Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Identification of the pollution sources and hidden clustering patterns for potentially toxic elements in typical peri-urban agricultural soils in southern China

Peri-urban agricultural soils are often contaminated by potentially toxic elements (PTEs) due to rapid urbanization, industrial activities, and agricultural practices. In this study, two advanced analytical methods including positive matrix factorization (PMF) model and K-means clustering algorithm were integrated to explore the potential sources and concealed contamination patterns of 8 PTEs in peri-urban soils in county Gaoming, China. Descriptive statistics showed average concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) as 19.11, 0.18, 35.69, 20.31, 18.26, 151.7, 67.75, and 0.29 mg/kg, respectively. The PMF model identified three primary sources: geogenic (Cr, Ni), industrial and traffic-related (Pb, Hg, Zn), and agricultural (As, Cd and Cu). The contribution of each source was quantified: geogenic sources contributed 55.6% to Cr and 52.3% to Ni, industrial sources accounted for 41.8% of Pb, 58.4% of Hg, and 41.9% of Zn, while agricultural practices contributed 88.1% of As, 77.9% of Cu, and 70.7% of Cd. Subsequently, K-means clustering classified the soil samples into three distinct clusters based on the derived factor contribution from PMF model, reflecting their clear spatial associations with different types of land use: large-scale agricultural areas (Cluster 1), natural vegetation (Cluster 2), and urbanized zones (Cluster 3). Furthermore, boxplots showed that the highest PTE concentrations were found in the third cluster, confirming the significant impact of human activities, while the lower concentrations in the second cluster indicated more natural conditions. These results underscored the dual influences of agriculture and urbanization on PTE contamination, which highlighted the need for targeted soil management strategies. Moreover, the integration of PMF and K-means clustering effectively reveals potential sources and concealed pollution patterns, providing insights for managing pollution and safeguarding environmental health in rapidly urbanized areas.

Geochemical assessment of groundwater quality using Nemerov's pollution index: a micro-level study in the groundwater critical zone of western Rajasthan, India

The pollution index is a helpful tool for assessing the quality of groundwater. To assess the water quality in the southern segment of Barmer District (Rajasthan), India, we collected 20 samples of groundwater from the post-monsoon 2021 and pre-monsoon 2022 periods. Physicochemical parameters such as pH, electrical conductivity (EC), total hardness, Cl-, SO4--, F-, NO3-, total dissolved solids, Ca2+, and Mg2+ were analyzed. To better understand the spatial and temporal variations, maps were generated in the Geographic Information System (GIS) environment in association with the seasonal correlation matrix. Nemerov's index method was used for determining the pollution level of groundwater sources. The results showed that there was significant spatial and temporal variation in the concentration level of physicochemical parameters. The correlation matrix revealed that the level of positive correlation among the parameters was higher during the pre-monsoon period of 2022 compared to the post-monsoon period of 2021. The result was evaluated using the standard set by the World Health Organization and the Bureau of Indian Standards. According to the result obtained from Nemerov's index technique, most parameters were within safe conditions in both seasons except for EC and NO3-. The results indicated that the improved Nemerov index technique can represent the status of groundwater more accurately.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23-30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.

Impacts on the quality of surface water in a urban perimeter of the Rio Grande watershed, Brazilian Cerrado

The aim of this study was to assess the spatiotemporal variation in water quality in the Grande River and the Ondas River, in the city of Barreiras, Bahia, Brazil. Water samples were collected at 11 points along the rivers, and eight physical-chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen, and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatiotemporal variation was assessed using the multivariate techniques of principal component analysis/factorial analysis (PCA/FA) and hierarchical cluster analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions were in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Assessment of water quality using entropy-weighted quality index, statistical methods and electrical resistivity tomography, Moti village, northern Pakistan

In this study, twenty-two water samples were collected from boreholes (BH), and streams to evaluate drinking water quality, its distribution, identification of contamination sources and apportionment for Moti village, northern Pakistan. An atomic absorption spectrophotometer (AAS) is utilized to determine the level of heavy metals in water such as arsenic (As), zinc (Zn), lead (Pb), copper (Cu), cadmium (Cd), manganese (Mn), and ferrous (Fe). Groundwater chemistry and its quantitative driving factors were further explored using multivariate statistical methods, Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) models. Finally, a total of eight electrical resistivity tomographs (ERTs) were acquired across i) the highly contaminated streams; ii) the villages far away from contaminated streams; and iii) across the freshwater stream. In the Moti village, the mean levels (mg/l) of heavy metals in water samples were 7.2465 (As), 0.4971 (Zn), 0.5056 (Pb), 0.0422 (Cu), 0.0279 (Cd), 0.1579 (Mn), and 0.9253 (Fe) that exceeded the permissible limit for drinking water (such as 0.010 for As and Pb, 3.0 for Zn, 0.003 for Cd and 0.3 for Fe) established by the World Health Organization (WHO, 2008). The average entropy weighted water quality index (EWQI) of 200, heavy metal pollution index (HPI) of 175, heavy metal evaluation index (HEI) of 1.6 values reveal inferior water quality in the study area. Human health risk assessment, consisting of hazard quotient (HQ) and hazard index (HI), exceeded the risk threshold (>1),indicating prevention of groundwater usage. Results obtained from the PCA and PMF models indicated anthropogenic sources (i.e. industrial and solid waste) responsible for the high concentration of heavy metals in the surface and groundwater. The ERTs imaged the subsurface down to about 40 m depths and show the least resistivity values (<11 Ωm) for subsurface layers that are highly contaminated. However, the ERTs revealed relatively high resistivity values for subsurface layers containing fresh or less contaminated water. Filtering and continuous monitoring of the quality of drinking water in the village are highly recommended.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.