Global occurrence characteristics, drivers, and environmental risk assessment of microplastics in lakes: A meta-analysis.

Microplastic (MP) pollution in lakes has received much attention as an increasing amount of plastic waste enters aquatic ecosystems. However, there is still a lack of comprehensive understanding of the global distribution patterns, environmental hazards, factors driving their presence, and the relationships between sources and sinks of MPs. In this study, we conducted a meta-analysis of drivers of lake MP pollution based on 42 articles on MP pollution from three different aspects: geographical distribution, driving factors and environmental risks. The results revealed differences in the MP pollution levels across the different sampling sites in the global lakes. Moreover, there is significant heterogeneity in the abundance of MPs among various lakes, whose distribution pattern is affected by geographical location, sampling method and extraction method. The size of the MPs differed significantly between water and sediment, and the proportion of small (<1 mm) MPs in sediment was significantly greater than that in water (72% > 46%). Environmental risk assessment reveals that the risk level of MP pollution in most lakes worldwide is low, and the environmental risk of pollution in lake water is higher than that in sediment. Based on the risk assessment and geographical location of the lake, the risk of MP pollution is related not only to human activities and economic development but also to local waste management practices, which directly impact the accumulation of MPs. Therefore, we suggest that the production of biodegradable low-risk polymer plastics instead of high-risk materials, and plastic solid waste recycling management should be strengthened to effectively mitigate the presence of MPs in the environment.

Sedimentary record of microplastics in coastal wetland, eastern China.

Microplastic (MP) dynamics can reflect history of plastic production and waste management in nearby areas. However, the stratigraphy of MPs in coastal wetlands and their link to policy and economic pattern changes are currently unclear. Here, MP stratigraphic records in sediment core from coastal wetlands in Yancheng, China, were used to reconstruct plastic pollution history. Neural network models simulated how policy intervention and economic development affected MP accumulation over time. We showed that MP abundance curves with boundaries from 1920 to 2019 had four stages. MP growth slowed or even decreased in the mid-to-late 1980s due to improved waste management and wastewater treatment since the late 1980s. Human activities were the primary factor affecting MP abundance and shape, followed by sediment properties. We predict that the environmental impact of MPs will continue to increase in the next decade. Current plastic policy measures focus on predictable waste emissions, but hidden sources like clothing fibers and tire wear that significantly contribute to MP pollution require further attention.

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.

A framework for systematic microplastic ecological risk assessment at a national scale.

Microplastic pollution is widespread in terrestrial and aquatic environments; however, a systematic assessment of the ecological risks of microplastics is lacking. This study collected research studies on microplastics in soil, aquatic and sediment environments, and screened 128 articles including 3459 sites to assess the ecological risks posed by microplastics in China following a literature quality assessment. We developed a systematic ecological risk assessment framework for microplastics in terms of spatial characterization, biotoxicity and anthropogenic impacts. The results of the pollution load index indicated that 74% and 47% of the soil and aquatic environments studied, respectively, faced a medium or higher level of pollution. Comparing predicted no effect concentrations (PNEC) and measured environmental concentrations (MECs), revealed that soil (97.70%) and aquatic (50.77%) environmental studies were at serious ecological risk from microplastics. The results of the pressure-state-response model showed that the microplastic pollution in Pearl River Delta was in a high-risk state. In addition, we found that ultraviolet radiation and rainfall exacerbate soil microplastic pollution, and higher river runoff may carry large amounts of microplastic from the source. The framework developed in this study will help assess the ecological risks of microplastics in the region to promote the mitigation of plastic pollution.

Farmland quality assessment using deep fully convolutional neural networks.

Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied recently in image recognition, semantic understanding, and many other application domains. In this research, we used fully convolutional networks (FCN) as the deep learning model to evaluate farmland grades. Normalized difference vegetation index (NDVI) derived from Landsat images was used as the input data, and the China National Cultivated Land Grade Database within Jiangsu Province was used to train the model on cloud computing. We also applied an image segmentation method to improve the original results from the FCN and compared the results with classical machine learning (ML) methods. Our research found that the FCN can predict farmland grades with an overall F1 score (the harmonic mean of precision and recall) of 0.719 and F1 score of 0.909, 0.590, 0.740, 0.642, and 0.023 for non-farmland, level I, II, III, and IV farmland, respectively. Combining the FCN and image segmentation method can further improve prediction accuracy with results of fewer noise pixels and more realistic edges. Compared with conventional ML, at least in farmland evaluation, FCN provides better results with higher precision, recall, and F1 score. Our research indicates that by using remote sensing NDVI data, the deep learning method can provide acceptable farmland assessment without fieldwork and can be used as a novel supplement to traditional methods. The method used in this research will save a lot of time and cost compared with traditional means.

Source Apportionment and Health Risk Assessment of Heavy Metals in Endemic Tree Species in Southern China: A Case Study of Cinnamomum camphora (L.) Presl.

As a developed economic region in China, the problem of heavy metals (HMs) pollution in the Yangtze River Delta has become increasingly prominent. As an important evergreen broad-leaved tree species in southern China, the camphor tree cannot only be used as a street tree but also its various tissues and organs can be used as raw materials for Chinese herbal medicine. In order to explore whether heavy metal contamination in the region threatens the safety of camphor trees as pharmaceutical raw materials, we collected the bark and leaves of the tree most commonly used for pharmaceuticals in Yixing City. Based on the determination of HMs content, the health risks after human intake are evaluated, the sources and contributions of HMs are analyzed, and then the health risks of pollution sources are spatially visualized. The results showed that under the influence of human activities, the camphor trees in the study area had obvious enrichment of HMs, and the over-standard rate of Pb in the bark was as high as 90%. The non-carcinogenic risks of bark and leaves are acceptable, but the carcinogenic risks are not acceptable. The bark had the highest average carcinogenic risk, approaching six times the threshold. The results of Pb isotope ratio analysis showed that the average contribution rate of industrial activities to HMs in camphor trees in the study area was the highest, reaching 49.70%, followed by fossil fuel burning (37.14%) and the contribution of natural sources was the smallest, only 13.16%. The locations of the high-risk areas caused by the three pollution sources in the study area are basically similar, mainly concentrated in the northwest, northeast, and southeast, which are consistent with the distribution of industries and resources in the study area. This study can provide a reference for the precise prevention of HMs pollution of camphor and the safe selection of its pharmaceutical materials.

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.

Characteristics, sources and health risk assessment of airborne particulate PAHs in Chinese cities: A review.

Polycyclic aromatic hydrocarbons (PAHs) are organic compounds composed of at least two benzene rings. This paper reviews the characteristics, sources and health risk of airborne particulate PAHs in Chinese cities. The airborne particulate PAH concentrations varied from 3.35 to 910 ng m-3, with an average of 75.0 ng m-3, and the pollution level of PAHs in northern cities was generally higher than that in southern cities. The PAH concentrations in different cities underwent similar seasonal variations, with high concentrations in the winter and relatively low concentrations in the summer. Many factors, such as meteorological conditions and source emissions, influenced the spatiotemporal pattern of PAHs. High temperatures, frequent flow exchanges, abundant rainfall and strong solar radiation reduced the level of particulate PAHs in the atmosphere. The historical changes in the level of airborne particulate PAHs in four cities were analyzed. The PAH concentrations in Beijing and Taiyuan presented a trend of first increasing and then decreasing, while the level of particulate PAHs in Nanjing and Guangzhou had a decreasing tendency from year 2000-2015. The airborne particulate PAHs in cities were derived from several sources, including coal combustion, vehicle emissions, coking industries, biomass burning and petroleum volatilization. The results of a health risk assessment indicated that the incremental lifetime cancer risk (ILCR) for people in the northern cities was higher than that for people in the other regions, especially during the cold season. Moreover, adults were at greater risk than people in other age groups, and the health risk to females was slightly higher than that to males. The potential risk of airborne particulate PAH exposure was relatively high in some cities, and controlling PAH emissions at the source should be required to prevent pollution.

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.

Heavy metals in food crops, soil, and water in the Lihe River Watershed of the Taihu Region and their potential health risks when ingested.

Environmental pollution by heavy metals resulting from rapid economic development is a major concern. Soil, water, wheat, and rice samples were collected from the Lihe River Watershed in the Taihu Region (east China). In this study area, many types of industrial plants, including ceramics factories, plants working with refractory materials, and chemical plants are densely distributed and cause serious heavy metal pollution. In addition, well-developed transportation and agricultural activities are also important sources of heavy metals. Thus, the concentrations of selected heavy metals including cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) in the samples were analyzed to evaluate their potential integral risk (IR) to the health of the local population. Accordingly, the spatial distribution pattern of the IR values was determined in the study. The soil in the study area showed heavy Cd pollution, whereas the pollution by other elements was relatively slight. When the proportions of grain samples in which the concentrations exceeded the tolerance limits were examined, the grains were primarily contaminated with Pb, Ni, Cd, and Zn; and less contaminated with Cu and Cr. The drinking water of the local inhabitants was safe. The average IR value was 3.53 for adults and 3.91 for children, indicating that both adults and children may experience adverse health effects. The spatial distribution pattern of the IR values among the exposed populations in the study area showed high values in the eastern and middle parts, with maximum values >5, and low values in the western part, with minimum values <2. This is consistent with the distributions of the industries and the population. The study may provide a basis for comparison to other regions both in China and worldwide.

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.

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations.

With China's rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.

[Evaluation of soil heavy metals accumulation in the fast economy development region].

Evaluation of soil heavy metals accumulation was studied in Kunshan City, a typical region of the fast economy development region in China. 126 soil samples were collected and analyzed, and evaluation indexes of soil heavy metal accumulation, which including total concentration of soil heavy metal index (THMI), soil available heavy metal index (AHMI) and fractionation of soil heavy metal index (FHMI), were established, and the heavy metal accumulation conditions of soil in this region were also discussed. Results showed as follows: the spatial variability of THMI was relative lower, with a mean value of 42.57%, whereas strong variability was found in AHMI and FHMI (especially active fraction of soil heavy metals), with the average value of 82.75% and 77.83%, respectively. Judging by each index reference standard of C Horizon, THMI was low-grade with a mean value of 1.01, while the AHMI and FHMI reached to medium accumulation and serious accumulation, with the average values of 2.46 and 4.32, respectively. The synthetic accumulation index of soil heavy metals (SHMI) was 2.56, reaching to medium grade level and with strong variability. 21.54% land area was in low-grade accumulation and 54.70% land area was in medium grade accumulation, while 23.76% land area was in serious accumulation under SHMI evaluation system. All the accumulation evaluation indexes in livestock breeding zone were the lowest, while the indexes in the smelting and plating zone were the highest, but the indexes difference between two zones were unobvious. There were markedly differences in soil types, which the accumulation indexes in Wushan soil were significantly higher than those in Huangni soil and Qingni soil.

Heavy metals in wheat grain: assessment of potential health risk for inhabitants in Kunshan, China.

Heavy metals (HMs) may cause deleterious effects on human health due to the ingestion of food grain grown in contaminated soils. Concentrations of HMs (Hg, As, Cr, Cu, Ni, Pb, Zn and Cd) in wheat grains were investigated in different areas of a developed industry city in Southeast China (Kunshan city), and their potential risk to health of inhabitants was estimated. The results showed that concentrations of HMs in the top soil (0-15 cm) were in this order: Zn>Cr>Ni>Pb>Cu>As>Hg>Cd. The Zn, Cr, Ni Cd and Hg concentrations of several soil samples exceeded the permissible limits of China standard. In addition, concentrations of HMs in wheat grain decreased in the order of Zn>Cu>Pb>Cr>Ni>Cd>As>Hg. There were 1, 6 and 10 wheat samples whose Zn, Pb and Cd concentrations were above the permissible limits of China standard, respectively. In relation to non-carcinogenic risks, Hazard Quotient (HQ) of individual metal presented values inside the safe interval. However, health risk due to the added effects of eight HMs was significant for rural children and rural adults, but not for urban adults and urban children. HQ (individual risk) and HI (Hazard Index of aggregate risk) to different inhabitants due to HMs followed the same sequence of: country children>country adults>urban children>urban adults. Amongst the HMs, potential health hazards due to As, Cu, Cd and Pb were great, and that due to Cr was the minimum. It was suggested to pay more attention on the potential added threat of HMs to the health of country inhabitants (both children and adults) through consumption of wheat in Kunshan.