Incorporating satellite-derived data with annual and monthly land use regression models for estimating spatial distribution of air pollution

Huang, Chun Sheng; Lin, Tang Huang; Hung, Hung; Kuo, Cheng-Pin; Ho, Chi-Chang; Guo, Yang; Chen, Kwang Cheng; Wu, Chang‐Fu

doi:10.1016/j.envsoft.2019.01.010

Cited by 10 publications

(1 citation statement)

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“…A total of 21 predictor variables were created: area of road, residence, industry, port, semi-natural, and forest and urban green; length of road and distance to road; number of population, household, emission sources, and temples; extracted AOD values. The predictor variables are similar to the ones in our previous study and can be found elsewhere [24]. For detailed information of predictors, please see Table S2 of Supplementary Materials.…”

Section: Collection Of Lur Predictorsmentioning

confidence: 92%

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

et al. 2021

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This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

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Section: Collection Of Lur Predictorsmentioning

confidence: 92%