.nc'
+OUTPUT_PATH = '/esarchive/recon/uchile/inema_v1/'
+SECTOR_LIST = {'Transport': None,
+               'Energy': None,
+               'Industry': None,
+               'Mining': None,
+               'Residential': ['Residential', 'Rural', 'Urban']}
+POLLUTANT_INFO = {'bc': 'BC', 'c6h6': 'C6H6', 'ch4': 'CH4', 'co2': 'CO2', 'co': 'CO', 'hg': 'Hg', 'nh3': 'NH3',
+                  'nmvoc': 'NMVOC', 'no': 'NO', 'no2': 'NO2', 'nox_no2': 'NOx', 'pb': 'Pb', 'pcddf': 'PCDDF',
+                  'pm10': 'PM10', 'pm25': 'PM25', 'pm': 'PM', 'so2': 'SO2', 'tol': 'Toluene', 'voc': 'VOC'}
+
+YEAR_LIST = [2016, 2017]
+# ==============================================================
+
+
+def do_transformation():
+    from hermesv3_gr.tools.netcdf_tools import write_netcdf
+    from hermesv3_gr.tools.coordinates_tools import create_bounds
+    import calendar
+    import numpy as np
+    from datetime import datetime
+
+    for sector in SECTOR_LIST.keys():
+        if SECTOR_LIST[sector] is None:
+            subsector_list = [sector]
+        else:
+            subsector_list = SECTOR_LIST[sector]
+        for subsector in subsector_list:
+            for year in YEAR_LIST:
+                for pollutant in POLLUTANT_INFO.keys():
+                    out_file_name = os.path.join(OUTPUT_PATH, 'yearly_mean',
+                                                 '{0}_{1}'.format(pollutant, subsector),
+                                                 "{0}_{1}.nc".format(pollutant, year))
+                    file_name = INPUT_PATH.replace('<year>', str(year)).replace('<sector>', sector).replace(
+                        '<pollutant>', POLLUTANT_INFO[pollutant]).replace('<subsector>', subsector)
+                    try:
+                        netcdf = Dataset(file_name, mode='r')
+                        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)
+                        if lons[-1] < lons[0]:
+                            flip = True
+                            lons = np.flip(lons)
+                        else:
+                            flip = False
+
+                        del lat_nc, lon_nc
+                        blats = create_bounds(lats)
+                        blons = create_bounds(lons)
+
+                        var = netcdf.variables[POLLUTANT_INFO[pollutant]][:]
+                        var = var.data
+                        var[var == netcdf.variables[POLLUTANT_INFO[pollutant]].getncattr('_FillValue')] = 0
+                        if flip:
+                            var = np.flip(var, axis=1)
+
+                        if calendar.isleap(year):
+                            days = 366
+                        else:
+                            days = 365
+
+                        # from ktonne/km2 year to kg/m2.s
+                        var = var * (1000000 / (days * 24 * 60 * 60 * 1000000))
+
+                        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)
+                    except FileNotFoundError:
+                        print("Variable {0} not found in {1}: {2}".format(pollutant, subsector, file_name))
+
+
+if __name__ == '__main__':
+    starting_time = timeit.default_timer()
+
+    do_transformation()
+
+    print('Time(s):', timeit.default_timer() - starting_time)