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1.
Sampling was conducted at three site groups, group E (in East Taihu Bay), G (in Gonghu Bay) and M (in Meiliang Bay) in Lake Taihu. TN and TP concentrations among site groups was in the increasing order of E < G < M. TP level at G sites is at the critical threshold for loss of submersed macrophytes. Mean values of DO and Transparence showed different trend, i.e., E > G > M. The mean phytoplankton fresh-weight biomass at M sites was 5.81 mg/l, higher than that at E sites (4.96 mg/l) and G sites (5.18 mg/l). Mean zooplankton fresh-weight biomass was in the decreasing order of M (6.4 mg/l) > G (4.9 mg/l) > E (2.7 mg/l). However, Rotifera density was in the sequence of E > G > M. Both zooplankton biomass and phytoplankton biomass increased with the rise of TN and TP concentrations. Relationships between zooplankton biomass and phytoplankton biomass showed that zooplankton played a limited role in the control of algae in eutrophic lakes. Nutrient availability is much more important than zooplankton grazing pressure in controlling phytoplankton growth in lakes. For most sites in Lake Taihu, reduction of nutrient loading, as well as macrophyte conservation, zappears to be especially important in maintaining high water quality and regulating lake biological structure, but for M sites, it’s urgent to control nutrient inputs rather than to restore macrophyte community.  相似文献   

2.
利用2001—2020年滇池水质监测数据,研究其水环境时空变化特征。结果表明:滇池水质呈现波动变化趋势,TN波动区间较大,呈现雨季<旱季的变化趋势。TP年内变化3—6月呈现增长趋势,空间上滇池北部TP值整体较高。Chl-a年内变化7—10月出现明显提升。IMn波动区间较大,空间上滇池南部呈现IMn值较高且稳定。NH3-N年内2—11月整体呈现连续下降趋势。2001—2020年滇池TLI整体呈现波动降低;空间上呈现由北向南递减,草海区域富营养化较为严重。滇池Chl-a与TN、TP、水温、pH值及降雨量呈现正相关性,与水位和透明度呈现负相关性。  相似文献   

3.
梅梁湖水体浮游植物与环境因子的关系   总被引:3,自引:1,他引:2  
根据2008年的4月—11月梅梁湖水域应急监测数据,探讨了梅梁湖水体浮游植物与环境因子的关系。相关性分析结果表明,蓝绿藻含量与TP、pH值和DO呈极显著正相关;与TN、SD和EC呈极显著负相关;与NH3-N呈显著负相关。多元逐步回归分析结果表明,梅梁湖浮游植物生长受多个环境因子的共同影响,但主要为TP、TN、水温和风速。  相似文献   

4.
2013年6月至2014年5月逐月对洞庭湖水体叶绿素a质量浓度和主要环境因子进行测定,分析洞庭湖水体叶绿素a质量浓度的时空分布特征,探讨洞庭湖水体叶绿素a质量浓度与环境因子的相关性。结果表明,洞庭湖水体叶绿素a质量浓度为0.11~8.62 mg/m~3,年均值为(1.89±1.23)mg/m~3,属贫营养;叶绿素a质量浓度随季节变化明显,总体呈现夏、秋季明显大于冬、春季的规律;在空间上,总体表现为西洞庭湖和东洞庭湖明显大于南洞庭湖。全湖叶绿素a质量浓度与水温、电导率、COD和TP呈极显著正相关,与DO、NH3-N、TN和TN/TP呈极显著负相关,与NO-3-N呈显著负相关,与p H和透明度无显著相关性。全湖TN/TP的年均值为28.5,磷可能是洞庭湖水体浮游植物生长的限制性营养盐。  相似文献   

5.
太湖氮磷大气干湿沉降时空特征   总被引:6,自引:0,他引:6  
为了探索太湖氮磷营养盐干湿沉降特征及对太湖营养盐输入的贡献,于2011年不同季节采集太湖不同位点的大气干湿沉降样品,分析干湿沉降中氮(N)和磷(P)的形态和沉降量。研究结果表明,输入太湖的磷以干沉降为主,而氮以湿沉降为主。在太湖干沉降中总无机氮(TIN)占总氮(TN)的77.1%,溶解性磷(DIP)占总磷(TP)的77.9%。干沉降中TIN主要以NH+4-N为主。西太湖是TN与TP通过大气干湿沉降输入太湖的最高湖区。太湖全年大气TN沉降总量为20 978 t,TP沉降总量为1 268 t,因此,氮磷大气干湿沉降是太湖营养盐输入的重要来源之一。  相似文献   

6.
生态工程治理玄武湖水污染效果的监测与评价   总被引:10,自引:1,他引:9  
选取总磷、总氮、叶绿素a、浮游生物、浮游植物等多项环境监测指标,对利用生态工程治疗玄武湖水环境污染的效果进行了环境监测与评价。指出生态工程治理玄武湖水环境污染效果显著,经过治理使湖水中生物多样性大大增加,浮游植物大幅减少,湖水透明度增加,总磷、总氮等主要指标大幅下降,生态工程区中的水环境已从高度富营养化降到中度富营养化。  相似文献   

7.
针对太湖湖滨带,均匀布设49个点位,分别于2009年12月、2010年4、8月开展浮游植物及水质监测。结果显示,湖滨带浮游植物群落多样性整体较低,优势种从枯水期到丰水期呈"鱼腥藻-鱼腥藻-微囊藻"的演变趋势;西北部湖区(竺山湖、梅梁湾、西部沿岸)浮游植物密度明显高于东南部湖区(东部沿岸、东太湖、南部沿岸);湖滨带浮游植物群落结构与湖体相似,密度比湖体高1个数量级;RDA排序筛选出在显著水平上解释浮游植物分布的最小变量组合为TN、CODMn、SS、p H、SD,且方差分解指出TN是相对最重要的变量;当物种适合度为50%~100%时,与TN具有较好梯度响应关系的是四尾栅藻及弓型藻,并且这2个种与TN、TP及综合营养状态指数的组合变量也有较好的梯度响应关系,具备指示太湖湖滨带富营养化的可能,但定量指示意义尚待进一步研究。  相似文献   

8.
根据2021年5月—2022年4月合溪新港河流水量、水质(TN和TP)的同步监测数据,利用通量模型核算了合溪新港污染物(TN和TP)通量。通过测算合溪新港TN、TP通量与断面降雨强度、水质的响应关系,分析了该区域的污染类型及特点,为后期水质污染调查及通量研究提供了新思路。结果表明:合溪新港流量与降雨量存在明显相关关系,在强降雨期(7—8月)水体流量最高,占监测周期总流量的57.77%;少雨期则流量最低,且会出现湖水倒灌现象(11—12月)。通过分析合溪新港TN、TP通量与流量、水质的相关关系,确定了该流域污染类型为点源污染及农业面源污染共存的混合型污染,且在高强度降雨时污染物负荷量较大。综上,可针对农业面源污染对该流域治理提出相关对策,建立农业面源污染防治体系,以有效降低TN和TP污染物的入湖通量,减少太湖TN和TP污染物负荷量。  相似文献   

9.
太湖浮游植物种类组成时空变化规律   总被引:2,自引:0,他引:2  
从2010年的3月到2010年的10月,通过春、夏、秋3个季度的采样,对太湖东部的浮游植物种类组成及其变化进行渊查,发现浮游植物92属279种,太湖东部五个采样点位的浮游植物常见种季节变化明显,而各湖区浮游植物种类绀成空间变化较小。通过对太湖东部浮游植物种类组成的时空变化的规律的初步探索,为预警监测及水环境保护提供技术支持。  相似文献   

10.
应用3S技术研究了太湖底质与水质总磷(TP)的分布情况,并结合水华频次分析了其相关性。结果表明:2016—2018年,太湖底质TP年均值在433~537 mg/kg波动,水质TP年均值从0.064 mg/L上升至0.087 mg/L。从空间分布来看,底质TP、水质TP和水华频次均呈现“西高东低”的规律,太湖西部区尤其是竺山湖区是需要开展治理的重点区域。3年间,太湖西部区水质TP上升,而底质TP与入湖河流TP下降,说明内源磷污染是太湖西部区水质TP升高的主要原因,须加强科学清淤。  相似文献   

11.
Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.  相似文献   

12.
对2015年以来的太湖TP变化趋势进行了研究,并对TP升高原因进行了分析。结果表明,2015—2019年,太湖TP浓度由0.059 mg/L上升至0.079 mg/L,涨幅34.1%。太湖TP上升的原因可能为:随入湖大量泥沙的带入而累积;东太湖水生植被覆盖面积急剧下降,使得湖体TP上升幅度明显;太湖磷营养盐浓度影响着蓝藻水华爆发的强度,蓝藻水华加快了湖体磷循环;夏季受台风天气影响,风浪较大的条件下造成底泥的再悬浮与释放而影响水质。  相似文献   

13.
2020年春、夏、秋、冬四季对阅海湿地进行水样采集并测定叶绿素a(Chl-a)等10种环境因子,采用相关分析、逐步回归分析和通径分析进行分析评价。结果表明:阅海湿地深水区与浅水区的Chl-a质量浓度季节差异较大,夏、秋季较高,春、冬季较低;空间上深水区高于浅水区。对深水区Chl-a质量浓度影响的环境因子依次为水温、IMn、DO,其中水温为正向直接作用,IMn、DO为负向直接作用;对浅水区Chl-a质量浓度影响的环境因子依次为TP、BOD5,均为正向直接作用。TN对阅海湿地Chl-a质量浓度影响不大,控制水体含磷营养盐类物质及有机物的输入量是防止阅海湿地Chl-a质量浓度过高的主要措施。  相似文献   

14.
Spatial structure analysis and kriging analysis have been identified to be useful tools in illustrating the spatial patterns of variables. Taihu Lake is one of the largest fresh water lakes in China, and has suffered serious eutrophication in recent years due to the rapid economic development and growing environmental pollution in the Taihu Catchment. In this paper, spatial structural analysis, kriging interpolation and eutrophication assessment were carried out for chlorophyll a in the lake. Studies show that spherical model could be applied to fit all experimental variograms. Positive nuggets were observed for three directions except NE–SW direction. The variograms show some anisotropy with anisotropic ratio falling within 1.76. The spatial structural patterns of chlorophyll a in the lake were affected by factors such as distribution of pollution sources, water flow and wind. Two-dimensional ordinary block kriging was applied for interpolation process. An eutrophication assessment map was also made based on a water-quality evaluation standard. Results show that the content of chlorophyll a in Taihu Lake was quite high. The whole lake has suffered serious eutrophication. However, the eutrophic situation varied in space. Higher contents of chlorophyll a appeared mainly in the northern part of the lake.  相似文献   

15.
Two sets of samples from Lake Päijänne and one from Lake Ladoga were used to examine the relations between the meiofauna and environmental variables. The most obvious indicators of an unpolluted environment were, in order of importance, the true meiofauna/total meiofauna ratio, the proportion of Aeolosomatidae, the proportion of Harpacticoida (excluding C. staphylinus), the meiofauna/macrofauna biomass ratio, the proportion of Naididae and the A. crassa + P. schmeili/true meiofauna ratio. Conversely, the clearest indicators of a polluted environment were the proportion of resting stages of Cyclopinae, the Nematoda/non-resting Copepoda ratio, and the proportions of Tubificidae, Oligochaeta, Cladocera, C. staphylinus and benthic Eucyclopinae. Oxygen saturation exercised a highly significant effect on the meiofauna variables, and the next in order of importance were sedimentation, sediment chlorophenols originating from the chlorobleaching of pulp, total phosphorus, COD and phytoplankton biomass. The dependence of the meiofauna on environmental variables was somewhat more pronounced in the deepest areas of Lake Päijänne than in the epiprofundal zone, and largely similar in the two lakes.  相似文献   

16.
对太湖东部水域9个点位浮游硅藻的群落结构进行了分析。结果表明,9个点位的生物多样性处于一般到较丰富状态,其水质为中污染到轻污染;硅藻群落的物种丰富度、生物量和密度因水域地形特点、换水周期等因素出现差异,在半封闭水体中的物种丰富度较差,生物量和藻密度较高,优势种所指示的水质处于中等偏下水平。硅藻相对多度和各项环境因子冗余分析显示,总磷(TP)对群落的组成和分布影响显著。东北部水域浮游硅藻群落主要受到TP和浊度(NTU)的影响,东南部水域浮游硅藻群落受到TP和NTU的影响则很小。  相似文献   

17.
富营养化与温度因素对太湖藻类生长的影响研究   总被引:1,自引:0,他引:1       下载免费PDF全文
为了研究气候变暖和富营养化对湖泊水生态系统的影响,应用阿列纽斯方程修正的Monod生长模型定量研究长期以来太湖藻类生物量与营养元素和温度的关系.研究表明,在近年来的富营养化状况下,年均气温每增加1.0℃,年均藻类生物量增加0.145倍.湖泊富营养化越严重.年平均气温对藻类生长的影响就越大,由此可以定量评估和预测年均气温...  相似文献   

18.
近年来,尽管太湖主要水质指标有所改善,但蓝藻水华暴发的频次和面积并未明显减少.为了探讨太湖蓝藻水华暴发的环境驱动因子,统计了2012—2020年历年4—10月预警期间的太湖蓝藻水华发生规模与频次,结合同步浮标自动监测数据和实验室分析数据,构建了蓝藻水华预测模型.以太湖蓝藻水华综合指数(Ic)表征蓝藻水华强度,并通过Ic...  相似文献   

19.
为探明太阳山湿地浮游植物优势功能群季节演替规律及其主要驱动因子,于2019年4月(春季)、7月(夏季)、10月(秋季)和2020年1月(冬季)采样分析了太阳山湿地浮游植物的种类组成、优势种、丰度、生物量及季节变化,同时测定了水环境理化因子指标,采用冗余分析方法研究了浮游植物优势功能群的优势度、丰度与水环境因子之间的关系。结果表明:太阳山湿地浮游植物可分为22个功能类群;优势功能群的季节演替和空间分异特征明显,存在一定的规律性。春、秋、冬3个季节的浮游植物以硅藻门为主,夏季以绿藻门和蓝藻门为主。春季优势功能群主要为D、C、P,以硅藻门种类为主;夏季优势功能群主要为J、Lo、TC、M、H1,以硅藻门、绿藻门、蓝藻门种类为主;秋季优势功能群主要为D、S1、MP,以硅藻门、绿藻门种类为主;冬季优势功能群主要为D、X3,以硅藻门种类为主。影响太阳山湿地浮游植物优势功能群季节演替的水环境因子有水温(WT)、pH、溶解氧(DO)、透明度(SD)、盐度(Sal)、氮磷营养元素含量、化学需氧量(CODCr)和高锰酸盐指数(CODMn)。4个湖区浮游植物优势功能群的时空差异与水环境因子密切相关,其中,西湖区浮游植物优势功能群的季节演替驱动因子为pH、DO、WT、总磷(TP),东湖区为pH、DO、WT、氮磷营养元素含量,南湖区为pH、DO、CODCr、五日生化需氧量(BOD5),小南湖区为pH、DO、WT、BOD5、CODCr、TP。pH、DO、WT、BOD5、SD等水环境因子的季节差异以及TP、TN、氨氮(NH3-N)、CODMn等水环境因子的湖区差异是太阳山湿地浮游植物优势功能群出现季节演替的主要原因。  相似文献   

20.
Seasonal variations in microcystin concentrations in Lake Taihu, China   总被引:1,自引:0,他引:1  
An enzyme-limited immunosorbent assay (ELISA) was used to monitor spatial and temporal variation of microcystins (MCs) in Lake Taihu. MC concentrations were higher in summer and autumn than in other seasons. Maximal MC concentration was 15.6 mug L(-1). Compared to central Lake Taihu and Wuli Bay, Meiliang Bay had higher MC concentrations due to high biomass of Microcystis.  相似文献   

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