The application of urban anthropogenic background to pollution evaluation and source identification of soil contaminants in Macau, China

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.

Comparative study on anthropogenic impacts on soil PAHs: Accumulation and source apportionment in tourist and industrial cities in Hebei Province, China

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous organic contaminants in urban soils. The accumulation and source identifications of PAHs within a city have been frequently studied. However, impacts of urbanization development modes on PAHs accumulation patterns by taking a city as a whole have been seldom reported. Four cities with two development modes in Hebei province, Chengde and Zhangjiakou (tourist cities) and Handan and Tangshan (industrial cities), were selected. The concentrations of 16 priority PAHs in soils in the study areas were investigated. The results showed that the average concentrations of Σ16PAHs in Handan (2517 μg/kg) and Tangshan (2256 μg/kg) were more than twice of those in Chengde (696 μg/kg) and Zhangjiakou (926 μg/kg) approximately. Lines of evidence, provided by a combination of diagnostic ratios, pairwise correlation, and PMF methods, revealed that the dominant sources of PAHs in either city were industrial emission, vehicle emission, and petrogenic/biogenic process but with different proportions. Linear fittings based on Bayesian kernel machine regression analysis (BKMR) were constructed to illustrate the impact of industrialization on PAHs accumulation. The probability of excessing the 10 % (376 μg/kg) and 50 % (1138 μg/kg) of current ∑16PAHs would be higher than 90 % given the gross industrial production per unit area >5.00 × 106 and 20.5 × 106 CNY/km2, respectively. The proposed threshold values of industrialization are of significance for determining industrial structure and proportion in urban management.

Assessment of metals (Ni, Ba) deposition in plant types and their organs at Mersin City, Türkiye

The increase in heavy metal concentrations in the air, especially after the Industrial Revolution, is notable for the scientific world because of the adverse effects that threaten environmental and human health. Among the trace elements, nickel (Ni) is carcinogenic, and all barium (Ba) compounds are toxic. Trace elements are critical for human and environmental health. Their threat further increases, especially in the urban areas and surroundings with a high population. In urban areas, the trace element contamination in the airborne can be reduced using plants. However, which plant and plant organs absorb trace elements could not be determined. In the present study, Ni and Ba concentrations in the branch, wood, and leaf samples of 14 species collected from the city center of Mersin province were determined. As a result, broad-leaved species' Ni and Ba concentrations in their leaf sample were generally higher than other species. Almost all species had the lowest Ni and Ba concentrations in their wood samples. Among these 14 species, it was found that Ni concentration was very high, especially in non-washed leaves of Platanus orientalis, Photinia serrulata, and Citrus reticulate, and Ba concentration was very high in Citrus reticulata, Chamaecyparis lawsoniana, Laurus nobilis, and Acer hyrcanum. Using broad-leaved species in urban areas where pollution is at high levels will significantly contribute to reducing Ni and Ba pollution. It is recommended that these points be considered in future urban landscaping projects.

Introducing a land use-based weight factor in regional health risk assessment of PAHs in soils of an urban agglomeration

The high heterogeneity of land uses in urban areas has led to large spatial variations in the contents and health risks of polycyclic aromatic hydrocarbons (PAHs) in soils. A land use-based health risk assessment (LUHR) model was proposed for soil pollution on a regional scale by introducing a land use-based weight factor, which considered the differences in exposure levels of soil pollutants to receptor populations between land uses. The model was applied to assess the health risk posed by soil PAHs in the rapidly industrializing urban agglomeration of Changsha-Zhuzhou-Xiangtan Urban Agglomeration (CZTUA). The mean concentration of total PAHs (∑PAHs) in CZTUA was 493.2 μg/kg, and their spatial distribution was consistent with emissions from industry and vehicles. The LUHR model suggested the 90th percentile health risk value was 4.63 × 10-7, which was 4.13 and 1.08 times higher than those of traditional risk assessments that have adopted adults and children as default risk receptors, respectively. The risk maps of LUHRs showed that the ratios of the area exceeding the risk threshold (1 × 10-6) to the total area were 34.0 %, 5.0 %, 3.8 %, 2.1 %, and 0.2 % in the industrial area, urban green space, roadside, farmland, and forestland, respectively. The LUHR model back-calculated the soil critical values (SCVs) for ∑PAHs under different land uses, resulting in values of 6719, 4566, 3224, and 2750 μg/kg for forestland, farmland, urban green space, and roadside, respectively. Compared with the traditional health risk assessment models, this LUHR model identified high-risk areas and drew risk contours more accurately and precisely by considering both the spatial variances of soil pollution and their exposure levels to different risk receptors. This provides an advanced approach to assessing the health risks of soil pollution on a regional scale.

Spatial distribution, sources, and risk assessment of potentially toxic elements in cultivated soils using isotopic tracing techniques and Monte Carlo simulation

Potentially toxic elements (PTEs) in cultivated lands pose serious threats to the environment and human health. Therefore, improving the understanding of their distinct sources and environmental risks by integrating various methods is necessary. This study investigated the distribution, sources, and environmental risks of eight PTEs in cultivated soils in Lishui City, eastern China, using digital soil mapping, positive matrix factorisation (PMF), isotopic tracing, and Monte Carlo simulation. The results showed that Pb and Cd are the main pollutants, which posed higher ecological risks in the study area than the other PTEs. Natural, mining, traffic, and agricultural sources were identified as the four determinants of PTE accumulation via a PMF model combined with Pearson correlation analysis, showing that their contribution rates were 22.6 %, 45.7 %, 15.2 %, and 16.5 %, respectively. Stable isotope analysis further confirmed that local mining activities affected the HM accumulation. Additionally, non-carcinogenic and carcinogenic risk values for children were 3.18 % and 3.75 %, respectively, exceeding their acceptable levels. We also identified that mining activities were the most important sources of human health risks (55.7 % for adults and 58.6 % for children) via Monte Carlo simulations coupled with the PMF model. Overall, this study provides insights into the PTE pollution management and health risk control in cultivated soils.

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.

Energy information flow-based ecological risk transmission among communities within the heavy metals contaminated soil system

An energy information flow-based ecological risk assessment framework (EIF-ERA) is developed for identifying ecological risk transmission rules among communities (i.e., vegetation E1, herbivorous animals E2, soil microorganisms E3, and carnivorous animals E4) within the heavy metals contaminated soil system. This framework is integrated with numerous techniques of carcinogenic risk evaluation, ecological risk assessment (ERA), and Monte Carlo simulation. Stepwise quadratic response surface analysis (SQRSA) is employed for reflecting the relation between contaminants' concentration and comprehensive risk. Two scenarios with respect to the environmental quality standards (scenarios 1) and carcinogenic risk reversion (scenarios 2) are merged into the EIF-ERA. A real-world mining area in Xinglong County in Chengde is selected to verify the developed framework's effectiveness. Results reveal that E3 is considered as the most sensitive community when contaminant interference occurs, and its 62.3% and 37.7% of comprehensive risk are contributed by initial and direct risks, respectively. Other communities can receive direct risk through control allocation (CA). Monte Carlo anlysis shows that there are 7.68% and 20.25% increase in the initial risk of Cd and Pb when their quantile statistics increase from 70% to 90%. Determination of an appropriate screening value is vital for contaminated mining soil remediation due to its inefficiency of remediation funds, especially when considering the trict standards of contaminants' concentration within scenarios 1. The surrogates obtained from the SQRSA display the relation of contaminant concentration and comprehensive risks with the adjusted R² greater than 0.77. These findings can be in support of system design, risk assessment, and site remediation.