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. 

Data Summary

Dataset Name: The NO2 Land-Use Regression Model Estimate Data

Data Source: Empirical Model Database

Years: All Data Contains measures between 2000 and 2010

Geographies: Data are collected at XY coordinates and made available at the census tract level.

Download the Air Pollution: NO2 Land-Use Regression Model Estimate Data documentation HERE.

Suggested 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.

Data Sources:

Empirical Model Database, http://spatialmodel.com/concentrations/Bechle_LUR.html

The data were obtained from Bechle et al (2016) whose work was supported by the National Science Foundation under Grant No. 1236800 and by the Minnesota Supercomputing Institute.