Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China

This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS Data) to monitor water quality parameters including chlorophyll-a, secchi disk depth, total phosphorus and total nitrogen at Chaohu Lake. In this paper, multivariate regression analysis, Back Propagation neural networks (BPs), Radial Basis Function neural networks (RBFs) and Genetic Algorithms-Back Propagation (GA-BP) were applied to investigate the relationships between water quality parameters and the MODIS bands combinations. The study results indicated that a simple, efficient and acceptable model could be established through multivariate regression analysis, but the model precision was relatively low. In comparison, BPs, RBFs and GA-BP were significantly advantageous in terms of sufficient utilization of spectra information and model reliance. The relative errors of BPs, RBFs and GA-BP were below 35%. Based on method comparison, it can be concluded that GA-BP is more suitable for simulation and prediction of water quality parameters by applying genetic algorithm to optimize the weight value of BP network. This study demonstrates that MODIS data can be applied for monitoring some of the water quality parameters of large inland lakes.     

陕北石油勘探开发区地表水污染特征分析

研究了陕北主要石油勘探开发地区地表水的水质状况,分析了pH、矿化度(全盐量)、硬度、六价铬、砷、镉、铅、氨氮、挥发酚、石油类、化学需氧量(COD)、氯化物、硫酸盐、硝酸盐氮、总磷、氰化物、氟化物等17个指标,结果显示砷、镉、铅、氟化物、硝酸盐、氰化物、氟化物的P_i值均小于1;挥发酚、总磷、石油类、氨氮、COD、六价铬、硫酸盐、矿化度、氯化物、硬度均超标。研究区东部和西部地表水呈现出不同的污染特征,通过分析不同区域污染物来源,提出了污染防治对策与建议。     

升钟湖水体叶绿素a动态特征及其影响因子分析

2011—2015年每月监测升钟湖湖区的4个代表性断面的水质指标,分析水体中叶绿素a时空分布特征、变化规律及影响水体水质状况的其他要素。结果表明:升钟湖的叶绿素a具有显著性的年际差异,表现为夏季高、冬季低且呈现逐年下降的变化趋势,总磷、总氮、氨氮、溶解氧、透明度监测指标同样表现为夏季高、冬季低的变化趋势,除溶解氧和透明度的监测指标逐年上升外,其他监测指标均逐年下降。构建了叶绿素a与总氮、总磷的多元回归方程模型,该模型可根据水体营养盐状态准确预测升钟湖叶绿素a的变化趋势。     

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland

A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment’s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations, of 0.6–1.1 and 0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class.     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

官厅水库入库断面水质多指标评价与演变特征分析

水体质量状况及其污染特征是流域水污染防治规划和治理措施制定的前提和基础。采用主成分分析和层次聚类分析等多元统计分析方法,选取溶解氧、高锰酸盐指数、化学需氧量等10个不同类型的监测指标,综合评价了官厅水库入库断面八号桥2006—2017年丰水期和枯水期水质年际变化特征,识别了不同阶段关键污染指标。结果表明:各年份丰水期(9月)大多数水质指标均优于枯水期(5月),特别是粪大肠菌群和氨氮,但总磷和高锰酸盐指数的差异较小。根据水质指标年际变化情况,可将研究期分为污染严重阶段(2006—2007年)、污染改善阶段(2008—2015年)和污染全面好转阶段(2016—2017年)。大部分水质指标呈现逐年好转的趋势,特别是粪大肠菌群、氨氮和五日生化需氧量,但总磷仍是官厅水库入库河流的重要污染指标。     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

浙江省水体富营养化特征及防治对策

为研究浙江省水体富营养化特征,对全省历年省控断面地表水水质、污染源和重点藻华高发河流营养盐通量等数据进行了统计分析。结果表明:全省水体营养盐水平总体仍处于高位,重点流域富营养化问题突出,省控断面富营养、重富营养占比分别为78.7%和43.0%,营养盐已持续成为地表水首要污染超标因子。通过近年来的集中整治,浙江省水体营养盐浓度下降明显。但治理过程中也出现了总氮减排滞后,部分水域总磷浓度反弹等不利现象。特别是相应的藻类生长势能尚未进入下降敏感区间,对藻华防控贡献有限。同时,全省入河营养盐城镇生活源和农业面源占比突出,总氮合计占89.27%,总磷占95.45%。重点流域地表径流对营养盐入河通量贡献明显,汛期其对河流总磷浓度的贡献明显高于固定源排放。建议在营养盐总量控制、跨部门协调机制、水资源配置、农业面源管理、城镇雨污管网建设、雨污处理以及河流生态修复等方面加强应对。     

Linking Land Cover and Water Quality in New York City’S Water Supply Watersheds

The Catskill/Delaware reservoirs supply 90% of New York City’s drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subjected to Agency’s administrative review and approved for publication as an EPA document.     

Multiple Effects of Environmental Factors on Algal Growth and Nutrient Thresholds for Harmful Algal Blooms: Application of Response Surface Methodology

Systematic understanding of the co-effects of environmental factors on phytoplankton proliferation can enable more effective control of harmful algal blooms in eutrophic lakes and reservoirs. A batch of statistically designed experiments using response surface methodology was recently conducted on mixed algae samples collected from Changtan Reservoir. The central composite designed response surface model was established to evaluate multiple effects of various physical and chemical factors (total nitrogen, total phosphorus, temperature, and light intensity) on algal density and chlorophyll a content. Analysis of variance indicated an excellent correlation between modeling results and experimental responses. Among the selected environmental variables, promotion of the interactive effects of nitrogen and phosphorus together with the optimum total nitrogen/phosphorus mass ratio (between 7.9 and 10.1) was determined to be the most significant stimulating parameter associated with algal blooming development dominated by non-nitrogen-fixing species. The favorable effects of strong illumination were shown to be greater than those of high temperature. The border values of total nitrogen and phosphorus concentrations leading to a critical value of algal density under different water temperatures and light intensities could be predicted as nutrient loading thresholds for harmful algal blooms by our second-order polynomial regression model.     

基于随机森林与神经网络的高光谱土壤重金属Zn含量反演

在河北省保定市白洋淀区域采集115个土壤样品进行重金属含量分析和室内光谱测量,分别将BP神经网络、随机森林、决策树、多元线性回归、K近邻回归、AdaBoost回归和偏最小二乘回归法应用于全部原始波谱数据和基于双层随机森林选择后的波段数据。结果表明,基于原始波谱数据的土壤重金属Zn元素含量的反演模型精度较低,而通过双层随机森林选择出光谱数据中与土壤重金属Zn信息相关的波段,减轻了网络模型的过拟合问题,提高了模型预测精度;与其他模型比较,结合双层随机森林和BP神经网络构建的反演模型对研究区土壤重金属Zn含量预测效果最佳。     

中国北方山区水库与引黄水库富营养化特征

于2012年10月—2013年8月对中国北方地区2种典型水库:山区水库(卧虎山、锦绣川)和引黄水库(鹊山、玉清)进行了采样调查,对其水质特征和富营养化现状进行分析研究。结果表明:2类水库水质的最大差异在于山区水库的氮含量显著高于引黄水库,这主要与水源、氮素来源和水库的进水方式有关。山区水库中SD与藻类生物量、TP关系密切;而引黄水库中,影响SD的环境因素较复杂,除藻类生物量和氮、磷外,碱度和水温也与SD密切相关。全年水平上,卧虎山水库营养状态指数显著高于其他水库,属中-富营养水体,锦绣川水库、鹊山水库和玉清水库均属中营养水平。时间上,同类型水库的营养状态指数季节变化基本一致。参与营养状态计算的4个因子中,Chla和TN对营养状态指数的贡献最大,表明这两者是引起2类水库营养状态加重的重要原因。典型的水源及进水方式、流域人类活动类型和强度是影响山区水库和引黄水库营养状态的主要外部因素。     

浑太河流域水质达标控制方法研究

结合浑太河流域水资源网络节点图,根据污染物来源、种类及其产生机理,针对计算单元水资源供需平衡预测数据、降雨径流值,分点源、非点源进行规划水平年污染物入河量的预测。根据浑太河流域地形特征、入河污染物降解特性,以水功能区水体纳污能力为计算单元污染物排放总量控制条件,给出基于规划水平年入河污染物变动特性的水质达标控制方法。运用该方法进行浑太河流域水质达标控制:2030年,流域总需水7.4亿立方米,缺水深度在1%以下,COD、NH3-N、TP、TN的入河量分别为28.5×104、2.8×104、3.1×104、4.6×104t,对应的入河控制量分别为9.3×104、0.6×104、0.8×104、0.9×104t,经控制后水功能区目标水质达标率100%。证实了该水质达标控制方法有效、可行。     

丹江口库区及其上游流域水质污染特征及评价

根据丹江口库区及其上游河流5个区域42个断面2012-2013年的水质监测数据，采用主成分分析法确定主要污染因子及权重，对不同流域的水质进行综合评价。第一主成分包括总氮、溶解氧、五日生化需氧量、总磷、氨氮、高锰酸盐指数，第二主成分为氟化物、粪大肠菌群，第三主成分为化学需氧量；其权重分别为5．022，2．256，1．508。评价结果表明，湖北十堰市和丹江口市流域水环境污染相对较重，其次为河南南阳市、陕西商洛市、陕西安康市以及陕西汉中市流域。     

太湖流域断头河道微循环水质效应与生物响应

针对太湖流域断头河道微循环过程中水质效应和生物响应不清楚的问题,笔者通过水槽模拟实验,分析了氮磷和有机物等水质指标随水体微循环时间、频率的变化关系;通过常州武进洛阳镇徐家头浜实际河道微循环实验,探讨了微循环运行过程中实际河道水质的稳定性,微循环过程中实际河道的生物响应,及浊度、溶解氧、pH等环境因素的影响。结果表明：水槽模拟实验条件下微循环对水体氨氮去除的影响最为明显（去除率达62.91%）,COD去除率变化幅度受微循环频率的影响最大（可达50%）;微循环设置后实际河道COD浓度下降趋势明显（降幅约30%）;微循环过程中河道水体浮游动物和浮游植物数量均呈上升趋势,而河道边坡和底泥中Proteobacteria、Bacteroidetes、Actinobacteria、Acidobacteria等优势微生物菌群的丰度却有不同程度的变化。同时,浊度对总磷和COD的影响程度相当,溶解氧和pH对水体氨氮浓度的影响均最显著。该研究为改善断头河道水质提供新方向,为微循环技术的实际应用提供数据参考。     

Modelling the Effects of Inflow Parameters on Lake Water Quality

A one-dimensional lake water quality model which includes water temperature, phytoplankton, phosphorus as phosphate, nitrogen as ammonia, nitrogen as nitrate and dissolved oxygen concentrations, previously calibrated for Lake Calhoun (USA) is applied to Uokiri Lake (Japan) for the year 1994. The model simulated phytoplankton and nutrient concentrations in the lake from July to November. Most of the water quality parameters are found to be the same as for Lake Calhoun. To predict probable lake water quality deterioration from algal blooming due to increased nutrient influx from river inflow, the model was run for several inflow water conditions. Effects of inflow nutrient concentration, inflow volume, inflow water temperatures are presented separately. The effect of each factor is considered in isolation although in reality more than one factor can change simultaneously. From the results it is clear that inflow nutrient concentration, inflow volume and inflow water temperature show very regular and reasonable impacts on lake water quality.     

太湖流域河流入河污染负荷通量与入湖水质响应关系分析——以殷村港为例

以2020年1—12月太湖主要入湖河流殷村港水质自动监测站的监测数据及2020年太湖水位资料为依据,构建了一维水量水质耦合数学模型,建立了入河污染负荷通量与入湖控制断面水质响应关系,以入太湖控制断面殷村港站达Ⅲ类水质水为目标,模拟计算了殷村港站主要污染物入湖水质变化过程。结果表明,殷村港站高锰酸盐指数、氨氮、总磷等水质指标浓度最大值均明显的降低,其中氨氮浓度降低幅度相对较大,主要集中于3—6月;高锰酸盐指数和总磷日均入河污染负荷通量变化相对较小,氨氮日均入河污染负荷通量降低幅度相对较大;殷村港站高锰酸盐指数、氨氮、总磷等水质指标年入河污染负荷削减量分别为24.17,41.43,3.87 t。提出,基于核算出的削减量需进一步结合污染负荷通量过程和污染源溯源分析,确定不同水质指标下入河污染负荷控制方向,为科学合理规划殷村港主要污染物的入河污染负荷总量控制提供科学依据。     

基于遥感影像的木兰溪水质参数反演

水质监测对水环境评价及污染预防至关重要，但地面监测成本高、监测面积有限等，难以满足实时、大范围监测的要求。为了更好地解决该问题，基于遥感影像的空中监测技术越来越得到研究人员的青睐。以木兰溪为研究区，利用和地面监测数据同步的Landsat-8卫星遥感影像数据，对木兰溪的典型水质参数总磷、总氮、溶解氧、高锰酸盐指数的反演问题进行研究。首先，根据Landsat-8的水体敏感波段，分别选取总磷、总氮、溶解氧、高锰酸盐指数的反演特征波段组合为(b1-b2)/(b2+b3),(b1-b2)/(b3-b4),b2/(b1+b4),b1/b2;其次，利用反演特征波段组合分别构建总磷、总氮、溶解氧、高锰酸盐指数浓度的SVR(Support Vector Regression)反演模型，通过IPSO算法对SVR模型的参数进行优选；然后，将IPSO-SVR反演模型和统计回归反演模型、广义回归神经网络(GRNN)反演模型在验证集上进行评估，以平均绝对误差和均方根误差作为评价指标进行对比分析，结果表明IPSO-SVR反演模型的平均绝对误差和均方根误差最小，说明IPSO-SVR反演模型具有较高的精度和较好的实用性...     

地表水监测误差分析与对策

分析和探讨了地表水环境监测过程中存在的误差。在13家监测站同步采样、同步分析的基础上，筛选出地表水监测实验室间比对结果偏差较大的石油类、挥发酚、氨氮、五日生化需氧量、化学需氧量、高锰酸盐指数和总磷等7个监测指标。从方法误差、仪器误差、试剂误差、操作误差、环境误差、过失误差和质控选择不当造成的误差等各方面探讨误差成因。根据实样测定结果，提出合理选择并统一分析方法、增加平行测定次数、加强质控措施和修改允许相对偏差等切实可行的对策和措施，为地表水监测和水环境管理提供技术依据。     

Atmospheric influences on water quality: a simulation of nutrient loading for the Pearl River Basin, USA

Knowledge of water quality conditions is essential in assessing the health of riverine ecosystems. The goal of this study is to determine the degree to which water quality variables are related to precipitation and air temperature conditions for a segment of the Pearl River Basin near Bogalusa, LA, USA. The AQUATOX ecological fate simulation model is used to estimate daily total nitrogen, total phosphorus, and dissolved oxygen concentrations over a 2-year period. Daily modeled output for each variable was calibrated against reliably measured data to assess the accuracy. Observed data were plotted against simulated data for controlled and perturbed models for validation, and stepwise multiple regression analysis was used to quantify the relationships between the water quality and meteorological variables. Results suggest that daily dissolved oxygen is significantly negatively correlated to concurrent daily mean air temperature with a total explained variance of 0.679 (p?<?0.01), and monthly dissolved oxygen is significantly negatively correlated to monthly mean air temperature with a total explained variance of 0.567 (p?<?0.01). Total mean monthly phosphorus concentration is significantly positively related to the previous month's precipitation with a total explained variance of 0.302 (p?<?0.01). These relationships suggest that atmospheric conditions have a strong influence on water quality in the Pearl Basin. Therefore, environmental planners should expect that future climatic changes are likely to alter water quality.