Land-Use Regression Model for Predicting Ambient NO2 at U.S. County Level

References1. United States, Environmental Protection Agency, 2002.

2. Henderson et al “Application of Land Use Regression to Estimate Long-Term Concentration of Traffic-Related Nitrogen Oxides and Fine Particulate Matter” Environmental Science & Technology 41 (2007) 2422 - 2428

3. US EPA AQS Data Mart,NO2 2013 https://ofmext.epa.gov/AQDMRS/aqdmrs.html

4. US EPA 2008 National Emissions Inventory - NOx emissions.http://www.epa.gov/ttnchie1/net/2011inventory.html

5. United State Census Bureau 2010 Tiger/Line shapefiles (primary and secondary roads) ftp://ftp2.census.gov/geo/tiger/TIGER2010/PRISECROADS/

6. United State Census Bureau 2010 Tiger/Line shapefiles (railroads) ftp://ftp2.census.gov/geo/tiger/TIGER2010/RAILS/

7. 1.Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864.

8. PRISM Climate Group at Oregon State University - PRISM_ppt_stable_4kmM3_2013 www.prism.oregonstate.edu

9. US Census Bureau , 2014 Population estimates http://www.census.gov/popest/data/index.html

By Sauda Ahmed, Karoline Gurdal, & Blair Robertson

Future Research● The accuracy and resolution of the regression model can be improved by

constructing different size circular buffers around each EPA monitoring site and building regression models based on the land use in each buffers.

● The LUR model can be modified to include direct indicators of NO2 emissions instead of proxies (e.g. traffic volumes instead of road lengths) as well as different meteorological variables (ex. temperature, solar radiation) and terrain variables to reduce the influence of confounding factors.

Land Use regressionWe used land use regression2 (LUR) to predict ambient NO2 levels in US Counties.LUR is a GIS- and statistics-based method that uses in situ concentration measurements and information about surrounding land-uses to develop a best fit linear regression which can be used to estimate concentrations for non-measurement locations. We used the following 7 datasets to obtain annual average NO2 measurements and land use variables:● US EPA 2013 NO2

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● US EPA 2011 NOx from the National Emissions Inventory4

● US Census Bureau 2010 US primary & secondary roads5

● US Census Bureau 2010 US railroads6

● 2011 National Land Cover Database7

● 2013 annual average rainfall from PRISM Climate group at Oregon state University8

● US Census Bureau 2014 population estimate9

Regression EquationFor each of the 245 counties with available EPA NO2 measurements , the following land use variables were extracted:● Road length● Railroad length● NOx point emissions sources● Total area of evergreen, deciduous, and mixed forests.● Population density● Annual Average RainfallLinear regression models were built in R statistical package.

Results● The model explains 56% of the variability of NO2 concentrations at US

counties.● The LUR model shows that Population is the biggest predictor of ambient

NO2.● For a 10 fold increase in railroad length , point emission and population,

NO2 is increased by 0.66 ppb, 0.37 ppb and 1.4 ppb respectively.● An increase in forest cover and precipitation causes a decrease in NO2

concentration.● Road length is not included in the final LUR model, since it’s effect was not

statistically significant.● During the aggregation of different predictor variables for all the counties,

some data was lost and recorded as NULL values. Because of this, some counties are missing NO2 concentrations in the attribute table and on the map.

The LUR model for annual average NO2 (R2 = 0.56) may be written as,

NO2(county) = 3.59 + 0.66*log(RR)+0.37*log(TN) +1.4*log(POP)- 0.08*log(TA) -0.003PP

Where: NO2(county) : Ambient Concentration of NO2 in the county, in ppb RR : Length of railroads within the county in km, TN : NO2 (tons) emitted from point sources within the county, POP: Population density of the county, TA: Area (km2) of vegetation within the county PP: Annual average precipitation , in mm

Purpose and Objective:● Within the U.S., there are 421 EPA monitoring stations that measures ambient

air pollutants in order to preserve and improve the air quality for the nation. However these monitoring stations are scarce in number and air monitoring is expensive.

Regression Diagnostics

● Epidemiologic studies require pollutant concentrations at a high spatial resolution to be used as exposure inputs

● Our objective is to develop a regression model for predicting ambient NO2 concentration of each county and to create a high resolution NO2 map for entire US.

What is NO2 ? Why does it matter?● Nitrogen dioxide, or NO2, is an urban air pollutant that is produced by combustion

processes. NO2 is a precursor to ozone and aerosol pollutants, which can be detrimental to health and the environment at local and global scales.

● In 20021, the primary sources of NO2 emissions were fuel combustion (~39 percent) and transportation (~56 percent)

● Dry deposition to trees and wet deposition due to rainfall are two important removal mechanisms for NO2.

● Current scientific evidence links short-term NO2 exposures, with adverse respiratory effects including airway inflammation in healthy people and increased respiratory symptoms in people with asthma.

This map details the predicted ambient NO2 concentrations in ppb per county in the U.S. The map was created using the values predicted by the regression equation.