首页|Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

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As most air quality monitoring sites are in urban areas worldwide,machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants.The bias stems from the issue of dataset shift,as the density distributions of predictor variables differ greatly between urban and rural areas.We propose a data-augmentation approach based on the multiple imputation by chained equations(MICE-DA)to remedy the dataset shift problem.Compared with the benchmark models,MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity(Chengdu)at the foot of the Tibetan Plateau,especially for correcting the estimation bias,with the mean bias decreasing from-3.4 μg/m3 to-1.6 μg/m3.As a complement to the holdout validation,the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area.The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth(AOD)during the data augmentation.Consequently,the importance of AOD is largely enhanced for predicting PM2.5,and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5％to 18.4％.This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies.The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.

Aerosol optical depthDataset shiftSpatiotemporal DistributionAir quality monitoringMultiple imputation by chained equations

Tan Mi、Die Tang、Jianbo Fu、Wen Zeng、Michael L.Grieneisen、Zihang Zhou、Fengju Jia、Fumo Yang、Yu Zhan

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College of Carbon Neutrality Future Technology,Sichuan University,Chengdu,Sichuan 610065,China

Department of Environmental Science and Engineering,Sichuan University,Chengdu,Sichuan 610065,China

Institute for Disaster Management and Reconstruction,Sichuan University,Chengdu,Sichuan 610200,China

Department of Land,Air,and Water Resources,University of California,Davis,CA 95616,United States

Chengdu Academy of Environmental Sciences,Chengdu,Sichuan 610072,China

Sichuan Chengdu Ecological and Environment Monitoring Center,Chengdu,Sichuan 610011,China

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国家自然科学基金Sichuan Key Research and Development ProjectChengdu Major Technology Application and Demonstration Project

220761292020YFS00552020-YF09-00031-SN

2024

10.1016/j.gsf.2023.101686

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(1)
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