Chemometrical analysis and quality control of pot water and its sources from Athens,Greece

The present study describes a chemometrical interpretation of a large data set from pot water quality control results. Sixteen chemical and physicochemical parameters constantly checked throughout the year were involved in the classification procedure. The data originate from seventeen sampling stations (pot water sources and pot water purification stations) and were collected in the period January ‐December 1993. Mean values for two months periods were introduced to the chemometrical procedure, so finally a data set 101x16 was formed. Additionally, quality control requirements of the European Union for pot water were added to the set to form an information input matrix of 103x16 elements. Hierarchical and non‐hierarchical clustering procedures were applied in order to reveal the internal structure of the data set. Some interesting conclusions like special water quality of lake Mornos, possibility of quality monitoring with lesser number of components etc., concerning the quality and stability of the pot water sources and quality characteristics of the samples are made.     

基于多元统计的引黄水库沉沙条渠泥沙与水质的变异分析

以济南市引黄供水鹊山水库沉沙条渠为研究对象,测定了9个断面（Y1—Y9）表层水体中的含沙量、总有机碳、总磷、总氮、叶绿素a等19个水质指标.运用主成分分析法和聚类分析法分别探讨水体的主要污染因子及监测断面的沿程归类,并通过室内模拟实验分析了泥沙对水体中N、P浓度的影响.结果表明,沉沙条渠中的水质总体特征以Ⅱ类水和Ⅲ类水为主,其中,超标水质指标为TN,达到劣Ⅴ类水标准,P为藻类生长的限制因子.19个水质指标可由6个主成分来反映,其贡献率分别为：F1（TN、NH3-N、NO3--N、NO2--N、总碱度、Br-、硫酸盐）34.41%,F2（含沙量、浊度、色度、电导率）22.78%,F3（pH和UV254）13.95%,F4（TP）6.280%,F5（CODCr）4.990%和F6（Chl-a）4.830%;9个监测断面归为4类,相应地整个沉沙条渠可划分为4个功能区：入渠布水区（Y1、Y2、Y3）、快速沉降区（Y4、Y5）、缓慢沉降区（Y6、Y7、Y8）和出水稳定区（Y9）.此外,水质与泥沙关系的室内模拟实验表明,条渠来水的P浓度随着泥沙颗粒的沉降而降低,TP在0—15 h内快速下降,而后缓慢下降,30 h内基本达到平衡;而泥沙颗粒对水中N浓度基本没有影响.     

Chemometrical analysis of waste water monitoring data from Yantra river basin,Bulgaria

A chemometrical study of waste water data is carried out in order to reveal some new information about the pollution events in Yantra river basin, Bulgaria. Cluster analysis shows that no substantial difference in the behaviour of various sites along the river could be found if all chemical pollutants are simultaneously considered as variables (both for direct and indirect inlet of pollutants). Further, the clustering of the variables indicates a separation between organic and nutrition polluting species. The principal components analysis offered information about the latent factors influencing the data structure. In principle, three to four such latent factors explain over 80% of the total variance of the system. These factors are conditionally named “organic pollution”;, “nutrition pollution”; or “natural”; and are slightly different for the cases of direct and indirect inlet.     

灰色聚类法综合评价滴水湖水系环境质量

将滴水湖水系看作一个灰色系统进行研究,利用灰色聚类理论,以地表水环境标准和富营养化分级标准为基础建立灰类评价体系,确定聚类指标的隶属度和标准灰类的权重,得到各聚类指标对标准灰类的聚类系数,最大聚类系数关联的等级即水体质量等级。2010年,每2周1次对滴水湖水系11个监测点的溶解氧（DO）、化学需氧量（CODMn）、五日生化需氧量（BOD5）、总磷（TP）、总氮（TN）、氨氮（NH3-N）、透明度（SD）及叶绿素a（Chl.a）进行监测,取各指标的年均值,建立灰类评价系统。结果表明：滴水湖水源大治河及引水干道随塘河处水质属于Ⅳ类～Ⅴ类,呈富营养化和极度富营养化状态;闸外引水河芦潮引河段水质类型为Ⅲ类,为富营养化水平;闸内引水河道水质总体呈现富营养化状态,水质类型为Ⅲ类;滴水湖湖区水体质量良好,水质类型为Ⅲ类,呈中营养水平。     

Relationships of nitrous oxide fluxes with water quality parameters in free water surface constructed wetlands

The effects of chemical oxygen demand (COD) concentration in the influent on nitrous oxide (N₂O) emissions, together with the relationships between N₂O and water quality parameters in free water surface constructed wetlands, were investigated with laboratoryscale systems. N₂O emission and purification performance of wastewater were very strongly dependent on COD concentration in the influent, and the total N₂O emission in the system with middle COD influent concentration was the least. The relationships between N₂O and the chemical and physical water quality variables were studied by using principal component scores in multiple linear regression analysis to predict N₂O flux. The multiple linear regression model against principal components indicated that different water parameters affected N₂O flux with different COD concentrations in the influent, but nitrate nitrogen affected N₂O flux in all systems.     

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Scenario analysis of water pollution control in the typical peri-urban river using a coupled hydrodynamic-water quality model

The water quality pollution and ecological deterioration in peri-urban rivers are usually serious under rapid urbanization and economic growth. In the study, a typical peri-urban river, Nansha River, was selected as a case study to discuss the scheme of peri-urban river rehabilitation. Located in the north part of the Beijing central region, the Nansha River watershed has been designated as an ecologically friendly garden-style area with high-tech industry parks and upscale residential zones. However, the Nansha River is currently seriously contaminated by urban and rural pollutants from both nonpoint sources (NPS) and point sources (PS). In this study, the pollutant loads from point sources and nonpoint sources in the Nansha River watershed were first assessed. A coupled model, derived from the Environmental Fluid Dynamics Code and Water Quality Analysis Simulation Program, was developed to simulate the hydrodynamics and water quality in the Nansha River. According to the characteristics of the typical peri-urban river, three different PS and NPS control scenarios were designed and examined by modeling analyses. Based on the results of the scenario analysis, a river rehabilitation scheme was recommended for implementation.     

Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling

This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN–GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns, along with an alternating fitness method to maintain solution diversity. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN–GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty.     

Application of cluster analysis to the geochemistry zonation of the estuary waters in the Tinto and Odiel rivers (Huelva,Spain)

The combination of acid water from mines, industrial effluents and sea water plays a determining role in the evolutionary process of the chemical makeup of the water in the estuary of the Tinto and Odiel rivers. This estuary is in the southwest of the Iberian Peninsula and is one of the estuarine systems on the northwest coast of the Gulf of Cádiz. From the statistical treatment of data obtained by analyzing samples of water from this system, which is affected by industrial and mining pollution processes, we can see how the sampling points studied form two large groups depending on whether they receive tidal or fluvial influences. Fluvial input contributes acid water with high concentrations of heavy metal, whereas industrial effluents are responsible for the presence of phosphates, silica and other nutrients. The estuarine system of the Tinto and Odiel Rivers can be divided into three areas – the Tinto estuary, the Odiel estuary and the area of confluence – based on the physical – chemical characteristics of the water.     

Southwest monsoon influences the water quality and waste assimilative capacity in the Mandovi estuary (Goa state,India)

The monsoon-dominated Mandovi estuary is located in Goa state – a global tourist destination along the centralwest coast of India. In addition to factor analyses of water quality data, the water quality index (WQI), trophic state index (TSI) and percentage of freshwater volume in the estuary are calculated in order to infer the general waste assimilative capacity and prevailing water quality conditions. Factor analysis showed a dominance of PO₄–P, NO₂–N, NH₃–N, total suspended solids (TSS) and turbidity during southwest (SW) monsoon relative to other seasons. The WQI suggested that an increase in nutrients, turbidity and TSS during SW monsoon increase the WQI values beyond 2, rendering the water at some locations slightly polluted. During pre-monsoon, considerable increase in the WQI is observed at all the upstream stations rendering slightly polluted water at these stations. The TSI showed an average value of 46.95 during SW monsoon, 42.43 during post-monsoon and 48.42 during the pre-monsoon seasons, suggesting better productivity level during pre-monsoon, followed by SW monsoon, but the least during the post-monsoon. All the seasons, however, indicated a mesotrophic condition in the estuary and the assimilative capacity of the estuary is found to be in good to fairly good state (pre-monsoon?相似文献   

Design of a water quality monitoring network in a large river system using the genetic algorithm

Despite several decades of operations and the increasing importance of water quality monitoring networks, the authorities still rely on experiential insights and subjective judgments in siting water quality monitoring stations. This study proposes an integrated technique which uses a genetic algorithm (GA) and a geographic information system (GIS) for the design of an effective water quality monitoring network in a large river system. In order to develop a design scheme, planning objectives were identified for water quality monitoring networks and corresponding fitness functions were defined using linear combinations of five selection criteria that are critical for developing a monitoring system. The criteria include the representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness levels were obtained through a series of calculations of the fitness functions using GIS data. A sensitivity analysis was performed for major parameters such as the numbers of generations, population sizes and probability of crossover and mutation, in order to determine a good fitness level and convergence for optimum solutions. The proposed methodology was applied to the design of water quality monitoring networks in the Nakdong River system, in Korea. The results showed that only 35 out of 110 stations currently in operation coincide with those in the new network design, therefore indicating that the effectiveness of the current monitoring network should be carefully re-examined. From this study, it was concluded that the proposed methodology could be a useful decision support tool for the optimized design of water quality monitoring networks.     

三峡库区高阳平湖水体富营养化主要驱动因子研究 ——基于主成分分析法

研究各种水质因子(水温、溶解氧、电导率、pH、营养盐等)对三峡库区支流水体富营养化的驱动效果,为库区支流水体富营养化的防控与治理提供科学依据.于2018年11月至2019年10月在三峡库区高阳平湖进行了12次样品采集,获取了水温、水体pH、营养盐、水体溶解氧等指标数据,利用主成分分析法研究三峡库区支流水体富营养化状况,...     

洱海流域水质时空变化特征

依据2009年洱海流域62个样点的水质调查数据,应用聚类分析对洱海流域水质时空变化特征进行分析,将样点在空间上分为4组,分别为无干扰的苍山林区源头溪流(A1)、受到一定程度人类干扰的流经农业区和城镇区溪流的中上游段及湖泊(A2)、干扰较严重的流经农业区和城镇区溪流的下游段(A3)以及干扰最为严重的洱海岸边带(A4),判别分析结果表明聚类准确率达95.2%。应用主成分分析方法得到影响洱海流域水质的4个主成分为:第1主成分,Alk、Ca2+、Mg2+、HS;第2主成分,TP;第3主成分,TN和NO3--N;第4主成分,DOC和TOC,这些主成分空间差异明显。在时间尺度上,Alk表现为丰水期高于枯水期;Ca2+表现为枯水期高于丰水期;TP没有表现出明显的季节性变化规律;TN在A1和A3组表现为丰水期高于枯水期。     

“十一五”期间中国化学需氧量减排与水环境质量变化关联分析

化学需氧量是中国主要污染物总量控制指标之一。分析化学需氧量排放变化及减排形势对今后化学需氧量及其他污染物减排具有借鉴意义。在分析＂十一五＂期间中国化学需氧量减排趋势和水环境质量变化趋势的基础上,定量和定性地考察两者之间的关系。结果表明：＂十一五＂期间化学需氧量排放量呈下降趋势,但生活化学需氧量排放量比重有所上升,中西部地区排放量下降趋势比东部地区要小;化学需氧量减排目标顺利实现,其中,工业源是减排主体,东部地区对减排贡献最大;中国地表水环境质量逐步改善,高锰酸盐指数质量浓度累计下降31.9%,Ⅰ～Ⅲ类水质断面比例累计提高14.4个百分点。无论是全国尺度还是流域尺度,随着化学需氧量排放量的减少,地表水水质均出现不同程度的改善,两者表现出一定程度的正相关关系,但是部分流域存在污染物排放总量与环境质量变化不完全协同的现象。     

Optimal locations of monitoring stations in water distribution systems under multiple demand patterns: a flaw of demand coverage method and modification

A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring stations is calculated by accumulating their demand coverage under each demand pattern, and the impact of temporal distribution between different time periods or demand patterns is ignored. This could lead to miscalculation of the optimal locations of the monitoring stations. To overcome this flaw, this paper presents a Demand Coverage Index (DCI) based method. The optimization considers extended period unsteady hydraulics due to the change of nodal demands with time. The method is cast in a genetic algorithm framework for integration with Environmental Protection Agency Net (EPANET) and is demonstrated through example applications. Results show that the set of optimal locations of monitoring stations obtained using the DCI method can represent the water quality of water distribution systems under multiple demand patterns better than the one obtained using previous methods.     

淮河流域农田碳收支特征及影响因素分析

采用开路涡度相关系统对淮河流域农田湍流特征进行了4年连续监测,以该地区主要农作物小麦和水稻为例,对CO2通量数据进行分析处理。研究结果表明：农田CO2通量的日变化随季节变化明显,春、夏变化幅度明显大于秋、冬。2007—2010年淮河流域夏季农田CO2通量日变化规律均呈单峰型,白天为明显的碳汇;其日最大累计吸收量出现在2007年,可达16.1mg.m-2.s-1,最小值出现在2009年,为11.1 mg.m-2.s-1。在水稻拔节期垂直风速（W）、潜热通量（LE）、显热通量（H）、光合有效辐射（PAR）、净辐射（Rn）与CO2通量均呈负相关,通过对各项指标进行主成分分析,得到了新的综合指标F=-0.36ZX1＋0.42ZX2＋0.42ZX3＋0.42ZX4＋0.39ZX5＋0.42ZX6＋0.13ZX7,并经过计算得出中午1点是最大的碳汇,凌晨12点是最大的碳源。     

碟形沼泽湿地水中氮的动态变化及影响因素分析

通过对三江平原碟形湿地不同植被类型沼泽水体中氮含量的研究表明,漂筏苔草、毛果苔草和乌拉苔草沼泽水体中各形态氮含量具有明显的季节变化特征.总氮(TN)含量在5-10月份的植物生长季节表现为先增加后降低的趋势,不同植被类型沼泽水体TN含量明显不同.NH4+-N是无机氮的主要存在形态,含量比例在44.81%～98.89%之间.在整个生长季,乌拉苔草沼泽水NH4+-N含量呈先增加后降低的趋势,毛果苔草沼泽水NH4+-N含量在8月份具有峰值,漂筏苔草沼泽水NH4+-N含量较低,波动不大.硝态氮(NO3--N)在生长季中期含量较低,而亚硝态氮(NO2--N)含量较高;生长季末期NO3--N含量增高,而NO2--N含量降低.沼泽水体中不同形态氮浓度与植被类型、植被生长阶段密切相关,并且受到水温、降雨、蒸发、农田排水等因素的影响.     

规模化牛场废水灌溉对土壤水分和冬小麦产量品质的影响

通过田间小区试验,设置不同的牛场废水灌溉次数,研究了冬小麦牛场废水灌溉过程中土壤水分和冬小麦产量品质的变化特征,结果表明,灌溉牛场废水土壤水分迁移和土壤贮水量与灌溉清水无显著差别,水质对土壤水分变化影响很小;冬小麦生育期内分别灌溉牛场废水2、3和4次与正常施肥灌溉施肥相比,冬小麦产量和灌溉水生产效率提高,分别提高了4.61%、6.48%、6.63%,4次牛场废水灌溉冬小麦产量略有下降,这说明灌溉牛场废水次数过多会对冬小麦造成一定的负面影响;牛场废水灌溉次数越多冬小麦籽粒中蛋白质质量分数越高,分别提高了2.50%、5.83%、8.03%,而全磷质量分数则有降低趋势。综合考虑,冬小麦生育期内牛场废水灌溉次数不应高于3次。