Center for Disease Control, Timothy B Norris (2026). 2014-2019 CDC PM 2.5 Monthly Predictions for US Counties [gis dataset]. University of Miami GDSC. last updated: 2026-09-30 (update frequency: Never). Aggregated monthly PM 2.5 modelled estimates that span 2014-01-01 to 2019-12-31 derived from the CDC Daily County-Level PM2.5 Concentrations 2001-2022 provided by the CDC National Environmental Public Health Tracking Network. From the CDC description: This dataset provides modeled predictions of PM2.5 levels from the EPA Downscaler model. Data are at the county level for 2001-2022. These data are used by the CDC National Environmental Public Health Tracking Network to generate air quality measures. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. Key words: environmental health, air pollution, asthma, pm2.5, particulate matter, air quality, national ambient air quality standards, national environmental health tracking network, United States Open Geospatial Consortium Standards:

dynamic vector tiles

Analytical Functions:

gdsc_get_attributes( table, lat, lng, identifier, normal )

ETL documentation

ETL for us_2014_2019_monthly_pm25_by_county_cdc

######### # GDSC ETL is performed in two steps: shell script to ETL data into postgres # and then additional transformations with SQL in postGIS (if needed). #### ######### # Step 1 - bash script (pseudo code) #### # create directory structure and move into it mkdir -p /data/us_2014_2019_monthly_pm25_by_county_cdc/download -p /data/us_2014_2019_monthly_pm25_by_county_cdc/etl chmod -R 777 /data/us_2014_2019_monthly_pm25_by_county_cdc cd /data/us_2014_2019_monthly_pm25_by_county_cdc # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # load us_2023_county_tl as dependency (see us_2023_county_tl for details). # download if update flag is set: if do_update == 1 then wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 -t 10 -O download/us_2014_2019_monthly_pm25_by_county_cdc.csv 'https://data.cdc.gov/api/v3/views/53mz-4zqd/query.csv?query=SELECT%0A%20%20%60year%60%2C%0A%20%20%60date%60%2C%0A%20%20%60statefips%60%2C%0A%20%20%60countyfips%60%2C%0A%20%20%60pm25_max_pred%60%2C%0A%20%20%60pm25_med_pred%60%2C%0A%20%20%60pm25_mean_pred%60%2C%0A%20%20%60pm25_pop_pred%60%0AWHERE%0A%20%20caseless_one_of(%60year%60%2C%20%222014%22%2C%20%222015%22%2C%20%222016%22%2C%20%222017%22%2C%20%222018%22%2C%20%222019%22)&app_token='"$CDC_APP_TOKEN"'' 2>&1 # remove spaces, periods, and leading numerical digits in column headers (if needed) if (head -n 1 download/us_2014_2019_monthly_pm25_by_county_cdc.csv | grep -qE '\s|\.|\"[0-9]'); then sed -i '1s/ /_/g; 1s/\.//g; 1s/\"\([0-9]\)/\"n\1/g' download/us_2014_2019_monthly_pm25_by_county_cdc.csv 2>&1 fi # load into postGIS with: ogr2ogr -lco GEOMETRY_NAME=geom -f PostgreSQL PG:"" download/us_2014_2019_monthly_pm25_by_county_cdc.csv -dialect sqlite -sql "WITH all_rows AS (SELECT year, DATE(SUBSTR(date, 6) || '-' || CASE SUBSTR(date, 3, 3) WHEN 'JAN' THEN '01' WHEN 'FEB' THEN '02' WHEN 'MAR' THEN '03' WHEN 'APR' THEN '04' WHEN 'MAY' THEN '05' WHEN 'JUN' THEN '06' WHEN 'JUL' THEN '07' WHEN 'AUG' THEN '08' WHEN 'SEP' THEN '09' WHEN 'OCT' THEN '10' WHEN 'NOV' THEN '11' WHEN 'DEC' THEN '12' END || '-01') AS start_date, FORMAT('%02d', statefips) AS statefips, FORMAT('%03d', countyfips) AS countyfips, MAX(CAST(pm25_max_pred AS decimal)) AS pm25_max_pred, MEDIAN(CAST(pm25_med_pred AS decimal)) AS pm25_med_pred, AVG(CAST(pm25_mean_pred AS decimal)) AS pm25_mean_pred, AVG(CAST(pm25_pop_pred AS decimal)) AS pm25_pop_pred FROM us_2014_2019_monthly_pm25_by_county_cdc WHERE pm25_max_pred != '' AND pm25_med_pred != '' AND pm25_mean_pred != '' AND pm25_pop_pred != '' GROUP BY statefips, countyfips, start_date) SELECT statefips, countyfips, statefips || countyfips as geoid, JSON_GROUP_OBJECT(start_date || '/' || DATE(start_date, '+1 month', '-1 day'), pm25_max_pred) AS pm25_max_pred, JSON_GROUP_OBJECT(start_date || '/' || DATE(start_date, '+1 month', '-1 day'), pm25_med_pred) AS pm25_med_pred, JSON_GROUP_OBJECT(start_date || '/' || DATE(start_date, '+1 month', '-1 day'), pm25_mean_pred) AS pm25_mean_pred, JSON_GROUP_OBJECT(start_date || '/' || DATE(start_date, '+1 month', '-1 day'), pm25_pop_pred) AS pm25_pop_pred FROM all_rows GROUP BY statefips, countyfips" -lco COLUMN_TYPES="pm25_mean_pred=jsonb,pm25_med_pred=jsonb,pm25_max_pred=jsonb,pm25_pop_pred=jsonb" -nlt multipolygon -nln us_2014_2019_monthly_pm25_by_county_cdc echo success: us_2014_2019_monthly_pm25_by_county_cdc.csv loaded with ogr2ogr ######### # Step 2 - SQL script (pseudo code): #### # remove duplicate points and make geometries valid: UPDATE us_2014_2019_monthly_pm25_by_county_cdc SET geom=ST_MakeValid(ST_RemoveRepeatedPoints(geom)); # add local geometry column and reproject existing geometries into local EPSG: ALTER TABLE us_2014_2019_monthly_pm25_by_county_cdc DROP COLUMN IF EXISTS geom_local CASCADE; SELECT AddGeometryColumn ( 'us_2014_2019_monthly_pm25_by_county_cdc', 'geom_local', 4269, 'multipolygon', 2 ); UPDATE us_2014_2019_monthly_pm25_by_county_cdc SET geom_local=ST_MakeValid(ST_RemoveRepeatedPoints(ST_Transform(ST_Multi(geom),4269))); CREATE INDEX us_2014_2019_monthly_pm25_by_county_cdc_geom_local_idx ON us_2014_2019_monthly_pm25_by_county_cdc USING GIST (geom_local); NOTIFY pgrst, 'reload schema'; -- join temp to us_2014_2019_monthly_pm25_by_county_cdc (column) ALTER TABLE us_2014_2019_monthly_pm25_by_county_cdc ADD COLUMN IF NOT EXISTS pm25_max_pred jsonb, ADD COLUMN IF NOT EXISTS pm25_med_pred jsonb, ADD COLUMN IF NOT EXISTS pm25_mean_pred jsonb, ADD COLUMN IF NOT EXISTS pm25_pop_pred jsonb; UPDATE us_2014_2019_monthly_pm25_by_county_cdc SET pm25_max_pred = temp.pm25_max_pred::jsonb, pm25_med_pred = temp.pm25_med_pred::jsonb, pm25_mean_pred = temp.pm25_mean_pred::jsonb, pm25_pop_pred = temp.pm25_pop_pred::jsonb FROM temp WHERE us_2014_2019_monthly_pm25_by_county_cdc.geoid = temp.geoid; -- drop foreign server(??) -- remove extension postgres_fwd (??) -- remove temporary table DROP TABLE temp CASCADE; " # Move into corrrect directory and create derivative directory in data package on osgeo cd /data/us_2014_2019_monthly_pm25_by_county_cdc/ mkdir -p derived # Create gpkg of table in derivate directory on osgeo rm -f derived/us_2014_2019_monthly_pm25_by_county_cdc.gpkg ogr2ogr -f GPKG -overwrite derived/us_2014_2019_monthly_pm25_by_county_cdc.gpkg PG:"" -select "state_fips,countyfips,geoid,pm25_max_pred,pm25_med_pred,pm25_mean_pred,pm25_pop_pred,geom" us_2014_2019_monthly_pm25_by_county_cdc # Create downloadable tarfile of gpkg in derivative directory on osgeo rm -f derived/us_2014_2019_monthly_pm25_by_county_cdc.gpkg.tar.gz tar -czf derived/us_2014_2019_monthly_pm25_by_county_cdc.gpkg.tar.gz meta_dcat_us_2014_2019_monthly_pm25_by_county_cdc.json -C derived us_2014_2019_monthly_pm25_by_county_cdc.gpkg # Move into correct directory cd /data/us_2014_2019_monthly_pm25_by_county_cdc/ # Create pg_dump of table SQL in derivate directory on postgis. pg_dump -h gdsc-postgis-node-2u3hmx5uzpp74jsn -t us_2014_2019_monthly_pm25_by_county_cdc > derived/us_2014_2019_monthly_pm25_by_county_cdc.sql # Create downloadable tarfile of SQL in derivative directory on postgis rm -f derived/us_2014_2019_monthly_pm25_by_county_cdc.sql.tar.gz tar -czf derived/us_2014_2019_monthly_pm25_by_county_cdc.sql.tar.gz meta_dcat_us_2014_2019_monthly_pm25_by_county_cdc.json -C derived us_2014_2019_monthly_pm25_by_county_cdc.sql