Identification Of Pollution Sources Research Articles

Identification of pollutant sources and evaluation of water quality improvement alternatives of a large river.

While pollutants are the most important factors for the deterioration of surface water quality, the identification of major pollutant sources for rivers is challenging, especially in areas with diverse land covers and multiple pollutant inputs. This study aims to identify the significant pollutant sources from the tributaries that are affecting the water quality and identify the limiting nutrient for algal growth in the Geum river to provide a management alternative for an improvement of the water quality. The positive matrix factorization (PMF) was applied for pollutant source identification and apportionment of the two major tributaries, Gab-cheon and Miho-cheon. Positive matrix factorization identifies three and two major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plants, urban, and agricultural pollution are identified as major pollutant sources. Furthermore, for Miho-cheon, agricultural and urban pollution were identified as major pollutant sources. Total phosphorus (TP) is also identified as a limiting nutrient for algal growth in the Geum river. Water quality control scenarios were formulated and improvement of water quality in the river locations was simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC). Scenario results were evaluated using a water quality index. The reduction of total phosphorus (TP) from the tributaries has greatly improved the water quality, especially algal bloom in the downstream stations.

Trajectory-geospatial approach using HYSPLIT Model in determining air pollution sources at Pasir Gudang industrial areas

Industrial air pollutants emissions can be hazardous to human health and the environment. This study aims to determine the source of air pollution exposure in Malaysia's busiest industrial area, Pasir Gudang by using the trajectory-geospatial approach. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to determine the pathway of air backwards-trajectory. While the geospatial analysis for air pollution sources identification in Pasir Gudang was conducted using Geographic Information System (GIS). Three days of air mass backwards-trajectories were successfully obtained, where the dominant air mass pathways direction came from West Sumatra, Indonesia, influenced by the Southwest monsoon wind. The air mass backwards-trajectories also show there are influenced by nearby factories' emissions. From the geospatial buffer analysis, about 17 to 60 factories were identified to be possible sources of air pollution exposure since the location of factories are within a 1 km buffer zone of six affected schools in Pasir Gudang. The meteorological factors that influence the flow of high emissions of air masses pollutant from nearby factories have worsened the exposure level of air pollution until impacted the health of school’s students. From the findings, a standard of procedure (SOP) for air pollution source identification using a trajectory-geospatial approach has been developed as a guideline for all the stakeholders involved and essential in understanding better strategies in monitoring and mitigating industrial air pollution, particularly in Pasir Gudang as the buffer zones required by the authorities are not actively functioning to mitigate the air pollution exposures. Keywords: Air pollution, buffer zones, HYSPLIT, industrial area, Pasir Gudang, trajectory-geospatial approach

Hydrogeochemical analysis and groundwater pollution source identification based on self-organizing map at a contaminated site

Groundwater contamination at the site has become a very serious problem. A clear understanding of the hydrogeochemical characteristics of groundwater is indispensable for pollution remediation. It requires taking a number of samples and continuous monitoring. However, it is challenging to interpret hydrogeochemical datasets with diverse compositions and wide range of concentration by linear method. In this work, combination of self-organizing map (SOM) and K-means clustering was applied to investigate the hydrogeochemical characteristics at a contaminated site. The results showed that shallow groundwater hydrogeochemical characteristics were performed by 42 neurons and were classified into 5 clusters. The NO3– in cluster 1 widely distributed in the site. The application of fertilizers led to high NO3– concentration in groundwater. Cluster 2 was dominated by Ca2+, Mg2+, Cr(Ⅵ) and NO2– and cluster 3 was characterized by TDS, Na+, Cl−, HCO3– and SO42−. Pollutants were mainly from the migration of components at the chromium slag heap under the effect of convection and dispersion. Cluster 4 was dominated by pH, As and CO32–. Furthermore, the pH with the minimum of 8.3 and the presence of CO32– in groundwater provided a favorable opportunity for arsenic enrichment. Pollutants in cluster 4 originated from rainfall leaching on the chromium slag. Moreover, the migration of components from cluster 4 to cluster 2 was also observed by SOM and numerical simulation. Cluster 5 was mainly dominated by Mn and Fe. Reduced environment and anthropogenic activities caused Fe and Mn to exceed standards. The deep groundwater characteristics were performed using 20 neurons and were identified into 4 clusters. Its contamination was due to the leakage of shallow groundwater. Finally, the Gibbs diagram and the saturation index method performed the chemistry control mechanisms of different clusters. This study demonstrated that SOM could be used to interpret nonlinear and complex contamination datasets.

KAJIAN STATUS MUTU AIR, INVENTARISASI SUMBER PENCEMAR DAN ALTERNATIF STRATEGI PENGENDALIAN PENCEMARAN SUNGAI BARABAI KABUPATEN HULU SUNGAI TENGAH

Barabai River is one of the rivers in Hulu Sungai Tengah District which is widely used by the surrounding community for household, agricultural and industrial activities. Community activities around the Barabai River certainly have an impact on the decline in river water quality because the waste produced generally goes directly to other environments without prior processing. This study aims to analyze the water quality status of the Barabai River, take an inventory of the potential sources of water pollution in the Barabai River and formulate alternative strategies for controlling water pollution in the Barabai River. The research was located on the Barabai River along ± 43.2 km which started from Tanjung Village, Hantakan District, towards Jaranih Village, Pandawan Subdistrict, to Tabat Village, Labuan Amas Utara Subdistrict. The water quality status of the Barabai River was calculated using secondary data from the 2017-2020 river water quality monitoring results sourced from the Department of Environment and Transportation of Hulu Sungai Tengah District using the pollutant index method. Potential sources of pollutants in the Barabai River were inventoried based on domestic, industrial and agricultural activities around the monitoring point for water quality sampling by collecting secondary data and field survey results, then a map of the distribution of each water pollutant source was described using the Geographic Information System (GIS) application. Alternative policy strategies for controlling pollution of the Barabai River were analyzed using the SWOT method based on the Regulation of the State Minister of the Environment Number 01 of 2010 concerning the management of water pollution control. The results showed that in 2018 the water quality status of the Barabai River met the quality standards while in 2017, 2019 and 2020 the status of the water quality was lightly polluted with parameters that did not meet the quality standards, namely Total suspended solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD) and Fecal Coli. Potential sources of pollutants in the Barabai River consisted of domestic pollutant sources originating from residential activities in the Barabai River area, potential sources of Pollution Point Source (Institutions) originating from ± 21 types of businesses /activities located in Hulu Sungai Tengah District and potential sources of Non-Point pollutants Source coming from agricultural activities of rice fields and plantation. Alternative policy strategies for controlling the pollution of the Barabai River can be done by adding water quality monitoring points in industrial areas, improving river water quality, raising public awareness of waste management through sustainable and targeted socialization, tightening supervision of compliance with business people, expanding coordination inter-agency, controlling waste entering rivers, determining the capacity of water pollutant loads, constructing a wastewater treatment plants (IPAL), carry out an inventory and identification of water pollutant sources based on the Regulation of the Minister of the Environment Number 01 of 2010, and stipulate a Regional Regulation on licensing for the disposal of wastewater to water sources.

Identification of nitrogen pollution sources and transport transformation processes in groundwater of different landforms using C, H, N, and O isotope techniques: an example from the lower Weihe River.

Nitrogen pollution in groundwater is an environmental issue of global concern. Identifying nitrogen pollution sources and determining migration and transformation processes are the major ways to prevent and control nitrogen pollution in the groundwater on a regional scale. In this study, groundwater in the lower Wei River was investigated by combining multi-isotope tracing techniques with the SIAR hybrid model (source resolution) to trace the nitrate sources and their contribution rate to nitrogen pollution in groundwater of different geomorphological units, considering types of geomorphology as the units. The multi-isotope tracing technique allows dynamic analysis of nitrate sources, and the combination of this technology can improve the accuracy of nitrogen source traceability. The results indicated that the pH of the water bodies in the study area ranged from 6.83 to 8.01, which is neutral and weakly alkaline. The nitrogen pollution was mainly due to nitrates. The significant factors affecting nitrogen migration in groundwater are the geomorphological type, the chemical characteristics of the groundwater, and the age of the groundwater. Nitrogen migration and transformation processes in the study area were dominated by nitrification, and sources of nitrate pollution were mainly animal manure and domestic sewage (32.6%), followed by atmospheric deposition (26.8%), soil nitrogen (20.9%), and chemical fertilizer (19.7%). The main sources of nitrate in groundwater from river flats, alluvial plains, and loess tableland were animal manure and domestic sewage (43.7%), animal manure and domestic sewage (59.1%), and atmospheric deposition (55.5%), respectively. The result is mainly related to the different structural characteristics of various geomorphic units and the intensity of human activities. This study can provide a theoretical basis for the relevant agencies to develop plans to combat groundwater pollution.

Application of chemometric modeling for identification of pollution sources from drains of Ghaggar River, Punjab, India

Application of chemometric modeling for identification of pollution sources from drains of Ghaggar River, Punjab, India

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

Protection of peri-urban groundwater catchments: a multi-tracer approach for the identification of urban pollution sources

Abstract. Groundwater catchment located in peri-urban areas may be impacted by many pollutants coming from different types of point or diffuse sources such as accidental spills, continuous hidden leaks in drainage networks, old landfills, treated/untreated wastewater and watercourses. In the scope of the CASPER project, a new methodological approach has been developed based on field survey and interpretation of the collected data in order to distinguish between the different sources of contamination and mixtures of pollutants. First, the groundwater catchment area corresponding to the land surface perimeter in which abstracted groundwater is recharged is determined and characterised in hydrogeological terms. The possible sources of pollution are identified. In a second step, a groundwater and surface water monitoring survey is established, and water samples are collected focusing on a combination of physicochemical parameters and set of various hydrochemical indicators. In particular, different stable isotopes are considered. The NO3- and Boron stable isotopes are used to distinguish between inputs linked to urban effluents, agricultural fertilisers and manure. Stable isotopes of SO42- are used to distinguish between sulphide minerals oxidation, sulphur-carbon compounds mineralisation, lixiviation and human pollution. Moreover, the occurrence of specific molecules like pharmaceutical and lifestyle products (carbamazepine, caffeine, etc.) are used as effective tracers of anthropogenic contamination. Microbiological analyses are also undertaken to identify microbial populations associated with specific sources of pollution or specific biochemical reactions occurring in soil and groundwater. The resulting hydrochemical dataset is then processed using multivariate and clustering analyses. In this context, the objective here is to describe the rigorous methodological approach to assess pollution sources and to illustrate the first steps of this process using a case study corresponding to a groundwater catchment is a chalk aquifer in Western Belgium.

Factors conditioning the content of chemical elements in soil and mosses in Armenia

In this research, chemical element contents were determined using the Epithermal neutron activation analysis (Cr, V, Ti, Sr, Rb, As, Zn, Co, Fe, Mn and Ba) and atomic-absorption spectrometry (Cu and Pb) for mosses and X-ray fluorescence spectroscopy (Cr, V, Ti, Sr, Rb, As, Zn, Cu, Co, Fe, Mn, Pb, and Ba) for soil samples. The research team combined compositional data analysis and geospatial mapping to reveal key factors conditioning moss and soil chemical composition. The results showed that significant correlation was detected between Cr, Sr, Rb, As, and Pb for Syntrichia ruralis moss and soil contents suggesting their origin stemming from soil resuspension. Meanwhile, in the case of pleurocarpus (Homalothecium philippeanum) moss species, the mechanisms conditioning the transfer of elements to mosses from dead or living plant materials were found to be responsible for mosses Zn, Sr, Mn, Rb, As, Ba, Cu contents. Both media were identified to display multivariate outliers in the same sampling sites which were characterized by the presence of natural mineralization and mining and smelting activities. The compositional PCA and k-means clustering show that the soil element content groupings are in line with the geological base, whereas in the case of mosses, the rural and urban gradient plays a pivotal role in the spatial separation of the identified clusters. In addition, moss samples included in a cluster representing the urban areas of the country have included typical anthropogenic elements (Pb, Cu, Zn etc.) confirming the applicability of the studied mosses in pollution source identification.

Artificial Intelligence for the Pollution Source Identification

Artificial Intelligence for the Pollution Source Identification

Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana

The emergence of low-cost sensors for atmospheric observations presents a new opportunity for identifying atmospheric emission sources based on high-resolution data reporting. Low-cost sensors have been widely assessed for use in source monitoring and identification of hotspots of key atmospheric species in advanced countries (e.g., for CO, NOx, CO2, SO2, O3, VOCs and PM (PM10, PM2.5 including emerging PM1). In contrast, research in recent years has focused on their utility for real-time monitoring, understanding precision and associated calibration requirements in technologically lagging environments. This leads to limited evidence on the utility of high-resolution data from low-cost sensor networks for air pollution source identification in Ghana and more widely across the African continent. In this paper, we demonstrate the potential of low-cost sensors for emission source apportionment in urban areas of Ghana when used with analytical tools such as sectoral and cluster analysis. With a 14-week dataset from a low-cost sensor deployment study at Cape Coast in the Central Region of Ghana, we aimed to identify sources of particulate matter (PM2.5 and PM10). PM pollution was local (associated with increased PM at wind speeds of ≤2 m s−1). High levels of PM during this study were associated with transport from the NNE. For coarse PM, hourly levels as high as 125 μg m−3 were observed at higher wind speeds (7-8 m s−1) indicating the importance of meteorology in the transport of PM. This study suggests that low-cost sensors could be deployed to (1) augment any existing sparsely distributed air quality monitoring and (2) undertake air quality monitoring for source apportionment studies in areas with no existing air quality observational capability (with appropriate calibration and operation in both cases). The urban landscape monitored in this study is typical of both Ghana and wider areas across Sub-Saharan Africa demonstrating the reproducibility of this study.

A comprehensive framework for identifying contributing factors of soil trace metal pollution using Geodetector and spatial bivariate analysis

The accurate identification of pollution sources is important for controlling soil pollution. However, the widely used Positive matrix factorization (PMF) model generally relies on knowledge and experience to accurately identify pollution sources; thus, this method faces significant challenges in objectively identifying soil pollution sources. Herein, we established a comprehensive source analysis framework using factor identification and geospatial analysis, and revealed the factors contributing to trace metal(loid) (TM) pollution in soil in the Pearl River Delta (PRD), China. Using the PMF model, we initially considered that the PRD may be affected by natural, atmospheric, traffic and industrial, and agricultural sources. Moreover, Geodetector model detected the relationship between TMs and 12 environmental variables based on the strong spatial “source–sink” relationship of pollutants. The parent material and digital elevation model were the key factors predicting the accumulation of Cr, Ni, and Cu. Industries and roads were the most important determinants of Pb, Zn, and Cd, whereas atmospheric deposition was more important for Hg accumulation. The accumulation of As was found to be closely related to agricultural activities such as the application of chemical fertilizers and pesticides. The spatial autocorrelation between soil TM pollution and environmental variables further supports this hypothesis. Overall, the obtained results showed that proposed approach improved the accuracy of source apportionment and provided a basis for soil pollution control.

The characteristics of nitrogen and phosphorus output in China's highly urbanized Pearl River Delta region

The nitrogen (N) and phosphorus (P) transportation due to the anthropogenic activities have strong correlations to the water pollution events. In the highly urbanized Pearl River Delta (PRD) region of China, the main input pathways for N and P have been changed. However, their main output pathways have not yet been understood. Based on the modified export coefficient model (ECM), we have quantified the N and P outputs and identified the main factors affecting the N and P outputs in highly urbanized areas such as PRD. The results showed that the N output intensity of the PRD has increased from 3010 to 3970 kg km−2·a−1 from 2008 to 2016. The P output exhibited a similar trend, from 549 to 769 kg km−2·a−1. In terms of spatial distribution, the output intensity gradually increased from economically underdeveloped regions to economically developed regions. N and P emissions in urban wastewater increased significantly with increasing urbanization rates, with output intensities increasing by 640 kg km−2·a−1 and 141 kg km−2·a−1 from 2008 to 2016, respectively. The correlation analysis showed that population density and urbanization rate were the most relevant factors with N and P outputs intensity in highly urbanized areas. This indicates that improving the effluent standards and utilization rates of wastewater treatment plants in these regions are effective measures to control N and P output. Our findings provide some new theoretical basis for the identification and management of pollution sources in highly urbanized areas for other regions, especially developing countries.

A city-scale inverse modelling method for air pollutant source determination

Urban air pollution is a major environmental hazard. Accurate and rapid identification of pollutant sources is a crucial component of efficient strategies to protect public health. The mostly used back-trajectory analysis and source-receptor likelihood function have limitations in real scenarios. Inverse prediction models such as the adjoint probability method have shown viability in indoor environment. A novel application of this method is conducted to determine the air pollutant source at city-scale. The adjoint probability method is physically deduced, parameterized, and first implemented in the Weather Research and Forecasting (WRF) model coupled with building effect parameterization (BEP) and building energy model (BEM). The predictive performance of the method is investigated using an ideal case and a real urban case study in the city of Hong Kong. The results show that the proposed method can precisely identify both the source location and strength. The main distinction of the method is the fast and accurate determination of the potential source with limited known information collected from three mobile sensors or three fixed sensors. The results of this study can provide insights on sensor distribution and sensor selection in the urban environment. The method of this study can be extended to other source-term problems.

Legal Framework for Environmental Protection in the Context of Sustainable Development

Modern society is characterized by dramatic climate changes and environment changes. There is an intensive use of natural resources and an increase in environmental pollution. The legal framework and its continuous development are essential for the protection of the environment and its sustainable development. The legal framework is a tool for identification and prevention of natural pollution sources, the prevention of environmental threats to the health of the population and the improvement of the quality of life of people in general. The volume of environmental legislation has increased significantly in recent years, but shortcomings in its implementation and enforcement are not sufficient to address environmental issues. The world community has relatively little knowledge of the potential for improving the legal regulation to strengthen environmental security by addressing global environmental risks.

Source apportionment of surface water pollution in North Anhui Plain, Eastern China, using APCS-MLR model combined with GIS approach and socioeconomic parameters

• APCS-MLR model combined with GIS approach and socioeconomic parameters were used in this study. • Northern and western parts of the North Anhui Plain were the most polluted. • The contributions of river pollution sources in the North Anhui Plain were quantified for the first time. • Consideration of socioeconomic data with hydrochemical data improved the identification of pollution source. Various human activities have led to the deterioration of river water quality in many plain areas, which threatens human health and restricts social development. Identifying the spatio-temporal distribution of river pollutants and the objective apportionment of pollution sources is important for effectively protecting water resources. In this study, a geographic information system (GIS approach), principal component analysis/factor analysis (PCA/FA), and the absolute principal component score-multiple liner regression (APCS-MLR) receptor model were applied to evaluate the water quality and identify pollution sources in rivers in the North Anhui Plain, Eastern China. River water quality data for 43 months (January 2017 to July 2020), comprising 10 physico-chemical parameters from 43 monitoring sites across the study area, were used. The average concentrations of total nitrogen (TN), chemical oxygen demand (COD Cr ), permanganate index (COD Mn ), fluoride (F − ), biochemical oxygen demand (BOD), total phosphorous (TP), and NH 4 + –N in 66.9 %, 59.7 %, 41.9 %, 41.25, 30 %, 27.1 %, and 14.7 % of the samples, respectively, exceeded the Chinese surface water standards. BOD, NH 4 + –N, TN, and TP showed a decreasing annual trend, and the northern and western parts of the study area were the most polluted. According to the PCA/FA and the spatial distribution characteristics of pollutants and socio-economic parameters, industrial wastewater and municipal effluents, agricultural cultivation and domestic sewage, geogenic processes and meteorological factors were identified as being responsible for the degradation of the river water quality. The mean contributions of these source were 28.6 %, 5.5 %, 16.1 % and 25.1 %, respectively. This study shows the objectivity and reliability of using a combined approach of GIS with multivariate statistics to evaluate the river water quality status, which can help decision makers to develop more effective strategies for water resource protection.

Assessing nutrient budget of ungauged catchment using intermittent water quality markers

This study is a preliminary spatial-temporal assessment method of the ungauged catchment to determine the variation in water quality (WQ) and the land use influence on river basins’ health. The intermittent WQ data, the principal component analysis, and the redundancy analysis were used to evaluate the (dis)similarity among the 10 ungauged streams and their significance in the entire catchment. These revealed some similarities/differences in nutrient pollution and latent land-use influence on the streams’ health. There were similarities between R6-R7, R9-R10, among R1 to R4 basins, while R5 and R8 had distinct variances in their WQ dynamics. The intensive vegetable and rice production in R5, R7, R8, R9, and R10 basins were the major sources of high nutrient concentrations. The unique variations, especially in R5 and R8 basins could be attributed to other different pollution sources. Hence, it’s of great significance to carry out comprehensive research in the above 5 river basins. That is the efficiency of management practices, identification of pollution sources, and the extent to which the elevated nutrients in the streams interact with biota within the river regime. This research offers a method to evaluate WQ dynamics in relation to human interferences in river basins of a catchment with limited data under similar climatic conditions.

An analytical solution for the advection-dispersion equation inversely in time for pollution source identification

Researchers have long explored inverse solution methods for identifying unknown pollutant sources in water resources. More often than rivers, these methods are used to study groundwater; therefore, employing efficient and up-to-date methods to determine the characteristics of unknown pollutants in rivers has been the cause for concern to water and environmental researchers. This study was thus designed to solve the pollutant transport equation in rivers as an inverse problem to reconstruct pollutant source intensity functions. A unique analytical method, based on the quasi-reversibility (QR) method and the Fourier transform tool, was developed to solve the advection-dispersion equation (ADE) in rivers inversely in time and a one-dimensional domain for various pollutant loading patterns. In this method, a stability term is incorporated into the original transport equation to prevent the problem from becoming ill-posed during the inverse solution process. The analytical solution results demonstrate its computational cost-effectiveness, high accuracy and potential capability for practical modes (particularly when applied to observational concentration data with errors).

Analysis of Contaminant Load Capacity of Nitrate and Phosphate in Krukut River

The Krukut River is one of the major rivers that flow in the Jakarta area and has an essential role in supporting public activities in the city. It is designated as raw water for drinking water. The study was conducted in the 44.30 km Krukut River flow from September 2021 until February 2022. The purpose of this study was to analyze the pollutant load capacity of nitrate and phosphate. Parameters measured in this study were temperature, turbidity, DHL, pH, DO, nitrate and phosphate. Identification of pollutant sources was carried out by field surveys. Water quality analysis is carried out following SNI (Indonesian Standard) and compared with quality standards according to Government Regulation no. 22 of 2021, as well as analysis of the pollutant load capacity of nitrate and phosphate. The search results for potential pollutants in the Krukut River are dominated by domestic waste. The air quality analysis simulation results show that the phosphate curve tends to increase with increasing distance, while the nitrate concentration fluctuates. The results of water quality analysis for nitrate parameters ranged from 1.52 mg/L – 2.94 mg/L, and phosphate ranged from 0.21 mg/L – 1.37 mg/L. The pollutant load carrying capacity results a (PLCC) average for nitrate parameters is 2431.94 kg/day, and phosphate is 48.64 kg/day, with an average pollutant load for nitrate parameters 579.84 kg/day, phosphate 243.02 kg/day.

Differential characteristics and source contribution of water pollutants before and after the extreme rainfall event in the Huaihe River Basin

Various pollutants are collected into rivers through runoff when rainstorms occur, which can harm the aquatic ecological environment, therefore, strengthening the analysis of river pollution sources before and after rainstorms can reduce the impact of rainstorm runoff periods in regard to the risk from river water pollutants. In this study, the water pollutant differential characteristics and pollution source identification in the Henan section in the Huaihe River Basin were explored before and after the extreme rainfall event, and the monitoring data for nine water quality indicators in the monitoring sections were selected. The changing and differential characteristics and processes of the water quality indicators in the Huaihe River Basin before and after the rainstorm were analyzed by using statistical analytical methods and the comprehensive water quality identification index (IWq), and the absolute contribution rates of pollution sources were calculated by the absolute principal components-multiple linear regression model (APCS-MLR). The results showed that the values of pH, DO (Dissolved oxygen) and EC (Electrical conductance) after the rainstorm were lower than those before the rainstorm, while the turbidity, CODMn (Permanganate index), NH3−N (Ammonia nitrogen), TP (Total phosphorus) and TN (Total nitrogen) increased after the rainstorm. The correlations between pH and DO and other water quality indicators increased after the rainstorm, while the correlations between WT (Water temperature), turbidity, CODMn, NH3−N, TP and TN decreased significantly after the rainstorm. The water pollution of each monitoring section increased after the rainstorm; for example, the water quality conditions at Zhoukou Kangdian (ZKKD), Xihua Zhifang (XHZF) and Suixian Banqiao (SXBQ) increased by 42.11, 36.84 and 25.73% to Class V of the environmental quality standards for surface water in China, respectively. The absolute contribution rates of the PC1 to CODMn and TP were 33.86 and 41.11% before the rainstorm, respectively. The absolute contribution rates of the PC2 to DO and TN were 50.77 and 60.26% after the rainstorm, respectively.