A Monte Carlo simulation-based health risk assessment of heavy metals in soils of the tropical region in southern China.

The disturbance of ecological stability may take place in tropical regions due to the elevated biomass density resulting from heavy metal and other contaminant pollution. In this study, 62 valid soil samples were collected from Sanya. Source analysis of heavy metals in the area was carried out using absolute principal component-multiple linear regression receptor modelling (APCS-MLR); the comprehensive ecological risk of the study area was assessed based on pollution sources; the Monte-Carlo model was used to accurately predict the health risk of pollution sources in the study area. The results showed that: The average contents of soil heavy metals Cu, Ni and Cd in Sanya were 5.53, 6.56 and 11.66 times higher than the background values of heavy metals. The results of soil geo-accumulation index (Igeo) showed that Cr, Mo, Mn and Zn were unpolluted to moderately polluted, Cu and Ni were moderately polluted, and Cd was moderately polluted to strongly polluted. The main sources of heavy metal pollution were natural sources (57.99%), agricultural sources (38.44%) and traffic sources (3.57%). Natural and agricultural sources were jointly identified as priority control pollution sources and Cd was the priority control pollution element for soil ecological risk. Heavy metal content in Sanya did not pose a non-carcinogenic risk to the population, but there was a carcinogenic risk to children. The element Zn had a high carcinogenic risk to children, and was a priority controlling pollutant element for the risk of human health, with agricultural sources as the priority controlling pollutant source.

Microplastics in agricultural soils on the coastal plain: Spatial characteristics, influencing factors and sources.

Farmland is a major sink for microplastics (MPs), but research on MPs in coastal plain soil, particularly their occurrence in agricultural areas with changing coastlines, is limited. Here, we investigated the distribution, influencing factors and sources of MPs in a typical agricultural county near the southeast coast of China considering different human activities and soil property changes. The average MP concentration in farmland soils was 314 items/kg, ranging from 70.2 to 851.3 items/kg. MPs increased first and then decreased from inland to the coast, and this trend was greatly affected by coastline expansion. Bulk density, clay and textile points of interest (POIs) are the major factors affecting MPs in farmland. Network analysis was used to divide the whole MP community into two modules, and the average similarity between each MP community and the other 25.5 MP communities was >0.5. Overall, the similarity of the MP community tended to decrease with increasing geographical distance (P < 0.01). In the soil environmental factors group, bulk density and clay affected the total MP abundance, accounting for 14.7 % and 9.4 % of MPs, respectively. After fitting 8 types of POIs and the total MP diversity integrated index (MDII) of farmland, washing POIs (R2 = 0.65, P < 0.01) displayed the greatest and most significant fit with MDII, followed by clothing POIs (R2 = 0.29, P < 0.01). The MDII-POI results showed that the major POI sources of soil MPs were clothing manufacturing and washing POIs. Unlike in urban areas, automobile service POIs, packaging POIs and textile POIs had no significant relationship with the MDII, which may be related to the population and economic development scale. The results emphasize the importance of investigating MP occurrence and sources in coastal agricultural areas to promote the effective management of MPs and plastic emissions in land-sea transition zones.

Determination of influencing factors on historical concentration variations of PAHs in West Taihu Lake, China.

The adsorption of polycyclic aromatic hydrocarbons (PAHs) by components such as elemental carbon (EC), total organic carbon (TOC), and particles is different, and EC and PAHs are good materials for reconstructing historical human activity patterns and pollution conditions. In this study, the effects of EC (soot and char), TOC and particles of different grain size on PAHs in surface sediments were quantitatively analysed, and their historical concentrations in a sediment core from western Taihu Lake were reconstructed. The contents of soot, TOC, clay, EC and char explained 57.2%, 27.6%, 26.0%, 24.0% and 16.4%, respectively, of the PAH concentrations in surface sediments. The correlation between the soot and PAH levels was significantly higher than that between the char, TOC, and clay contents and PAH levels, and PAHs were mainly affected by the local economic development and human activity, as indicated by metrics of population, highway mileage, coal burning, and industrial output. With the development of the economy of the Taihu Lake Basin, the composition of PAHs in the sediments has changed: the proportion of low-molecular-weight PAHs decreased from 42.4% to 17.5%, and that of high-molecular-weight PAHs increased from 58.7% to 82.5%. The concentration of PAHs in pore water from Taihu Lake over the past 100 years was reconstructed and ranged from 43.1 to 961.2 µg L-1, with an average of 180.7 µg L-1. After China's reform and opening up, the concentrations of various PAHs in Taihu Lake changed from safe to chronic pollution levels. The ratios of lead (Pb) isotopes and the diagnostic ratios of PAHs showed that the main sources of PAHs in western Taihu Lake sediments were human activities such as coal and petroleum combustion.

Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China.

In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950⁻2003 and 1950⁻2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 µg kg-1 and 407.8 ng g-1, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.