Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology

Tongshu Zheng,Michael Bergin,Guoyin Wang,David Carlson

doi:10.3390/rs13071356

Tongshu Zheng, Michael Bergin + Show 2 more

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https://doi.org/10.3390/rs13071356

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Abstract

Satellite-based rapid sweeping screening of localized PM2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM2.5 estimates. The downstream LCN processes the estimated PM2.5 maps to reveal local PM2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM2.5 and the local variations in PM2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m−3 difference in PM2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m−3 from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM2.5 exposures even within the same local neighborhoods between local hotspots and coolspots.

Highlights

Exposure to fine particulate matter (PM2.5, with an aerodynamic diameter of 2.5 μm and smaller) has wide-ranging adverse health effects on, for example, cardiovascular, cardiopulmonary and respiratory wellness [1]
Remote Sens. 2021, 13, 1356 than existing rather sparse regulatory air quality monitoring (AQM) stations, which have advanced our understanding of the adverse impacts of highly dynamic and heterogenous air pollutants, such as PM2.5, at higher spatiotemporal resolutions; these two approaches can be further complemented by a satellite-based modeling approach that requires much less manpower for sampling or instrument calibration and maintenance to potentially rapidly screen localized PM2.5 hotspots over wider spatial areas
We evaluated the random forest (RF)–convolutional neural network (CNN) joint model’s PM2.5 estimation performance on the test dataset

Summary

Introduction

Exposure to fine particulate matter (PM2.5, with an aerodynamic diameter of 2.5 μm and smaller) has wide-ranging adverse health effects on, for example, cardiovascular, cardiopulmonary and respiratory wellness [1]. 2021, 13, 1356 than existing rather sparse regulatory air quality monitoring (AQM) stations, which have advanced our understanding of the adverse impacts of highly dynamic and heterogenous air pollutants, such as PM2.5 , at higher spatiotemporal resolutions; these two approaches can be further complemented by a satellite-based modeling approach that requires much less manpower for sampling or instrument calibration and maintenance to potentially rapidly screen localized PM2.5 hotspots over wider spatial areas. Few of the studies in [19,20,21,22,23,24,25,26]

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