Influential factors detection for surface water quality with geographical detectors in China
文献类型：期刊论文
| 作者 | Wang, Jiaxin1,3; Hu, Maogui3,4; Zhang, Fengsong3; Gao, Bingbo2 |
| 刊名 | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
 |
| 出版日期 | 2018-09-01 |
| 卷号 | 32期号:9页码:2633-2645 |
| 关键词 | Surface water quality Spatial-temporal analysis Geographical Detector CCME-WQI |
| ISSN号 | 1436-3240 |
| DOI | 10.1007/s00477-018-1532-2 |
| 通讯作者 | Hu, Maogui(humg@lreis.ac.cn) |
| 英文摘要 | Surface water quality is a matter of serious concern in China. This study quantitatively analyzes the spatial-temporal characteristics of surface water quality among 100 monitoring stations in China during 2015. A geographical detector was used to detect the influential annual and seasonal factors. Surface water quality is primarily controlled by the content of nutrient pollutants and organic pollutants. Natural factors (precipitation, temperature, soil erosion, and terrain) and anthropogenic factors [land use type, population density, and per capita gross domestic product (GDP-per-capita)] were selected as geographical proxies to be tested for their explanatory power for surface water quality. Results indicated that the top three factors influencing the annual mean of nutrient pollutants were the population density, terrain, and precipitation, the explanatory power of which was 0.82, 0.35, and 0.24, respectively. The interactive explanatory power for population density and terrain was 0.88 and for population density and precipitation was 0.87, both exhibiting enhanced interaction relationships. The top three factors influencing the annual mean of organic pollutants were population density, temperature, and basin, the explanatory power of which was 0.46, 0.29, and 0.27, respectively. The interactive explanatory power for population density and basin was 0.80 and for terrain and precipitation was 0.82, both demonstrating a nonlinear enhanced interaction relationship. For seasonal changes, the nutrient pollutants and organic pollutants were both affected by agricultural runoff due to seasonal farming. This study revealed that anthropogenic factors influenced surface water quality two to three times more than natural factors. |
| WOS关键词 | MULTIVARIATE STATISTICAL TECHNIQUES ; RIVER-BASIN ; LAND-USE ; TEMPORAL VARIATIONS ; RISK-ASSESSMENT ; CLIMATE-CHANGE ; INDEXES ; IDENTIFICATION ; POLLUTION ; ESTUARINE |
| 资助项目 | National Natural Science Foundation of China[41771434] ; National Natural Science Foundation of China[41531179] ; Project of Cultivation and Development for Science and Technology Innovation Base of Beijing[Z161100005016110] |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000442996700012 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China ; Project of Cultivation and Development for Science and Technology Innovation Base of Beijing |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/54306]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Maogui |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jiaxin,Hu, Maogui,Zhang, Fengsong,et al. Influential factors detection for surface water quality with geographical detectors in China[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,2018,32(9):2633-2645. |
| APA | Wang, Jiaxin,Hu, Maogui,Zhang, Fengsong,&Gao, Bingbo.(2018).Influential factors detection for surface water quality with geographical detectors in China.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,32(9),2633-2645. |
| MLA | Wang, Jiaxin,et al."Influential factors detection for surface water quality with geographical detectors in China".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 32.9(2018):2633-2645. |
入库方式： OAI收割
来源：地理科学与资源研究所
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