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landfills QA/QC updates for gridding #61

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landfills QA/QC updates for gridding #61

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2 changes: 2 additions & 0 deletions gch4i/emis_processing/task_industrial_landfills_emis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -404,13 +404,15 @@ def task_get_industrial_landfills_food_beverage_inv_data(
.drop(columns=["tot_ch4_kt", "nr_ch4_kt"])
.rename(columns={"r_ch4_kt": "ghgi_ch4_kt"})
.dropna()
.query("ghgi_ch4_kt > 0")
.reset_index(drop=True)
)

nonreporting_fb_emi_df = (corrected_fb_emi_df
.drop(columns=["tot_ch4_kt", "r_ch4_kt"])
.rename(columns={"nr_ch4_kt": "ghgi_ch4_kt"})
.dropna()
.query("ghgi_ch4_kt > 0")
.reset_index(drop=True)
)

Expand Down
1 change: 1 addition & 0 deletions gch4i/emis_processing/task_msw_landfills_emis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,6 +216,7 @@ def task_msw_landfills_emi(
pd.concat(emi_df_list)
.groupby(["state_code", "year"])["ghgi_ch4_kt"]
.sum()
.query("ghgi_ch4_kt > 0")
.reset_index()
)
# Save the emissions data to the output path
Expand Down
75 changes: 34 additions & 41 deletions gch4i/proxy_processing/task_industrial_landfills_proxy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,7 @@
def task_get_reporting_industrial_landfills_pulp_paper_proxy_data(
subpart_tt_path = "https://data.epa.gov/efservice/tt_subpart_ghg_info/pub_dim_facility/ghg_name/=/Methane/CSV",
state_path: Path = global_data_dir_path / "tl_2020_us_state.zip",
reporting_pulp_paper_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path
/ "ind_landfills_pp_r_proxy.parquet",
reporting_pulp_paper_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path / "ind_landfills_pp_r_proxy.parquet",
):
"""
Relative emissions and location information for reporting facilities are taken from
Expand Down Expand Up @@ -99,7 +98,7 @@ def task_get_reporting_industrial_landfills_pulp_paper_proxy_data(
),
)
.drop(columns=["facility_id", "latitude", "longitude", "city", "zip"])
.loc[:, ["facility_name", "state_code", "geometry", "year", "rel_emi"]]
.loc[:, ["year", "state_code", "geometry", "rel_emi"]]
)

reporting_pulp_paper_gdf.to_parquet(reporting_pulp_paper_proxy_output_path)
Expand All @@ -112,8 +111,7 @@ def task_get_nonreporting_industrial_landfills_pulp_paper_proxy_data(
frs_naics_path = global_data_dir_path / "NATIONAL_NAICS_FILE.CSV",
frs_facility_path = global_data_dir_path / "NATIONAL_FACILITY_FILE.CSV",
mills_online_path: Path = V3_DATA_PATH / "sector/landfills/Mills_OnLine.xlsx",
nonreporting_pulp_paper_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path
/ "ind_landfills_pp_nr_proxy.parquet",
nonreporting_pulp_paper_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path / "ind_landfills_pp_nr_proxy.parquet",
):
"""
The Mills OnLine database of facilities is compared against the Subpart HH
Expand Down Expand Up @@ -243,7 +241,7 @@ def task_get_nonreporting_industrial_landfills_pulp_paper_proxy_data(

FRS_facility_locs = frs_naics.merge(frs_main, how='inner', on='registry_id')

#try to match mills database with FRS database, based on state and city -
# try to match mills database with FRS database, based on state and city -
# only need to find locations for where 'ghgrp_match' = 0
mills_locs['FRS_match'] = 0
for ifacility in np.arange(0, len(mills_locs)):
Expand All @@ -256,29 +254,24 @@ def task_get_nonreporting_industrial_landfills_pulp_paper_proxy_data(
mills_locs.loc[ifacility,'FRS_match'] = 1
elif len(imatch)>1:
FRS_temp = FRS_facility_locs.loc[imatch,:]
new_match = np.where(np.max(FRS_temp['accuracy_value']))[0]
if len(new_match) >0:
mills_locs.loc[ifacility,'lat'] = FRS_facility_locs.loc[imatch[new_match[0]],'latitude']
mills_locs.loc[ifacility,'lon'] = FRS_facility_locs.loc[imatch[new_match[0]],'longitude']
mills_locs.loc[ifacility,'FRS_match'] = 1
# use the location of the first occurance of the facility with the highest accuracy value
new_match = np.argmax(np.max(FRS_temp['accuracy_value']))
mills_locs.loc[ifacility,'lat'] = FRS_facility_locs.loc[new_match,'latitude']
mills_locs.loc[ifacility,'lon'] = FRS_facility_locs.loc[new_match,'longitude']
mills_locs.loc[ifacility,'FRS_match'] = 1
else:
continue

print('Not Found:',len(mills_locs)-(np.sum(mills_locs.loc[:,'FRS_match'])+np.sum(mills_locs.loc[:,'ghgrp_match'])), 'of',len(mills_locs))

# keep mills that only have FRS locations (mills that are not already covered by
# subpart tt and mills that are not missing locations)
mills_locs = mills_locs.query(
"ghgrp_match == 0 & FRS_match == 1").drop(
columns=["state_name", "county", "city", "grades", "ghgrp_match", "FRS_match"]).rename(
columns={"lat": "latitude", "lon": "longitude"}).dropna()

# add a column to equally allocate unaccounted for GHGI emissions to all non-reporting mills
mills_locs["ch4_kt"] = 1.0
mills_locs = mills_locs.reset_index(drop=True)

nonreporting_pulp_paper_df['rel_emi'] = nonreporting_pulp_paper_df.groupby(["state_code"])['ch4_kt'].transform(lambda x: x / x.sum() if x.sum() > 0 else 0)
nonreporting_pulp_paper_df = nonreporting_pulp_paper_df.drop(columns='ch4_kt')
mills_locs = (mills_locs
.query("ghgrp_match == 0 & FRS_match == 1")
.drop(columns=["state_name", "county", "city", "grades", "ghgrp_match", "FRS_match"])
.rename(columns={"lat": "latitude", "lon": "longitude"})
.dropna()
)

nonreporting_pulp_paper_gdf = (
gpd.GeoDataFrame(
Expand All @@ -290,7 +283,7 @@ def task_get_nonreporting_industrial_landfills_pulp_paper_proxy_data(
),
)
.drop(columns=["facility_id", "latitude", "longitude"])
.loc[:, ["state_code", "geometry", "rel_emi"]]
.loc[:, ["state_code", "geometry"]]
)

nonreporting_pulp_paper_gdf.to_parquet(nonreporting_pulp_paper_proxy_output_path)
Expand All @@ -300,8 +293,7 @@ def task_get_nonreporting_industrial_landfills_pulp_paper_proxy_data(
def task_get_reporting_industrial_landfills_food_beverage_proxy_data(
subpart_tt_path = "https://data.epa.gov/efservice/tt_subpart_ghg_info/pub_dim_facility/ghg_name/=/Methane/CSV",
state_path: Path = global_data_dir_path / "tl_2020_us_state.zip",
reporting_food_beverage_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path
/ "ind_landfills_fb_r_proxy.parquet",
reporting_food_beverage_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path / "ind_landfills_fb_r_proxy.parquet",
):
"""
Relative emissions and location information for reporting facilities are taken from
Expand Down Expand Up @@ -362,21 +354,20 @@ def task_get_reporting_industrial_landfills_food_beverage_proxy_data(
),
)
.drop(columns=["latitude", "longitude", "city", "zip"])
.loc[:, ["facility_id", "facility_name", "state_code", "geometry", "year", "rel_emi"]]
.loc[:, ["year", "state_code", "geometry", "rel_emi"]]
)

reporting_food_beverage_gdf.to_parquet(reporting_food_beverage_proxy_output_path)
return None


def task_get_nonreporting_industrial_landfills_food_beverage_proxy_data(
state_path: Path = global_data_dir_path / "tl_2020_us_state.zip",
subpart_tt_path = "https://data.epa.gov/efservice/tt_subpart_ghg_info/pub_dim_facility/ghg_name/=/Methane/CSV",
frs_naics_path = global_data_dir_path / "NATIONAL_NAICS_FILE.CSV",
frs_facility_path = global_data_dir_path / "NATIONAL_FACILITY_FILE.CSV",
food_manufacturers_processors_path = V3_DATA_PATH / "sector/landfills/Food Manufacturers and Processors.xlsx",
nonreporting_food_beverage_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path
/ "ind_landfills_fb_nr_proxy.parquet",
state_path: Path = global_data_dir_path / "tl_2020_us_state.zip",
subpart_tt_path = "https://data.epa.gov/efservice/tt_subpart_ghg_info/pub_dim_facility/ghg_name/=/Methane/CSV",
frs_naics_path = global_data_dir_path / "NATIONAL_NAICS_FILE.CSV",
frs_facility_path = global_data_dir_path / "NATIONAL_FACILITY_FILE.CSV",
food_manufacturers_processors_path = V3_DATA_PATH / "sector/landfills/Food Manufacturers and Processors.xlsx",
nonreporting_food_beverage_proxy_output_path: Annotated[Path, Product] = proxy_data_dir_path / "ind_landfills_fb_nr_proxy.parquet",
):
"""

Expand Down Expand Up @@ -547,24 +538,26 @@ def task_get_nonreporting_industrial_landfills_food_beverage_proxy_data(
food_beverage_facilities_locs.loc[ifacility,'FRS_match'] = 1
elif len(imatch)>1:
FRS_temp = FRS_facility_locs.loc[imatch,:]
new_match = np.where(np.max(FRS_temp['accuracy_value']))[0]
if len(new_match) >0:
food_beverage_facilities_locs.loc[ifacility,'lat'] = FRS_facility_locs.loc[imatch[new_match[0]],'latitude']
food_beverage_facilities_locs.loc[ifacility,'lon'] = FRS_facility_locs.loc[imatch[new_match[0]],'longitude']
food_beverage_facilities_locs.loc[ifacility,'FRS_match'] = 1
# use the location of the first occurance of the facility with the highest accuracy value
new_match = np.argmax(FRS_temp['accuracy_value'])
food_beverage_facilities_locs.loc[ifacility,'lat'] = FRS_facility_locs.loc[imatch[new_match],'latitude']
food_beverage_facilities_locs.loc[ifacility,'lon'] = FRS_facility_locs.loc[imatch[new_match],'longitude']
food_beverage_facilities_locs.loc[ifacility,'FRS_match'] = 1
else:
continue

print('Not Found:',len(food_beverage_facilities_locs)-(np.sum(food_beverage_facilities_locs.loc[:,'FRS_match'])+np.sum(food_beverage_facilities_locs.loc[:,'ghgrp_match'])), 'of',len(food_beverage_facilities_locs))

# Find missing locations by Geocoding addresses
# If this portion of the code fails due to timeout error (1), just re-run and it will work.
# This section takes ~152 minutes to run.
geolocator = Nominatim(user_agent="myGeocode")
geopy.geocoders.options.default_timeout = None
print(geolocator.timeout)

food_beverage_facilities_locs['geo_match'] = 0
for ifacility in np.arange(0,len(food_beverage_facilities_locs)):
if food_beverage_facilities_locs.loc[ifacility,'FRS_match'] ==0 and food_beverage_facilities_locs.loc[ifacility,'ghgrp_match'] == 0:
if food_beverage_facilities_locs.loc[ifacility,'FRS_match'] == 0 and food_beverage_facilities_locs.loc[ifacility,'ghgrp_match'] == 0:
location = geolocator.geocode(food_beverage_facilities_locs['full_address'][ifacility])
if location is None:
continue
Expand Down Expand Up @@ -602,7 +595,7 @@ def task_get_nonreporting_industrial_landfills_food_beverage_proxy_data(
#count -= 1
food_beverage_facilities_locs.loc[ifacility,'lat'] = location2.latitude
food_beverage_facilities_locs.loc[ifacility,'lon'] = location2.longitude
food_beverage_facilities_locs.loc[ifacility,'geo_match']=1
food_beverage_facilities_locs.loc[ifacility,'geo_match'] = 1
else:
#count -= 1
food_beverage_facilities_locs.loc[ifacility,'lat'] = location.latitude
Expand Down Expand Up @@ -631,7 +624,7 @@ def task_get_nonreporting_industrial_landfills_food_beverage_proxy_data(
"excessfood_tonyear_lowest", "excessfood_tonyear_highest",
"full_address", "partial_address", "lat", "lon",
"ghgrp_match", "FRS_match", "geo_match"])
.loc[:, ["facility_id", "state_code", "geometry", "rel_emi"]]
.loc[:, ["state_code", "geometry", "rel_emi"]]
)

nonreporting_food_beverage_gdf.to_parquet(nonreporting_food_beverage_proxy_output_path)
Expand Down
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