Development of spatiotemporal models to predict ambient ozone and NOx concentrations in Tianjin, China.

Nitrogen oxides (NO x) and ozone (O 3) are important air pollutants that are associated with adverse health effects. Land-use regression (LUR) models have been widely developed to estimate air pollution concentrations. Due to data availability, however, such models are usually not applied in developing countries. We aimed to characterize NO x and O 3 concentrations and develop LUR models to predict their spatial and temporal distributions using publicly-available data in Tianjin, a heavily polluted city in China. Seasonal samples were collected across Tianjin at 29 locations for O 3 and 49 locations for NO x. Heavy-duty vehicle counts estimated from 0.5 m × 0.5 m satellite images correlated well with field-measured counts, thus supporting the use of high-resolution satellite images to assess vehicle traffic. Concentrations of NO x were highest in winter, while the opposite pattern was observed for O 3. The majority of the variance in NO x was explained by season (36.2%) and heavy vehicle traffic (19.8%). For O 3 , the variance was explained by season (80.7%) in a pooled model, and by distance to roads (43.4%) and distance to coal plants (26.2%) in a summer model. Cross-validation showed reasonable practicability for NO x (R2 = 0.53 with field-measured heavy-duty vehicle count; R2 = 0.46 with satellite-based heavy-duty vehicle count) and O 3 (R2 = 0.90 for pooled model; R2 = 0.70 for summer model) 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. • Ambient NO x mostly explained by season and heavy-duty vehicle traffic. • Ambient O 3 mostly explained by season and by distance to roads. • Concentration of NO x was highest in winter while O 3 was highest in summer. • Satellite images can be used to count vehicles for air pollution modeling. [ABSTRACT FROM AUTHOR]