Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Geospatial prediction of total soil carbon in European agricultural land based on deep learning

Accurate geospatial prediction of soil parameters provides a basis for large-scale digital soil mapping, making efficient use of the expensive and time-consuming process of field soil sampling. To date, few studies have used deep learning for geospatial prediction of soil parameters, but there is evidence that it may provide higher accuracy compared to machine learning methods. To address this research gap, this study proposed a deep neural network (DNN) for geospatial prediction of total soil carbon (TC) in European agricultural land and compared it with the eight most commonly used machine learning methods based on studies indexed in the Web of Science Core Collection. A total of 6209 preprocessed soil samples from the Geochemical mapping of agricultural and grazing land soil (GEMAS) dataset in heterogeneous agricultural areas covering 4,899,602 km2 in Europe were used. Prediction was performed based on 96 environmental covariates from climate and remote sensing sources, with extensive comprehensive hyperparameter tuning for all evaluated methods. DNN outperformed all evaluated machine learning methods (R2 = 0.663, RMSE = 9.595, MAE = 5.565), followed by Quantile Random Forest (QRF) (R2 = 0.635, RMSE = 25.993, MAE = 22.081). The ability of DNN to accurately predict small TC values and thus produce relatively low absolute residuals was a major reason for the higher prediction accuracy compared to machine learning methods. Climate parameters were the main factors in the achieved prediction accuracy, with 23 of the 25 environmental covariates with the highest variable importance being climate or land surface temperature parameters. These results demonstrate the superiority of DNN over machine learning methods for TC prediction, while highlighting the need for more recent soil sampling to assess the impact of climate change on TC content in European agricultural land.

Identification and analysis of accident black spots using Geographic Information System (GIS): A study on Kushtia-Jhenaidah national highway (N704), Bangladesh

Road accidents, mostly on national highways, pose a significant public health and economic burden in Bangladesh, requiring in-depth analysis for road safety measures. This study comprehensively analyzes accident trends, characteristics, causes, and consequences by identifying the accident black spots on the Kushtia-Jhenaidah National Highway (N704). Accident records from 2017 to 2021 were collected from nearby police stations. Additionally, using a cluster random sampling approach, a questionnaire survey with 100 respondents (50% drivers and 50% general road users) was also conducted to capture diverse perceptions and behaviors. The study utilizes descriptive methods, such as trends analysis and frequency distributions, alongside spatial analysis techniques, including severity index, Kernel Density Estimation, and hotspot analysis. Findings indicate a decrease in accidents from 2018 to 2021, yet a concerning rise in fatalities in 2021. Trucks (47.9%) emerge as the primary contributor among 169 vehicles involved in accidents. Head-on collisions (36%) are prevalent, attributed to both human and environmental factors, including driver inexperience (56%), mobile phone use while driving (78%), lack of proper training (12%), overspeeding (28.3%), and nighttime driving (54%) influenced by seasons and land use. Mostly, victims aged from 20 to 40, where men are more affected by fatalities (70.7%) and women by injuries (86.3%). Out of 35 identified accident spots, including Battail, Bittipara Bazar, Laxmipur Bazar, Modhupur Bazar, IU Main Gate, Sheikhpara Bazar, and DM College Front, are designated as blackspot zones based on the frequency of accidents, deaths, and injuries. The study concludes by recommending targeted interventions, driver training, infrastructure improvements, regulatory measures, and victim assistance in collaboration with local and national agencies to enhance road safety and mitigate accident risks.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

Environmental risk assessment of PPP application in European soils and potential ecosystem service losses considering impacts on non-target organisms

The use of Plant Protection Products (PPPs) is leading to high exposure scenarios with potential risk to soil organisms, including non-target species. Assessment of the effects of PPPs on non-target organisms is one of the most important components of environmental risk assessment (ERA) since they play crucial functions in ecosystems, being main driving forces in different soil processes. As part of the framework, EFSA is proposing the use of the ecosystem services approach for setting specific protection goals. In fact, the services provided by soil organisms can be impacted by the misuse of PPPs in agroecosystems. The aim of this work was to assess PPPs potential risk upon ecosystem services along European soils, considering impacts on earthworms and collembola. Four well-known (2 insecticides-esfenvalerate and cyclaniliprole- and 2 fungicides - picoxystrobin and fenamidone-) worst case application (highest recommended application) were studied; exploring approaches for linked observed effects with impacts on ecosystem services, accounting for their mode of action (MoA), predicted exposure, time-course effects in Eisenia fetida and Folsomia sp. and landscape variability. The selected fungicides exerted more effects than insecticides on E. fetida, whereas few effects were reported for both pesticides regarding Folsomia sp. The most impacted ecosystem services after PPP application to crops appeared to be habitat provision, soil formation and retention, nutrient cycling, biodiversity, erosion regulation, soil remediation/waste treatment and pest and disease regulation. The main factors to be taken into account for a correct PPP use management in crops are discussed.

Cadmium accumulation in paddy soils affected by geological weathering and mining: Spatial distribution patterns, bioaccumulation prediction, and safe land usage

The abnormal enrichment of cadmium (Cd) in soil caused by rock weathering and mining activities is an issue in southern China. Although the soil Cd content in these regions is extremely high, the bioavailability of Cd in the soils differs significantly. The carbonate area (CBA) and tin-mining area (TIA) in Hezhou City were investigated to determine the primary features of soil Cd mobility in these regions and improve environmental management. Lateral and vertical spatial distributions revealed different accumulation and migration mechanisms of soil Cd in the CBA and TIA. Further analyses revealed that mining activities and geological weathering resulted in different soil geochemical parameters, thus yielding significantly lower levels of Cd in rice grains in the CBA than in the TIA. The random forest (RF) model predicted the bioaccumulation factor (BAF) (R2 = 0.69) better than the support vector machine (SVM) model (R2 = 0.68). Subsequently, a novel land management scheme was proposed based on soil Cd and the prediction of Cd in rice to optimize the spatial resources of agricultural land and ensure the safety of rice for consumption. This study provides a novel approach for land management in Cd-contaminated areas.

Identification of possible sources for potentially toxic elements and polycyclic aromatic hydrocarbons and their spatially varying relationships in urban soils of Dublin, Ireland

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn-Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies.

Relation disentanglement, the potential risk assessment, and source identification of heavy metals in the sediment of the Changzhao Reservoir, Zhejiang Province

Heavy metal contamination in the water body is a distinctly important issue for the water security of the reservoir. 114 sediment samples of Changzhao Reservoir were collected to investigate the spatial (horizontal and vertical) distribution characteristics, risk assessment, and source identification of heavy metals. The concentrations of heavy metals at the surface layer of sediment were slightly higher compared with that at the middle and bottom layer sediment in the most sampling sites. The concentration of Zn and Cd was significantly different in the different depths of sediment (P ≤ 0.01, Tukey HSD test). pH and Cd were identified as the key factors for TOC in the sediment by the Boruta algorithm. The proportion of "uncontaminated to moderately contaminated" for Cd, Zn, and As in the surface layer was 84.21%, 47.37%, and 34.21%, which indicated that the quality of sediment was mostly impacted by Cd, Zn, and As. The agricultural non-point source pollution is dominant according to the source identification method of APCS-MLR. Overall, this paper presents the distribution and conversion trends of heavy metals and provides the insights of the reservoir protection in the future work.

Identification of soil parent materials in naturally high background areas based on machine learning

Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

Identification of the spatial patterns and controlling factors of Se in soil and rice in Guangxi through hot spot analysis

Selenium (Se) is essential to human health, anti-cancer, possessing antioxidant, and antiviral properties. In this study, the spatial patterns of rice Se and their varying relationship with soil Se on a regional scale were studied using hot spot analysis for the agricultural soils in Guangxi. According to the hot and cold spot maps, rice Se correlates positively with soil Se in Guangxi agricultural soils. High rice Se accompanies high soil Se in the central part of Guangxi (e.g., Liuzhou, Laibin), and low rice Se is in line with low soil Se in the western part (e.g., Baise). However, the hot spot analysis maps indicate that southwestern Guangxi exhibits a special characteristic of low rice Se with high soil Se (e.g., Chongzuo). This special pattern is strongly associated with the high concentrations of Fe₂O₃ (ferromanganese nodules) in the carbonate rock area. The hot spot analysis proves useful in revealing the spatial patterns of rice Se in Guangxi and identifying the hidden patterns.

Degraded land rehabilitation through agroforestry in India: Achievements, current understanding, and future prospectives

Land degradation is one of the most important factors responsible for the alarming situation of food security, human health, and socioeconomic development in the country. Currently, 120.7 M ha of land in the country is affected by land degradation, out of which 85.7 M ha of land is affected by soil erosion caused by water and wind. Moreover, physical, chemical, and biological degradation are the major forms of land degradation in the country. Deforestation or tree cover loss (2.07 M ha) from 2001 to 2021, intensive rainfall (>7.5 mm ha−1), uncontrolled grazing (5.65 M ha), indiscriminate use of fertilizers (32 MT year−1), and shifting cultivation (7.6 M ha) are other major factors that further aggravate the process of land degradation. In order to alleviate the problem of land degradation, numerous agroforestry technologies have been developed after years of research in different agroclimatic zones of the country. The major agroforestry systems observed in the country are agri-horticulture, silvipasture, and agri-silviculture. This review indicates the potential of agroforestry in enhancing carbon sequestration (1.80 Mg C ha−1 year−1 in the Western Himalayan region to 3.50 Mg C ha−1 year−1 in the island regions) and reduced soil loss and runoff by 94% and 78%, respectively, in Northeast India. This can be concluded that the adoption of the agroforestry system is imperative for the rehabilitation of degraded lands and also found to have enough potential to address the issues of food, environmental, and livelihood security. This review’s findings will benefit researchers, land managers, and decision-makers in understanding the role of agroforestry in combating land degradation to enhance ecosystem service in India and planning suitable policies for eradicating the problem effectively.

Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning

Soil pH and carbonates (CaCO₃) are important indicators of soil chemistry and fertility, and the prediction of their spatial distribution is critical for the agronomic and environmental management. Digital soil mapping (DSM) techniques are widely accepted for the geospatial analysis of the soil properties. They are rapid and cost-efficient approaches that can provide quantitative prediction. However, the digital mapping of soil pH and CaCO₃ are not well studied, especially at a continental scale. In this research, we mapped the soil pH and CaCO₃ at the European scale using multisource environmental variables and machine learning approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) products, terrain attributes, and climatic variables were considered. Meanwhile, nine machine learning algorithms, namely, three linear and six nonlinear models, were used for the spatial prediction of soil pH and CaCO₃. The land use and cover area frame statistical survey (LUCAS) 2015 topsoil dataset provided by the European Soil Data Centre was utilised. The performances of different models were compared and analysed in terms of coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). Specifically, nonlinear machine learning models outperformed the linear ones, and extremely randomized trees (ERT) gave the most satisfactory result for soil pH (R² = 0.70, RMSE = 0.75, and RPD = 1.84) and CaCO₃ (R² = 0.53, RMSE = 93.49 g/kg, and RPD = 1.46). The results revealed that MODIS products and climatic variables were important in predicting soil pH and CaCO₃. Moreover, spatial distribution of soil pH and CaCO₃ in Europe were mapped at 250 m resolution, and the areas with high CaCO₃ content always showed high soil pH value.

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

An overview of plutonium isotopes in soils, China: Distribution, spatial patterns, and sources

Plutonium (Pu) is an anthropogenic radionuclide which has drawn significant attentions due to its radiotoxicity, and the sources of plutonium linked with nuclear accidents and contaminations. The ²⁴⁰Pu/²³⁹Pu atom ratio is source dependent and can be used as a fingerprint to determine the sources of radioactive contaminant. However, the distribution and sources of plutonium in soils of China have not yet been systematically studied at a national scale up to date. The distribution, spatial patterns, and sources of plutonium in soils of China were discussed in this work. The concentrations of ^239,240Pu are in the range of 0.002-4.824 mBq/g with a large variation, and the ^239,240Pu concentrations in surface soils increase with the increasing latitude, which affects by multi-factors such as organic matter and particle size, etc. The inventories of ^239,240Pu are in the range of 7.31-554 Bq/m². The weighted average of ²⁴⁰Pu/²³⁹Pu atom ratios (0.180 ± 0.004) in all surface samples is good agreement with the ratio of global fallout (0.180 ± 0.014) of the nuclear weapons tests, this indicate that the major source of plutonium in China is global fallout. However, among some sites, distinctly lower ²⁴⁰Pu/²³⁹Pu atom ratio compared to the global fallout values were observed in the northwest China, indicating a significant contribution from other source besides the global fallout. Furthermore, the spatial clustering patterns of hot spots (high values) and cold spots (low values) for plutonium showing the clear associations with nuclear tests, especially the Chinese Lop Nor nuclear weapons tests (CNTs) and the Semipalatinsk nuclear weapons tests (STS). Radioactive material including plutonium from the STS or CNTs was transported by the prevailing westerlies to the northwest China. This review about the fingerprints and distribution of plutonium in soils of China will help researchers to establish a reference database for future radiation risk assessment and environmental radioactive management.

Risk assessment and early warning of the presence of heavy metal pollution in strawberries

Heavy metal pollution is a major threat to agricultural produce and it can pose potential ecological risks which subsequently impacts on human health. Strawberries are an economically important produce of China. The intrinsic link of heavy metal pollution risk in the soil-strawberry ecosystem is of concern. In this study, the pollution index of heavy metal pollutants in farmlands of different provinces were evaluated, and the results showed significantly high levels of cadmium. In addition, Nemerow integrated pollution index analysis showed that low-pollution farmlands only accounted for 14.07% of the total arable land area. Then, the transfer factors were used to calculate the migration of heavy metals from the soil into strawberries. The results showed that cadmium and nickel were relatively high in strawberries from the Guangxi province. Similar results were found for mercury in Jiangxi Province. The pollution index of single food pollution also showed that mercury in strawberries from Jiangxi Province was at a moderate pollution level. The comprehensive pollution index indicated that heavy metal pollution in strawberries in Central China may be severe. In addition, spatial clustering analysis showed that cadmium, chromium, lead, arsenic and zinc in strawberries had significant hotspot clustering in central, south and southwest China. Finally, our studies also suggested that the risk of carcinogenic and non-carcinogenic diseases was higher in the (2, 4] years age group than in other age groups. People in Yunnan Province were also found to have a higher non-carcinogenic risk than those in other provinces and cities in China. This study provides a comprehensive view of the potential risks of heavy metal contamination in strawberries, which could provide assistance in the design of regulatory and risk management programs for chemical pollutants in strawberries, thus ensuring the safety of consumption of these edible fruits.

Predicting environmental concentrations and the potential risk of Plant Protection Products (PPP) on non-target soil organisms accounting for regional and landscape ecological variability in european soils

Plant Protection Products (PPP) raise concerns as their application may cause effects on some soil organisms considered non-target species which could be highly sensitive to some pesticides. The European Food and Safety Authority (EFSA), in collaboration with the Joint Research Centre (JRC) of the European Commission, has developed guidance and a software tool, Persistence in Soil Analytical Model (PERSAM), for conducting soil exposure assessments. EFSA PPR Panel has published recommendations for the risk assessment of non-target soil organisms. We have used PERSAM for calculating PPPs predicted environmental concentrations (PECs); and used the estimated PEC for assessing potential risks using Toxicity Exposure Ratios (TER) for selected soil organisms and good agricultural practices. Soil characteristics and environmental variables change along a latitudinal axis through the European continent, influencing the availability of PPP, their toxicity upon soil biota, and hence, impacting on the risk characterization. Although PERSAM includes as input geographical information, the information is aggregated and not further detailed in the model outputs. Therefore, there is a need to develop landscape based environmental risk assessment methods addressing regional variability. The objective was to integrate spatially explicit exposure (PECs) and effect data (biological endpoints i.e. LC₅₀, NOEC, etc.) to estimate the risk quotient (TER) of four PPP active substances (esfenvalerate, cyclaniliprole, picoxystrobin, fenamidone) on non-target species accounting European landscape and agricultural variability. The study was focused on the effects produced by the above-mentioned pesticides on two soil organisms: E. fetida earthworms and Folsomia sp. collembolans. After running PERSAM assuming a worst case application of PPPs, PECs in total soil and pore water were obtained for different depths in northern, central and southern European soils. With this data, soil variability and climatic differences among soils divided in three large Euroregions along a latitudinal transect (Northern, Central, Southern Europe) were analysed. Summarising, a trend to accumulate higher PECs and TERs in total soil was observed in the north decreasing towards the south. Higher PECs and TERs could be expected in pore water in southern soils, decreasing towards the north. The risk disparity between pollutant concentrations at different soils compartments should be taken into account for regulatory purposes, as well as the potential landscape variabilities among different Euroregions.

High spatial resolved cropland coverage and cultivation category determine neonicotinoid distribution in agricultural soil at the provincial scale

Croplands are experiencing increasing neonicotinoid pollution and ecological health problems, which are especially widely applied in China. However, the large regional scale distribution of neonicotinoids and the key factors have seldom been determined. We show that the total residual concentration of neonicotinoids ranged from 13.4 to 157 ng/g with an average level of 75.8 ng/g and imidacloprid which was the dominant compound ranged from 10.4 to 81.3 ng/g during 2017-2021 in the Yangtze River Delta, China. In comparison, the neonicotinoid residues detected here were mostly higher than those in other regions. We further show that the 1-km spatial resolution cropland coverage (78.0%) and crop type (18.1%) predominantly contributed to the large spatial variation of neonicotinoids after adjusting for the factors including temperature, soil pH, soil moisture, and precipitation via automatic linear regression modeling at the provincial scale. Additional analyses revealed that tea croplands had significantly lowest concentration and fruit fields had the highest level due to the different application methods. Our findings provide new insight into key factors quantifying the high spatial resolved distribution of neonicotinoids and urgently call for reasonable application methods against rapidly growing ecology threats from neonicotinoid pollution in China.

Effect of TOC Concentration of Humic Substances as an Electron Shuttle on Redox Functional Groups Stimulating Microbial Cr(VI) Reduction

Humic substances as an electron shuttle play an essential role in the biogeochemistry processes. However, the influence of total organic carbon (TOC) concentrations of humic substances on microbial Cr(VI) reduction remains unclear. In this study, the rates and extents of Cr(VI) reduction by Shewanella oneidensis MR-1 in the presence of Leonardite humic acids (LHA) and Pahokee peat humic acids (PPHA) with different TOC concentrations were evaluated. We found that the enhanced reduction in Cr(VI) was associated with TOC concentrations of 2.5-50 mg C/L of HA samples. The result shows that HA as an electron shuttle impacted both rates and extents of microbial Cr (VI) reduction, which delivered differently in terms of low TOC concentration range of 2.5 to 15 mg C/L and high concentration range of 15-50 mg C/L. The rates of Cr(VI) reduction significantly enhanced in the low TOC concentration range of HA compared to a high concentration range. The highest acceleration rate of Cr(VI) reduction was achieved at 15 mg C/L of HA. The quinone-like fluorophore was responsible for the main redox-active functional groups of HA by the three-dimensional excitation-emission spectroscopy. The fluorescence intensity of quinone-like fluorophore of HA in the low TOC concentration range was positively correlated with its acceleration coefficient, corresponding to the highest microbial Cr(VI) reduction rate obtained in 15 mg C/L of HA. These findings highlighted the effect of the TOC concentration of HA on microbial Cr(VI) reduction processes. It emphasized that the low TOC concentration of HA contributed to the high rates of Cr(VI) reduction, which is critical for better understanding the fate of Cr(VI) and evaluating the effectiveness of Cr(VI) restoration strategies in the future.

Effects of industrial agglomeration and environmental regulation on urban ecological efficiency: evidence from 269 cities in China

The ecological environment and economic development are double-edged swords. Nevertheless, we can still achieve green and coordinated development through environmental regulations and industrial agglomeration. Based on the panel data from 269 cities in China from 2008 to 2017, using the SBM-DEA model, the Malmquist-Luenberger (ML) index, and the spatial Durbin model (SDM) under different weight matrices, this paper explored the spatial pattern of ecological efficiency, the internal evolution mechanism, and the spillover effects of industrial agglomeration and environmental regulation on ecological efficiency. The results demonstrated that China's urban ecological efficiency had an obvious spatial pattern of "high in the east and low in the west." Due to the different life cycles of cities, the internal evolution mechanism of urban ecological efficiency had significant differences. Pure technological efficiency (PEFFCH), technological progress (TECH), and scale efficiency (SECH) have contributed the most to the ecological efficiency of the eastern, central, and western regions, respectively. Furthermore, a significant U-shaped relationship existed between industrial agglomeration and ecological efficiency. In particular, urban ecological efficiency will be improved when the industrial agglomeration level exceeds a certain scale. However, the spillover effects of industrial agglomeration were more sensitive to distance factors, leading to failure of the significance test under the economic distance and asymmetric economic distance matrix. The "innovation compensation effect" of environmental regulation was greater than the "compliance cost," which verified the applicability of the "Porter Hypothesis" in urban ecological efficiency to a certain extent. Finally, the geographical detector showed that each variable had a certain impact on the urban ecological efficiency, and the impact of the interaction term was greater than that of a single variable.

Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord G_i* statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.

Statement of the PPR Panel on a framework for conducting the environmental exposure and risk assessment for transition metals when used as active substances in plant protection products (PPP)

The European Commission asked the European Food Safety Authority (EFSA) to prepare a statement on a framework for the environmental risk assessment (ERA) of transition metals (e.g. iron and copper) used as active substances in plant protection products (PPPs). Non-degradability, essentiality and specific conditions affecting fate and behaviour as well as their toxicity are distinctive characteristics possibly not covered in current guidance for PPPs. The proposed risk assessment framework starts with a preliminary phase, in which monitoring data on transition metals in relevant environmental compartments are provided. They deliver the metal natural background and anthropogenic residue levels to be considered in the exposure calculations. A first assessment step is then performed assuming fully bioavailable residues. Should the first step fail, refined ERA can, in principle, consider bioavailability issues; however, non-equilibrium conditions need to be taken into account. Simple models that are fit for purpose should be employed in order to avoid unnecessary complexity. Exposure models and scenarios would need to be adapted to address environmental processes and parameters relevant to the fate and behaviour of transition metals in water, sediment and soils (e.g. speciation). All developments should follow current EFSA guidance documents. If refined approaches have been used in the risk assessment of PPPs containing metals, post-registration monitoring and controlled long-term studies should be conducted and assessed. Utilisation of the same transition metal in other PPPs or for other uses will lead to accumulation in environmental compartments acting as sinks. In general, it has to be considered that the prospective risk assessment of metal-containing PPPs can only cover a defined period as there are limitations in the long-term hazard assessment due to issues of non-degradability. It is therefore recommended to consider these aspects in any risk management decisions and to align the ERA with the goals of other overarching legislative frameworks.

Investigating spatially varying relationships between total organic carbon contents and pH values in European agricultural soil using geographically weighted regression

Total organic carbon (TOC) has received increased attention in recent years, not only as an important indicator in soil fertility, but also due to its close relationship with the atmosphere. Generally, soil TOC and pH values follow a negative correlation, which was revealed by traditional statistical methods. However, the conventional global models lack the ability to capture the spatial variation locally. In this study, spatially varying local relationships between TOC and pH values are studied by geographically weighted regression (GWR) on continental-scale data of European agricultural soil from the project 'Geochemical Mapping of Agricultural and Grazing land Soil' (GEMAS). In this study, TOC is the dependent and pH the independent variable. Both negative and positive local correlation coefficients are observed, showing the existence of 'special' spatially varying relationships between TOC and pH values. Original negative relationships change to positive values in more than 50% of the study area. Novel finding of significant positive correlations is observed in central-eastern Europe, while negative correlations are found mainly in northern Europe. Mixed relationships occur in southern Europe. These special patterns are strongly associated with specific natural factors, especially the extensive occurrence of quartz-rich soil in the central-eastern part of Europe. Anthropogenic inputs may have also played a role in the mixed southern European areas. The GWR technique is powerful and effective for revealing spatially varying relationships at the local level. Thus, it provides a new way to further explore the related influencing factors on the TOC and pH spatial distribution.

Distributional Characteristics and Source Identification of Cadmium in Soils of the Pearl River Delta, China

The results of the Multi-Purpose Geochemical Survey in the Pearl River Delta (PRD) show that the pollution is serious. In this study, the influence of geological genesis, soil-forming process, and human activities on soil quality in PRD is analyzed, and the influence factors, genesis and spatial distributional characteristics of cadmium (Cd) in different soil depths are studied by inverse distance weighted (IDW) and hot spot analysis. The results show that the spatial distribution of Cd is significantly different in PRD and high-value is mainly concentrated in the central cities of Guangzhou-Foshan-Jiangmen-Zhongshan-Zhuhai. Moreover, hot spots with higher Cd content in deep are mainly along Beijiang, Dongjiang, and Pearl River Estuary (PRE). Overall, our findings suggest that the high background value areas formed by marine-land and fluvial sediments as well as intensive human activities that make PRD become an area under the dual restriction of geological genesis and human activities, pollution control cannot be ignored.

Residual concentrations and ecological risks of neonicotinoid insecticides in the soils of tomato and cucumber greenhouses in Shouguang, Shandong Province, East China

Neonicotinoid insecticides (NNIs) are the most widely used insecticides in China and worldwide. Continuous use of NNIs can lead to their accumulation in soil, causing potential ecological risks due to their relatively long half-life. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the residual levels of nine neonicotinoids in greenhouse soils in Shouguang, East China, at different soil depths and with different crops (tomato and cucumber) after varying periods of cultivation. Seven neonicotinoids were detected in the soils of the tomato greenhouses and six were detected in the soils of the cucumber greenhouses, with total concentrations ranging from 0.731 to 11.383 μg kg^-1 and 0.363 to 19.224 μg kg^-1, respectively. In all samples, the neonicotinoid residues in the soils cultivated for 8-9 years were lower than in those cultivated for 2 years and 14-17 years. In the tomato greenhouse soils, the residual levels of NNIs were highest in the topsoil, with progressively lower concentrations found with depth. Under cucumber cultivation, the NNI residue levels were also highest in the topsoil but there was little difference between the middle and lower soil layers. Total organic carbon (TOC) decreased with soil depth while pH showed the opposite trend, showing a significant negative correlation in both types of soils (tomato soils ρ = -0.900, p = .001; cucumber soils ρ = -0.883, p = .002). Furthermore, TOC was significantly positively correlated, and pH was negatively correlated, with total NNI concentrations in both types of soils (TOC: tomato soils ρ = 0.800, p = .010; cucumber soils ρ = 0.881, p = .004; pH: tomato soils ρ = -0.850, p = .004; cucumber soils ρ = -0.643, p = .086). The results of an ecological risk analysis showed that acetamiprid represents a particularly high toxicity risk in these soils. Based on our analysis, NNI residues in the soils of tomato greenhouses and their associated ecological risks deserve more attention than those of cucumber greenhouse soils.

Contamination, ecological and health risks of trace elements in soil of landfill and geothermal sites in Tibet

Due to the utilization of landfill technology and geothermal energy production in Tibet, the contamination of the soils and underground water by trace element has currently become a serious problem, both ecologically and to the human health point of view. However, relevant studies concerning this critical problem, particularly in the Tibet area has not been found. Therefore, this study investigated the soil contamination and the spatial distribution of the trace elements in the areas surrounding the Tibetan landfill sites (LS) and geothermal sites (GS) through several pollution evaluation models. In addition, the possible sources of trace elements and their potential impact on public health were also investigated. Results showed that the trace elements in soils nearby LS and GS had moderate to high contamination risk. In soils surrounding LS, mercury had the highest concentration of 0.015 mg/kg and was 6 times higher than the background value of 0.008 mg/kg while in GS, arsenic had the highest concentration of 66.55 mg/kg, and exceeded the soil contamination risk value of 25 mg/kg. Maizhokunggar LS was the most polluted site with an average pollution load index value of 2.95 compared to Naqu, Nyingchi, Shigatse, and Lhasa. 42% of LS were with considerable ecological risk, and all GS had low ecological risk. Both carcinogenic and non-carcinogenic risk for children and adults (male, female) were within the acceptable range. According to the source analysis, unscientific anthropogenic activities including accumulated MSW, industrial discharges, and vehicle emissions significantly contributed 51.83% to soil trace element contamination. Considering that Tibet is an environment-ecologically vulnerable region with very weak self-adjustment ability, accumulated municipal solid waste in the landfill sites should be well disposed of, and even soil remediation should be well implemented.

Assessing Soil Acidification of Croplands in the Poyang Lake Basin of China from 2012 to 2018

Soil acidification, caused by intensified fertilizer application and acid deposition, has threatened the sustainability of agricultural ecosystems and soil quality in parts of China since the 1980s. However, little is known about the spatio-temporal change of soil pH in cropland at a large basin scale. Poyang Lake Basin of China was selected as the study area to identify the spatio-temporal change of cropland pH and detect potential soil acidification factors. A total of 507 and 503 topsoil samples were collected in 2012 and 2018, respectively, and methods including one-way analysis of variance (ANOVA), Pearson’s correlation analyses, and Inverse Distance Weighted (IDW) were applied. Results showed that soil pH ranged from 3.96 to 7.95 in 2012 and from 3.34 to 8.19 in 2018, with most samples being acidic (pH < 7) in both sets of data. The two soil datasets showed a significant decline (p < 0.05) of 0.1 pH units over the past six years and several soil samples that exhibited obvious uptrends in the groups of pH < 4.5 and 4.5–5.0 from 2012 to 2018. Overall, the distribution patterns of pH at the two sampling dates were similar, whereas local details of the pH spatial distribution patterns differed. While we found a significant correlation (p < 0.05) between soil pH and aspect, elevation and slope showed no significant correlation with pH. ANOVA showed that pH values in the water density (river or lake network density) range of 6.27–19.94 were significantly higher (p < 0.05) than the other water densities. Large amounts of precipitation with low pH values were found to significantly influence soil pH, whereas N-fertilizer inputs exerted limited effects on soil pH over the entire study area. These findings provided new insights on soil acidification assessment and potential factor detection at the basin scale.