Response of water quality in major tributaries to the difference of multi-scale landscape indicators in Dongting Lake basin, China.

River water quality has been increasingly deteriorated because of the influence of natural process and anthropogenic activities. Quantifying the influence of landscape metrics, namely topography and land use pattern, which encompass land use composition and landscape configuration, across different spatial and seasonal scales that reflect natural process and anthropogenic activities, is highly beneficial for water quality protection. In this study, we focused on investigating the effects of topography, landscape configuration and land use composition on water quality at different spatial scales, including 1-km buffer and sub-watershed, and seasonal scales, including wet and dry season, based on the monthly water quality data in 2016 of Dongting Lake in China. Multivariate statistical analysis of redundancy analysis and partial redundancy analysis was used to quantify the contributions of these factors under different scales. Our results showed that among the three environmental groups, topography made the greatest pure contribution to water quality, accounting for 11.4 to 30.9% of the variation. This was followed by landscape configuration, which accounted for 9.4 to 23.0%, and land use composition, which accounted for 5.9 to 15.7%. More specifically, water body made the greatest contribution to the water quality variation during dry season at both spatial scales, contributing 16.6 to 17.2% of the variation. In contrast, edge density was the primary interpreter of the variability in water quality during wet season at both spatial scales, accounting for 9.9 to 11.1% of the variation. The spatial variability in the influence of landscape metrics on water quality was not markedly distinct. However, these metrics have a minimal impact difference on water quality at the buffer scale and the sub-watershed scale. Moreover, the contribution of landscape configuration varied the most from the buffer to sub-watershed scales, indicating its importance for the spatial scale difference in water quality. The findings of this study offer useful insights into enhancing water quality through improved handling of landscape metrics.

Natural background levels and driving factors of aluminum in shallow groundwater of an urbanized delta: Insight from eliminating anthropogenic-impacted groundwaters.

Knowledge on natural background levels (NBLs) of aluminum (Al) in groundwater can accurately assess groundwater Al contamination at a regional scale. However, it has received little attention. This study used a combination of preselection and statistic methods consisting of the oxidation capacity and the boxplot iteration methods to evaluate the NBL of shallow groundwater Al in four groundwater units of the Pearl River Delta (PRD) via eliminating anthropogenic-impacted groundwaters and to discuss driving factors controlling high NBLs of Al in groundwater in this area. A total of 280 water samples were collected, and 18 physico-chemical parameters including Redox potential, dissolved oxygen, pH, total dissolved solids, HCO3 -, NH4 +, NO3 -, SO4 2-, Cl-, NO2 -, F-, K+, Na+, Ca2+, Mg2+, Fe, Mn, and Al were analyzed. Results showed that groundwater Al NBLs in groundwater units A-D were 0.11, 0.16, 0.15, and 0.08 mg/L, respectively. The used method in this study is acceptable for the assessment of groundwater Al NBLs in the PRD, because groundwater Al concentrations in various groundwater units in residual datasets were independent of land-use types, but they were opposite in the original datasets. The dissolution of Al-rich minerals in sediments/rocks was the major source for groundwater Al NBLs in the PRD, and the interaction with Al-rich river water was secondary one. The high groundwater Al NBL in groundwater unit B was mainly attributed to the acid precipitation and the organic matter mineralization inducing the release of Al in Quaternary sediments. By contrast, the high groundwater Al NBL in groundwater unit C mainly was ascribed to the release of Al complexes such as fluoroaluminate from rocks/soils into groundwater induced by acid precipitation, but it was limited by the dissolution of Mg minerals (e.g., dolomite) in aquifers. This study provides not only useful groundwater Al NBLs for the evaluation of groundwater Al contamination but also a reference for understanding the natural geochemical factors controlling groundwater Al in urbanized deltas such as the PRD. PRACTITIONER POINTS: The natural background level (NBL) of groundwater aluminum in the Pearl River Delta (PRD) was evaluated. The dissolution of aluminum-rich minerals in sediments/rocks was the major source for groundwater aluminum NBLs in the PRD. The acid precipitation and organic matter mineralization contribute to high groundwater Al NBL in the groundwater unit B. The acid precipitation contributes to high groundwater Al NBL in the groundwater unit C, while dissolution of magnesium minerals limits it.

Combining Google Earth historical imagery and UAV photogrammetry for urban development analysis.

Rural-urban migration often triggers additional demand for housing and infrastructural development to cater for the growing population in urban areas. Consequently, town planners and urban development authorities need to understand the urban development trend to make sustainable urban planning decisions. Yet, methods to analyse changes and trends in urban spatial development are often complex and require costly data collection. This article thus presents a simplified method to analyse the urban development trend in an area. The method integrates Google Earth (GE) historical imagery (baseline data) and unmanned aerial vehicle (UAV) photogrammetry (recent data) to quantify the changes over time. This approach can be applied to study the urban development trends in low-income countries with budget constraints. The method is discussed under four main headings: (1) background, (2) method details, (3) limitations, and (4) conclusion.•Google Earth historical image can be extracted with its associated world file.•The population of an area can be estimated by using average household size data and the number of residential buildings in the area.•The building height ratio can be used to ascertain if the land is being used parsimoniously.

Assessing changes in the ecosystem service value in response to land use and land cover dynamics in Malawi.

Land use and land cover (LULC) changes are inevitable outcomes of socioeconomic changes and greatly affect ecosystem services. Our study addresses the critical gap in the existing literature by providing the first comprehensive national analysis of LULC changes and their impacts on ecosystem service values (ESVs) in Malawi. We assessed changes in ecosystem service values (ESVs) in response to LULC changes using the benefit transfer method in ArcGIS 10.6 software. Our findings revealed a significant increase in grasslands, croplands, and urban areas and a notable decline in forests, shrubs, wetlands, and water bodies. Grassland, cropland, and built-up areas expanded by 52%, 1%, and 23.2%, respectively. In contrast, permanent wetlands, barren land, and water bodies declined by 27.6%, 34.3%, and 1%, respectively. The ESV declined from US$90.87 billion in 2001 to US$85.60 billion in 2022, marking a 5.8% reduction. Provisioning services increased by 0.5% while regulating, supporting, and cultural ecosystem service functions declined by 12.2%, 3.16%, and 3.22%, respectively. The increase in provisioning services was due to the expansion of cropland. However, the loss of regulating, supporting, and cultural services was mainly due to the loss of natural ecosystems. Thus, environmental policy should prioritise the conservation and restoration of natural ecosystems to enhance the ESV of Malawi.

Spatial variation of anthropogenic disturbances within watersheds determines dissolved organic matter composition exported to oceans.

Global land-use changes alter the delivery of fluvial dissolved organic matter (DOM) along land-to-sea continuum. To study how spatial variations in watershed anthropogenic disturbances control chemodiversity and reactivity of DOM exported to oceans, we used fluorescent and ultra-high-resolution mass spectrometry to investigate spatial and seasonal variations of DOM properties along two subtropical coastal rivers with contrasting anthropogenic land-use distributions (North and West tributaries of Jiulong River, southeast China). Dissolved organic carbon (DOC) concentration and humic- and protein-like fluorescent DOM (FDOM) intensities were high in the mixed urban-agricultural impacted upper North River and lower West River. DOM molecular signatures suggested that the urban-sourced DOM is dominated by bio-labile, S-rich compounds, whereas the agricultural-sourced DOM is characterized by a mixture of bio-labile CHONS and bio-refractory CHON. This anthropogenic-induced spatial variation in DOM signatures was especially prominent during the dry season. Molecular analysis indicated that heteroatomic-containing (phosphorus-sulfur-nitrogen) DOM compounds are more biologically degradable, whereas most of the heteroatom-depleted and highly unsaturated CHO was stable during transport. Due to a longer transit distance and reservoir impoundment in North River, the urban-sourced aliphatic compounds were largely microbially removed or transformed into bio-refractory components, resulting in lower DOC fluxes and an increase of recalcitrance in the DOM exported to the ocean. Conversely, shorter transit times for anthropogenic inputs from the middle/lower West River increased watershed yield and export fluxes of DOC with higher bio-lability. Our study documents that transit history plays a crucial role in assessing the fate of anthropogenic DOM along the land-to-ocean continuum.

Multi-scenario analysis and optimization strategy of ecological security pattern in the Weihe river basin.

For many years, the Weihe River Basin (WRB) has struggled to achieve a balance between ecological protection and economic growth. Constructing an Ecological Security Pattern (ESP) is extremely important for ensuring ecological security (ES). This study employed a coupling of multi-objective programming (MOP) and the patch-generating land use simulation (PLUS) model to project land use change (LUCC) in 2040 across three scenarios. Leveraging circuit theory, we generated ecological corridors and identified key ecological nodes, enabling a comparative analysis of ESPs within the WRB. The main results showed that: (1) The Ecological Protection (EP) scenario showed the highest proportions of forestland, grassland, and water, indicating an optimal ecological environment. Conversely, the Economic Development (ED) scenario features the greatest proportion of construction land, particularly evident in the rapid urban expansion. The Natural Development (ND) scenario exhibits a more balanced change, aligning closely with historical trends. (2) The ecological source areas in the EP scenario is 13,856.70 km2, with the largest and most intact patch area. The ecological source patches that have been identified in the ED scenario exhibit fragmentation and dispersion, encompassing a total area of 8018.82 km2. The ecological source areas in the ND scenario is most similar to the actual situation in 2020, encompassing 8474.99 km2. (3) The EP scenario demonstrates minimal landscape fragmentation. The ED scenario presents a more intricate corridor pattern, hindering species and energy flow efficiency. The ND scenario is more similar to the actual distribution in 2020. Protecting and restoring key ecological nodes, and ensuring the integrity and connectivity of ecological sources are crucial for ESP optimization in various scenarios. Combining all results, we categorize the WRB's spatial pattern into "three zones, three belts, and one center" and offer strategic suggestions for ecological preservation, promoting sustainable local ecological and socioeconomic development.

[Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016].

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.

[Land Use Optimization and Carbon Reserve Assessment in the Shiyang River Basin].

Studying the response relationship and spatial distribution characteristics of carbon reserve and land use change and predicting the change trend of carbon reserve caused by the change of land use type in the future can provide some reference for watershed policy formulation, land use structure adjustment, and the realization of the "two-carbon" goal. Based on the land use data from 2000, 2010, and 2020, the InVEST model was used to calculate carbon reserves and analyze the change characteristics and to simulate the land use change and its impact on carbon reserves in natural development, urban development, and ecological protection in 2030 with the help of the PLUS model. The study found that ① the main land types in the Shiyang River Basin from 2000 to 2020 were cultivated land, grassland, and unused land. The area of cultivated land, water area, and construction land in the Shiyang River Basin showed a significant increasing trend, and the construction land area increased the most. ② In the natural development scenario of 2030, cultivated land, water area, and construction all increased by 6.15%, 9.56%, and 29.9%, respectively. In the urban development scenario, the area of construction land increased the most. Compared with that in the other two scenarios, the area of forest land and grassland increased in the ecological protection scenarios. ③ The carbon reserves of the Shiyang River Basin from 2000 to 2020 showed a steady increase, with an overall increase of 0.035×108 t. The increased carbon reserves were mainly due to the increase in cultivated land area. ④ In 2030, the carbon reserves of the Shiyang River Basin showed an increasing trend in all three scenarios. The carbon reserves in the three scenarios were 5.65×108, 5.64×108,and 5.73×108 t, respectively, with the largest increase in carbon reserves in the ecological conservation scenario, mainly due to the increase in grassland and woodland. The results showed that the expansion of construction land was the main cause of the loss of carbon reserves. If effective ecological protection measures are taken, the carbon reserves in the Shiyang River Basin will be improved, and the problem of the loss of carbon reserves caused by economic development can be solved.

Land use and spatial contiguity are key driven factors of antibiotic multimedia patterns in the megacity river network.

The widespread spread of antibiotics in the environment poses a growing threat to human health. This study investigated the distribution and fate of antibiotics concerning land use characteristics, hydrological conditions, and spatial contiguity within a megacity river network. Temporally, the average concentrations of twenty antibiotics in water (354 ng/L), suspended particulate matter (SPM) (46 ng/L), and sediment (151 ng/g) during dry season were notably higher than that in the corresponding environment media (water: 127 ng/L, SPM: 2 ng/L, and sediment: 49 ng/g) during the wet season. Moreover, the inter-annual variation of antibiotics in water showed a decreasing trend. Spatially, substantial antibiotic contamination was observed in a human-intensive watershed, particularly in the upstream and central city sections. The macrolides in water were most affected by land use types and hydrological processes. Antibiotic contamination in water exhibited a stronger spatial autocorrelation compared to other media. Nevertheless, the interconnectedness of antibiotic contamination in sediments during the wet season warrants attention, and relevant authorities should enhance environmental monitoring in watersheds with pollution hotspots. Certain antibiotics, such as sulfamethoxazole, enrofloxacin, and florfenicol, were transported via urban rivers to the ocean, potentially posing environmental risks to coastal water quality. Local sources accounted for the predominant portion (>50 %) of most antibiotics in various media. The correlation distances of antibiotics in waters during the wet season could screen ecological risk prioritization in aquatic environments.

Coupled natural-social water system in a subwatershed of the Tennessee River Basin.

Regional water cycle systems are increasingly characterized by the dual effect of natural and social processes, which have profound impacts on global water security. However, accurately interpreting the changes in the coupled natural-social water system and identifying the driving factors pose significant challenges. Here, we attempted to model a coupled natural-social water system in the East Fork Poplar Creek (EFPC) watershed of the Tennessee River, United States. The study area features two social water cycle components: a local water transfer project and the Oak Ridge Wastewater Treatment Facility (ORWTF). We conducted the Soil and Water Assessment Tool (SWAT) modeling in the open-source light-weight QGIS software, with the synthesis of various climate and land use change scenarios in both historical periods (1980-2016) and future periods (2017-2050). We achieved more accurate and realistic model simulations when considering the social water cycle components, indicating that the social water cycle accounted for 13-18 % of the observed streamflow. Climate variation/change dominates natural runoff changes. Though land use and cover change (LUCC) had minimal effect on natural runoff, it had a profound impact on the process of runoff generation, i.e., surface runoff (RS) and subsurface runoff (RSS). Specifically, LUCC would be responsible for 152 % and 45 % of the changes in RS and RSS, respectively, in future periods. This study highlights the significance of artificial water discharge and withdrawal impacts on the water cycle and emphasizes the need for water resources management measures that fully consider natural-social hydrological processes.

Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation.

In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in areas where tea cultivation is carried out at the micro-watershed scale and the predictability of soil quality using ANN were evaluated. According to the results, the soil quality indices of tea-growing areas were generally classified as "medium" between 55 and 70 %. Among the evaluated features for determining soil quality, the highest relative importance value was for soil organic carbon content (13 %) and potential mineralizable nitrogen (13 %), whereas the lowest values were for exchangeable potassium (4 %) and sodium adsorption ratio (SAR) (4 %). In addition, when comparing the actual and predicted values for soil quality prediction using ANN, the Lin's concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and R2 values were found to be 0.93, 2.95, and 0.89, respectively. Significant properties for the determined values within a 90 % predicted interval were found to be organic matter, microbial biomass carbon, bulk density, and aggregate stability of the soils. Moreover, the uncertainty values (standard deviation) in the model predictions were determined to be within the range of 1.01-4.56 %. Consequently, the Soil Quality Index (SQI) obtained from the SMAF model using 12 soil properties in tea-growing areas could be accurately predicted using ANN. As a result of this study, digital maps showing the spatial distribution of SQI and the predicted uncertainties can help monitor SQI levels in this area.

The spatial overlay effect of urban waterlogging risk and land use value.

Urban waterlogging poses a severe threat to lives and property globally, making it crucial to identify the distribution of urban value and waterlogging risk. Previous research has overlooked the heterogeneity of value and risk in spatial distribution. To identify the overlay effect of urban land value and risk, this study employs the Entropy Weighting Method (EM) to assess urban value, Principal Component Analysis (PCA) to determine waterlogging risk and key areas (RK), local Moran's I (SC) to identify key areas (HK), and finally Bivariate local Moran's I (DC) to comprehensively evaluate urban value and waterlogging risk to delineate key areas (BH). The results indicate that waterlogging risk is primarily influenced by proximity to water systems (PCA coefficient: 0.567), population density (0.550), and rainfall (0.445). There is a positive correlation between urban value and waterlogging risk, with a global Moran's I of 0.536, indicating that areas with higher urban value also face greater waterlogging risk. The DC method improved identification precision, reducing the BH area by 6.42 and 3.51 km2 compared to RK and HK, accounting for 25.50 % and 15.76 % of the RK and HK identified areas, respectively. At present, rescue resources can access less than one-third of the area within 5 min, but with the DC method, during the centennial rainfall scenario, the accessibility rate within 5 min for the BH area reaches 63 %, and all BH key areas can be covered within 15 min. This study provides a new methodology for identifying key areas of waterlogging disasters and can be used to enhance urban rescue efficiency and the precision management of flood disasters.

New insights into assembly processes and driving factors of urban soil microbial community under environmental stress in Beijing.

Rapid urbanization leads to drastic environmental changes, directly or indirectly affecting the structure and function of soil microbial communities. However, the ecological response of soil microbes to environmental stresses has not yet been fully explored. In this study, we used high-throughput sequencing to analyze the assembly mechanism and driving factors of soil microbial community under environmental stresses. The results indicated that environmental stresses significantly affected soil properties and the levels of beryllium, cobalt, antimony, and vanadium contamination in soil generally increased from the suburban areas toward the city core. The composition and distribution of soil microbial communities demonstrated clear differences under different levels of environmental stress, but there was no significant difference in microbial diversity. Random forest and partial least squares structural equation modeling results suggested that multiple factors influenced microbial diversity, but antimony was the key driver. The influence of environmental stress led to deterministic processes dominating microbial community assembly processes, which promoted the regional homogenization of soil microbes. Therefore, this study provides new insights into urban soil microbial management under environmental stresses.

Evaluating the quality of sediments in streams draining contrasting land-use areas in Osogbo metropolis, southwestern Nigeria.

The attendant effects of urbanization on the environment and human health are evaluable by measuring the potentially harmful element (PHE) concentrations in environmental media such as stream sediments. To evaluate the effect of urbanization in Osogbo Metropolis, the quality of stream sediments from a densely-populated area with commercial/industrial activities was contrasted with sediments from a sparsely-populated area with minimal anthropogenic input.Forty samples were obtained: 29 from Okoko stream draining a Residential/Commercial Area (RCA, n = 14) and an Industrial Area (IA, n = 15), and 11 from Omu stream draining a sparsely-populated area (SPA). The samples were air-dried, sieved to < 75 micron fraction, and analysed for PHEs using inductively-coupled plasma atomic emission spectrometry (ICP-AES). Index of geoaccumulation (Igeo), pollution index (PI), ecological risk factor (Er) and index (ERI) were used for assessment. Inter-elemental relationships and source identification were done using Pearson's correlation matrix and principal component analysis (PCA).PHE concentrations in the stream sediments were RCA: Zn > Pb > Cu > Cr > Sr > Ni > Co, IA: Zn > Cr > Ni > Co > Pb > Cu > Sr and SPA: Zn > Co > Cr > Cu > Sr > Ni > Pb. Igeo calculations revealed moderate-heavy contamination of Cu, Pb and Zn in parts of RCA, moderate-heavy contamination of Zn in IA while SPA had moderate contamination of Co and Zn. PI values revealed that stream sediments of RCA are extremely polluted, while those of IA and SPA are moderately and slightly polluted, respectively.The pollution of the stream sediments in RCA and IA is adduced to anthropogenic activities like vehicular traffic, automobile repairs/painting, blacksmithing/welding and metal scraping. In SPA however, the contamination resulted from the application of herbicides/fertilizers for agricultural purposes.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Ecosystem health assessment of East Kolkata Wetlands, India: Implications for environmental sustainability.

The East Kolkata Wetlands (EKW) in Kolkata, India, span 12,500 ha and are a vital ecological zone providing several benefits, including water purification, flood control, and biodiversity support. This study investigated land use and land cover (LULC) alterations in the EKW from 1991 to 2023, using a random forest (RF) machine learning model. Significant LULC changes were observed over the 32 years, with wetland areas decreasing from 91.2 km2 in 1991 to 33.4 km2 in 2023, reflecting substantial habitat loss and reduced ecosystem services. Conversely, agricultural land expanded from 27.8 km2 to 58.7 km2, driven by economic and food production needs, and built-up areas increased dramatically from 0.2 km2 to 10.5 km2, indicating rapid urbanization. This study evaluated the health, resilience, and ecosystem functionality of EKW by analysing human-induced land use changes and using ecological indicators and landscape metrics. Landscape and class level metrics such as PLAND, largest patch index (LPI), total edge (TE), edge density (ED), number of patches (NP), and patch density (PD) were used to analyse the spatiotemporal dynamics of the wetlands. This study revealed a significant increase in fragmentation, with the number of patches increasing from 2689 in 1991 to 4532 in 2023, despite a consistent decrease in core wetland areas. Ecosystem health indicators, such as the ecosystem structure index (ESI) and landscape deviation degree (LDD), were used to assess landscape metrics and fragmentation changes. The ESI and other metrics revealed significant temporal fluctuations, providing insights into landscape structure, connectivity, and heterogeneity. The ESI improved from 0.87 in 1991 to 1.03 in 2023, indicating enhanced connectivity and diversity. Conversely, the LDD increased from 20.6% to 56.85%, indicating a shift towards impervious surfaces. The vegetation productivity and ecosystem health index (EHI) decreased, indicating biodiversity loss and reduced carbon sequestration. The EHI also dropped from 0.67 to 0.55, signifying ongoing environmental stress. This study emphasizes the need for conservation efforts to maintain the ecological integrity of the EKW amidst urbanization and land use changes and recommends a balanced approach for sustainable urban development and enhanced wetland resilience.

Assessing the impact of three biosphere reserves on the conservation of coastal ecosystems.

Biosphere Reserves (BR) manage large territories with diverse natural covers and land uses to preserve biodiversity, promote local development and preserve ecosystems. This study evaluated how their zoning (buffer and core) and policy timeframes (decree period, management plan period, and land planning period) influence four landscape management outcomes: deforestation, natural cover recovery, and anthropic and natural permanence. For three Mexican BR case studies, land use and cover transitions were calculated and compared to contrafactual sites. Observed rates of land cover change were marginal within all three BR zoning and across their policy timeframe (<0.02 % change rate), suggesting that BR effectively promote the permanence of both natural and anthropic covers. Nevertheless, the predicted probability of uncommon deforestation and recovery outcomes at local levels showed that the effect of a BR over its regulated landscape is not spatiotemporally static, contrasting the effect of individual allocation vs a group or network. Poverty, land tenure, agriculture aptitude and distance to markets adds to this dynamic and is modelled and discussed. This study shows that BR zoning schemes and its regulatory sequence influence the rates of land cover change and the predicted probability of landscape management outcomes across space and time.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Environmental vulnerability applied to the territorial planning of a tropical semiarid basin.

Tropical semiarid regions are naturally prone to environmental damage. Human activity can worsen this situation. To understand how human actions affect the ecosystem, plan land use effectively, and establish targeted management practices, assessing environmental vulnerability is crucial. This study focuses on a sub-basin receiving water transfers from the São Francisco River in Brazil's semiarid region. Here, we map and evaluate how land use and occupation alter natural vulnerability. We also propose zoning strategies to support water resource management and implement sustainable development policies in the region. To achieve this, we conducted an integrated analysis of physical factors (soil types, geology, climate, vegetation, and landforms) and spatial land-use data using geographic information systems (GIS) and map algebra techniques. Map algebra allowed us to combine these various datasets within the GIS environment, enabling the creation of maps that synthesize both natural and environmental vulnerability across the study area. Following analysis of these vulnerability maps, our findings reveal a high level of vulnerability. The areas with high to very high degrees of natural vulnerability coincide with the places that have high slopes, high altitudes, Lithic Neosols, and thick vegetation. Furthermore, the interaction between environmental factors and human activity exacerbates vulnerability. Based on the environmental vulnerability assessment, we defined four environmental management zones. These zones require distinct protection measures and management approaches. As a method to potentially improve the basin's vulnerability scenario, soil conservation measures are recommended. This approach is highly relevant for managing land in tropical semiarid regions and, with adaptations to specific regional factors, can be applied globally.

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method.

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region.