Method on site-specific source apportionment of domestic soil pollution across China through public data mining: A case study on cadmium from non-ferrous industries

Spatial and temporal variations in environmental impacts of heavy metal emissions from China's non-ferrous industry: An enterprise-specific assessment

In China, the non-ferrous metal industry is the sector with the highest emissions of arsenic, cadmium, mercury and lead, causing serious impacts on human health and the ecosystem. However, current heavy metal emission inventories are inadequate for figuring out their exposures and associated environmental impacts due to the lack of detailed data. Here, we constructed a high-resolution, enterprise-specific, and long-term dataset detailing heavy metal emissions from the non-ferrous industry in China from 1981 to 2020, using comprehensive enterprise information. Furthermore, an environmental impact assessment was performed using the characterization factors of the IMPACT World + model. Results show that: (1) from 1981 to 2020, the total heavy metal emissions of China's non-ferrous industry reached 144,697 tons (t), with atmospheric emissions (104,524 t) exceeding aquatic ones (40,173 t). (2) The industry's emissions showed a rising and then declining trend, with significant spatial heterogeneity, where heavy metal emissions concentrated in the central and western parts of Yunnan, the southern part of Hunan, the northern part of Guangxi, Henan along the Yellow River, the intersection of Gansu and Shaanxi, the central and eastern parts of Liaoning, and the eastern part of Inner Mongolia. (3) The environmental impact on human health was 1.19 × 107 DALY, and the value of ecosystem quality was 7.26 × 109 species·yr. The top 10 % of enterprises with the largest environmental impacts contributed over 60 % of human health risks and 62 % of ecosystem quality impacts. Improving the removal efficiency of heavy metals by 10 % within the four major industry classes could lead to a 9.92 % reduction in human health impacts and a 9.77 % reduction in ecosystem quality impacts within the non-ferrous metals industry. The findings of this study can provide insights for pollution control, environmental risk reduction, and sustainable development in the non-ferrous metals industry.

Sources apportionments of heavy metal(loid)s in the farmland soils close to industrial parks: Integrated application of positive matrix factorization (PMF) and cadmium isotopic fractionation

Understanding the source identification and distribution of heavy metal(loid)s in soil is essential for risk management. The sources of heavy metal(loid)s in farmland soil, especially in areas with rapid economic development, were complicated and need to be explored urgently. This study combined geographic information system (GIS) mapping, positive matrix factorization (PMF) model and cadmium (Cd) isotope fingerprinting methods to identify heavy metal(loid) sources in a typical town in the economically developed Yangtze River Delta region of China. Cd, As, Cu, Zn, Pb, Ni and Co in different samples were detected. The results showed that Cd was the most severely contaminated element, with an exceedance rate of 78.0 %. GIS mapping results indicated that the hotspot area was located in the northeastern area with prolonged operational histories of electroplating and non-ferrous metal smelting industries. The PMF model analysis also identified emissions from smelting and electroplating enterprises as the main sources of Cd in the soil, counted for 49.28 %, followed by traffic (25.66 %) and agricultural (25.06 %) sources. Through further isotopic analysis, it was found that in soil samples near the industrial park, the contribution of electroplating and non-ferrous metal smelting enterprises to cadmium pollution was significantly higher than other regions. The integrated use of various methodologies allows for precise analysis of sources and input pathways, offering valuable insights for future pollution control and soil remediation endeavors.

Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

Spatiotemporal assessment and scenario simulation of the risk potential of industrial sites at the regional scale

Spatiotemporal risk and future evolutionary distribution characteristics of industrial sites are crucial for regional environmental supervision. However, traditional site survey methods have long cycles, high costs, and small coverage and usually only consider the static risk of a single industrial site to a single receptor. Low-cost, large-scale, and long-term multi-source data can compensate for the shortcomings of traditional site surveys. Previous studies have rarely considered the spatiotemporal heterogeneity of industrial sites and assessed their dynamic risks at the regional scale. This study used China's Yangtze River Delta Urban Agglomeration as the study area. We assessed the risk potential of industrial sites from 2000 to 2020 using multi-source and multiperiod data. We also simulated the risk potential for 2030 and 2050 using a patch-generating land use simulation (PLUS) model under different scenarios. The results indicated that the proportion of medium- and high-risk potential grids from 2000 to 2020 ranged from 2.53 % to 5.61 % in the study area, with the vast majority of areas (94.39 %-97.47 %) having low- or no-risk potential. The PLUS model exhibited remarkable reliability from 2005 to 2020, with the overall accuracy, Kappa coefficient, and Moran's index ranging from 83 % to 89 %, 0.38 to 0.59, and 0.34 to 0.56, respectively. The future prediction results indicated that the number of high-risk potential grids (>5 %) showed an upward trend under natural development scenarios in 2030 and 2050 and a downward trend under the ten-chapter soil pollution action plan or strict control scenarios. This study provides vital information for addressing the challenges of industrial site management and environmental risks in similar regions.

Foliar zinc reduced Cd accumulation in grains by inhibiting Cd mobility in the xylem and increasing Cd retention ability in roots1

Cadmium (Cd) pollution endangers the safe utilization of paddy soils, and foliar zinc (Zn) can reduce the toxic effects of Cd. However, little is known about the effects of foliar Zn application on the transport and immobilization of Cd in key rice tissues and the physiological state of rice plants. A pot experiment was conducted to explore the effects of spraying 0.2% and 0.4% Zn (ZnSO4) during the early grain-filling stage on Cd transport in rice, photosynthesis, glutathione (GSH) levels, Cd concentrations in xylem sap, and the expression of Zn transporter genes. The results showed that grain Cd concentrations in the 0.2% Zn and 0.4% Zn treatments were 24% and 31% lower, respectively, than those of the control treatments at maturity. Compared with the control treatments, the 0.4% Zn treatment increased Cd by 60%, 69%, 23%, and 22% in husks, rachises, first internodes, and roots, respectively. Application of Zn reduced xylem Cd content by up to 26% and downregulated transporter genes (OSZIP12, OSZIP4, and OSZIP7a) in flag leaves. Foliar Zn increased Cd bioaccumulation in roots while decreasing Cd bioaccumulation in grains. Zn reduced GSH concentration in flag leaves and stems, inhibiting photosynthesis (intercellular CO2 concentration, transpiration rate). Taken together, foliar Zn can reduce the expression of Zn transporter genes and the mobility of Cd in the xylem, promoting the fixation of Cd in husks, rachises, first internodes, and roots, ultimately reducing Cd concentration in rice grains.

Method for establishing soil contaminant discharge inventory: An arsenic-contaminated site case study

The existing method to survey site pollution is generally based on soil-groundwater sampling and instrumental analysis, which enables us to access the detailed soil pollution status while lacking quantitative association with industrial activities. It is urgent to understand contaminant discharge modes and establish a discharge inventory for achieving process-targeted pollution control. This study took a 40-year phosphate fertilizer-sulfuric acid site as an example and constructed a contaminant tracing method based on on-site investigations and detailed industrial data. These investigations and data were combined to determine the characteristic pollutant of this site, arsenic. And the calculation process of four-pathway pollution modes (atmospheric deposition, wastewater, solid waste leaching, and storage dripping) is derived from the existing acceptance criteria and risk assessment guidelines. They are set to calculate the arsenic's factory-to-soil discharge flux. The absent process contaminant release information and parameters, such as discharge coefficient, were obtained from soil-groundwater pollution control standards and discharge handbooks. It was found that the high concentration of arsenic (around 1930 mg g^-1) was preponderantly caused by sulfur-iron slag and tailing leaching (96.19%), while the other pathways accounted for only 0.13% (atmospheric deposition), 3.59% (wastewater) and 0.09% (storage tank). Results were verified by the measured arsenic concentration, and the difference was +16.29%, which was acceptable. Finally, a contaminant discharge inventory was established with high-resolution spatial distribution and time-scale (historical discharge) evolution. The innovation of this study lies in the preliminary construction of a method for formulating soil discharge inventory. This study would contribute to the refined management of site pollution and reduction of source contaminants discharge. In addition, it will help infer the pollution condition of sites that are difficult to sample so as to help the government achieve precise source control.

Pollution and risk assessment of potentially toxic elements in soils from industrial and mining sites across China

Potentially toxic elements in soils (SPTEs) from industrial and mining sites (IMSs) often cause public health issues. However, previous studies have either focused on SPTEs in agricultural or urban areas, or in a single or few IMSs. A systematic assessment of the pollution and risk levels of SPTEs from IMS at the national scale is lacking. Here, we obtained SPTE (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) concentrations from IMSs across China based on 188 peer-reviewed articles published between 2004 and 2022 and quantified their pollution and risk levels using the pollution index and risk assessment model, respectively. The results indicated that the average concentrations of the eight SPTEs were 4.42-270.50 times the corresponding background values, and 19.58% of As, 14.39% of Zn, 12.79% of Pb, and 8.03% of Cd exceeded the corresponding soil risk screening values in these IMSs. In addition, 27.13% of the examined IMS had one or more SPTE pollution, mainly distributed in the southwest and south central China. On the examined IMSs, 81.91% had moderate or severe ecological risks, which were mainly caused by Cd, Hg, As, and Pb; 23.40% showed non-carcinogenic risk and 11.70% demonstrated carcinogenic risk. The primary exposure pathways of the former were ingestion and inhalation, while that for the latter was ingestion. A Monte Carlo simulation also confirmed the health risk assessment results. As, Cd, Hg, and Pb were identified as priority control SPTEs, and Hunan, Guangxi, Guangdong, Yunnan, and Guizhou were selected as the key control provinces. Our results provide valuable information for public health and soil environment management in China.

A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.

The high-dimensional geographic dataset revealed significant differences in the migration ability of cadmium from various sources in paddy fields

Cadmium (Cd) contamination in paddy fields and its subsequent transfer in soil-rice systems are of particular concern. Significant discrepancies exist in the transfer process of Cd pollution sources from soil to rice. Here, we proposed a novel hybrid framework to reveal the priority of controlling Cd pollution sources in soil-rice systems, based on a high-dimensional geographical database. We further defined transfer potential (TP) to describe the ability of Cd from soil to rice (TP^r = Cd_r/Cd_s) and activated status (TP^a = Cd_a/Cd_s), respectively, to reveal the priority sources of Cd pollution at the regional scale. The mining source has both high levels of TP^r and TP^a, which should be a controlled priority. Followed by traffic sources with a higher value of TP^r, showing the risk to rice rather than the soil. The activated and enriched capacities of soil Cd are unequal in different sources that we attribute to the disparities of Cd transport in soil-rice systems. Cd contamination shows a significant spatial heterogeneity due to the difference in its transport performance. Our findings provide support for designing site-specific and pollution-targeted control priorities for suitable Cd pollution mitigation strategies at the regional scale.

Assessing the suitable regions and the key factors for three Cd-accumulating plants (Sedum alfredii, Phytolacca americana, and Hylotelephium spectabile) in China using MaxEnt model

Phytoremediation is an effective way to remove metals from contaminated soil, and selecting remediation plants suitable for climate conditions is a prerequisite for effective phytoremediation. In this study, a MaxEnt model was applied to investigate the potential distribution and habitat suitability of three Cd-accumulating plants in China- Sedum alfredii, Phytolacca americana, and Hylotelephium spectabile and explore the key environmental factors that affect their habitat suitability. A total of 44 environmental parameters, including bioclimatic variables, altitude, and soil property parameters were used. The results showed that: (1) For S. alfredii, suitable areas account for 14.9 % of the area of China, which are mainly distributed in the middle and lower reaches of the Yangtze River. (2) The suitable areas of P. americana account for 22.7 % of China and are mainly located in the regions of the Qinling Mountains and the south of China. (3) While that for H. spectabile are mainly located in the regions of northeastern China and certain regions of central China, with suitable areas account for 8.3 % of the area of China. (4) The distribution of these three plants is significantly affected by precipitation; specifically, solar radiation is an influential factor for the distribution of S. alfredii and H. spectabile, and temperature limits the distribution of P. americana. The selection and agronomic management of hyperaccumulators for phytoremediation requires multifactor consideration (e.g., climate, soil conditions and planting patterns). The results can provide guidance for identifying suitable areas for planting these three accumulating plants, which could not only prevent the unscientific cultivation of them in unsuitable habitats but also enhance the efficiency of phytoremediation. Meanwhile, these findings are expected to contribute to agronomic management for improved phytoremediation effects in different Cd-contaminated regions of China.

Construction of bifunctional bacterial community for co-contamination remediation: Pyrene biodegradation and cadmium biomineralization

Polycyclic aromatic hydrocarbons and heavy metals are typical pollutants in the non-ferrous metal smelting industry. The combination of biodegradation and biomineralization has great development potential for co-contamination removal as an environmentally friendly method. Pyrene (Pyr) and cadmium (Cd) were regarded as model pollutants of co-contamination in this study. A bifunctional bacterial community named Ycp was screened from a non-ferrous smelting slag field soil. The 16S rRNA gene high throughput sequencing analysis showed that Enterobacter was the dominant genus (99.1%). Ycp had adaptability under a wide range of environmental conditions (pH 3-9, salinity 0-10 g L^-1 NaCl, Pyr concentration 0-50 mg L^-1, Cd concentration 0-100 mg L^-1), and the removal rate of Pyr and Cd reached 41.8%-76.9%, 82.8%-98.8%, respectively. It was found that compound carbon sources had promoting effect on the removal of Pyr and Cd, with the maximum removal rate of 88.3% and 98.0%. According to the degradation products of Pyr by LC-MS analysis and the mineralized products of Cd²⁺ by XRD and SEM-EDS analysis, the mechanism of Ycp for co-contamination remediation was: Ycp biodegraded Pyr through salicylic acid and phthalic acid metabolic pathways, and biomineralized Cd²⁺ into CdCO₃ through microbially induced carbonate precipitation. This study provided a basis for microbial remediation of co-contamination.