面源污染

面源污染，尤其是农业面源污染，已成为全球范围内威胁水环境安全与生态系统健康的关键因素。其具有分散性、隐蔽性、随机性和滞后性等特征，使得监测、溯源和治理难度远高于点源污染。本文系统梳理了面源污染的基本特征与主要来源，综述了从经验统计到机理性模型在内的主要研究方法和模型应用进展，并重点探讨了包括“源头减量–过程阻断–末端治理”在内的多种污染控制措施及其效果评估。同时还深入分析了当前面源污染防治在法律法规、监测技术、模型精度和跨部门协同等方面面临的困境，并对未来研究方向，如模型本土化、多技术融合、基于风险分区的精准治理等进行了展望，以期为我国面源污染的系统治理和流域水环境的可持续发展提供科学参考与决策支持。

农业面源污染第三方治理机制是解决农业面源污染治理顶层设计针对性不足、根除与归责困难、治理效果欠佳等问题的新型治理模式，对农业面源污染的治理现状极具必要性。文章构建农业面源污染第三方治理机制于农业种植生产污染、规模化养殖污染以及农业生产累积性污染中的应用模式，以及针对主体、适用审查、治理过程与结果评估四个方面的具体制度，使农业面源污染第三方治理机制具象化，实现创新治理机制与难以在传统规制手段下有所成效的农业面源污染治理之间的融合最大化，为国家治理体系现代化进程提供新思路。

以安徽省耕地、水体环境保护以及人体健康为出发点，依据2001~2016年安徽省化肥、畜禽养殖以及人口数据，估算安徽省农业面源排放污染物的产生量，在此基础上计算耕地氮、磷和COD污染负荷、水体等标污染指数以及对地下水硝态氮的贡献程度。结果表明，安徽省整体耕地氮素、磷素污染负荷严重，预警级别均在Ⅲ级及以上；污染物对地表水污染呈现由南至北依次增加的趋势，北部最高，中东部次之；氮素会通过淋溶作用进入地下水导致硝态氮浓度升高，根据估算，超过80%的城市地下水硝态氮浓度存在超过20 mg∙L−1的国家限定标准；地下水健康风险指数超过“1”的城市总共13座城市，占城市总数的76.5%，其中合肥风险指数最高，已经达到3.996，应注意饮用水质量。

本文以江西省抚州市东乡区小璜水流域面源污染问题为导向；以“源头减量、过程阻断、养分再利用、生态修复”4R策略为思路；主要论述了污染现状、治理方案及工程设施匹配，为东乡区小璜水流域面源污染问题的解决提供科学的可行性方案，使流域面源污染得到治理。同时也为小流域面源污染治理提供科学的参考方案。

农业增长依赖于生产要素的集约投入，这种高产、低效、高投入的农业模式在水环境中造成了严重的面源污染问题，其中农业面源污染威胁最大。本文通过系统性梳理国内外对于农业面源污染负荷分析的方法，并重点阐述了我国农业面源污染负荷现场监测与模型模拟的发展方向，为我国农业高质量发展和生态文明建设提供借鉴。通过构建基于大数据平台的农业面源污染在线监测系统，开展对农业面源污染风险源的静态评估，建立集现场监测与模型模拟为一体的农业面源污染负荷分析体系，是实现国家“绿水青山就是金山银山”和“粮食安全”战略的关键手段，是保障我国农业高质量发展的必需要素。

随着经济的发展，点源污染逐渐被重视和得到治理，使得面源污染在环境污染中所占的比例越来越大，我国农业面源污染已成为当前环境污染的重要影响因素，制约农业可持续发展和危害农村生态环境。本文通过对国内外关于典型农业面源污染控制技术——生态沟渠进行系统梳理，并重点阐述了生态沟渠控制对农业面源污染的影响机制和控制效果评估的发展方向，以期为生态文明建设和农业面源污染防控提供借鉴。明确生态沟渠控制农业面源污染的影响机制，建立多尺度下生态沟渠对农业面源污染的控制效果评估，是全面落实流域生态保护和高质量发展重要讲话重要指示精神、全面打好污染防治攻坚战的具体体现。

随着关中平原区集约化农业的快速发展，农业面源污染问题日益凸显。本研究聚焦气候湿润的关中平原集约化农区，深入剖析农业面源污染演变机制。在全球气候变化背景下，关中平原极端水文事件频发，农业面源污染问题突出。通过对该区域集约化农业现状、农业面源污染现状的调查与分析，明确了气温、降水量等气候因素对农业面源污染的影响，旨在为该地区水资源规划管理、农业可持续发展以及生态环境保护提供科学依据与决策支持。

面源污染防治是困扰农村生态环境保护的一大难题。因此需要分析农业面源污染的具体概念及其特征，并对江苏具体防治措施的法律规制进行分析；针对立法无系统性、执法力量不足、司法薄弱等问题，建议江苏省可以制定《江苏省农业面源污染防治条例》或者先由省政府或农业农村厅出台相关的地方政府规章或规范性文件；结合新修改的《行政处罚法》，可将生态环保领域的行政处罚下放到乡镇；结合江苏“9 + 1”司法改革，江苏省可将农业面源污染纳入司法防治机制之中，也可提起公益诉讼；基层执法机关更应组织多样性的农业面源污染法治宣传，探讨从源头上减少面源污染的可能性，以实现农业发展与生态环境保护的和谐共存。

为了更加全面地了解中国西北地区某省的农业面源污染特征，选取化肥施用强度、农药施用强度、农膜使用密度、畜禽养殖化学污染物排泄总量、农村居民年生活垃圾量作为农业面源污染的主要评测指标，结合第一次全国污染源普查数据，根据统计年鉴基础数据资料核算出五类农业面源污染指标，对人均农林牧渔产值与农业面源污染的环境库兹涅茨拟合曲线(EKC)进行关系验证。结果表明：研究时段内，该地区农业面源污染整体上呈逐年上升趋势。从农业面源污染与人均农林牧渔产值的EKC拟合曲线检验可以看出，化肥施用强度、农膜使用密度与人均农林牧渔业产值呈较为显著的倒“U”型关系；畜禽养殖化学污染物排泄总量与人均农林牧渔产值之间呈正“N”型关系；而农药施用强度、农村居民年生活垃圾总量与人均农林牧渔业产值呈线性上升关系，不符合库兹涅茨曲线。结合实证分析结果，提出有益于改善农业面源污染状况，提高农业经济增长，促进农业环境保护协调绿色发展的建议与措施。

为研究抚仙湖入湖流域农业污染对入湖水质的影响，通过实验监测尖山河流域雨季时不同形态的氮、磷以及化学需氧量(COD)的质量浓度，对入湖河流氮、磷、COD在2021年雨季时月变化和氮、磷、COD，2020年、2021年年监测分布特征进行分析。结果表明：2020年尖山河雨季入湖水体总氮的质量浓度为3.88~2.70 mg/L，平均值为3.36 mg/L，硝态氮平均浓度为2.03 mg/L，占总氮的59.73%，是最主要的赋存形态；氨氮平均浓度为0.50 mg/L，占总氮的15.02%，总磷的质量浓度为0.26~0.21 mg/L，平均值为0.23 mg/L，正磷酸盐平均质量浓度为0.21 mg/L，占总磷的89.67%，COD质量浓度为10.33~6.33 mg/L，平均值为7.93 mg/L。2021年尖山河雨季入湖水体总氮的质量浓度为2.60~0.94 mg/L，平均值为1.91 mg/L，硝态氮平均浓度为0.16 mg/L，占总氮的10.43%，氨氮平均浓度为1.12 mg/L，占总氮的57.86%，是最主要的赋存形态；总磷的质量浓度为0.28~0.19 mg/L，平均值为0.25 mg/L，正磷酸盐平均质量浓度为0.13 mg/L，占总磷的51.33%，COD质量浓度为11.00~5.00 mg/L，平均值为8.33 mg/L。整体较2020年呈现明显的下降趋势。

近年来，农业化学品的过量使用增加了农田面源污染的风险，特别在西北干旱半干旱地区，这种污染对当地水资源和生态系统造成了严重影响。本文系统梳理了国内外关于农田面源污染控制技术，着重阐述了植物根系对土壤性质和氮迁移转化的影响，并提出了建立农田植物根系特征为主的面源污染控制方法的建议。通过开展对农田氮污染控制效果评估，可有效控制农田径流中氮污染，为城市生态文明建设和面源污染防控提供重要借鉴。这种基于植物根系的污染控制方法有望在农业生产实践中得到应用，助力改善农田面源污染问题，促进生态环境的健康与可持续发展。

随着点源污染治理力度加大，农业面源污染正逐渐成为水环境治理新热点。借助模型准确有效核算面源污染负荷，对于流域综合治理具有重要意义。与机理模型相比，非机理模型具有需要数据量少、应用简便、适用性广等优势。迄今为止，统计模型、输出系数模型、降雨量差值法和径流量插值法等核算模型被应用于面源污染治理的研究工作中，对它们的基本原理、计算方法、应用限制和未来情景进行阐述和讨论。输出系数模型是当前应用最广的非机理模型，但仍然受到数据精度和估算尺度的限制。非机理模型和“3S”技术结合是当前的研究热点。

目前我国很多农村地区的生活污水还得不到集中处理，其中的有机物、氮、磷等对周边水体造成一定污染。在南水北调中线工程中，为了确保进京水质的安全优良，库区汇水流域的农村面源污染问题必须引起重视。本文对适合处理农村生活污水的技术与方法进行了综述，包括人工湿地污水处理系统、稳定塘处理系统、生物膜处理技术等，并在此基础上介绍了复合人工快渗系统以及其的应用，并对南水北调水源区农村面源污染防治的实践进行了归纳。 Presently, the domestic sewage in rural area could not be collected and disposed properly. Organic matters, nitrogen, phosphorus in the sewage would pollute the surrounding water. In the South-to- North water diversion project, to ensure high quality water for Beijing, we must pay attention to rural non-point source pollution of the reservoir converge basin. In this paper, the suitable techniques and methods of rural domestic sewage were summarized, including Constructed Wetland Sewage Treatment System, Stabilization Pond System, Membrane Biology Technology, etc. And on this basis, this paper introduces the Compound Constructed Rapid Infiltration System and its applications. The paper also summarizes practice cases of rural non-point source pollution prevention and control in water source area of the South-to-North water diversion project.

农业面源污染已成为我国水污染的主要来源，农田径流是农业面源污染的末端环节，也是农业面源污染负荷的主要来源。本文通过系统梳理我国农田径流污染现状及典型农田径流污染控制技术的实践经验，为我国农田径流污染的产业化发展提供借鉴。进一步加强对农田径流污染的重视，建立农田径流污染控制及水资源回收利用新技术，是新时代现代农业可持续发展和高标准农田建设的关键要素。

鉴于初期雨水面源污染突发性、无规律性、非连续性等特点，常规的活性污泥处理工艺难以有效应对。结合九江市中心城区降雨特征及工程实际应用需要，首先提出MBBR工艺在雨天满负荷旱天低负荷运行的思路。发现雨天满水量负荷0.44 m3/h，旱天在低水量负荷(10%雨天满负荷)情况下已经能够正常维持微生物活性并发挥良好的有机物去除效果，进一步提高旱天水量负荷(提升至30%、50%、70%)对CODcr和NH3-N去除率无明显提高。其次研究了MBBR系统在长期未降雨时，能接受的最大停置时间。结果表明，在原水CODcr和NH3-N浓度分别98.67 mg/L和13.01 mg/L，停置期为4 d、6 d和8 d时，出水CODcr浓度均能达到地表IV类水标准；停置期为4 d时，出水NH3-N浓度可达IV类水标准，超过4 d后，MBBR系统对NH3-N的去除效果大幅度降低，出水远超IV类水标准。此运行思路和试验参数可为实际工程运维调度及相关工程方案设计提供一定参考。

随着城市化进程的加快，降雨径流产生的城市内涝及径流污染问题日益凸显，降雨径流的研究对城市规划建设提供科学依据。本研究应用SWMM模型，以深圳市宝安区前海片区为研究区域，构建符合研究区域实际情况的降雨模型，模拟不同重现期下降雨径流情况及径流污染产生和分布情况。结果表示，随着降雨量的不断增加，地表径流量、洪峰流量逐渐增加，产生洪峰流量的时间逐渐提前，径流系数增加，且增加幅度有逐渐减小的趋势。此外，随着降雨量的不断增加，由于冲刷产生的径流污染不断增加，城市受纳水体的承污压力不断加大。研究结果表明了不同重现期下城市内涝及径流污染的实际情况，对城市内涝治理及径流污染控制有重要意义。

开展水源地水库的污染来源解析，掌握影响水质的主要污染源，是改善水库水环境和保障饮用水源安全的基础。目前，国内关于水源地水环境污染物源解析的研究相对较少。本文以南方某水库为例开展水库污染来源解析，探索水库污染来源解析方法。研究结果表明，南方某水库NH3-N、TN、TP主要污染来源是外来引水，贡献占比高达81.9%、95.7%和92%，入库支流、散流入库、大气沉降、底泥释放污染负荷贡献均较小。

在总结分析前人有关黄河下游水体污染物浓度研究成果的基础上，利用三门峡、花园口和高村水文站逐日水沙资料，以氨氮和全磷为指标，分析了三门峡、花园口和高村断面因水土流失引起的非点源污染负荷。氨氮非点源污染负荷占总污染负荷的比例分别为11.8%~88.4%、8.8%~85.1%和6.6%~89.5%，平均值53.0%；全磷分别为34.3%~99.7%、32.4%~96.2%和39.2%~94.8%，平均值为78.8%。参照地表水环境质量标准，得到下游河道达到Ⅲ类和Ⅱ类水质标准的临界输沙量，三门峡至高村河段平均为7.93和5.20亿t。将对控制黄河下游水体水质健康具有重要的指导意义。

为了解决非点源污染在缺资料地区难以模拟计算和综合评估的问题，本文以柏条河流域为例，采用主成分分析法和综合污染指数法对研究区水质进行了综合评价，通过结合源强系数法(source strength coefficient method)和输出系数法(the export coefficient model)对研究区2017年非点源污染情况进行模拟计算，并采用等标污染负荷法进行污染综合评估，以明确研究区主要污染物和主要污染源。结果表明：1) 柏条河流域水质整体偏好，个别时段存在水质超标现象，枯水期水质整体优于丰水期；2) 研究区非点源污染比较严重，主要污染物为TN，其等标污染负荷占比为56.17%；3) 农村生活污水和农业面源为主要污染源，其等标污染负荷占比分别为69.53%和12.49%；4) 污染最严重的乡镇为丽春镇、其次为唐昌镇，需采取相应的措施加以治理。

非点源与城市污水排放的复合输入显著加剧流域富营养化风险。以浑太河流域为例，应用SWAT模型模拟分析流域非点源氮磷污染的时空分布特征。研究结果表明：(1) SWAT模型在率定期、验证期的R2、Ens均大于0.7，说明模型在研究区具有较高适用性；(2) 时间分布：2010~2022年间TN和TP总体负荷呈下降趋势，年内变化表现为“汛期高、非汛期低”的特征。(3) 空间分布：TN和TP的高值区主要分布在上中游及库区周边，下游地区负荷相对较低。进一步分析表明，土地利用变化对氮磷输出具有显著影响：耕地和建设用地是流域氮磷输出的主要“源区”，而林地、草地与水域则发挥“汇区”功能，有效削减非点源污染。本研究揭示了浑太河流域非点源氮、磷污染的时空格局及其驱动机制，可为流域水环境管理提供科学依据。

以三峡库区香溪河流域为研究区，利用现场观测实验及查阅文献等方法确定不同污染源负荷模型输出系数，估算得出香溪河小流域土地利用、畜牧养殖及居民生活三种污染源负荷。结果表明，林地和旱地是土地利用污染源负荷中贡献率最大的，香溪河小流域总氮负荷量(TN)由高到低依次为畜牧养殖污染 > 土地利用污染 > 居民生活污染，而总磷负荷量(TP)由高到低依次为土地利用污染 > 畜牧养殖污染 > 居民生活污染。因此，选择合理的农田管理措施，有效控制林地、旱地施肥量及畜牧养殖污水排放，是防控三峡库区香溪河流域非点源污染的关键。

非点源污染是导致地表和地下水环境恶化的重要原因之一。优先管理区(Priority Management Areas, PMAs)识别对非点源污染精准高效管控至关重要。然而，在已有PMAs识别研究中很少考虑河流系统中的传输损失，且缺乏对识别结果准确性和合理性的验证。基于此，本文通过耦合SWAT模型与空间Markov算法，构建了一个考虑河流滞留效应和削减负荷的多级PMAs识别框架，应用于海河水系的王快水库流域，并验证PMAs识别结果在不同管理措施下的准确性。主要结果如下：(1) 在校准和验证期SWAT模型性能良好，纳什效率系数NSE > 0.60、R2 > 0.79；(2) 河道氮磷滞留系数与径流量呈非线性关系，存在显著的径流阈值。当径流量介于1.14~8.23 m3/s，总氮(TN)和总磷(TP)的滞留系数趋于稳定。(3) TN的PMAs均为#29和#30号子流域，负荷贡献为74.35% (枯水年)、58.45% (平水年)和76.46% (丰水年)，面积占流域总面积的14.22%。(4) TP的PMAs为#33 (枯水年)、#28和#33 (平水年)、#29和#30 (丰水年)号子流域，负荷贡献分别达到53.19%、78.83%和69.69%，面积占1.20%、2.95%和14.22%。(5) 仿真结果显示，PMAs削减效率约为非PMAs的4.25倍(TN)和5.37倍(TP)，证实了PMAs识别结果的合理性。该研究结果可为相关流域的非点源污染控制提供有效方法支撑。

明确河流主要水污染负荷的贡献类型，将对污染源监控和管理有重要作用，并直接决定污染治理投资的方向及强度。本文提出了一套采用控制断面实测通量识别河流水污染负荷贡献类型的方法。建立了一个简单的适合点源和非点源多种情况的时段通量计算方法，改进了常规的点源和非点源分割方法——枯水期平均通量法，提出了水污染负荷贡献类型的判别标准。应用实例表明，该方法是可行的，可为流域水污染控制规划提供科学依据和技术支持。

本文对尤溪河流域从点源、面源污染现状展开调查，通过污染负荷分析和估算方法对尤溪河流域水的污染源进行总体评价。研究结果表明：尤溪流域污染源以面源污染为主，点源污染所占比例极小。全流域各类污染源的污染物入河量中，畜禽养殖污染源和农闲污染源的COD、NH3-N的入河量比率最大，工业污染源的入河量是最小的；其中农田排放COD，新洋溪流域最大，占全流域总量的24%，其它支流依次为新桥溪、干流、源湖溪、青印溪、吉木溪、清溪。氨氮排放量，以青印溪排放量最大，占全流域总量的23%；另外生活污水排放，青印溪生活排放COD总量最大，占全流域总量的21%，其它依次为源湖溪、新桥溪、新洋溪、干流、清溪、吉木溪。总之全流域生活污水具有排污总量大、治理水平低的特点，生活污水对水体质量的危害程度十分明显。

随着经济发展和城市化进程加速，磷污染成为我国河、湖水体污染的主要原因之一，威胁着人民生产生活用水安全。本研究以深圳麻雀坑水流域为例，通过实地采样和化学测量分析，评估了不同来水和土地利用条件下水、土环境介质中的磷污染及其空间分布特征，识别了磷污染的主要来源，得出以下结论：1) 麻雀坑水流域中磷的形态变化易受环境因素影响；2) 磷的赋存形态随空间变化显著；3) 面源污染和湿地内源是造成该流域磷污染的主要原因。

金华江为钱塘江上游最大的支流，水质污染较重，氨氮、总磷污染尤为突出。该文针对金华江水质污染的特点，对金华江流域沿河典型乡镇干流及主要支流进行了高密度监测，通过对监测数据的详细分析，对各沿江乡镇地表水水质污染现状进行了评价。结果表明，金华江流域的污染以氨氮为主，总磷为第二污染物，上游乡镇未受明显污染，中游乡镇氨氮污染最为突出，下游乡镇污染趋势于下降，针对各乡镇不同的水质污染特征分析了成因并提出控制对策。

入河排污口是陆域污染物进入水体的关键节点，其精细化管理是流域水环境系统治理的核心环节。本研究以云南省保山市龙陵县为对象，基于2023~2024年度入河排污口排查与溯源工作成果，系统分析了怒江、瑞丽江干流及城市建成区水体的排污口结构特征、污染来源与空间分布规律。研究共识别出入河排污口587个，其中以自然冲沟、农田退水口等面源型排口占主导(占比达97.96%)，表明区域水污染输入结构已由传统点源为主转向“面源污染绝对主导”的新阶段。水质监测结果显示，32个排口存在超标现象，空间上高度集中于城市建成区水体及怒江碧寨乡段，超标因子主要为化学需氧量、氨氮与总磷。通过“资料–人工–技术”三级溯源分析，超标排口的污染来源呈现高度复合化特征，主要形成“乡村复合面源”与“城镇雨污混流”两类典型污染链。基于上述发现，本文提出以“流域统筹、三端协同”为核心的系统治理策略，涵盖源头管控、过程拦截与末端修复的全链条措施，并建议构建智慧监管平台与多元共治格局，以期为西南山地县域的水环境精细化治理提供科学依据与实践路径。

抚仙湖为深水型淡水湖泊，近十年，随着临湖经济的快速发展，入湖河道污染物氮磷浓度提升显著，富营养化加剧。为了找出抚仙湖的主要污染源，为其保护和治理提供参考方向和理论指导，本文基于多年实地调研和化学需氧量(COD)、总氮、总磷含量等指标检测分析，分别从农村生活污染、农田径流污染、城镇生活污染、废弃磷矿区污染、旅游污染等方面对抚仙湖流域的污染现状进行了初步分析与评价。结果显示，抚仙湖流域污染主要来源于农村面源污染、城镇生活污染、废弃磷矿区污染、旅游污染等方面，其中，最大的污染源是农村面源污染，包括流域村落污染、人畜粪便污染、农村垃圾污染和农田径流污染。农村面源污染的COD、总氮、总磷分别占各自所有入湖污染量的94.81%，94.85%，87.79%。其中，农田径流污染又是农村面源污染中最主要的组成部分。

本研究以涟江流域作为研究对象，采用路线调查法对流域河岸带类型进行调查，根据土地利用方式、植被类型、地形地貌、土壤质地、人类活动强度、植被覆盖度等指标对涟江流域河岸带分类，选取不同类型河岸带运用径流槽模拟地表径流冲刷，测定土壤水分入渗和地表径流中氮流失量，以期能为流域面源污染防治以及良好河岸带结构的构建提供参考。研究表明：草地 + 壤土 + 山地、V型河谷 + 重度干扰类型河岸带地表径流产生量最大，单次冲刷最大地表径流量为757 mL；农业用地 + 水稻田撂荒地 +丘陵、U型沟谷 + 壤土 + 重度人类干扰类型与农业用地 + 坡耕地裸地 + 丘陵、U型沟谷 + 壤土 + 重度人类干扰类型产生量最低，6次冲刷均未产生地表径流。水分入渗量则相反，这三种类型单次冲刷入渗量分别为43 mL、800 mL、800 mL。不同类型河岸带模拟冲刷径流中总氮、铵态氮、硝态氮流失量均随冲刷次数增多而降低，其中硝态氮流失量大于铵态氮流失量。总氮、铵态氮、硝态氮流失量最大为建设用地 + 裸地 + 壤土 + 山地、V型河谷+重度干扰类型河岸带，流失量分别为4.60 mg/L、1.74 mg/L、2.11 mg/L；流失量最小为草地 + 壤土 + 山地、V型河谷 + 重度干扰类型河岸带，流失量分别为1.56 mg/L、0.45 mg/L、0.47 mg/L。本研究从局域尺度揭示了不同河岸带氮流失特征。

种植业面源污染成为我国农村地区环境污染的主要来源，有效控制种植业面源污染，是保护土壤与水资源，促进种植业可持续发展的必然要求。本文针对我国种植业面源污染特点，主要介绍了我国种植业面源污染防治的主要技术研究进展并对农业可持续性发展进行了展望。

黄河流域作为中国重要的水源地，近年来也面临着日益严重的微塑料污染威胁。本研究通过对比分析国内外关于黄河流域微塑料污染的研究现状及出台的相关治理措施，分析了黄河流域微塑料污染的来源、分布特征及生态危害，并探讨了微塑料污染的监测、处理技术以及治理策略的发展现状。研究表明，黄河流域的微塑料污染主要来源于工业废水、农业径流、生活垃圾等，其治理难度较大，需要多层次、系统性的防治手段。本论文旨在深入研究黄河流域微塑料的现状、来源、危害及治理措施，为黄河流域生态保护和高质量发展提供科学依据。

根据农田生态系统建设需求，选取浠水县郁港村为研究对象，分析了农田生态系统存在的问题及产生问题的原因，并从生态水系建设和农田生态系统养分流失阻控两方面提出了农田生态修复的对策。通过建设贯穿果园、农田、山林的生态廊道及具有区域特色的生态小水系，串联水体，改善区域生态系统结构；完善控释肥技术、生态沟渠和人工生物浮岛工程，解决项目区环境恶化，水功能退化的问题，以期提高农业可持续发展水平。

乡村水生态环境质量影响流经的河流水质，是保障河流水质需要解决的关键问题。利用ArcGIS空间分析功能，水文分析模块和最小耗费距离模型，分析了辇头村水生态环境质量的现状，得出了生态源斑块数量占比少，生态廊道不足，无法形成生态网，生态韧性差。生活污水和农田径流直接进入大沽河引发河流水质超标。依据生态连通性原理，规划了21条生态廊道，形成了村庄的生态网，增强了村域的生态韧性。对村庄生活污水进行集中处理和深度净化，在大沽河沿岸规划了宽度为100 m的缓冲带，减少农田径流污染，保障了河流水质。

Rural population aging is a major social issue in China, but its impact on agricultural nonpoint source pollution has been largely overlooked by researchers. To explore the relationship between rural population aging and agricultural nonpoint source pollution, we used over 290,000 tracking survey data from the Rural Fixed Observation Points Survey (2003–2021), which is the most representative data set in China, and identified the long-term association between them. We found that rural population aging has a significant reduction effect on agricultural nonpoint source pollution, with each 1% increase in the proportion of elderly people in rural households reducing agricultural nonpoint source pollution by 0.0656%, and this reduction effect gradually weakening over time. This study provides a new perspective and evidence for developing effective policies to mitigate agricultural nonpoint source pollution.

Water security is a basic requirement of a region’s residents and also an important point of discussion worldwide. The middle route of the south-to-north water diversion project (MR-SNWDP) represents the most extensive inter-basin water allocation scheme globally. It is the major water resource for the Beijing–Tianjin–Hebei region, and its security is of great significance. In this study, 28 indicators including society, nature, and economy were selected from the water sources of the MR-SNWDP from 2000 to 2017. According to the Drivers-Pressures-States-Impact-Response (DPSIR) framework principle, the entropy weight method was used for weight calculation, and the comprehensive evaluation method was used for evaluating the water security of the water sources of the MR-SNWDP. This study showed that the total loss of nonpoint source pollution (NPSP) in the water source showed a trend of slow growth, except in 2007. Over the past 18 years, the proportion of pollution from three NPSP sources, livestock, and poultry (LP) breeding industry, planting industry, and living sources, were 44.56%, 40.33%, and 15.11%, respectively. The main driving force of water security in all the areas of the water source was the total net income per capita of farmers. The main pressure was the amount of LP breeding and the amount of fertilizer application. The largest impact indicators were NPSP gray water footprint and soil erosion area, and water conservancy investment was the most effective response measure. Overall, the state of the water source safety was relatively stable, showing an overall upward trend, and it had remained at Grade III except for in 2005, 2006, and 2011. The state of water safety in all areas except Shiyan City was relatively stable, where the state of water safety had fluctuated greatly. Based on the assessment findings, implications for policy and decision-making suggestions for sustainable management of the water sources of the MR-SNWDP resources are put forward. Agricultural cultivation in water source areas should reduce the application of chemical fertilizers and accelerate the promotion of agricultural intensification. Water source areas should minimize retail livestock and poultry farming and promote ecological agriculture. The government should increase investment in water conservancy and return farmland to forests and grasslands, and at the same time strengthen the education of farmers’ awareness of environmental protection. The evaluation system of this study combined indicators such as the impact of agricultural nonpoint source pollution on water bodies, which is innovative and provides a reference for the water safety evaluation system.

This paper presents a modeling framework for nonpoint source (NPS) pollution control which involves the use of the MapShed hydrological model and the Water Quality Analysis Simulation Program (WASP8) to identify critical source areas (CSAs) for improving in-stream water quality at the watershed scale based on the efficiencies of agricultural best management practices (BMPs) for NPS load reductions. The study area is the coastal watershed of the Lower Aksu Stream in the south of Turkey. Extensive data collection facilitated model calibration, validation, and scenario analyses. There was a good agreement between the model predictions and measurements related to flow rate and water quality parameters. The most effective scenario (S3) combining BMPs for agriculture, pastureland, and animal waste management achieved significant annual load reductions: approximately 40% for total nitrogen (TN) and 25% for total phosphorus (TP). Two sub-watersheds were defined as the CSAs based on the contributions to total NPS pollution loads and load reductions by the investigated BMPs. Pastureland and animal waste management practices (S1 Scenario) were most effective in sub-watershed 5 with contributions of approximately 32% for both TN and TP annual load reductions. Scenario S2 (agricultural waste management) and Scenario S3 were most successful in sub-watershed 8 with contributions of 30.3% and 27.3% for TN and 35.7% and 28.7% for TP annual load reductions, respectively. The identification of CSAs enhances the effectiveness of BMPs for NPS pollution control.

Understanding the impact of climate change on agricultural nonpoint source (NPS) pollution is crucial for developing effective adaptation strategies and reducing vulnerabilities where such challenges exist. This study evaluated the impact of precipitation and temperature variations on Total Inorganic Nitrogen (TIN), Total Inorganic Phosphorus (TIP), and sediment loads in the Sand River Catchment (SRC) using the Soil and Water Assessment Tool plus (SWAT+). One-way analysis of variance (ANOVA) was used to determine the significance (p < 0.05) of the relationships (R2) between precipitation and temperature on sediment, TIN, and TIP loads in the SRC. SWAT+ calibration and validation demonstrated that the statistical indices (NSE and R2 ≥ 0.72; −17.30 ≤ PBIAS ≤ 14.74) fell within an acceptable range. Results indicated a significant influence of average monthly precipitation (p < 0.0001) and temperature (p ≤ 0.004) on sediment, TIN, and TIP loads. In addition, a decrease in average annual precipitation led to a decline in sediment, TIN, and TIP loads (R2 ≥ 0.55), with the average annual temperature increasing in the same period (R2 ≤ 0.23). This study confirms that climate change contributes to agricultural NPS pollution in the SRC and highlights the need to employ suitable adaptation strategies for pollution control in the catchment.

Numerical models are commonly used to support the management of diffuse pollution sources in large agricultural landscapes. This paper investigates the suitability of a three-dimensional groundwater streamline-based nonpoint source (NPS) assessment tool for agricultural aquifers. The streamline approach is built on the assumption of steady-state groundwater flow and neglects the effect of transverse dispersion but offers considerable computational efficiency. To test the practical applicability of these assumptions, two groundwater transport models were developed using the standard three-dimensional advection–dispersion equation (ADE): one with steady-state flow and the other with transient flow conditions. The streamline approach was compared with both ADE models, under various nitrate management practice scenarios. The results show that the streamline approach predictions are comparable to the steady-state ADE, but both steady-state methods tend to overestimate the concentrations across wells by up to 10% compared to the transient ADE. The prediction of long-term attenuation of nitrate under alternative land management scenarios is identical between the streamline and the transient ADE results.

The export coefficient model (ECM) remains widely applied in estimating agricultural Non-point Source Pollution (NPSP) due to its simplicity, minimal parameter requirements, and relatively high accuracy. However, its reliance on empirical export coefficient (EC) limits its ability to accurately quantify pollutant loads in large and complex watersheds. This necessitates the development of more advanced approaches for improved pollutant load estimation. To overcome these challenges, the EC-ICM integrates environmental factors with optimized EC values for various land use types, enhancing its adaptability for watershed management. Empirical EC values were derived using genetic algorithm (GA) and Latin hypercube sampling, then improved EC and corrected EC were employed to estimate pollutant discharge and water inflow across land uses. The model incorporates multiple factors—such as surface runoff, topographic influence, landscape interception, soil erosion, pollutant production, water leaching, and cost-distance—allowing for more accurate NPSP load assessments. Pollution factors were classified using the natural breaks method, with Entropy Weight method determining weights for a comprehensive multi-factor evaluation and risk-level assignment. Compared to ECM optimized solely with GA, the EC-ICM demonstrates improved accuracy, reducing the relative error of total nitrogen and total phosphorus by 9.66% and 6.68%, respectively. Land use contributes the highest share of TN loads, particularly from cropland and grassland, followed by livestock and population sources. TP loads are primarily attributed to livestock and poultry farming, followed by land use and population sources. The Longdong Loess Plateau, responsible for approximately 12% of total NPSP loss, is identified as a high-risk area. Targeted zoning management strategies based on risk analysis prioritize these high-risk regions, providing practical recommendations for pollution control and comprehensive watershed environmental management. Future research can further explore the impact of improving temporal resolution, future climate change and combining hydrodynamic models on the ability to simulate the amount of pollutants entering the river.

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To address the challenges in simulating nonpoint source pollution inflow, pollutant source distribution, and migration pathways in plain river network regions, this study innovatively proposes an optimized technical framework based on the NDR module of the InVEST model. Through land use data reconstruction, DEM negative value correction, and flow accumulation threshold optimization, the framework effectively resolves key issues including pollutant receiving water identification, runoff path simulation, and pollutant migration termination determination, significantly enhancing the model’s applicability to complex river systems. Using the Taipu River as a case study, this research investigates the spatial distribution characteristics of nonpoint source pollution load inflow and its sources in major rivers within plain river network regions. Results show that in 2023, total nitrogen and total phosphorus inflows into the Taipu River were 1004.11 t/a and 83.80 t/a, respectively, with pollution loads primarily originating from the Wangning Polders in the midstream, Chengnan New District Small Watersheds and Chang Yang River Small Watersheds, mainly entering the Taipu River through tributaries such as the Beijing-Hangzhou Grand Canal and Nanzha Port. Calculations based on measured data indicate total nitrogen and total phosphorus inflows into the Taipu River were approximately 1300 t/a and 90 t/a, respectively, consistent with model predictions. Building on environmental capacity assessment results, this study proposes targeted recommendations for precision-based nonpoint source pollution control in the Taipu River basin. The findings provide scientific evidence and technical paradigms for nonpoint source pollution management and sustainable management in plain river network regions.

Agricultural production is the largest contributor of nitrogen and phosphorus pollution in lakes, rivers, and streams in the United States. The effectiveness of agricultural conservation programs that encourage farmers to adopt certain practices to reduce this water pollution, once implemented, is an open question. We develop a unique data set combining the spatial structure of the watershed river system, the timing of federal conservation contracts, water quality measurements, land use, land cover, and weather data to study the effect of conservation contracts on nitrogen and phosphorus levels in the Wabash River watershed, which drains Indiana and Illinois. We develop econometric models that generate a causal understanding of the effectiveness of these conservation contracts for reducing nitrogen and phosphorus levels in the surface water system. We find that at current treatment levels, these programs reduce surface water pollution only during relatively dry periods. The efficacy of these programs in the study area is highly sensitive to precipitation to the extent that average precipitation can eliminate the nutrient loss reduction benefits of conservation program installations at current treatment levels. Therefore, we find weak evidence to support ambient downstream water quality improvements resulting from program investment levels to date. We anticipate this work will motivate further inquiry into the manner in which these conservation programs have or have not been effective.

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The integrated application of hydrological models and best management practices (BMPs) serves as a pivotal decision-making tool for managing nonpoint source (NPS) pollution in watersheds. Optimizing and selecting BMP options are critical challenges in managing NPS pollution, as these processes are typically computationally expensive and involve mixed discrete-continuous decision variables. Our study integrated a novel method, the multi-objective adaptive surrogate modeling-based optimization for constrained hybrid problems (MO-ASMOCH), with the distributed Soil and Water Assessment Tool (SWAT) model to efficiently optimize the deployment of BMPs in the Four Lakes watershed of China. We compared the optimization results with those obtained using the traditional non-dominated sorting genetic algorithm (NSGA-II) method. Our results demonstrate that MO-ASMOCH significantly outperforms NSGA-II in computational efficiency, achieving comparable Pareto-optimal solutions with just 1,150 model evaluations compared to NSGA-II's requirement of 10,000 model evaluations. This demonstrates that MO-ASMOCH is a more efficient optimization algorithm for BMP optimization problems with both discrete and continuous decision variables. We selected representative scenarios to calculate in-lake concentrations of total phosphorus (TP) and total nitrogen (TN) pollutant loads. The largest reduction scenario could reduce TN and TP loads by 18.3 % and 20.7 %, respectively, at a cost of 1.54 × 108 Chinese Yuan. Under this scenario, the water quality classification level of TN improves from inferior Class V to Class IV-V, while TP attains Class III throughout the year. The methods of this study could enhance our capability to manage NPS pollution in watersheds effectively and provide targeted decision-making insights for environmental management practices.

Abstract This study evaluates the impact of green finance (GF) on agricultural nonpoint source pollution (ANPSP) control and emission reduction in 30 Chinese provinces from 2005 to 2021. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of GF development and ANPSP. It employs a mediation effect model to empirically assess the pollution control efficacy of GF and to elucidate the mechanisms underlying its influence. The findings indicate that GF development significantly curtails ANPSP emissions. It achieves this through government environmental regulation (ER) and land transfer mechanisms. Heterogeneity test results show that GF has a stronger impact on ANPSP in regions with lower economic development level and GF reform policies. Therefore, the study suggests strengthening the GF infrastructure in rural areas, aligning GF policies with ER, promoting large-scale land operations, and implementing tailored strategies for regions with different levels of economic development and GF reform policies.

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China repeatedly surpasses international fertilizer safety limits, resulting in significant fertilizer nonpoint source pollution (denoted as FNSP), which adversely affects food security and agricultural sustainability. Simultaneously, farmland transfer has emerged as a pivotal strategy for transitioning between agricultural production methods. The present study aims to investigate the relationship between farmland transfer and FNSP. In line with the aim of the study, based on China’s panel data from 2005 to 2020, the fixed-effect model, mediating-effect model, spatial Durbin model, and threshold regression model are employed. The findings reveal that farmland transfer exerts a significant inhibitory effect on FNSP. The reduction in FNSP through farmland transfer is facilitated by the decrease in fertilizer application intensity and increase in compound fertilizer application. Further, farmland transfer demonstrates a significant spatial spillover effect on FNSP, mitigating pollution levels within regions and influencing neighboring areas. Moreover, a nonlinear relationship between farmland transfer and FNSP is observed. These findings contribute to understanding the intricate dynamics between agricultural land management strategies and environmental sustainability, offering valuable insights for policymakers and stakeholders engaged in promoting green and sustainable agricultural practices.

The Chesapeake Bay watershed is representative of governance challenges relating to agricultural nonpoint source pollution and, more generally, of sustainable resources governance in complex multi-actor settings. We assess information flows around Best Management Practices (BMPs) undertaken by dairy farmers in central Pennsylvania, a subregion of the watershed. We apply a mixed-method approach, combining Social Network Analysis, the analysis of BMP-messaging (i.e. information source, flow, and their influences), and qualitative content analysis of stakeholders’ interviews. Key strategic actors were identified through network centrality measures such as degree of node, betweenness centrality, and clustering coefficient. The perceived influence/credibility (by farmers) of BMP-messages and their source, allowed for the identification of strategic entry points for BMP-messages diffusion. Finally, the inductive coding process of stakeholders’ interviews revealed major hindrances and opportunities for BMPs adoption. We demonstrate how improved targeting of policy interventions for BMPs uptake may be achieved, by better distributing entry-points across stakeholders. Our results reveal governance gaps and opportunities, on which we draw to provide insights for better tailored policy interventions. We propose strategies to optimize the coverage of policy mixes and the dissemination of BMP-messages by building on network diversity and actors’ complementarities, and by targeting intervention towards specific BMPs and actors. We suggest that (i) conservation incentives could target supply chain actors as conservation intermediaries; (ii) compliance-control of manure management planning could be conducted by accredited private certifiers; (iii) policy should focus on incentivizing inter-farmers interaction (e.g. farmers’ mobility, training, knowledge-exchange, and engagement in multi-stakeholders collaboration) via financial or non-pecuniary compensation; (iv) collective incentives could help better coordinate conservation efforts at the landscape or (sub-)watershed scale; (v) all relevant stakeholders (including farmers) should be concerted and included in the discussion, proposition, co-design and decision process of policy, in order to take their respective interests and responsibilities into account.

This study addresses the pressing issue of nonpoint source water pollution in Kentucky, particularly associated with large-scale agriculture. Centered on the outer bluegrass region of Central Kentucky, the research examines the water quality of headwater streams during the agricultural season. The approach involves geospatial land cover classification using aerial imagery. Water quality data were collected during the agricultural growing season from May to October 2018. Land cover classification utilized ERDAS Imagine 2016 and ESRI ArcGIS 10.6 GIS software, while conventional water quality parameters were measured with a YSI ProDSS® multiparameter water probe and a Marsh-McBirney Flo-Mate 2000 flow meter. Statistical analyses show significant differences in stream water chemistry, suggesting the impact of agricultural nonpoint source pollution. Forested streams exhibited more varied conditions, indicating a potentially better environment. As agricultural land percentage increased, water chemistry variation suggested a measurable threshold for changes. Significant differences in water quality between agricultural and forested streams highlight the potential benefits of expanding riparian zones beyond regulations. Enlarging these zones is proposed as a strategy to mitigate nonpoint source pollution in Kentucky’s waterways.

Mitigating nonpoint source pollution from stormwater runoff demands effective strategies for treating the first flush depth. Whether through off-stream storage or pass-through treatment devices, designing diversion structures and filtering materials is critical. This study proposes a streamlined procedure for determining first flush design flow rates, employing the modified rational method and rainfall intensity–duration equations applicable to any U.S. location. The dimensionless solution, which is presented as an equation requiring an iterative calculation for the desired flow rates, is complemented by precision graphs. Examples from the semi-arid Southwestern United States illustrate the methodology’s utility.

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Nonpoint source pollution (NPS) has become the most important reason for the deterioration of water quality, while relevant studies are often limited to African river and lake basins with insufficient data. Taking the Simiyu catchment of the Lake Victoria basin as the study area, we set up a NPS model based on the soil and water assessment tool (SWAT). Furthermore, the rationality of this model is verified with the field-measured data. The results manifest that: (1) the temporal variation of NPS load is consistent with the variation pattern of rainfall, the average monthly output of total nitrogen (TN) and total phosphorus (TP) in the rainy season was 1360.6 t and 336.2 t, respectively, while in the dry season was much lower, only 13.5 t and 3.0 t, respectively; (2) in view of spatial distribution among 32 sub-basins, TN load ranged from 2.051 to 24.288 kg/ha with an average load of 12.940 kg/ha, and TP load ranged from 0.263 to 8.103 kg/ha with an average load of 3.321 kg/ha during the 16-month study period; (3) Among the land use types, the cropland contributed the highest proportion of TN and TP pollution with 50.28% and 76.29%, respectively, while the effect of forest on NPS was minimal with 0.05% and 0.02% for TN and TP, respectively. (4) Moreover, the event mean concentration (EMC) values of different land use types have been derived based on the SWAT model, which are key parameters for the application of the long-term hydrological impact assessment (L-THIA) model. Therefore, this study facilitates applying the L-THIA model to other similar data-deficient catchments in view of its relatively lower data requirement.

Nonpoint source (NPS) pollution has emerged as the predominant water environment issue confronting plateau lakes in central Yunnan. Quantitative analysis of the impact of NPS pollution on water quality constitutes the key to preventing and controlling water pollution. However, currently, there is a dearth of research on identifying NPS pollution risks and exploring their relationship with water quality based on the Minimum Cumulative Resistance (MCR) model in the plateau lake basins of central Yunnan. Particularly, studies on the spatial heterogeneity of the impact of NPS pollution on water quality from a multi-scale perspective are scarce. Therefore, this study focuses on three typical lake basins in the Central Yunnan Plateau–Fuxian Lake, Xingyun Lake, and Qilu Lake (the Three Lakes). Utilizing the MCR model to identify NPS pollution risks, the study analyzes seven different scales, including sub-basins, riparian buffer zones (100 m, 300 m, 500 m, 700 m, and 1,000 m) and lakeshore zones, to reveal the multi-scale effects of NPS pollution on water quality through correlation analysis. The results indicate that: (1) Over 60% of the areas in the Three Lakes Basin are at high or extremely high risk, mainly concentrated in flat terrain and around inflow rivers; (2) The area of NPS pollution from paddy field source landscape (PFSL) is greater than that from construction land source landscape (CLSL), and the high-risk areas of NPS pollution are also larger for PFSL compared to CLSL; (3) The mean resistance values of PFSL and CLSL show a significant negative correlation with monthly mean values of water quality indexes (NH3-N, TP, CODCr), with the 1,000 m riparian buffer zone scale showing the greatest correlation with most water quality indexes, especially NH3-N; (4) The correlation between the mean resistance value of CLSL and the monthly mean values of water quality indexes is significantly higher than that of PFSL, indicating a greater impact of CLSL on water quality compared to PFSL. In summary, PFSL and CLSL are the primary sources of NPS pollution in the Three Lakes Basins. The 1,000 m riparian buffer zone scale is the most sensitive to the impact of NPS pollution on water quality. This study provides scientific references for landscape pattern optimization and precise control of NPS pollution risks in the Central Yunnan Plateau lake basins and offers a new research perspective for exploring multi-scale effects of NPS pollution on water quality.

Temporal and spatial changes in non-point source pollution, driven by significant alterations in land use due to increased human activity, have considerably affected the quality of groundwater, surface water, and soil environments in the region. This study examines the Weihe River basin in greater detail, an area heavily impacted by human activity. The study developed the River Section Potential Pollution Index (R-PPI) model using the Potential Non-Point Source Pollution Index (PNPI) model in order to investigate the dynamic changes in River Section Potential Pollution (R-PP) over a 31-year period and its associated risks, especially those related to land use and land cover change (LUCC). The predominant land uses in the Weihe River Basin are cropland, grassland, and forest, making up around 97% of the basin’s total area. The Weihe River Basin underwent a number of soil and water conservation initiatives between 1990 and 2020, which significantly decreased the potential pollution risk in the river segment. The research separated the R-PP risk values in the area into five different categories using a quantile classification technique. According to the data, there is a polarization of R-PP risk in the area, with downstream parts especially having an increased risk of pollution in river segments impacted by human activity. On the other hand, river segments in the middle and upper reaches of the basin showed a discernible decline in possible pollution risk throughout the study period. The Weihe River Basin’s rapid urbanization and land degradation are to blame for the current increase in R-PP risk. The substantial influence of LUCC on the dynamic variations in R-PP risk in the Weihe River Basin is highlighted by this study. Additionally, it offers crucial information for upcoming conservation initiatives and urban planning guidelines meant to enhance the area’s ecological well-being and environmental standards.

Climate change scenarios have been used to evaluate future climate change impacts and develop adaptation measures to mitigate potential damage. This study investigated strategies to reduce nonpoint source loads in an agriculturally dominated watershed and adapt to climate change despite uncertainty. We also investigated strategies for adapting to future meteorological conditions characterized by uncertainty. We utilized the latest future climate change scenarios—shared socioeconomic pathways—and explored measures to reduce nonpoint source loads by implementing nonpoint pollution abatement facilities in a watershed model. The simulation results indicate that the future frequency of rainfall events may decrease based on observations and the types and features of rainfall events in the scenarios. However, the variability of runoff loads in the context of future climate scenarios may increase because of factors influencing surface runoff, including the amount and intensity of rainfall. Nonpoint source loads are expected to exhibit high uncertainty in the future. Finally, the optimal solution can be determined through a simulated evaluation of the cost–benefit of installing the abatement facilities, considering the abatement efficiency and maintenance period. Overall, implementing effective management practices is crucial for reducing nonpoint source loads resulting from agricultural activities while adapting to increasingly variable meteorological conditions.

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Accurately identifying pollution sources within a watershed is crucial for water pollution prevention and precise management. The Simeng River Basin was once one of the 32 key pollution control sub-watersheds in Sichuan Province, characterized by significant agricultural non-point source pollution. Before environmental remediation, the water pollution prevention and control situation in the watershed was not optimistic. This paper takes this basin as the research object, uses the Soil and Water Assessment Tool (SWAT) model to simulate the pollution sources (total phosphorus) within the basin. Based on the Total Maximum Daily Loads (TMDL) plan, it calculates the allowable load flux and the actual current load flux of the water body, determines whether the water quality meets the standards, and identifies the point and non-point sources of pollution exceeding the standards, determining the reduction rate for the excess pollution load.

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Diffuse pollution, including that in the lower and middle reaches of the Yangtze River, is the primary source of pollution in several agricultural watersheds globally. As the largest river basin in China, the Yangtze River Basin has suffered from total phosphorus (TP) pollution in the past decade owing to diffuse pollution and aquatic ecology destruction, especially in the midstream tributaries and mid-lower reaches of the lakes. However, the transport dynamics of diffuse pollutants, such as phosphorus (P) from land to water bodies have not been well evaluated, which is of great significance for quantifying nutrient loss and its impact on water bodies. In this study, diffuse pollution estimation with remote sensing (DPeRS) model coupled with Soil and Water Assessment Tools (SWAT) was utilized to simulate the transport dynamics of P, investigate the spatial heterogeneity and P sources in the Poyang Lake Basin. Additionally, the P transport mechanism from land to water and the migration process in water bodies were considered to investigate the impact of each loss unit on the water body and evaluate the load generated by diverse pollution types. The estimated diffuse TP loss was 6016 t P·yr-1, and the load to inflow rivers and to Poyang Lake were 11,619 and 9812 t P·yr-1, respectively. Gan River Basin (51.09%) contributed most TP to Poyang Lake among five inflow rivers, while waterfront area demonstrated the highest TP load per unit area with 0.057 t km-2·yr-1. Our study also identified P sources in the sub-basins and emphasized agricultural diffuse sources, especially planting, as the most significant factor contributing to TP pollution. Additionally, to improve the aquatic environment and water ecological conditions, further nutrient management should be applied using a comprehensive approach that encompasses the entire process, from source transportation to the water body.

Rainfall plays a crucial role in influencing the loss of agricultural diffuse pollution. The middle Yangtze River region is well-know for its humid climate and numerous agricultural activities. Thus, this study quantitatively analyzed the concentration and distribution of nitrogen (N) and phosphorus (P) load and loss in a major tributary of the middle Yangtze River under different rainfall patterns by using sampling analysis and SWAT model simulation. The total nitrogen (TN) and nitrate-nitrogen (NO3-) concentrations were 1.604-3.574 and 0.830-2.556 mg/L, respectively. The total phosphorous (TP) and Soluble Reactive Phosphorus (SRP) were 2-148 and 2-104 μg/L, respectively. The modeling results demonstrated that higher rainfall intensity led to greater load and loss flux of diffuse pollutant at the outlet. Organic nitrogen (ORGN) is the main nitrogen form transported from the subbasin to the reach, while organic phosphorus (ORGP) and inorganic phosphorus (INORGP) were transported at similar amounts. Under the condition of conventional rainfall, the outlet reaches mainly transported NO3-, and ORGN gradually increased when rainstorm events occurred. The ratio of INORGP to ORGP was relatively stable. During extreme rainfall event, rainfall is the dominant element of agricultural diffuse pollution. A strong positive correlation exists between rainfall intensity and pollution loss during rainstorms. Storm rain events were the main source of TN and TP losses. Few storm rain days generated pollutants that accounted for a large proportion of the total loss, and their impact on TP loss was significantly greater than that of TN. The influence of storm rain on TN is mainly the increase in runoff, while TP is sensitive to the runoff and sediment transport promoted by rainfall. By setting different precipitation scenarios, it was confirmed that under the same rainfall amount, short-term storm rain has the most significant impact on the TN load, whereas TP load may be influenced more by the combined effects of rainfall duration and intensity. Therefore, to reduce the impact of agricultural diffuse pollution, it is important to take targeted measures for the rainstorm days.

The detection of non-point pollution in large rivers requires high-frequency sampling over a longer period of time, which, however presumably provides data with large spatial and temporal variance. Variability may mean that data sets recorded upstream and downstream from a densely populated area overlap, suggesting at first glance that the urban area did not affect water quality. This study presents a simple way to explore trend-like effects of non-point pollution in the Danube based on data that varied strongly in space and time. For one year, biweekly sampling was carried out upstream and downstream from a large city with negligible emission of untreated wastewater and the surrounding settlements, industrial and agricultural areas. Although most of the values of the 34 examined physicochemical characteristics fell within the range of data previously published for the Danube, and the mean values of all parameters indicated unpolluted surface water, different water quality was revealed upstream and downstream from the metropolitan area at each sampling time. Since the physicochemical characteristics causing the separation also differed from time to time, univariate tests and consensus ordination were used to determine which variables changed similarly during most of the examined period. With this evaluation method, several diffuse pollutants of anthropogenic origin contaminating the Danube in the long term were identified, such as nitrogen, phosphorus, sulphate, chloride, potassium and vanadium. The results demonstrated that trend-like effects of non-point pollution can be detected even in a large river, where physicochemical measurements can vary strongly in space and time.

Abstract The Soil and Water Assessment Tool (SWAT) simplified the hydrological processes in large patches of paddy fields. Based on the original SWAT code, the main equations are modified and validated with the long-term field observations in freezing-thawing watershed. The original SWAT and SWAT with paddy module (SWAT-P) both perform well in the simulation of stream flow at the basin scale (NSE ≥ 0.7). SWAT-P performs better in the soil water simulation (RMSE = 24.22 mm, smaller than 104.63 mm of SWAT). The monthly diffuse nitrogen loadings simulated by SWAT-P are within a reasonable range and close to the monitored nitrate loads in 2011 and 2012. SWAT-P represents the ponding water depth dynamics during the rice growing period. The original SWAT overestimates the diffuse pollution loads in the freeze-thaw period. SWAT-P has better performance for water cycles and diffuse pollution load simulations in watersheds with large rice paddy fields.

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters need to be accurately calibrated for models to produce reliable predictions for streamflow, evapotranspiration, snow water equivalent, and nutrient loading. Existing parameter estimation methods are time-consuming, inefficient, and computationally intensive, with reduced accuracy when estimating high-dimensional parameters. In this paper, we present a fast, accurate, and reliable methodology to calibrate the SWAT model (i.e., 21 parameters) using deep learning (DL). We develop DL-enabled inverse models based on convolutional neural networks to ingest streamflow data and estimate the SWAT model parameters. Hyperparameter tuning is performed to identify the optimal neural network architecture and the nine next best candidates. We use ensemble SWAT simulations to train, validate, and test the above DL models. We estimated the actual parameters of the SWAT model using observational data. We test and validate the proposed DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the DL models-based calibration is better than traditional parameter estimation methods, such as generalized likelihood uncertainty estimation (GLUE). The behavioral parameter sets estimated by DL have narrower ranges than GLUE and produce values within the sampling range even under high relative observational errors. This narrow range of parameters shows the reliability of the proposed workflow to estimate sensitive parameters accurately even under noise. Due to its fast and reasonably accurate estimations of process parameters, the proposed DL workflow is attractive for calibrating integrated hydrologic models for large spatial-scale applications.

The Soil and Water Assessment Tool (SWAT) model is extensively used globally for hydrological and water quality assessments but encounters challenges in karst regions due to their complex surface and groundwater hydrological environments. This study aims to refine the delineation of hydrological response units within the SWAT model by combining geomorphological classification and to enhance the model with an epikarst zone hydrological process module, exploring the accuracy improvement of SWAT model simulations in karst regions of Southwest China. Compared with the simulation results of the original SWAT model, we simulated runoff and nutrient concentrations in the Mudong watershed from January 2017 to December 2021 using the improved SWAT model. The simulation results indicated that the modified SWAT model responded more rapidly to precipitation events, particularly in bare karst landform, aligning more closely with the actual hydrological processes in Southwest China’s karst regions. In terms of the predictive accuracy for monthly loads of total nitrogen (TN) and total phosphorus (TP), the coefficient of determination (R2) value of the modified model increased by 10.3% and 9.7%, respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) increased by 11.3% and 9.9%, respectively. The modified SWAT model improves prediction accuracy in karst areas and holds significant practical value for guiding non-point source pollution control in agricultural watersheds.

Ecohydrological models are critical for understanding coupled hydrologic–biogeochemical processes in tile-drained watersheds and for assessing management options. Despite recent advances in SWAT’s hydrological and biogeochemical processes, there has been limited evaluation of both the original and new tile drainage and nitrogen (N) modules. We therefore applied a comparative modeling approach in a typical Midwestern tile-drained watershed, evaluating eight configurations that vary tile-drainage module (original/new), tile parameter treatment (calibrated/default), and N module (original/new) to assess performance for N-loss simulation. Using daily streamflow and nitrate (NO 3 − ) load records from three monitoring sites, we conducted calibration, validation, sensitivity analysis, and uncertainty assessment. Each configuration effectively reproduced daily and monthly dynamics, although high-flow and associated NO 3 − load peaks were underestimated. We found that the new tile module generally improved streamflow simulations, particularly under tile parameter calibration conditions, while the new N module consistently enhanced NO 3 − load simulations compared to the original module. Despite improvements in streamflow and NO 3 − loads with the new tile and N modules, the additional processes in the new N module can magnify uncertainty in N-gas-flux estimates when calibration observations are scarce. We recommend applying the new N module in conjunction with additional measurements—such as soil moisture and nitrous oxide (N 2 O) fluxes—to constrain better N gas flux estimates beyond outlet NO 3 − load data. If such observations are unavailable, careful calibration with reasonable estimates may still help constrain soil N cycling and improve overall N budget accuracy.

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