1
|
Wang Y, Zou B, Li S, Tian R, Zhang B, Feng H, Tang Y. A hierarchical residual correction-based hyperspectral inversion method for soil heavy metals considering spatial heterogeneity. JOURNAL OF HAZARDOUS MATERIALS 2024; 479:135699. [PMID: 39226683 DOI: 10.1016/j.jhazmat.2024.135699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/19/2024] [Accepted: 08/27/2024] [Indexed: 09/05/2024]
Abstract
Promising hyperspectral remote sensing exhibits substantial potential in monitoring soil heavy metal (SHM) contamination. Nevertheless, the local spatial perturbation effects induced by environmental factors introduce considerable variability in SHM distribution. This engenders non-stationary relationship between SHM concentrations and spectral reflectance, posing challenges for accurate inversion of SHM globally. Addressing this gap, a novel Hierarchical Residual Correction-based Hyperspectral Inversion Method (HRCHIM) is proposed for SHM, considering their spatial heterogeneity. Initially, a global model is constructed using ground hyperspectral data to predict SHM concentration, capturing overarching contamination trends. Subsequently, four hierarchical levels, segmented by residual standard deviation (SD) intervals, identify critical environmental factors via Geodetector. These factors inform local residual correction models, refining global model predictions. HRCHIM aims to synergize global trends and local stochasticity to enhance prediction accuracy and interpretation of SHM spatial heterogeneity. Validated through a case study of a Cadmium(Cd)-contaminated mine area, six critical environmental factors were identified, exhibiting significant differences across hierarchical levels. By incorporating hierarchical correction models, HRCHIM demonstrated superior inversion performance compared to other conventional methods, achieving optimal prediction accuracies (Rv2 = 0.94, RMSEv = 0.21, and RPDv = 4.11). This innovative method can facilitate more precise and targeted strategies for preventing and controlling SHM contamination.
Collapse
Affiliation(s)
- Yulong Wang
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Chinese Ministry of Education, Changsha 410083, China.
| | - Sha Li
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
| | - Rongcai Tian
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
| | - Bo Zhang
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
| | - Huihui Feng
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Chinese Ministry of Education, Changsha 410083, China
| | - Yuqi Tang
- School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Chinese Ministry of Education, Changsha 410083, China
| |
Collapse
|
2
|
Saha A, Sen Gupta B, Patidar S, Hernández-Martínez JL, Martín-Romero F, Meza-Figueroa D, Martínez-Villegas N. A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico. ENVIRONMENTAL RESEARCH 2024; 260:119619. [PMID: 39009213 DOI: 10.1016/j.envres.2024.119619] [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: 09/18/2023] [Revised: 06/10/2024] [Accepted: 07/12/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND This study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico. OBJECTIVES The primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination. METHODS Soil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health. RESULTS The study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children. CONCLUSION This study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.
Collapse
Affiliation(s)
- Arnab Saha
- Institute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
| | - Bhaskar Sen Gupta
- Institute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
| | - Sandhya Patidar
- Institute of Infrastructure and Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
| | | | - Francisco Martín-Romero
- Department of Geochemistry, Institute of Geology, Universidad Nacional Autónoma de México, Alcandia Coyoacán., Ciudad de México., 04510, Mexico.
| | - Diana Meza-Figueroa
- Department of Geology, UNISON, University of Sonora, Rosales y Encinas S/n, C.P. 83000, Hermosillo, Sonora, Mexico.
| | | |
Collapse
|
3
|
Ma X, Guan DX, Zhang C, Yu T, Li C, Wu Z, Li B, Geng W, Wu T, Yang Z. Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135407. [PMID: 39116745 DOI: 10.1016/j.jhazmat.2024.135407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 08/10/2024]
Abstract
The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.
Collapse
Affiliation(s)
- Xudong Ma
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Dong-Xing Guan
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaosheng Zhang
- International Network for Environment and Health, School of Geography, Archaeology and Irish Studies, University of Galway, Ireland
| | - Tao Yu
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Cheng Li
- Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin, Guangxi 541004, China
| | - Zhiliang Wu
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Bo Li
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Wenda Geng
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
| | - Tiansheng Wu
- Guangxi Institute of Geological Survey, Nanning 530023, China
| | - Zhongfang Yang
- School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China.
| |
Collapse
|
4
|
Zeng Y, Liu X, Li Y, Jin Z, Shui W, Wang Q. Analysis of driving factors for potential toxic metals in major urban soils of China: a geodetetor-based quantitative study. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:389. [PMID: 39172173 DOI: 10.1007/s10653-024-02163-4] [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: 12/19/2023] [Accepted: 08/01/2024] [Indexed: 08/23/2024]
Abstract
Potential toxic metal (PTM) is hazardous to human health, but the mechanism of spatial heterogeneity of PTM at a macro-scale remains unclear. This study conducts a meta-analysis on the data of PTM concentrations in the soil of 164 major cities in China from 2006 to 2021. It utilizes spatial analysis methods and geodetector to investigate the spatial distribution characteristics of PTMs. The geographic information systems (GIS) and geodetector were used to investigate the spatial distribution characteristics of PTMs, assess the influence of natural factors (NFs) and anthropogenic factors (AFs) on the spatial heterogeneity of PTMs in urban soils, and identified the potential pollution areas of PTMs. The results indicated that the pollution levels of PTMs in urban soils varied significantly across China, with higher pollution levels in the south than in the north. Cd and Hg were the most severely contaminated elements. The geodetector analysis showed that temperature and precipitation in NFs and land use type in AFs were considered as the main influencing factors, and that both AF and NF together led to the PTM variation. All these factors showed a mutually enhancing pattern which has important implications for urban soil management. PTM high-risk areas were identified to provide early warning of pollution risk under the condition of climate change.
Collapse
Affiliation(s)
- Yue Zeng
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education of China, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350108, People's Republic of China
| | - Xinyu Liu
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China
| | - Yunqin Li
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
| | - Zhifan Jin
- Fujian Provincial Fuzhou Environmental Monitoring Center Station, Fuzhou, 350013, People's Republic of China
| | - Wei Shui
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education of China, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350108, People's Republic of China
| | - Qianfeng Wang
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education of China, Fuzhou University, Fuzhou, 350108, People's Republic of China
- Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350108, People's Republic of China
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Wang H, Zhao M, Huang X, Song X, Cai B, Tang R, Sun J, Han Z, Yang J, Liu Y, Fan Z. Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133745. [PMID: 38401211 DOI: 10.1016/j.jhazmat.2024.133745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/26/2024]
Abstract
The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000-2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.
Collapse
Affiliation(s)
- Huijuan Wang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; School of Resoureces and Environment, Anqing Normal University, Anqing 246133, China
| | - Menglu Zhao
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xinmiao Huang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaoyong Song
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Boya Cai
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Rui Tang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jiaxun Sun
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Department of Geographical Sciences, University of Maryland, College Park 20742, the United States
| | - Zilin Han
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jing Yang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510530, China
| | - Yafeng Liu
- School of Resoureces and Environment, Anqing Normal University, Anqing 246133, China.
| | - Zhengqiu Fan
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
| |
Collapse
|
7
|
Xu X, Wang Z, Song X, Zhan W, Yang S. A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest. ENVIRONMENTAL RESEARCH 2024; 240:117570. [PMID: 37939802 DOI: 10.1016/j.envres.2023.117570] [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: 08/10/2023] [Revised: 10/04/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023]
Abstract
The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral-temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R2 = 0.85; RMSE = 0.80 mg kg-1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management.
Collapse
Affiliation(s)
- Xibo Xu
- Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China.
| | - Zeqiang Wang
- Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China; College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
| | - Xiaoning Song
- College of Tourism and Environment Resource, Zaozhuang University, Zaozhuang 277160, China
| | - Wenjie Zhan
- College of Tourism and Environment Resource, Zaozhuang University, Zaozhuang 277160, China
| | - Shuting Yang
- Institute of Agricultural Economy and Information Technology, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China
| |
Collapse
|
8
|
Zeng W, Wan X, Gu G, Lei M, Yang J, Chen T. An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159698. [PMID: 36309258 DOI: 10.1016/j.scitotenv.2022.159698] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/20/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.
Collapse
Affiliation(s)
- Weibin Zeng
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoming Wan
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Gaoquan Gu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mei Lei
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Yang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tongbin Chen
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
9
|
Xu Y, Bi R, Li Y. Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 249:114436. [PMID: 36525951 DOI: 10.1016/j.ecoenv.2022.114436] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 11/23/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The concentrations of trace elements in agricultural soils directly affect the ecological security and quality of agricultural products. A comprehensive study aimed at quantitatively analyze the effects of anthropogenic and natural environmental factors on the spatial distribution of heavy metals (HMs) and selenium (Se) in agricultural soils in a typical grain producing area of China. Factors considered in this study were parent rock, soil physicochemical properties, topography, precipitation, mine activity, and vegetation. Results showed that the median values of Zn, Cd, Cr, and Cu of 111 topsoil samples exceeded the background values of Guangxi province but were lower than the relevant national soil quality standards, and 85% of soil samples were classified as having rich Se levels (0.40 -3.0 mg kg-1). The potential ecological risk index of soil heavy metals as a whole was low, with Cd in 9% of the samples posing moderate ecological risk. The concentrations of heavy metals and Se were relatively high in soils from shale rock. Soil properties, mainly Fe2O3 and Mn played a dominant role on soil HMs and Se concentrations. Based on GeoDetector, we found that the interaction effects of two factors on the spatial differentiation of soil HMs and Se were greater than their sum effect. Among the factors, Mn enhanced the explanatory power of the model the most when interacting with other factors for soil Zn; the greatest interactive effect was between distance from mining area and Mn for Cd (q = 0.70); Fe2O3 significantly promoted the spatial differentiation of soil Cr, Cu and Se when interacting with other factors (q > 0.50). These findings contribute to a better understanding of the factors that drive the distribution of HMs and Se in agricultural soils.
Collapse
Affiliation(s)
- Yuefeng Xu
- College of Resources and Environment, Shanxi Agricultural University, Taigu, Shanxi 030801, China.
| | - Rutian Bi
- College of Resources and Environment, Shanxi Agricultural University, Taigu, Shanxi 030801, China
| | - Yonghua Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| |
Collapse
|
10
|
Yin G, Chen X, Zhu H, Chen Z, Su C, He Z, Qiu J, Wang T. A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 825:153948. [PMID: 35219652 DOI: 10.1016/j.scitotenv.2022.153948] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
Collapse
Affiliation(s)
- Guangcai Yin
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Xingling Chen
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Hanghai Zhu
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhiliang Chen
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Chuanghong Su
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
| | - Zechen He
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Jinrong Qiu
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Tieyu Wang
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China.
| |
Collapse
|
11
|
Qiao P, Lai D, Yang S, Zhao Q, Wang H. Effectiveness of predicting the spatial distributions of target contaminants of a coking plant based on their related pollutants. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:33945-33956. [PMID: 35034303 DOI: 10.1007/s11356-021-17951-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction in the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging was 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The RMSEs calculated for the multivariable cokriging were lower than those obtained by only combining one related pollutants, and two co-variables should be better. (3) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (4) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (5) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively, with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.
Collapse
Affiliation(s)
- Pengwei Qiao
- Institute of Resources and Environment, Beijing Academy of Science and Technology, Beijing Key Laboratory of Remediation of Industrial Pollution Sites, Beijing, 100089, China
| | - Donglin Lai
- YuHuan Environmental Technology Co., Ltd, Shijiazhuang, 050051, China
| | - Sucai Yang
- Institute of Resources and Environment, Beijing Academy of Science and Technology, Beijing Key Laboratory of Remediation of Industrial Pollution Sites, Beijing, 100089, China.
| | - Qianyun Zhao
- YuHuan Environmental Technology Co., Ltd, Shijiazhuang, 050051, China
| | - Hengqin Wang
- YuHuan Environmental Technology Co., Ltd, Shijiazhuang, 050051, China
| |
Collapse
|
12
|
Sakizadeh M, Rodríguez Martín JA. Spatial methods to analyze the relationship between Spanish soil properties and cadmium content. CHEMOSPHERE 2021; 268:129347. [PMID: 33359986 DOI: 10.1016/j.chemosphere.2020.129347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/10/2020] [Accepted: 12/14/2020] [Indexed: 06/12/2023]
Abstract
In this study, concentrations of cadmium using 3778 samples encompassing the total size of Spain (about 505 km2) were investgated. Two novel spatial methods namely Moran eigenvector spatially varying coefficient (MESVC) and spatially filtered unconditional quantile regression (SF-UQR) were employed with the aim of avoiding the problem of local collinearity which is prevalent in regression models. Additionally, the spatially varying coefficients methods were applied to assess the influence of soil properties together with soil texture on the spatial variations of cadmium. It was indicated that the overall level of cadmium is low compared to the concentrations found around the world. In particular, the values of Cd varied between 0.01 and 2.00 mgkg-1, with the median of 0.23 mgkg-1. The residual standard error and adjusted R2 produced by MESVC were 0.16 and 0.69, respectively which are better than 0.21 and 0.39 yielded by the SF-UQR model. Both of these models outperformed compared to the geographically weighted regression (GWR) and the performance of MESVC was also better than the traditional method of kriging. For instance, in terms of willmott index (d) and root mean squared relative error (RMSRE), the MESVC had superior performance with values equal to 0.612 and 0.275 compared to 0.399 and 0.379 obtained for the ordinary kriging. The MESVC and GWR demonstrated that CaCO3, sand, silt and clay had a negligible influence on spatial variations of cadmium whereas, EC had the largest contribution followed by SOM and pH.
Collapse
Affiliation(s)
- Mohammad Sakizadeh
- Environmental Engineering and Management Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Jose Antonio Rodríguez Martín
- Dept. Environment, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (I.N.I.A), Ctra. de ACoruña 7.5, 28040, Madrid, Spain
| |
Collapse
|
13
|
Determinants of Residents’ Willingness to Accept and Their Levels for Ecological Conservation in Ganjiang River Basin, China: An Empirical Analysis of Survey Data for 677 Households. SUSTAINABILITY 2019. [DOI: 10.3390/su11216138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Using the contingent valuation method and the Heckman two-stage model, we explore residents’ willingness to accept (WTA) compensation and their WTA level for ecological conservation compensation in the upstream of the Ganjiang River Basin in China. The findings reveal that 86.26% of the respondents are willing to accept compensation, and the average compensation level is ¥789.60/household per year. The residents’ gender, annual disposable income, residential location, decision on whether or not the watershed environment is important, and their satisfaction with water quality and quantity are significantly related to their WTA. The influencing factors that significantly affect compensation level are residents’ occupation, educational background, annual disposable income, family size, residential location, decision on whether or not the watershed environment is important, and their satisfaction with water quality and quantity. The results of this empirical research have important policy implications: the government should strengthen advocacy and education of watershed ecological environment protection, intensify farming and other agricultural activities, establish a differentiated and diversified compensation strategy, so as to protect and improve the ecological environment of the Ganjiang River Basin.
Collapse
|
14
|
Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. REMOTE SENSING 2019. [DOI: 10.3390/rs11141683] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.
Collapse
|