广东省水质现状及驱动因素分析
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广东省科学院广州地理研究所

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广东省科技计划项目(2018B030320002,2019A050506001);广东省自然科学基金项目(2018B030311059,2021A1515012579)


Analysis of water quality status and driving factors in Guangdong Province
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1.Guangzhou Institute of Geography, Guangdong Academy of Sciences;2.Guangzhou Institute of Geography,Guangdong Academy of Sciences

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Science & Technology Plan Project of Guangdong Province of China (2018B030320002, 2019A050506001);Natural Science Fund grant (2018B030311059, 2021A1515012579) of Guangdong Province, China

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    摘要:

    水质现状评估及其驱动因素分析是实现水生态保护、水资源利用和水污染治理的关键,对于水生态系统的可持续发展具有重要意义。以广东省七大流域为研究区,基于2019-2020年间的溶解氧(DO)、透明度(SDD)、悬浮物(SPM)、叶绿素a(Chla)、氨氮(NH3N)、总氮(TN)、总磷(TP)等7个指标的水质监测数据,综合运用单因子指数法(SI)和综合水质指数(WQI)评价方法,分丰水期(N=66)和枯水期(N=54)评估研究区的水质现状,并探讨水质参数与地形、气象、社会经济和土地覆被类型等驱动因素之间的相关关系。SI评估结果显示广东七大流域主要以工业污水、农业面源等造成的Chla和TN 浓度超标、部分水体富营养化严重为主,同时伴有溶解氧浓度偏低的问题;WQI评估结果显示研究区有57%以上的采样点属于中等以下水质。Chla、SPM、NH3N和TP浓度具有显著的季节和驱动因素差异:丰水期的Chla和TP浓度低于枯水期,但SPM和NH3N浓度高于枯水期。枯水期DO、TN和WQI的显著性影响因子为丰水期的1/3左右;这种季节差异可能是流域内降雨、营养盐负荷和土地覆被类型导致的复杂地表径流及面源污染所致。珠江三角洲河网区、粤西诸河、韩江下游以及粤东诸河练江流域的水质问题突出。未来水生态系统的可持续发展研究可以借助长时间序列、多频次、高分辨率的遥感监测手段和多种数值模拟方法以及常规水质评估模型,探讨气候变化、河岸带产业结构和流域土地利用方式对面源污染的影响,以进一步厘清降雨强度、三产结构和土地利用方式转变对区域水质变化的影响。

    Abstract:

    Water quality evaluation and driving forces analysis, two key factors to achieve ecological protection, resource utilization, and pollution control of water, are pivotal to the sustainable development of aquatic ecosystem. We utilized the in-situ experiment data of seven water quality parameters – dissolved oxygen (DO), transparency (Secchi disk depth, SDD), suspended particulate matters (SPM), chlorophyll a (Chla), ammonia nitrogen (NH3N), total nitrogen (TN), and total phosphorus (TP) – which were collected from seven major basins in Guangdong province during 2019-2020. This paper evaluates the water quality status of Guangdong Province in the high-flow season (HFS, N=66) and low-flow season (LFS, N=54) using the single factor index method (SI) and the comprehensive water quality index (WQI). The correlation between water quality parameters and driving factors, such as topography, meteorology, socio-economic and land cover types, were analyzed using Pearson correlation coefficient. The SI-based evaluation results show that the seven basins in Guangdong province are mainly faced with problems such as exceeding standard of Chla and TN concentration, and low DO concentration, which were caused by industrial sewage and agricultural non-point sources. The WQI evaluation results show that 57% of the sampling points in the study area are below the moderate level. The concentrations of Chla, SPM, NH3N and TP have significant differences in water period and driving factors: the concentrations of Chla and TP in HFS are lower than that in LFS, but the concentrations of SPM and NH3N are opposite in the two periods. The number of significant factors affecting the evaluation results of DO, TN, and WQI in LFS is about 1/3 of those in HFS. The seasonal difference is caused by the changes of complicated surface runoff and non-point source pollution, such as precipitation, nutrient load and land cover types in the basins. Water quality problems are prominent in the river network area of the Pearl River Delta, the lower reaches of the Hanjiang River, the rivers in western Guangdong, and the Lianjiang Basin of the eastern Guangdong. The water quality problems in the Pearl River Delta and the West River are mainly exceeded concentrations of TN and SPM. The main water quality problem of North River and East River is high TN concentration. The rivers in western Guangdong have insufficient DO, low SDD, and high concentrations of Chla and NH3N. The conditions of TN and DO for some sampling points in the rivers of eastern Guangdong and the Hanjiang River are not optimistic. In the future, the integrated methods combining remote sensing monitoring, numerical simulation, and regular water quality evaluation models should be enhanced to explore the effects of non-point source pollution from climate change, riparian industrial structure, and adjustment of watershed land cover patterns. These integrated methods can help to further clarify the water quality impact of changes in rainfall intensity, production structure, and land cover patterns, which greatly promote the sustainable development of aquatic ecosystems.

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陈金月,付娆,陈水森,尹小玲,王重洋,李丹,彭咏石.广东省水质现状及驱动因素分析.生态学报,,(). http://dx. doi. org/[doi]

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