Evaluating meteorological comparability in air quality studies: Classification and regression trees for primary pollutants in California's South Coast Air Basin

Meteorology confounds the comparison of air quality data across time and space. This presents challenges, for example, to comparisons of pollutant concentration data obtained with mobile monitoring platforms on different days and/or locations within the same airshed. In part to address this challenge, we employed a classification and regression tree (CART) modeling approach that can serve as a useful and straightforward tool in such air quality studies, to determine the comparability of meteorological conditions between measurement days and locations as well as to compare primary pollutant concentrations corrected by meteorological conditions. Specifically, regression trees were developed to obtain representative concentrations of traffic-related primary air pollutants such as NOx and CO, based on meteorological conditions for 2007–2009 in the California South Coast Air Basin (SoCAB). The resulting regression trees showed strong correlations between the regression classifications developed for different pollutant metrics, such as daily CO and NOx maxima, as well as between monitoring sites. For the SoCAB, the most important meteorological parameters controlling primary pollutant concentrations were the mean surface wind speed, geopotential heights at 925 mbar, the upper air north–south pressure gradient, the daily minimum temperature, relative humidity at 1000 mbar, and vertical stability, in approximate order of importance. The value of developing a regression tree for a single season was also explored by performing CART analysis separately on summer data. Although seasonal classifications were similar to those developed from annual data, the standard deviations of the classification groups were somewhat reduced.

► Regression trees for primary pollutants were created using a CART model. ► Primary pollutant levels are largely under control of meteorology in the SoCAB. ► The most important meteorological variables are wind speed and geopotential height. ► Spatial variances in pollutants are well correlated with meteorological conditions. ► CART analysis provides an effective tool for meteorological comparability.