Estimating monthly wet sulfur (S) deposition flux over China using an ensemble model of improved machine learning and geostatistical approach. (1st October 2019)

• Record Type: Journal Article
• Authors: Li, Rui; Cui, Lulu; Zhao, Yilong; Meng, Ya; Kong, Wang; Fu, Hongbo
• Is Part Of: Atmospheric environment. Volume 214(2019)
• Publication Date: 2019-10-01
• Subjects: Wet S deposition -- Machine learning -- Geostatistical approach -- China; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2019.116884
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The wet S deposition was treated as a key issue because it played the negative on the soil acidification, biodiversity loss, and global climate change. However, the limited ground-level monitoring sites make it difficult to fully clarify the spatiotemporal variations of wet S deposition over China. Therefore, an ensemble model of improved machine learning and geostatistical method named fruit fly optimization algorithm-random forest-spatiotemporal Kriging (FOA-RF-STK) model was developed to estimate the nationwide S deposition based on the emission inventory, meteorological factors, and other geographical covariates. The ensemble model can capture the relationship between predictors and S deposition flux with the better performance (R 2 = 0.68, root mean square error (RMSE) = 7.51 kg ha −1 yr −1 ) compared with the original RF model (R 2 = 0.52, RMSE = 8.99 kg ha −1 yr −1 ). Based on the improved model, it predicted that the highest and lowest S deposition flux were mainly concentrated on the Southeast China (69.57 kg S ha −1 yr −1 ) and Inner Mongolia (42.37 kg S ha −1 yr −1 ), respectively. The estimated wet S deposition flux displayed the remarkably seasonal variation with the highest value in summer (22.22 kg S ha −1 sea −1 ), follwed by ones in autumn (18.30 kg S ha −1 sea −1 ), spring (16.27 kg S ha −1 sea −1 ), and the lowest one in winter (14.71 kg S ha −1 sea −1 ), which was closely associated with the rainfall amounts. The study provides a novel approach for the S deposition estimation at a national scale. Highlights: A novel model (FOA-RF-STK) is developed to estimate the national S deposition flux. FOA-RF-STK showed the better performance in predicting wet S deposition flux. The hotspot of wet S deposition occurs in Southeast China (69.57 kg S ha −1 yr −1 ).