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• This study proposed an effective scheme of PM 2.5 and O 3 control strategy. • This multi-technique-integrated scheme markedly improves air quality. • PM 2.5 and O 3 control calls for spatial-temporally differentiated strategies. • Emphasis should be put on areas with sensitive pollutant-precursor responses. Previous air pollution control strategies didn't pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM 2.5 and O 3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, "2 + 26" cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM 2.5 -dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O 3 -dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM 2.5 and MDA8 O 3 concentrations in 28 cities by 3.2%–8.2% and 3.9%–9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%–18.0% for PM 2.5 and 3.3%–14.2% for MDA8 O 3 , respectively). This study proposed an effective scheme for the collaborative control of PM 2.5 and O 3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems. [Display omitted] [ABSTRACT FROM AUTHOR]

Fluoride enrichment (> 1.5 mg/L) in groundwater has become a global threat, particularly given the hazards to human health. This study collected 58 unconfined groundwater samples from Fengpei Plain in June 2022 for hydrochemical and stable isotope analyses combined with multiple methods to explore sources, influencing factors, and potential health hazards of groundwater F−. The results showed that groundwater F− concentration ranged from 0.08 to 8.14 mg/L, with an average of 1.91 mg/L; over 41.4% of them exceeded the acceptable level of 1.5 mg/L prescribed by the World Health Organization (WHO). The dominant hydrochemical facies changed from Ca·Mg-HCO3 and Ca·Mg-SO4·Cl type in low-F– groundwater to Na-HCO3 and Na-SO4·Cl water types in high-F– groundwater. The Self-Organizing Map (SOM) and ionic correlation analysis indicated that F− is positively correlated to pH, EC, Na+, K+, SO42−, and TDS, but negatively to Ca2+ and δ18O. Groundwater F− accumulation was primarily driven by F−-bearing minerals dissolution such as fluorite. Simultaneously, the carbonates precipitation, positive cation exchange processes, and salt effect were conducive to groundwater F− enrichment. However, competitive adsorption between OH−/HCO3− and F−, evaporation, and anthropogenic activities only had a weak effect on the F− enrichment in groundwater. The hazard quotient (HQ) assessment results show that 67.2% of groundwater samples pose a non-carcinogenic risk (HQ > 1) for infants, followed by 53.4% for children, 32.8% for females, and 25.9% for males. The Monte Carlo simulation results agreed with those of the deterministic model that minors are more susceptible than adults. These findings are vital to providing insights into the geochemical behavior, driving factors, and drinking water safety of high-F− groundwater worldwide. [ABSTRACT FROM AUTHOR]

The study of tradeoffs/synergies among ecosystem services (ESs) is highly significant for land-use planning and regional ecosystem optimization. Land-use change and topographic factors have important implications for ESs. Strengthening the comparative analysis of the capacity of ESs provided by different land-use types in specific regions, studying the topographic gradient effects of ecosystem service trade-offs/synergies with slope changes, and identifying the dominate trade-off/synergy relationship among ESs will help us to carry out ecosystem regulation according to local conditions through land-use layout optimization at a fine scale. Our research site was located in the Dabie Mountains of western Anhui Province, China (DBM), where, based on the InVEST software, R language, self-organizing maps (SOM), and GeoDA, the temporal and spatial variations of five typical ESs, including food supply, soil retention, water yield, carbon storage, and biodiversity maintenance from 2005 to 2020, were analyzed, and spatial distributions of the different ESs clusters were also recognized by using the SOM method. Moreover, the impacts of land-use type and slope on ESs, and the characteristics of trade-offs/synergies among the five ESs, were discussed. Results showed, firstly, that the total values of ESs showed a changing trend of "three increases and two decreases" from 2005 to 2020. Among the ESs, food supply, soil retention and water yield showed upward trends, with annual growth rates of 2.83%, 6.50% and 2.98%, respectively, whereas carbon storage and biodiversity maintenance showed downward trends, with annual decline rates of 0.03% and 0.07%, respectively. Second, the results showed that the Moran's I index of the total ESs was 0.3995 in 2005 and 0.4305 in 2020, respectively, indicating that they had significant spatial clustering characteristics. The Low-Low clustering regions with reduced changes were mainly in the central and northern parts of the study area, whereas the High-High clustering regions with increased changes were found distributed mainly in the south of the study area. Thirdly, it was found that cropland and woodland were the main contributors to the total amounts of ESs, but the supply capacity of ESs per unit area of woodland was the largest, constituting nearly 1/3 of the total supply capacity of the ESs. Last, the slope effect on trade-offs and synergies was significant between typical ecosystem service pairs in the study area; trade-offs were the main relationships between the pairs of ESs in the study area, which accounted for nearly 60% of all types of trade-offs/synergies during the 15 years. In addition, the spatial distributions of the trade-offs/synergies between ESs pairs were visualized clearly, and the six ES bundles were identified by using the SOM method at the township administrative scale. The identification of ecosystem service bundles is of great significance for the division of ecological functional zones and ecological regulation in the DBM. [ABSTRACT FROM AUTHOR]

Purpose: Stable soil pH is the key property to maintain soil biogeochemical balance, structure, and function stability of the terrestrial ecosystem. Understanding the soil pH change and its influencing factors is critical for maintaining soil quality and the sustainability of agricultural resources. Materials and methods: In our study, 215 soil samples were collected using a paired comparison method from a typical hickory plantation region between 2008 and 2018 in southeastern China. The geostatistics and kriging interpolation method were used to evaluate the change in pH (CIP). The structural equation model (SEM) was combined to quantify the contribution of driving factors to CIP. Results and discussion: The results suggested that the pH value of the soil had a significant neutralization effect which was manifested by increased pH value of the relatively acidic soil but decreased pH value of the relatively alkaline soil. The soil pH threshold (pHτ, the point at which the soil pH changes from positive to negative) was 5.96. The spatial distribution trend of pH between 2008 and 2018 was similar, while the local variation was different. Soil initial pH (IpH) was the most critical negative driving factor influencing CIP, while the mean annual temperature (MAT) and the mean annual precipitation (MAP) had positive effects on CIP. In addition, soil organic matter content (SOM) and bulk density were also important factors affecting CIP. Conclusions: Our results indicated that long-term management could increase, decrease, or remain the soil pH value, with CIP mainly dependent on IpH, climatic factors, soil organic matter content, and bulk density. Lime, sod cultivation, and organic fertilizers are recommended to increase soil pH. [ABSTRACT FROM AUTHOR]

Streamflow forecasting is of great significance for water resources planning and management. In recent years, numerous data-driven models have been widely used for streamflow forecasting. However, the traditional single data-driven model ignores the utilization of different streamflow regimes. This study proposed an integrated framework for daily streamflow forecasting based on the regime recognition of flow sequences. The framework integrates self-organizing maps (SOM) for identifying streamflow sub-sequences, the random forests (RF) algorithm to select input variables for different streamflow sub-sequences, and a deep belief network (DBN) for establishing complex relationships between the selected input variables and streamflows for different sub-sequences. Specifically, the integrated framework was applied to forecast daily streamflow at the Xiantao hydrological station in the Hanjiang River Basin, China. The results show that the developed integrated framework has higher streamflow prediction accuracy than the single data-driven model (i.e., the DBN model in this study), with Nash efficiency coefficient (NSE) of 0.91/0.81 and coefficient of determination (R2) of 0.93/0.89 for the integrated framework/DBN model during the validation period, respectively. Additionally, the prediction accuracy of the peak flood was also improved. The relative error of the peak flood derived from the integrated framework was reduced by 4.6%, compared with the single DBN model. Overall, the constructed integration framework, considering the complex characteristic of different flow regimes, could improve the accuracy for daily streamflow forecasting. [ABSTRACT FROM AUTHOR]

Karst ecosystem services (ES) are a key component of terrestrial ES in the world. Karst rocky desertification is one of the biggest ecological and environmental challenges in China. The Karst Rocky Desertification Project (RDPs) help to promote land use and land cover and management practices, which aimed to alter the supply of ecosystem services and affect the well-being of both humanity and nature to alleviate ecological degradation and ameliorate regional poverty in southwest China. The spatial aggregation and distribution of ecosystem services is a wise evaluation of ecosystem management and landscape planning. The ecological efficiency of KRD restoration control measures on a regional scale can be explored by ecosystem services bundles. Our study takes the Huajiang-Guangling karst plateau-valley experimental site—a core area implementing ecological restoration projects in Guizhou province, China—as a case study. Nine ESs (e.g. soil conservation, habitat quality, carbon sequestration, etc.) were quantified using different models of InVEST. A self-organizing map (SOM) approach is used to define and map the ESB. The results showed that (1) the overall trend of rocky desertification has improved during the study period, especially in the mild and potential rocky desertification areas. (2) The ESs has improved, particularly agricultural provisioning services and soil conservation, habitat quality, and carbon sequestration. (3) There is a stable synergy relationship between provisioning services and most regulatory services, while the trade effects between ESs are weakened, indicating a subtle functional interdependence between ESs. (4) The changes of bundling patterns showed that the change of ES bundle can promote the transfers of several ESs supply and regulation services in a different project promoting periods. This study provides a new perspective for inspecting the interactions between multiple ESs at the small regional ecological scale. And these results provide an important scientific basis for ecological restoration and reconstruction in karst areas, which are of great significance for improving policy responses and ecosystem management. [ABSTRACT FROM AUTHOR]

This study aimed to reveal the main environmental factors affecting PH, SOM, SOC, TN, and AHN in mountainous areas of Beijing, using the Chaoguan Xigou watershed as the research object. The relationship among pH, SOM, SOC, TN, AHN, topographic factors, and vegetation factors was analyzed by correlation and redundancy analysis (RDA). The results showed that altitude was significantly positively correlated with vegetation types in the study area (p < 0.01). Menhinnick richness index (D), Shannon–Wiener diversity index (H), and Alatalo evenness index (E) ranged from 0.35–0.79, 0.86–1.73, and 0.39–0.7, respectively, indicating moderate variations. E was significantly positively correlated with stand type (p < 0.05), altitude (p < 0.05), and H (p < 0.01). The variation ranges of PH, SOM, SOC, TN, and AHN in soil were 5.78–7.13, 54.73–90.38 g/kg, 23.77–60.25 g/kg, 1.71–4.22 g/kg, and 95.64–223.26 mg/kg, respectively. All soil nutrient indexes had medium variation except for pH (weak variation). In this study, RDA results showed that altitude is the main environmental factor affecting the soil pH, SOM, SOC, TN, and AHN in this area and could explain 25.9% of the total variance. However, the effects of factors associated with altitude on pH, SOM, SOC, TN, and AHN need to be further studied. [ABSTRACT FROM AUTHOR]

Background: Our study aimed to compare the reference distributions of serum creatinine and urea obtained by direct sampling technique and two indirect sampling techniques including the Gaussian Mixture Model (GMM) and the Self-Organizing Map (SOM) clustering based on clinical laboratory records, so that the feasibility as well as the potential limitations of indirect sampling techniques could be clarified.Methods: The direct sampling technique was used in the Pediatric Reference Interval in China (PRINCE) study, in which 15,150 healthy volunteers aged 0 to 19 years were recruited from 11 provinces across China from January 2017 to December 2018. The indirect sampling techniques were used in the Laboratory Information System (LIS) database of Beijing Children's Hospital, in which 164,710 outpatients were included for partitioning of potential healthy individuals by GMM or SOM from January to December 2016. The reference distributions of creatinine and urea that were established by the PRINCE study and the LIS database were compared.Results: The density curves of creatinine and urea based on the PRINCE data and the GMM and SOM partitioned LIS data showed a large overlap. However, deviations were found in reference intervals among the three populations.Conclusions: Both GMM and SOM can identify potential healthy individuals from the LIS data. The performance of GMM is consistent and stable. However, GMM relies on Gaussian fitting, and thus is not suitable for skewed data. SOM is applicable for high-dimensional data, and is adaptable to data distribution. But it is susceptible to sample size and outlier detection strategy. [ABSTRACT FROM AUTHOR]

Heavy metal(loid)s (HMs) in soils of Southwest China naturally have a high background and are superimposed with intensive anthropogenic activities, resulting in serious accumulation of HMs in regional agricultural soils, which poses threat to ecological security and agricultural product quality. The accumulation and source apportionment of HMs in agricultural soils are not only one of the focuses of global concern but also a challenging task worldwide. In the present study, a total of 395 topsoil samples (0-20 cm) and 19 deep soil samples (150-180 cm) were collected from typical artisanal zinc smelting regions in northwestern Guizhou Province, China to investigate the spatial distribution, accumulation and source apportionment of HMs (As, Cr, Cu, Hg, Ni, Pb, Cd and Zn) based on the combined approach of enrichment factor (EF), self-organizing map (SOM), geographical information system (GIS), and positive matrix factorization (PMF). The mean contents of As, Cr, Cu, Hg, Ni, Pb, Cd and Zn in agricultural soils all exceeded the corresponding local background values, and obvious enrichment of Cd was found in the studied topsoil, followed by Pb, Cu, and Zn. Combined with SOM and PMF analysis, five potential HMs sources were identified: Cd (74.58%), As (32.05%), Zn (30.57%), Hg (23.16%), and Pb (11.38%) were associated with artisanal Zn smelting activities; Cu (66.08%), Zn (18.12%), Ni (17.25%), Cd (16.95%), and Pb (10.08%) were mainly from agricultural activities; Pb (68.9%), Zn (25.73%), and As (22.64%) were mainly from traffic emissions and smelting-related activity sources; Hg (68.55%), As (45.31%), Cr (38.63%), and Ni (32.44%) were primarily from atmospheric deposition associated with industrial activities and coal combustion; and Cr (61.01%), Ni (44.52%), Zn (17.32%), Cu (16.11%), and Cd (8.39%) mainly originated from soil parent materials. The proportions of the five potential sources were 14.5%, 20.9%, 19.8%, 17.8%, and 27%, respectively. Anthropogenic sources accounted for the largest contribution (approximately 73%) to the soil HMs, especially the artisanal zinc smelting activities to Cd, Zn, As, Hg, and Pb, which was significantly higher than that of geological sources, suggesting that intense human activities produced a large amount of HMs enrichment in topsoil. The SOM clustering and source apportionment of soil HMs are of great significance for reducing HMs pollution sources, and can provide scientific guidance for pollution prevention and regional remediation of HMs in agricultural soils. [ABSTRACT FROM AUTHOR]

[Display omitted] Heavy metal(loid)s (HMs) in soils of Southwest China naturally have a high background and are superimposed with intensive anthropogenic activities, resulting in serious accumulation of HMs in regional agricultural soils, which poses threat to ecological security and agricultural product quality. The accumulation and source apportionment of HMs in agricultural soils are not only one of the focuses of global concern but also a challenging task worldwide. In the present study, a total of 395 topsoil samples (0–20 cm) and 19 deep soil samples (150–180 cm) were collected from typical artisanal zinc smelting regions in northwestern Guizhou Province, China to investigate the spatial distribution, accumulation and source apportionment of HMs (As, Cr, Cu, Hg, Ni, Pb, Cd and Zn) based on the combined approach of enrichment factor (EF), self-organizing map (SOM), geographical information system (GIS), and positive matrix factorization (PMF). The mean contents of As, Cr, Cu, Hg, Ni, Pb, Cd and Zn in agricultural soils all exceeded the corresponding local background values, and obvious enrichment of Cd was found in the studied topsoil, followed by Pb, Cu, and Zn. Combined with SOM and PMF analysis, five potential HMs sources were identified: Cd (74.58%), As (32.05%), Zn (30.57%), Hg (23.16%), and Pb (11.38%) were associated with artisanal Zn smelting activities; Cu (66.08%), Zn (18.12%), Ni (17.25%), Cd (16.95%), and Pb (10.08%) were mainly from agricultural activities; Pb (68.9%), Zn (25.73%), and As (22.64%) were mainly from traffic emissions and smelting-related activity sources; Hg (68.55%), As (45.31%), Cr (38.63%), and Ni (32.44%) were primarily from atmospheric deposition associated with industrial activities and coal combustion; and Cr (61.01%), Ni (44.52%), Zn (17.32%), Cu (16.11%), and Cd (8.39%) mainly originated from soil parent materials. The proportions of the five potential sources were 14.5%, 20.9%, 19.8%, 17.8%, and 27%, respectively. Anthropogenic sources accounted for the largest contribution (approximately 73%) to the soil HMs, especially the artisanal zinc smelting activities to Cd, Zn, As, Hg, and Pb, which was significantly higher than that of geological sources, suggesting that intense human activities produced a large amount of HMs enrichment in topsoil. The SOM clustering and source apportionment of soil HMs are of great significance for reducing HMs pollution sources, and can provide scientific guidance for pollution prevention and regional remediation of HMs in agricultural soils. [ABSTRACT FROM AUTHOR]

As one of the most important drivers of global climate change, land use change (LUC) has markedly altered the regional and global carbon (C) cycles. However, the geographic variations and the key drivers in the effects of LUC on temperature sensitivity (Q 10) of soil microbial respiration (R s) are still not fully elucidated, hence impeding the spatially explicit predictions of soil C cycling under climate change. Here, we used a paired-plot approach with data for 19 locations distributed from the tropical to temperate zones in eastern China, and compared the temperature responses of R s between forest and cropland soil. Results showed that the latitudinal patterns of Q 10 in forest soils were better explained by climatic variables; whereas in cropland, soil Q 10 trended higher with increasing latitude, with climatic factors, pH, clay, and soil organic C (SOC) jointly modulating the spatial variations in Q 10. Overall, the values of Q 10 tended to converge with latitude between forests and croplands, with change in Q 10 from forest to cropland, Δ Q 10 , significantly decreasing from the tropical region (9.23 ± 3.58 %) to the subtropical (0.58 ± 1.93 %) and temperate (−0.97 ± 1.11 %) regions. Moreover, the spatial variations of Δ Q 10 were significantly affected by climatic factors, ΔpH, Δmicrobial biomass C (ΔMBC), and their interactions. Our findings highlight the potential impacts of LUC-related biogeographic variations in the temperature response of R s , and emphasize the importance of incorporating the land-use effects on the temperature sensitivity of soil microbial respiration into terrestrial C cycle models to improve predictions of carbon-climate feedbacks in the future. • Differences in soil Q 10 were compared between forest and cropland for 19 paired-plots. • Land use change (LUC) effects on Q 10 vary spatially from tropical to temperature zones. • Change in Q 10 from forest to cropland, Δ Q 10 , generally decreased with latitude. • Geographical variations of Δ Q 10 were mainly driven by climate, ΔpH, and ΔMBC. • Carbon-climate models should incorporate the geographical effects of LUC on Q 10. [ABSTRACT FROM AUTHOR]

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Soil heavy metal pollution threatens ecological health and food security. It is significant to classify pollution risk management and control zones, which can effectively cope with soil pollution and scientifically carry out soil remediation projects. In this study, based on 665 soil samples collecting from Ningbo (southeast China), single pollution index and Nemerow composite pollution index (NCPI) were measured to assess soil pollution risk, and self-organization mapping model was applied to classify management and control zones. Results showed that the heavy metal pollution in the northwest part was more serious, while the east part was less polluted. Although more than 75% soil samples had negligible risks, the Hg and Cu pollution was greatly influential and notable as their polluted samples accounted for 24.21% and 12.48% respectively. Moreover, about 55.34% soil samples and more than half study region had pollution grades, and NCPI values were obviously high with the center of northwest study area. Results also showed that the study region could be classified into four zones with good spatial variabilities. Specifically, Monitored Zone with High-risk Pollution had the highest NCPI caused by human activities, while Controlled Zone with Severe Pollution had relatively high NCPI caused by industrial and agricultural production. Protected Zone with Ecological Conservation and Restricted Zone with Potential Pollution had low NCPIs attributing to historical or natural factors. Our study implies that the classified zones can provide fundamental and momentous information for establishing appropriate priorities of heavy metal risk management and control. [ABSTRACT FROM AUTHOR]

The rapid mining activities of mica mines in Giridih district, India, have led to toxic metal pollution of agricultural soil. This is a key concern for environmental risk and human health. 63 top soil samples were collected at a distance of 10 m (Zone 1), 50 m (Zone 2), and 100 m (Zone 3) from near 21 mica mines with agriculture fields. The mean concentration of total and bio-available toxic elements (TEs - Cr, Ni, Pb, Cu, Zn, and Cd) was higher in zone 1 across three zones. The Positive matrix factorization model (PMF) and Pearson Correlation analysis were used to identify waste mica soils with TEs. Based on PMF results, Ni, Cr, Cd, and Pb were the most promising pollutants and carried higher environmental risks than the other TEs. Using the self-organizing map (SOM), zone 1 was identified as a high-potential source of TEs. Soil quality indexes for TEs risk zone 1 were found to be higher across three zones. Based on the health risk index (HI), children are more adversely affected than adults. Monte Carlo simulations (MCS) model and sensitivity analysis of total carcinogenic risk (TCR), children were more affected by Cr and Ni than adults through ingestion exposure pathways. Finally, a geostatistical tool was developed to predict the spatial distribution patterns of TEs contributed by mica mines. In a probabilistic assessment of all populations, non-carcinogenic risks appeared to be negligible. The fact that there is a TCR can't be ignored, and children are more likely to develop it than adults. Mica mines with TEs contamination were found to be the most significant anthropogenic contributor to health risks based on source-oriented risk assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Long-term continuous cropping affects the biochemical quality of soil organic matter (SOM), but whether the effects are relevant with their thermal stability is less clear. In northeast China, long-term continuous cropping occurred frequently owing to higher yield and economic interest requirement. To verify the thermal stability properties of SOM affected by the long-term continuous cropping, the study focused on 23-yr continuous cultivated maize and soybean plots, where the effect of cropping is likely to be detected. Bulk soils sampled in 1991 and 2014 were studied by thermogravimetry and differential scanning calorimetry (DSC). The results showed typical bimodal peaks in DSC curve in bulk Mollisols. A labile fraction peak was observed at 354-366 °C low-temperature zone and recalcitrant fraction one at high temperature of 430-438 °C. Energy density (J mg OM) was greater in soybean plots compared to maize plots; in contrast, long-term continuous maize cultivation also increased energy density, in reverse in soybean plots after 23-yr cultivation. The DSC-T, temperature at which half of energy release occurred, typically showed larger responses to long-term cultivation than crop species. Results obtained support the hypothesis of a potential link between long-term continuous cropping and the thermal stability of SOM, and a correlation with crop species. [ABSTRACT FROM AUTHOR]

This study explores the influence of socio-economic inequalities on the occupational safety situation in counties in Central China. Self-organizing feature maps (SOFMs) are applied to cluster the occupational safety situation in 346 counties in Central China. The results show that some socio-economic factors, such as the secondary industry added value, scalable industrial output value, infrastructure investment, and gross domestic product, and social factors, such as medical beds, fiscal expenditure, deposits from residents, and employed persons, had significant effects on the occupational safety situation in counties. An S-shaped relationship was also found between the increase of socio-economic development level and the death toll among clusters. Socio-economic inequalities could influence occupational safety on the county level and the significant growth in social level can improve occupational safety situation. [ABSTRACT FROM AUTHOR]

Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geo-accumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate soils with different erosion degrees. Two methods were compared: direct identification by inherent spectral characteristics and indirect identification by predictions of critical soil properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter (SOM), iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between −0.3 to −0.5, which were worse than the other two groups. However, the maximum value of R was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state (Fep), Aluminium amorphous state (Alo) and the modelling results for soil organic matter (SOM). The study proved that it is feasible to identify different degrees of soil erosion by hyper-spectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion. [ABSTRACT FROM AUTHOR]

Abstract: This study, undertaken from the perspective of statistical dynamics, proposes the treatment of financial centres as an ecosystem, creates a multidimensional financial centre niche (FC-niche) under given generalised entropy and constraints, and interprets the evolutionary process of an FC-niche with dynamic equations obtained from the maximum generalised entropy principle (MGEP). To solve these dynamic equations, a self-organised feature map (SOM) is designed. Finally, the values and evolutionary rules of FC-niches in China’s 29 major cities are simulated as a case study. [Copyright &y& Elsevier]

Heavy metal pollution control zoning aiming at the health maintenance of watershed soil ecosystem has become an important means of soil environmental protection and governance. Based on the self-organizing map (SOM), this study classifies the data sets of eight heavy metals such as Co, Cd, Zn, Cr, Cu, Pb, Ni, and Tl in 354 samples, calculates the potential ecological risk value of soil heavy metals in combination with the potential Hakansom Risk index (HRI), and uses the geographic information system (GIS) for visualization. In the research results, SOM has divided five soil ecological risk categories. The highest average ecological risk value of 85.95 is found in cluster IV, which is clustered and distributed in urban development areas in the upper reaches of the river. The average ecological risk values of cluster I and cluster V are relatively close at 79.64 and 79.19, respectively. Cluster I and cluster V are distributed in the north of the river in a linear and cluster manner, respectively, and are located on a concave bank with a relatively gentle slope. The average ecological risk of soil pollution in cluster II is 77.59, which is linearly distributed on both banks of the river. The ecological risk of soil pollution in cluster III is the lowest (74.39), mainly scattered in the south of rivers with less human activities. The study further identified the environmental factors that affect the soil ecological risk value in different cluster units and put forward the classified and differentiated management and control strategies for different cluster units. The research shows that SOM can cluster the data sets of heavy metals with high sensitivity and low threshold through competitive learning to effectively provide the distribution information of abnormal soil ecological risk areas. This information is helpful for urban environmental management departments and planning departments to take targeted management and recovery measures to avoid the health risks related to soil heavy metal pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Hyperspectral reflectance is widely used for determining important properties of soil erosion. However, there have been few studies which focus on the influence of soil erosion intensity on the characteristics of hyperspectral reflectance, and such information would provide a new tool to improve quantitative understanding of soil erosion. In this study, 35 soil samples were collected from three regions with different erosion intensities in Changting County, a typical severely eroded county in the ferralic cambisol region of southern China, and classified into three groups according to different erosion controlling status. All the samples were scanned at wavelengths from 400 to 2,498 nm by an ASD Field Spec Portable Spectrometer, and the erosion intensity of each sample was calculated using the Revised Universal Soil Loss Equation. Multivariate stepwise linear regression was then employed to model the soil erosion intensity based on reflectance. The results suggested that the absorption peaks of each sample were in a similar wavelength range, while the absorption depth varied with different erosion status, and the reflectance of extremely eroded soil samples were the highest. During modelling of erosion intensity, the result was poor when all the samples were combined, but improved greatly at certain wavelength ranges when samples were classified into three groups based on different erosion controlling status. The extreme erosion group markedly outperformed the other two groups, in which the R values between the actual and predicted erosion intensity were 0.67, 0.85 and 0.80 for each spectral type. The results indicated that hyperspectral reflectance is a promising method for accurately monitoring erosion intensity. [ABSTRACT FROM AUTHOR]

The effect of soil organic matter (SOM) content and composition on sorption behavior of toluene for fluvo-aquic, red and black soils in China was investigated in batch experiments. Tested SOM was fractionated into two primary sorptive domains ('soft' and 'hard') to explain the dependence of sorption behavior on SOM composition. All the tested soils exhibited similar sorption kinetics and nonlinear sorption isotherms. Soils with high SOM content possessed a high sorptive capacity. Clay minerals also contributed to the sorptive capacity, especially for fluvo-aquic and red soils with low SOM content. In comparison, after removing most 'soft' SOM fraction from soils through hydrogen peroxide treatment, the residual 'hard' SOM fraction of three soil samples exhibited a slower sorption rate and a less sorption capacity compared to the untreated soil samples. The nonlinear degree of sorption was positively correlated with the content of 'hard' SOM for all soil samples. This is explained by the combined effect of SOM content and the composition on toluene sorption rates, sorption capacity and nonlinear degree of sorption of three typical soils in China. [ABSTRACT FROM AUTHOR]

Abstract: The research proposes a hybrid knowledge-sharing model, which integrates the concepts of the self-organizing feature map optimization, fuzzy logic control, and hyper-rectangular composite neural networks, to provide 32 rules that suggest performing or not performing foreign construction investment. The database is derived from 520 quarterly financial reports of all listed construction companies in Taiwan that have now or in the past five years made foreign investment in China’s construction industry. The input variables are set to all 25 financial ratios assessable in public, reducing to 11 ratios after feature deduction using t-test. The model yields a high successful classification rate of 90.6% and generates 14 and 18 rules for Taiwan construction companies performing or not performing foreign investment in China, respectively. The valuable rules give user a closer look at what is the appropriate corporate financial status, what knowledge can be shared from the interpretations of the rules, and the impact by investment on corporate finance. [Copyright &y& Elsevier]

Abstract: Accidents to pipelines because of the third-party interference have been recorded and they often result in catastrophic consequences for environment and society with a great deal of economic loss. The third-party interference resulting from complicated origins occurs randomly, and is hard to be forecasted or controlled in advance, so it becomes a serious threat to the safe operation of long transmission pipeline. This paper focuses on the application of self-organizing maps (SOMs) to assess the risk of third-party interference and classify their risk patterns. In this work, fault tree is used first to establish the risk assessment index system, and then SOM is used in multi-parameter risk pattern classification approach, which is proposed to present various risk maps, incorporating the factors of pipeline laying conditions, historical damage records, safety-related actions, management measures and the environment around the underling pipeline. A field case study of Shaanxi–Beijing gas pipeline in China is undertaken so that the effectiveness of the proposed approach could be verified. By taking the classification results into consideration, the decision maker may well get precious and differentiated information about the pipeline risk distribution of third-party interference and make appropriate safety-related actions to prevent the damage. [Copyright &y& Elsevier]

Nationwide O 3 has been deteriorating and shows an obvious spatial aggregation effect (SAE), while there are still many cities not meeting Chinese national standards for PM 2.5 , demonstrating the urgency for the collaborative control of PM 2.5 and O 3. This study adopted multiple mathematic models and data mining technologies, including Moran's I (MI), the self-organizing map (SOM), the distributed lag nonlinear model (DLNM), multivariate meta-analysis, and univariable multivariate meta-regression, and aimed to explore the spatio-temporal trends and influencing mechanisms of PM 2.5 and O 3 in different regions. Results revealed that PM 2.5 and O 3 showed nonlinear and lagged associations with meteorology and precursors and relatively large spatial heterogeneity existed in the influencing mechanisms. The eight clusters, divided with SOM based on air pollution, can explain a substantial part of spatial heterogeneity in influencing mechanisms, which means influencing mechanisms are more consistent in regions with similar pollution characteristics. PM 2.5 and O 3 heavily polluted regions showed strong SAE according to Moran's I index (LMI), and showed sensitive responses to meteorology and precursors according to meta-analysis of DLNM. Results also suggested that simultaneously mitigating PM 2.5 and O 3 showed a promising long-term prospect, and that NOx reduction should be strengthened in PM 2.5 dominated months and lightened in O 3 dominated months at current O 3 -NOx-VOC regime. This study, with multi-technology fusion, provides systematic understanding of PM 2.5 and O 3 pollution in China and scientifically backed support for the next-stage collaborative control. • Spatial distribution of O 3 showed a rising SAE and evident seasonal differences. • PM 2.5 and O 3 showed nonlinear and lagged associations with multiple factors. • Similarly polluted regions showed similar influencing mechanisms. • Heavily polluted regions showed sensitive response in influencing mechanisms. [ABSTRACT FROM AUTHOR]

In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency. [ABSTRACT FROM AUTHOR]

Different land-use managements accompany different land-use objectives in the changing phases of household economic development. These management changes lead to a number of changes in soil nutrients such as in soil organic matter (SOM). This article, with support from global positioning system (GPS) and geographic information system (GIS) monitoring, analyses and compares SOM change in an area of urban fringe in north China from 1982 to 2000 and then from 2000 to 2006. SOM increased by 27% (5.43 g kg-1-6.90 g kg-1) during the first period but decreased by 7% during the second. In the first period, with the implementation of the "Household Responsibility System", material inputs, especially of fertiliser was very high when the central household objective was to "have a full stomach". Yields also increased quickly, so there were corresponding increases in the amount of organic materials returned to the fields and SOM also increased during this earlier period. In the second period, household objectives developed making a trade-off between consumption and income above subsistence level. Land-use types evolved from subsistence field cropping to the growing of cash crops. Changing material inputs and managements between the different cropping systems affected soil nutrients such as SOM. SOM decreased in most of the region during this second period. Dynamic monitoring (GPS and GIS) is needed in the region to conduct scientific research on fertiliser use and management. [ABSTRACT FROM AUTHOR]

The article presents abstracts of several studies on environmental geochemistry. Some of the abstracts presented include "Biogeochemical Cycling of Nutrients in Karstic Catchments, Southwestern China: Linkages to Changes of Eco-Environments," by Congqiang Liu and "Emerging Organic Contamination in China," by Guibin Jiang and colleagues.

Beijing-Tianjin-Hebei and the surrounding area (BTHSA) shows the poorest air quality in China, reflected in sub-standard PM 2.5 and increasingly pronounced O 3 pollution, stressing the urgency for regional cooperation and collaborative control of PM 2.5 and O 3 . With the aim to explore the cooperative regions and response mechanisms of PM 2.5 and O 3 in BTHSA, this study applied multiple mathematical models and analytical indicators to multiple data sources, including applying self-organizing map (SOM), response surface model (RSM), random forest (RF), distributed lag nonlinear models (DLNMs), and meta-analysis, on ground observations of air quality and meteorology, ozone monitoring instrument (OMI) observations, and air pollutant emission inventory. The results revealed that BTHSA exhibited clear regional characteristics of air pollution and can be divided into four clusters for enhanced intercity cooperation. Over 2015-2020, anthropogenic factors played more important roles than meteorological ones on the alleviation of PM 2.5 and the deterioration of O 3 . RSM based on observations and RF based on emissions both suggested that, in the near future, strengthened abatement of SO 2 , PM 2.5 and VOC can be beneficial for controlling PM 2.5 and O 3 pollution, while intensive NOx reduction in PM 2.5 -dominant months and mitigatory NOx reduction in O 3 -dominant months should be formulated before certifying an obvious transition of O 3 -NOx-VOC sensitivity. This study, with multi-model and multi-data fusion, can be expected to provide synthesized fact- and science-based guidance for the next-stage collaborative control of PM 2.5 and O 3 in BTHSA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Phthalic acid esters (PAEs) are predominant hazardous substances and endocrine-disrupting compounds to be controlled in soil. The degradation behaviors of PAEs in soil had been long term concerned. Thus, the degradation rate (K) is important for assessing theexposure risk and is of great significance in evaluating the ecological risk of PAEs in soil environment. But by far, quantitative structure activity relationship (QSAR) models for PAEs degradation have rarely been considered in soil environment. In this study, quantum chemical parameters were considered along with soil properties as two kinds of descriptors in QSAR model. A total of 32 logk of PAEs were collected from reference and experiment. Degradation kinetics in soils were determined by pseudo-first order kinetic models. The residual concentration of PAEs in Udic ferrosols and Aquic cambisols suggesting a potential expose risks of PAEs to ecosystem in soil. The QSAR model between logk and quantum chemical parameters revealed that E HOMO and qC- are two predominant factors in determining logk value. Furthermore,our study further indicated that soil organic matter (SOM) as new predictor contributes more to predict logk values of PAEs during degradation process than pH. Results from this study make a new contribution for methods to predict the degradation of PAEs in soil environment and highlight the potential to evaluate the environmental risks of degradation of PAEs., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Rapid urbanization and industrial growth have triggered heavy metal contamination in agricultural soil in Dhaka, which is a serious concern for ecological risk and public health issues. In this study, fifty-four soil samples from agricultural lands of Dhaka had been analyzed for assessing accumulation, spatial enrichment, ecological risk and sources apportionment of heavy metals using a combined approach of self-organizing map (SOM), positive matrix factorization (PMF), geographical information system (GIS), and enrichment factor (EF). The results of the enrichment factor, geoaccumulation index and contamination factor index showed that more than 90% of the soil samples were polluted by higher levels of Cr and Cd. The mean pollution load index (PLI) results demonstrated that about 73% of soil samples were moderately polluted by heavy metals. Based on SOM and PMF analysis, four potential sources of heavy metals were found in this study area: (i) agrochemical and sewage irrigation (Cd-As); (ii) combined effect of agriculture, industrial and natural sources (Mn, Co, Ni and Zn); (iii) atmospheric deposition and industrial emission (As-Pb); (iv) chemical and leather tanning industries (Cr). The ecological risk index demonstrated that in terms of Cd content, about 75% of soil samples were moderate to high risk, and 20% were moderate to considerable ecological risk, which was the serious environmental, ecological, and public health concern. The spatial projection of ecological risk values showed that the southern part of Dhaka (Keraniganj Upazila) is a high ecological risk in terms of heavy metal pollution. These risk maps in agricultural soils may play a vital role in reducing pollution sources; so that zonal pollution control, as well as ecological protection, may be achieved in this resource-based agricultural land., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The Qinghai-Tibetan Plateau (QTP), the source and upper reaches of many Asian rivers, are crisscrossed by rivers and dotted with lakes. Schizothoracinae fishes, species native to the QTP, are distributed widely through these rivers and lakes. Over the past decades, ecological protection has become increasingly intense. The rapid acquisition of the genetic information and accurate gene sequence database are assumed to play an important role in the conservation of species diversity and biodiversity. In this study, 153 COI sequences (648bp in length) covering 13 species in 8 genera of Schizothoracinae fishes in Qinghai Province were used to determine whether barcode could identify Schizothoracinae species accurately. The average Kimura two parameter (K2P) genetic distances within and among species were 0.35% and 8.83%, respectively. The maximum K2P distance within species was observed in Gymnocypris eckloni (1.36%) while minimum K2P distance among species was observed between Chuanchia labiosa and Schizopygopsis pylzovi (0.23%). Overlaps existed in K2P distance intra- and inter- species based on both the genes. Eleven groups with 9 single-species groups and 2 multi-species groups were identified through Automatic Barcode Gap Discovery System, which were consistent with the overlaps of K2P distance. 96.7% as the accurate ratio for COI barcode was calculated and high solution was observed in the phylogenetic trees based on COI gene and Cyt b gene. Except for the similar results based on two genes above, COI barcode was more economical than Cyt b gene. The SOM model successfully predicted characteristic-diagnostic sites at species level: 36 characteristic-diagnostic sites from eight species, in which 12 from Gmnodiptychus pachycgeilus , 2 from Platypharodon extremus , 7 from Ptychobarbus kaznakovi , 2 from Schizopygopsis anteroventris , 2 from Schizopygopsis malacanthus , 3 from Schizopygopsis malacanthus chengi , 3 from Schizothorax dolichonema and 5 from Schizothorax lantsangensis . Our results show that Schizothoracinae fishes can be identified validly by using COI DNA barcode. Thirty-six characteristic-diagnostic sites were proposed to be applied into works of species identification for the Schizothoracinae fishes in Qinghai Province.

The Lancang-Mekong River is a trans-boundary river which provides a livelihood for over 60 million people in Southeast Asia. Its environmental security is vital to both local and regional inhabitants. Efforts have been undertaken to identify controlling factors of the distribution of trace metals in sediments and soils of the Manwan Reservoir catchment in the Lancang-Mekong River basin. The physicochemical attributes of 63 spatially distributed soil and sediment samples, along with land-use, flooding, topographic, and location characteristics, were analyzed using the Self-Organizing Map (SOM) methodology. The SOM permits the analysis of complex multivariate datasets and gives a visual interpretation that is generally not easy to obtain using traditional statistical methods. Across the catchment, enrichments of trace metals are rare overall, despite the severely enriched cadmium (Cd). The analysis of SOM showed that flooded levels and land-use types were associated with high concentrations of Cd. Sediments and inundated soils covered with shrub and open woodlands in downstream always have a high concentration of Cd. The results demonstrate that SOM is a useful tool that can aid in the interpretation of complex datasets and help identify the environment of enriched metals on a catchment scale.