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Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

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Coking industry is a potential source of heavy metals (HMs) pollution. However, its impacts to the groundwater of surrounding residential areas have not been well understood. This study investigated the pollution characteristics and health risks of HMs in groundwater nearby a typical coking plant. Nine HMs including Fe, Zn, Mo, As, Cu, Ni, Cr, Pb and Cd were analyzed. The average concentration of total HMs was higher in the nearby area (244.27 µg/L) than that of remote area away the coking plant (89.15 µg/L). The spatial distribution of pollution indices including heavy metal pollution index (HPI), Nemerow index (NI) and contamination degree (CD), all demonstrated higher values at the nearby residential areas, suggesting coking activity could significantly impact the HMs distribution characteristics. Four sources of HMs were identified by Positive Matrix Factorization (PMF) model, which indicated coal washing and coking emission were the dominant sources, accounted for 40.4%, and 31.0%, respectively. Oral ingestion was found to be the dominant exposure pathway with higher exposure dose to children than adults. Hazard quotient (HQ) values were below 1.0, suggesting negligible non-carcinogenic health risks, while potential carcinogenic risks were from Pb and Ni with cancer risk (CR) values > 10-6. Monte Carlo simulation matched well with the calculated results with HMs concentrations to be the most sensitive parameters. This study provides insights into understanding how the industrial coking activities can impact the HMs pollution characteristics in groundwater, thus facilitating the implement of HMs regulation in coking industries.

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China's groundwater is facing a significant threat from nitrate pollution. Here we analyzed 2348 regional surveys of groundwater nitrate levels in China from 1990 to 2020, examining distribution, trends, and drivers. This study uncovers a concerning rise in nitrate pollution, with estimated median nitrate levels climbing from 3.84 mg/L in 1990 to 6.94 mg/L in 2020. A stark contrast is observed between regions: the northern areas have a median nitrate concentration of 8.54 mg/L, significantly higher than the southern regions, where the median is just 7.15 mg/L. From 1990 to 2020, agricultural activity consistently emerges as the dominant driver of changes in groundwater nitrate concentrations, while groundwater exploitation, domestic pollution, and industrial production also contribute to varying degrees. This analysis highlights the urgency for region-specific policies and interventions to address the escalating nitrate pollution in China's groundwater.

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Plant hormones are essential and structurally diverse molecules that regulate various aspects of plant growth, development, and stress responses. However, the precise analysis of plant hormones in complex biological samples poses a challenge due to their low concentrations, dynamic levels, and intricate spatial distribution. Moreover, the complexity and interconnectedness of hormone signaling networks make it difficult to simultaneously trace multiple hormone distributions. In this review, we provide an overview of the currently recognized small-molecule plant hormones, signal peptide hormones, and plant growth regulators, along with the analytical methods employed for their analysis. We delve into the latest advancements in mass spectrometry imaging and in situ fluorescence techniques, which enable the examination of the spatial distribution of plant hormones. The advantages and disadvantages of these imaging techniques are further discussed. Finally, we propose potential avenues for future research in this field to further enhance our understanding of plant hormone biology.

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This study introduces an improved Ski Climate Index (SCI) designed to assess skiing suitability in China by applying fuzzy logic. Using daily meteorological data from 733 weather stations for the periods 1961-1990 and 1991-2020, the study identifies significant changes in SCI distribution over time. Additionally, a coupled analysis is performed, integrating the SCI results with the distribution and spatial vitality of 389 ski resorts in China. This analysis provides a comprehensive understanding of the interplay between actual ski resources and the ongoing evolution of the skiing industry in China and three significant results:1) The snow module has a major impact on SCI distribution, while other non-snow natural elements, such as sunshine duration, wind speed, and thermal comfort, influence the overall SCI assessment less; 2) High SCI values are concentrated in Northwestern and Northeastern China, with increased ski climate resources being observed in Shaanxi-Gansu-Ningxia, Southwest Tibet, and Sichuan due to climate change and noticeable declines in the Southern regions of Northeast China.; 3) In terms of the distribution and vitality of ski resorts, the SCI also partially reflects the development of ski resorts. This skiing suitability model uses climate resources to offer valuable insights for key decision-making in resort development and operation, thereby supporting advancement of the ice-snow economy.

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Novel brominated flame retardants (NBFRs) have emerged as an alternative to traditional brominated flame retardants (BFRs) and may pose risks to the environment and human health. However, the distribution pattern of NBFRs in urbanized zones and their association with multiple socioeconomic variables have not been adequately explored. Herein, seven NBFRs were investigated in surface soil samples from Tianjin, China, a typical urbanized area. The ∑7NBFRs ranged from n.d. to 101 ng/g, dry weight (dw) (mean: 12.6 ± 17.6 ng/g dw), which exhibited a relatively elevated level compared to NBFRs in soils from other regions worldwide. Decabromodiphenylethane (DBDPE) was the main contaminant, and its concentration ranged from 0.378 to 99 ng/g, dry weight (dw) (mean: 11.4 ± 17.0 ng/g dw), accounting for 81 % of the ∑7NBFRs. Notably, NBFRs exhibited peak concentrations within residential zones, significantly surpassing those recorded in the remaining four regions (green, farmland, water environment and other) (p < 0.05). Furthermore, the concentration of NBFRs in the soil of the Binhai New District within Tianjin was the highest, significantly exceeding that of other administrative areas, which was closely related to the intensive industrial activities in this region. The above results indicate that human activities are a key factor affecting the concentration of NBFRs in the soil. Moreover, a variety of statistical methods were employed to investigate the correlation between socioeconomic variables and the distribution of NBFRs. The concentration of NBFRs showed a significant correlation with population density and the gross domestic product (GDP) (p < 0.05), and the incorporation of administrative regional planning into structural equation models demonstrated an indirect influence on the spatial distribution of NBFRs concentration, mediated by its impact on population density. These results emphasize the association between NBFRs contamination and the degree of urbanization, thereby providing valuable insights for assessing the exposure risk of NBFRs among urban residents.

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Spatial variability of throughfall (i.e. the non-uniform characteristics of throughfall at different canopy positions) and its temporal persistence (i.e. time stability) are related to the quantity and efficiency of soil moisture replenishment, and affect plant competition and community succession dynamics by affecting resource availability. We carried out a meta-analysis with 554 papers (from 2000 to 2022) retrieved from Web of Science and China National Knowledge Infrastructure (CNKI) based on keyword search, quantified and compared the amount, spatial heterogeneity, and temporal stability characteristics of penetrating rain in different climate zones and plant functional types. Our results that throughfall proportion was lower in arid regions (72.0%±13.6%) than humid (75.1%±9.3%) and semi-humid areas (79.9%±10.4%). Cold climates had lower values (74.1%±14.6%) than temperate (74.2%±7.5%) and tropical climates (80.9%±14.6%). Shrubs (68.9%±14.9%) generally had lower throughfall proportion than trees (76.7%±9.1%). Broad-leaved trees (75.2%±11.1%) and conifers (75.1%±9.9%) showed similar throughfall proportions, as did evergreen (76.7%±10.0%) and deciduous species (74.7%±11.9%). Additionally, spatial variability (coefficient of variation) did not significantly differ across rainfall zones, temperature zones, or vegetation types. The spatial distribution of throughfall was relatively stable. Canopy structure was the dominant factor affecting temporal stability of throughfall. However, there was a lack of comparison between typical geographic units (i.e. spatial units with basically consistent geographical environmental conditions) at various temporal scales. Future research should expand upwards to the summary of global spatial scale rules and downwards to the analysis of process based temporal scale mechanisms, to depict the dynamic distribution of penetrating rain and unify observation standards to enhance comparability of different studies, in order to efficiently promote research on canopy penetrating rain and provide ecological and hydrological basis for protecting nature, managing artificial activities, and restoring degraded ecosystems.

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Land suitability (LS) classifications are essential for efficient and sustainable agricultural land use. With climate change, future LS classifications are necessary to ensure that crop growth remains sustainable and prevents land degradation. This study develops a current LS classification for rainfed corn (Zea mays) growth in the state of Georgia, USA, which is validated using historical census data on yield, acres planted, and corn crop lost. Significant (p < 0.05) differences were found between yield, acres planted, and crop loss percentage across LS classes for many years. Soil factors (Ph and soil texture) showed significant differences in fewer years compared to climate and topography factors, as soil factors can be altered by management practices such as liming and irrigation. Future LS classes determined by climate factors indicated a shift to the northwest of 150-300 km by the year 2100 based on the RCP4.5 or RCP8.5 emissions scenarios. The northwards shift in more suitable land due to rising maximum temperatures is expected to limit rainfed corn growth in Georgia in the future. As urban areas become more suitable for corn growth, farmers may need to plant crops earlier, irrigate, or switch to different crops. These results have important implications for agricultural planning and policy in the state of Georgia.

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In recent decades, China's rapid development has led to significant environmental pollution from the widespread use of chemical products. Per- and polyfluoroalkyl substances (PFAS) are among the most concerning pollutants due to their persistence and bioaccumulation. This article assesses PFAS exposure levels, distribution, and health risks in Chinese blood, environment, and food. Out of 4037 papers retrieved from November 2022 to December 31, 2023, 351 articles met the criteria. Findings show perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) as the main PFAS in both Chinese populations and the environment. The highest PFOA levels in Chinese populations were in Shandong (53.868 ng/mL), while Hubei had the highest PFOS levels (43.874 ng/mL). Similarly, water samples from Sichuan (2115.204 ng/L) and Jiangsu (368.134 ng/L) had the highest PFOA and PFOS levels, respectively. Although localized areas showed high PFAS concentrations. Additionally, developed areas had higher PFAS contamination. The researches conducted in areas such as Qinghai and Hainan remain limited, underscoring the imperative for further investigation. Temporal analysis indicates declining levels of some PFAS, but emerging alternatives require more research. Limited studies on PFAS concentrations in soil, atmosphere, and food emphasize the need for comprehensive research to mitigate human exposure.

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This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-temporal distribution of Chlorophyll-a (Chl-a) and Secchi Disk Depth (SDD) in the Gulf of Izmit using in-situ measurements and Sentinel-2 satellite imagery from October 2021 and 2022. Among the models tested, the Support Vector Regression (SVR) model showed better predictive performance, achieving the lowest RMSE for SDD (1.11-1.70 m) and Chl-a (1.16-4.97 mg/m3) and the lowest MAE for SDD (0.86-1.43 m) and Chl-a (1.03-3.17 mg/m3). Additionally, the study observed a shift from hypertrophic to eutrophic Chl-a conditions and from mesotrophic-eutrophic to oligotrophic SDD conditions between 2021 and 2022, aligning with SVR model predictions and in-situ observations. These findings underscore the potential of ML models to enhance the accuracy of water quality monitoring and management in marine ecosystems.

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The research, focusing on the analysis of nine trace elements, namely As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, completely analyzed their quantities in both water and sediment inside the Rabnabad Channel. Samples were collected during the post-monsoon and analyzed by ICP-OES following acid digestion. The mean concentrations of elements in water and sediments are as follows: Fe > Mn > Pb > Cu > Ni > Zn > Cr > As>Cd, and Zn > Fe > Pb > Mn > As>Cu > Cr > Ni > Cd. To understand the state of ecological and human health risk, several indices were incorporated. Health risk assessment revealed that children posed higher risk than adults. PERI, TRI, and Igeo indices for water sediment indicate a significant ecological risk. Moreover, Mn and Pb exhibit elevated HPI values and contribute substantially to contamination factors. Correlation and PCA implicate both anthropogenic and geogenic sources, such as agricultural practices, coal-based power plants, and the Payra seaport, in the elevated concentrations of Cd, Cr, Mn, and Fe in both water and sediment samples.

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With the spread of an aging society, the demand for aged care institutions among older adults is increasing. The inadequate supply and distribution of aged care institutions have led to an increasing concern about spatial equity in aged care institutions. Most studies have utilized accessibility to assess spatial equity from the supply perspective, while the demand perspective has received little attention. In addition, few studies have evaluated the spatial equity of aged care institutions at grid resolution. Therefore, this study takes Shanghai as an example to analyze aged care institutions from both the supply and demand perspectives. By proposing an improved potential model, at a network resolution of 500 × 500, the spatial equity of aged care institutions is more refined. The results show that aged care institutions and the older population in Shanghai are predominantly concentrated in the downtown area and surrounding regions. However, the results obtained from the Lorenz curve and Gini coefficient indicate the allocation of pension beds based on population size is proportional across different districts of Shanghai. When considering the quality indicators of aged care institutions and introducing the improved potential energy model to calculate spatial accessibility, an imbalance between regions in Shanghai still exists and needs further optimization.

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Alarm calls produced by basal prey have a well-known informative value. In multi-predator communities, mesopredators, when faced with top predators, may emit alarm calls that could inform basal prey about their lowered predation risk. To test this unexplored possibility, we conducted one field and one mesocosm experiment in which we simulated alarm and non-alarm calls from little owls (Athene noctua) as mesopredators and measured their effects on grasshoppers as prey of little owls but not of top predators. In the field experiment, we found that grasshopper species were significantly more abundant in patches where we simulated either the presence of scared little owls (alarm treatment) or no owls (control treatment) compared to patches where the presence of non-scared little owls (non-alarm treatment) was simulated. In the mesocosm experiment, locusts (Locusta migratoria) moved significantly more to exposed areas when we simulated the presence of scared little owls (alarm treatment) or of a granivorous bird (control treatment), while they moved to sheltered areas when we simulated the presence of non-scared owls (non-alarm treatment). These results show that prey could cue on predators' calls to assess their predation risk and make decisions, revealing unprecedented potential ecological consequences of alarm calls in invertebrate communities.

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Schistosomiasis remains a formidable challenge to global public health. This study aims to predict the spatial distribution of schistosomiasis seropositive rates in Hunan Province, pinpointing high-risk transmission areas and advocating for tailored control measures in low-endemic regions. Six machine learning models and their corresponding hybrid machine learning-Kriging models were employed to predict the seropositive rate. The optimal model was selected through internal and external validations to simulate the spatial distribution of seropositive rates. Our results showed that the hybrid machine learning-Kriging model demonstrated superior predictive performance compared to basic machine learning model and the Cubist-Kriging model emerged as the most optimal model for this study. The predictive map revealed elevated seropositive rates around Dongting Lake and its waterways with significant clustering, notably in the central and northern regions of Yiyang City and the northeastern areas of Changde City. The model identified gross domestic product, annual average wind speed and the nearest distance from the river as the top three predictors of seropositive rates, with annual average daytime surface temperature contributing the least. In conclusion, our research has revealed that integrating the Kriging method significantly enhances the predictive performance of machine learning models. We developed a Cubist-Kriging model with high predictive performance to forecast the spatial distribution of schistosomiasis seropositive rates. These findings provide valuable guidance for the precise prevention and control of schistosomiasis.

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Dominant species occupy a pivotal role in plant community, influencing the structure and function of the ecosystem. The spatial distributions of dominant species can react to the effect of different grazing intensities, thereby reflecting their tolerance and adaptive strategies toward grazing. In this study, geostatistical methods were mainly used to study the spatial distribution characteristics of Stipa krylovii Roshev. and Leymus chinensis (Trin.) Tzvel. species at two interval scales (quadrat size 5 m × 5 m, 10 m × 10 m) and two treatments (free grazing, FG, 1.66 sheep·ha- 1·a- 1; control, CK, 0 sheep·ha- 1·a- 1) in typical steppe of Inner Mongolia. A systematic sampling method was used in each 100 m × 100 m representative sample plots to obtain the height, coverage, and density of all species in the community. The results showed that grazing altered the concentrated distribution of S. krylovii and the spatial mosaic distribution pattern of S. krylovii and L. chinensis while having no effect on the spatial clumped distribution of L. chinensis. It also found that the spatial distributions of dominant species are primarily affected by structural factors, and random factors such as long-term grazing led to a transition of S. krylovii from a concentrated distribution to a small patchy random pattern should not be overlooked. Our findings suggest that long-term grazing alters the spatial distribution pattern of dominant species and that adaptive strategies may be the key for maintaining the dominant role of structural factors.

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BACKGROUND: Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identification methods to recognize the damage, studies recommending fast and accurate identification of rice leafroller damage are rare. In this study, we employed an ultra-lightweight unmanned aerial vehicle (UAV) to eliminate the influence of the downwash flow field and obtain very high-resolution images of the damaged areas of the rice leafroller. We used deep learning technology and the segmentation model, Attention U-Net, to recognize the damaged area by the rice leafroller. Further, a method is presented to count the damaged patches from the segmented area. RESULTS: The result shows that Attention U-Net achieves high performance, with an F1 score of 0.908. Further analysis indicates that the deep learning model performs better than the traditional image classification method, Random Forest (RF). The traditional method of RF causes a lot of false alarms around the edge of leaves, and is sensitive to the changes in brightness. Validation based on the ground survey indicates that the UAV and deep learning-based method achieve a reasonable accuracy in identifying damage patches, with a coefficient of determination of 0.879. The spatial distribution of the damage is uneven, and the UAV-based image collecting method provides a dense and accurate method to recognize the damaged area. CONCLUSION: Overall, this study presents a vision to monitor the damage caused by the rice leafroller with ultra-light UAV efficiently. It would also contribute to effectively controlling and managing the hazardous rice leafroller. © 2024 Society of Chemical Industry.

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Soil in karst areas commonly exhibits characteristics of heavy metal enrichment. Accurate identification of soil heavy metal distribution, risks, and sources are crucial for preventing soil heavy metal pollution in karst areas. In this study, 2467 topsoil samples (0-20 cm) and 620 subsoil samples (150-200 cm) were collected using a grid-based sampling method in Tianyang County. Statistics, geo-statistics, correlation analysis, principal component analysis, and the absolute principal component-multiple linear regression model were utilized to analyze the content, spatial distribution and sources of heavy metals. The geo-accumulation index and the potential ecological risk index were employed to assess the ecological risks of heavy metals in the topsoil, with the subsoil content as baseline. The results showed that the study area's soil exhibited high heavy metal content, significantly exceeding Chinese background values. The content of heavy metals in the karst area's soil was notably higher than that in the non-karst area. The fitted semi-variogram models and the spatial distribution map revealed that the heavy metals' content was generally dominated by the geological background. As, Cr, Cu, Hg, Ni, Pb, and Zn displayed low levels of pollution in the topsoil and posed low ecological risk, with over 90 % of samples classified as unpolluted and low risk. Cd exhibited high levels of pollution and ecological risks, with 52.28 % of samples classified as polluted and 60.81 % classified as moderate to high risk. For Hg, despite only 6.94 % of samples showing polluted, the ecological risks were not negligible, with 40.65 % of samples in moderate to high risk. Natural source and anthropogenic source contribute to the heavy metals on average by 81.49 % and 18.51 %, respectively. This study provides a reference for the risk assessment of soil heavy metals, and its findings offer valuable scientific insights for the prevention of heavy metal pollution in the study area.

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Evolutionary theory predicts that the species of an evolutionarily successful taxon would not overlap in spatial distribution. To test the prediction, we document our research on the spatial associations of mustelids, an evolutionarily successful group of order Carnivore, using infrared camera trap data on species distribution collected from the national nature reserves (NNRs) of Liancheng, Wolong, Tangjiahe and Heizhugou in China in 2017-2021. Data showed seven mustelid species occurring in the study area, including Arctonyx collaris, Meles leucurus, Martes foina, Martes flavigula, Mustela altaica, Mustela nivalis and Mustela sibirica. Following Ricklef's definition of biological community, we identified five networks of species associations. The mustelids occurred in the networks. Species from the same genus, such as M. foina and M. flavigula, stayed in different networks to avoid competition owing to similar feeding habits or habitat preferences. Species with different feeding habits or habitat preferences either occurred in different networks, such as M. altaica and M. flavigula, or coexisted in the same networks but avoided direct spatial associations, such as M. foina and A. collaris. Asymmetrical associations were found between different genera, such as M. foina and M. altaica, or between different subfamilies, such as M. flavigula and A. collaris. These associations may be attributed to interspecific killing or seed dispersal. However, these associations accounted for only a small proportion and would not impact the species diversity of Mustelidae. It is thus concluded that the prediction is supported by our research findings and that spatial avoidance may be the biogeographic strategy of maintaining the species diversity of the family. We also found that the well protection of the mustelids may benefit the overall biodiversity conservation in Heizhugou, an NNR that has experienced severe deforestation.

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In this study, we measured 15 common organophosphate flame retardants (OPFRs) in six categories of tea samples across China. OPFRs were found in all the tea samples, with the total concentrations of OPFRs (∑OPFRs) at 3.44-432 ng/g [geometric mean (GM): 17.6 ng/g]. Triphenyl phosphate (TPhP) was the dominant OPFR, accounting for 39.0-76.2% of ∑OPFRs across all tea categories. The potential factors influencing the residual OPFRs in tea were thoroughly examined, including the agricultural environment, fermentation, and packaging of teas. Tea packaging materials (TPMs) were then identified as the primary sources of OPFRs in teas. The migration test revealed that OPFRs with lower molecular weights and log Kow values exhibited a higher propensity for facilitating the migration of OPFRs from TPMs to teas. The estimated daily intakes of OPFRs from teas were relatively higher for the general populations in Mauritania, Gambia, Togo, Morocco, and Senegal (3.18-9.79 ng/kg bw/day) than China (3.12 ng/kg bw/day). The health risks arising from OPFRs in Chinese teas were minor. This study established a baseline concentration and demonstrated the contamination sources of OPFRs in Chinese tea for the first time, with an emphasis on enhancing the hygiene standards for TPMs.

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There have been growing concerns regarding the health and environmental impacts of trace organic pollutants (TOPs). However, fresh leachate from municipal solid waste (MSW) has been overlooked as a potential reservoir of TOPs. Therefore, we investigated 90 legacy and emerging TOPs in fresh leachate from 14 provinces and municipalities in China. Additionally, the fate and final discharge impacts of TOPs in 14 leachate treatment systems were analyzed. The results revealed that the detection rate of 90 TOPs was over 50 % in all samples. Notably, polychlorinated biphenyls, banned for 40 years, were frequently detected in fresh leachate. The concentration of pseudo-persistent TOPs (105-107 ng/L) is significantly higher than that of persistent TOPs (102-104 ng/L). Spatial distribution patterns of TOPs in fresh leachate suggest that economy, population, climate, and policies impact TOPs discharge from MSW. For example, economically developed and densely populated areas displayed higher TOPs concentrations, whereas warmer climates facilitate TOPs leaching from MSW. We confirmed that waste classification policies were a key driver of the decline in multiple TOPs in leachate. Mass balance analysis shows that the final effluent and sludge from current dominant leachate treatment systems contain refractory TOPs, especially perfluoroalkyl acids, which must be prioritized for control. This paper was the first comprehensive investigation of multiple TOPs in fresh leachate at a large geographic scale. The factors affecting the occurrence, spatial distribution, and fate of TOPs in fresh leachate were revealed. It provides a valuable reference for the establishment of policies for the management of TOPs in MSW and the associated leachate.

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