Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure

Version 1 : Received: 20 April 2021 / Approved: 21 April 2021 / Online: 21 April 2021 (16:49:09 CEST)
Version 2 : Received: 30 April 2021 / Approved: 5 May 2021 / Online: 5 May 2021 (12:41:17 CEST)

How to cite: Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. Preprints 2021, 2021040588 (doi: 10.20944/preprints202104.0588.v2). Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. Preprints 2021, 2021040588 (doi: 10.20944/preprints202104.0588.v2).

Abstract

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM2.5 concentrations on average (16.60 ± 4.29 µg/m3), while tgbwp hosted the highest BC concentrations (2.31 ± 1.94 µg/m3). Furthermore, STURLA combined with machine learning modeling was able to correlate PM2.5 (R2= 0.68, RMSE 2.82 µg/m3) and BC (R2 = 0.64, RMSE 0.75 µg/m3) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

Subject Areas

Air Pollution; STURLA; Urban Structure; Mobile Monitoring; Spatial Prediction

Comments (1)

Comment 1
Received: 5 May 2021
Commenter: Justin Stewart
Commenter's Conflict of Interests: Author
Comment: Updated title.
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