Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx using generalized additive models and machine learning methods: A case study in London

TY - JOUR

T1 - Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx using generalized additive models and machine learning methods: A case study in London

AU - Analitis, Antonis

AU - Barratt, Ben

AU - Green, David

AU - Beddows, Andrew

AU - Samoli, Evangelia

AU - Schwartz, Joel

AU - Katsouyanni, Klea

PY - 2020/11/1

Y1 - 2020/11/1

N2 - The adverse health effects of air pollutants, especially those of PM 2.5, are well documented. However, a lack of adequate monitoring and weaknesses in modelling approaches do not allow a good assessment of health effects in many areas of the World. Advances in computational methods and the availability of new data sets, e.g. satellite remote observations, have enlarged the possibilities of modelling for application in large scale health effects studies. However, PM 2.5 monitoring is very recent in most of the World and more limited compared to other pollutants, and understanding how to use PM 10 monitors to estimate PM 2.5 exposure is therefore important. Since interest in these methods is relatively recent, there is a need for testing their performance against ambient measurements, but long term PM 2.5 datasets are less readily available than PM 10 in many regions. In the present study we report the methodology and results of using regression modelling and a machine learning method (Random Forest-RF), as well as a combination of the two, to enhance a PM 2.5 measurement data base in London using PM 10 and NO x measurements as well as other predictors and compare the relative performance of each method. We found that the combination of predictions by the regression model and the RF performs best and we obtain a cross-validation R 2 of 99.29% and 98.22% for the 5-year periods 2004–2008 and 2009–2013, respectively, and a Mean Square Error near 1. Our enhanced data base for PM 2.5 is available for use by other researchers.

AB - The adverse health effects of air pollutants, especially those of PM 2.5, are well documented. However, a lack of adequate monitoring and weaknesses in modelling approaches do not allow a good assessment of health effects in many areas of the World. Advances in computational methods and the availability of new data sets, e.g. satellite remote observations, have enlarged the possibilities of modelling for application in large scale health effects studies. However, PM 2.5 monitoring is very recent in most of the World and more limited compared to other pollutants, and understanding how to use PM 10 monitors to estimate PM 2.5 exposure is therefore important. Since interest in these methods is relatively recent, there is a need for testing their performance against ambient measurements, but long term PM 2.5 datasets are less readily available than PM 10 in many regions. In the present study we report the methodology and results of using regression modelling and a machine learning method (Random Forest-RF), as well as a combination of the two, to enhance a PM 2.5 measurement data base in London using PM 10 and NO x measurements as well as other predictors and compare the relative performance of each method. We found that the combination of predictions by the regression model and the RF performs best and we obtain a cross-validation R 2 of 99.29% and 98.22% for the 5-year periods 2004–2008 and 2009–2013, respectively, and a Mean Square Error near 1. Our enhanced data base for PM 2.5 is available for use by other researchers.

KW - Ensemble methods

KW - Environmental exposure

KW - London case study

KW - PM prediction

KW - Random forest

UR - http://www.scopus.com/inward/record.url?scp=85089500551&partnerID=8YFLogxK

U2 - 10.1016/j.atmosenv.2020.117757

DO - 10.1016/j.atmosenv.2020.117757

M3 - Article

SN - 1352-2310

VL - 240

JO - ATMOSPHERIC ENVIRONMENT

JF - ATMOSPHERIC ENVIRONMENT

M1 - 117757

ER -