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Interpretable machine learning tools to analyze PM2.5 sensor network data so as to quantify local source impacts and long-range transport
Journal article   Peer reviewed

Interpretable machine learning tools to analyze PM2.5 sensor network data so as to quantify local source impacts and long-range transport

Benjamin de Foy, Ross Edwards, Khaled Shaifullah Joy, Shahid Uz Zaman, Abdus Salam and James J. Schauer
Atmospheric research, v 311, 107656
01 Dec 2024
DOI: https://doi.org/10.1016/j.atmosres.2024.107656
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

eXplainable artificial intelligence Generalized additive model Interpretable machine learning Low cost sensor PM2.5 Sensor network
Sensor networks provide spatially resolved information about the time variation of PM2.5 concentrations in urban areas around the world. With relatively simple improvements to the control of the temperature and humidity of incoming air, and with proper quality assurance and calibration protocols, a low cost monitor was developed that provides measurements that were highly correlated with a reference PM2.5 monitor. These have sufficient resolution and reliability to quantify small differences within an urban area. Nevertheless, many sites report similar concentrations and it can be difficult to interpret the results or distinguish local from regional effects. Generalized Additive Models are an effective Machine Learning method to distinguish the impact of factors across very different scales. As a type of Interpretable Machine Learning / eXplainable Artificial Intelligence, they provide direct information on the link between specific factors and PM2.5 concentrations. GAM simulations were developed for sensors located around Dhaka, Bangladesh, for both the dry and the wet seasons. The simulations show that the largest contributor to high PM2.5 concentration variations across both urban and peri-urban sites is the boundary layer height which represent the vertical mixing of the urban plume. Using Trajectory Cluster Concentration Impacts, the simulations showed that robust estimates of long-range transport could be obtained from measurements located within a polluted environment, and the model further showed that enhancements of more than 40μg/m3 were associated with air transport from the Indo-Gangetic Plain in the dry season. Finally, interaction maps of the effect of horizontal wind speed and direction showed that these could be associated with up to +/−20 % variation in PM2.5 from site to site. Most of the enhancements are related to very calm winds and appear to be more strongly associated with road emissions than with point sources. Overall, the sensor network shows that air is polluted throughout the Dhaka area and into the peripheries, and that a multipronged approach will be needed to improve air quality for its inhabitants. •GAM simulations identify the impacts of local sources within the urban area.•Monitoring sites on the periphery are impacted by industrial sources and brick kilns.•Trajectory Cluster Concentration Impacts provide reliable estimates of long-range transport.•Vertical mixing, representative of urban emissions, is the largest factor in the dry season.•Network map shows strong similarities in hourly PM 2.5 across the Dhaka metropolis.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#13 Climate Action
#11 Sustainable Cities and Communities

Source: SDGs in the Output

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Meteorology & Atmospheric Sciences
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