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Developing an advanced PM 25 exposure model in Lima, Peru

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dc.contributor.author Vu, B.N.
dc.contributor.author Sánchez, O.
dc.contributor.author Bi, J.
dc.contributor.author Xiao, Q.
dc.contributor.author Hansel, N.N.
dc.contributor.author Checkley, W.
dc.contributor.author Gonzales Rengifo, Gustavo Francisco
dc.contributor.author Steenland, K.
dc.contributor.author Liu, Y.
dc.date.accessioned 2019-07-04T16:59:28Z
dc.date.available 2019-07-04T16:59:28Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/20.500.12866/6765
dc.description.abstract It is well recognized that exposure to fine particulate matter (PM 2.5 ) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM 2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM 2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM 2.5 concentrations at a 1 km 2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from theWeather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R 2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m 3 ). Mean PM 2.5 for ground measurements was 24.7 μg/m 3 while mean estimated PM 2.5 was 24.9 μg/m 3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m 3 (Std.Dev. = 5.97 μg/m 3 ), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM 2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM 2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM 2.5 measurements at 1 km 2 spatial resolution to support future epidemiological studies. en_US
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartofseries Remote Sensing
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Lima en_US
dc.subject Peru en_US
dc.subject Air pollution en_US
dc.subject Remote sensing en_US
dc.subject Machine learning en_US
dc.subject Random forests en_US
dc.subject Decision trees en_US
dc.subject Image resolution en_US
dc.subject Land use en_US
dc.subject Learning systems en_US
dc.subject MAIAC AOD en_US
dc.subject PM 2.5 en_US
dc.subject PM2.5 en_US
dc.subject Random forest en_US
dc.subject Solar radiation en_US
dc.subject Topography en_US
dc.subject Weather forecasting en_US
dc.subject WRF-chem en_US
dc.title Developing an advanced PM 25 exposure model in Lima, Peru en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.3390/rs11060641
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#1.05.00
dc.relation.issn 2072-4292

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