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.     

Seasonal dynamics of phytoplankton and its relationship with the environmental factors in Dongping Lake, China

Dongping Lake is the final adjusting and storing lake in the east route of the South-to-North Water Diversion Project in China, and there has been serious concern regarding the water quality. Understanding the process of phytoplankton variation can be particularly useful in water quality improvement and management decisions. In this study, the phytoplankton taxonomic composition, abundance, temporal variations, spatial distribution, and diversity were studied based on a monthly sampling campaign from three sampling stations between May 2010 and May 2011. A total of 132 species (8 phyla, 72 genera), including 64 species of Chlorophyta, 26 species of Bacillariophyta, 21 species of Cyanophyta, 12 species of Euglenophyta, 3 species of Cryptophyta, 2 species of Xanthophyta, 1 species of Pyrrophyta, and 3 species of Chrysophyta were identified. Average phytoplankton diversity index and evenness values were 3.83 and 0.77, respectively, revealing a high biodiversity of phytoplankton community. The phytoplankton abundance averaged 5.11?×?10⁶ cells/L, with Bacillariophyta dominant in winter and spring, but Cyanophyta in summer and autumn. There were 14 predominant species including Pseudanabaena limnetica, Chlamydomonas simplex, Cyclotella stelligera, and Chroomonas acuta. Phytoplankton community structure and water quality variables changed substantially over the survey period; redundancy analysis, Pearson correlations, and regression analysis as an integrated approach were applied to analyze the relationships among them. Total phosphorus and ammonium played governing roles in the phytoplankton dynamics of Dongping Lake during all periods investigated.     

Surface Water Quality Assessment by Environmetric Methods

This environmetric study deals with the interpretation of river water monitoring data from the basin of the Buyuk Menderes River and its tributaries in Turkey. Eleven variables were measured to estimate water quality at 17 sampling sites. Factor analysis was applied to explain the correlations between the observations in terms of underlying factors. Results revealed that, water quality was strongly affected from agricultural uses. Cluster analysis was used to classify stations with similar properties and results distinguished three groups of stations. Water quality at downstream of the river was quite different from the other part. It is recommended to involve the environmetric data treatment as a substantial procedure in assessment of water quality data.     

Serial Correlation and Inter-annual Variability in Relation to the Statistical Power of Monitoring Schemes to Detect Trends in Fish Populations

We studied the effects of inter-annual variability and serial correlation on the statistical power of monitoring schemes to detect trends in biomass of bream (Abramis brama) in Lake Veluwemeer (The Netherlands). In order to distinguish between 'true' system variability and sampling variability we simulated the development of the bream population, using estimates for population structure and growth, and compared the resulting inter-annual variabilities and serial correlations with those from field data. In all cases the inter-annual variability in the field data was larger than in simulated data (e.g. for total biomass of all assessed bream sigma = 0.45 in field data, and sigma = 0.03-0.14 in simulated data) indicating that sampling variability decreased statistical power for detecting trends. Moreover, sampling variability obscured the inter-annual dependency (and thus the serial correlation) of biomass, which was expected because in this long-lived population biomass changes are buffered by the many year classes present. We did find the expected serial correlation in our simulation results and concluded that good survey data of long-lived fish populations should show low sampling variability and considerable inter-annual serial correlation. Since serial correlation decreases the power for detecting trends, this means that even when sampling variability would be greatly reduced, the number of sampling years to detect a change of 15%.year(-1) in bream populations (corresponding to a halving or doubling in a six-year period) would in most cases be more than six. This would imply that the six-year reporting periods that are required by the Water Framework Directive of the European Union are too short for the existing fish monitoring schemes.     

Developing an optimal sampling design. A case study in a coastal marine ecosystem

The development of a sampling design for optimisingsampling site locations collected from a coastalmarine environment has been the purpose of the presentwork; application of statistical analysis and spatialautocorrelation methods have been carried out. Thedataset included data collected from 34 sampling sitesspaced out in the Strait of Lesbos, Greece, arrangedin a 1×1 NM grid. The coastal shallow ecosystem wassubdivided into three zones, an inner one (7stations), a middle one (16 stations) and an offshorezone (11 stations). The standard error of thechlorophyll-a concentrations in each zone hasbeen used as the criterion for the sampling designoptimisation, resulting into reallocation of thesampling sites into the three zones. The positions ofthe reallocated stations have been assessed byestimation of the spatial heterogeneity and anisotropyof chlorophyll-a concentrations usingvariograms. Study of the variance of the initialdataset of the inner zone taking into account spatialheterogeneity, revealed two different sub-areas andtherefore, the number of the inner stations has beenreassessed. The proposed methodology eliminates thenumber of sampling sites and maximises the informationof spatial data from marine ecosystems. It isdescribed as a step-by-step procedure and could bewidely applied in sampling design concerning coastalpollution problems.     

Water quality assessment, by statistical analysis, on rural and urban areas of Chocancharava River (Río Cuarto), Córdoba, Argentina

Water quality has degraded dramatically in the Chocancharava River (Río Cuarto, Córdoba, Argentina) due to point and non-point sources. This paper aims to assess spatial and temporal variations of physical and chemical parameters of the river. Six sampling sites and six sampling campaigns were developed. During the period 2007–2008, wet and dry seasons were included. A statistical analysis was carried out with 23 physical and chemical variables. Then, a new statistical analysis was carried out including the Riparian Corridors Quality Index and the physical and chemical variables (24 variables). Considering a multivariate system, analysis of variance, principal component analysis and cluster analysis were used. From the statistical analysis, the river was divided into two zones with different degrees of contamination. The most polluted zone is due to pollution inputs of urban, industrial and agricultural sources. This area showed a remarkable deterioration in water quality, mainly due to wastewater discharges. According to Riparian Quality, better results were found in sections of poor water quality, due to the fact that the river bank forest was less degraded downstream of the sewage discharge.     

成都市河流生态健康评价

研究以成都市域内河流为研究对象,通过采样分析得到水质、生境状况、浮游藻类、底栖动物等相关指标,利用综合指标体系法,评价成都市域内河流生态系统健康状况,并结合成都市人口、土地利用、地区生产总值等因素分析人类活动与河流生态健康的关系。结果表明:成都市河流浮游藻类以硅藻和绿藻为主,部分中下游采样点出现以蓝藻为主的情况;底栖动物以指示有机污染的颤蚓科出现的频率最高,约48%的采样点以颤蚓科为优势种;约74%的河流生态健康状况为"差"或"很差",河流生态健康状况整体呈现中上游优于下游、城市周边优于市区的特征;人口密度、人类活动用地比例与河流中氨氮呈显著正相关,人类活动会显著影响河流生态系统健康。     

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.     

Improving the sampling strategy of the Joint Danube Survey 3 (2013) by means of multivariate statistical techniques applied on selected physico-chemical and biological data

The concept of basin-wide Joint Danube Survey (JDS) was launched by the International Commission for the Protection of the Danube River (ICPDR) as a tool for investigative monitoring under the Water Framework Directive (WFD), with a frequency of 6 years. The first JDS was carried out in 2001 and its success in providing key information for characterisation of the Danube River Basin District as required by WFD lead to the organisation of the second JDS in 2007, which was the world’s biggest river research expedition in that year. The present paper presents an approach for improving the survey strategy for the next planned survey JDS3 (2013) by means of several multivariate statistical techniques. In order to design the optimum structure in terms of parameters and sampling sites, principal component analysis (PCA), factor analysis (FA) and cluster analysis were applied on JDS2 data for 13 selected physico-chemical and one biological element measured in 78 sampling sites located on the main course of the Danube. Results from PCA/FA showed that most of the dataset variance (above 75 %) was explained by five varifactors loaded with 8 out of 14 variables: physical (transparency and total suspended solids), relevant nutrients (N–nitrates and P–orthophosphates), feedback effects of primary production (pH, alkalinity and dissolved oxygen) and algal biomass. Taking into account the representation of the factor scores given by FA versus sampling sites and the major groups generated by the clustering procedure, the spatial network of the next survey could be carefully tailored, leading to a decreasing of sampling sites by more than 30 %. The approach of target oriented sampling strategy based on the selected multivariate statistics can provide a strong reduction in dimensionality of the original data and corresponding costs as well, without any loss of information.     

Evaluation of spatial and temporal variations in marine sediments quality using multivariate statistical techniques

Spatial and temporal variations of sediment quality in Matanzas Bay (Cuba) were studied by determining a total of 12 variables (Zn, Cu, Pb, As, Ni, Co, Al, Fe, Mn, V, CO₃ ^2?, and total hydrocarbons (THC). Surface sediments were collected, annually, at eight stations during 2005–2008. Multivariate statistical techniques, such as principal component (PCA), cluster (CA), and lineal discriminant (LDA) analyses were applied for identification of the most significant variables influencing the environmental quality of sediments. Heavy metals (Zn, Cu, Pb, V, and As) and THC were the most significant species contributing to sediment quality variations during the sampling period. Concentrations of V and As were determined in sediments of this ecosystem for the first time. The variation of sediment environmental quality with the sampling period and the differentiation of samples in three groups along the bay were obtained. The usefulness of the multivariate statistical techniques employed for the environmental interpretation of a limited dataset was confirmed.     

Phytoplankton variation and its relationship with the environmental factors in Nansi Lake, China

Nansi Lake is an important storage lake in the east route of the South-to-North Water Diversion Project in China, about which there has been serious concern regarding the water quality. In this study, the phytoplankton taxonomic composition, abundance, temporal variations, spatial distribution, and diversity were studied based on a monthly sampling campaign from five sampling stations between January 2010 and December 2010. A total of 159 species (8 phyla, 79 genera), including 74 species of Chlorophyta, 36 species of Bacillariophyta, 19 species of Cyanophyta (including 2 water bloom causative species), 21 species of Euglenophyta, 3 species of Cryptophyta, 5 species of Xanthophyta, 2 species of Pyrrophyta, and 2 species of Chrysophyta, were identified. Average phytoplankton diversity index and evenness values were 4.33 and 0.81, respectively, revealing high biodiversity of phytoplankton community. The phytoplankton abundance averaged at 9.51?×?10⁶ cells L^?1 and was much higher than previous investigations carried out in 1983–1984. The dominant species were Bacillariophyta, Chlorophyta in winter and spring, and Chlorophyta and Cyanophyta in summer and atutumn. There were 14 predominant species including Chlorella vulgaris, Cyclotella stelligera, Pseudanabaena limnetica, and Chroomonas acuta. Phytoplankton community structure and environmental variable changed substantially over the survey period. Redundancy Analysis was used to analyze the relationship between them. Temperature was considered to be the key factor driving the change in phytoplankton community composition in Nansi Lake during the 2010 study period.     

Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP)

River water quality sampling frequency is an important aspect of the river water quality monitoring network. A suitable sampling frequency for each station as well as for the whole network will provide a measure of the real water quality status for the water quality managers as well as the decision makers. The analytic hierarchy process (AHP) is an effective method for decision analysis and calculation of weighting factors based on multiple criteria to solve complicated problems. This study introduces a new procedure to design river water quality sampling frequency by applying the AHP. We introduce and combine weighting factors of variables with the relative weights of stations to select the sampling frequency for each station, monthly and yearly. The new procedure was applied for Jingmei and Xindian rivers, Taipei, Taiwan. The results showed that sampling frequency should be increased at high weighted stations while decreased at low weighted stations. In addition, a detailed monitoring plan for each station and each month could be scheduled from the output results. Finally, the study showed that the AHP is a suitable method to design a system for sampling frequency as it could combine multiple weights and multiple levels for stations and variables to calculate a final weight for stations, variables, and months.     

Assessing BMP effectiveness: multiprocedure analysis of observed water quality data

Observed water quality data obtained from eight stream monitoring locations within Richland–Chambers Watershed in north central Texas were analyzed for trends using box-and-whisker plots, exceedance probability plots, and linear and Mann–Kendall statistical methods. Total suspended solids decreased at seven out of eight stations, and at two of these stations, the decrease was significant. Mixed results were obtained for nitrogen across the stations. A nonsignificant and significant increase in nitrite plus nitrate nitrogen (nitrite + nitrate N) was noticed in two stations each, whereas at the other four stations showed nonsignificant decrease. The results of organic nitrogen (Org N) was similar to nitrite + nitrate N except that the two stations that showed significant increase in nitrite + nitrate N showed nonsignificant decrease in Org N. Mixed results were also noticed for orthophosphorus (Ortho P) including nonsignificant decrease at two stations, significant decrease and increase at one station each, and nonsignificant increase in four stations. In general, total phosphorus (TP) decreased at all stations, significantly at some, except one station where it increased significantly. Decreasing trends in sediment, Org N, Ortho P, and TP were likely related to implementation of best management practices (BMPs). Increasing trends in dissolved constituents including Ortho P and nitrite + nitrate N were likely due to increased surface residue as a result of some BMPs such as conservation tillage.     

Multivariate Analysis of Interactions Between Phytoplankton Biomass and Environmental Variables in Taihu Lake, China

Phytoplankton variation in large shallow eutrophic lakes is characterized by high spatial and temporal heterogenity. Understanding the pattern of phytoplankton variation and the relationships between it and environmental variables can contribute to eutrophic lakes management. In this study Taihu Lake, one of the largest eutrophic fresh water lake in China, was taken as study area. The water body of Taihu Lake was divided into five regions viz. Wuli bay (WB), Meilian Bay (MB), West Taihu Lake (WTL), Main Body of Taihu Lake (MBTL) and East Taihu Lake (ETL). Concentrations of chlorophyll-a and the related environmental variables were determined in each region in the period 2000–2003. Factor analysis and multivariate analysis were applied to evaluate the interactions between phytoplankton variation and environmental variables. Results showed that the highest average concentrations of TN, TP and Chl-a were observed in WB, followed in a descending order by MB and WTL, and the lowest concentrations of TN, TP and Chl-a were observed in MBTL and ETL. Chl-a and TP concentrations in most regions (except ETL) declined during the study period. It suggested that to some extent the lake was recovering from eutrophication. However, persistent ascending of TN and NH₄–N in all five regions indicated the deteriorating of water quality in the study period. Results of multivariate showed that the relationships between phytoplankton biomass and environmental variables varied among regions. TP illustrated itself a controlling role on phytoplankton in WB, MB, WTL and MBTL according to the significant positive relations to phytoplankton biomass in these regions. Nitrogen could be identified as a limiting factor to phytoplankton biomass in ETL in view of the positive correlations between TN and phytoplankton and between NH₄–N and phytoplankton. Spatial variation of interactions between phytoplankton and environmental parameters suggested proper eutrophication control measures were needed to restore ecological system in each region of Taihu Lake.     

Maryland Biological Stream Survey: Development of a Fish Index of Biotic Integrity

As a step towards determining the extent of degradation in non-tidal streams, a multi-metric Index of Biotic Integrity (IBI) based on fish assemblages was developed for the Maryland Biological Stream Survey (MBSS). The MBSS is a probability-based statewide sampling program designed to assess the status of biological resources and to evaluate the effects of anthropogenic activities. We used data from 419 MBSS sites sampled in 1994-95 to develop the IBI. Two distinct geographic strata, corresponding with ecoregional and physiographic boundaries, were identified via cluster analysis and multivariate analysis of variance (MANOVA) as supporting distinctly different species groups. Reference conditions were based on minimally degraded sites. We quantitatively evaluated the ability of various attributes of the fish assemblage (candidate metrics) to discriminate between these reference sites and sites known to be degraded, using statistical tests and classification efficiency. Provisional formulations of the IBI were selected for each region based on high classification efficiency and broad representation of fish assemblage attributes. Fish IBI scores for 1995 MBSS sites spanned a wide range of biological conditions, from good to very poor. Over all six basins sampled in 1995, half of the stream miles fell into the range of good to fair. Roughly 25% of stream miles showed some degradation. The IBI will be used in conjunction with physical and chemical data to answer critical questions about the health of Maryland streams and the relative impacts of human-induced stresses on the state's aquatic systems.     

Organochlorine pesticide residues in Songkla Lake

The water quality of Songkla Lake for organochlorine (OC) pesticide contamination was studied from chosen sampling sites (stations) using the lakes geographic data and pollution sources. The water samples were collected monthly from 13 stations in Songkla Lake during September 1991–November 1992. The OC pesticide residues found were Heptachlor, Heptachlor Epoxide, DDD, DDE, DDT and Aldrin in the range 0–0.5690 ppm. This study showed that DDT and its metabolite products (DDD and DDE) dominated OC pesticide residues in the lake. The concentration of OC residues also depended on the season, i.e. at some sampling stations the concentration of OC residues in the dry season (July–September) were higher than in the wet season (October–January).     

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.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey

The application of different multivariate statistical approaches for the interpretation of a complex data matrix obtained during the period 2004-2005 from Uluabat Lake surface water is presented in this study. The dataset consists of the analytical results of a 1 year-survey conducted in 12 sampling stations in the Lake. Twelve parameters (T, pH, DO, [Formula: see text], NH(4)-N, NO(2)-N, NO(3)-N, [Formula: see text], BOD, COD, TC, FC) were monitored in the sampling sites on a monthly basis (except December 2004, January and February 2005, a total of 1,296 observations). The dataset was treated using cluster analysis, principle component analysis and factor analysis on principle components. Cluster analysis revealed two different groups of similarities between the sampling sites, reflecting different physicochemical properties and pollution levels in the studied water system. Three latent factors were identified as responsible for the data structure, explaining 77.35% of total variance in the dataset. The first factor called the microbiological factor explained 32.34% of the total variance. The second factor named the organic-nutrient factors explained 25.46% and the third factor called physicochemical factors explained 19.54% of the variances, respectively.     

Intertidal macrofaunal communities in an intensely polluted estuary

The Bilbao estuary (northern Spain) has suffered a serious environmental deterioration due to industrial and domestic discharges during decades. A total of 21 sampling stations were selected in order to: (1) study the intertidal fauna distribution on both hard and soft bottoms; (2) link this information with the prevalence of certain environmental variables at the sampling sites; and (3) define distinct biological zones with all the information gathered. Up to 50 taxa were identified mainly in the outer estuary with an abrupt decrease upstream. In general, fauna on hard substrates was more diverse, while soft bottom communities exhibited a more consistent structure. Sporadic hypoxic events were recorded in the water column of all the sites studied, which affects the distribution of species. Three major biotic zones are proposed in the estuary. This provides a reliable biomonitoring tool for assessing the effectiveness of a sewerage and remediation scheme ongoing on the river and evaluating the future use of each area in the urban development of the city of Bilbao and conurbation.