The NO2 land-use regression model estimate data includes national-scale estimates of NO2 in the United States. It provides predictions for annual average NO2 concentrations (ppb) using the land-use regression models. In order to predict NO2 estimates at the national level, the following input data were used; Fixed-site regulatory monitors, Satellite-derived NO2 estimates and GIS-derived land-use data.

Source: Empirical Model Database

Citation: Jennifer Ailshire and Hyewon Kang. 2018. Contextual Data Resource (CDR): Nitrogen Dioxide LUR Model Estimates, 2000-2010, Version 1.0. Los Angeles, CA: USC/UCLA Center on Biodemography and Population Health.

Years: 2000-2010

Global and regional PM2.5 concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from PM2.5 measurements.. Annual and monthly mean PM2.5 [ug/m3] at 0.01° × 0.01°.

Global and regional PM2.5 concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from PM2.5 measurements.. Annual and monthly mean PM2.5 [ug/m3] at 0.1° × 0.1°.

The Fused Air Quality Surface Using Downscaling (FAQSD) Files is derived from data on Ozone (O3). The data provides predictions for national-scale annual average concentration of O3 (μg/m3) in the United States using monitoring data and Community Multiscale Air Quality (CMAQ) output.

The Fused Air Quality Surface Using Downscaling (FAQSD) Files is derived from data on Particulate Matter 2.5 (PM2.5). The data provides predictions for national-scale annual average concentration of PM2.5 (μg/m3) in the United States using monitoring data and Community Multiscale Air Quality (CMAQ) output.

The Food Access Research Atlas dataset provides information on food environment factors—such as store/restaurant proximity, food prices, food and nutrition assistance programs, and community characteristics—interact to influence food choices and diet quality. These interactions are complex and more research is needed to identify causal relationships and effective policy interventions.

The USDA Food Environment and Access dataset provides information on geographic variation in access and proximity to grocery stores, restaurant availability and expenditures, food insecurity and assistance, food prices and taxes, and other food-related measures, as well as fine-grained data on food access for various demographic groups.

The Street Connectivity dataset contains measures of street connectivity (how well streets connect with one another) within United States census tracts. This includes measures of the number of street segments (links) and intersections (nodes) per tract, street length within tracts, and indices representing overall connectivity within the tract.