Tracing the potential pollution sources of the coastal water in Hong Kong with statistical models combining APCS-MLR

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

A critical review of the recent trends in source tracing of microplastics in the environment

Microplastics are found across the globe because of their size and ability to transport across environments. The effects of microplastics on the micro- and macro-organisms have brought out concern over the potential risk to human health and the need to regulate their distribution at the source. Control of microplastic pollution requires region-specific management and mitigation strategies which can be developed with the information on sources and their contributions. This review provides an overview of the sources, fate, and distribution of microplastics along with techniques to source-trace microplastics. Source-tracing approaches provide both qualitative and quantitive information. Since better outcomes have been produced by the integration of techniques like backward trajectory analysis with cluster analysis, the significance of integrated and multi-dimensional approaches has been emphasized. The scope of the plastisphere, heavy metal, and biofilm microbial community in tracing the sources of microplastics are also highlighted. The present review allows the researchers and policymakers to understand the recent trends in the source-tracing of microplastics which will help them to develop techniques and comprehensive action plans to limit the microplastic discharge at sources.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Sewage treatment decreased organic carbon resources in Hong Kong waters during 1986-2020

Riverine organic carbon (OC) transport plays a role in regulating terrestrial and marine carbon pools and deteriorating coastal water quality. However, long-term OC transport in Asian rivers and its diffusion in marginal seas have remained unreported. This study reported the spatiotemporal variations in OC resources for Hong Kong waters, China, based on monthly monitoring data collected at 82 river stations and 94 ocean sites during 1986-2020. The station-based riverine OC varied spatially and was generally high, with a mean value of 1.4-52.0 mg/L. Moreover, along with improving water quality, OC at 97.6% of the river stations decreased during 1986-2020; overall, sewage treatment accounted for 83.4% of the exponential decrease in riverine OC (R2 = 0.68, p < 0.01). However, the reduction in riverine OC accounted for only 10.4% of the reduction in the marine five-day biochemical oxygen demand (BOD5), which occurred at 70.2% of the ocean sites, especially those closest to the shore. The linear reduction in the marine BOD5 (R2 = 0.24, p < 0.01) was mainly attributed to reduced OC input from the adjacent Pearl River (61.9%) and decreases in phytoplankton growth (19.0%). These results indicated that sewage treatment improved water quality and decreased OC resources in Hong Kong waters, which can serve as a sustainable development model for other coastal cities. This study has important implications for mitigating organic pollution in the context of human efforts to manage the water environment.

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

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Pollution source identification of nitrogen and phosphorus in the lower West Main Canal, the Ganfu Plain irrigation district (South China)

The degradation of surface water quality has been a widespread concern around the world. However, irrigation canal water does not attract much attention although it is important to agriculture and population. In this study, a 5-year water quality monitoring of surface water was conducted in the lower West Main Canal of the Ganfu Plain irrigation district to identify the levels and pollution sources of nitrogen and phosphorus.Over 75% of samples had total phosphorus (TP) concentrations of > 0.02 mg/L, and all samples had total nitrogen (TN) concentrations of > 0.2 mg/L, indicating a risk of eutrophication. The concentrations of NO3--N and NH4+-N averagely occupied 57% and 18% of TN, respectively. PCA analysis showed that phosphorus and nitrogen in canal water were associated with meteorological factors, urban life and surface runoff, agricultural cultivation, livestock-poultry breeding, and water-sediment interaction in the wet season, whereas they were affected by meteorological factors, industrial effluent, urban domestic sewage, and livestock-poultry breeding in the dry season. Absolute principal component score-multiple linear regression (APCS-MLR) model results revealed that (1) agricultural cultivation plus livestock-poultry breeding contributed 43.2% of TP in canal water in the wet season, while livestock-poultry breeding contributed 52.9% in the dry season, and (2) domestic sewage plus surface runoff contributed 29.4% of TN in the wet season, while livestock-poultry breeding contributed 45.9% in the dry season. The unidentified sources had significant contributions of > 20% for almost all variables. So further investigations are required for determining unidentified sources, and anthropogenic pollution control is imperative for canal water quality protection.

Relation disentanglement, the potential risk assessment, and source identification of heavy metals in the sediment of the Changzhao Reservoir, Zhejiang Province

Heavy metal contamination in the water body is a distinctly important issue for the water security of the reservoir. 114 sediment samples of Changzhao Reservoir were collected to investigate the spatial (horizontal and vertical) distribution characteristics, risk assessment, and source identification of heavy metals. The concentrations of heavy metals at the surface layer of sediment were slightly higher compared with that at the middle and bottom layer sediment in the most sampling sites. The concentration of Zn and Cd was significantly different in the different depths of sediment (P ≤ 0.01, Tukey HSD test). pH and Cd were identified as the key factors for TOC in the sediment by the Boruta algorithm. The proportion of "uncontaminated to moderately contaminated" for Cd, Zn, and As in the surface layer was 84.21%, 47.37%, and 34.21%, which indicated that the quality of sediment was mostly impacted by Cd, Zn, and As. The agricultural non-point source pollution is dominant according to the source identification method of APCS-MLR. Overall, this paper presents the distribution and conversion trends of heavy metals and provides the insights of the reservoir protection in the future work.

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.

Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in the vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that the APCS-MLR and UNMIX models could verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41~38.42%), followed by agricultural sources (29.35~31.65%) and traffic emission sources (21.03~21.51%); and the smallest proportion was from natural sources of pollution (11.2~14.42%). The PMF model was easily affected by outliers and its fitting degree was not ideal, leading to be unable to get more accurate results of source analysis. The combination of multiple models could effectively improve the accuracy of pollution source analysis of soil heavy metals. These results provide some scientific basis for further remediation of heavy metal pollution in farmland soil.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China

Heavy metal(loid)s accumulation in groundwater has posed serious ecological and health concerns worldwide. Source-specific risk apportionment is crucial to prevent and control potential heavy metal(loid)s pollution in groundwater. However, there is very limited comprehensive information on the health risk apportionment for groundwater heavy metal(loid)s in arid regions. Thus, the Zhangye Basin, a typical arid oasis region in Northwest China, was selected to investigate the contamination characteristics, possible pollution sources, and source-specific health risks of groundwater heavy metal(loid)s. The heavy metal pollution index (HPI), the Nemerow index (NI), and the contamination degree (CD) were adopted to assess the pollution level of heavy metal(loid)s; then source-specific health risk was apportioned integrating the absolute principal component scores-multiple linear regression (APCS-MLR) with health risk assessment. Noticeable accumulation of Mn, Fe, and As was observed in this region with especially Fe/As in 12.68%/2.11% of the samples revealing significant enrichment. Approximately 3.5% of the groundwater samples caused moderate or higher pollution level based on the HPI. The APCS-MLR model was more physically applicable for the current research than the positive matrix factorization (PMF) model. Industrial-agricultural activity factor (12.56%) was the major source of non-cancer (infants: 59.15%, children: 64.87%, teens: 64.06%, adults: 64.02%) and cancer risks (infants: 77.36%, children: 77.35%, teens: 77.40%, adults: 77.41%). Industrial-agricultural activities should be given priority to control health risks of heavy metal(loid)s in groundwater. These findings provide fundamental and significant information for mitigating health risks caused by heavy metal(loid)s in groundwater of typical arid oasis regions by controlling priority sources.

Statistical analyses of hydrochemistry in multi-aquifers of the Pansan coalmine, Huainan coalfield, China: implications for water-rock interaction and hydraulic connection

Understanding the groundwater hydrogeochemical processes and aquifer hydraulic connections are essential for effective prevention of water inrush in concealed coal mines. In this study, 40 groundwater samples were collected from the loose layer aquifer (LA), coal measure aquifer (CA), and limestone aquifer (LA) in the Pansan coal mine, Huanan coalfield, China, and the major ion concentrations were analyzed by bivariate diagrams (Na⁺ + K⁺ - Cl⁻ versus Ca²⁺ + Mg²⁺ - SO₄²⁻ - HCO₃⁻ and CAI-I versus CAI-II), multivariate statistical methods, and receptor model in order to identify the water-rock interactions and aquifer hydraulic connections. Piper diagram showed that groundwater in LA and TA was dominated by the Na–Cl type, while groundwater in CA was mainly of the Na–HCO₃ type. Based on the results of bivariate diagrams and PCA/FA, weathering of silicate minerals and cation exchange (source 1), sulfate dissolution (source 2) and chloride dissolution (source 3) were the main processes controlling the groundwater chemistry. Unmix model revealed that the mean contribution of source 1 to CA samples was 74%, while LA and TA samples have higher contributions from evaporite dissolution (source 2 and source 3) relative to CA samples. Moreover, both clustering analysis methods (Q-type hierarchical and K-means cluster) confirmed the existence of a hydraulic connection between LA and TA in the northeastern part of the study area. It is concluded that the application of multivariate statistical analysis to interpret groundwater chemistry can provide useful guidance to prevent water inrush in coal mines.

APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Nitrate (NO₃^-) contamination in groundwater has diverse sources and complicated transformation processes. To effectively control NO₃^- pollution in groundwater systems, quantitative and accurate identification of NO₃^- sources is critical. In this work, we applied hydrochemical characteristics and isotope analysis to determine NO₃^- source apportionment. For the first time, the NO₃^- source contributions were calculated using hydrochemical indicators combined with multivariate statistical model (PCA-APCS-MLR). The results interpret that chemical fertilizers (58.11%) and natural sources (22.69%) were the primary NO₃^- sources in the vegetable cultivation area (VCA) which were rather close to the estimation by Bayesian isotope mixing model (SIAR). In particular, the contributions of chemical fertilizers in the VCA differed by only 3.79% between the two methods. Compared with previous approaches e.g. SIAR, the key advantage of the proposed PCA-APCS-MLR model is that it only requires the hydrochemical indicators which can be easily measured. A series of complicated experiments including measurement of isotope data of NO₃^- in groundwater, monitoring of in-situ pollution source information and calculation of isotopic enrichment factor can be simply avoided. The PCA-APCS-MLR model offers a much more convenient and faster method to determine the contribution rates of NO₃^- pollution sources in groundwater.

Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

This study aimed to transform the input of a large dataset into the output of interpretable information. Hence, multivariate statistical methods were carried out to analyze physicochemical parameters in 34 rivers during a 17-year period (1997-2014). Cluster analysis divided the study area into spatially different riverine water quality sub-regions described in ascending order of water quality as severely polluted (SP), highly polluted (HP), polluted (P), moderately polluted (MP), lightly polluted (LP), and not polluted (NP). By diagnosing threats and identifying fragile zones, water contamination sources responsible for impaired water quality in the study area recognized as natural pollutants in LP, municipal wastes in P, discharge of industrial effluents in MP, natural geochemical formations in SP and HP, and superficial flows of agricultural lands in SP, HP, and MP. The dominant water type in each zone was classified into Na-Cl, Na-Cl, Na-Mg-Ca-Cl-SO₄, Na-Ca-Mg-Cl-SO₄, Na-Ca-Cl, and Ca-Mg-HCO₃-SO₄ groups for SP, HP, P, MP, LP, and NP, respectively. To explore aesthetic aspects of drinking water application, hazard quotient (HQ) was applied for children and adults in terms of ingestion and dermal exposure. Overall health risk assessment revealed the order of impacts of the secondary water quality parameters as Cl^-  > Na⁺  > total dissolved solids (TDS) > Ca²⁺  > SO₄^2-  > Mg²⁺. Furthermore, hazard index (HI) ranged from 0.011 to 31.439 and 0.010 to 30.122 for children and adults, respectively, indicating a potential health risk regarding chloride throughout the whole region excluding NP. To identify significant agents in water quality, principal component analysis extracted 3 varifactors (VFs), with the eigenvalues of 4.74, 1.19, and 0.85, respectively, explained about 83% of the variance. The most important parameters in the first factor were TDS, electrical conductivity, SAR, TH, Na⁺, Cl^-, and SO₄^2- accounting for 58% of the total variance. The most influenced parameters in the second and third factors were pH and HCO₃^-, respectively, with variance coverage of 26%. These factors indicated that the hydrochemical characteristics of the water originated by natural interactions (existing salt domes, evaporation, weathering, and soil erosion) and anthropogenic activities (fertilizer-rich flows of agro-fields and domestic/industrial disposals), which must be minimized in rivers to supply the population with hygienic water.

Source identification of surface water pollution using multivariate statistics combined with physicochemical and socioeconomic parameters

Accurate identification of potential contamination sources of river water is a basis for effective pollution control and sustainable water management. Pollution source identification based on physicochemical-parameters-only method may lead to uncertainty and subjectivity. In this study along with hydrochemistry parameters (HPs), socioeconomic parameters (SPs) were considered as an auxiliary in multivariate statistics to achieve a comprehensive assessment on pollution sources with accurate estimates of source identification and apportionment. Fifteen physicochemical parameters were combined with twelve socioeconomic parameters in multivariate statistics to quantitatively assess potential pollution sources and their contributions to river water pollution. Multivariate statistics in the study included regression analysis, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR). Regression analysis between hydro-chemical parameters and socioeconomic parameters indicated that industrial and population growths were the main factors related to ammonium nitrogen (NH₄⁺-N), total nitrogen (TN) contamination, while total phosphorus (TP) was more correlated with domestic discharge and poultry breeding. Based on the results of PCA, four latent factors were extracted for hydrochemistry parameters (HPs) and socioeconomics parameters (SPs), accounting for 68.59% and 82.40% of the total variance, respectively. With integrating the PCA results of the two parameter groups, pollution sources were ranked as industrial effluents > rural wastewater > municipal sewage > phytoplankton growth and agricultural cultivation. Source apportionment in APCS-MLR revealed that industrial wastewater and rural wastewater averagely contributed 35.68% and 25.08% of pollution, respectively, followed by municipal sewage (18.73%) and phytoplankton pollution (15.13%) with relatively small percentage of unrecognized source. It is concluded that socioeconomic parameters assisting hydrochemistry parameters in multivariate statistics can improve the accuracy and certainty of pollution source identification, supporting decision makers to formulate strategies on protection of river water quality.

Spatiotemporal Evaluation of Water Quality and Hazardous Substances in Small Coastal Streams According to Watershed Characteristics

In this study, spatial and temporal changes of eight water quality indicators and 30 types of hazardous substances including volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), pesticides, and inorganic matters for the small coastal streams along the West Coast of South Korea were investigated. In coastal streams with clear seasonal changes in water quality, larger watershed areas led to greater contamination by particulate matter (i.e., suspended solids, r = 0.89), and smaller watershed areas led to greater contamination by organic matter (i.e., BOD, r = −0.78). The concentration of VOCs and pesticides was higher in agricultural areas, and those of SVOCs and metals were often higher in urban areas. According to the principal component analysis (PCA), during the wet season, the fluctuation in the water quality of coastal streams was higher in urban areas than in agricultural areas. Furthermore, coastal streams in residential areas exhibited higher levels of SVOCs, and those in industrial areas exhibited higher levels of metallic substances. Based on these results, the spatial and temporal trends of water quality and hazardous substances were obtained according to watershed characteristics, thereby clarifying the pollution characteristics of small-scale coastal streams and the major influencing factors.

Source apportionment and natural background levels of major ions in shallow groundwater using multivariate statistical method: A case study in Huaibei Plain, China

Understanding the sources, natural background levels (NBLs), and threshold values (TVs) of the major ions in groundwater is essential for the effective protection of water resources. In this study, a total of 70 shallow groundwater samples were collected in Suzhou, Huaibei Plain, China. A variety of statistical methods and cumulative probability distribution techniques were performed to identify the sources, NBLs, and TVs of the major ions. The major ion concentrations found in decreasing order as follows: HCO₃^- > SO₄^2- > NO₃^- > Cl^- and Na⁺ > Ca²⁺ > Mg²⁺. Piper diagram for hydrochemical types shows that groundwater types were Mg-HCO₃ (36%), Ca-HCO₃ (34%), and Na-HCO₃ (30%). According to the factor and the Unmix model analysis, anthropogenic (agriculture-related) and geogenic source (water-rock interactions-related) were identified to be responsible for the chemical composition of the groundwater in the study area, and their mean contributions for the major ion concentrations are 47.9% and 52.1%, respectively. The NBLs for Na⁺, Ca²⁺, Mg²⁺, Cl^-, SO₄^2-, and NO₃^- were determined to be 29.5-44.2, 26.2-38.9, 18.9-39.5, 1.0-9.9, 12.9-19.4, and 2.1-16.5 mg/L, respectively, and the TVs were calculated as 122.1, 169.5, 39.5, 129.6, 134.7, and 18.3 mg/L, respectively. Moreover, this study shows the feasibility and reliability of using these multivariate statistical methods and natural background levels to evaluate the status of groundwater quality.

Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China

We collected 682 topsoil samples (0-20cm) from agricultural lands of Luhe County in East China, and analyzed the spatial distribution patterns and potential sources of four major heavy metals. High Pb and Cr were mainly in the southeast adjacent to the Yangtze River, and Cd were characterized by an increasing trend from northwest to southeast, while high Hg mainly occurred in the areas near downtown. Spatially-continuous sources dominated the soil heavy metal concentrations. Contributions of spatially-continuous natural source (soil parent material) to Cr and Cd were 97.0% and 77.7%, respectively, whereas contributions of spatially-continuous anthropogenic source such as diffuse pollution to Pb and Hg were 75.7% and 86.7%, respectively. The distance to factories was the most influential anthropogenic factor for localized anomaly patterns of Pb, Cd, and Cr, while the intensive agricultural land uses associated with the rapid urban expansion were particularly relevant to the anomaly patterns of Hg.

Spatiotemporal Variation Characteristics of Water Pollution and the Cause of Pollution Formation in a Heavily Polluted River in the Upper Hai River

Blackening and odorization of heavily polluted rivers has become a serious concern and threat to ecological and human health. This paper aims to gain a deeper understanding of changes in water pollution and the cause of pollution formation in a heavily polluted river in the upper Hai River. In this study, comprehensive water quality index (CWQI) and multivariate statistical techniques (MSTs) were applied to assess the spatiotemporal variation characteristics of water pollution and to identify potential pollution sources. The seasonal Mann–Kendall (SMK) test and the SMK test of flow-adjusted concentrations were effectively used to explore the temporal variation trends of major pollutants and the causes of their formation. Data of 15 water quality parameters were analyzed during 1980–2018 at 19 monitoring sites in the mainstream and major tributaries of the Xinxiang Section of the Wei River (XSWR). The results showed that the rivers were seriously polluted from 1991 to 2009, but the water quality improved after 2010. Nineteen sampling sites were divided into a low pollution region and a high pollution region. In the flood season, the pollution sources were mainly domestic sewage, industrial wastewater, agricultural runoff, biochemical pollution, and natural sources. In the nonflood season, the pollution sources were mainly domestic sewage and industrial wastewater. In recent years, the water quality of seriously polluted river has generally improved, mainly due to reductions in pollutant discharge from point sources and nonpoint sources.

Spatial variation and source apportionment of surface water pollution in the Tuo River, China, using multivariate statistical techniques

The increasingly serious water pollution of rivers has attracted wide attention from all countries in the world. Investigating spatial variations of water pollution and source apportionment is particularly important for the effective management of river quality. The water samples collected every two months at 31 sampling sites containing 12 water quality parameters during 2018 and 2019 were analyzed to investigate the spatial patterns and the apportionment of the pollutants in the Tuo River. Cluster analysis (CA), pollution index (PI), factor analysis (FA), principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) were used in the current study. The PI found that the Tuo River was most severely polluted with phosphorus and nitrogen. Additionally, compared with that in 2018, the water quality in the Tuo River has significantly improved in 2019. The CA divided the sampling sites into three categories, which are defined as clean, low-polluted, and moderate-polluted areas, respectively. FA/PCA resulted in four latent pollution sources, explaining 74.09% of the total variance. The contributions of the identified pollution sources to pollutants were realized using APCS-MLR. Most variables were mainly affected by the pollution of agricultural runoff, industrial wastewater, domestic sewage, and soil weathering. According to the results, we can also find that agricultural runoff and industrial wastewater were dominating in the Tuo River. These results provide a scientific basis for formulating more reasonable and strict pollution control strategies for the Tuo River.

Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China

Anthropogenic activities pose challenges on security of water quality. Identifying potential sources of pollution and quantifying their corresponding contributions are essential for water management and pollution control. In our study, 2-year (2017-2018) water quality dataset of 15 parameters from eight sampling sites in tributaries and mainstream of the Min River was analyzed with multivariate statistical analysis methods and absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique to reveal potential sources of pollution and apportion their contributions. Temporal and spatial cluster analysis (CA) classified 12 months into three periods exactly consistent with dry, wet, and normal seasons, and eight monitoring sites into two regions, lightly polluted (LP) and highly polluted (HP) regions, based on different levels of pollution caused by physicochemical properties and anthropogenic activities. The principal component analysis (PCA) identified five latent factors accounting for 75.84% and 73.46% of the total variance in the LP and HP regions, respectively. The main pollution sources in the two regions included agricultural activities, domestic sewage, and industrial wastewater discharge. APCS-MLR results showed that in the LP region, contribution of five potential pollution sources was ranked as agricultural non-point source pollution (22.13%) > seasonal effect and phytoplankton growth (19.86%) > leakage of septic tanks (15.73%) > physicochemical effect (12.86%) > industrial effluents and domestic sewage (11.59%), while in the HP region ranked as point source pollution from domestic and industrial discharges (20.81%) > municipal sewage (16.66%) > agricultural non-point source pollution (15.23%) > phytoplankton growth (14.82%) > natural and seasonal effects (12.67%). Based on the quantitative assessment of main pollution sources, the study can help policymakers to formulate strategies to improve water quality in different regions.

Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China

The quality of groundwater in a region is regarded as a function of natural and anthropogenic factors. Receptor models have advantages in source identification and source apportionment by testing the physicochemical properties of receptor samples and emission sources. In our study, receptor models PMF and PCA-APCS-MLR were developed to qualitatively identify the latent sources of groundwater pollution in the study area and quantitatively evaluate the contribution of each source to groundwater quality. The performances of PMF and APCS-MLR models were compared to test their applicability on the assessment of groundwater pollution sources. Results showed that both of the models identified five sources of groundwater contamination with similar main load species of each potential source. The comparable source apportionment of species NO₂^- and NO₃^- with two models indicated the reliable source estimation for these species, whereas the contributions of sources to species Fe, Mn, Cl^-, SO₄^2- and NH₄⁺ were significantly different due to the large variability of data, difference of uncertainty analysis and algorithm of unexplained variability in the two models. R-squared value between observation and model prediction was 0.603-0.931 in PMF and 0.497-0.859 in PCA-APCS-MLR. The significant disagreement of average source contribution was detected in agricultural source and unexplained variability using PMF and PCA-APCS-MLR models. Average contributions of other sources to groundwater quality parameters had similar estimates between the two models. Higher R² and smaller proportion of unexplained variability in the PMF model suggested that PMF approach could provide more physically plausible source apportionment in the study area and a more realistic representation of groundwater pollution than solutions from PCA-APCS-MLR model. The study showed the advantages of application of multiple receptor models on achieving reliable source identification and apportionment, particularly, providing a better understanding of applicability of PMF and PCA-APCS-MLR models on the assessment of groundwater pollution sources.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.