Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Some comments on analysis techniques for censored water quality data

When investigating trace substances in ambient water, a proportion of water sample concentrations is usually below limits of detection. In medical and industrial reliability studies, comparisons are often made of time to event data which includes right censored observations indicating only that an observation is greater than a specified value. In this paper consideration is given to the application of non-parametric procedures, widely used in the analysis of time to event data, to water quality data which is left censored.A non-parametric estimate of the cumulative distribution function for left censored water quality data can be generated quite easily. For the comparison of levels of trace substances it is necessary to combine an unconditional likelihood for the proportion of observations below a detection limit with a partial likelihood for the portion of the distribution above the detection limit in order to make use of regression methodology. The details of this are outlined and an example is given which compares levels of toxic substances at the head and mouth of the Niagara river.When comparisons are based on matched pair data, further modifications are necessary. A development paralleling that for time to event data is given. Consideration is also given to model extensions which allow for a dependence between observations at the same location over a period of time.The presentation is introductory and designed to illustrate the potential of some available methodology for use in the analysis of water quality data.     

Assessing water quality of drinking water distribution system in the South Taiwan

This investigation conducted a full-scale survey the drinking water distribution system in Kaohsiung city, Taiwan. The aim was to investigate whether the distribution system was capable of maintaining high water quality from the water treatment facilities through to the end user. The results showed that the distribution system can maintain high water quality, except for suitable chlorine residuals. The authors plotted chlorine residual contour maps to identify areas with low chlorine residuals, helping them prioritize sections that must be flushed or renewal. The contour maps also provide sufficient and clear information for locating booster chlorination stations. Contour maps enable water facilities to identify how water quality decays in the distribution systems and the locations of such decay. Water quality decay can be caused by properties of pipeline materials, hydraulic conditions, and/or biofilm thickness. However, understanding the exact reasons is unnecessary because the contour maps provide sufficient information for trouble-shooting the distribution systems.     

Evaluating the effectiveness of routine water quality monitoring in Miyun reservoir based on geostatistical analysis

This study used geographic information system techniques and geostatistics methods to evaluate the effectiveness of routine water quality monitoring in the western segment of the Miyun reservoir in Beijing. Methodologies as well as the sampling design are evaluated. The single-layer evaluation and three integrated evaluation methods including principal component analysis (PCA), ordinary kriging (OK)_Mean, and Mean_Layers were used to validate the effectiveness of evaluation methods, and the effectiveness of each sampling design was validated by comparing their errors. Results indicated that, while a single-layer evaluation only shows the trophic state of water at a specific level, an integrated evaluation synthetically analyzes and evaluates the trophic state of the entire water body. Furthermore, results of the integrated analysis show that a PCA method is more accurate and can represent the trophic state of the entire water body. The OK_Mean and Mean_Layers methods are only able to represent the mean level for trophic state of the entire water body but cannot reflect local trophic state and distribution details. Although methods used in the routine monitoring of Miyun reservoir have some similarities to the OK_Mean and Mean_Layers methods, their range of errors and uncertainty are greater because of a lack of detailed spatial continuous information. The analysis on the number of sampling points shows that, within a certain range of error, minor changes of sampling points will have no obvious impact on the monitoring results. For the routine monitoring of western Miyun reservoir, using only three to five sampling points for monitoring is inadequate. According to our analysis, it is more appropriate to use at least ten sampling points for monitoring these areas.     

Landsat and limnologically derived water quality data: A perspective

Fixed station sampling is the conventional method used to obtain data on the median water quality of reservoirs. A major source of uncertainty associated with this technique is that water quality at the fixed stations may not be representative of the ambient water quality in the reservoir at the time of sampling. This problem is particularly relevant for water quality variables such as chlorophyll, which have a markedly patchy spatial distribution. The use of Landsat reflectance data to estimate median chlorophyll concentrations in Roodeplaat Dam was investigated. A linear polynomial regression model for estimating chlorophyll concentrations from Landsat reflectance data, was firstly calibrated with chlorophyll concentration data obtained by sampling seven fixed stations on the reservoir at the time of the satellite overflight to produce an individual calibration. Secondly, the model was calibrated with a pooled set of sampled data obtained from five separate overflights, to obtain a generalised calibration.It was found that median chlorophyll concentrations determined from Landsat-derived data were similar to median chlorophyll concentrations estimated from fixed station data. However, the range of chlorophyll concentrations in the reservoir estimated from Landsat data was considerably larger than that estimated from fixed station data. Landsat derived estimates of chlorophyll concentrations have the added advantage of providing information on the spatial distribution of chlorophyll in the reservoir.     

Sensitivity analysis of TOPSIS method in water quality assessment II: sensitivity to the index input data

This is the second part of the study on sensitivity analysis of the technique for order preference by similarity to ideal solution (TOPSIS) method in water quality assessment. In the present study, the sensitivity of the TOPSIS method to the index input data was investigated. The sensitivity was first theoretically analyzed under two major assumptions. One assumption was that one index or more of the samples were perturbed with the same ratio while other indices kept unchanged. The other one was that all indices of a given sample were changed simultaneously with the same ratio, while the indices of other samples were unchanged. Furthermore, a case study under assumption 2 was also carried out in this paper. When the same indices of different water samples are changed simultaneously with the same variation ratio, the final water quality assessment results will not be influenced at all. When the input data of all indices of a given sample are perturbed with the same variation ratio, the assessment values of all samples will be influenced theoretically. However, the case study shows that only the perturbed sample is sensitive to the variation, and a simple linear equation representing the relation between the closeness coefficient (CC) values of the perturbed sample and variation ratios can be derived under the assumption 2. This linear equation can be used for determining the sample orders under various variation ratios.     

地理信息系统在大窑湾水质现状评价中的应用

借助mapinfo软件,利用离散点插值扩展法对大窑湾海域的水质污染空间分布情况进行了环境评价,通过计算污染区域的面积得出了各类水质的海域空间分布,克服了以往逐点评价海域的片面性,对管理和控制海域污染范围有一定的指导意义.     

Assessing the value of information for water quality management: a watershed perspective from China

To tackle China’s pervasive water pollution, tremendous efforts are needed to achieve more and better information. However, resources for information collection (e.g., water quality monitoring, field experiments, etc.) are very limited for large watersheds with significant nonpoint source pollution. Thus, it is crucial to identify the priority of information acquisition. Based on the theory of value of information (VOI), a stochastic optimization approach was developed in this study to evaluate the importance of information. The approach was applied to several key polluted water bodies in China (e.g., Lake Taihu, Lake Chaohu, and Lake Dianchi). The major findings include: (1) because of the severe pollution and large uncertainty, the VOI for the targeted water bodies is substantial; (2) when the uncertainty is significant, a stricter regulation would result in a higher VOI, and therefore provide more incentives for data collection; (3) due to the interaction among different information sources, collecting multiple types of information simultaneously could be more valuable than collecting one after another; and (4) the importance of a specific type of information could vary significantly across watersheds. The proposed approach can be readily extended to more complex models and more sophisticated watershed cases. It could effectively support watershed management in China, as well as in other countries.     

Water quality analysis of surface water: a Web approach

The chemical, physical and biological characteristics of water with respect to its suitability describe its quality. Concentration of pesticides or fertilisers degrades the water quality and affects marine life. A comprehensive environmental data information system helps to perform and complete common tasks in less time with less effort for data verification, data calculations, graph generation, and proper monitoring, which helps in the further mitigation step. In this paper, focus is given to a web-based system developed to express the quality of water in the imprecise environment of monitoring data. Water samples were analyzed for eight different surface water parameters, in which four parameters such as pH, dissolved oxygen, biochemical oxygen demand, and fecal coliform were used for the water quality index calculation following MPCB Water Quality Standards of class A-II for best designated use. The analysis showed that river points in a particular year were in very bad category with certainty level of 0–38 % which is unsuitable for drinking purposes; samples in bad category had certainty level that ranged from 38 to 50 %; samples in medium to good category had certainty levels from 50 to 100 %, and the remaining samples were in good to excellent category, suitable for drinking purposes, with certainty levels from 63 to 100 %.     

供水厂水质状况分析

通过对西江某供水厂的水源水与出厂水水质进行分析研究 ,可知供水厂的水处理效果主要表现在对降低出水的浊度、色度和总细菌数等指标上 ,对大部分水溶性物质的减少无效或效果较低。为保证和提高饮用水的安全性和水质 ,除需提高供水厂的水处理效果和能力外 ,根本出路在于从环境管理与立法等角度 ,加大对水源水的保护力度 ,减少对饮用水源的排污     

湖库水质评价污染因子选择方案探讨

目前我国湖库水质评价中,评价项目不统一,造成评价结果不具可比性.为抓住水体污染的主要矛盾,同时节约监测成本,对有代表性的湖库监测数据进行分析,提出了湖库水质评价项目的选择方案.     

Assessing the water quality index of water treatment plant and bore wells, in Delhi, India

Water quality monitoring exercise was carried out with water quality index (WQI) method by using water characteristics data for bore wells and a water treatment plant in Delhi city from December 2006 to August 2007. The water treatment plant received surface water as raw water, and product water is supplied after treatment. The WQI is used to classify water quality as excellent, good, medium, bad, and very bad. The National Sanitation Foundation WQI procedure was used to calculate the WQI. The index ranges from 0 to 100, where 100 represents an excellent water quality condition. Water samples were collected monthly from a bore well in Nehru Camp (site 1), a bore well in Sanjay Gandhi pumping station (site 2), and water treatment plant in Haiderpur (site 3). Five parameters were analyzed, namely, nitrate, pH, total dissolved solids, turbidity, and temperature. We found that the WQI was around 73–80 in site 3, which corresponds to “good,” and it decreased to 54.32–60.19 and 59.93–70.63 in site 1 and site 2, respectively, indicating that these bore wells were classified as “medium” quality.     

Cluster analysis of Delhi's ambient air quality data

The purpose of this study was to study the spatial patterns of ambient air quality in Delhi in the absence of extensive datasets needed for space-time modeling. A spatial classification was attempted on the basis of ambient air quality data of nine years (1998 is latest year for which published data were available) for three criteria pollutants--nitrogen dioxide, sulfur dioxide, and suspended particulate matter. Monitoring stations take 24-hour samples twice a week. Published monthly average concentration data were used in this study. A hierarchical agglomerative algorithm using the average linkage between groups method and the Euclidean distance metric was used. Cluster analysis indicated that till 1998, by and large, two distinct classes existed. The results of cluster analysis prompted an investigation of systematic biases in the monitored data. No statistically significant differences in the mean concentration of all pollutants were observed between stations belonging to different land-use types (residential and industrial). This fact would be useful, if and when the authorities consider modifying the network or expanding it in Delhi. The results also support the recommendation that Delhi have a uniform standard across all areas. This study has provided a methodology for Indian researchers and practitioners to do an exploratory study of spatial patterns of air pollution and data quality issues in Indian cities using the National Ambient Air Quality Monitoring System data.     

Assessing possible visitor-use impacts on water quality in Yosemite National Park, California

There is concern that visitor-use associated activities, such as bathing, dish washing, wastewater production, and stock animal use near lakes and streams, could cause degradation of water quality in Yosemite National Park. A study was conducted during 2004-2007 to assess patterns in nutrient and Escherichia coli (E. coli) concentrations in the Merced and Tuolumne Rivers and characterize natural background concentrations of nutrients in the park. Results indicated that nutrient and E. coli concentrations were low, even compared to other undeveloped sites in the United States. A multiple linear regression approach was used to model natural background concentrations of nutrients, with basin characteristics as explanatory variables. Modeled nitrogen concentrations increased with elevation, and modeled phosphorus concentrations increased with basin size. Observed concentrations (±uncertainty) were compared to modeled concentrations (±uncertainty) to identify sites that might be impacted by point sources of nutrients, as indicated by large model residuals. Statistically significant differences in observed and modeled concentrations were observed at only a few locations, indicating that most sites were representative of natural background conditions. The empirical modeling approach used in this study can be used to estimate natural background conditions at any point along a study reach in areas minimally impacted by development, and may be useful for setting water-quality standards in many national parks.     

A multi-variate methodology for analyzing pre-existing lake water quality data

Environmental agencies are given the task of monitoring water quality in rivers, lakes, and other bodies of water, for the purpose of comparing the results with regulatory standards. Monitoring follows requirements set by regulations, and data are collected in a systematic way for the intended purpose. Monitoring enables agencies to determine whether water bodies are polluted. Much effort is spent per monitoring event, resulting in hundreds of data points typically used solely for comparison with regulatory standards and then stored for little further use. This paper devises a data analysis methodology that can make use of the pre-existing datasets to extract more useful information on water quality trends, without new sample collection and analysis. In this paper, measured lake water quality data are subjected to statistical analyses including Principal Component Analysis (PCA) to deduce changes in water quality spatially and temporally over several years. It was found that the lake as a whole changed temporally by season, rather than spatially. Storm events caused the greatest shifts in water quality, though the shifts were fairly consistent across sampling stations. This methodology can be applied to similar datasets, especially with the recent emphasis by the U.S. EPA on protection of lakes as water sources. Water quality managers using these techniques may be able to lower their monitoring costs by eliminating redundant water quality parameters found in this analysis.     

Application of water quality indices and analysis of the surface water quality monitoring network in semiarid North-Central Chile

Surface water quality has increasing importance worldwide and is particularly relevant in the semiarid North-Central Chile, where agriculture and mining activities are imposing heavy pressure on limited water resources. The current study presents the application of a water quality index in four watersheds of the 29°-33°S realm for the period 1999-2008, based on the Canadian Council of Ministers for the Environment approach and the Chilean regulation for irrigation water quality. In addition, two modifications to the index are tested and a comprehensive characterization of the existing monitoring network is performed through cluster analysis. The basins studied show fairly good water quality in the overall, specially the Limarí basin. On the other hand, the lower index values were obtained for the headwaters of Elqui, associated with the El Indio mining district. The first modification of the indicator (i.e., to consider parameters differentially according to their effect on human health or the environment) did not produce major differences with respect to the original index, given the generally good water quality. The second modification (i.e., to consider as threshold values the more restrictive figures derived from a set of regulations) yielded important differences in the indicator values. Finally, an adequate characterization of the monitoring network was obtained. The results presented spatial coherence and the information can be used as a basis for the optimization of the monitoring network if required.     

Improving data quality on low level mercury wastewater analysis

In order to compare treatability test results evaluating low-level mercury (Hg) removal from oil refinery wastewater, improvements in Hg analytical methods were conducted at two US EPA certified analytical labs. The revisions in the analytical protocols improved Hg recoveries and hence enabled more reliable data interpretation and comparison for the specific wastewater tested. Nevertheless, significant differences between results from the two laboratories were identified in a split-sample experiment.     

Assessing water quality in a tropical lake using biomarkers in zebrafish embryos: developmental toxicity and stress protein responses

In order to achieve a more substantial appraisal of lake water quality, the assessment must not be based only on chemical measurements and analyses of the water itself, but even more so on the impact of existing conditions on aquatic biota. This is possible with the use of biotests or biomarkers, e.g. investigations of the developmental parameters (96-h embryotoxicity evaluate) or of the induction of heat shock proteins (proteotoxicity evaluate). To evaluate the suitability of these tests for environmental screening, fertilized zebrafish eggs were exposed to water samples collected from five sites of varying levels of stress from Laguna Lake, Philippines. Reconstituted water was used as laboratory control while water samples from a highly polluted freshwater source was used as positive control. Developmental parameters were noted and described within 48 and 96 h of exposure. Dilution experiments of the positive control were also done to further assess and compare toxicity potentials of Laguna Lake waters with those originating from a polluted freshwater. After the 96-h exposure, the levels of stress proteins (hsp 70) were determined in embryos from all exposure groups. Results showed 100% mortality in embryos exposed to undiluted positive control (PC) within only 12 h. Increasing dilution levels, however, resulted in lower mortality and lower abnormality rates. No detectable developmental differences were noted among embryos exposed to either the laboratory control or Laguna Lake waters at the end of 96 h, regardless of the source. Very high survival rates and high hatching success rates were observed in embryos exposed to lake waters as well as laboratory control, and the data did not differ significantly among the groups. Likewise, no significant malformations were noted among all developing embryos throughout the exposure period. However, the levels of heat shock proteins in the two sites located closest to Manila, the Philippine capital (Northern West Bay and Central West Bay), showed a pronounced elevation relative to the control, indicating that these stress proteins protect the embryos from the detrimental effects of pollutants in the water. Based on the 96-h early life stage (ELS) test, the water quality of the lake is good for fishery propagation despite the current levels of pollutants in the water. This finding is in accordance with the Class C status (i.e. suitable for fish growth and propagation) as given to the lake by the local environmental agency. On the other hand, data on proteotoxicity showed that the fish are under stress, presumably deriving from pollutants. This calls for a continuous monitoring and improvement of the lake water. The present study indicates that the two biomarker methods are very easy to use, practical, rapid, and sensitive for assessing water quality in a tropical lake and recommends for their incorporation into the future monitoring program of Laguna Lake.     

Assessing the impact of FDI on PM2.5 concentrations: A nonlinear panel data analysis for emerging economies

While emerging markets have obtained powerful growth for foreign direct investment (FDI) inflows, they are facing severe smog pollution, and this contradiction has become increasingly prominent since the financial crisis. Assessing the influence of FDI on pollutant emissions is of great significance for determining how to attract FDI to promote environmental sustainability. The present study simultaneously investigates the direct and indirect effects of FDI on PM_2.5 contamination for emerging countries spanning the period 2010–2016. Due to the features of the nonlinear analysis, a generalized panel smooth transition regression (GPSTR) model was introduced, and cross-sectional dependence, heterogeneity, nonlinear unit root, nonlinear cointegration tests and non-parametric kernel density estimation were applied to achieve this goal. The results reveal that FDI directly contributes to decreasing PM_2.5, but indirectly has on increasing PM_2.5 emissions. The total effect of FDI on PM_2.5 concentrations is proven to be negative, which confirms the pollution halo hypothesis. Moreover, the connection between FDI inflows and PM_2.5 emissions displays a threshold and dynamic characteristic and is “S-shaped”. At lower levels of FDI, the inflows of FDI exert a positive effect on reducing PM_2.5 concentrations, whereas when FDI exceeds the threshold of 23.2981, such influence is gradually weakened with an increase in its own accumulation. The study provides new assessments on FDI's contribution to pollutant emissions and evidence for environmental sustainability in the post-financial crisis era.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.