Electrochemical system for anaerobic oxidation of methane by DAMO microbes with nitrite as an electron acceptor
文献类型：期刊论文
| 作者 | Chai, Fengguang; Li, Lin; Xue, Song; Xie, Fei; Liu, Junxin |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
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| 出版日期 | 2021-10-10 |
| 卷号 | 225页码:- |
| 关键词 | PM2.5 Machine learning GA-SVM Land use regression Method improvement Spatial clustering |
| ISSN号 | 0147-6513 |
| 英文摘要 | With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters <= 2.5 mu m (PM2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R-2) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 mu g/m(3) and 10.07 mu g/m(3), respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM2.5 pollution, and the spatial spillover effect of PM2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/46850]  |
| 专题 | 生态环境研究中心_环境水质学国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Environm Aquat Chem, 18 Shuangqing Rd, Beijing 100085, Peoples R China 2.Tsinghua Univ, Sch Environm, Environm Simulat & Pollut Control State Key Joint, State Environm Protect Key Lab Microorganism Appl, Beijing 100084, Peoples R China 3.Univ Chinese Acad Sci, Natl Engn Lab VOCs Pollut Control Mat & Technol, Beijing 101408, Peoples R China 4.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chai, Fengguang,Li, Lin,Xue, Song,et al. Electrochemical system for anaerobic oxidation of methane by DAMO microbes with nitrite as an electron acceptor[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2021,225:-. |
| APA | Chai, Fengguang,Li, Lin,Xue, Song,Xie, Fei,&Liu, Junxin.(2021).Electrochemical system for anaerobic oxidation of methane by DAMO microbes with nitrite as an electron acceptor.SCIENCE OF THE TOTAL ENVIRONMENT,225,-. |
| MLA | Chai, Fengguang,et al."Electrochemical system for anaerobic oxidation of methane by DAMO microbes with nitrite as an electron acceptor".SCIENCE OF THE TOTAL ENVIRONMENT 225(2021):-. |
入库方式： OAI收割
来源：生态环境研究中心
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