A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimating PM2.5 in Southern California using satellite data: factors that affect model performance
Background: Studies of PM _2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM _2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM _2.5 /AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM _2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM _2.5 concentration for the study domain was 8.84 µ g m ^−3 . Linear regression between CV predicted PM _2.5 concentrations and observations had an R ^2 of 0.80 and RMSE 2.25 µ g m ^−3 . The ratio of PM _2.5 to PM _10 proved an important variable in modifying the AOD/PM _2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R ^2 and a 0.56 µ g m ^−3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM _2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas.
Estimating PM2.5 in Southern California using satellite data: factors that affect model performance
Background: Studies of PM _2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM _2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM _2.5 /AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM _2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM _2.5 concentration for the study domain was 8.84 µ g m ^−3 . Linear regression between CV predicted PM _2.5 concentrations and observations had an R ^2 of 0.80 and RMSE 2.25 µ g m ^−3 . The ratio of PM _2.5 to PM _10 proved an important variable in modifying the AOD/PM _2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R ^2 and a 0.56 µ g m ^−3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM _2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas.
Estimating PM2.5 in Southern California using satellite data: factors that affect model performance
Jennifer D Stowell (author) / Jianzhao Bi (author) / Mohammad Z Al-Hamdan (author) / Hyung Joo Lee (author) / Sang-Mi Lee (author) / Frank Freedman (author) / Patrick L Kinney (author) / Yang Liu (author)
2020
Article (Journal)
Electronic Resource
Unknown
pm2.5 , air quality , pm10 , AOD , satellite , remote sensing , Environmental technology. Sanitary engineering , TD1-1066 , Environmental sciences , GE1-350 , Science , Q , Physics , QC1-999
Metadata by DOAJ is licensed under CC BY-SA 1.0
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