Use of Spatial Models to Predict Ambient O3 and NOx Concentrations in Tianjin, China

Abstract

Nitrogen oxides (NOx) and ozone (O3) are important ambient air pollutants that have been associated with a variety of adverse health effects. In North America and Europe, land-use regression (LUR) models have been widely developed to estimate pollution concentrations at a high spatial resolution. Although these models included traffic fleet composition and/or traffic counts/density as important predictors, such information is usually unavailable in developing countries. In China, LUR has only recently been applied in limited areas. We aimed to characterize NOx and O3 concentrations and develop LUR models to predict their spatial distributions using solely publicly-available data in Tianjin, a major coastal city in China that is heavily polluted by traffic and industrial emissions. Seasonal NOx and O3 samples were collected at 49 locations across Tianjin. Heavy-duty vehicle counts estimated from 0.5 m x 0.5 m satellite images correlated well with field-measured counts collected at a different time, thus supporting the use of high-resolution satellite images to assess vehicle traffic. We found that concentrations of NOx were highest in winter, while the opposite pattern was observed for O3. The majority of the variance in NOx concentrations was explained by heavy vehicle traffic, tree cover, and season. For O3, the variance was mostly explained by distance to airports, distance to roads, and distance to coal plants. Measured vs. predicted NOx concentrations following model calibration showed good predictive ability for NOx (R2=0.64 with field-measured heavy-duty vehicle count; R2=0.59 with satellite-based heavy-duty vehicle count) and O3 (R2=0.70) models. This study provides utility for researchers investigating air pollution in regions where field-measured vehicle traffic data are not available, as well as for policy makers and public health officials seeking to understand the sources and spatial distribution of air pollution in Tianjin.