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

Chen, Kai; Liu, Qimeng; Jiang, Quanliang; Hou, Xueling; Gao, Wei

doi:10.1016/j.ecolind.2022.109324

Cited by 18 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Henan Province's secondary sector is dominated by industry. The large amount of industrial wastewater discharged leads to an increase in the concentration of ammonia nitrogen and chemical oxygen demand, resulting in the pollution of water bodies (Chen et al, 2022;Giao, 2022;Zhao et al, 2022). Therefore, the discharge of large quantities of industrial wastewater is one of the main causes of the deterioration of water quality in heavily polluted areas.…”

Section: Analysis Of Pollution Sources In Heavily Polluted Areasmentioning

confidence: 99%

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Wei,

Qiu,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

Maintaining good surface water quality is essential to protecting ecosystems and human health, and different targeted measures for different polluted areas are an effective way to maintain good water quality. This paper takes Henan as an example to study the spatial dependence of surface water quality and explore its spatial clustering pattern, and find out the main driving factors affecting the water quality and analyze the sources of heavily polluted areas by random forest. The results indicate that the spatial pollution pattern of surface water quality in Henan Province can be roughly categorized as insignificant pollution in the northern part, heavy pollution in the central part, and light pollution in the southern part. The heavily polluted areas are mainly located in Zhengzhou, Luoyang and Kaifeng cities. The main indicators affecting water quality in heavily polluted areas are NH3-N, CODMn and TP. The main causes of the deterioration in the region are urban sewage and industrial wastewater discharges. The results not only provide a scientific basis for the systematic management of surface water quality pollution in Henan Province, but also provide a new method for regional water pollution management.

show abstract

Section: Analysis Of Pollution Sources In Heavily Polluted Areasmentioning

confidence: 99%

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Wei,

Qiu,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The spatial distribution of water hydrochemical components based on inverse distance weight interpolation is essential to understand the distribution and evolution of groundwater contamination at spatial scales (Nsabimana and Li, 2023). Land use types are an important tool for identifying regional urbanization processes and the causes of anthropogenic groundwater pollution (Chen et al, 2023).…”

Section: Self-organizing Map (Som) With K-means Clusteringmentioning

confidence: 99%

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Yan,

Zhang,

Yao

et al. 2024

Preprint

View full text Add to dashboard Cite

An in-depth understanding of nitrate-contaminated groundwater quality and risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices and human health risk model. Groundwater samples were subdivided into two clusters by SOM analysis. Cluster Ⅰ including three samples were Ca-SO4 type and cluster Ⅱ of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for Cluster I samples, and (2) calcite dissolution, silicate weathering and positive cation exchange for Cluster Ⅱ samples. Nitrate contamination occurred in both Cluster I and Ⅱ samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis.

show abstract

“…Suzhou (115°53′-118°10′E, 32°43′-34°38′N) is located in the north of Anhui Province, and the climate belongs to warm temperate zone and the semi-humid monsoon section, with an average annual temperature of 14.7ºC, and annual rainfall around 774-896 mm [16]. The terrain is mainly plain, which belongs to the southern part of Huanghe-Huaihe Plain and the main agricultural crops include wheat, corn, soybean and cotton, etc [17].…”

Section: Study Sitementioning

confidence: 99%

Environmental Risk Assessment of Heavy Metals in Cultivated Soils Around Industrial Area and Source Identification under APCS-MLR Model: a Case of Suzhou in Northern China

Chen¹,

Xu²,

Feng³

2023

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

To investigate the impact of industrial activities on the environmental accumulation and the health risks to humans of heavy metals in urban soils, the cultivated soil samples around different industrial areas were collected and analyzed. The heavy metal concentrations in the soil samples were in the decreasing order Mn>Zn>Cr>Ni>Cu>Pb>As>Sn>Cd>Hg, and the average concentrations of all heavy metals exceeding the corresponding background values. In addition to Cd and Hg, all the other eight metals were classified as low ecological risk; Cd had low, moderate, and considerable ecological risk and Hg were in low, high, and very high ecological risk. The proportion of RI in the four ecological risk levels of low risk, moderate risk, considerable risk and high risk were 69.6%, 13%, 8.7% and 8.7%, respectively. All Mn and some Cr pose non-carcinogenic risk to children primarily through inhalation exposure. Carcinogenic risk is Cr>Ni>As>Pb>Cd, and the exposure route is mainly by ingestion. For children, Cr, Ni and As were high carcinogenic risk and Pb, Cd were acceptable carcinogenic risk; for adults, Cr and Ni were high risk, As was acceptable risk, and Pb and Cd were no risk. The results of the APCS-MLR receptor model showed that the percentages of vehicle emission sources, coal transport industrial sources, coal-fired power plant sources and natural sources were 27.8%, 25.2%, 8.7% and 38.3%, respectively.

show abstract

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

Cited by 18 publications

References 46 publications

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Environmental Risk Assessment of Heavy Metals in Cultivated Soils Around Industrial Area and Source Identification under APCS-MLR Model: a Case of Suzhou in Northern China

Contact Info

Product

Resources

About