The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing.

The effects of the urban morphological characteristics on the spatial variation of near-surface PM2.5 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. The urban morphological characteristics explained up to 73% of the variance in normalized PM2.5 concentrations in street canyons, indicating that the spatial variation of the near-surface PM2.5 air quality was mostly defined by the characteristics studied. The fraction of urban trees nearby the stations was found to be the most important urban morphological characteristic in explaining the PM2.5 air quality, followed by the height-normalized roughness length as the second important parameter. An increase in the fraction of trees within 50 m of the stations from 25 percentile to 75 percentile (i.e. by the interquartile range, IQR) increased the normalized PM2.5 concentration by up to 24% in the street canyons. In open areas, an increase in the trees by the IQR actually decreased the normalized PM2.5 by 6% during the pre-COVID period. An increase in the height-normalized roughness length by the IQR increased the normalized PM2.5 by 9% in the street canyons. The results obtained in this study can help urban planners to identify the key urban characteristics affecting the near-surface PM2.5 air quality and also help researchers to evaluate how representative the existing measurement stations are compared to other parts of the cities.

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. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

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. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.