diff --git a/preproc/hemco_soilnox_v202112_preproc.py b/preproc/hemco_soilnox_v202112_preproc.py
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+++ b/preproc/hemco_soilnox_v202112_preproc.py
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+#!/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 sys
+import numpy as np
+import pandas as pd
+import timeit
+from datetime import datetime, timedelta
+import xarray as xr
+
+# ============== README ======================
+"""
+downloading website: 
+reference: 
+Besides citing HERMESv3_GR, users must also acknowledge the use of the corresponding emission inventories in their works
+"""
+
+# ============== CONFIGURATION PARAMETERS ======================
+# The original emissions are downloaded from the following link:
+# downloading website: http://geoschemdata.wustl.edu/ExtData/HEMCO/OFFLINE_SOILNOX/v2021-12/0.25x0.3125/
+# reference: https://doi.org/10.1038/s41597-020-0488-5
+
+INPUT_PATH = '/esarchive/recon/pku/hemco_soilnox-v202112/original_files/<MM>/soilnox_025.<YYYYMMDD>.nc'
+OUTPUT_PATH = '/esarchive/recon/pku/hemco_soilnox-v202112/daily_mean/<pollutant>_<sector>/<pollutant>_<YYYYMMDD>.nc'
+SECTOR_LIST = ['soil']
+POLLUTANT_INFO = {"EmisNO_Fert": "nox_n_fert", "SOIL_NOx": "nox_n_tot"}
+
+START_DAY = datetime(year=2019, month=1, day=1)
+END_DAY = datetime(year=2019, month=12, day=31)
+# END_DAY = datetime(year=2019, month=1, day=1)
+
+XARRAY = True
+# ==============================================================
+
+
+def create_bounds(coords, number_vertices=2):
+    """
+    Calculate the vertices coordinates.
+
+    :param coords: Coordinates in degrees (latitude or longitude)
+    :type coords: numpy.array
+
+    :param number_vertices: Non mandatory parameter that informs the number of vertices that must have the boundaries.
+            (by default 2)
+    :type number_vertices: int
+
+    :return: Array with as many elements as vertices for each value of coords.
+    :rtype: numpy.array
+    """
+    import numpy as np
+
+    interval = coords[1] - coords[0]
+
+    coords_left = coords - interval / 2
+    coords_right = coords + interval / 2
+    if number_vertices == 2:
+        bound_coords = np.dstack((coords_left, coords_right))
+    elif number_vertices == 4:
+        bound_coords = np.dstack((coords_left, coords_right, coords_right, coords_left))
+    else:
+        raise ValueError('The number of vertices of the boudaries must be 2 or 4')
+
+    return bound_coords
+
+
+def parse_path(file_path, date_aux=None, sector=None, pollutant=None):
+    """
+    :param file_path: Path to parse
+    :type file_path: str
+
+    :param date_aux: Date to parse
+    :type date_aux: datetime, None
+
+    :param sector: Sector name
+    :type sector: str, None
+
+    :param pollutant: Pollutant name
+    :type pollutant: str, None
+
+    :return: Complete path
+    :rtype: str
+    """
+    if date_aux is not None:
+        file_path = file_path.replace('<YYYYMMDD>', date_aux.strftime("%Y%m%d"))
+        file_path = file_path.replace('<MM>', date_aux.strftime("%m"))
+    if pollutant is not None:
+        file_path = file_path.replace('<pollutant>', pollutant)
+    if sector is not None:
+        file_path = file_path.replace('<sector>', sector)
+    return file_path
+
+
+def make_dir(file_path):
+    parent_dir = os.path.dirname(file_path)
+    if not os.path.exists(parent_dir):
+        os.makedirs(parent_dir)
+    return True
+
+
+def make_grid(template_path):
+    grid = xr.open_dataset(template_path)
+
+    grid.coords['cell_area'] = grid['AREA']
+    grid.coords['cell_area'].attrs = {"long_name": "area of the grid cell",
+                                      "standard_name": "cell_area",
+                                      "units": "m2"}
+
+    grid.coords['crs'] = int(0)
+    grid.coords['crs'].attrs = {"grid_mapping_name": "latitude_longitude",
+                                "semi_major_axis": 6371000.,
+                                "inverse_flattening": 0}
+
+    lat_bnds = create_bounds(grid.coords['lat'])
+    lat_bnds = lat_bnds.reshape((lat_bnds.shape[-2], lat_bnds.shape[-1]))
+    grid.coords['lat_bnds'] = (('lat', 'nv'), lat_bnds)
+    grid.coords['lat'].attrs['bounds'] = 'lat_bnds'
+
+    lon_bnds = create_bounds(grid.coords['lon'])
+    lon_bnds = lon_bnds.reshape((lon_bnds.shape[-2], lon_bnds.shape[-1]))
+    grid.coords['lon_bnds'] = (('lon', 'nv'), lon_bnds)
+    grid.coords['lon'].attrs['bounds'] = 'lon_bnds'
+
+    var_list = list(grid.keys())
+    for var_name in var_list:
+        del grid[var_name]
+
+    return grid
+
+
+def main_xarray():
+
+    grid_nc = make_grid(parse_path(INPUT_PATH, date_aux=START_DAY))
+
+    day_aux = START_DAY
+    for sector in SECTOR_LIST:
+        while day_aux <= END_DAY:
+            print(day_aux)
+            in_nc = xr.open_dataset(parse_path(INPUT_PATH, date_aux=day_aux))
+            for poll_in_name, poll_out_name in POLLUTANT_INFO.items():
+                out_nc = grid_nc.copy()
+                out_nc[poll_out_name] = (('lat', 'lon'), (in_nc[poll_in_name][:].mean(dim='time').data))
+                out_nc[poll_out_name].attrs['cell_measures'] = "area: cell_area"
+                out_nc[poll_out_name].attrs['grid_mapping'] = "crs"
+
+                out_nc['time'] = [day_aux]
+
+                out_nc.attrs['comment'] = "Re-writing done by Carles Tena (carles.tena@bsc.es) from the BSC-CNS " + \
+                                          "(Barcelona Supercomputing Center)"
+
+                out_path = parse_path(OUTPUT_PATH, date_aux=day_aux, sector=sector, pollutant=poll_out_name)
+                make_dir(out_path)
+                out_nc.to_netcdf(out_path)
+
+            day_aux += timedelta(days=1)
+
+    return True
+
+
+def main():
+    day_aux = START_DAY
+
+    while day_aux <= END_DAY:
+        in_path = parse_path(INPUT_PATH, day_aux)
+
+        day_aux += timedelta(days=1)
+
+    return True
+
+
+if __name__ == '__main__':
+    initime = timeit.default_timer()
+
+    if XARRAY:
+        main_xarray()
+    else:
+        main()
+
+    print("\t{0:.3f}s".format(timeit.default_timer() - initime))
+    sys.exit(0)