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Alzahrani H, El-Sorogy AS, Okok A, Shokr MS. GIS- and Multivariate-Based Approaches for Assessing Potential Environmental Hazards in Some Areas of Southwestern Saudi Arabia. TOXICS 2024; 12:569. [PMID: 39195671 PMCID: PMC11359128 DOI: 10.3390/toxics12080569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 08/29/2024]
Abstract
Soil contamination is a major issue that endangers the ecology in most countries. Total concentrations of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, VFe, and Zn were determined by analyzing soil samples from 32 surface soil samples in southwest Saudi Arabia, including certain areas of Al-Baha. Kriging techniques were used to create maps of the distribution of metal. To assess the levels of soil contamination in the research area, principal component analysis (PCA), contamination factors (CF), and pollution load index were used. The results show the stable model gave the best fit to the As and Zn semivariograms. The circular model fits the Cd, Co, and Ni semivariograms the best, while the exponential model fits the Cr, V, and Fe semivariograms the best. For Ni and Pb, respectively, spherical and Gaussian models are fitted. The findings demonstrated two clusters containing different soil heavy metal concentrations. According to the data, there were two different pollution levels in the research region: 36.58% of it is strongly contaminated, while 63.41% of it has a moderate level of contamination (with average levels of these metals 5.28 ± 5.83, 0.81 ± 0.19, 18.65 ± 6.22, 45.15 ± 23.25, 60.55 ± 23.74, 972.30 ± 223.50, 33.45 ± 14.11, 10.05 ± 5.13, 84.15 ± 30.72, 97.40 ± 30.05, and 43,245.00 ± 8942.95 mg kg-1 for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, Fe, and Zn, respectively). The research area's poor management practices are reflected in the current results, which raised the concentration of harmful elements in the soil's surface layers. Ultimately, the outcomes of pollution concentration and spatial distribution maps could aid in informing decision-makers when creating suitable heavy metal mitigation strategies.
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Affiliation(s)
- Hassan Alzahrani
- Geology and Geophysics Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (H.A.); (A.S.E.-S.)
| | - Abdelbaset S. El-Sorogy
- Geology and Geophysics Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (H.A.); (A.S.E.-S.)
| | - Abdurraouf Okok
- Earth Sciences and Engineering Department, Missouri University of Science and Technology, McNutt Hall, 1400 N. Bishop Ave, Rolla, MO 65401, USA;
| | - Mohamed S. Shokr
- Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
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Rashid A, Ayub M, Gao X, Khattak SA, Ali L, Li C, Ahmad A, Khan S, Rinklebe J, Ahmad P. Hydrogeochemical characteristics, stable isotopes, positive matrix factorization, source apportionment, and health risk of high fluoride groundwater in semiarid region. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:134023. [PMID: 38492393 DOI: 10.1016/j.jhazmat.2024.134023] [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: 01/30/2024] [Revised: 03/02/2024] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
Chronic exposure to high fluoride (F-) levels in groundwater causes community fluorosis and non-carcinogenic health concerns in local people. This study described occurrence, dental fluorosis, and origin of high F-groundwater using δ2H and δ18O isotopes at semiarid Gilgit, Pakistan. Therefore, groundwater (n = 85) was collected and analyzed for F- concentrations using ion-chromatography. The lowest F- concentration was 0.4 mg/L and the highest 6.8 mg/L. F- enrichment is linked with higher pH, NaHCO3, NaCl, δ18O, Na+, HCO3-, and depleted Ca+2 aquifers. The depleted δ2H and δ18O values indicated precipitation and higher values represented the evaporation effect. Thermodynamic considerations of fluorite minerals showed undersaturation, revealing that other F-bearing minerals viz. biotite and muscovite were essential in F- enrichment in groundwater. Positive matrix factorization (PMF) and principal component analysis multilinear regression (PCAMLR) models were used to determine four-factor solutions for groundwater contamination. The PMF model results were accurate and reliable compared with those of the PCAMLR model, which compiled the overlapping results. Therefore, 28.3% exceeded the WHO permissible limit of 1.5 mg/L F-. Photomicrographs of granite rocks showed enriched F-bearing minerals that trigger F- in groundwater. The community fluorosis index values were recorded at > 0.6, revealing community fluorosis and unsuitability of groundwater for drinking.
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Affiliation(s)
- Abdur Rashid
- State Key Laboratory of Biogeology and Environmental Geology, School of Environmental Studies, China University of Geosciences, Wuhan 430074, PR China; National Centre of Excellence in Geology, University of Peshawar, 25130, Pakistan.
| | - Muhammad Ayub
- Department of Botany Hazara University, Mansehra PO 21300 Pakistan
| | - Xubo Gao
- State Key Laboratory of Biogeology and Environmental Geology, School of Environmental Studies, China University of Geosciences, Wuhan 430074, PR China.
| | - Seema Anjum Khattak
- National Centre of Excellence in Geology, University of Peshawar, 25130, Pakistan
| | - Liaqat Ali
- National Centre of Excellence in Geology, University of Peshawar, 25130, Pakistan
| | - Chengcheng Li
- State Key Laboratory of Biogeology and Environmental Geology, School of Environmental Studies, China University of Geosciences, Wuhan 430074, PR China
| | - Ajaz Ahmad
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Sardar Khan
- Department of Environmental Sciences, University of Peshawar, PO 25120, Pakistan
| | - Jörg Rinklebe
- University of Wuppertal, School of Architecture and Civil Engineering, Laboratory of Soil, and Groundwater-Management, Pauluskirchstraße 7, 42285 Wuppertal, Germany
| | - Parvaiz Ahmad
- Department of Botany, GDC, Pulwama 192301, Jammu and Kashmir, India
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Halder B, Bandyopadhyay J, Ghosh N. Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:37075-37108. [PMID: 38760605 DOI: 10.1007/s11356-024-33603-4] [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: 12/07/2023] [Accepted: 05/03/2024] [Indexed: 05/19/2024]
Abstract
Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.
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Affiliation(s)
- Bijay Halder
- Department of Earth Sciences and Environment, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia UKM, 43600, Bangi, Selangor, Malaysia.
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | | | - Nishita Ghosh
- Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, 721102, India
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Du Y, Tian Z, Zhao Y, Wang X, Ma Z, Yu C. Exploring the accumulation capacity of dominant plants based on soil heavy metals forms and assessing heavy metals contamination characteristics near gold tailings ponds. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119838. [PMID: 38145590 DOI: 10.1016/j.jenvman.2023.119838] [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: 09/12/2023] [Revised: 11/10/2023] [Accepted: 11/28/2023] [Indexed: 12/27/2023]
Abstract
Heavy metal contamination of soil commonly accompanies problems around gold mine tailings ponds. Fully investigating the distribution characteristics of heavy metals and the survival strategies of dominant plants in contaminated soils is crucial for effective pollution management and remediation. This study aims to investigate the contamination characteristics, sources of heavy metals (As, Cd, Pb, Hg, Cu, Zn, Cr, and Ni) in soils around gold mine tailings ponds areas (JHH and WZ) and to clarify the form distribution of heavy metals (As, Cd, Pb, Hg) in contaminated plots as well as their accumulation and translocation in native dominant plants. The results of the study showed that the concentrations of As, Pb, Cd, Cu, and Zn in soil exceeded the national limits at parts of the sampling sites in both study areas. The Nemerow pollution index showed that both study areas reached extreme high pollution levels. Spatial analysis showed that the main areas of contamination were concentrated around metallurgical plants and tailings ponds, with Cd exhibiting the most extensive area of contamination. In the JHH, As (74%), Cd (66%), Pb (77%), Zn (47%) were mainly from tailings releases, and Cu (52%) and Hg (51%) were mainly from gold ore smelting. In the WZ, As (42%), Cd (41%), Pb (73%), Cu (47%), and Zn (41%) were mainly from tailings releases. As, Cd, Pb, and Hg were mostly present in the residue state, and the proportion of water-soluble, ion-exchangeable, and carbonate-bound forms of Cd (19.93%) was significantly higher than that of other heavy metals. Artemisia L. and Amaranthus L. are the primary dominating plants, which exhibited superior accumulation of Cd compared to As, Pb, and Hg, and Artemisia L. demonstrated a robust translocation capacity for As, Pb, and Hg. Compared to the concentrations of other forms of soil heavy metals, the heavy metal content in Artemisia L correlates significantly better with the total soil heavy metal concentration. These results offer additional systematic data support and a deeper theoretical foundation to bolster pollution-control and ecological remediation efforts in mining areas.
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Affiliation(s)
- Yanbin Du
- School of Chemical & Environmental Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China
| | - Zhijun Tian
- Beijing Institute of Mineral Geology, Beijing, 101500, China
| | - Yunfeng Zhao
- Beijing Institute of Mineral Geology, Beijing, 101500, China
| | - Xinrong Wang
- School of Chemical & Environmental Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China
| | - Zizhen Ma
- School of Chemical & Environmental Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China
| | - Caihong Yu
- School of Chemical & Environmental Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China.
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Mhana KH, Norhisham SB, Katman HYB, Yaseen ZM. Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe. Heliyon 2023; 9:e19413. [PMID: 37809986 PMCID: PMC10558544 DOI: 10.1016/j.heliyon.2023.e19413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/29/2023] [Accepted: 08/22/2023] [Indexed: 10/10/2023] Open
Abstract
Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.
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Affiliation(s)
- Khalid Hardan Mhana
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Civil Engineering Department, College of Engineering, University Of Anbar, Iraq
| | - Shuhairy Bin Norhisham
- Institute of Energy Infrastructure (IEI) and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
| | - Herda Yati Binti Katman
- Institute of Energy Infrastructure (IEI) and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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Yang Y, Lu X, Yu B, Zuo L, Wang L, Lei K, Fan P, Liang T, Rennert T, Rinklebe J. Source-specific risk judgement and environmental impact of potentially toxic elements in fine road dust from an integrated industrial city, North China. JOURNAL OF HAZARDOUS MATERIALS 2023; 458:131982. [PMID: 37413801 DOI: 10.1016/j.jhazmat.2023.131982] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/27/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
The contamination of potentially toxic elements (PTEs) in road dust of large industrial cities is extremely serious. Determining the priority risk control factors of PTE contamination in road dust is critical to enhance the environmental quality of such cities and mitigate the risk of PTE pollution. The Monte Carlo simulation (MCS) method and geographical models were employed to assess the probabilistic pollution levels and eco-health risks of PTEs originating from different sources in fine road dust (FRD) of large industrial cities, and to identify key factors affecting the spatial variability of priority control sources and target PTEs. It was observed that in FRD of Shijiazhuang, a typical large industrial city in China, more than 97% of the samples had an INI > 1 (INImean = 1.8), indicating moderately contaminated with PTEs. The eco-risk was at least considerable (NCRI >160) with more than 98% of the samples, mainly caused by Hg (Ei (mean) = 367.3). The coal-related industrial source (NCRI(mean) = 235.1) contributed 70.9% to the overall eco-risk (NCRI(mean) = 295.5) of source-oriented risks. The non-carcinogenic risk of children and adults are of less importance, but the carcinogenic risk deserves attention. The coal-related industry is a priority control pollution source for human health protection, with As corresponding to the target PTE. The major factors affecting the spatial changes of target PTEs (Hg and As) and coal-related industrial sources were plant distribution, population density, and gross domestic product. The hot spots of coal-related industrial sources in different regions were strongly interfered by various human activities. Our results illustrate spatial changes and key-influencing factors of priority source and target PTEs in Shijiazhuang FRD, which are helpful for environmental protection and control of environmental risks by PTEs.
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Affiliation(s)
- Yufan Yang
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Xinwei Lu
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China.
| | - Bo Yu
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Ling Zuo
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Lingqing Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Kai Lei
- School of Biological and Environmental Engineering, Xi'an University, Xi'an 710065, China
| | - Peng Fan
- Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Tao Liang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Thilo Rennert
- Department of Soil Chemistry and Pedology, Institute of Soil Science and Land Evaluation, University of Hohenheim, 70593 Stuttgart, Germany
| | - Jörg Rinklebe
- University of Wuppertal, School of Architecture and Civil Engineering, Institute of Foundation Engineering, Water, and Waste-Management, Soil-and Groundwater-Management, Pauluskirchstraße 7, 42285 Wuppertal, Germany
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Halder B, Ahmadianfar I, Heddam S, Mussa ZH, Goliatt L, Tan ML, Sa'adi Z, Al-Khafaji Z, Al-Ansari N, Jawad AH, Yaseen ZM. Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine. Sci Rep 2023; 13:7968. [PMID: 37198391 DOI: 10.1038/s41598-023-34774-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
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Affiliation(s)
- Bijay Halder
- Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, 721102, India
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Thi-Qar, 64001, Iraq
| | - Iman Ahmadianfar
- Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
| | - Salim Heddam
- Agronomy Department, Faculty of Science, University, 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda, Algeria
| | | | - Leonardo Goliatt
- Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Mou Leong Tan
- GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, Penang, Malaysia
- School of Geographical Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Zulfaqar Sa'adi
- Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment, Universiti Teknologi Malaysia (UTM), 81310, Sekudai, Johor, Malaysia
| | - Zainab Al-Khafaji
- Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah, 51001, Iraq
| | - Nadhir Al-Ansari
- Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden.
| | - Ali H Jawad
- Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
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An W, Wang B, Duan L, Giovanni C, Yu G. Emerging contaminants in the northwest area of the Tai Lake Basin, China: Spatial autocorrelation analysis for source apportionment and wastewater-based epidemiological analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161176. [PMID: 36581295 DOI: 10.1016/j.scitotenv.2022.161176] [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: 10/04/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
In the present study, 60 emerging contaminants (ECs) were detected from 88 target compounds in the district of Wujin, which is the northwest area of Tai Lake Basin, China. Among them, CF (caffeine), a type of PhAC (pharmaceutically active compound), was detected as the pollutant with the highest concentration. It was observed that the removal efficiencies of PFASs (per-/polyfluoroalkyl substances) in wastewater treatment plants were lower than those of pesticides; further, those of pesticides were lower than those of PhACs. Based on the spatial agglomeration estimated by the spatial autocorrelation model, the probable sources of 28 contaminants were identified. This model provided a new perspective that would help to clarify the location of sources with high accuracy. The point sources of 6 PFASs and 14 PhACs were successfully found; in contrast, the main source of pesticides was identified as an agricultural nonpoint source. Finally, the potential risks of the ECs were also assessed in this study, including their aquatic ecological risks and human exposure risks. It was concluded that pesticides generally had the highest ecological risk, followed by PFASs and PhACs. To evaluate the population risk of pesticides, the wastewater-based epidemiological model was extended to back-calculate the per capita pesticide consumption, which was 0.22 g d-1 (103capita)-1.
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Affiliation(s)
- Wenkai An
- School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), Tsinghua University, Beijing 100084, PR China
| | - Bin Wang
- School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), Tsinghua University, Beijing 100084, PR China; Research Institute for Environmental Innovation (Suzhou), Tsinghua, Suzhou 215163, PR China.
| | - Lei Duan
- School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), Tsinghua University, Beijing 100084, PR China
| | - Cagnetta Giovanni
- School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), Tsinghua University, Beijing 100084, PR China
| | - Gang Yu
- School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), Tsinghua University, Beijing 100084, PR China; Research Institute for Environmental Innovation (Suzhou), Tsinghua, Suzhou 215163, PR China
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9
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Tao H, Al-Hilali AA, Ahmed AM, Mussa ZH, Falah MW, Abed SA, Deo R, Jawad AH, Abdul Maulud KN, Latif MT, Yaseen ZM. Statistical and spatial analysis for soil heavy metals over the Murray-Darling river basin in Australia. CHEMOSPHERE 2023; 317:137914. [PMID: 36682637 DOI: 10.1016/j.chemosphere.2023.137914] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 12/21/2022] [Accepted: 01/18/2023] [Indexed: 06/17/2023]
Abstract
Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.
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Affiliation(s)
- Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China; School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, China; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah, Alam, Selangor, Malaysia.
| | | | - Ali M Ahmed
- Engineering Department, Al-Esraa University College, Baghdad, 10011, Iraq.
| | | | - Mayadah W Falah
- Building and Construction Engineering Technology Department, AL-Mustaqbal University, College, Hillah, 51001, Iraq.
| | | | - Ravinesh Deo
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia.
| | - Ali H Jawad
- Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.
| | - Khairul Nizam Abdul Maulud
- Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia; Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
| | - Mohd Talib Latif
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia.
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
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10
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Tian M, Wang X, Liu F, Hu Q, Qiao Y, Wang Q. Spatial-temporal variability and influence factors of Cd in soils of Guangxi, China. PLoS One 2023; 18:e0279980. [PMID: 36626378 PMCID: PMC9831335 DOI: 10.1371/journal.pone.0279980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
In this study, the regional spatial-temporal variability of cadmium (Cd) in the topsoil of Guangxi, China from 2010 to 2016 was studied from data obtained from the China Geochemical Baseline Project (CGB Ⅰ and CGB Ⅱ). The driving forces of natural and anthropogenic variables were quantitatively analyzed using a geographically and temporally weighted regression model. The results showed that 1) soil Cd was highly enriched in 2010 and in soils of Hechi city in northwest Guangxi, a non-ferrous metal mining and metallurgy area, ~17% of the samples exceeded the soil contamination risk limit. In contrast, in 2016, the topsoil Cd content decreased significantly, with 7% of sites exceeding the soil risk limit. 2) Multiple factors jointly influenced the regional spatial variability of Cd. pH and organic carbon were found to be the main factors influencing Cd content and were strongly spatially correlated with Cd. Anthropogenic activities, including mining and industrial emissions, resulted in significant Cd enrichment in local areas, whereas agricultural and domestic pollutants were relatively weakly correlated with Cd. The weathering products of carbonates were significantly enriched in Cd; thus, the geological background played a significant role in the spatial variability of Cd. Soil-forming factors, including temperature, precipitation, and elevation influenced the spatial distribution of Cd, especially in the Cd background area. 3) Anthropogenic activities were the key factors influencing temporal changes in Cd. Mining caused significant enrichment of Cd in CGB Ⅰ, while industrial emissions were the primary factor for Cd enrichment in CGB Ⅱ. In addition, natural factors also played an important role; the increased Normalized Difference Vegetation Index suggested reduced desertification and reduction of soil erosion in the watershed and in pollutants transported from upstream.
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Affiliation(s)
- Mi Tian
- Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang, Hebei, China
- UNESCO International Center on Global-scale Geochemistry, Langfang, Hebei, China
| | - Xueqiu Wang
- Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang, Hebei, China
- UNESCO International Center on Global-scale Geochemistry, Langfang, Hebei, China
- * E-mail:
| | - Futian Liu
- Lanzhou University & Key Laboratory of Strategic Mineral Resources of the Upper Yellow River, School of Earth Sciences, Ministry of Natural Resources, Lanzhou, Gansu, China
| | - Qinghai Hu
- Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang, Hebei, China
- UNESCO International Center on Global-scale Geochemistry, Langfang, Hebei, China
| | - Yu Qiao
- Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang, Hebei, China
- UNESCO International Center on Global-scale Geochemistry, Langfang, Hebei, China
| | - Qiang Wang
- Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang, Hebei, China
- UNESCO International Center on Global-scale Geochemistry, Langfang, Hebei, China
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11
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Ning W, Yang P, Wang H, Han L, Cao M, Luo J. Evaluating a Sampling Regime for Estimating the Levels of Contamination and the Sources of Elements in Soils Collected from a Rapidly Industrialized Town in Guangdong Province, China. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2022; 82:403-415. [PMID: 35246725 DOI: 10.1007/s00244-022-00916-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
Gaogang Town, a typical urban center within the Pearl River Delta region of China, suffers contamination of soils with metals/metalloids due to rapid development of industrial activities and agriculture. Few studies have been conducted to systematically describe the main sources, influencing factors, and ecological risks of metals/metalloids in soils in China. In this study, 312 surface soil samples were collected, and 15 elements were detected by plasma emission spectroscopy, atomic fluorescence spectroscopy, and atomic emission spectrometry. Element content features were analyzed by index of geo-accumulation (Igeo), pollution load index (PLI), potential ecological risk index (RI), positive matrix factorization model (PMF), and geostatistical analysis. The PLI value is between 0 and 1, indicating that the whole study area is lightly polluted. Combining PMF model and geostatistical analysis, soil elements in surface soils of Gaogang town were quantitatively apportioned into four sources: parent material and basic substances (23.5%), natural sources (32.2%), agricultural activities and industrial pollution (22.9%), and transportation (21.4%). The comprehensive analysis results show that polluted areas are mainly distributed on roads, rivers, and industrial and human activity areas. The main sources of ecological risks are factory pollution and human activity. Finally, we found that a quarter of the sampling density was the best sample size for this study.
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Affiliation(s)
- Wenjing Ning
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Pan Yang
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Hanzhi Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510535, China
| | - Lijie Han
- China University of Geosciences, Wuhan, 430074, China
| | - Min Cao
- University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Jie Luo
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China.
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12
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Zierold KM, Myers JV, Brock GN, Sears CG, Sears LL, Zhang CH. Nail Samples of Children Living near Coal Ash Storage Facilities Suggest Fly Ash Exposure and Elevated Concentrations of Metal(loid)s. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:9074-9086. [PMID: 34132542 PMCID: PMC10725724 DOI: 10.1021/acs.est.1c01541] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Children who live near coal-fired power plants are exposed to coal fly ash, which is stored in landfills and surface impoundments near residential communities. Fly ash has the potential to be released as fugitive dust. Using data collected from 263 children living within 10 miles of coal ash storage facilities in Jefferson and Bullitt Counties, Kentucky, USA, we quantified the elements found in nail samples. Furthermore, using principal component analysis (PCA), we investigated whether metal(loid)s that are predominately found in fly ash loaded together to indicate potential exposure to fly ash. Concentrations of several neurotoxic metal(loid)s, such as chromium, manganese, and zinc, were higher than concentrations reported in other studies of both healthy and environmentally exposed children. From PCA, it was determined that iron, aluminum, and silicon in fly ash were found to load together in the nails of children living near coal ash storage facilities. These metal(loid)s were also highly correlated with each other. Last, results of geospatial analyses partially validated our hypothesis that children's proximity to power plants was associated with elevated levels of concentrations of fly ash metal(loid)s in nails. Taken together, nail samples may be a powerful tool in detecting exposure to fly ash.
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Affiliation(s)
- Kristina M Zierold
- Department of Environmental Health Sciences, University of Alabama at Birmingham, Birmingham 35294, Alabama, United States
| | - John V Myers
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, Columbus 43210, Ohio, United States
| | - Guy N Brock
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, Columbus 43210, Ohio, United States
| | - Clara G Sears
- Department of Epidemiology, Brown University, Providence 02912, Rhode Island, United States
| | - Lonnie L Sears
- Department of Pediatrics, University of Louisville, Louisville 40292, Kentucky, United States
| | - Charlie H Zhang
- Department of Geography & Geosciences, University of Louisville, Louisville 40292, Kentucky, United States
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13
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Wang Z, Chen X, Yu D, Zhang L, Wang J, Lv J. Source apportionment and spatial distribution of potentially toxic elements in soils: A new exploration on receptor and geostatistical models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 759:143428. [PMID: 33168250 DOI: 10.1016/j.scitotenv.2020.143428] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 05/27/2023]
Abstract
Potentially toxic element (PTE) pollution is considered as the main soil environmental problem in the world. Source apportionment and spatial pattern of soil PTEs are essential for soil management. US-EPA positive matrix factorization (EPAPMF) and sequential Gaussian simulation (SGS) are general modeling tools for source apportionment and spatial distribution, respectively. Factor analysis with nonnegative constraints (FA-NNC) and stochastic partial derivative equations (SPDE) provided potential tools for this issue. We compared the performance of FA-NNC with PMF and the performance of SPDE with SGS, based on a dataset containing 9 PTEs in 285 topsoil samples. Three factors were determined by the two receptor models, and the source contributions were similar, suggesting that FA-NNC can validly identify quantitative sources of soil PTEs. The average source contributions were calculated based on the PMF and FA-NNC. Natural sources dominated the contents of As, Co, Cr, Cu, Ni, and Zn and affected 56.0%, 38.7%, and 84.8% of the Cd, Hg, and Pb concentrations, respectively. A total of 59.8% of Hg and 12.0% of Pb were associated with atmospheric deposition from coal combustion, industrial and traffic emissions, respectively. Agricultural and industrial activities contributed 37.2% of Cd concentration. SPDE proved to be an effective geostatistical technique to simulate the spatial patterns of soil PTEs with higher prediction accuracy than SGS. Co, Cr, Cu, and Ni had similar spatial patterns with hotspots randomly distributed across the study area. The common hotspots of As, Cd, Hg, Pb, and Zn in central parts inherited their high geochemical background in mudstone, while intensive human inputs in these areas also contributed to the accumulation of Cd, Hg, and Pb.
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Affiliation(s)
- Zheng Wang
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China
| | - Xiaomei Chen
- Natural Resources and Planning Bureau of Linyi, Linyi 276000, China
| | - Deqin Yu
- Shandong Institute of Geological Survey, Jinan 250013, China
| | - Lixia Zhang
- Shandong Geo-Environmental Monitoring Station, Jinan 250014, China
| | - Jining Wang
- Shandong Geo-Environmental Monitoring Station, Jinan 250014, China
| | - Jianshu Lv
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China.
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