A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields.

Soil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.

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.

Integration of land ecological consolidation and ecosystem product value realization: A case from the Yangtze riverside industrial park in Changzhou, China.

Traditional industries and industrialization have led to widespread environmental pollution and ecosystem degradation in major river basins globally. Strategies centered on ecological restoration and ecological economy are emerging as essential tools for effective environmental governance. This study aims to investigate how a multifaceted framework for land ecological consolidation, with various developmental goals, can effectively support ecological restoration and sustainability. Through quantitative analysis and in-depth interviews, we investigated the case of Yangtze riverside chemical industrial park in Changzhou. This park pursues ecological and economic sustainability through chemical industry transformation, ecological restoration and protection, ecological management, and ecological industry development. The results show that this practice established a multi-objective action framework rooted in urban renewal, land consolidation, ecological restoration, industrial transformation, and rural revitalization. Through multiplanning integration, integrated implementation and full-cycle profit distribution, the aim of ecological protection has been initially achieved, offering a crucial guarantee for sustainable development. A total of 96.47 ha ecological space expanded, which can generate ecological product worth CNY 7.283 billion, alongside a net economic benefit of CNY 978 million over three decades. The top-down ecological responsibilities, coupled with local developmental demands, have stimulated collaborations within a bottom-up endogenous network comprising government, enterprises, and residents.

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.

Vertical distribution and weathering characteristic of microplastics in soil profile of different land use types.

After deposition on the topsoil, microplastics (MPs) may be vertically migrated to deeper soil layers over time or eventually enter the groundwater system, leading to more widespread environmental and ecological issues. However, the vertical distribution of MPs in natural soils are not yet fully understood. In this study, we collected soil profiles (0-100 cm) from four different land use types on the west bank of Taihu Lake in China to investigate the vertical distribution and weathering characteristics of MPs. The average abundance of soil MPs followed the pattern of paddy field (490 ± 82 items/kg) > dryland (356 ± 55 items/kg) > tea garden (306 ± 32 items/kg) > woodland (171 ± 27 items/kg) in the 0-10 cm layer, and the abundance of MPs decreased linearly with soil depth (r = -0.89, p < 0.01). Compared to tea garden and woodland, MPs in dryland and paddy field have migrated to deeper soil layers (80-100 cm). The carbonyl index of polyethylene and polypropylene MPs increased significantly with soil depth (r = 0.96, p < 0.01), with values of 0.58 ± 0.30 and 0.54 ± 0.33, respectively. The significant negative correlation between MPs size and carbonyl index confirmed that small-sized MPs in deeper soil layers originated from the weathering and fragmentation of MPs in topsoil. The results of structural equation model showed that roots and soil aggregates may act as filters during the vertical migration of MPs. These findings contribute to a better understanding of the environmental fate of MPs in soil and the assessment of associated ecological risks.

Trends in the occurrence and accumulation of microplastics in urban soil of Nanjing and their policy implications.

Urban soil is an important sink of terrestrial microplastics (MPs), and understanding their distribution over time is essential for effective pollution management. Here, based on soil MP data from Nanjing, a typical megacity in eastern China, this study analyzed MP accumulation trends using decision tree and time series network based on soil attributes, POI (point of interest), and human activity factors such as urban industrial structure, transportation, water use. We also evaluated the impact of plastic policy interventions. In the past 15 years, MPs in urban soil in Nanjing have gradually increased, and highly polluted areas have also grown. From 2010 to 2020, the concentration of MPs in urban soil increased from 326.7 items/kg to 480.9 items/kg, with high pollution areas expanding from only 2.0 km2 (0.7 %) to 48.7 km2 (14.9 %). The accumulation of MPs was also influenced by changing factors due to urbanization. In the early 21st century, residential areas had the largest effect, while in the later period, public passenger transport and domestic water consumption were the dominant factors. The scenarios simulation suggests recent plastic intervention policies have helped alleviate this rate of increase, but MP source management (e.g., laundry fibers, tire wear) still needs improvement. By the proposed method, the past trend of microplastics in urban soil and their relationship with soil properties and human activities can be accurately revealed, which will be helpful for the formulation of countermeasures to mitigate regional soil MP pollution.

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.

Role of microplastics in microbial community structure and functions in urban soils.

Evidence from the laboratory suggests that microplastics (MPs) can harm soil microorganisms, affecting the structures and functions of microbial communities. The impact of soil MPs on microbes in actual urban environments with high human activity levels, however, has not been well reported. To investigate the MP effect on urban soil microorganisms under complex scenarios, we analyzed 42 soil samples from standardized plots of 7 urban functional zones. We report that urban green spaces are important for studying microbial diversity in the study area, and they also contribute to the global homogenization of soil microbes and genes. Bacterial communities in soils enriched with various MPs showed greater differences in OTUs than fungi. Compared to low-MP soils, most ARGs and nutrient cycling genes had similar or slightly lower abundances in soils with high levels of MPs. The coupling of pollutant factors with MPs as independent variables had significant explanatory power for both positive and negative correlations in PLS-PM analysis. Specifically, PET and PP MPs explained 3.54% and 6.03%, respectively, of the microbial community and functional genes. This study fills knowledge gaps on the effects of MPs on urban soil microbial communities in real environments, facilitating better management of urban green spaces.

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.

Translocation pattern of heavy metals in soil-rice systems at different growth stages: A case study in the Taihu region, Eastern China.

Rice production is crucial for human nutrition and food safety globally. However, it has been a significant sink for potentially harmful metals because of intensive anthropogenic activities. The study was conducted to characterize heavy metal translocation from soil to rice at the filling, doughing and maturing stages, and influencing factors of their accumulation in rice. The distribution and accumulation patterns varied for metal species and growth stages. Cd and Pb accumulation mainly occurred in roots, Cu and Zn were readily transported to stems. Cd, Cu, and Zn accumulation in grains had a descending order of filling > doughing > maturing. Soil heavy metals, TN, EC, and pH exerted important impacts on heavy metals uptake by roots during the period from filling stage to maturing stage. Concentrations of heavy metals in grains were positively correlated with the translocation factors TFstem-grain (from stem to grain) and TFleaf-grain (from leaf to grain). Grain Cd exhibited significant correlations with total Cd and DTPA-Cd in the soil at each of the three growth stages. Moreover, Cd in maturing grain could be effectively predicted by soil pH and DTPA-Cd at the filling stage.

Effects of land use on the distribution of soil microplastics in the Lihe River watershed, China.

Soil contamination by microplastics (MPs) has gained widespread attention, whose fate may be influenced by land use types. The effects of land use types and the intensity of human activities on the distribution and sources of soil MPs at the watershed scale are unclear. In this study, 62 surface soil sites in representing five land use types (urban, tea garden, dryland, paddy field and woodland) and 8 freshwater sediment sites were investigated in the Lihe River watershed. MPs were detected in all samples, and the average abundance of soil and sediments was 401.85 ± 214.02 and 222.13 ± 54.66 items/kg, respectively. The soil MPs abundance followed the sequence: urban > paddy field > dryland > tea garden > woodland. Soil MP distribution and MP communities were significant different (p < 0.05) among land use types. The similarity of MP community highly correlated with geographic distance, and woodlands and freshwater sediments may be a potential fate for MPs in the Lihe River watershed. Soil clay, pH, and bulk density significantly correlated with MP abundance and fragment shape (p < 0.05). The positive correlation between population density, Total- Point of Interest (POI) and MP diversity indicates the importance of intensity of human activities in exacerbating soil MP pollution (p < 0.001). Plastic waste sources accounted for 65.12%, 58.60%, 48.15% and 25.35% of MPs in urban, tea garden, dryland and paddy field soils, respectively. Differences in the intensity of agricultural activities and cropping patterns were associated with different percentages of mulching film sources in the three types of agricultural soils. This study provides new ideas for the quantitative analysis of soil MP sources in different land use types.

Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China.

Soil microplastic (MP) pollution has recently become increasingly aggravated, with severe consequences being generated. Understanding the spatial distribution characteristics of soil MPs is an important prerequisite for protecting and controlling soil pollution. However, determining the spatial distribution of soil MPs through a large number of soil field sampling and laboratory test analyses is unrealistic. In this study, we compared the accuracy and applicability of different machine learning models for predicting the spatial distribution of soil MPs. The support vector machine regression model with radial basis function (RBF) as kernel function (SVR-RBF) has a high prediction accuracy (R2 = 0.8934). Among the six ensemble models, random forest (R2 = 0.9007) could better explain the significance of source and sink factors affecting the occurrence of soil MPs. Soil texture, population density, and MPs point of interest (MPs-POI) were the main source-sink factors affecting the occurrence of soil MPs. Furthermore, the accumulation of MPs in soil was significantly affected by human activity. The spatial distribution map of soil MP pollution in the study area was drawn based on the bivariate local Moran's I model of soil MP pollution and the normalized difference vegetation index (NDVI) variation trend. A total of 48.74 km2 of soil was in an area of serious MP pollution, mainly concentrated in urban soil. This study provides a hybrid framework that includes spatial distribution prediction of MPs, source-sink analysis, and pollution risk area identification, providing scientific and systematic methods and techniques for pollution management in other soil environments.

A novel methodological framework for risk zonation and source-sink response concerning heavy-metal contamination in agroecosystems.

The accurate identification of high-risk zones and risk source-sink responses for heavy-metal (HM) contamination of agroecosystems remains challenging due to involving multiple environmental media such as soils, dustfall, and crops and a wide range of evaluation criteria and constraints. This study established a novel evaluation model based on the integration of Geographical Information Systems (GIS) and multi-criteria decision analysis (MCDA) to assess agroecosystem risk in the Lihe River watershed, China. Bivariate local indicators of spatial association (LISA) were adopted to explore the spatial interaction of risk sources and sinks with outputs ranging from 0.0003 (no risk) to 0.83 (high risk). Areas with moderate, considerable, and high risk constituted 67.4 % of the total land area, and only 1.8 % of the area was classed as low risk. Central urban and eastern areas around Taihu Lake were risk accumulation regions that needed more remedial attention. Risk cluster zones in the central urban area involved significant source-sink response relationships with the spatial distribution of industries, whereas eastern zones were linked to vehicular traffic distribution, accounting for 27.5 % and 16.5 % of the total area, respectively. This study provides a new methodological framework for the assessment of environmental risk, risk zonation, and risk source-sink spatial interaction in agroecosystems.

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.

Soil microplastic characteristics and the effects on soil properties and biota: A systematic review and meta-analysis.

The soil environment serves as an assembling area for microplastics, and is an important secondary source of microplastics in other environmental media. Recently, soil microplastics have been extensively studied; however, high variability is observed among the research results owing to different soil properties, and the complexity of soil microplastic composition. The present study amassed the findings of 2886 experimental groups, across 38 studies from 2016 to 2022, and used meta-analysis to quantitatively analyze the differences in the effects of microplastic exposure on soil physicochemical properties and biota. The results showed that among the existing soil microplastic research, agricultural soils maintained a higher environmental exposure distribution than other environments. Microplastic fibers and fragments were the predominant shapes, indicating that the extensive use of agricultural films are the primary influencing factor of soil microplastic pollution at present. The results of the meta-analysis found that microplastic exposure had a significant negative effect on soil bulk density (lnRR = -0.04) and aggregate stability (lnRR = -0.085), indicating that microplastics may damage the integrity of soil structure or damage the soil surface. The significant changes in plant root biomass and soil phosphatase further signified the potential impact of microplastics on soil nutrient and geochemical element cycling. We further constructed species sensitivity distribution curves, revealing that invertebrates had a higher species sensitivity to microplastics, as they can pass through the gut wall of soil nematodes, causing oxidative stress and affecting gene expression. In general, soil is an interconnected complex, and microplastic exposure can directly or indirectly interact with environmental chemical processes in the soil environment, potentially harming the soil ecosystem; however, current research remains insufficient with respect to breadth and depth in terms of the comprehensive "source-sink" mechanism of soil microplastics, the hazard of exposure, and the overall toxic effects.

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction.

The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

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.

Arable land and water footprints for food consumption in China: From the perspective of urban and rural dietary change.

Since the adoption of the open-door policy, the Chinese dietary pattern has changed greatly. Based on the dietary changes, this study analyzed the arable land and water footprints (WFs) for the food consumption of urban and rural residents in China. The results showed that the arable land demand and WFs for meat, vegetable oil, soybeans and liquor exceeded those for other foods, and the per capita arable land and WFs for food consumption of urban residents were higher than those of rural residents. The total arable land and WFs for the food consumption of residents increased by 16.9 million ha (from 91.1 to 108 million ha) and 214.5 billion m3 (from 457.9 to 672.4 billion m3), respectively, from 1983 to 2017. Specifically, the total arable land and WFs for the food consumption of urban residents increased by 45.9 million hm2 (from 22.6 to 68.5 million hm2) and 318.3 billion m3 (from 113.2 to 431.5 billion m3), respectively. Additionally, those of rural residents decreased by 29.7 million hm2 (from 69.2 to 39.5 million hm2) and 103.9 billion m3 (344.8 to 240.9 billion m3), respectively, mainly due to the migration of the rural population to cities and the reductions in per capita arable land and WFs due to increased crop yields. The arable land and blue WFs required for food consumption will reach 127.7 million hm2 and 221.1 billion m3, respectively, in 2030. However, these values will be reduced by approximately 23% and 20%, respectively, to 98.9 million hm2 and 177.8 billion m3 under a balanced dietary pattern. Measures such as improving the investment in agricultural research and development, advocating a balanced diet, and increasing the import of resource-intensive foods could alleviate the pressure on land and water resources.

Microplastics in urban soils of Nanjing in eastern China: Occurrence, relationships, and sources.

Although microplastic (MP) pollution has been defined as a new global challenge by the United Nations Environment Programme, their abundance and composition has only been studied in-depth within farmland soil, while minimal attention has been placed on urban soil contamination. Accordingly, within the current study, MP abundance and composition is investigated within urban soil from green spaces in Nanjing, eastern China. The average MP abundance in soil was 461 ± 222 items/kg and primarily comprised fibers (39.1%) and fragments (37.7%). MPs <1000 µm in size accounted for 83.7% of the total content and white MPs were the most abundant (26.5%). The dominant polymers were polyethylene glycol terephthalate (32.0%) and polypropylene (20.5%). Moreover, relationship network analysis generated three distinct MP modules based on community similarity. Indeed, the degree of similarity increased by ∼26.8% per kilometer. Furthermore, application of a forward selective optimal multiple regression model identified clay, sand, longitude, and points of interest for recycling bins (RecyclePOI) as the primary spatial and soil environmental factors affecting MP abundance and composition. Additionally, five potential sources of MPs were identified based on the MP diversity integrated index fitting results, and point of interest density (MDII-POI) source analysis (R2 = 0.21-0.62; P < 0.05). In particular, the point of interest of express delivery points (ExpressPOI) were important sources of plastic emissions as they are widely distributed throughout urban and fringe areas. Collectively, the findings of this study provide novel insights regarding quantitative source appointment and regional ecological control of MPs in urban soil.