| 黄河流域甘肃段工业行业水污染物空间排放特征 |
| 摘要点击 1763 全文点击 556 投稿时间：2021-08-23 修订日期：2021-10-19 |
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| 中文关键词 黄河流域甘肃段 工业 水污染物 空间排放特征 环境污染综合评价指数 |
| 英文关键词 Gansu section of Yellow River basin industry water pollutants spatial emission characteristics environment pollution composite index |
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| 中文摘要 |
| 为研究黄河流域甘肃段工业废水污染的空间排放特征，运用系统聚类法，构建水污染物空间分类模型，引入污染综合评价指数，测算空间关联特征，揭示黄河流域甘肃段工业水污染物排放空间集聚效应.结果表明，2017年黄河流域甘肃段总工业企业数量为7224家，企业类型以小型和微型为主.化学需氧量（COD）排放主要集中在安定区、麦积区和西固区，氨氮（NH4+-N）、总氮（TN）和总磷（TP）排放主要集中在西固区、安宁区和红古区，农副食品加工业、酒/饮料和精制茶制造业、化学原料和化学制品制造业及电力/热力生产和供应业是主要贡献源.黄河流域甘肃段57个县区可分6种污染类型，水环境污染综合评价指数在空间整体上存在明显空间正相关，高值簇和低值簇集聚现象显著.研究结果可为管理部门制定差异化的水环境管控对策，实行分区分级的精细化管理提供科学支撑. |
| 英文摘要 |
| In order to study the spatial discharge characteristics of industrial water pollutants in the Gansu section of the Yellow River basin, the systematic clustering method was used to construct the spatial classification model of water pollutants, and the environment pollution composite index was introduced to measure the spatial correlation characteristics and reveal the spatial agglomeration effect of industrial water pollutants in the Gansu section of the Yellow River basin. The results showed that there were 7224 industrial enterprises in the Gansu section of the Yellow River basin in 2017, and the types of enterprises were mainly small and micro. The chemical oxygen demand (COD) emissions were mainly concentrated in the Anding District, Maiji District, and Xigu District, whereas ammonia nitrogen (NH4+-N), total nitrogen (TN), and total phosphorus (TP) emissions were mainly concentrated in the Xigu District, Anning District, and Honggu District. The agricultural and sideline food processing industry, wine/beverage and refined tea manufacturing, chemical raw materials and chemical products manufacturing, and electricity/heat production and supply were the main contribution sources. The 57 districts in the Gansu section of the Yellow River basin could be divided into six pollution types. The environment pollution composite index had an obvious spatial positive correlation, and the clustering of high-value clusters and low-value clusters was significant. These results can provide scientific support for management departments to formulate differentiated water environment management and control countermeasures and to implement subregional and hierarchical refined management. |
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