Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed

Spatiotemporal evolution and interaction of water constraints and their socio-ecological drivers in the Taihu Lake Basin

To effectively manage water constraints (WCs) within a basin, it is crucial to first scientifically delineate their spatial distribution and thoroughly understand the interactions between WCs. Investigating the complex driving mechanisms at multiple scales is also essential. In this study, a basin WC evaluation framework is constructed using a conflict risk assessment model. The spatiotemporal variations of four types of WCs across three spatial scales in the Taihu Lake Basin (TLB) are thoroughly investigated. Furthermore, the study quantifies the trade-offs, synergy effects, and bundle patterns of these water constraints. The study employs the Optimal Parameters-based Geographic Detector (OPGD) and multivariate linear regression to identify the key socio-ecological drivers of WCs. Our findings indicate that between 2010 and 2020, water resource constraint (WREC), water environment constraint (WENC), water safety constraint (WSAC), water ecology constraint (WECC), and the comprehensive WC (CWC) displayed varying degrees of heterogeneity. Particularly, the mean values of WSAC, WECC, and CWC witnessed an increase over the decade. Additionally, all WCs exhibited a strong positive spatial autocorrelation. Synergistic interactions among WCs were predominantly observed in pairs such as WREC-WSAC, WREC-WECC, and WSAC-WECC, while a weaker trade-off effect was noted in the WENC-WECC pair. At multiple scales, we identified eight types of WC bundles capable of undergoing mutual transformations, especially at the basin scale. The primary drivers of WCs varied across different stages and scales, with most factors collectively exerting a more significant impact than individually. Notably, factors like secondary and tertiary industry GDP (X2), population density (X3), precipitation (X6), and elevation (X7) were identified as core drivers influencing the evolution of WCs in the TLB. Integrating these spatiotemporal characteristics and driving mechanisms of WC interactions into basin planning and management can significantly support the alleviation of multidimensional water constraints in territorial spaces.

Spatio-temporal assessment of landscape ecological risk using spatial statistical analysis in a basin of Turkiye

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas.

Research on ecological risk assessment and risk level prediction in the central urban area of Chongqing, China

Complex geological conditions, coupled with urban expansion, resource consumption, and rapid economic development, make the ecological environment of Chongqing's central urban area more vulnerable. To enhance the carrying capacity of resources and the environment in this region, it is significant to scientifically assess the trend of ecological risk changes in Chongqing. The article developed an ecological risk assessment index system for Chongqing, utilizing the "pressure-state-response" framework. The entropy weight method (EWM) is employed to assign weights to each variable, subsequently establishing a grey weighted clustering evaluation model (GWCEM). We evaluated the ecological risks of nine central urban areas in Chongqing from 2005 to 2021 and projected the ecological risk levels and changes from 2022 to 2025. Our research indicates that the comprehensive ranking of influencing factors of ecological risk in Chongqing follows this order: response factor > pressure factor > state factor. Throughout the study period, we observed a decrease in the ecological risk values of Ba'nan, Shapingba, Jiulongpo, Nan'an and Yubei Districts by more than 50%. These decline rates are accelerating and regional differences in ecological risk levels are diminishing. From 2022 to 2025, except Shapingba, Jiangbei, Yuzhong, and Nan'an District which consistently maintained a "low-risk" level, the ecological risk levels of all other areas continue to decrease, aligning with a "low-risk" classification by 2025. Based on the results of ecological risk assessment and ecological risk level prediction, corresponding recommendations are proposed for ecological environment protection and ecological risk management in the central urban area of Chongqing.

Spatial-temporal variation and correlation analysis of ecosystem service values and ecological risks in winter city Shenyang, China

Ecosystems in winter cities are complex and fragile, experiencing significant changes due to climate variations and human construction activities. Previous studies on the assessment of overall ecosystem service value (ESV) and ecological risk index (ERI) in winter cities are scarce. In this study, we constructed ESV and ERI measurement models using land use data in 2000, 2010, and 2020 using the improved value per unit area factor method and the landscape pattern index method, respectively, to reveal their spatial and temporal change characteristics. Geographic detectors were used to explore the driving roles of natural and artificial factors on the changes of ESV and ERI. The combination in ESV and ERI can then provide a more quantitative and accurate basis for policy decisions, identify priority areas for urban ecological restoration, and reduce the risk to ecosystems. The results of the study show that the total ESV of Shenyang city decreased from 273.97 × 108 CNY to 270.38 × 108 CNY during 2000-2020. Although the decrease is not large, the ESV changes structurally with the advancement of urbanization. During the 20 years, the construction land with the lowest ecological service function continues to expand, increasing by 354 km2, the grassland decreased by 215.9 km2, and the arable land decreased by 196.6 km2. The ecological service function of the water area is the strongest, with an increase of 51.3 km2 in the water area, ensuring that there is no significant decline in ESV. The size of the ERI is Very high, High, and Medium value zones remained relatively stable, while the size of the Very Low-value zone decreased by 12.78% and the size of the Low-value zone increased by 13.21%. The interaction factors that contributed most to the changes in ESV and ERI were annual evapotranspiration (EVP)/ Normalized Difference Vegetation Index (NDVI) and Annual sunshine hours (SSD)/ Digital Elevation Model (DEM) , respectively. There was a spatial correlation between ESV and ERI. The areas with the highest ESV supply capacity and at the same time facing severe ecological risks to the landscape pattern are distributed in the northeastern hilly lands. This area should be prioritized to develop planning and control measures to prevent further erosion of forest lands and grasslands and reduce ecological risks. These results provide a theoretical basis for ensuring ecological security and sustainable development in winter cities.

Construction of ecological security pattern of arid area based on landscape ecological risk assessment: a case study of the Wu-Chang-Shi urban agglomeration

The construction of ecological security pattern (ESP) holds paramount importance in ensuring regional environment sustainability. This study introduces an innovative approach to ESP construction grounded in landscape ecological risk (LER) assessment, with Wu-Chang-Shi urban agglomeration in Xinjiang, China, serving as a case study. Initially, LER within the area was evaluated using the LER Index (LERI) method. Subsequently, the Geodetector model was employed to discern the relationship between multi-source data and LER. Furthermore, ecological resistance and corridors were delineated utilizing the minimum cumulative resistance (MCR) model. Lastly, the corridors were optimized using the gravity model, finalizing the ESP construction. Study results reveal that LER was always fluctuating from 1990 to 2010, and tended to stabilize from 2010 to 2020. Factor detection underscores the predominant influence of land use on LER, followed by elevation and vegetation distribution. The ESP shows the imperative for improving connectivity of the natural areas that are fragmented by urban land, highlighting the great significance of the woodland-originating corridors. Finally, strategies are proposed to enhance woodland and water coverage, boost landscape diversity in nature reserves, and prioritize ecological conservation in corridor regions. In summation, the study furnishes a framework for analyzing arid regions in Eurasia. Furthermore, the research idea of evaluation-analysis-remodeling also offers insights into environmental management in developing areas with more diverse climate types.

A novel quantity assessment of landscape ecological risk using human-nature driving mechanism for sustainable society

The rapid advancement of global economic integration and urbanization has severely damaged the stability of the ecological environment and hindered the ecological carbon sink capacity. In this study, we evaluated the spatiotemporal evolution pattern of landscape ecological risk (LER) in the Loess Plateau from 2010 to 2020. This was examined under the driving mechanism of human and natural dual factors. We combined the random forest algorithm with the Markov chain to jointly simulate and predict the development trend of LER in 2030. From 2010 to 2020, LER on the Loess Plateau showed a distribution pattern with higher values in the southeast and lower values in the northwest. Under the interaction of human and natural factors, annual precipitation exerted the strongest constraint on LER. The driving of land use and natural factors significantly influenced the spatial differentiation of the LER, with a q-value >0.30. In all three projected scenarios for 2030, there was an increase in construction land area and a significant reduction in cultivated land area. The urban development scenario showed the greatest expansion of high-risk areas, with a 5.29 % increase. Conversely, the ecological protection scenario showed a 1.53 % increase in high-risk areas. The findings have provided a reference for ecological risk prevention and control, and sustainable development of the ecological environment in arid regions.

Identification of ecological security patterns of alpine wetland grasslands based on landscape ecological risks: A study in Zoigê County

Climate change and human activities have increased ecological risks and degraded ecosystem functions in alpine wetland grassland regions, where ecological security remains largely unexplored. The construction of ecological security patterns (ESP) can help to synchronize regional ecological security and sustainable development and provide ideas to address these challenges. This article determines the current ESP of Zoigê County, China, by analyzing the spatial and temporal characteristics of landscape ecological risk (LER) and generating an ecological network by combining the InVEST model, the landscape connectivity index, and the circuit theory model. Management zoning and targeted conservation recommendations are proposed. The results indicate that the region has significant spatial heterogeneity in IER. Ecological risk exposure is increasing, with high values mainly concentrated in the central part of the region. Meanwhile, ecological protection areas were identified, which included 2578.44 km2 of ecological sources, 71 key ecological corridors, 25 potential ecological corridors, 4 river ecological corridors, 66 pinch points, and 58 barriers. This study provides a valuable reference for the ecological development of Zoigê County, as well as insights into the formation of ESP in other alpine wetland grassland regions.

Distribution characteristics of soil carbon density and influencing factors in Qinghai-Tibet Plateau region

The Qinghai-Tibet Plateau has low anthropogenic carbon emissions and large carbon stock in its ecosystems. As a crucial region in terrestrial ecosystems responding to climate change, an accurate understanding of the distribution characteristics of soil carbon density holds significance in estimating the soil carbon storage capacity in forests and grasslands. It performs a crucial role in achieving carbon neutrality goals in China. The distribution characteristics of carbon and carbon density in the surface, middle, and deep soil layers are calculated, and the main influencing factors of soil carbon density changes are analyzed. The carbon density in the surface soil ranges from a minimum of 1.62 kg/m2 to a maximum of 52.93 kg/m2. The coefficient of variation for carbon is 46%, indicating a considerable variability in carbon distribution across different regions. There are substantial disparities, with geological background, land use types, and soil types significantly influencing soil organic carbon density. Alpine meadow soil has the highest carbon density compared with other soil types. The distribution of soil organic carbon density at three different depths is as follows: grassland > bare land > forestland > water area. The grassland systems in the Qinghai-Tibet Plateau have considerable soil carbon sink and storage potential; however, they are confronted with the risk of grassland degradation. The grassland ecosystems on the Qinghai-Tibet Plateau harbor substantial soil carbon sinks and storage potential. However, they are at risk of grassland degradation. It is imperative to enhance grassland management, implement sustainable grazing practices, and prevent the deterioration of the grassland carbon reservoirs to mitigate the exacerbation of greenhouse gas emissions and global warming. This highlights the urgency of implementing more studies to uncover the potential of existing grassland ecological engineering projects for carbon sequestration.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas

The eastern coastal areas of Japan are threatened by multiple ecological risks due to frequent natural disasters, climate changes, human activities, etc. Identification spatio-temporal variations and driving mechanisms of landscape ecological risk could be used as significant basis for policymakers. In this study, taking the eastern coastal areas of Japan affected by the 2011 Great East Japan Earthquake and Tsunami Disaster as the study area, the "Nature-Landscape Pattern-Human Society" (NA-LP-HS) multi-dimensional ecological risk assessment framework was established to analyze the spatio-temporal patterns, and identity driving factors using spatial cluster analysis and spatial principal component analysis (SPCA) based on ArcGIS from 2009 to 2021. The findings revealed the distinct geographic patterns in landscape ecological risk, with a noticeable decline from the southwest to the northeast. During the period from 2009 to 2015, the driving factors leading to a sharp risk increase were natural disasters and vegetation coverage. These high-risk areas were concentrated in Sendai Bay and its surroundings. From 2015 to 2021, ecological instability was primarily attributed to a reduction in vegetation coverage, the occurrence of natural disasters, and heightened rainfall erosion. These high-risk areas were mainly clustered within the Tokyo-centered urban agglomeration. Spatial clustering of ecological risks was obvious across all time periods. The key factors contributing to the clustering of high ecological landscape risks focused on the "landscape pattern" criterion, specifically including vegetation coverage, land use land cover. This study demonstrated the ability of multi-dimensional ecological risk assessment to identify high-risk areas and driving factors, and these results could provide a visual analysis and decision-making basis for sustainable development of coastal areas.

Environmental DNA biomonitoring reveals the human impacts on native and non-native fish communities in subtropical river systems

Subtropical rivers are one of the hotspots of global biodiversity, facing increased risks of fish diversity changes and species extinction. However, until now, human impacts on native and non-native fish communities in subtropical rivers still lack sufficient effort. Here, we used the environmental DNA (eDNA) approach to investigate fish communities in the Dongjiang River of southeast China, a typical subtropical river, and explored the effects of regional land use and local water pollution on fish taxonomic and functional diversity. Our data showed that 90 species or genera of native fish and 15 species or genera of non-native fish were detected by the eDNA approach, and there was over 85% overlap between eDNA datasets and historical records. The taxonomic and functional diversity of all, native and non-native fish communities showed consistent spatial patterns, that is, the upstream of the tributary was significantly higher than that of the mainstream and downstream. Land use and water pollution such as COD and TP were the determinants in shaping the spatial structure of fish communities, and water pollution explained 31.56%, 29.88%, and 27.80% of the structural variation in all, native and non-native fish communities, respectively. The Shannon diversity and functional richness of native fish showed a significant downward trend driven by COD (pShannon = 0.0374; pfunctional = 0.0215) and land use (pShannon = 0.0159; pfunctional = 0.0441), but they did not have significant impacts on non-native fish communities. Overall, this study emphasizes the inconsistent response of native and non-native fish communities to human impacts in subtropical rivers, and managers need to develop strategies tailored to specific fish species to effectively protect water security and rivers.

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.

Spatiotemporal patterns and prediction of landscape ecological security in Xishuangbanna from 1996-2030

In recent years, the landscape ecological security of Xishuangbanna in southwest China has become an essential factor affecting the cross-border ecological security in South Asia and Southeast Asia. Based on the change of land use in Xishuangbanna, with the help of "3S" technology, landscape ecology theory, and gray prediction model, the spatial and developmental trends of landscape ecological security in Xishuangbanna from 1996-2030 could be determined. In more than 20 years, the woodland landscape area in Xishuangbanna decreased, and the fragmentation of construction land has increased overall. In 1996, the overall landscape ecological safety was good, with 63.5% of the total area of grade I and II. In 2003, the proportion of the grade I and grade II areas decreased, with landscape ecological security problems appearing. In 2010, the overall landscape ecological security area reached 74.5%, the largest proportion in more than 20 years. The grade V area accounted for only 9% and was mainly distributed on the border of Menghai County and central Jinghong City. In 2017, The grade IV and V areas was further increased, and the ecological security problem intensified. The prediction results showed that from 2023 to 2030, the regions of grades I and II increased, but the proportion of level V regions increased. Furthermore, the grade IV transformed to grade V rapidly, reaching its highest value in more than 20 years. From 1996 to 2030, the landscape ecological security space significantly evolved, showing an evident "east-south" trend in movement and eventually shifting to the southeast.

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria

In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%-45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

Recent advancements in MXene-based nanocomposites as photocatalysts for hazardous pollutant degradation - A review

The recent expeditious industrialization and urbanization showcase the increasing need for renewable and non-renewable energy and the severe environmental crisis. In this regard, numerous 2-dimensional (2D) nanomaterials have been developed as a facile approach to meet the futuristic energy essentials and to resolve the crisis. In contrast, the newly explored 2D MXenes (transition metal carbide/nitrides/carbonitride) have been employed as an intriguing material for various environmental applications. This development is accredited to their unique properties, which include a vast surface area, strong electrical conductivity, fascinating photophysical properties, high mechanical properties, stability in an aqueous medium, high hydrophilicity, biocompatibility, ease of functionalization, and excellent thermal properties. MXenes act as a potential candidate in water desalination, energy storage devices such as electrodes of Li-ion batteries and pseudo capacitors, hydrogen production, sensors, and wastewater treatment. This review article deliberates the synthesis of MXene and nanocomposites of MXene and their photo-catalytic actions against various toxic pollutants such as organic dyes and heavy metals in wastewater. This review also precises the various preparation methods of MXene-based photocatalyst and the enhanced photocatalytic activity of MXene and MXene-based nanocomposites in wastewater treatment. Also, it details the attempts made to improve the photocatalytic activity of MXene-based nanocomposites in terms of their structural compositions. In addition, the merits and demerits of the MXene-based photocatalysts are deliberated, which may pave the way for future research in this arena.

Does a scaling exist in urban ecological infrastructure? A case for sustainability trade-off in China

So far, urban scaling theory has proven that urban area, infrastructure, and economic output have a scaling relation with population. But if we consider ecological space as a part of urban infrastructure, would the same scaling characteristics exist? What is the scaling relationship between ecological spaces and economic social development in different stages of urbanization? This paper is based on this question and explores the trade-off between social economic system and ecosystem in 370 cities of China. The results show that the relationship between population and urban ecological space generally follows the scaling theory in terms of different types of ecological spaces and ecosystem services. For every 10-fold increase in population size, the total area of ecological space and ecosystem services increase by approximately 4 times. The manifestation of ecological space following the scaling laws is the aggregation behavior of better network connectivity. There is a trade-off between urban ecological space and socioeconomic development, with flow equilibrium reached at a population of 2 million and efficiency equilibrium reached at a population of 1 million. Starting from type I and type II megapolis, urban development gradually tends to stabilize, and there may even be a trend of slow decline in urban development potential. In the absence of ecological space, virtual network space can serve as a substitute for ecological space. The driving factors affect scaling behavior of ecological space, including connectivity of ecological space, spatial heterogeneity of natural conditions, and disturbance of economic and social activities. This research can help city to expand ecological space, promoting the added value of urban ecological assets and keeping the urban development potential within the optimal threshold range continuously.

Latest advances in layered covalent organic frameworks for water and wastewater treatment

This review provides an overview of recent progress in the development of layered covalent organic frameworks (LCOFs) for the adsorption and degradation of pollutants in water and wastewater treatment. LCOFs have unique properties such as high surface area, porosity, and tunability, which make them attractive adsorbents and catalysts for water and wastewater treatment. The review covers the different synthesis methods for LCOFs, including self-assembly, co-crystallization, template-directed synthesis, covalent organic polymerization (COP), and solvothermal synthesis. It also covers the structural and chemical characteristics of LCOFs, their adsorption and degradation capacity for different pollutants, and their comparison with other adsorbents and catalysts. Additionally, it discussed the mechanism of adsorption and degradation by LCOFs, the potential applications of LCOFs in water and wastewater treatment, case studies and pilot-scale experiments, challenges, and limitations of using LCOFs, and future research directions. The current state of research on LCOFs for water and wastewater treatment is promising, however, more research is needed to improve their performance and practicality. The review highlights that LCOFs have the potential to significantly improve the efficiency and effectiveness of current water and wastewater treatment methods and can also have implications for policy and practice.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Woody and herbaceous wastes for the remediation of polluted waters of wetlands

Plants wastes play an important role during water remediation in wetlands. Plant waste is made into biochar, which is usually used directly or as a water biofilter to remove pollutants. While, the water remediation effect of combination for biochar from woody and herbaceous wastes coupling with substrate types in CWs have not been fully explored. To explore the water remediation effect of combination for biochar coupling with substrate on pH, Turbidity, COD, NH₄⁺-N, TN and TP, four plant configuration modes combining seven woody plants and eight herbaceous plants (Plants A, Plants B, Plants C, Plants D) were coupled with three substrate types (Substrate 1, Substrate 2, Substrate 3) as 12 experiment groups, using water detection methods and significant differences test (LSD) to analyze. Results showed: (1) Compared to Substrate 3, Substrate 1 and Substrate 2 removed significantly higher in pollutants concentration (p < 0.05); (2) NH₄⁺-N final concentration in Plants C and Plants D were both significantly lower than Plants A and Plants B coupling with Substrate 1 and Substrate 2 (p < 0.05). The TN final concentration of Plants C was significantly lower than Plants A in Substrate 1 (p < 0.05), and Plants A's turbidity was significantly lower than Plants C and Plants D's in Substrate 2 (p < 0.05); (3) The pollutants removal of group A1, A2, B1, B2, C1, C2, D1 and D2 were significantly higher than other experiment groups (p < 0.05). Group A2, B2, C1 and D1 had the best water remediation effect and better stability of plant community. Findings in this study will be beneficial for remediating polluted water and building sustainable wetlands.

Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China

It is of great significance for regional ecological protection and sustainable development to quickly and effectively assess and monitor regional ecological quality and identify the factors that affect ecological quality. This paper constructs the Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE) platform to analyze the spatial and temporal evolution of ecological quality in the Dongjiangyuan region from 2000 to 2020. An ecological quality trend analysis was conducted through the Theil-Sen median and Mann-Kendall tests, and the influencing factors were analyzed by using a geographically weighted regression (GWR) model. The results show that (1) the RSEI distribution can be divided into the spatiotemporal characteristics of "three highs and two lows," and the proportion of good and excellent RSEIs reached 70.78% in 2020. (2) The area with improved ecological quality covered 17.26% of the study area, while the area of degradation spanned 6.81%. The area with improved ecological quality was larger than that with degraded ecological quality because of the implementation of ecological restoration measures. (3) The global Moran's I index gradually decreased from 0.638 in 2000 to 0.478 in 2020, showing that the spatial aggregation of the RSEI became fragmented in the central and northern regions. (4) Both slope and distance from roads had positive effects on the RSEI, while population density and night-time light had negative effects on the RSEI. Precipitation and temperature had negative effects in most areas, especially in the southeastern study area. The long-term spatiotemporal assessment of ecological quality can not only help the construction and sustainable development of the region but also have reference significance for regional ecological management in China.

Novel Google Maps and Google Earth application for chemical industry disaster risk assessment during complex emergencies in Eastern Ukraine

The war in Ukraine has led to complex emergencies, humanitarian crises, and other severe consequences, such as chemical industry disasters. The chemical industry is one of the principal sectors of Ukraine's economy. In 2019, Ukraine had a total volume of hazardous chemical accumulation of more than a 5.1billion tons. Therefore, an attack on chemical industrial facilities will lead to catastrophic consequences such as chemical disasters. This paper aims to study the disaster risk of chemical industrial facilities and its effects on public health and the environment during complex emergencies in Eastern Ukraine. Observational cross-sectional risk assessment method was utilized to assess hazard, vulnerability, and exposure of the chemical industry in Eastern Ukraine in Donetsk Oblast and Luhansk Oblast. Data on chemical factories in Eastern Ukraine was collected on Google Maps and Google Earth on May 2022. Lastly, the semi-quantitative risk assessment method was utilized to describe the risk from the perspective of consequences for life and health, the environment, property, and speed of development. Our disaster risk assessment found more than 1 million people (1,187,240 people) in Donetsk Oblast and more than 350 thousand people (353,716 people) in Luhansk Oblast are exposed to potential hazards from the chemical facilities clusters. The aggregation risk of bombardment of chemical facilities cluster in Eastern Ukraine is also high due to ongoing war. Therefore, the chemical industry disaster risks for Eastern Ukraine during complex emergencies in Donetsk Oblast and Luhansk Oblast are high in terms of likelihood and consequences to life and health, environment, property, and speed of development.

Mapping the social stock and spatiotemporal distribution of high-tech minerals from wasted mobile phones in China: 2001-2019

In the twenty-first century, mobile phones have become one of the most indispensable electronic products in the international community. The pollution of wasted mobile phones has become an urgent problem worldwide and needs special attention. In this paper, we applied the consumption and usage method to calculate the high-tech mineral elements in China from 2001 to 2019. To analyze the spatial distribution of per capita high-tech minerals in China, we proposed a model (3D GHM) through which a 3D grid of high-tech minerals in wasted mobile phones can be obtained in 1 km resolution. The results showed that the total amount of wasted mobile phones in China from 2001 to 2019 was 8.6 billion, with a growth rate of 1026.7% in 2019 compared with 2001. Moreover, the spatiotemporal distribution of wasted mobile phones is characterized by more in the east and less in the west. The total amount of cobalt, palladium, antimony, beryllium, neodymium, praseodymium, and platinum in wasted mobile phones from 2001 to 2019 reached 42,422.4 tons. Based on our results, we proposed a system for efficient collecting and recycling of wasted mobile phones in China.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM_2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM_2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM_2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM_2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R² is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM_2.5 predictions. In addition, it evaluates different machine learning approaches in PM_2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta

The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R² ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R² ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R² ˃ 0.57), summer (R² ˃ 0.76) and autumn (R² ˃ 0.38). However, in winter the composite model outperformed others (R² ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41＜R² ＜0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.

Spatiotemporal dynamics of ecological security in a typical conservation region of southern China based on catastrophe theory and GIS

Ecological security assessment can effectively reflect the ecological status of a region and reveal its level of sustainable development. In this paper, an ecological security-oriented evaluation system was constructed, and the ecological security level of the Dongjiangyuan region from 2000 to 2020 was evaluated based on catastrophe theory and GIS. The results were as follows: (1) As shown in the land use and cover maps, by 2020, the forestland area had decreased the most, and the artificial surface area had increased the most. (2) The ecological security index of the Dongjiangyuan region showed a low trend in the artificial surface area and its surrounding areas. The quite low values of the ecological security index in 2000 and 2010 were improved in 2020 due to the increase in ecological services capacity. The increased vegetation cover from 2000 to 2020 promoted the improved ecological service capacity. (3) The rapid urbanization process in the Dongjiangyuan region resulted in a lower ecological sensitivity index value. Notably, the ecological sensitivity index of the study area had a slightly decreasing trend. (4) The spatial autocorrelation showed that the proportion of hot and cold spots from 2000 to 2020 decreased by 2.96% and 6.91%, respectively. This study can provide a scientific basis and decision-making guidance for ecological management in the Dongjiangyuan region in the future.

Risk assessment of heavy metal and pesticide mixtures in aquatic biota using the DGT technique in sediments

Heavy metals and pesticides (HMPs) are common contaminants due to their extensive use worldwide. Diffusive gradients in thin films (DGT) are a good method for measuring the bioavailable concentration of pollutants. This study represents the first evaluation of HMP toxicity in aquatic biota using the DGT technique in sediments. Zhelin Bay was selected as the case study site because it has been contaminated by pollutants. Nonmetric multidimensional scaling (NMS) analysis reveals that a diverse range of pollutants (V, Cr, Ni, Cu, Zn, As, Se, InHg, Mo, Cd, Sb, W, Pb, CLP, PYR) are mainly influenced by sediment characteristics. Assessment of single HMP toxicity found that the risk quotient (RQ) values for Mn, Cu, inorganic Hg (InHg), chlorpyrifos (CLP) and diuron (DIU) are significantly higher than 1, indicating that the adverse effects of these single HMPs should not be ignored. The combined toxicity of HMP mixtures based on probabilistic ecotoxicological risk assessment shows that Zhelin Bay surface sediments had a medium probability (54.6%) of toxic effects to aquatic biota.