1
|
Xu H, Hu P, Wang H, Croot P, Li Z, Li C, Xie S, Zhou H, Zhang C. Identification of the pollution sources and hidden clustering patterns for potentially toxic elements in typical peri-urban agricultural soils in southern China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2025; 370:125904. [PMID: 39988249 DOI: 10.1016/j.envpol.2025.125904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 01/25/2025] [Accepted: 02/21/2025] [Indexed: 02/25/2025]
Abstract
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.
Collapse
Affiliation(s)
- Haofan Xu
- Department of Space Information and Resources Environment, School of Architecture and Planning, Foshan University, Foshan, Guangdong, 528000, China; School of Environmental and Chemical Engineering, Foshan University, Foshan, Guangdong, 528000, China
| | - Peng Hu
- Department of Space Information and Resources Environment, School of Architecture and Planning, Foshan University, Foshan, Guangdong, 528000, China; School of Environmental and Chemical Engineering, Foshan University, Foshan, Guangdong, 528000, China
| | - Hailong Wang
- School of Environmental and Chemical Engineering, Foshan University, Foshan, Guangdong, 528000, China
| | - Peter Croot
- Irish Centre for Research in Applied Geoscience (iCRAG), Earth and Ocean Sciences, School of Natural Sciences and Ryan Institute, University of Galway, Galway, H91 CF50, Ireland
| | - Zhiwen Li
- Department of Space Information and Resources Environment, School of Architecture and Planning, Foshan University, Foshan, Guangdong, 528000, China
| | - Cheng Li
- Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/ International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi, 541004, China
| | - Shaowen Xie
- Department of Space Information and Resources Environment, School of Architecture and Planning, Foshan University, Foshan, Guangdong, 528000, China
| | - Hongyi Zhou
- Department of Space Information and Resources Environment, School of Architecture and Planning, Foshan University, Foshan, Guangdong, 528000, China
| | - Chaosheng Zhang
- International Network for Environment and Health (INEH), School of Geography, Archaeology & Irish Studies, University of Galway, Galway, H91 CF50, Ireland.
| |
Collapse
|
2
|
Parihar S, Jadhav R, Pachak SK, Solanki VS, Alarifi SS, Yadav KK, Agarwal N, Ghosh T, Yadav VK. Geochemical assessment of groundwater quality using Nemerov's pollution index: a micro-level study in the groundwater critical zone of western Rajasthan, India. JOURNAL OF WATER AND HEALTH 2025; 23:16-25. [PMID: 39882851 DOI: 10.2166/wh.2024.113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 11/26/2024] [Indexed: 01/31/2025]
Abstract
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.
Collapse
Affiliation(s)
- Sangeeta Parihar
- Department of Chemistry, Jai Narain Vyas University, Jodhpur, Rajasthan, India
| | - Raina Jadhav
- Department of Chemistry, ISR, IPS Academy Indore, Indore, Madhya Pradesh, India
| | - Suresh Kumar Pachak
- Department of Chemistry, Jai Narain Vyas University, Jodhpur, Rajasthan, India
| | | | - Saad S Alarifi
- Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Krishna Kumar Yadav
- Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai-602105, Tamil Nadu, India
| | - Neha Agarwal
- Department of Chemistry, Navyug Kanya Mahavidhyalaya, University of Lucknow, Lucknow, Uttar Pradesh, India
| | - Tathagata Ghosh
- Department of Geography, Balurghat Mahila Mahavidyalaya, Balurghat, Dakshin Dinajpur, West Bengal 733101, India
| | - Virendra Kumar Yadav
- Marwadi University Research Center, Department of Microbiology, Faculty of Sciences, Rajkot 360003, Gujrat, India
| |
Collapse
|
3
|
Soetan O, Nie J, Polius K, Feng H. Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:61643-61659. [PMID: 39433627 PMCID: PMC11541290 DOI: 10.1007/s11356-024-35330-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 10/13/2024] [Indexed: 10/23/2024]
Abstract
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.
Collapse
Affiliation(s)
- Oluwafemi Soetan
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ, 07043, USA
| | - Jing Nie
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ, 07043, USA
| | - Krishna Polius
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ, 07043, USA
| | - Huan Feng
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ, 07043, USA.
| |
Collapse
|
4
|
Fuentes TGQ, de Castro Oliveira GL, de Jesus Souza E, da Glória França Nascimento N, da Silva Marques SJ, de Souza Guedes S, de Melo DC, Prudencio CV, Portella RB, Chiarelotto M. Impacts on the quality of surface water in a urban perimeter of the Rio Grande watershed, Brazilian Cerrado. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:1027. [PMID: 39373797 DOI: 10.1007/s10661-024-13198-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 09/30/2024] [Indexed: 10/08/2024]
Abstract
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.
Collapse
|
5
|
Song T, Tu W, Su M, Song H, Chen S, Yang Y, Fan M, Luo X, Li S, Guo J. Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:856. [PMID: 39196401 DOI: 10.1007/s10661-024-13017-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 08/15/2024] [Indexed: 08/29/2024]
Abstract
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.
Collapse
Affiliation(s)
- Tao Song
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Weiguo Tu
- Sichuan Provincial Academy of Nature Resources Sciences, Sichuan, 610015, China
| | - Mingyue Su
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Han Song
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Shu Chen
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Yuankun Yang
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
| | - Min Fan
- School of Environment and Resource, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China.
- Tianfu Institute of Research and Innovation, Southwest University of Science and Technology, Chengdu, 610299, China.
| | - Xuemei Luo
- Sichuan Provincial Academy of Nature Resources Sciences, Sichuan, 610015, China
| | - Sen Li
- Sichuan Provincial Academy of Nature Resources Sciences, Sichuan, 610015, China
| | - Jingjing Guo
- Sichuan Provincial Academy of Nature Resources Sciences, Sichuan, 610015, China
| |
Collapse
|
6
|
Nisar UB, Rehman WU, Saleem S, Taufail K, Rehman FU, Farooq M, Ehsan SA. Assessment of water quality using entropy-weighted quality index, statistical methods and electrical resistivity tomography, Moti village, northern Pakistan. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 264:104368. [PMID: 38776561 DOI: 10.1016/j.jconhyd.2024.104368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
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.
Collapse
Affiliation(s)
- Umair Bin Nisar
- Department of Meteorology, COMSATS University Islamabad, Park Road, Tarlai Kalan 45550, Islamabad, Pakistan
| | - Wajeeh Ur Rehman
- Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Defence Road, 54000 Lahore, Pakistan
| | - Saher Saleem
- Department of Statistics, Lahore College for Women University, Jail Road, Lahore, Pakistan
| | - Kashif Taufail
- Department of Physics, COMSATS University Islamabad, Lahore Campus, Defence Road, 54000 Lahore, Pakistan
| | - Faizan Ur Rehman
- Department of Earth sciences, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, KPK, Pakistan
| | - Muhammad Farooq
- Institute of Geology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan
| | - Siddique Akhtar Ehsan
- Department of Physics, COMSATS University Islamabad, Lahore Campus, Defence Road, 54000 Lahore, Pakistan.
| |
Collapse
|
7
|
Zhang H, Ren X, Chen S, Xie G, Hu Y, Gao D, Tian X, Xiao J, Wang H. Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123771. [PMID: 38493866 DOI: 10.1016/j.envpol.2024.123771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/26/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
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.
Collapse
Affiliation(s)
- Han Zhang
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
| | - Xingnian Ren
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Sikai Chen
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Guoqiang Xie
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Yuansi Hu
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Dongdong Gao
- Sichuan Academy of Environmental Science, Chengdu, 610000, China
| | - Xiaogang Tian
- Sichuan Academy of Environmental Science, Chengdu, 610000, China
| | - Jie Xiao
- Ya'an Ecological and Environment Monitoring Center Station, Ya'an, 625000, China
| | - Haoyu Wang
- School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| |
Collapse
|
8
|
Ren X, Zhang H, Xie G, Hu Y, Tian X, Gao D, Guo S, Li A, Chen S. New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment. CHEMOSPHERE 2023; 334:138967. [PMID: 37211163 DOI: 10.1016/j.chemosphere.2023.138967] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/23/2023]
Abstract
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.
Collapse
Affiliation(s)
- Xingnian Ren
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Han Zhang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Guoqiang Xie
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Yuansi Hu
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Xiaogang Tian
- Sichuan Academy of Environmental Science, Chengdu, 610000, China
| | - Dongdong Gao
- Sichuan Academy of Environmental Science, Chengdu, 610000, China.
| | - Shanshan Guo
- China 19th Metallurgical Corporation, Chengdu, 610031, China
| | - Ailian Li
- College of Environment Sciences, Sichuan Agricultural University, Chengdu, 611130, China
| | - Sikai Chen
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
| |
Collapse
|