Two-dimensional empirical mode decomposition of heavy metal spatial variation in agricultural soils, Southeast China

Spatial distribution and potential ecological risk of metal(loid)s in cultivated land from Xianjia Town in Fujian, Southeast China

Metal(loid)s in cultivated land become an important issue with respect to human health and food security. However, it remains challenging to identify metal(loid) pollution characteristics due to varying environmental settings at the local scale. In this study, the geographic information system and categorical regression model were applied to analyze the spatial distribution and influencing factors of metal(loid)s in cultivated land using 90 sampling sites in Xianjia Town, Southeast China. The pollution levels and ecological risks of five metal(loid)s-Cd, Pb, Cr, Hg, and As-were further investigated using the single pollution index (PI), Nemerow comprehensive pollution index (PN), and potential ecological risk index (RI). The results indicate that the cultivated soils were affected by Cd and Pb pollution, with 3.06 and 6.30 times higher average concentrations than the soil environment background values (SEBV) of Fujian Province, respectively. Based on the CATREG model, crop type had a great impact on Pb and Hg contents. Cr contents were higher in rice fields, while Hg and As concentrations were higher in turmeric fields. Cr and Hg contents under five crop types did not exceed the SEBV of Fujian Province. The average Pb contents in rice fields were 1.25 and the Cd contents in vegetable fields 1.09 times higher than the average value in sampled soils. According to the RI, 63.66% of the sampling points were at medium to high risk. These findings enhance our understanding of the metal(loid)s pollution characteristics and their ecological risks in cultivated land at the local scale.

A simple method to determine the sampling numbers in decision-making units with unknown variations of soil cadmium

Sampling number is one critical issue to achieve credible results when surveying soil contamination and making remediation decisions. Traditional methods based on a normal distribution for determining numbers of samples are not always optimal because most distributions of soil heavy metal concentrations followed a log-normal distribution. Moreover, the variation of soil heavy metal concentrations is a prerequisite for previous methods to determine sampling numbers. Unfortunately, the variation is often unknown before soil sampling. Therefore, a simple method under the log-normal distribution without relying on variation to determine quickly the sampling number (QSN) was developed for soil cadmium and compared with other methods based on classical statistics and Chebyshev inequality. Results showed that an equation as a function of sampling areas could be used to determine QSN (QSN = 18.44 × A^0.54 + 8.69, A is sampling areas, km²), with acceptable errors ranging from 13 to 33% at the sampling areas of 0.03-10 km². The developed simple method for QSN was easy to use and cost-effective without prerequisite on the estimation of variation. Moreover, when the sampling cost was enough and the improved accuracy was requested, the increased sampling numbers were recommended as 1.53 times as the number calculated by the simple method. Therefore, the proposed method is believed as a simple and cost-effective method to determine the sampling numbers of soil Cd in decision-making units with unknown variations.

Quantitative analysis of the main sources of pollutants in the soils around key areas based on the positive matrix factorization method

Quantitative identification of the main sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in soils around multiple types of key areas is of great significance for blocking pollution sources. However, there is a lack of more comprehensive relevant research. In this study, Beijing was taken as the research area and four main sources were identified using the positive matrix factorization (PMF) method. The concentration of Pb, PAHs, Cr, and Hg in soils was significantly affected by the presence of landuse type, road traffic, natural factor, and industrial production, respectively, and the farmland, distance to main road, Proterozoic Changcheng-Jixian parent material and cinnamon soil type, and the gross industrial production make greater contributions to these four factors respectively than other variables. Moreover, the uncertainty of the PMF indicates that this four-factor PMF solution is stable and appropriate. These results provide support for the comprehensive control of soil environmental risks.

Status of cadmium accumulation in agricultural soils across China (1975-2016): From temporal and spatial variations to risk assessment

Based on 1186 published studies, the first national-scale assessment of cadmium (Cd) contamination in agricultural soils across China was conducted. Cd concentrations, temporal and spatial variations, and ecological and health risks resulted from Cd exposure were analyzed. A small part of sampling sites with Cd concentration surpass the screening value and the control value (GB15618-2018), respectively. Soil Cd concentrations in South China were higher than other regions. Ecological risks resulting from Cd contamination were low. Soil Cd concentrations accumulated gradually from 1981 to 2016. Cd mainly came from anthropogenic activities, such as mining, smelting, sewage irrigation, and fertilization. Linear correlations were observed between application amounts of fertilizers and Cd concentrations in soil, indicating that the application of nitrogen, phosphorus, potassium, and compound fertilizers is an important contributor of Cd in soils. This study details the overall Cd contamination status of agricultural soils in China, thus can provide insights for policymakers regarding contamination prevention measures.

Soil physiochemical properties and landscape patterns control trace metal contamination at the urban-rural interface in southern China

This study examined the influences of three subsets of environmental factors (i.e. soil physicochemical properties including pH, organic matters and soil texture, landscape patterns, and parent materials) on the spatial variations and sources of soil trace metal contamination across an urban-rural environmental gradient in Guangzhou City, southern China. We collected 318 surface soil samples from forests, orchards, farmlands, and urban lawns using a random tessellation design for selecting sample sites. The geo-accumulation indices showed that 18%-88% of soil samples were contaminated: moderate to high contamination with Cd and Hg, low to moderate contamination with Cu, Pb, Zn and Ni, and low contamination with As and Cr. However, less than 13% of soil samples were considered to have exceeded the national standards causing environmental and human health concerns. The mean geo-accumulation indices increased in the order of forest, paddy field/orchard, vegetable, road/residential, and park/residential areas for As, Cd, Ni, Pb, Zn, closely following a land disturbance gradient. Spearman Correlation and Cluster Analyses showed that Pb-Cu-Zn had traffic-related origins, Cd-Hg were mainly influenced by fertilization or industrial emissions, and As-Cr-Ni had geogenic origins for agricultural soils. In contrast, the Ni, Hg and Cd contamination sources for urban soils included both anthropogenic and geogenic origins. The Stepwise Regression and Partial Redundancy Analyses showed that three subsets of environmental factors explained 43%-87% of variations of soil contamination for both agricultural and urban soils. We concluded that soil contamination was mainly controlled by soil physiochemical properties followed by landscape patterns. Soil absorption of aerial loads of trace metal pollutants dominated the soil contamination processes. Our findings implied that improving soil physiochemical properties and landscape designs can strengthen environmental buffering and carrying capacity, thus alleviating soil contamination and reducing non-point-source pollution in the study region.

Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector

With the rapid development of modern industry, heavy metals in the soil introduce the risk of serious pollution. To reduce this pollution risk, the following four research questions needed to be addressed: What are the main influencing factors of soil pollution? What is the degree of influence? Do factors operate independently or are they interconnected? Which regions have high pollution risk and should be paid more attention? The study area was in Huanjiang County, with 273 km², and geographical detector proved to be a useful tool to solve these four problems. We found that mine activity and pH value were the primary influencing factors for total and water-soluble heavy metals. The interaction effects of mine activity and soil type, pH values, and normalized difference vegetation index (NDVI) for total heavy metals, as well as pH value and mine activity for water-soluble heavy metals, were greater than the sum effect of two factors. Zones with a high concentration of heavy metals were closer to the road and farther from the mine area, which had low NDVI, large slope, high terrain, and large pH values. Concentrations of total heavy metals were higher in calcareous soils and in dryland and forests. Zones with a higher concentration of water-soluble heavy metals were closer to the mine and river, which had lower DEM and pH values. The uncertainty of geographical detector was also analyzed on the basis of their interpolation accuracy and the stratification number of influencing factors, and we found that the existing sample numbers and the stratification number of influencing factors met the needs of geographical detector calculation. This study's conclusions are useful for soil pollution control and restoration.

A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment

Soil heavy metal pollution has been becoming serious and widespread in China. To date, there are few studies assessing the nationwide soil heavy metal pollution induced by industrial and agricultural activities in China. This review obtained heavy metal concentrations in soils of 402 industrial sites and 1041 agricultural sites in China throughout the document retrieval. Based on the database, this review assessed soil heavy metal concentration and estimated the ecological and health risks on a national scale. The results revealed that heavy metal pollution and associated risks posed by cadmium (Cd), lead (Pb) and arsenic (As) are more serious. Besides, heavy metal pollution and associated risks in industrial regions are severer than those in agricultural regions, meanwhile, those in southeast China are severer than those in northwest China. It is worth noting that children are more likely to be affected by heavy metal pollution than adults. Based on the assessment results, Cd, Pb and As are determined as the priority control heavy metals; mining areas are the priority control areas compared to other areas in industrial regions; food crop plantations are the priority control areas in agricultural regions; and children are determined as the priority protection population group. This paper provides a comprehensive ecological and health risk assessment on the heavy metals in soils in Chinese industrial and agricultural regions and thus provides insights for the policymakers regarding exposure reduction and management.

Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.