Paper Info
Title
Local Pm2.5 Hotspot Detector At 300 M Resolution: A Random Forest-Convolutional Neural Network Joint Model Jointly Trained On Satellite Images And Meteorology
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 similar to 31% and normalized mean absolute error of within similar to 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 mu g 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 mu g 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.
Year
DOI
Venue
2021
10.3390/rs13071356
REMOTE SENSING
Keywords
DocType
Volume
fine particulate matter, machine learning, remote sensing, computer vision, satellite imagery, convolutional neural network (CNN), random forest (RF), hotspot, exposure, risk
Journal
13
Issue
Citations 
PageRank 
7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Tongshu Zheng100.34
Michael Bergin200.68
Guoyin Wang3247.38
David E. Carlson418215.35