•BME combined with PMF are proposed for spatial quantitatively source apportionment.•1/5 of soils show different pollution degrees and Cd, Cu, As are the main pollutants.•Cd and As are the main ...elements causing ecological and health risks, respectively.•Industry like electroplating is the main source for As in northwest and northeast.•Agricultural activities are the main source for Cd in northwestern and north-central.
Soil pollution by toxic metals has become an important environmental problem over the last several decades. Because of environmental factor variation, specific spatial patterns of pollution and sources exist. However, commonly used methods rarely take natural spatial heterogeneity into account. Positive matrix factorization and Bayesian maximum entropy models combined with specific environmental factors were proposed for quantitative source apportionment to account for spatial heterogeneity. The proposed method was implemented in a region located in southeastern China using dense samples (3627 total samples containing Cd, Hg, As, Pb, Cr, Cu, Zn and Ni data). The results showed that more than one-fifth of soils in the northwest, north-central and southeast of the study region exhibited different degrees of integrated pollution. Cd, Cu and As were the main pollutants, with proportions that exceeded the national standards of 26%, 10% and 7%, respectively. In addition, Cd was the primary element responsible for ecological risk, and As was the greatest hazard to human health. Five main pollution sources were extracted: 72.11% of the toxic metal pollution could be ascribed to anthropogenic sources, and natural sources explained the remaining 27.89%. Traffic emissions (24.31%) consistent with the major road distribution were the main source of Pb and Zn, and atmospheric deposition during the coal combustion (18.04%) distributed across the study area, except for the southeastern mountain areas, was the main source of Hg. Agricultural activities (16.81%) distributed mainly in the north-central regions contributed the most to Cd and Cu, and industrial activities (12.95%) clustered in the northwestern areas contributed the most to As. In addition, natural sources were closely linked to Ni and Cr in the southeastern mountain areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Comparative evaluation of SOC baselines between global soil database (HWSD) and the recent soil survey for farmlands using a random forest model.
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•Total of 29,927 farmland sites in ...Zhejiang, East China were surveyed for SOC stock.•Random forest model showed high predictive performance with R2 of 0.76.•Maps of fine-resolution SOC stock baseline and its uncertainty were estimated.•Considerable spatial discrepancies between this study and HWSD were revealed.•Carbon accounting based on SOC content of HWSD should be reinvestigated.
Soil organic carbon (SOC) is important to soil fertility and the global carbon cycle. Accurate estimates of SOC stock and its dynamics are critical for managing agricultural ecosystems and carbon accounting under climate change, especially for highly cultivated regions. We extensively surveyed the SOC levels in 29,927 sites in Zhejiang province, an intensively cultivated region of East China, from year 2007 to 2008. We then estimated the spatial distribution of topsoil (0–30cm) organic carbon stock using a random forest (RF) model, which is a powerful machine learning algorithm with superior predictive performance over parametric statistical models. The final RF model contained 23 predictor variables, covering soil properties, vegetation, climate, topography, land cover, farming practices, and locations. The RF model showed high performance in predicting the SOC stock, with a coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 10.63tCha−1. This performance was superior to the General Linear Model (GLM) (R2=0.35, RMSE=19.93tCha−1) and the ordinary kriging (OK) method (R2=0.57, RMSE=14.44tCha−1), and was equivalent to Boosted Regressing Trees (BRT) (R2=0.73, RMSE=11.26tCha−1). According to the variable importance evaluation, soil properties were the most important predictor variables, followed by climate and location, with relative importance values of 61%, 17%, and 14%, respectively. The predicted SOC stock ranged from 14.8 to 125.5tCha−1, with an average±standard deviation of 50.1±12.3tCha−1. The mean SOC level obtained from this survey was considerably lower than the value of 60.5tCha−1 reported for the same region in the Harmonized World Soil Database (HWSD), which is the most commonly used soil database worldwide. A large spatial discrepancy of SOC stock was observed between this survey and HWSD in regional and sub-regional levels. This study provided an updated regional baseline map of SOC levels for improving farmland management and refining carbon accounting under climate change.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Landscape connectivity is important for all organisms as it directly affects population dynamics. Yet, rapid urbanization has caused serious landscape fragmentation, which is the primary contributor ...of species extinctions worldwide. Previous studies have mostly used spatial snap-shots to evaluate the impact of urban expansion on landscape connectivity. However, the interactions among habitats over time in dynamic landscapes have been largely ignored. Here, we demonstrated that overlooking temporal connectivity can lead to the overestimation of the impact of urban expansion. How much greater the overestimation is depends on the amount of net habitat loss. Moreover, we showed that landscape connectivity may have a delayed response to urban expansion. Our analysis shifts the way to understand the ecological consequences of urban expansion. Our framework can guide sustainable urban development and can be inspiring to conservation practices under other contexts (e.g., climate change).
Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date ...is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992–2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling.
Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a case study location, this research proposed the synthesis application of positive ...matrix factorization (PMF) and GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area. Although the mean concentrations of As, Cd, Cr, Hg, and Pb in the soils were lower than the National Environmental Quality Standards for Soils in China, the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively, indicating that heavy metals have been enriched in topsoil. Agricultural activities, industrial activities, and soil parent materials were the main sources of heavy metal pollution in the soils, accounting for 63.4%, 19.8%, and 16.8% of the total heavy metal accumulation, respectively. Cr was derived mainly from soil parent materials (80.72%). Cd was closely associated with agricultural activities (73.68%), such as sewage irrigation and application of fertilizer. Mercury was mainly attributed to industrial activities (92.38%), such as coal mining and smelting. As was related to agricultural (57.83%) and natural (35.56%) sources, and Pb was associated with industrial (42.42%) and natural (41.83%) sources. The new synthesis models are useful for estimating the source apportionment of heavy metals in soils.
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•A novel framework based on spatial analysis for source apportionment is proposed.•Combined with auxiliary data, the new model provides foundations for source analysis.•Cr (80.72%) was derived mainly from natural sources while As and Pb had mix sources.•Cd (73.68%) was closely associated with agricultural activities.•Hg (92.38%) was mainly attributed to industrial activities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Heavy metal pollution in soil has received much attention in recent decades. Many studies have analyzed the contamination status, spatial distribution, and pollution sources of heavy metals. Little ...information is available on the interaction between cultivated land quality and soil heavy metal pollution. Combining soil quality information and intensive heavy metal sampling surveys, this study analyzed heavy metal contamination and the ecological and health risks of various soils with different quality levels. Additionally, through the PMF model and risk assessment techniques, the ecological and health risks of specific pollution sources and their interaction with soil quality were investigated. The results showed that the mean content of the studied elements followed the increasing order of Hg (0.12 mg/kg) < Cd (0.19 mg/kg) < As (6.98 mg/kg) < Pb (25.57 mg/kg) < Cr (72.02 mg/kg). In addition, with increasing soil quality, the concentrations of Pb, Cr and Hg as well as the overall ecological risk increased significantly. Regarding health risks, heavy metal pollution posed a higher risk to children than adults, and ingestion was the main exposure pathway. The total hazard index and carcinogenic risk also increased with increasing soil quality. The PMF analysis showed that Pb and Cr mainly came from industrial activities, As could be attributed to natural sources, Cd was mainly derived from agricultural activities, and Hg pollution was determined by coal combustion. Considering the risks of specific pollution sources, agricultural activities and coal combustion were the major reasons for high ecological risks, whereas industrial activities and coal combustion posed significantly higher risks in suburban high-quality soil. Industrial activities mainly determined the health risk, which contributed more than 50% to the total risk. There was an upward health risk trend with increasing soil quality. Industrial activities in high-quality suburban soil posed the highest health risk to both adults and children. Reasonable and effective policies should be formulated to control industrial pollution and improve the ecological environment in this area.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Soil heavy metal pollution can be a serious threat to human health and the environment. The accurate mapping of the spatial distribution of soil heavy metal pollutant concentrations enables the ...detection of high pollution areas and facilitates pollution source apportionment and control. To make full use of auxiliary soil properties information and show that they can improve mapping, a synthesis of the Bayesian Maximum Entropy (BME) theory and the Geographically Weighted Regression (GWR) model is proposed and implemented in the study of the Shanghai City soils (China). The results showed that, compared to traditional techniques, the proposed BME-GWR synthesis has certain important advantages: (a) it integrates heavy metal measurements and auxiliary information on a sound theoretical basis, and (b) it performs better in terms of both prediction accuracy and implementation flexibility (including the assimilation of multiple data sources). Based on the heavy metal concentration maps generated by BME-GWR, we found that the As, Cr and Pb concentration levels are high in the eastern part of Shanghai, whereas high Cd concentration levels were observed in the northwestern part of the city. Organic carbon and pH were significantly correlated with most of the heavy metals in Shanghai soils. We concluded that Cd pollution is mainly the result of agricultural activities, and that the Cr pollution is attributed to natural sources, whereas Pb and As have compound pollution sources. Future studies should investigate the implementation of BME-GWR in the case of space-time heavy metal mapping and its ability to integrate human activity information and soil category variables.
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•BME-GWR performs better in terms of prediction accuracy and performance flexibility.•Organic carbon and pH were significantly correlated with most of the heavy metals.•Cr was higher in eastern region, which is attributed to natural sources.•Cd was higher in northwestern region, which is mainly due to agricultural activities.•Pb and As were higher in eastern region having compound pollution sources.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an ...integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.
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•The pollution threshold was evaluated by a finite mixture distribution model.•Only the pollution threshold for Cd was higher than its risk screening value.•27.9% of the samples showed various pollution levels with distinct spatial features.•BME indicated 3.73% of the study area was defined as risk at 95% confidence level.•5.34% and 8.22% of the area exhibited risk at the 90% and 75% confidence levels.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Heavy metals, and especially Cd, Pb and Hg, accumulate heavily in soils.•Industrial production is responsible for 72.85% and 51.16% of Hg and Pb pollution.•40.08% and 52.23% of Cr ...and As came from natural sources,•81.38% of Cd originated from agriculture activities.•Soils with different fertility grades exhibit different heavy metal contamination.
For the purpose of pollution assessment and source apportionment of heavy metals in agricultural soils with different fertility levels, an intensive sampling (1848 samples) was conducted in Shanghai city. Various indices, including the pollution index (PI), Nemerow integrated pollution index (NIPI), geoaccumulation index (GI) and potential ecological risk index (RI), were employed to assess the pollution status caused by heavy metals. In view of the spatial heterogeneity of heavy metal concentrations, in this work a synthesis of principal component analysis (PCA) with spatial lag modeling (SLM) is proposed to explore quantitatively the sources of heavy metals and the contributions of each component. The results showed that the mean heavy metal concentrations followed a decreasing order: Cr (72.02 ± 8.90 mg/kg) > Pb (25.57 ± 7.38 mg/kg) > As (6.98 ± 1.97 mg/kg) > Cd (0.19 ± 0.10 mg/kg) > Hg (0.12 ± 0.08 mg/kg). Although the mean heavy metal concentrations did not exceed the corresponding national standards, the percentages of sampling locations exhibiting higher concentrations relative to the background values were found to be equal to 43.29% for Pb, 32.63% for Cd, 27.54% for Hg, 11.26% for Cr, and 10.93% for As, which indicate significant local accumulation of heavy metal pollutants within the study area. Industrial activities, natural sources and agricultural activities are the main pollution sources that account for 27.92%, 27.48% and 20.64% of the total pollution, respectively. Industrial activities with high Pb and Hg loadings have a large contribution to good fertility soils (39.38%). It was found that agricultural activity is the main contributor of Cd pollution, having a large contribution (33.46%) to low fertility soils. Cr and As pollution comes mainly from natural sources, with relatively equal contribution to soils with various fertility levels. The present study improves understanding of the pollution status of heavy metals in Shanghai agricultural soils, and also serves as reference for pollution source apportionment in other regions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively ...extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil. Of the samples with Pb, Cd and Hg, 84.12 %, 68.03 % and 41.57 % exceeded the background values, and 5.36 %, 6.42 % and 5.43 % were moderately contaminated according to the geoaccmulation index, respectively. Industrial activities, with relative importance values of 17.82 %, 31.54 % and 26.51 % for Cd, Hg and Pb, respectively, were the predominant source of these metals especially, in their high-content cluster areas (central urban areas). Soil available phosphorus was another important factor (relative importance values of 13.03 %, 13.41 % and 25.55 % for Cd, Hg and Pb, respectively), and agricultural activities (especially the overuse of phosphoric fertilizers) were identified as an anthropogenic source of these toxic metals. Soil parent material had the greatest influence on As and Cr, with relative importance values of 19.88 % and 19.09 %, respectively, especially in their high-content accumulation area (the eastern coastal area), indicating that these toxic metals mainly come from natural sources. Slope had important impacts on toxic metal accumulation (relative importance values of 17.48 %, 21.22 %, 12.40 % and 16.13 % for Cd, Hg, Cr and As, respectively) by influencing industrial distribution and pollutant migration. By changing the soil adsorption capacity, soil organic matter (explaining 13.01 % of Pb) and soil pH (explaining 14.58 % of As and 12.40 % of Cr) strongly influenced toxic metal accumulation. This study highlights the benefits of the integrated Catreg-GWR model for analyzing multiple spatially heterogeneous environmental data types (numerical and categorical variables), providing a potential foundation for local pollution prevention.
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•Catreg was used to handle numerical and categorical source proxies simultaneously.•GWR was used to analyze the distribution of those sources under spatial heterogeneity.•Industrial activities was the main source Cd, Hg and Pb in their high cluster area.•Natural source was the main reason of As and Cr in their high accumulation area.•SOM and pH impacted toxic metal accumulation by changing the soil adsorption capacity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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