Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran

Contamination Assessment and Potential Human Health Risks of Heavy Metals in Urban Soils from Grand Forks, North Dakota, USA

Heavy metal (HM) pollution of soil is an increasingly serious problem worldwide. The current study assessed the metal levels and ecological and human health risk associated with HMs in Grand Forks urban soils. A total 40 composite surface soil samples were investigated for Mn, Fe, Co, Ni, Cu, Zn, As, Pb, Hg, Cr, Cd and Tl using microwave-assisted HNO3-HCl acid digestion and inductively coupled plasma mass spectrometry (ICP-MS) analysis. The enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), ecological risk and potential ecological risk index were used for ecological risk assessment. The park soils revealed the following decreasing trend for metal levels: Fe > Mn > Zn > Cr > Ni > Cu > Pb > As > Co > Cd > Tl > Hg. Based on mean levels, all the studied HMs except As and Cr were lower than guideline limits set by international agencies. Principal component analysis (PCA) indicated that Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Pb, Cr and Tl may originate from natural sources, while Hg, Pb, As and Cd may come from anthropogenic/mixed sources. The Igeo results showed that the soil was moderately polluted by As and Cd and, based on EF results, As and Cd exhibited significant enrichment. The contamination factor analysis revealed that Zn and Pb showed moderate contamination, Hg exhibited low to moderate contamination and As and Cd showed high contamination in the soil. Comparatively higher risk was noted for children over adults and, overall, As was the major contributor (>50%), followed by Cr (>13%), in the non-carcinogenic risk assessment. Carcinogenic risk assessment revealed that As and Cr pose significant risks to the populations associated with this urban soil. Lastly, this study showed that the soil was moderately contaminated by As, Cd, Pb and Hg and should be regularly monitored for metal contamination.

Human-induced pollution and toxicity of river sediment by potentially toxic elements (PTEs) and accumulation in a paddy soil-rice system: A comprehensive watershed-scale assessment

This study aimed to assess pollution by potentially toxic elements (PTEs) in the Zarjoub and Goharroud river basins in northern Iran. Due to exposure to various types of pollution sources, these rivers are two of the most polluted rivers in Iran. They also play an important role in irrigation of paddy fields in the study area, increasing concerns about the contamination of rice grains by PTEs. Hence, we analyzed the concentrations of eight PTEs (i.e., As, Co, Cr, Cu, Mn, Ni, Pb, and Zn) at ten channel bed sediment sampling sites in each river, fifteen samples of paddy soils and fifteen co-located rice samples in the relevant watersheds. Results of the index-based assessment indicate moderate to heavy pollution and moderate toxicity for sediments in the Goharroud River, while both pollution and toxicity of the Zarjoub River sediment were characterized as moderate. Paddy soils in the watersheds were found to be moderate to heavily polluted by PTEs, but the values of the rice bioconcentration factor (RBCF) indicated intermediate absorption for Cu, Zn, and Mn, and weak and very weak absorption for Pb/Ni and As/Co/Cr, respectively. The concentration of Zn, Cu, Pb, and Cr was negatively correlated to the corresponding values of RBCF, highlighting the ability of rice grains to control bioaccumulation and regulate concentrations. Industrial/agricultural effluents, municipal wastewater, leachate of solid waste, traffic-related pollution, and weathering of parent materials were found to be responsible for pollution of the Zarjoub and Goharroud watersheds by PTEs. Mn, Cu, and Pb in rice grains might be responsible for non-carcinogenic diseases. Although weak absorption was observed for As and Cr in rice grains, the concentrations of these elements in rice grains indicate a high level of cancer risk if ingested. This study provides insights to control the pollution of sediment, paddy soils, and rice.

Spatial changes of nutrients and metallic contaminants in topsoil with multi-geostatistical approaches in a large-size watershed

Appropriate assessment on concerned soil contaminants spatially is of importance for decision-makers and stakeholders to make efficient mitigation countermeasures. In this study, we applied multiple geostatistical approaches to explore soil nutrient and metallic contaminant distributions in a large river watershed in Thailand, and to compare their performances in predicting spatial distribution patterns of the concerned soil contaminants under suitable application scenarios. The total carbon, nitrogen and phosphorous in surface soils over the whole watershed were measured with their maximum concentrations up to 131.47, 9.24, 5.33 g·kg^-1, respectively, while the concentrations of eight metallic elements (Cu, Zn, Pb, Cd, Hg, As, Cr, and Ni) were 933.00, 6862.50, 373.00, 6.22, 1.15, 178.53, 761.11, and 372.44 mg·kg^-1, respectively. It was found that the conditional interpolation approaches such as land use stratified inverse distance weighted and land use stratified original kriging provided better boundary details than original interpolations, with substantially reduced root mean square errors (up to 28% for nutrients and 54% for specific metals) and mean relative errors (up to 38% for nutrients and specific metals respectively) in predicting the spatial patterns of soil nutrients and several land use specific metals (Cu, Zn, Cd, and Pb). The global accuracies were equivalent or higher than those of geographically weighted regression. Nonetheless, the prediction accuracy for Cr, Ni, As, and Hg could not be improved using the land use stratified interpolation because their sources and pathways were not significantly correlated with land use types in the watershed, as reflected by the results of analysis of variance with post hoc test (p ≤ 0.05) and principal component analysis.

A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

Evaluation of heavy metal contamination and ecological risk of soil adjacent to Saravan municipal solid waste disposal site, Rasht, Iran

This study was performed on the soil of the Hyrcanian forests near Saravan municipal solid waste dumpsite, Rasht, Iran. In this research, the contents of metals (As, Pb, Cr, Cd, Cu, Hg, and Zn) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The geoaccumulation index (I_geo), contamination factor (CF), and enrichment factor (EF), as well as pollution load index (PLI), were used to evaluate the metals contamination. The ecological risk factor ([Formula: see text]) and the potential ecological risk index (PERI) were applied to assess ecological risk. Pearson's correlation coefficients and the principal component analysis (PCA) were used to determine the possible origin of the metals. The metal concentrations were as follows: Zn > Pb > Cu > Cr > As > Cd > Hg. The results of the statistical tests showed that, except for Cr, the other elements had a significant difference with the control station (P < 0.05). The results of the Pearson's correlation coefficients, the PCA, and the I_geo revealed that the possible source of As, Hg, and Pb was the waste dumpsite activities and other anthropogenic origins, while Cd, Cu, Zn, and Cr probably have geogenic sources. The PLI was < 1, in unpolluted grade for all stations. The [Formula: see text] of the metals ranged as follows Hg > Cd > As > Pb > Zn, Cu > Cr, which implies that Cd and Hg play a key role in determining the ecological risk. The mean value of the PERI was 192.11 that represented a moderate ecological risk.

Environmental Health and Ecological Risk Assessment of Soil Heavy Metal Pollution in the Coastal Cities of Estuarine Bay-A Case Study of Hangzhou Bay, China

Shanghai is the major city on the north shore of Hangzhou Bay, and the administrative regions adjacent to Hangzhou Bay are the Jinshan district, Fengxian district, and Pudong new area (Nanhui district), which are the main intersection areas of manufacturing, transportation, and agriculture in Shanghai. In this paper, we collected a total of 75 topsoil samples from six different functional areas (agricultural areas (19), roadside areas (10), industrial areas (19), residential areas (14), education areas (6), and woodland areas (7)) in these three administrative regions, and the presence of 10 heavy metals (manganese(Mn), zinc(Zn), chromium(Cr), nickel(Ni), lead(Pb), cobalt(Co), cadmium(Cd), mercury(Hg), copper(Cu), and arsenic(As)) was investigated in each sample. The Nemerow pollution index (NPI), pollution load index (PLI), and potential ecological risk index (PERI) were calculated to assess the soil pollution levels. The hazard quotient (HQ) and carcinogenic risk (CR) assessment models were used to assess the human health risks posed by the concentrations of the heavy metals. The CR and HQ for adults and children in different functional areas descended in the following order: industrial areas > roadside areas > woodland areas > residential areas > education areas > agricultural areas. The HQ of Mn for children in industrial areas was higher than 1, and the risk was within the acceptable range.

The high levels of heavy metal accumulation in cultivated rice from the Tajan river basin: Health and ecological risk assessment

Consumption of food crops contaminated with heavy metals (HMs) is a significant risk factor for human health and safety. We evaluated the health risks of HMs in contaminated food crops irrigated with surface water. Results showed there is a substantial buildup of HMs in rice, collected from the Tajan river basin, Iran. The transfer factor (TF) value for toxic elements Cd (3.6-12.4) and Pb (4.9-23.6) were significantly high and exceeded the permissible limits for crops set by WHO. The principal component analysis was used to analyze the relevance of different metals and identify the primary sources. The results showed that two factors dominated the metals variability (94.10% of total variance) that Cr, Fe, Cd, and Pb were dominated by PC1 whereas another factor charged Zn and Cu. The average total hazard quotient (THQ) values for Pb, Fe, Cr, and Cd were 13.8, 7.7, 5.5, and 1.5, respectively, that suggest a considerable risk to the health of regular rice consumers. The high hazard index (HI) value (29.2) demonstrated that the exposure concentration was very high compared to the effective threshold, and it may have potentially harmful implications for human health. To sum up, these results proved that rice from this basin could be a serious dietary source of Pb and Cd exposure to the consumer population.

Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas

Based on the results of an extensive literature research, we summarize the research progress of remote sensing monitoring in terms of identifying mining area boundaries and monitoring land use or land cover changes of mining areas. We also analyze the application of remote sensing in monitoring the biodiversity, landscape structure, vegetation change, soil environment, surface runoff conditions, and the atmospheric environment in mining areas and predict the prospects of remote sensing in monitoring the ecological environment in mining areas. Based on the results, the accurate classification of land use or land cover and the accurate extraction of environmental factors are the basis for remote sensing monitoring of the ecological environment in mining areas. In terms of the extraction of ecological factors, vegetation extraction is relatively advanced in contrast to the extraction of animal and microbial data. For the monitoring of environmental conditions of mining areas, sophisticated methods are available to identify pollution levels of vegetation and to accurately monitor soil quality. However, the methods for water and air pollution monitoring in mining areas still need to be improved. These limitations considerably impede the application of remote sensing monitoring in mining areas. The solving of these problems depends on the progress of multi-source remote sensing data and stereoscopic monitoring techniques.

Quantification of heavy metal pollution for environmental assessment of soil condition

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

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.

Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil

The spatial distribution of potentially toxic metals (PTMs) has been shown to be related to anthropogenic activities. Several auxiliary variables, such as those related to remote sensing data (e.g. digital elevation models, land use, and enhanced vegetation index) and soil properties (e.g. pH, soil type and cation exchange capacity), have been used to predict the spatial distribution of soil PTMs. However, these variables are mostly focused on natural processes or a single aspect of anthropogenic activities and cannot reflect the effects of integrated anthropogenic activities. Nighttime lights (NTL) images, a representative variable of integrated anthropogenic activities, may have the potential to reflect PTMs distribution. To uncover this relationship and determine the effects on evaluation precision, the NTL was employed as an auxiliary variable to map the distribution of PTMs in the United Kingdom. In this study, areas with a digital number (DN) ≥ 50 and an area > 30 km² were extracted from NTL images to represent regions of high-frequency anthropogenic activities. Subsequently, the distance between the sampling points and the nearest extracted area was calculated. Barium, lead, zinc, copper, and nickel concentrations exhibited the highest correlation with this distance. Their concentrations were mapped using distance as an auxiliary variable through three different kriging methods, i.e., ordinary kriging (OK), cokriging (CK), and regression kriging (RK). The accuracy of the predictions was evaluated using the leave-one-out cross validation method. Regardless of the elements, CK and RK always exhibited lower mean absolute error and root mean square error, in contrast to OK. This indicates that using the NTL as the auxiliary variable indeed enhanced the prediction accuracy for the relevant PTMs. Additionally, RK showed superior results in most cases. Hence, we recommend RK for prediction of PTMs when using the NTL as the auxiliary variable.

Assessment of heavy metal pollution and human health risks in urban soils around an electronics manufacturing facility

Heavy metal pollution has pervaded many parts of the world, especially in developing countries. The purpose of this study was to determine the concentrations and health risks of heavy metals in urban soils around an electronics manufacturing site in the Hubei Province of China. Soils samples were collected from commercial, roadside, farmland, and residential areas around the electronics manufacturing facility. A total of 136 topsoil samples were collected, and these samples were analyzed for Cr, Cu, Zn, As, Cd, Ni, and Pb. The geoaccumulation index (Igeo), pollution index (PI), and potential ecological risk index (PER) were calculated to assess the soil pollution levels. The hazard index (HI) was used to assess the human health risks posed by the presence of heavy metals. The total concentrations of the seven congeners (∑metals) ranged from 3738.86 to 5173.25mgkg^-1, and the concentrations were highest in the commercial area followed (in decreasing order) by the roadside, farmland, and residential areas. The HI for children and adults descended in the order of Cr>As>Pb>Cd>Cu>Ni>Zn. The carcinogenic risks of two metals, namely, Cr and As, for children and adults were higher than 10^-4, and children faced greater health risks.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km², with a median of 0.4 samples per km². The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA).