Predicting fine particulate matter (PM2.5) in the greater london area: An ensemble approach using machine learning methods

TY - JOUR

T1 - Predicting fine particulate matter (PM2.5) in the greater london area

T2 - An ensemble approach using machine learning methods

AU - Yazdi, Mahdieh Danesh

AU - Kuang, Zheng

AU - Dimakopoulou, Konstantina

AU - Barratt, Benjamin

AU - Suel, Esra

AU - Amini, Heresh

AU - Lyapustin, Alexei

AU - Katsouyanni, Klea

AU - Schwartz, Joel

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km x 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.

AB - Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km x 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.

KW - Air pollution

KW - Exposure modeling

KW - Machine learning

KW - Particulate matter

UR - http://www.scopus.com/inward/record.url?scp=85082296257&partnerID=8YFLogxK

U2 - 10.3390/rs12060914

DO - 10.3390/rs12060914

M3 - Article

AN - SCOPUS:85082296257

SN - 2072-4292

VL - 12

JO - REMOTE SENSING

JF - REMOTE SENSING

IS - 6

M1 - 914

ER -