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Atmosphere 2018, 9(9), 335; https://doi.org/10.3390/atmos9090335

Modeling Wildfire Smoke Pollution by Integrating Land Use Regression and Remote Sensing Data: Regional Multi-Temporal Estimates for Public Health and Exposure Models

1
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of History, Archaeology, Geography, and Fine & Performing Arts, University of Florence, 50129 Florence, Italy
*
Author to whom correspondence should be addressed.
Received: 15 July 2018 / Revised: 20 August 2018 / Accepted: 21 August 2018 / Published: 27 August 2018
(This article belongs to the Special Issue Impacts of Air Pollution on Human Health)
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Abstract

To understand the health effects of wildfire smoke, it is important to accurately assess smoke exposure over space and time. Particulate matter (PM) is a predominant pollutant in wildfire smoke. In this study, we develop land-use regression (LUR) models to investigate the impact that a cluster of wildfires in the northwest USA had on the level of PM in southern Alberta (Canada), in the summer of 2015. Univariate aerosol optical depth (AOD) and multivariate AOD-LUR models were used to estimate the level of PM2.5 in urban and rural areas. For epidemiological studies, it is also important to distinguish between wildfire-related PM2.5 and PM2.5 originating from other sources. We therefore subdivided the study period into three sub-periods: (1) Pre-fire, (2) during-fire, and (3) post-fire. We then developed separate models for each sub-period. With this approach, we were able to identify different predictors significantly associated with smoke-related PM2.5 verses PM2.5 of different origin. Leave-one-out cross-validation (LOOCV) was used to evaluate the models’ performance. Our results indicate that model predictors and model performance are highly related to the level of PM2.5, and the pollution source. The predictive ability of both uni- and multi-variate models were higher in the during-fire period than in the pre- and post-fire periods. View Full-Text
Keywords: particulate matter PM2.5; AOD (aerosol optical depth); wildfire smoke; LUR (land use regression); spatial analysis; public health particulate matter PM2.5; AOD (aerosol optical depth); wildfire smoke; LUR (land use regression); spatial analysis; public health
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mirzaei, M.; Bertazzon, S.; Couloigner, I. Modeling Wildfire Smoke Pollution by Integrating Land Use Regression and Remote Sensing Data: Regional Multi-Temporal Estimates for Public Health and Exposure Models. Atmosphere 2018, 9, 335.

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