Multivariate statistical assessment of a polluted river under nitrification inhibition in the tropics

Multiple stressors determine the process of the benthic diatom community assembly and network stability in urban water bodies in Harbin

Human activities have triggered biodiversity loss, often resulting in biotic homogenization, which poses a threat to human well-being. Nevertheless, the overall influence of diverse environmental stressors on intra- and inter-community diversity remains insufficiently elucidated. This study aimed to quantify and reveal the impact of environmental stressors on the alpha and beta diversities of benthic diatom communities in the Harbin urban river network during the summer and autumn of 2022 and spring of 2023. The marked seasonal variations observed in alpha and beta diversity indices highlighted the distinct community compositions. Nonetheless, varying types of urban water pollutants were the primary drivers of biotic homogenization in terms of both taxonomic and functional diversities and played a prominent role in steering diversity shifts. These pollutants indirectly led to biotic homogenization by altering water quality parameters and affecting the ecological dynamics of benthic diatom communities. Furthermore, diverse responses to stressors were identified in taxonomic and functional diversities, providing additional insights for understanding ecological shifts in communities. Taxonomic beta diversity was related to environmental filtering, whereas functional beta diversity resulted from stressor-spatial dimension interactions. Our study emphasises that relying solely on traditional water quality monitoring may not fully reveal the current state of river ecosystem protection, and the need to study the continuous changes in biodiversity across seasons in urban waterbodies from the perspective of various stressors is highlighted.

Groundwater Contamination Monitoring for Pollution Measurement and Transmission Applying WQI Approaches from a Region of the Erode District, Tamilnadu, India

The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.

Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD₅ and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO₄³) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD₅, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO₄^3-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.

Spatiotemporal Variations in Physicochemical and Biological Properties of Surface Water Using Statistical Analyses in Vinh Long Province, Vietnam

In this study, spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam, were conducted using entropy weighting, water quality index (WQI), and multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The samples collected at 63 monitoring locations in March, June, and September were measured for 15 parameters. Compared to the Vietnamese standard, surface water was contaminated with organic matters, nutrients, microorganisms, and salinity. DA identified the most typical parameters (pH, turbidity, TSS, EC, DO, Cl−, E. coli, coliform) in distinguishing temporal variations in water quality with greater than 75% of the correction. CA group 63 sampling sites into 22 clusters representing different land use patterns. WQI determined the worst water quality was found in the agricultural areas. Based on the results of entropy weighting, EC, coliform, N-NH4+, BOD, N-NO3−, and Fe had significantly controlled surface water quality. Four principal components obtained from PCA explained 66.45% of the variance, suggesting the influences of geohydrological factors and anthropogenic activities, such as domestic, market area, agriculture, and industry. The findings of this study can provide useful information for authorities to evaluate the effectiveness of monitoring systems and plan for water quality management strategies.

Trends of nutrients and metals in precipitation in northern Germany: the role of emissions and meteorology

We analyzed the precipitation chemistry for a maritime region in northern Germany (Schleswig-Holstein) from 1997 to 2017 in order to reveal temporal and spatial patterns and to evaluate the role of meteorological factors relative to emission reductions in Germany and Europe. Therefore, we applied several statistical methods such as time series decomposition, principal component, and redundancy analysis. We extracted two main groups: (i) a marine group (Cl, Na, Mg) that was related to natural processes like sea spray input and (ii) an anthropogenic group (Pb, Cd, As, Zn, and nitrogen species) with a terrestrial subgroup (Fe, Al, Mn), which were both related to emissions. These groups were valid for the spatial, seasonal, and annual trend data. Other elements, like Ca, K, total P, and sulfate, were influenced by natural and anthropogenic processes. The seasonal variation of ammonium deposition was caused primarily by ammonia emissions and ancillary by precipitation. Most heavy metals as well as sulfate, nitrate, and ammonium showed decreasing trends in concentrations and deposition fluxes. Only Hg did not show any trend. The decreasing depositions of sulfate and total nitrogen were correlated to emission reductions in Germany. The deposition of most heavy metals was influenced by emission reductions on European scale and meteorological factors such as wind speed and humidity. Hg did not show any correlation with the emission time series in Europe. Instead, it was correlated to the NAO index and wind, implying that global emissions and transport pathways determine the temporal development of Hg depositions. Overall, the study reveals that emission reductions positively influence regional depositions for most investigated substances. The regional spatial patterns of depositions were also influenced by local meteorological factors.

Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil

The objective of the present study was to evaluate the water quality data in the Minas Gerais portion of the Doce River basin in order to analyze the current monitoring network by identifying the main variables to be maintained in the network, their possible sources of pollution, and the best sampling frequency. Multivariate statistical techniques (factor analysis/principal components analysis, FA/PCA and cluster analysis, CA) complemented by the analysis of violation of the framing classes were used for this purpose. Water quality variables common to 64 monitoring sites were analyzed for the base period from 2010 to 2017. The water quality variables were analyzed considering the different monitoring campaigns: (a) partial campaigns; (b) total campaigns; and (c) monthly campaigns. It was identified from the FA/PCA results, that, when the partial campaign data were analyzed, the variables selected represent the high susceptibility that the basin presents to erosion and the release of domestic effluents in its water bodies. When the data of total campaigns were evaluated, representative variables of the contamination by heavy metals from industrial and mining activities were included. Therefore, the analysis of violation of the framing classes made possible to identify five critical variables: thermotolerant coliforms, dissolved iron, total phosphorus, and total manganese, which reinforced the results obtained in FA/PCA. Based on the results of the analyses, it was recommended to include variables associated with heavy metal contamination in the partial campaigns, prioritizing the dissolved iron and total manganese, as well as total chloride sampling only for the total campaigns. The evaluated data from the monthly campaigns, the CA showed that although the quarterly monitoring frequency is satisfactory, the monthly monitoring is more appropriate for the monitoring of water quality in the Minas Gerais portion of the Doce River basin.

The impact of small-scale land cover and groundwater interactions on base flow solute and nutrient export in a small agricultural stream

Central Kentucky could be considered a critical source area of nutrients in water ways because of low permeability soils, fast groundwater flow through bedrock fractures, and pervasive agriculture and development. Of particular concern is rising development in rural areas, which creates mixed land cover (MLC) watersheds, i.e., watersheds with development, agriculture, and other land cover types. MLC watersheds add complexity to spatial and temporal releases of dissolved constituents, leading to less predictable water quality patterns. The goal of this research was to examine the export of dissolved substances from a small, upland MLC catchment in central Kentucky with a focus on how the interaction between discharges from developed agricultural land cover and groundwater influence base flow water quality. Our approach was to spatially sample a representative catchment monthly over 1 year, characterize the major dissolved constituents, and evaluate catchment processes with statistical analyses and Piper diagrams. Principal component analysis, factor analysis, and Piper diagrams indicate base flow was composed of groundwater influenced by two different host rocks and an outfall draining a developed region. Base flow nutrient export was dominated by mixing nitrate-sulfate rich groundwater with ammonium-phosphate-chloride rich outfall drainage. High nitrate groundwater dominated nitrogen export in the winter, whereas high ammonium outfall drainage dominated summer export. Spatial analysis revealed that ~ 10% of the basin may have similar land cover and hydrologic processes, suggesting that MLC catchments are small but collectively significant nitrogen sources to river networks due to development and agriculturally impacted groundwater.

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.