太湖地区“禁磷”措施的效果及在富营养化控制中的作用

通过太湖地区采取“禁磷”措施前后城市生活污水、主要入湖河水和湖体水域中磷浓度与富营养化指数变化的分析，证实“禁磷”措施对降低居民生活污水中磷浓度的作用较为明显，降幅为24％左右，但对入湖河道和湖体水域中磷浓度与水体富营养化的影响则不明显。表明太湖富营养化的改善，除了实施“禁磷”措施外，尚需结合域内其它污染治理措施，进行综合治理，才能取得较好的效果。     

太湖表层沉积物中多磷酸盐检出的环境意义

在氮限制型富营养化湖泊中,沉积多磷酸盐(Poly-P)可以用来示踪营养盐磷的输入和湖泊的富营养化过程。太湖是一个典型的磷限制型富营养化湖泊,通过对太湖表层沉积物中总有机碳(TOC)、总氮(TN)、总磷(TP)、氢氧化钠可提取磷(NaOH-P)以及Poly-P的提取分析,初步探讨了太湖营养盐磷输入的历史记录,并揭示了该湖Poly-P的主要来源和保存机制。太湖Poly-P的质量浓度较低,变化范围为0.004~0.065mg·g-1。NaOH-P占TP组成的22%,是太湖沉积物总磷的主要组成部分之一。结果显示,在湖泊水体藻类生物量较大和NaOH-P是沉积物TP重要组成部分的磷限制型湖泊中,Poly-P也是沉积物磷汇组成的一个重要部分,同时还是一个可以反映由人为磷输入增大导致湖泊富营养化程度加剧的敏感指标。     

洞庭湖不同形态氮、磷和叶绿素a浓度的时空分布特征

洞庭湖水体主要污染物为氮和磷,而有关洞庭湖营养盐赋存形态与叶绿素a的关系鲜有报道。为研究洞庭湖氮与磷的时空分布特征及其对叶绿素a(Chl-a)的影响,2017年在洞庭湖湖体、出湖口及8条入湖河流共20个断面采集了水样,分析了水体中不同形态氮、磷和Chl-a的质量浓度。结果表明,洞庭湖水体中总氮(TN)、溶解态总氮(DTN)、氨氮(NH_4~+-N)、硝酸盐氮(NO_3~--N)质量浓度年均值分别为1.83、1.69、0.26、1.27 mg·L~(-1),总磷(TP)、溶解态总磷(DTP)、磷酸盐(DPO)、颗粒态磷(PP)质量浓度年均值分别为0.081、0.059、0.049、0.022 mg·L~(-1),Chl-a质量浓度平均值为4.84μg·L~(-1)。空间分布上,各形态氮和磷的质量浓度总体表现为:入湖口出湖口湖体,其中,区间入湖口水体中ρ(TN)、ρ(NH_4~+-N)、ρ(TP)、ρ(PP)最高,而ρ(NO_3~--N)、ρ(DTP)、ρ(DPO)在松滋口最高。ρ(Chl-a)表现为区间湖体出湖口松滋口四水。时间分布上,各形态氮与磷的质量浓度具有明显的季节变化特征,均表现为枯水期平水期丰水期;ρ(Chl-a)总体上呈现丰水期平水期枯水期的趋势。可见入湖河流对洞庭湖氮与磷的时空分布起了至关重要的作用,入湖污染负荷和人类活动(包括采沙和生产生活)是洞庭湖氮与磷空间分布的重要影响因素,而入湖水量可在一定程度上解释洞庭湖氮与磷的时间分布。总体而言,洞庭湖未出现明显的富营养化现象,这可能得益于其独特的水文条件(水循环周期短,流速较快),但流速较低的六门闸和大小西湖断面ρ(Chl-a)较高,夏季水华频发,应引起高度重视。     

网围养殖对骆马湖水体富营养化的影响

对骆马湖1998年度湖体内网围养殖入湖营养盐的研究结果表明,入湖氮、磷量分别为339 和57 t,分别占湖体滞留氮、磷总量的27%和33%,说明网围养殖对湖体水质有一定影响.另外,从近10 a湖区网围养殖面积和产量变化状况分析,1995年以来网围养殖面积趋于稳定,因此网围养殖对湖体水质富营养化的影响也趋于稳定.     

网围养殖对骆马湖水体富营养化的影响

对骆马湖 1 998年度湖体内网围养殖入湖营养盐的研究结果表明 ,入湖氮、磷量分别为 3 3 9和 5 7t,分别占湖体滞留氮、磷总量的 2 7%和 3 3 % ,说明网围养殖对湖体水质有一定影响。另外 ,从近 1 0a湖区网围养殖面积和产量变化状况分析 ,1 995年以来网围养殖面积趋于稳定 ,因此网围养殖对湖体水质富营养化的影响也趋于稳定。     

网围养殖对骆马湖水体富营养化的影响

对骆马湖1998年度湖体内围养殖入湖营养盐的研究结果表明，入湖氮、磷量分别为339和57t，分别占湖体滞留氮、磷总量的27％和33％，说明网围养殖对湖体水质有一定影响。另外，从近10a湖区网围养殖面积和产量变化状况分析，1995年以来围网养殖面积趋于稳定，因此网围养殖对湖体水质富营养化的影响也趋于稳定。     

基于AQUATOX模型的乌梁素海富营养化模拟及控制研究

乌梁素海是典型的寒旱区湖泊,具有多草、多藻、水浅及寒区气候等特征。针对乌梁素海湖泊富营养化及水华问题,运用AQUATOX模型对乌梁素海湖区主要营养盐的季节变化以及不同藻类(蓝藻、绿藻、硅藻)的演替规律进行了模拟。模拟结果显示,该研究建立的乌梁素海生态系统模型,能较好的模拟水域中营养盐的动态变化以及不同藻类(蓝藻、绿藻、硅藻)的季节演替规律,与乌梁素海实际水环境状况大体吻合。在模拟的基础上,对控制入湖氮、磷污染负荷后营养盐浓度及藻类生物量的响应关系进行探讨。结果显示,在氮磷与营养物响应关系方面,消减入湖氮磷负荷能有效降低乌梁素海湖区相应营养物浓度,且随消减量的加大,其浓度也不断降低。同时消减入湖N,P负荷的20%,营养物TN,TP浓度分别降低17.06%和4.83%;消减入湖N,P负荷的30%,营养物TN,TP浓度分别降低25.62%和6.92%;消减入湖N,P负荷的50%,营养物TN,TP浓度分别降低42.58%和11.56%。在氮磷与藻类响应关系方面,消减氮磷负荷可以有效降低湖内绿藻、蓝藻生物量。同时消减入湖N,P负荷的20%、30%和50%,绿藻及蓝藻生物量平均降幅分别为7.77%、12.60%、19.54%和6.05%、10.60%、13.12%。单一控制磷负荷亦能有效降低绿藻和蓝藻生物量,且作用更加明显,而单一控制氮反而会使蓝藻的生物量增加。因此,在乌梁素海富营养化的治理中,需采取同时消减入湖氮磷负荷或者单一消减磷负荷的措施,以实现抑制水华暴发、改善区域生态的目的。     

规模化控养水葫芦改善滇池外海水质效果研究

2011─2013年,在滇池外海水域实施了规模化控养水葫芦(Eichhornia crassipes)修复富营养化湖泊水体的试验性工程,以及外海湖体与控养区内、外水质的变化分析,探讨规模化控养水葫芦对外海水质的改善效果。结果表明:水葫芦在滇池外海控养水域生长迅速,安全可控,并未出现逃逸泛滥现象。2012和2013年水葫芦最大覆盖面积分别为556.39和257.87hm2,通过对其采收共计从外海水体带走氮691.89 t,磷57.40 t;工程实施期间,外海全湖水质得到一定的改善,水体TP平均浓度始终维持在较低水平,水体TN平均浓度显著下降,尤其2012年,外海湖体TN平均质量浓度由2011年的2.80 mg·L-1降至1.96 mg·L-1,同比下降了30.00%;水葫芦控养区内水体TN和TP平均浓度较控养区外分别下降了28.52%和13.04%。综上分析,规模化控养水葫芦可削减水体大量的氮、磷,尽管水葫芦覆盖面积不足外海水域面积的2.00%,但仍对外海全湖水质改善具有一定效果。该研究为规模化控养水葫芦应用于大型富营养化湖泊的治理提供了工程实践与理论依据。     

河湖相连水系水体污染控制技术与策略

太湖流域上游洮滆水系河湖相连,湖荡密布。以滆湖—太滆运河、漕桥河—太湖为例,分析了该区域水系特征、生态状况、水环境污染现状及存在的主要问题,提出河湖相连水系污染治理控制方案。通过加强流域污染物的源头控制,削减污染负荷,控污与生态修复相结合,提高湖荡自然净化能力和生态系统自我修复能力,拦截并净化水系入湖污染物,为入湖河流提供清洁水源;对入湖河流集水区各类污染源进行治理,拦截和控制沿线污染物进入入湖河流,建成清水河道,提高入湖河流自然净化能力,使污染物在入湖河流运移过程中进一步削减,在河湖连结处等关键水域构建强化净化污染物的生态拦截工程,再次削减入湖污染物,并对水系各污染控制要素进行系统调控和优化配置,在上游地区构建太湖污染防控和水环境保护的安全屏护体系。     

滇池湖泊生态系统水动力学模拟

针对滇池富营养化状况,依据滇池生态环境建设规划,在以前研究工作的基础上,综合考虑湿地植物、底泥、水域条件变化,根据水动力学原理建立了垂向平均的二维生态系统水动力学模型,模拟了滇池现有水体、拆除防浪堤水域扩展后水体和滇池湿地建成后总水体3种情况的流场和浓度场,以及入滇河道达标排放后的水质浓度场,分析了湿地植物和水域扩展对滇池流场的影响,以及水域扩展、湿地植物、入湖河流达标排放对滇池水质的影响。结果表明,水域拓展对滇池流场的影响是局部的,湿地植物对滇池流场的影响主要发生在湿地区域。水域拓展对滇池水质具有一定影响,TN、TP平均质量浓度由2.08和0.19 mg.L-1(现有水体)降至1.69和0.16 mg.L-1;人工湿地建设对滇池水体TN、TP浓度影响较大,湿地建成后平均质量浓度降至0.76和0.05 mg.L-1,基本达到GB 3838—2002《地表水环境质量标准》Ⅲ类水质标准;人工湿地建设完成并且入滇池河流达标排放后,TN、TP平均质量浓度分别降至0.17和0.01 mg.L-1,水质明显改善。因此,人工湿地建设及入滇池河流的达标排放对滇池生态环境的改善具有重要作用。     

Assessment on contaminations in sediments of an intake and the inflow canals in Taihu Lake,China

As the second largest freshwater lake in China, Taihu Lake provides water supply to approximately 32 million inhabitants around the lake. However, dramatically increased pollution has threatened the safety of drinking water supply in recent years. In the present study, we investigated the contaminations of nutrients and heavy metals in the sediments of an intake and inflow canals in Gonghu Bay, Taihu Lake. Moreover, we also examined the impact of human activities on spatial distribution characteristics of contaminations. Our results showed that the intake presented relatively lower concentrations of phosphorus and nitrogen compared with inflow canals. However, the concentrations of Cr, Ni, Cu, Zn and Pb in the sediments of the intake exceeded the lowest effect level (LEL) values, indicating a potential risk to drinking water resource. In addition, the concentrations of Ni in the sediments of Tianji Canal and Jinshu Canal exceeded the severe effect level (SEL) value. More importantly, the concentrations of Cu in the sediments of Tianji Canal exceeded three times of the SEL value. Multivariate statistical analysis confirmed that the domestic sewage primarily contributed to the nutrient accumulation, and the leakage of electronic trash dominated the enrichment of metals in the sediments. Taken together, more effort should be made to ensure the security of water resources in Taihu Lake, especially for the treatment of domestic sewage and industrial wastewater.     

Assessment of nutrients and heavy metals enrichment in surface sediments from Taihu Lake,a eutrophic shallow lake in China

Concentrations of the nutrients (TN and TP), phosphorus fractions and heavy metals (Co, Cr, Cu, Mn, Ni, Pb, Sr, Ti, V, Zn and Hg) in 40 surface sediment samples collected from Taihu Lake, a eutrophic shallow lake in China, were determined. The results showed that the northwest region of the lake possessed higher concentrations of TN and TP, as well as the similar spatial distribution trend in the water column. This should be related to excessive anthropogenic input from industrial effluents and domestic sewage in surrounding areas. Similarly, the concentrations of P fractions exhibited significant regularity. In addition, except for Sr showing low concentration, the rest of the heavy metals in the surface sediments had two- to four-folds of magnitude of the concentrations compared with the reference values in earth's crust. In the past decade, concentrations of heavy metals had undergone different levels of variations. Principal component analysis (PCA) and enrichment factors (EFs) of the compositional data aiming at heavy metals showed that Taihu Lake was slightly exposing to heavy metal contamination except Sr. High concentrations of heavy metals were ascribed to the discharge of untreated and partially treated industrial waste water via rivers. Co, Cr, Cu, Mn, Ni, Pb, V and Zn positively correlated with each other (R = 0.78–0.92), that indicated they had analogous sources and/or kindred geochemistry characteristics. Differing from nutrients, randomness in the space indicated that heavy metals had a complex distribution.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

太湖重污染湖区和水源地水质概况及藻毒素污染环境风险

近年来太湖流域局部水质状况有所改善,但太湖藻型生境条件还未根本改变,水污染防治任务依然艰巨。确保太湖湖体水质稳定达标,尤其是加强对太湖重点湖区和水源地重点污染物的调查研究十分重要。在此背景下,本文调研了太湖重点湖区和水源地水质概况、藻毒素污染时空分布特征、环境影响因子和迁移转化规律,并总结了藻毒素的环境和健康风险研究的最新进展,指出了太湖西部湖区和饮用水源地的主要环境风险,以及未来太湖藻毒素污染相关研究需解决的关键技术问题,以期为促进太湖流域重点污染物的控制和治理,确保太湖饮用水源地安全提供有益借鉴。     

Eutrophication status and control strategy of Taihu Lake

The water quality and eutrophication status of Taihu Lake in recent years are presented and the pollution trends are analyzed. It is shown that because of unreasonable industrial structures, pollution discharge per GDP is high within the Taihu basin, and the pollution discharge from point and non-point sources exceed the basin’s environmental carrying capacity. Especially, excessive pollutants containing nitrogen and phosphorus are being discharged. Moreover, eutrophication may also result from internal pollution sources such as the release of nutrient elements from sediment. All these factors have resulted in the water quality deterioration of Taihu Lake. To solve this environmental problem, possible control strategies are summarized, including the control of internal pollution sources and inflow-river pollution, ecological restoration and reconstruction of the degraded lakeside zone ecosystem, clean water diversion, dredging, and manual algae removal.     

Control concept and countermeasures for shallow lakes’ eutrophication in China

Research on lake eutrophication in China began in the early 1970s, and many lakes in China are now known to be in meso-eutrophic status. Lake eutrophication has been showing a rapidly increasing trend since 2000. Investigations show that the main reasons for lake eutrophication include a fragile lake background environment, excessive nutrient loading into lakes, excessive human activities, ecological degeneration, weak environmental protection awareness, and lax lake management. Major mechanisms resulting from lake eutrophication include nutrient recycling imbalance, major changes in water chemistry (pH, oxygen, and carbon), lake ecosystem imbalance, and algal prevalence in lakes. Some concepts for controlling eutrophication should be persistently proposed, including lake catchment control, combination of pollutant source control with ecological restoration, protection of three important aspects (terrestrial ecology, lake coast zone, and submerged plant), and combination of lake management with regulation. Measures to control lake eutrophication should include pollution source control (i.e., optimize industrial structural adjustments in the lake catchment, reduce nitrogen and phosphorus emission amounts, and control endogenous pollution) and lake ecological restoration (i.e. establish a zone-lake buffer region and lakeside zone, protect regional vegetation, utilize hydrophytes in renovation technology); countermeasures for lake management should include implementing water quality management, identifying environmental and lake water goals, legislating and formulating laws and regulations to protect lakes, strengthening publicity and the education of people, increasing public awareness through participation in systems and mechanic innovations, establishing lake region management institutions, and ensuring implementation of governance and management measures.     

无锡市水环境变化及其引发的地质灾害问题与对策

随着经济的快速发展，无锡市水环境质量发生了很大的变化。由于在太湖、五里湖等湖泊周围大规模围湖造田，水域生态环境和原有水系格局被破坏，五里湖、梅梁湖水质恶化，每10年下降一个等级，太湖水质总体上呈现富营养化状态。水环境变化在一定程度上导致了地面沉降的发生，有的地区地下水位呈加速下降的趋势，有的地区已出现多个地下水位降落漏斗，后者面积达220km^2。应该加快区域供水建设步伐，调整工业结构，加强对水资源的管理和调控。     

太湖入湖河流中精神活性物质污染特征与生态风险

精神活性物质是一类摄入人体后对中枢神经系统具有强烈兴奋或抑制作用的新型污染物,其在水环境中的存在可能对水生生物、水生态系统甚至人体健康产生潜在的危害。为评价太湖中精神活性物质的污染水平和生态风险,利用超高效液相色谱-质谱联用法检测了太湖19条入湖河流中13种典型精神活性物质的质量浓度和空间分布规律。结果表明,在太湖19条入湖河流中除苯甲酰牙子碱(BE)和去甲氯胺酮(NK)外,其余11种目标物均有检出,质量浓度范围为n.d.～43.2 ng·L~(-1)。其中麻黄碱(EPH)的检出率和中间浓度最高,分别为100%和11.0 ng·L~(-1);其次为甲基苯丙胺(METH),检出频率为58%,浓度中值为1.0 ng·L~(-1);苯丙胺(AMP)在东部湖区均未检出。大部分精神活性物质浓度水平较高的河流分布在竺山湾和西太湖,而海洛因(HR)的高值区主要在南太湖。运用风险熵方法对其进行风险评估,结果显示,太湖流域地表水中检出的13种精神活性物质的风险熵值均<0.1,生态风险较低,但其对水生生态系统的长期和综合风险值得关注。     

Organophosphate esters in sediment from Taihu Lake,China: Bridging the gap between riverine sources and lake sinks

• Eleven OPEs were detected in river sediment and lake sediment in Taihu Lake. • TnBP dominated in river sediment, while TBEP dominated in lake sediment. • A strong correlation existed between logK_oc and logK_ow of OPEs. • Vertical profiles of OPEs in sediment cores varied according to sampling location. Surface sediment samples from Taihu Lake in China and its inflow rivers, along with two lake sediment core samples, were collected and analyzed for organophosphate esters (OPEs). The concentrations of total OPEs varied from 28.60 ng/g to 158.72 ng/g (median: 54.25 ng/g) in river surface sediment and from 62.57 ng/g to 326.84 ng/g (median: 86.37 ng/g) in lake sediment. Tributyl phosphate (TnBP) was the predominant compound in river surface sediment, and tris(2-butoxyethyl) phosphate was predominant in lake sediment. High contamination occurred in the north-west region, which was related to the high level of urbanization and high usage of OPEs. The sediment–water partition coefficients of OPEs (logK_oc) were calculated, showing a significant correlation with logK_ow (p<0.05). The concentration and composition of OPEs in two sediment cores varied due to the different sampling locations, with more OPE species found in the northern region than in the southern one. Principal component analysis and positive matrix factorization indicated that sewage discharges, vehicle emissions, and atmospheric deposition were the possible sources of OPEs in Taihu Lake sediments. Tris(1-chloro-2-propyl) phosphate, tris(2-chloroethyl) phosphate, and TnBP were the main OPEs causing ecological risks.