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

《地学前缘(英文版)》2024,15(1):101686

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 PM_2.5 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 PM_2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM_2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM_2.5, 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.  相似文献   

论四川盆地下中三叠统(T1j-T2L)地下水的形成   总被引：3，自引：0，他引：3       下载免费PDF全文

林耀庭 《盐湖研究》2005,13(1):1-7

四川盆地下中三叠统嘉陵江组—雷口坡组(T1j-T2L)地下水分布广泛,资源丰富,类型各异。其展布规律及水文地球化学景观系在漫长的地质发展历史阶段中,经沉积、掺杂、淋滤、火山活动、生物化学及剥蚀淋滤等多种因素综合作用形成。对其形成分布的研究论述,有助于对地下水资源的开发利用。  相似文献