Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy
Introduction
With a population of 16.7 million, the Los Angeles Metropolitan Area (hereafter referred to as LA) is the largest urban area in the state of California and the second-largest in the United States. LA is also consistently ranked as one of the most polluted metropolitan areas in the US, partially due to heavy reliance on automobiles for transportation, and because more than 40% of all goods transported into the United States move through the Long Beach/Los Angeles port complex, which generates thousands of truck trips per day (Hricko, 2008). The LA Basin is also susceptible to atmospheric inversions (Bytnerowicz and Fenn, 1996), which trap exhaust from on-road vehicles, airplanes, and locomotives, as well as from shipping, manufacturing and other industrial sources in the troposphere. Additionally, with an average rainfall of only 381 mm/year, Los Angeles experiences minimal pollution removal by precipitation. The high level of auto-dependence in LA is replicating in other major conurbations around the world, particularly in newly industrialized countries (Walsh, 2008). Lessons learned from studying air pollution exposures and subsequent health effects in Los Angeles may therefore have widespread applicability to other large urban areas.
This paper reports the first attempt to model NO, NO2 and NOX concentrations in LA (Fig. 1) using a land use regression (LUR) approach. The LUR was developed as part of a study sponsored by the California Air Resources Board (CARB) to examine the impacts of outdoor air pollution on respiratory health in children living in LA. The LUR method seeks to predict pollution concentrations at a given site based on surrounding land use, road network, traffic, physical environment and population characteristics using a series of buffers (Su et al., 2009). Geospatial modeling techniques including LUR are an attractive alternative to ambient (government) air monitoring data for assessing traffic pollutant exposures, since they can be applied to large populations and account for neighborhood-scale variations in pollutant concentrations. To our knowledge, LUR to date has not been applied to large urban areas with population over 10 million for traffic-related air pollution (NOX) but only to estimate more spatially homogenous air pollutant concentrations such as fine particulate matter (Moore et al., 2006; Ross et al., 2007).
Section snippets
Sampling location determination
We selected neighborhood monitoring locations (n=201) using a location-allocation algorithm (Kanaroglou et al., 2005) that took into account variability in traffic pollution and the spatial distribution of our childhood respiratory health study population, specifically, participants in the Los Angeles Family and Neighborhood Study (LA FANS). The estimation domain for locating optimal sampling sites covered more than 10,000 km2 of LA. Briefly, the location-allocation algorithm involves a two-step
NOX measurements
Based on the field blank measures, sampler detection limits were <0.14 μg for NO2 and <0.76 μg for NOX. Our duplicate measurements indicated that the average coefficient of variation was low (3.3% for NO2 and 2.1% for NOX). Measured pollutant levels ranged from 5.33 to 42.73 ppb for NO2 (median=27.29 ppb) and from 8.08 to 156.95 ppb for NOX (median=60.88 ppb), after correcting for blank concentrations. Annual means measured by 14 monitors in the Southern California Air Quality Management District
Discussion and conclusions
In this paper, we modeled NO, NO2 and NOX concentrations for the LA metropolitan area using the ADDRESS modeling strategy. Our final three prediction models explained 81%, 86% and 85% of NO, NO2 and NOX variances, respectively. The models presented here have a higher power of prediction (R2) than a large majority of previously developed LUR models (Jerrett et al., 2007; Henderson et al., 2007; Hoek et al., 2008). To our knowledge, this is the first application of an intensive air pollution
Acknowledgments
This work was funded by the California Air Resources Board (CARB) under contract 04-323 “Traffic-Related Air Pollution and Asthma in Economically Disadvantaged and High Traffic Density Neighborhoods in Los Angeles, California”, CARB 06-332 “Spatialtemporal Analysis of Air Pollution and Mortality in California Based on the American Cancer Society Cohort” and by the California Office of Environmental Health Hazard Assessment 07-E0009 “Understanding and Acting on Cumulative Impacts on California
References (28)
- et al.
Nitrogen deposition in California forests: a review
Environ. Pollut.
(1996) - et al.
A review of land-use regression models to assess spatial variation of outdoor air pollution
Atmos. Environ.
(2008) - et al.
Establishing an air pollution monitoring network for intra-urban population exposure assessment; a location-allocation approach
Atmos. Environ.
(2005) Satellite remote sensing of surface air quality
Atmos. Environ.
(2008)- et al.
A land use regression for predicting fine particulate matter concentrations in the New York City region
Atmos. Environ.
(2007) - et al.
Passive measurement of nitrogen oxides to assess traffic-related pollutant exposure for the east bay children's respiratory health study
Atmos. Environ.
(2004) - et al.
New high resolution maps of estimated background ambient NOX and NO2 concentrations in the UK
Atmos. Environ.
(1997) - et al.
A distance decay variable selection strategy for optimized land use regression modeling
Sci. Total Environ.
(2009) - et al.
Estimating urban morphometry at the neighborhood scale for improvement in modeling long-term average air pollution concentrations
Atmos. Environ.
(2008) - et al.
Development of an individual exposure model for application to the Southern California children's health study
Atmos. Environ.
(2005)
Interactive Spatial Data Analysis
GIS Fundamentals: A First Text on Geographic Information Systems
Statistics for Spatial Data
A physically-based transformation of thematic mapper data—the TM tasseled cap
IEEE Trans. Geosci. Remote Sensing
Cited by (120)
Long-term air pollution exposures on type 2 diabetes prevalence and medication use
2023, Hygiene and Environmental Health AdvancesRerouting urban construction transport flows to avoid air pollution hotspots
2023, Transportation Research Part D: Transport and EnvironmentDeveloping Machine learning models for hyperlocal traffic related particulate matter concentration mapping
2022, Transportation Research Part D: Transport and EnvironmentAircraft noise and vehicle traffic-related air pollution interact to affect preterm birth risk in Los Angeles, California
2022, Science of the Total Environment