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.     

Revising river water quality monitoring networks using discrete entropy theory: the Jajrood River experience

This paper aims at evaluating and revising the spatial and temporal sampling frequencies of the water quality monitoring system of the Jajrood River in the Northern part of Tehran, Iran. This important river system supplies 23% of domestic water demand of the Tehran metropolitan area with population of more than 10 million people. In the proposed methodology, by developing a model for calculating a discrete version of pair-wise spatial information transfer indices (SITIs) for each pair of potential monitoring stations, the pair-wise SITI matrices for all water quality variables are formed. Also, using a similar model, the discrete temporal information transfer indices (TITIs) using the data of the existing monitoring stations are calculated. Then, the curves of the pair-wise SITI versus distance between monitoring stations and TITI versus time lags for all water quality variables are derived. Then, using a group pair-wise comparison matrix, the relative weights of the water quality variables are calculated. In this paper, a micro-genetic-algorithm-based optimization model with the objective of minimizing a weighted average spatial and temporal ITI is developed and for a pre-defined total number of stations, the best combination of monitoring stations is selected. The results show that the existing monitoring system of the Jajrood River should be partially strengthened and in some cases the sampling frequencies should be increased. Based on the results, the proposed approach can be used as an effective tool for evaluating, revising, or redesigning the existing river water quality monitoring systems.     

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.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

Design of River Water Quality Monitoring Networks: A Case Study

Karoon River, from Gotvand Dam to Persian Gulf with more than 450 km in length and an annual discharge of 11,891 million cubic meters, is the largest river in Iran. Increasing water withdrawal from and wastewater discharge to the river has endangered the aquatic life of this important ecosystem. Furthermore, the drinking and in-stream water quality standards have been violated in many instances. In this paper, a river water quality monitoring network is designed, including determination of sampling frequencies as well as location of water quality monitoring stations. In this regard, two models are developed. The first model is a Genetic Algorithm-based optimization model and the second one is a combination of Kriging method and Analytical Hierarchy Process. The temporal variation of the concentration of water quality variables along Karoon and Dez Rivers are evaluated and the main water quality indicators are selected. Then, thirty five stations are selected and the application of Entropy Theory in calculating the sampling frequency is demonstrated. The results show the significant value of the proposed methodology in the design of monitoring network.     

A cost-precision model for marine environmental monitoring,based on time-integrated averages

Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6–12.4%) compared to variability between stations in a single day (CV range 2.4–88.6%), or variability over time for a single station (CV range 0.4–110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

Lognormality of trace contaminant concentrations in sewage effluents

It is important to understand the statistical distribution of monitoring data for them to be of value in determining the parameters of environmental models. No such distributional information has been available for many trace contaminants in sewage effluents. This paper applies the data of a major UK sewage works’ effluent monitoring programme to determine the validity of the common assumption that data are lognormally distributed. Effluent quality was monitored at 162 wastewater treatment works over 1 year, generating over 3,000 results for each of over 40 substances, including metals, trace organic substances and pharmaceuticals. It is demonstrated that the lognormal assumption is clearly justified for the great majority of substances in the spatial case—for annual average effluent concentrations across different treatment works. In the site-specific, temporal case—for individual determinations of concentration at a single site over an annual period—lognormality is generally supported but not demonstrated so unequivocally for all site/substance combinations. The principal source of uncertainty was lack of sufficient numbers of observations reported to adequately low reporting limits.     

Temporal and spatial trends in water quality of Lake Taihu,China: analysis from a north to mid-lake transect, 1991–2011

Interpretations of state and trends in lake water quality are generally based on measurements from one or more stations that are considered representative of the response of the lake ecosystem. The objective of this study is to examine how these interpretations may be influenced by station location in a large lake. We addressed this by analyzing trends in water quality variables collected monthly from eight monitoring stations along a transect from the central lake to the north in Lake Taihu (area about 2,338 km²), China, from October 1991 to December 2011. The parameters examined included chlorophyll a (Chl a), total nitrogen (TN), and total phosphorus (TP) concentrations, and Secchi disk depth (SD). The individual variables were increasingly poorly correlated among stations along the transect from the central lake to the north, particularly for Chl a and TP. The timing of peaks in individual variables was also dependent on station location, with spectral analysis revealing a peak at annual frequency for the central lake station but absence of, or much reduced signal, at this frequency for the near-shore northern station. Percentage annual change values for each of the four variables also varied with station and indicated general improvement in water quality at northern stations, particularly for TN, but little change or decline at central lake stations. Sediment resuspension and tributary nutrient loads were considered to be responsible for some of the variability among stations. Our results indicate that temporal trends in water quality may be station specific in large lakes and that calculated whole-lake trophic status trends or responses to management actions may be specific to the station(s) selected for monitoring and analysis. These results have important implications for efficient design of monitoring programs that are intended to integrate the natural spatial variability of large lakes.     

基于GIS的石家庄市PM10和PM2.5时空分布研究

利用2018年261个乡镇环境空气自动监测站监测数据,结合GIS空间分析技术,对石家庄市PM10和PM2.5的时空污染特征进行了研究。结果表明,石家庄地区PM10和PM2.5污染的空间分布整体表现为西北部山区好于东南部的平原地区,主城区好于周边县(市、区)的特征。采暖期PM10和PM2.5的污染程度明显重于非采暖期。PM2.5稳定性差于PM10,PM10和PM2.5的稳定性与污染程度具有一定的负相关性,表现出污染越轻的区域稳定性越差。两者的日均值浓度变化在时间序列上呈极强正相关,且污染越重的区域时间相关性越强。与日均值相关性不同,污染程度越轻的区域PM10和PM2.5年均值的线性相关性越强。     

Detecting water quality patterns in New Hampshire’s estuaries using National Coastal Assessment probability-based survey data

Between 2000 and 2006, the New Hampshire Department of Environmental Services and the University of New Hampshire collected water quality samples at 25 to 40 stations per year in a 56.5-km² estuary as part of the Environmental Protection Agency’s National Coastal Assessment program. Due to the high density of stations, probabilistic statistics for the estuary could be calculated with low uncertainty. The proportions of the estuary exceeding thresholds in each year were calculated for temperature, salinity, dissolved oxygen, chlorophyll a, nitrogen as nitrate and nitrite, nitrogen as ammonium, phosphorus as orthophosphate, total suspended solids, and fecal coliform bacteria. These values were tested for trends over time and correlations with climate variables. The same statistical tests were applied to monthly grab sample data from a representative station in the estuary. The outcomes of the statistical tests on the two datasets were compared to determine if they provided similar information to coastal managers. Trends and correlations were equally likely to be detected using the probability-based data and the fixed station data, but the results were different for the two datasets. The differences were likely due to the distributed nature of the probability-based sampling design, which places stations in all sections of the estuary. In addition, expressing the probabilistic datasets as estimated proportions reduced variability in volatile parameters, such as bacteria, relative to the grab sample dataset. It will be important to develop tools to rectify trends from probability-based surveys with fixed station monitoring to provide clear information to managers.     

Spatial and seasonal patterns in water quality in an embayment-mainstem reach of the tidal freshwater Potomac River, USA: a multiyear study

Spatial and temporal patterns in water quality were studied for seven years within an embayment-river mainstem area of the tidal freshwater Potomac River. The purpose of this paper is to determine the important components of spatial and temporal variation in water quality in this study area to facilitate an understanding of management impacts and allow the most effective use of future monitoring resources. The study area received treated sewage effluent and freshwater inflow from direct tributary inputs into the shallow embayment as well as upriver sources in the mainstem. Depth variations were determined to be detectable, but minimal due mainly to the influence of tidal mixing. Results of principal component analysis of two independent water quality datasets revealed clear spatial and seasonal patterns. Interannual variation was generally minimal despite substantial variations in tributary and mainstem discharge among years. Since both spatial and seasonal components were important, data were segmented by season to best determine the spatial pattern. A clear difference was found between a set of stations located within one embayment (Gunston Cove) and a second set in the nearby Potomac mainstem. Parameters most highly correlated with differences were those typically associated with higher densities of phytoplankton: chlorophyll a, photosynthetic rate, pH, dissolved oxygen, BOD, total phosphorus and Secchi depth. These differences and their consistency indicated two distinct water masses: one in the cove harboring higher algal density and activity and a second in the river with lower phytoplankton activity. A second embayment not receiving sewage effluent generally had an intermediate position. While this was the most consistent spatial pattern, there were two others of a secondary nature. Stations closer to the effluent inputs in the embayment sometimes grouped separately due to elevated ammonia and chloride. Stations closer to tributary inflows into the embayment sometimes grouped separately due to dilution with freshwater runoff. Segmenting the datasets by spatial region resulted in a clarification of seasonal patterns with similar factors relating to algal activity being the major correlates of the seasonal pattern. A basic seasonal pattern of lower scores in the spring increasing steadily to a peak in July and August followed by a steady decline through the fall was observed in the cove. In the river, the pattern of increases tended to be delayed slightly in the spring. Results indicate that the study area can be effectively monitored with fewer study sites provided that at least one is located in each of the spatial regions.     

Methodology for designing air quality monitoring networks: I. Theoretical aspects

An objective methodology is presented for determining the number and disposition of ambient air quality stations in a monitoring network for the primary purpose of compliance with air quality standards. The methodolgy utilizes a data base with real or simulated data from an air quality dispersion model for application with a two-step process for ascertaining the optimal monitoring network. In the first step, the air quality patterns in the data base are collapsed into a single composite pattern through a figure-of-merit (FOM) concept. The most desirable locations are ranked and identified using the resultant FOM fields. In the second step the network configuration is determined on the basis of the concept of spheres of influence (SOI) developed from cutoff values of spatial correlation coefficients between potential monitoring sites and adjacent locations. The minimum number of required stations is then determined by deletion of lower-ranked stations whose SOIs overlap. The criteria can be set to provide coverage of less than some fixed, user-provided percentage of the coverage of tha SOIs of the higher ranked stations and for some desired level of minimum detection capability of concentration fluctuations.The methodology is applied in a companion paper (McElroy et al., 1986) to the Las Vegas, Nevada, metropolitan area for the pollutant carbon monoxide.Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through Contract No. 68-03-2446 to Systems Applications, Inc., it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

A feasible method to assess inaccuracy caused by patchiness in water quality monitoring

Patchiness is a typical property of water quality in lakes. However, in conventional water quality monitoring, patchiness is usually too expensive to take into account, due to the high number of required samples. This study examines a feasible methodology developed for estimating the representativeness of discrete chlorophyll a measurements. Four spatially extensive data sets were collected from the Enonselkä basin of Lake Vesijärvi in Southern Finland, using a flow trough system with a fluorometer in a moving boat. Data sets were used to estimate (1) the spatial representativeness of discrete sampling; (2) the effect of varying sample size on the detected mean chlorophyll a concentration and on the observed proportion of variance. Spatial representativeness was assessed using semivariogram analysis. Results indicate that the spatial representativeness of discrete sampling can remain undesirably low. Furthermore, in monitoring programs involving just one or only a few samples, there is a significant risk of obtaining a false estimate for the mean value and variance of chlorophyll a concentration over the whole monitoring area.     

Spatial and temporal trends in water quality in a Mediterranean temporary river impacted by sewage effluents

This paper analyzes how changes in hydrological conditions can affect the water quality of a temporary river that receives direct inputs of sewage effluents. Data from 12 spatial surveys of the Vène river were examined. Physico-chemical parameters, major ion, and nutrient concentrations were measured. Analyses of variance (ANOVA) and multivariate analyses were performed. ANOVA revealed significant spatial differences for conductivity and major ion but no significant spatial differences for nutrient concentrations even if higher average concentrations were observed at stations located downstream from sewage effluent discharge points. Significant temporal differences were observed among all the parameters. Karstic springs had a marked dilution effect on the direct disposal of sewage effluents. During high-flow periods, nutrient concentrations were high to moderate whereas nutrient concentrations ranged from moderate to bad at stations located downstream from the direct inputs of sewage effluents during low-flow periods. Principal component analysis showed that water quality parameters that explained the water quality of the Vène river were highly dependent on hydrological conditions. Cluster analysis showed that when the karstic springs were flowing, water quality was homogeneous all along the river, whereas when karstic springs were dry, water quality at the monitoring stations was more fragmented. These results underline the importance of considering hydrological conditions when monitoring the water quality of temporary rivers. In view of the pollution observed in the Vène river, “good water chemical status” can probably only be achieved by improving the management of sewage effluents during low-flow periods.     

大宁河水体营养盐状况与水华爆发之间关系分析

通过对大宁河常规监测和水华爆发期间加密监测断面的氮、磷营养盐指标统计,运用综合评价、相关性分析等方法对大宁河氮、磷营养盐分布和叶绿素a与总磷、总氮的关系进行了分析.结果显示,水华爆发期间叶绿素a与总磷、总氮呈正相关关系,总磷是大宁河水华爆发的限制因子.氮磷水平呈现从上游到入长江口逐渐递增趋势,长江回灌作用对大宁河输入部分氮、磷营养盐.     

基于LSTM-GCN的PM2.5浓度预测模型

应用机器学习算法开展空气质量预测已成为当前研究热点之一,空气质量监测数据具有显著的时空特征,即具有时间维度时序特征和空间维度传输演化特征。面向空气质量监测数据,联合LSTM提取的时间特征和GCN提取的空间特征,提出预测PM_2.5浓度的LSTM-GCN组合模型。以北京市35个空气质量监测站2018—2020年监测数据进行仿真实验,并将LSTM-GCN模型与LSTM模型、GCN模型以及时空地理加权回归模型(GTWR)进行对比,结果显示:LSTM-GCN模型相较于LSTM模型均方根误差(RMSE)、平均绝对误差(MAE)分别降低了11.68%、7.34%;相较于GCN模型RMSE、MAE分别降低了40.22%、36.37%;相较于GTWR模型RMSE、MAE分别降低了17.52%、23.69%,表明所提出LSTM-GCN模型在准确率上有所提升。用LSTM-GCN模型预测2021年1—7月PM_2.5浓度,结果显示预测效果较好。