Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring

Abstract

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form–air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

A Review of Literature on the Usage of Low-Cost Sensors to Measure Particulate Matter

“…Two studies [15,56] deployed their LCS in urban locations near residential non-smoking environments. The most common emissions in urban and suburban sites are industrial, traffic exhaust, and vehicular emissions [1][2][3][4]6,[8][9][10][11][12][13][15][16][17]21,28,[31][32][33][34][35][36][37][38][39][40][41][43][44][45][46][47][49][50][51][52][53][55][56][57][58][59]. Transportation (or traffic) emissions, including gas, vehicle, and diesel combustions, were exposed in forty-three of the studies.…”

“…PM ambient air pollutants in the urban area, with roads and highways, result in a significant emission source from traffic and vehicular exhaust [1][2][3][4]6,[8][9][10][11][12][13][15][16][17]21,28,[31][32][33][34][35][36][37][38][39][40][41][43][44][45]47,[49][50][51][52][53][55][56][57][58][59]. With the influx of in-vehicle use in the city, PM (a common pollutant from vehicle use) is exposed more frequently, resulting in chronic and acute human health effects [13].…”

Hyperlocal Air Pollution in London: Validating Low-Cost Sensors for Mobile Measurements from Vehicles

“…LCS can be mounted to vehicles that are already present in the target area, either privately owned vehicles driving according to the users’ individual time–activity patterns or commercial vehicles driving fixed routes. Vehicle-based mobile monitoring with LCS has already been performed using many types of vehicles, including, cars/taxis, − buses, − light rail/street cars, , waste trucks, , and bicycles. , LCS have also been used as personal exposure monitors in studies where they are moved between various environments, including vehicles. ,, If LCS are combined with small drones, then myriad uses become possible, including monitoring industrial emissions and conducting atmospheric chemistry research …”