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| Method of monitoring and calculating monthly export coefficient of farmland non-point source pollution at outlets into rivers in the plain river network areas |
| Received:October 12, 2024 |
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| KeyWord:plain river network area;collaborative monitoring of hydrology and water quality;export coefficient of farmland non-point source pollution at outlets into rivers;automatic sampling;classified load estimation;calculus algorithm |
| Author Name | Affiliation | E-mail | | CHEN Cheng | Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants of Ministry of Ecology and Environment, Shanghai Academy of Environmental Sciences, Shanghai 200233, China | | | HUAI Hongyan | Shanghai Environmental Monitoring Centre, Shanghai 200235, China | | | WU Enuo | Shanghai Environmental Monitoring Centre, Shanghai 200235, China | | | LIU Yiyang | Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants of Ministry of Ecology and Environment, Shanghai Academy of Environmental Sciences, Shanghai 200233, China | | | CHEN Xiaohua | Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants of Ministry of Ecology and Environment, Shanghai Academy of Environmental Sciences, Shanghai 200233, China | | | SHEN Genxiang | Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants of Ministry of Ecology and Environment, Shanghai Academy of Environmental Sciences, Shanghai 200233, China | shengx@saes.sh.cn |
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| Abstract: |
| In order to sufficiently consider the unique characteristics of geographical environment and river systems in the plain river network areas, and accurately investigate the export amount and characteristics of farmland non-point source pollution, a complete series of technical method system was established for monitoring and calculating the monthly export coefficient of non-point source pollution at outlets into rivers based on Shanghai “4+9” （four municipal pilots + nine district-level pilots）farmland non-point source pollution pilot monitoring and evaluation. Farmland non-point source pollution monitoring and calculation in the plain river network areas require ten important steps, including determination of monitoring areas, collection of basic documents, delimitation of monitoring units, selection of monitoring points, choosing of sampling and monitoring period and mode, setting of triggering criteria and monitoring frequency, selection of monitoring index, calculation of（net）export coefficients at outlets into rivers, classified evaluation of load and quality control. Highfrequency online monitoring of discharge was conducted at farmland outlets into rivers. Procedure was reasonably set up to trigger automatic sampling for water quality analysis so as to capture the entire process of field-scale runoff events. The sampling frequency was set up to frequent at first and then gradually to sparse according to the pulse-type export characteristics of farmland non-point source pollution. The precipitation, irrigation and backflow events were recognized with hydrology monitoring data. Non-point source pollution monitoring was conducted during typical runoff events according to the fertilization time. Loads and net loads discharged into the river were respectively calculated. The loads discharged into the river during monitoring and non-monitoring periods were estimated with the application of calculus algorithm and mean concentration methods, respectively. Consequently, the accurate estimation of loads during the whole period was accomplished and the influence of human（controllable）and natural（uncontrollable）factors on non-point source pollution export was quantificationally analyzed. A typical farmland non-point source pollution export event was monitored during the first landing of two successional typhoons in Shanghai in September, 2024 since records began. The monitoring and estimation results indicated that the export coefficient at the outlet into the river of total phosphorus, phosphate, total nitrogen, ammonia nitrogen, nitrate nitrogen and permanganate index were 0.225, 0.093, 1.719, 0.597, 0.775 kg·hm-2·month-1 and 4.916 kg·hm-2·month-1, respectively. |
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