The effect of urban morphological characteristics on the spatial variation of PM air quality in downtown Nanjing.

The effects of the urban morphological characteristics on the spatial variation of near-surface PM air quality were examined. Unlike previous studies, we performed the analyses in real urban environments using continuous observations covering the whole scale of urban densities typically found in cities. We included data from 31 measurement stations divided into 8 different wind sectors with individually defined morphological characteristics leading to highly varying urban characteristics.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA.

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data.

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots.