Evaluation of heavy metal distribution characteristics of agricultural soil-rice system in a high geological background area according to the influence index of comprehensive quality (IICQ)

Quantitative analysis and risk assessment of heavy metal pollution in an intensive industrial and agricultural region

In recent decades, the study of heavy metal pollution has garnered significant attention owing to the advancement of industrialization. To explore the contamination of heavy metals in an intensive industrial and agricultural region in Shandong province, China, 101 surface soil samples and 80 samples of edible crop parts were collected from the vicinity of an industrial park. A positive matrix factorization-multilayer perceptron model (PMF-MLP) was established to identify the sources of heavy metals and quantify the complex relationships between pollution sources, crop types, and pollution status. Index and human health-risk methods were used to assess the heavy metal pollution. The results show that (1) lead (Pb) and cadmium (Cd) in the soil may originate from industrial-traffic mixed pollution sources in the surrounding industrial park, whereas copper (Cu) is derived from agricultural pollution sources in the southern farmland. (2) Pollution is primarily concentrated in the central and northern regions of the study area. The analysis of the PMF-MLP model indicates that human activities account for the majority (79.6 %) of the risk of associated with heavy metal pollution. Among them, industrial, traffic, and agricultural mixed pollution sources, agricultural pollution sources associated with northern livestock farms, and crop types contribute to 49.3, 24.5, and 5.80 % of the total risk, respectively. (3) The oral intake of heavy metals represents the primary route of entry into the human body. Cd and Cu are the most significant elements associated with adverse human health, with Cd and Cu contributing the most to carcinogenic and non-carcinogenic risks in both adults and children, respectively. The results will provide references for the formulation of control strategies to curb heavy metal pollution.

Response of soybean Cd to soil Cd and pH and its associated health risk in a high geological background area in Guizhou Province, Southwest China

This study comprehensively examined the accumulation of cadmium (Cd) in soybeans grown in low- and high-Cd soils around the high geological background areas in Guizhou province. The aim was to analyze the relationship between soybean Cd and soil pH and soil Cd, alongside assessing the potential carcinogenic and non-carcinogenic risks associated with Cd in soybeans. Cd content of soybeans cultivated in the high-Cd area (0.430 mg/kg) was significantly higher than that in low-Cd areas (0.156 mg/kg) (P < 0.05). Biological concentration factors (BCFs) of soybean for Cd in low- and high-Cd areas were 0.282 and 0.314, respectively, with no significant differences (P > 0.05). Multiple linear regression results indicated that soil pH was a determining factor for Cd accumulation in soybeans in both areas. Furthermore, soil pH and soil Cd could accurately predict Cd accumulation in soybeans according to the neural network model. These findings suggest that regulating soil pH could reduce Cd accumulation in soybeans in areas with high geological background. In both areas, there was no significant non-carcinogenic risk for the adult population (HQ value < 1) through soybean consumption. However, according to the Monte Carlo model, the percentage of Cd in soybeans exceeding the acceptable range (CR value > 1.00 × 10 -04) in areas was 99.18%, indicating an unacceptable carcinogenic risk for the adult population. Our discussion revealed that reducing the soybean intake and increasing soil pH did not effectively lower the carcinogenic risk of Cd in soybeans to an acceptable range (CR value ≤ 1.00 × 10 -04). These findings necessitate further exploration of alternative remediation strategies to ensure the safe production of soybeans, such as screening for low-Cd accumulation soybean varieties and implementing the combined remediation strategies.

A Collaborative Approach for Metal Pollution Assessment in Production System of Plastic-Shed Vegetables Near Industrial Areas

A collaborative assessment approach, including impact index of comprehensive quality (IICQ), food pollution index (FPI), and single factor pollution index (PI), was used to simultaneously select priority metal pollutants and assess metal contamination status in the plastic-shed soil (PSS)-vegetable system of the industrial towns situated in the Yangtze River Delta, China. Overall, significant Cr increment as well as Cd and Cu pollution in PSS existed, which was related to anthropogenic activities, especially industrial wastewater irrigation. The evaluation using PI and FPI demonstrated that priority metal pollutants were Cu and Cd in PSS while Cr and Cd in vegetables. Additionally, the estimation using IICQ method revealed that 23.3% and 13.3% of the sampling sites were sub-moderately and heavily contaminated by metals, respectively. These sites especially with heavy pollution need priority pollution management. These data will be beneficial to metal pollution control in PSS-vegetable system around industrial areas.

Water and sediment chemistry drivers of chlorophyll-a dynamics within a Ramsar declared floodplain pan wetland system

Floodplain pans are hydrologically dynamic in nature and characterised by variables such as chlorophyll-a (chl-a), water, and sediment chemistry over their hydroperiods. The present study investigated the spatio-temporal variations in water and sediment physico-chemical, and chlorophyll-a concentration characteristics of six floodplain pans found in the Ramsar declared Makuleke wetlands, Kruger National Park, South Africa. The water and sediment physico-chemical variable values were generally elevated during the high-water period, whereas chlorophyll-a concentrations varied across pans and hydroperiod. Benthic chl-a concentration significantly varied across pans with concentrations ranging from 161 to 1036.2 mg m2. The two-way ANOVA showed significant differences in benthic chl-a concentration among hydroperiods, and no significant differences were observed in pelagic chl-a across pans and hydroperiods. Generally, pelagic and benthic chl-a concentration increased as water and sediment chemistry variables increased. Furthermore, three sediment variables, i.e. pH, calcium, and magnesium, and water conductivity were found to be significant in structuring benthic chlorophyll-a dynamics in pans. However, none of the sediment and water variables had a significant effect on pelagic chl-a. Hydroperiod had a significant effect on influencing chl-a concentration, with high and low water level periods being characterised by low and high chl-a concentration, respectively. The n-MDS results showed strong overlaps in chl-a biomass among the Makuleke floodplain pans across hydroperiods. The increasing chl-a concentration in these floodplain pans due to potential bioturbation effects as a result of large mammals could potentially lead to eutrophication, which in turn could affect the system's primary productivity and aquatic biota. Therefore, it is important to establish a continuous monitoring programme on these pans to inform sound management decisions.

Application of the partial least square regression method in determining the natural background of soil heavy metals: A case study in the Songhua River basin, China

The "background" is an essential index for identifying anthropogenic inputs and potential ecological risks of soil heavy metals. However, the lithology of bedrock can cause significant spatial variation in the natural background of soil elements, posing considerable difficulties in estimating background values. In this study, an attempt was made to calculate the natural background through regression analysis of soil chemical composition, and reasonably evaluate the impact of lithology. A total of 1771 surface soil samples were collected from the Songhua River Basin, China, for chemical composition analysis, and the partial least square regression (PLSR) method was employed to establish the relationship between heavy metals (As, Hg, Cr, Cd, Pb, Cu, Zn, and Ni) and soil chemical composition/environmental parameters (SiO2, Al2O3, TFe2O3, MgO, CaO, K2O, Na2O, La, Y, Zr, V, Sc, Sr, Li and pH). The result shows that As, Cr, Pb, Cu, Zn, and Ni have significant linear relationships with soil chemical composition. Each of these six heavy metals obtained 1771 regression background values; some were higher than the uniform background value obtained from the boxplot, while others were lower. The regression background values recognized not only subtle anthropogenic inputs and potential ecological risks in low-background regions but also spurious contamination in high-background areas. All these indicate that the PLSR method can effectively improve the determination accuracy of the natural background of soil heavy metals. More attention should be paid to the serious anthropogenic inputs appearing in some places of the study area.

Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory

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.

Microbial community composition analysis to decipher the possible role of inherent bacteria for in-situ arsenic (As) bioremediation

Biogeochemical reduction and mobilization of sediment-bound arsenic (As) is the major concern for widespread groundwater As contamination in the middle Gangetic plains. The present work examines a microcosm based bio-stimulation study and substrate amendments over 45 days to analyze the bacterial community structure and distribution to indicate the possible in-situ bioremediation strategy in the area. Initially, Bacterial phyla Proteobacteria was predominantly present in all the samples, followed by Actinobacteria, Bacteroidetes, and Firmicutes whereas Cyanobacteria was noted as the minor group. In genus level, Delftia, Acinetobacter, Lysobacter, Bacillus, and Pseudomonas were the major groups of bacteria in the As-rich aquifer system, while Planctomycetes dominated the bio-stimulated samples, followed by a minute portion of Proteobacteria. Alpha diversity and Chaol curve further determined the species richness in the samples with an As tolerant capacity of 152.28 ppb. The presence of γ-Proteobacteria as the dominating member in high As-content water indicated their predominant role in As mobilization, whereas, dominance of α-Proteobacterial members in low As-content water indicated their involvement in As detoxification. The complete change in microbial community structure within the bio-stimulated conditions indicated the extensive role of arsenite-oxidizing microbial communities within different levels of As-contaminated areas in Bihar that will enlighten the significant role of these communities in As-biogeochemical cycle.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03612-0.

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R² values for mapping results of different heavy metals by 12.35-24.32%, and decreased the RMSE values by 30.35%-42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

Pollution characteristics and risk assessment of antimony and arsenic in a typical abandoned antimony smelter

Antimony (Sb) and arsenic (As) co-contamination occurs in Sb smelting areas and is harmful to the surrounding ecological environment. The purpose of this study is to explore the spatial distribution characteristics of Sb and As in abandoned Sb smelting area and carry out risk assessments. Soil samples were collected from the smelting area profile and background points, and groundwater samples were also collected. Samples from two geological background sections were collected to understand the geological background characteristics of Sb and As. The spatial distribution was drawn via the inverse distance weighted interpolation method. The hazard assessment was carried out by the geo-accumulation index and potential ecological hazard methods. The results showed that special high geological background value of Sb and As in study area. Sb and As co-contamination is one of the characters in soil. And the contents of Sb and As decrease as depth increases, reflecting the weak migration capacity. The spatial distribution of Sb and As is affected by slag distribution and rainfall leaching. The Sb content in groundwater was higher in the wet and normal seasons than in the dry season, slag leaching may be one of the elements. The potential ecological hazards of Sb and As are high and considerable, respectively. In abandoned smelting area with high geological background values, it is necessary to focus on the pollution abatement and protection of ecological health.

Hidden risks from potentially toxic metal(loid)s in paddy soils-rice and source apportionment using lead isotopes: A case study from China

Pyrite is a typical sulfide mineral which contains various potentially toxic metal(loid)s (PTMs). The pyrite smelting and subsequent industrial utilization activities usually release numerous amounts of PTMs into nearby ecosystem, which may be enriched in the nearby farmland soils and crops, leading to hidden but irreversible harm to human health via the food chain. Herein, the distribution pattern, source apportionment, and potential health risks of Pb, Zn, Cu, Cd and multiple seldom monitored PTMs (Ag, Bi, Sb, Sr, Th, U, W, and V) in the paddy soils and different organs of the rice plants from ten various sites in a typical industrial zone were investigated, where pyrite ores were used for the production of sulfuric acid and subsequent cement over several decades. The results showed that the contents of Cd, Pb and Zn in studied paddy soils generally exceeded the maximum permissible level (MPL) in China, and the contents of Sb and V were approaching the MPL. Moreover, the rice is easier to bioaccumulate Cd, Cu, and Zn than the other studied elements. The hazard quotient (HQ) calculations indicate that the rice containing such multiple elements may cause a high potential non-carcinogenic and carcinogenic health risk for residents, particularly for the senior group. The Pb isotope tracing method combined with PCA (principal component analysis) further uncovered that the pyrite industrial utilization contributed 18.58-55.41 % to the highly enriched PTMs in paddy soils. All these findings indicate that the paddy soil system has been contaminated by the pyrite industrial activities and certain distances or areas should be rigidly forbidden from rice cultivation in the proximity of the pyrite smelting and related industrial sites.

Micronutrients and heavy metals in rice farms: the case of Ahvaz and Bawie Counties, Khuzestan Province, Iran

This study addressed micronutrients (Fe, Zn, Cu, Mn, Co) and heavy metals (As, Pb) in the soil and rice crop in Khuzestan Province, Iran. Twenty-eight composite soil and grain samples from the intended rice farms were garnered during harvest time. Concentrations of the elements in the samples and in the grains were, respectively, determined by inductively coupled plasma optical emission spectrometry and inductively coupled plasma mass spectrometry device. The average concentration of As, Fe, Co, Cu, Mn, Pb, and Zn in soil of crop were 2.71, 20,065.8, 10.43, 22.28, 422.28, 5.85, and 47.07 mg/kg, respectively. The physicochemical properties of soil, bioconcentration factor, daily intakes, and health risk assessment of the elements were calculated. The results revealed that the area covered by alkaline saline soils is poor in micronutrients. Bioconcentration factor values of all elements were less than 1. Low levels of bioconcentration factor may be for low levels of nutrients in the soil and physicochemical conditions of the soil. Furthermore, the daily intake of Co (adults' group) and Fe and Zn (children group) was very low. Health risk assessment showed only adults are threatened by non-cancerous diseases due to excessive value of all the elements (HI = 2.53) and cancerous diseases caused by excessive As and Pb (2.86E-04 and 2.01E-05, respectively). Considering that Khuzestan Province is the fourth largest producer of rice in Iran, the lack of micronutrients and the presence of heavy metals in rice produced in the study area can adversely affect consumers. Further investigation is therefore a must in the region.

Accumulation and risk assessment of heavy metals in rice: a case study for five areas of Guizhou Province, China

In the present study, the concentration and accumulation abilities of five heavy metals (Cd, Hg, As, Pb, Cr) in rice were assessed and their human health risk to local citizens had been evaluated. Soil and rice samples (125 samples) were collected from Guiyang (GY), Qiannan (QN), Bijie (BJ), Tongren (TR), and Zunyi (ZY) in Guizhou Province. Heavy metals were measured by inductively coupled plasma-mass spectrometry (ICP-MS) after microwave digestion. The mean concentrations of Cd, Hg, As, Pb, and Cr were 0.58, 0.65, 12.31, 38.70, and 87.30 mg/kg in soil and were 0.05, 0.005, 0.11, 0.07, and 0.34 mg/kg in rice, respectively. The bioconcentration factors (BCF) decreased with the order Cd > Hg > As > Cr > Pb. Non-carcinogenic risk in this study was evaluated using the method of the hazard quotient (HQ) and hazard index (HI). The mean HQ values for Cd, Hg, Pb, and Cr were all lower than the standard limit (1.0) for children and adults, except As with the mean HQ for children of 2.79. The mean HI values for children and adults were 4.22 and 1.42, which exceeded 1.0. The mean carcinogenic risk (CR) values of As and Pb for children and adults were higher than the upper limit of the acceptable range (1 × 10^-4) established by the United States Environmental Protection Agency (USEPA). In a conclusion, the non-carcinogenic and carcinogenic risks induced by heavy metals for children were higher than that for adults. This study revealed that consumption of rice in study areas may pose potential non-carcinogenic and carcinogenic risks to humans, and As was the largest contributor.

Risk Identification of Heavy Metals in Agricultural Soils from a Typically High Cd Geological Background Area in Upper Reaches of the Yangtze River

This study adopted two risk assessment models to estimate the potential risk of heavy metals (HMs) in agricultural soils from a high Cd geological background area. Results were as follows: HMs posed an extremely high potential ecological risk (PER) (PER = 2051 > 1200) ascribed to Cd contribution. The overall non-carcinogenic risk (HI) of HMs for children (HI > 1) was unacceptable. Contributions of HMs to HI generally decreased in the order of Cr > As > Pb > Cd > Ni > Cu > Zn. The total carcinogenic risk (TCR) value for adults (2.1 × 10^-4) and children (2.2 × 10^-4) surpassed the reference value (1.0 × 10^-4), indicating that the risks were unacceptable. Contributions of HMs to TCR generally decreased in the order of Cr ≈ Ni > Cd > As > Pb. These results suggested that risk control policies should not only consider the potential ecological risk of Cd but also take into consideration of the carcinogenic and non-carcinogenic risk of Cr and Ni in a typically high Cd geological background area.

Comprehensive evaluation of high-oleic rapeseed (Brassica napus) based on quality, resistance, and yield traits: A new method for rapid identification of high-oleic acid rapeseed germplasm

To scientifically evaluate and utilize high-oleic acid rape germplasm resources and cultivate new varieties suitable for planting in the Hunan Province, 30 local high-oleic acid rape germplasms from Hunan were used as materials. The 12 personality indices of quality, yield, and resistance were comprehensively evaluated by variability, correlation, principal component, and cluster analyses. The results of variability showed that except for oleic acid, the lowest coefficient of variation was oil content, which was 0.06. Correlation analysis showed that oil content was positively correlated with main traits such as yield per plant and oleic acid, which could be used in the early screening of high-oleic rape germplasm. The results of principal component analysis showed that the 12 personality indicators were integrated into four principal components, and the cumulative contribution rate was 62.487%. The value of comprehensive coefficient ‘F’ was positively correlated with the first, second, and fourth principal components and negatively correlated with the third principal component. Cluster analysis showed that 30 high-oleic rape germplasms could be divided into four categories consisting of 9 (30%), 6 (20%), 7 (23%), and 8 (27%) high-oleic rape germplasms, each with the characteristics of "high disease resistance", "high yield", "high protein", and "more stability". This study not only provides a reference basis for high-oleic rape breeding but also provides a theoretical basis for their early screening.

Identification of heavy metal pollutants and their sources in farmland: an integrated approach of risk assessment and X-ray fluorescence spectrometry

Investigation and assessment of farmland pollution require an efficient method to identify heavy metal (HM) pollutants and their sources. In this study, heavy metals (HMs) in farmland were determined efficiently using high-precision X-ray fluorescence (HDXRF) spectrometer. The potential ecological risk and health risk of HMs in farmland near eight villages of Wushan County in China were quantified using an integrated method of concentration-oriented risk assessment (CORA) and source-oriented risk assessment (SORA). The CORA results showed that Cd in farmland near the villages of Liuping (LP) and Jianping (JP) posed a "very high" potential ecological risk, which is mainly ascribed to soil Cd (single potential ecological risk index ([Formula: see text]) of Cd in villages LP and JP, [Formula: see text] = 2307 and 568 > 320). A "moderate" potential ecological risk was present in other six villages. The overall non-carcinogenic risk (hazard index (HI) = 1.2 > 1) of HMs for children in village LP was unacceptable. The contributions of HMs decrease in the order of Cr > As > Cd > Pb > Ni > Cu > Zn. The total carcinogenic risk (TCR = 2.1 × 10^-4 > 1.0 × 10^-4) of HMs in village LP was unacceptable, with HMs contributions decreasing in the order of Cr > Ni > Cd > As > Pb. Furthermore, three source profiles were assigned by the positive matrix factorization: F1: agricultural activity; F2: geological anomaly originating from HMs-rich rocks; F3: the natural geological background. According to the results of SORA, F2 was the highest contributor to PER in village LP, up to 64.4%. Meanwhile, the contributions of three factors to HI in village LP were 19.0% (F1), 53.6% (F2), and 27.4% (F3), respectively. It is worth noting that TCR (1.2 × 10^-4) from F2 surpassed the threshold of 1.0 × 10^-4, with an unacceptable carcinogenic risk level. As mentioned above, the HM pollutants (i.e., Cd and Cr) and their main sources (i.e., F2) in this area should be considered. These results show that an integrated approach combining risk assessments with the determination of HM concentration and identification of HM source is effective in identifying HM pollutants and sources and provides a good methodological reference for effective prevention and control of HM pollution in farmland.

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Mining activities have increased the accumulation of heavy metals in farmland soil and in food crops. To identify the key soil properties influencing heavy metal bioavailability and accumulation in food crops, 81 crop samples and 81 corresponding agricultural soil samples were collected from rape, wheat, and paddy fields. Heavy metal (copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), iron (Fe), and manganese (Mn)) concentrations in soils and rape, wheat, rice grains were determined using inductively coupled plasma atomic emission spectroscopy, and soil physicochemical properties (pH, organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium (AK)) were analyzed. Soil extractable metals were extracted using various single extractants (DTPA, EDTA, NH₄OAc, NH₄NO₃, and HCl). The average concentrations of Cu, Zn, Pb, Cd, and Mn in the soil samples all exceeded the local geochemical background value (background values of Cu, Zn, Pb, Cd, and Mn are 43.0, 81.0, 28.5, 0.196, and 616 mg/kg, respectively), and Cd over-standard rate was the highest, at 98%. Furthermore, soil total Cd concentrations (0.1–24.8 mg/kg) of more than 86% of the samples exceeded the soil pollution risk screening value (GB 15618-2018). The sources of Cu, Zn, Pb, Cd, and Mn in soils were mainly associated with mining activities. The key factors influencing heavy metal bioavailability were associated with the types of extractants (complexing agents or neutral salt extractants) and the metals. Cd and Pb concentrations in most wheat and rice grain samples exceeded the maximum allowable Cd and Pb levels in food, respectively, and Cd concentrations in approximately 10% of the rice grain samples exceeded 1.0 mg/kg. Furthermore, rice and wheat grains exhibited higher Cd accumulation capacity than rape grains, and despite the high soil Cd concentrations in the rape fields, the rape grains were safe for consumption. High soil pH and AK restricted Cd and Cu accumulation in wheat grains, respectively. Soil properties seemed to influence heavy metal accumulation in rice grains the most.

Optimizing Shade Cultivation Method and Irrigation Amount to Improve Photosynthetic Characteristics, Bean Yield, and Quality of Coffee in a Subtropical Monsoon Climate

Reasonable water and light management technology can improve economic benefits, coffee yield, and quality. We used cluster analysis and principal component analysis to evaluate and optimize the water and light management technology with high coffee yield, quality, and economic benefits in a subtropical monsoon climate region of China. The experiment was arranged in a randomized complete block design with two factors (3 irrigation levels × 4 shade cultivation treatments) replicated four times during 2016-2017. The irrigation levels consisted of full irrigation (FI) and two deficit irrigations (DI _L : 75% FI, DI _S : 50% FI). The shade cultivation treatments consisted of no shade cultivation (S₀) and three shade cultivation modes (S _L : intercropping with four lines of coffee and one line of banana; S _M : intercropping with three lines of coffee and one line of banana; S _S : intercropping with two lines of coffee and one line of banana). The results showed that the effects of irrigation level and shade cultivation mode on growth, crop yield, most of the photosynthetic characteristics, and nutritional quality were significant (p < 0.05). Regression analysis showed that the leaf radiation use efficiency (RUE) showed a significant negative exponential relation or logistic-curve variation with photosynthetically active radiation (PAR). The bean yield increased with an increase of the shade degree when water was seriously deficient, whereas it first increased and then decreased with an increase of the shade degree under FI and DI _L . Based on both cluster analysis and principal component analysis, the FIS _S treatment resulted in the highest comprehensive quality of coffee, followed by the FIS _M treatment; the DI _S S₀ treatment obtained the lowest quality. Compared with the FIS₀ treatment, the FIS _M treatment increased the 2-year average bean yield and net income by 15.0 and 28.5%, respectively, whereas the FIS _S treatment decreased these by 17.8 and 8.7%, respectively. To summarize, FIS _S treatment significantly improved the nutritional quality of coffee, and FIS _M treatment significantly increased the dry bean yield and economic benefits of coffee. The results of the study could provide a theoretical basis for water-saving irrigation and shade cultivation management of coffee in a subtropical monsoon climate region of China.

Recommended risk screening values for Cd in high geological background area of Guangxi, China

Because of unusually high cadmium concentrations in the soil, the risk screening values of soil Cd in the existing standard is not applicable to the Cd high geological background areas. The aim of our study is to explore recommended risk screening values applicable for Cd high geological background areas of Guangxi, China, to help locals with land management and guarantee the quality and safety of crops as well as providing the theoretical basis for guiding the production safety. A total of 903 pairs of rice samples and root soil samples were collected. The Cd concentration of soil-rice samples and soil pH were determined. The scatter diagram method was used to gradually increase the screening values, and the value with the most samples in the correct interval was counted as the recommended risk screening value. The soil Cd concentrations ranged from 0.06 to 7.08 mg·kg^-1 and the rice Cd ranged from 0.002 to 1.488 mg·kg^-1; 64.89% of soil samples exceed the RSVs and 27.8% of rice samples exceed the allowable limit of Cd. The recommended risk screening values of Cd in study area were 0.5, 0.7, 1.5, and 2.4 mg·kg^-1 for soil with pH ≤ 5.5, 5.5 < pH ≤ 6.5, 6.5 < pH ≤ 7.5, and pH > 7.5, respectively. Compared with the standard screening value, the accuracy of using the recommended screening value as the reference value to judge whether the Cd concentration in rice exceeds the standard was increased by 12%, 20%, 21%, and 47%, respectively. The recommended screening value can be used as the standard value to better indicate the soil environmental quality in the study area.

Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops

Heavy metal pollution is a notable threat to agricultural production. Soil heavy metal pollution can cause potential ecological risk (ERI), and crop heavy metal pollution can cause human health risk (HRI). However, most previous studies partially focused on heavy metal pollution in soil or crop but often neglected the relationship between them. Actually, soil heavy metal can pollute crops to some extent, while not all heavy metal pollution in crops comes from soil. The inner relationship of pollution risk in soil-crop system is worth attention. In this study, we selected Ningbo as the study region and used sample data to assess both soil and crop heavy metal risks, in order to explore the differences between heavy metal contamination risks in soil and crops as well as the relationships between heavy metal contents in soil and crops. Our results showed that Hg was the most polluted heavy metal in soil, which led to the highest ecological risk in Jiangbei (Comprehensive ERI = 567) with the maximum ERI of Hg (430). However, As in crops contributed the most to health risk and caused the highest health risk in Fenghua (HRI = 10) with the largest contribution of 64.5%. Such differences of pollution risk assessment indicated that the contents of the same heavy metal were inconsistent in soil and crops. Our results further showed that the heavy metals in soil had the greatest influence on Zn in crops. Pb and Cr in soil had synergistic effects on the crop absorption of Zn, whereas As, Hg and Cu played antagonistic roles in the crop absorption of Zn. Our study confirms that heavy metals in soil would variously influence heavy metals in crops and the interaction of heavy metals is very important for pollution risk control, which have been largely ignored yet.

Spatial Distribution, Environmental Risk and Safe Utilization Zoning of Soil Heavy Metals in Farmland, Subtropical China

Heavy metal (HM) accumulation in farmland soil can be transferred to the human body through the food chain, posing a serious threat to human health. Exploring the environmental risk and safe utilization zoning of soil HMs in farmland can provide the basis for the formulation of effective control strategies. Soil samples from typical subtropical farmland were collected in Jinhua City and analyzed for HMs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn). The objective of this study was to explore the spatial distribution and environmental risk of soil HMs, and then divide the safe utilization area of soil HMs of farmland in Jinhua City. The results showed that the mean concentrations of soil HMs were, in descending order: Zn (76.05 mg kg−1) > Cr (36.73 mg kg−1) > Pb (32.48 mg kg−1) > Cu (18.60 mg kg−1) > Ni (11.95 mg kg−1) > As (6.37 mg kg−1) > Cd (0.18 mg kg−1) > Hg (0.11 mg kg−1), and all determined soil HMs did not exceed the risk screening values for soil contamination of agricultural land of China. The fitted semi-variogram showed that the spatial autocorrelation of Cd, Hg, Pb, and Zn was weak, with island-shaped distribution, while As, Cr, Cu, and Ni had medium spatial autocorrelation, with strip-shaped and island-shaped distribution. The hot spot analysis and environmental risk probability showed that the environmental risks of As, Cd, Cu, Pb, Zn, and Cu were relatively high, whereas those of Cr, Hg, and Ni were relatively low. Safe utilization zones and basic safe utilization zones accounted for 89.35% and 8.58% of the total farmland area in Jinhua, respectively, and only a small part of the farmland soil was at risk of use.

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

Environmental burden of unprocessed solid waste handling in Enugu State, Nigeria

Improper waste management has assumed a worrisome dimension in cities across many developing countries. One of its commonest features is open dumps. Open dumps in Enugu and Nsukka were investigated in this study. Waste samples were collected from ten dumps located in low-income, low-to-middle income, and high-income zones of the study area. The composition of waste was determined following standard methods and results obtained subjected to statistical analyses. Selected open dumps were subjected to detailed inspection in order to identify possible environmental impacts. Soil samples were also collected from the top soil and subsoil (15 cm) of selected dumps and analyzed for As, Cd, Cr, Cu, Hg, Mn, Pb, Ni, Cd, and Zn. The sources of contamination were determined using the principal component analysis (PCA) and cluster analysis (CA). Results of heavy metal analyses were used to determine extent of soil pollution. Food waste ranged from 29.6 to 56.5% with an average of 42.2%. Analysis along income line showed a decline in the proportion of food and rubber waste from lower to high income. The order of heavy metals concentration in waste dump soils investigated was as follows: Mn > Zn > Cu > Cr > Pb > As > Ni > Cd > Hg. The pollution indices (PI) of the dumpsites ranged from 1.87 for Ni to 1634.6 for Cu in the topsoil, and 0.62 for Ni to 1354.74 for Cu in the subsoil, indicating a severe level of pollution. Pollution load index (PLI) ranged from 25.38 to 75.07 with a mean of 60.75 for the dump surface and from 51.46 to 21.7 with a mean of 33.86 below the dump soil. Forty-three percent (43%) of the topsoil and 40% of the subsoil exhibited ecological risk index values greater than 320, indicating extreme degree of ecological risk. The first principal and second principal components with 36% and 28.2% variance respectively represent the growing impact of electronic waste disposal, specifically mobile phones, personal computers, and other potable electronic devices with short life span on waste dumps. The third principal component (10.2%) represents input from households and other forms of chemicals such as insecticides, paints, and detergents.

Contamination assessment, health risk evaluation, and source identification of heavy metals in the soil-rice system of typical agricultural regions on the southeast coast of China

To quantitatively assess heavy metal accumulation and potential ecological and human health risks as well as analyze the sources of metals in a typical soil-rice system located on the southeast coast of China, 120 topsoil samples and corresponding rice grain samples were collected across the study area. The concentrations of As, Cd, Pb, Cr, Hg, Zn, Cu, and Ni were analyzed. The results revealed that Hg, Cd, and Cu were the main pollutants in soils. Besides, according to geo-accumulation value of Hg, 18.3% of samples were at or above moderate contamination levels. Additionally, the soil was in moderate ecological risk from combined heavy metal pollution, and 49.7% and 27.0% of this risk could be attributed to Hg and Cd pollution, respectively, due to their high toxic-response factors. For the rice samples, Cd content showed the highest biological accumulation coefficient value (40.8%) in rice grains and was slightly greater than its maximum allowable value (MAV) (0.2 mg/kg) in 7.5% of samples, whereas the other metals were all lower than their corresponding MAVs. Heavy metal exposure (especially As exposure) via rice consumption causes significant carcinogenic and non-carcinogenic risks to adults, and non-carcinogenic risk to children, while the carcinogenic risk to children was at tolerable level. Greater rice consumption might be responsible for the greater health risk to adults than children. Natural sources (loaded heavily with Cr and Ni) such as lithogenic components and soil parent materials, agricultural activities (loaded heavily with Cd, Cu, and Zn), especially excessive use of pesticides and fertilizers, and industrial activities (loaded heavily with Hg, Pb, and As) including vehicle emissions, coal combustion, and those of the textile and chemical industries were identified as the main sources. Effective regulations should be enforced to guarantee the safety of farm produce and protect ecological and human health in the study area.

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.