平原感潮河网地区农业非点源污染产污规律

平原感潮河网地区非点源污染严重,同时由于本身具有的交叉污染等特性,造成了整个平原河网存在严重的环境污染和环境安全问题。文章在南通平原河网地区选择圩区作为典型区,以野外观测和室内分析相结合的方法开展野外原位试验,研究平原河网典型圩区各形态污染物随降雨径流的迁移特征,建立了稻季农田营养盐的迁移通量与径流通量、施肥量及降雨距施肥时间间隔三者之间的定量化关系。     

平原感潮河网地区非点源污染监测方法

平原感潮河网地区非点源污染严重以及交叉污染等特性，造成了严重的环境污染和环境安全问题。由于非点源污染监测资料不完善，相关研究的深度、广度以及监测管理存在不足，确定非点源污染大小、少资料区域污染定量化预测等问题都是亟待解决的难点。以南通平原感潮河网地区为例，对非点源污染中的不确定性及监测方法进行探讨，提出分别针对平原河网圩区和平原河网非圩区的非点源污染监测方法。     

北京市水环境非点源污染监测与负荷估算研究

文章对北京全市域范围开展水环境非点源污染监测以及污染负荷估算研究。监测结果表明,天然降雨氨氮、总氮污染程度高;城区典型下垫面降雨径流的有机污染十分严重,其中屋面降雨径流总氮和氨氮污染最严重,路面降雨径流COD和总磷污染最严重;下垫面降雨径流汇入城市排水管网后,由于冲洗下水道中的沉积物,使得水质污染进一步恶化。农业典型小流域面源污染对水质影响也很明显。城市非点源污染负荷估算选用SWMM暴雨径流模型,农业非点源污染负荷模型选用改进的输出系数模型,估算结果表明:城市非点源污染主要来自大气湿沉降、综合用地、路面和屋面等,农业非点源污染主要来自耕地和林地;全市污染物排放总量中,点源排放总量与非点源排放总量基本各占50％左右。为进一步挖掘污染减排空间,完善总量减排体系提供了依据。     

平原感潮河网地区不同土地利用下营养盐迁移特征

分别在南通平原河网地区选择典型非圩区开展野外原位试验,用野外观测和室内分析相结合的方法,研究平原非圩区典型试验小区不同土地利用下营养盐在自然降雨—径流驱动下迁移的时空分布特征.结果表明,导致营养盐迁移时空分布存在显著差异的主要原因为不同的土地利用类型、施肥条件及植被覆盖度等,不同土地利用下的径流量差异是导致营养盐迁移通...     

南昌市区初期雨水污染研究与防治对策

随着城市污水和工业废水等点源污染控制水平的提高，城市非点源污染的严重性日益表现出来，是仅次于农业非点源污染的第二大污染源．降雨后在形成的地表径流中污染物的浓度与未经处理的城市污水基本相同，成为我国城市水体水质恶化的主要原因之一．在充分调查研究之后，分析了南昌市区非点源特征，探讨了非点源污染控制的主要对策和措施，以便为该地区城市非点源污染供科学依据．     

降雨特征对合流制排水系统径流污染负荷的影响

通过对昆明主城区典型合流制排水系统10次降雨径流过程水质水量的监测,计算次降雨污染负荷,并采用M(V)曲线和初期冲刷系数方法对降雨特征与次降雨污染、初期冲刷负荷之间的关系进行探讨。结果表明:10次降雨事件径流污染负荷存在明显的差异,SS、COD、TP和TN的次降雨污染负荷平均值分别为21.5 kg/hm~2、16.9 kg/hm~2、0.3 kg/hm~2、3.9 kg/hm~2;降雨量、最大雨强和平均雨强与次降雨污染负荷及初期冲刷强度呈显著正相关,是影响其初期冲刷的重要水文参数;降雨历时和前期晴天与次降雨污染负荷和初期冲刷强度没有相关性,二者对次降雨污染负荷和初期冲刷无明显影响。     

百年一遇大旱后城区降雨径流中重金属与悬浮物相关性分析

为了探讨2009年入秋至2010年春,西南百年一遇大旱后城市降雨径流中重金属污染特性,对昆明市交通干道路面及路旁一处混凝土屋面的3次降雨径流进行了监测,研究了城区降雨径流中Cu、Zn、Cd、Pb、Fe、Mn、Cr的变化过程,分析了不同重金属之间、重金属与悬浮物(SS)之间的相关性。3次降雨径流中重金属质量浓度随降雨呈不同程度下降。大旱后的首次降雨径流重金属污染最严重,次日的降雨径流重金属污染最轻。降雨径流中各重金属均与SS在含量上明显相关,径流中的重金属主要以吸附在SS上的不溶态存在。屋面与路面径流中的大多数重金属具有良好的同源性。     

内河涌氨氮污染的特征及其来源的研究

以佛山市雅瑶涌为研究实例,探讨了珠江三角洲河网地区围内河涌氨氮污染的特征,并对氨氮的来源进行了研究。内河涌的氨氮污染逐年严重,并呈现季节性的变化,已成为最主要的污染因子。研究结果表明,畜牧水产业和非点源都是比较显著的氨氮污染源,但未经集中式污水处理厂处理的生活污水,则成为内河涌最重要的氨氮污染源。     

北京市城市非点源污染特征的研究

通过监测降雨径流水质,研究了北京市城市非点源污染的特征。结果表明,北京市城市地表径流水排入任何地表水体都会对其造成污染,且城市地表径流水的大部分水质指标已经达到了污水综合排放的三级标准,因此,我们对待城市地表径流水应该如对待污水一样处理。对于TN、TP、CODCr、BOD5浓度,路面径流要高于屋顶径流,而对于SS浓度,屋顶径流高于路面径流。总磷TP颗粒吸附态的污染物对总污染物的贡献最大,对于路面径流高达83.1%,对于屋顶径流为68.6%,其次是CODCr,总氮TN的颗粒吸附态的贡献较低。通过沉积或过滤去除城市地表径流中的悬浮颗粒物,可以提高城市地表径流的水质。所有污染物随降雨过程变化的总体趋势为雨水初期径流污染物浓度很高,随降雨历时的延长,污染物浓度逐渐下降并趋于稳定。初期径流危害较大。     

滇池流域城市降雨径流污染负荷定量化研究

借助遥感影像处理软件ERDAS IMAGINE9．2和GIS技术对研究区QuickBird影像图进行下垫面类型分类和统计,在对降雨产流过程及地表径流污染特征研究的基础上,根据2008年的降雨总量,定量计算滇池流域全年城市降雨径流污染负荷。结果表明,流域内城镇区域屋顶、庭院、道路、绿地及其他类型面积比例分别为13．8％、11．6％、5．2％、3．8％及65．6％。2008年滇池流域城市降雨径流污染负荷COD、TN、TP分别为2．95×10^4t、1．24×10^3t、103t。滇池北岸昆明主城区内的建成区全年城市降雨径流污染负荷COD、TN、TP的产生量分别为2．39×10^4t、9．89×10^3t、8．24×10^3t,对滇池流域COD、TN、TP的贡献率总和分别为81．2％、79．5％、80．3％。     

滇池东南岸农业和富磷区入湖河流地表径流及污染特征

应用聚类分析与因子分析方法,通过8次常规监测,对滇池东南岸10条以农业面源和受磷矿开采区影响的入湖河流的地表径流及其水质污染特征进行了分析,并探讨了其空间差异性。在南岸选取降雨过程相同的3条河流,开展暴雨径流监测,探讨污染物在降雨过程中的流失特征。结果表明,新宝象河的平均流量为2.6 m3/s,占总入湖流量的26.5%;总氮、总磷、化学需氧量、悬浮物是滇池的主要污染指标,许多河流均已严重超标。河流水质在空间上可分为3类,具有明显的空间差异性。总氮、总磷、溶解磷、硝态氮对水质污染的贡献率达到了53.636%,氮、磷含量是河流水质污染的主要贡献因子。降雨条件下化学需氧量、悬浮物浓度增长迅速,流量、悬浮物与大多数水质指标均有相关性,磷矿开采对河流水质的影响在降雨条件下更加明显,其悬浮物浓度在降雨条件下比只受农业面源影响的河流最高高出1.9倍。     

Evaluating Phosphorus Loss for Watershed Management: Integrating a Weighting Scheme of Watershed Heterogeneity into Export Coefficient Model

The identification of critical source areas (CSAs) and critical source periods (CSPs) are essential prerequisites for cost-effective practices of non-point source (NPS) pollution control. A simple empirical tool combining Export Coefficient Model (ECM) and a Geographic Information Systems (GIS)-based weighting scheme of watershed heterogeneity was proposed to estimate annual and monthly phosphorus loss, to identify critical source areas and periods, and to assess pollution control practices. The GIS-based weighting scheme was developed to represent the transport potential of runoff to move phosphorus from the land surfaces to waters, as a supplement to the source-based ECM. The empirical tool was applied to the Dianchi Lake watershed of China. The results showed that the total phosphorus loss from NPS in 2008 was 352.3 tons. The agricultural land was recognized as the largest and the most spatially various source type. The lakeside plain and the terraces of the watershed were identified as CSAs, which generated more than 90 % of non-point phosphorus. The early part of wet season (from May to August) was the CSPs, when about 70 % of non-point phosphorus was lost. The reduction of phosphorus fertilizers and the vegetated buffer strips (VBS) were effective in controlling phosphorus loss from NPS in the watershed. A reduction of 20 % in phosphorus fertilizer application combined with the set-up of VBS in both riparian area of the main watercourses and the lakeside areas would decrease 25 % of phosphorus loss.     

基于农业面源污染风险区的生态保护红线优化方法分析

将农业面源污染风险区划入生态保护红线中，防范由此导致的饮用水水源富营养化现象，是值得深入探讨的科学问题。以南水北调中线重要水源地丹江口水库流域十堰段为例，基于农业面源污染风险区的识别，通过情景分析探讨生态保护红线优化方法，改善区域生态环境，推动绿色发展。结果表明：将农业面源污染极高风险区划入生态保护红线，区域氮、磷流失削减率可分别达35.9%和26.33%,在一定程度上增强了生态系统连通性，且人口生态压力指数较小(0.23),可统筹生态效益和经济效益的发展。研究结果有望为存在农业面源污染风险的丘陵山区提供一种红线优化新思路。     

Contribution of point sources and non-point sources to nutrient and carbon loads and their influence on the trophic status of the Ganga River at Varanasi,India

To determine the possible contributions of point and non-point sources to carbon and nutrient loading in the Ganga River, we analyzed N, P, and organic carbon (OC) in the atmospheric deposits, surface runoff, and in the river along a 37-km stretch from 2013 to 2015. We also assessed the trophic status of the river as influenced by such sources of nutrient input. Although the river N, P, and productivity showed a declining trend with increasing discharge, runoff DOC and dissolved reactive phosphorus (DRP) increased by 88.05 and 122.7% between the Adpr and Rjht sites, indicating contributions from atmospheric deposition (AD) coupled with land use where agriculture appeared to be the major contributor. Point source input led to increased river concentrations of NO₃ ^?, NH₄ ⁺, DRP, and DOC by 10.5, 115.9, 115.2, and 67.3%, respectively. Increases in N, P, and productivity along the gradient were significantly negatively correlated with river discharge (p < 0.001), while river DOC and dissolved silica showed positive relationships. The results revealed large differences in point and non-point sources of carbon and nutrient input into the Ganga River, although these variations were strongly influenced by the seasonality in surface runoff and river discharge. Despite these variations, N and P concentrations were sufficient to enhance phytoplankton growth along the study stretch. Allochthonous input together with enhanced autotrophy would accelerate heterotrophic growth, degrading the river more rapidly in the near future. This study suggests the need for large-scale inter-regional time series data on the point and non-point source partitioning and associated food web dynamics of this major river system.     

Analysis of spatial–temporal distributions of nitrate-N concentration in Shitoukoumen catchment in northeast China

This article discusses the generation and migration process of nitrate-N pollution in shallow groundwater caused by agricultural nonpoint source pollution in the catchment area of Shitoukoumen Reservoir in northeast China. By monitoring the shallow groundwater nitrate-N in the low-water period, the normal season, and high-flow period in the study area for a year, it was found that the nitrate-N concentration in the shallow groundwater of this area had a seasonal variation in both spatial and time distribution. In the time distribution, the peak value appeared in July, the high-flow period, and the valley value appeared in April, the low-water period, and showed a significant correlation with the time distribution of fertilization rate and rainfall. In the spatial distribution of nitrate-N pollution, when the distribution in shallow groundwater was analyzed separately in the three different periods (low-water period, the normal season, and high-flow period) and the discipline transference and enrichment of nitrate-N pollution in shallow groundwater was determined, this indicated that the region in the southeast study area where runoff conditions were better was less contaminated, and the region where runoff conditions were poor, as well as the region along the river were seriously polluted. The nitrate-N concentration in shallow groundwater was distributed mainly along the path of groundwater flow and was excreted in the drainage region. This showed that the spatial distribution of nitrate-N concentration in the shallow groundwater of the entire region was mainly controlled by the groundwater flow system. At the same time, in the middle and lower reaches of the study area, the seasonal changes in the recharged–excreted relationship between groundwater and river caused seasonal differences in the spatial distribution of nitrate-N pollution in groundwater. The combined effects of the groundwater mobility and the surface river resulted in a poor correlation between the groundwater nitrate-N concentration and land-use types. Only in the plain area where there was little influence from groundwater runoff and the surface river did the groundwater nitrate-N concentration correlate with land-use types. The spatial and time distribution of nitrate-N concentration in the shallow groundwater of the study area was impacted by agricultural nonpoint source pollution, the groundwater flow system, and the surface river and formed a concentration response system which uses basins as a unit.     

Estimating nutrient loadings using chemical mass balance approach

The river Hindon is one of the important tributaries of river Yamuna in western Uttar Pradesh (India) and carries pollution loads from various municipal and industrial units and surrounding agricultural areas. The main sources of pollution in the river include municipal wastes from Saharanpur, Muzaffarnagar and Ghaziabad urban areas and industrial effluents of sugar, pulp and paper, distilleries and other miscellaneous industries through tributaries as well as direct inputs. In this paper, chemical mass balance approach has been used to assess the contribution from non-point sources of pollution to the river. The river system has been divided into three stretches depending on the land use pattern. The contribution of point sources in the upper and lower stretches are 95 and 81% respectively of the total flow of the river while there is no point source input in the middle stretch. Mass balance calculations indicate that contribution of nitrate and phosphate from non-point sources amounts to 15.5 and 6.9% in the upper stretch and 13.1 and 16.6% in the lower stretch respectively. Observed differences in the load along the river may be attributed to uncharacterized sources of pollution due to agricultural activities, remobilization from or entrainment of contaminated bottom sediments, ground water contribution or a combination of these sources.