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

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R²) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

Seasonal behavior and accumulation of some toxic metals in commercial fishes from Kirtankhola tidal river of Bangladesh - A health risk taxation

Contamination of fish with heavy metals (Heavy metals) is one of the most severe environmental and human health issues. However, the contamination levels in tropical fishes from Bangladesh are still unknown. To this end, the evaluated concentrations of arsenic (As), chromium (Cr), cadmium (Cd), and lead (Pb) in 12 different commercially important fish species (Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Glossogobius giuris, Pseudeutropius atherinoides, Polynemus paradiseus, Sillaginopsis panijus, Corica soborna, Amblypharyngodon mola, Trichogaster fasciata, and Wallago attu) were collected from the Kirtankhola River assess human health risk for the consumers, both in the summer and winter seasons. Toxic metals surpassed the acceptable international limits in P. atherinoides, P. paradiseus, S. panijus, C. soborna, and W. attu. The target hazard quotient (THQ) revealed that non-carcinogenic health effects (HI < 1) for children and adults, and the carcinogenic risk (CR) indicated safety. Results show that children are more susceptible to carcinogenic and non-carcinogenic hazards from higher As. The multivariate analysis justified that heavy metals were from anthropogenic actions. The lessening of toxic metals might need strict rules and regulations as metal enrichment would continue to increase in this tidal river from both the anthropogenic and natural sources.

Zooplankton Fluctuations in the Surface Waters of the Estuary of a Large Subtropical Urban River

The Danshuei River has a third largest catchment area and third longest in Taiwan. It flows through the capital, Taipei, and more than six million people live within its catchment area. Its estuary is characterized by a highly variable chemical and physical environment that is affected by the interaction of inland freshwater runoff with wastewater, and toward the coast is also influenced by the China Coastal Current and the Kuroshio Current. By collecting zooplankton bimonthly in 2014 from the surface layer (0–2 m depth) at five sites in the estuary, we were able to demonstrate that the composition of the zooplankton, and particularly its copepod fraction, varied significantly among sampling stations and months, thereby revealing seasonal succession. Fourteen higher taxa or other categories of zooplankter were identified, with the following being most common taxa: Decapoda, Copepoda (including Calanoida, Cyclopoida, and Harpacticoida), and “other larvae.” The Copepoda comprised 44 taxa (including eight only identified to genus) belonging to 3 orders, 17 families, and 29 genera, the five most abundant of which were Bestiolina n. sp. (undescribed), Corycaeus spp., Parvocalanus crassirostris, Acartia sp., and Paracalanus parvus. The highest and lowest copepod abundances were recorded in July (2557.88 inds. m–3) and January (1.3 inds. m–3), respectively. Observed changes in abundance of many kinds of copepod appeared to be significantly related to changes in physico-chemical parameters (e.g., salinity, temperature, pH, and dissolved oxygen concentration). Cluster analysis confirmed the existence of distinct copepod communities, each characterized by a preference for a different set of environmental conditions. Our comprehensive literature review of the copepod biodiversity of Taiwan’s major rivers for comparison with similar data compiled for other estuaries in the world, the first time such a review has been compiled, shows that 32 copepod taxa have been recorded from the brackish and freshwater parts of the Danshuei River. They represent 58.2% of the total number of brackish- and freshwater copepod species in Taiwan, and five of them have so far only been recorded in the Danshuei River: the calanoids Acartiella sinensis and Pseudodiaptomus forbesi, the cyclopoids Oithona fragilis and Oithona simplex, and the harpacticoid Tachidius (Tachidius) discipes.

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources.

Using multi-threshold regression techniques to assess river fecal pollution in the highly urbanized Tamsui River watershed

Rivers are an important urban water resource. This study adopted multivariate linear regression (MLR) and logistic regression (LR) with multiple thresholds to assess river fecal pollution in the Tamsui River watershed using auxiliary environmental data. First, environmental data between 2015 and 2017 on land use, antecedent precipitation, population density, sewerage infrastructure, and river water quality were obtained using geographic information systems and served as explanatory variables. River fecal coliforms (FC), the dependent variable, were also collected for the same period. Then, MLR was used to establish an overall prediction model after validation, and to determine significant factors influencing the level of river fecal pollution. Finally, after stratifying the fecal pollution as low, medium, and high levels, LR with multiple thresholds was employed to explore key factors affecting different FC pollution levels. The study results revealed that land use type and river water quality (other than FC) strongly affected river FC pollution. The discharge of household sewage and wastewater from urban areas was a major source of river FC pollution, particularly for low and medium pollution levels, while farmland land use was negatively correlated with the medium and high levels of river FC pollution in the highly urbanized watershed. Biochemical oxygen demand and suspended solids were highly correlated with medium and high pollution levels in river water.

A spatiotemporal analysis of water quality characteristics in the Klip river catchment, South Africa

Understanding the spatial and temporal patterns of water quality is central to its management as it provides information essential to the restoration as well as protection of water resources. The main objectives of this study were (i) to analyze the spatial and temporal trends of water quality and (ii) to identify the critical sources of pollution in the Klip River catchment (KRC). Water samples were collected at 12 sampling points along the Klip River, monthly from February 2016 to January 2017 and analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and spectrophotometry for heavy metals and nutrients, respectively. Multivariate statistical techniques (cluster analysis and discriminant analysis) were used to delineate homogeneous water quality zones and seasons, and principal component analysis was used to identify pollution sources. Comprehensive pollution index (CPI) was also computed to classify the overall pollution of the river. The spatial grouping yielded two homogenous water quality zones namely upstream and downstream. Temporal grouping yielded two clusters, which were attributed to the effects of the El Nino (2015/16 season) and La Nina phenomena (2016/17 season). The CPI revealed that the KRC was critically polluted in the upstream for domestic (162.16-323.28) and aquatic uses (617.70-837.09) in both the 2015/2016 and 2016/2017 seasons. It can be concluded that pollutants, which influence water quality in the KRC in one season and/or location, may not necessarily be the same in the other season or location. Therefore, there is need to develop a water quality management plan in the KRC that targets the most impaired uses, pollutants of priority, and the critically polluted areas.

Microplastic pollution of the Tamsui River and its tributaries in northern Taiwan: Spatial heterogeneity and correlation with precipitation

The microplastic pollution and its effects on ecosystem in the marine environment has been well studied over the past decade. In contrast, the impact of microplastic pollution in freshwater environments was understudied, e.g., only a few studies examined the amount and distribution of microplastic in rivers, as well as the contribution of rivers to the microplastic pollution in oceans. In this study we investigated the microplastic pollution in the Tamsui River and its tributaries in northern Taiwan. We collected samples with a manta net from the Tamsui River, the Dahan River, the Keelung River and the Xindian River every two weeks over a time period of three months in 2018. Additionally, we took samples from the Xindian River during a heavy rain event in February 2019. Microplastic particles in the size range of 0.3-5 mm were visually identified. Unknown particles were identified using FTIR spectroscopy. The extracted particles were counted and classified according to their shape and color. We found microplastic of varying amounts in each of the samples, which indicates a wide spread pollution in the Tamsui River and its tributaries. The amount varies between rivers and ranges in average from 2.5 ± 1.8 particles per m³ in the Xindian River to 83.7 ± 70.8 particles per m³ in the Dahan River. Our data shows a positive correlation between precipitation and amount of microplastic particles found in the rivers. Moreover, in each river we could observe a large spatial and temporal variation of the microplastic amount between the left, middle and right sections of the stream. Due to this heterogeneous distribution of particles, we suggest that samples for microplastic analysis should be taken from multiple places across a river, as well as over a certain period to account for the heterogeneous microplastic distribution in the river water.

Evaluation of Water Quality and Heavy Metals in Wetlands along the Yellow River in Henan Province

Assessing spatiotemporal variation in water quality and heavy metals concentrations in wetlands and identifying metal contamination source are crucial steps for the protection and sustainable utilization of water resources. Using the water quality identification index (Iwq), heavy metal pollution index (HPI), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), we evaluated spatiotemporal variation in water quality and heavy metals concentrations, and their interrelation in wetlands along the middle and lower Yellow River. The average Iwq was highest during flood season but the average HPI was lowest in the same season. Meanwhile, the trend in mean HPI across three hydrological seasons was the opposite to that of mean Iwq. There was significant variation in wetlands water pollution status across seasons. During the flood season, the wetlands in the affected area with hanging river were seriously polluted. In other seasons, pollution in the artificial wetlands was even more severe. Moreover, serious pollution of wetlands in belt transect #03 (Yuanyang-Zhongmu) was more frequent. Dissolved oxygen and chemical oxygen demand strongly influenced heavy metal concentrations, while other water quality parameters had different influences on heavy metal concentrations in different hydrological seasons. The causes of water pollution were divided into natural factors and human disturbance (with potential relationships between them). The polluted wetlands were greatly affected by the Yellow River during the flood season while they were more impacted by agricultural and domestic sewage discharge in other seasons. However, heavy metal deposition and leaching into riparian wetlands were still affected by diverse channel conditions. If this trend is allowed to continue unabated, wetlands along the middle and lower Yellow River are likely to lose their vital ecological and social functions.

Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

Spatial and Temporal Variability of Water Quality in the Bystrzyca River Basin, Poland

The aim of the study was to analyze the results of surface water quality tests carried out in the Bystrzyca river basin. The study was conducted over four years in four seasons. The following chemometric techniques were used for the purposes of statistical analyses: the principal component analysis with factor analysis (PCA/FA), the hierarchical cluster analysis (HCA), and the discriminant analysis (DA). The analyses allowed for determining the temporal variability in water quality between the seasons. The best water quality was recorded in summer and the worst in autumn. The analyses did not provide a clear assessment of the spatial variability of water quality in the river basin. Pollution from wastewater treatment plants and soil tillage had a similar effect on water quality. The tested samples were characterized by very high electrolytic conductivity, suspended solids and P-PO4 concentrations and the water quality did not meet the standards of good ecological status.

Spatiotemporal assessment of water quality monitoring network in a tropical river

Managers of water quality and water monitoring programs are often faced with constraints in terms of budget, time, and laboratory capacity for sample analysis. In such situation, the ideal solution is to reduce the number of sampling sites and/or monitored variables. In this case, selecting appropriate monitoring sites is a challenge. To overcome this problem, this study was conducted to statistically assess and identify the appropriate sampling stations of monitoring network under the monitored parameters. To achieve this goal, two sets of water quality data acquired from two different monitoring networks were used. The hierarchical agglomerative cluster analysis (HACA) were used to group stations with similar characteristics in the networks, the time series analysis was then performed to observe the temporal variation of water quality within the station clusters, and the geo-statistical analysis associated Kendall's coefficient of concordance were finally applied to identify the most appropriate and least appropriate sampling stations. Based on the overall result, five stations were identified in the networks that contribute the most to the knowledge of water quality status of the entire river. In addition, five stations deemed less important were identified and could therefore be considered as redundant in the network. This result demonstrated that geo-statistical technique coupled with Kendall's coefficient of concordance can be a reliable method for water resource managers to identify appropriate sampling sites in a river monitoring network.

Developing Egyptian water quality index for drainage water reuse in agriculture

In Egypt, water quality (WQ) is an ordinary activity. However, the calculation of a tailor-made Egyptian WQ index is not. Therefore, this research attempts to develop an index for the agricultural drainage water (EG-DWQI) that can be reused for irrigation based on collected WQ data measured monthly from August 2000 to July 2015. The development of the EG-DWQI was carried out in four main steps, namely, parameters selection, parameter transformation to a common scale, assignment of parameters weights, and aggregation of subindices to produce a final index score. In its final form, the index can be easily estimated using a set of rating curves and their corresponding equations that were statistically tested and proved to be verified. The developed index was successfully employed to delineate the drainage WQ status that can be reused for irrigation in the Nile Delta of Egypt as benchmarking for future comparisons. The EG-DWQI is proved to be easy, fast, and does not entail complex mathematics and can be easily managed by means of a spreadsheet. PRACTITIONER POINTS: This research develops a tailor made water quality index for assessing agricultural drainage water that can be re-used for irrigation. Then, the developed index was successfully employed to delineate the water quality in the main drains located in the Nile Delta of Egypt as benchmarking for future comparisons. The application of the index is proved to be easy, fast and does not entail complicated mathematics especially with the new computer facilities.

Assessment of spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin

Rivers are an important urban resource, and water quality influences the use of river water. Thus, analyzing spatiotemporal variations in river water quality is crucial for sustainable use and management of water resources in a highly urbanized region. This study employed river pollution index (RPI) data obtained in 2013 to assess spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin. First, ordinary kriging was adopted to analyze monthly RPI distributions. Subsequently, different percentiles of monthly estimated RPI distributions were probabilistically determined at a river segment. Finally, three measurement methods of local uncertainty, namely-conditional variance, local entropy, and interquartile range-were used to characterize spatiotemporal variations in river water quality in the Danshui River basin. Assessment results revealed that more highly polluted river water quality resulted in higher seasonal variations. Moreover, high and very high seasonal variations were mainly concentrated in urban river segments, whereas low and very low seasonal variations were primarily located in upstream river segments. Thus, to achieve sustainable development goals, artificial wetlands should be established at downstream and midstream urban riverbanks and urban recreational activities should be developed in upstream riverbank parks in the Danshui River basin before the comprehensive improvement of river water quality.

Spatial dynamic assessment of health risks for urban river cruises

River cruising ships move along river courses, and thus health risks to passengers may vary spatially due to the accidental exposure of river fecal pollution. This study performed a spatial dynamic assessment of health risks for river cruises in the highly urbanized Tamsui River Basin. First, the spatial distributions of river Escherichia coli (E. coli) were probabilistically characterized using indicator kriging (IK). Moreover, the current river cruise information was surveyed to obtain cruise routes and transit times. Then, to explore the parametric uncertainty of quantitative microbial risk assessment (QMRA), the ingestion rate (IR) for boating was determined using Monte Carlo simulation (MCS). Moreover, river E. coli distributions were estimated using nonparametric MCS according to multi-threshold IK estimates. Eventually, after combining the distribution of the joint probability of the IR and E. coli in QMRA, the β-Poisson dose-response function was adopted to analyze risks to river cruise passengers at discretized segments of cruise routes. Health risks to river cruise passengers were integrated at the discretized segments to explore suitable recreational strategies for river cruises. The research results indicate that all health risks do not exceed a daily target level of 8 illnesses per 1000 exposures for single-trip cruise routes. However, health risks to passengers can exceed this level for round-trip cruise routes along highly polluted urban river courses.

A cost-effective and efficient framework to determine water quality monitoring network locations

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata-Markov chain model (CA-Markov) was applied to simulate future change in non-point sources during the period 2026-2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks

Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poço Tarzan (PT) and the least is Parque Petrópolis (PP).

Spatial and Seasonal Surface Water Quality Assessment in a Tropical Urban Catchment: Burío River, Costa Rica

Water quality assessments are essential for providing information regarding integrated water resource management processes. This study presents the results of a spatial and seasonal surface water quality assessment of the Burío river sub-catchment in Costa Rica. Fourteen sample campaigns were conducted at eight sample sites between 2005 and 2010. Seasonal variations were evaluated using linear mixed-effects models where dissolved oxygen, total solids, and nitrate showed significant differences between dry and wet seasons (p < 0.05). Cluster analysis identified three clusters at the top, middle, and bottom of the catchment that were consistent with land use patterns, and principal component analysis identified the main parameters that were affecting 84% of the total variance in water quality (biochemical oxygen demand, dissolved oxygen, total phosphate, and nitrate). The National Sanitation Foundation Water Quality Index (NSF-WQI) results indicated the majority of the river consisted of mainly “medium” water quality, although “bad” and “good” water quality results were identified depending on sample site and season. This methodological approach provides a useful monitoring technique for local governments that can be used for further remediation strategies.

Water Quality Assessment of River Soan (Pakistan) and Source Apportionment of Pollution Sources Through Receptor Modeling

The present study was designed to determine the spatiotemporal patterns in water quality of River Soan using multivariate statistics. A total of 26 sites were surveyed along River Soan and its associated tributaries during pre- and post-monsoon seasons in 2008. Hierarchical agglomerative cluster analysis (HACA) classified sampling sites into three groups according to their degree of pollution, which ranged from least to high degradation of water quality. Discriminant function analysis (DFA) revealed that alkalinity, orthophosphates, nitrates, ammonia, salinity, and Cd were variables that significantly discriminate among three groups identified by HACA. Temporal trends as identified through DFA revealed that COD, DO, pH, Cu, Cd, and Cr could be attributed for major seasonal variations in water quality. PCA/FA identified six factors as potential sources of pollution of River Soan. Absolute principal component scores using multiple regression method (APCS-MLR) further explained the percent contribution from each source. Heavy metals were largely added through industrial activities (28 %) and sewage waste (28 %), nutrients through agriculture runoff (35 %) and sewage waste (28 %), organic pollution through sewage waste (27 %) and urban runoff (17 %) and macroelements through urban runoff (39 %), and mineralization and sewage waste (30 %). The present study showed that anthropogenic activities are the major source of variations in River Soan. In order to address the water quality issues, implementation of effective waste management measures are needed.

Using probability-based spatial estimation of the river pollution index to assess urban water recreational quality in the Tamsui River watershed

The Tamsui River watershed situated in Northern Taiwan provides a variety of water recreational opportunities such as riverbank park activities, fishing, cruising, rowing, sailing, and swimming. However, river water quality strongly affects water recreational quality. Moreover, the health of recreationists who are partially or fully exposed to polluted river water may be jeopardized. A river pollution index (RPI) composed of dissolved oxygen, biochemical oxygen demand, suspended solids, and ammonia nitrogen is typically used to gauge the river water quality and regulate the water body use in Taiwan. The purpose of this study was to probabilistically determine the RPI categories in the Tamsui River watershed and to assess the urban water recreational quality on the basis of the estimated RPI categories. First, according to various RPI categories, one-dimensional indicator kriging (IK) was adopted to estimate the occurrence probabilities of the RPI categories. The maximum occurrence probability among the categories was then employed to determine the most suitable RPI category. Finally, the most serious categories and seasonal variations of RPI were adopted to evaluate the quality of current water recreational opportunities in the Tamsui River watershed. The results revealed that the midstream and downstream sections of the Tamsui River and its tributaries with poor river water quality afford low water recreational quality, and water recreationists should avoid full or limited exposure to these bodies of water. However, the upstream sections of the Tamsui River watershed with high river water quality are suitable for all water recreational activities.

Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

Multivariate statistical techniques, such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA), were used to investigate the temporal and spatial variations and to interpret large and complex water quality data sets collected from the Kaduna River. Kaduna River is the main tributary of Niger River in Nigeria and represents the common situation of most natural rivers including spatial patterns of pollutants. The water samples were collected monthly for 5 years (2008-2012) from eight sampling stations located along the river. In all samples, 17 parameters of water quality were determined: total dissolved solids (TDS), pH, Thard, dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), NH4-N, Cl, SO4, Ca, Mg, total coliform (TColi), turbidity, electrical conductivity (EC), HCO3 (-), NO3 (-), and temperature (T). Hierarchical CA grouped 12 months into two seasons (dry and wet seasons) and classified eight sampling stations into two groups (low- and high-pollution regions) based on seasonal differences and different levels of pollution, respectively. PCA/FA for each group formed by CA helped to identify spatiotemporal dynamics of water quality in Kaduna River. CA illustrated that water quality progressively deteriorated from headwater to downstream areas. The results of PCA/FA determined that 78.7 % of the total variance in low pollution region was explained by five factor, that is, natural and organic, mineral, microbial, organic, and nutrient, and 87.6 % of total variance in high pollution region was explained by six factors, that is, microbial, organic, mineral, natural, nutrient, and organic. Varifactors obtained from FA indicated that the parameters responsible for water quality variations are resulted from agricultural runoff, natural pollution, domestic, municipal, and industrial wastewater. Mann-Whitney U test results revealed that TDS, pH, DO, T, EC, TColi, turbidity, total hardness (THard), Mg, Ca, NO3 (-), COD, and BOD were identified as significant variables affecting temporal variation in river water, and TDS, EC, and TColi were identified as significant variables affecting spatial variation. In addition, box-whisker plots facilitated and supported multivariate analysis results. This study illustrates the usefulness of multivariate statistical techniques for classification and processing of large and complex data sets of water quality parameters, identification of latent pollution factors/sources and their spatial-temporal variations, and determination of the corresponding significant parameters in river water quality.

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.