Mini-Open Dumps in the Paraná River Floodplain: Local Problems with Global Effects.

The growing production of urban solid waste is a structural problem faced by most cities around the world. The proliferation of mini-open dumps (MOD; small spontaneous open-air waste dumps formed in urban and peri-urban areas) on the banks of the Paraná River is particularly evident. During the historical drought (June-December 2021), we carried out sampling campaigns identifying MODs of the Santa Fe River, a secondary channel of the Paraná River. MOD were geolocated, measured, described and classified by origin. The distance to the river and other sensitive places was considered (houses-schools-health facilities). Our results suggested a serious environmental issue associated with poor waste management. MOD were extremely abundant in the study area, being mostly composed of domestic litter. Plastics clearly dominated the MOD composition. Burning was frequently observed as a method to reduce the volume of MOD. We concluded that the proliferation of MOD is a multi-causal problem associated with a failure of public policies and a lack of environmental education.

Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration.

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

Modelling relationships between land use and water quality using statistical methods: A critical and applied review.

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Environmental impact assessment of ocean energy converters using quantum machine learning.

The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources from the oceans, such as waves and tides, due to their high potential. On the other hand, the large-scale deployment of ocean energy converters to meet future energy needs requires the use of large farms of these converters, which may have negative environmental impacts on the ocean ecosystem. In the meantime, a very important point is the volume of data produced by different methods of collecting data from the ocean for their analysis, which makes the use of advanced tools such as different machine learning algorithms even more colorful. In this article, some environmental impacts of ocean energy devices have been analyzed using machine learning and quantum machine learning. The results show that quantum machine learning performs better than its classical counterpart in terms of calculation accuracy. This approach offers a promising new method for environmental impact assessment, especially in a complex environment such as the ocean.

Spatiotemporal assessment of pathogenic Leptospira in subtropical coastal watersheds.

The World Health Organization classifies leptospirosis as a significant public health concern, predominantly affecting impoverished and unsanitary regions. By using the Pensacola Bay System as a case study, this study examines the underappreciated susceptibility of developed subtropical coastal ecosystems such as the Pensacola Bay System to neglected zoonotic pathogens such as Leptospira. We analyzed 132 water samples collected over 12 months from 44 distinct locations with high levels of Escherichia coli (>410 most probable number/100 mL). Fecal indicator bacteria (FIB) concentrations were assessed using IDEXX Colilert-18 and Enterolert-18, and an analysis of water physiochemical characteristics and rainfall intensity was conducted. The LipL32 gene was used as a quantitative polymerase chain reaction (qPCR) indicator to identify the distribution of Leptospira interrogans. The results revealed 12 instances of the presence of L. interrogans at sites with high FIB over various land cover and aquatic ecosystem types. Independent of specific rainfall events, a seasonal relationship between precipitation and elevated rates of fecal bacteria and leptospirosis was found. These findings highlight qPCR's utility in identifying pathogens in aquatic environments and the widespread conditions where it can be found in natural and developed areas.

Interplay of natural and anthropogenic inputs on the groundwater contamination of Beenaganj-Chachura block.

The present research work investigates the impact of natural and anthropogenic inputs on the chemistry and quality of the groundwater in the Beenaganj-Chachura block of Madhya Pradesh, India. A total of 50 groundwater samples were examined for nitrates, fluoride, chlorides, total dissolved solids, calcium, magnesium, pH, total hardness, and conductivity, and their impact on entropy-weighted water quality index and pollution index of groundwater (PIG) was investigated via the response surface methodology (RSM) using the central composite design. According to analytical findings, Ca, Mg, Cl-, SO42-, and NO3- exceed the desired limit and permitted limit set by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). According to PIG findings, 76, 16, and 8% of groundwater samples, respectively, fell into the insignificant, low, and moderate pollution categories. The regression coefficients of the quadratic RSM models for the experimental data provided excellent results. Thus, RSM provides an excellent means to obtain the optimized values of input parameters to minimize the PIG values.

Fresh records on Ichthyofaunal diversity of River Panjkora Dir, Khyber Pakhtunkhwa, Pakistan.

This study was designed to assess the ichthyofaunal diversity of River Panjkora, Khyber Pakhtunkhwa, Pakistan. For this purpose, a total of 1189 fish from six different sites were collected along the river and identified using standard keys. The fish collected and identified were representing 38 species, belonging to 7 families. The investigation spanned a year, from July 2021 to May 2022. The most dominant family was Cyprinidae 76% (n=906/1189), followed by Nemacheilidae 5.8% (n=69/1189), Channidae 5.2% (n=62/1189), Sisoridae 5.1% (n=61/1189), Mastacembelidae 4.9% (n=58/1189), Salmonidae 2.6% (n=31/1189) and least was Bagridae 0.17 (n=2/1189). The most abundant speices was Schizothorax plagiostomus with relative density of 16.8. Family Cyprinidae was represented by 21 species, Sisoridae by 7 species, Nemacheilidae by 5 species, Channidae by 2 species, while Bagridae, Salmonidae and Mastacembelidae, were each represented by a single species. PAST 3, XLSTAT and EXCEL 2019 were used for principal component analysis to study correlation of fish diversity and richness. Eigenvalue obtained from Kumrat to Busaq were 3.32, 1.01, 0.80, 0.44, 0.31 and 0.10 respectively. The higher value at Kumrat shows higher diversity. The water quality assessment showed average value of water temperature 10.4 ͦC, pH 7.0, electrical conductivity 184 mg/L, dissolved oxygen 7.9 mg/L, turbidity 43.73 mg/L, total dissolved solids 101 mg/L, total suspended solids 34.72 mg/L, total solids 135.53 mg/L, total alkalinity 75.77 mg/L, total hardness 58.37 mg/L, ammonia 0.46 mg/L, sulphate 26.03 mg/L, chloride 14.67 mg/L, calcium 69.11 mg/L, chromium 0.18 mg/L, copper 0.03, cobalt mg/L 0.04, nickel 0.039 mg/L, lead 0.02 mg/L and Zinc 0.35 mg/L. The findings of this study indicated that most of the physicochemical parameters remained within the acceptable limits throughout the study period. Analysis of fish gut contents included; nymphs, insect larvae, the presence of algae, protozoans and macroinvertebrates in the river ecosystem.

Predictive modelling reveals Australian continental risk hotspots for marine debris interactions with key threatened species.

Anthropogenic debris is a global threat that impacts threatened species through various lethal and sub-lethal consequences, as well as overall ecosystem health. This study used a database of over 24,000 beach surveys of marine debris collated by the Australian Marine Debris Initiative from 2012 to 2021, with two key objectives: (1) identify variables that most influence the occurrence of debris hotspots on a continental scale and (2) use these findings to identify likely hotspots of interaction between threatened species and marine debris. The number of particles found in each beach survey was modelled alongside fifteen biological, social, and physical spatial variables including land use, physical oceanography, population, rainfall, distance to waste facilities, ports, and mangroves to identify the significant drivers of debris deposition. The model of best fit for predicting debris particle abundance was calculated using a generalized additive model. Overall, debris was more abundant at sites near catchments with high annual rainfall (mm), intensive land use (km2), and that were nearer to ports (km) and mangroves (km). These results support previous studies which state that mangroves are a significant sink for marine debris, and that large ports and urbanized catchments are significant sources for marine debris. We illustrate the applicability of these models by quantifying significant overlap between debris hotspots and the distributions for four internationally listed threatened species that exhibit debris interactions; green turtle (26,868 km2), dugong (16,164 km2), Australian sea lion (2903 km2) and Flesh-footed Shearwater (2413 km2). This equates to less than 1% (Flesh-footed Shearwater, Australian sea lion), over 2% (green sea turtle) and over 5% (dugong) of their habitat being identified as areas of high risk for marine debris interactions. The results of this study hold practical value, informing decision-making processes, managing debris pollution at continental scales, as well as identifying gaps in species monitoring.

Monitoring of toxic cyanobacterial blooms in Lalla Takerkoust reservoir by satellite imagery and microcystin transfer to surrounding farms.

Cyanobacterial harmful algal blooms (CyanoHABs) threaten public health and freshwater ecosystems worldwide. In this study, our main goal was to explore the dynamics of cyanobacterial blooms and how microcystins (MCs) move from the Lalla Takerkoust reservoir to the nearby farms. We used Landsat imagery, molecular analysis, collecting and analyzing physicochemical data, and assessing toxins using HPLC. Our investigation identified two cyanobacterial species responsible for the blooms: Microcystis sp. and Synechococcus sp. Our Microcystis strain produced three MC variants (MC-RR, MC-YR, and MC-LR), with MC-RR exhibiting the highest concentrations in dissolved and intracellular toxins. In contrast, our Synechococcus strain did not produce any detectable toxins. To validate our Normalized Difference Vegetation Index (NDVI) results, we utilized limnological data, including algal cell counts, and quantified MCs in freeze-dried Microcystis bloom samples collected from the reservoir. Our study revealed patterns and trends in cyanobacterial proliferation in the reservoir over 30 years and presented a historical map of the area of cyanobacterial infestation using the NDVI method. The study found that MC-LR accumulates near the water surface due to the buoyancy of Microcystis. The maximum concentration of MC-LR in the reservoir water was 160 µg L-1. In contrast, 4 km downstream of the reservoir, the concentration decreased by a factor of 5.39 to 29.63 µgL-1, indicating a decrease in MC-LR concentration with increasing distance from the bloom source. Similarly, the MC-YR concentration decreased by a factor of 2.98 for the same distance. Interestingly, the MC distribution varied with depth, with MC-LR dominating at the water surface and MC-YR at the reservoir outlet at a water depth of 10 m. Our findings highlight the impact of nutrient concentrations, environmental factors, and transfer processes on bloom dynamics and MC distribution. We emphasize the need for effective management strategies to minimize toxin transfer and ensure public health and safety.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data.

In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are provided. To derive the unified representation of multi-granularity linguistic assessment information, a fuzzy quantitative transformation for multi-granularity uncertain linguistic information is proposed. The proofs of the desirable properties and some normalized formulas for the trapezoidal fuzzy numbers are presented simultaneously. Next, the objective weight of each assessment indicator is further determined by calculating the Jaccard-Cosine similarity between the trapezoidal fuzzy numbers. Moreover, the trapezoidal fuzzy numbers corresponding to the comprehensive assessment values of each alternative are obtained. The alternatives are effectively ranked according to the distance from the centroid of the trapezoidal fuzzy number to the origin. Finally, based on the proposed consistent fuzzy assessment method, the suitability assessment of forest land resources is achieved under a multi-source heterogeneous data setting.

Seasonal studies of aquatic humic substances from Amazon rivers: characterization and interaction with Cu (II), Fe (II), and Al (III) using EEM-PARAFAC and 2D FTIR correlation analyses.

Aquatic humic substances (AHS) are defined as an important components of organic matter, being composed as small molecules in a supramolecular structure and can interact with metallic ions, thereby altering the bioavailability of these species. To better understand this behavior, AHS were extracted and characterized from Negro River, located near Manaus city and Carú River, that is situated in Itacoatiara city, an area experiencing increasing anthropogenic actions; both were characterized as blackwater rivers. The AHS were characterized by 13C nuclear magnetic ressonance and thermochemolysis GC-MS to obtain structural characteristics. Interaction studies with Cu (II), Al (III), and Fe (III) were investigated using fluorescence spectroscopy applied to parallel factor analysis (PARAFAC) and two-dimensional correlation spectroscopy with Fourier transform infrared spectroscopy (2D-COS FTIR). The AHS from dry season had more aromatic fractions not derived from lignin and had higher content of alkyls moities from microbial sources and vegetal tissues of autochthonous origin, while AHS isolated in the rainy season showed more metals in its molecular architecture, lignin units, and polysacharide structures. The study showed that AHS composition from rainy season were able to interact with Al (III), Fe (III), and Cu (II). Two fluorescent components were identified as responsible for interaction: C1 (blue-shifted) and C2 (red-shifted). C1 showed higher complexation capacities but with lower complexation stability constants (KML ranged from 0.3 to 7.9 × 105) than C2 (KML ranged from 3.1 to 10.0 × 105). 2D-COS FTIR showed that the COO- and C-O in phenolic were the most important functional groups for interaction with studied metallic ions.

Characteristics and risk assessment of potentially toxic elements pollution in river water and sediment in typical gold mining areas of Northwest China.

To assess the concentration characteristics and ecological risks of potential toxic elements (PTEs) in water and sediment, 17 water samples and 17 sediment samples were collected in the Xiyu River to analyze the content of Cr, Ni, As, Cu, Zn, Pb, Cd and Hg, and the environmental risks of PTEs was evaluated by single-factor pollution index, Nemerow comprehensive pollution index, potential ecological risk, and human health risk assessment. The results indicated that Hg in water and Pb, Cu, Cd in sediments exceeded the corresponding environmental quality standards. In the gold mining factories distribution river section (X8-X10), there was a significant increase in PTEs in water and sediments, indicating that the arbitrary discharge of tailings during gold mining flotation is the main cause of PTEs pollution. The increase in PTEs concentration at the end of the Xiyu River may be related to the increased sedimentation rate, caused by the slowing of the riverbed, and the active chemical reactions at the estuary. The single-factor pollution index and Nemerow pollution index indicated that the river water was severely polluted by Hg. Potential ecological risk index indicated that the risk of Hg in sediments was extremely high, the risk of Cd was high, and the risk of Pb and Cu was moderate. The human health risk assessment indicated that As in water at point X10 and Hg in water at point X9 may pose non-carcinogenic risk to children through ingestion, and As at X8-X10 and Cd at X14 may pose carcinogenic risk to adults through ingestion. The average HQingestion value of Pb in sediments was 1.96, indicating that the ingestion of the sediments may poses a non-carcinogenic risk to children, As in the sediments at X8-X10 and X15-X17 may pose non-carcinogenic risk to children through ingestion.

Can a naturally depauperate Ephemeroptera, Plectoptera and Trichoptera (EPT) fauna track river degradation in south-western Australia?

Freshwater aquatic ecosystems are threatened globally. Biological monitoring is required to deliver rapid and replicable assessment of changes in habitat quality. The Ephemeroptera, Plectoptera, Trichoptera (EPT) index is a globally recognised rapid bioassessment that measures taxa richness of three insect orders whose larvae are considered sensitive to freshwater habitat degradation. South-western Australia contains threatened freshwater ecosystems but has depauperate EPT fauna and high endemism, potentially reducing the capacity of the EPT index to track degradation. This study investigated if EPT species richness, composition or individual species tracked physical or chemical river degradation in three catchments in south-western Australia. We sampled EPT fauna and measured water chemistry, erosion, sedimentation, riparian vegetation cover and instream habitat at 98 sites in the winters of 2007 and 2023. We found 35 EPT taxa across the study area with a median number of species per site of two. EPT species richness had weak positive associations with a composite water quality index and dissolved oxygen and weak negative associations with electrical conductivity and total nitrogen. No association was found between physical and fringing zone degradation measures and EPT species richness. EPT community structure generally did not distinguish between sites with high or low degradation levels. The presence of the mayfly Nyungara bunni tracked salinity, dissolved oxygen and nitrogen levels, but its usefulness as a bioindicator could be limited by its restricted range. This study suggests that the EPT index would need modification or combination with other indices to be a useful rapid bioassessment in south-western Australia.

Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions.

Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world.

Utilization of metal and radionuclide concentrations to assess the influence of shrimp farming on the geochemical characteristics of sediments.

This article assesses the environmental impacts of aquatic biota cultivation, focusing on shrimp farming in Brazil's Northeast, as this practice has proven to be one of the main sources of economic growth in the region. For this purpose, sediment samples were collected from areas impacted and not directly impacted by shrimp farming, and concentrations of key geochemical parameters such as salinity, various elements (K, P, Cu, Mn, Pb, Zn, Al, Ca, Fe, Mg, and Na), and natural radionuclides (K-40, Ra-226 and Ra-228) were compared using statistical tools. Element concentrations were determined using ICP-OES, and naturally occurring radionuclide concentrations were obtained through gamma spectrometry. Statistical tests, such as ANOVA and/or Mann-Whitney, cluster analysis, and principal component analysis, were applied to the results. Additionally, the ERICA Tool software was employed to estimate deleterious effects on both human and non-human biota. Descriptive statistics reveal variability in sediment parameters around shrimp farming. ANOVA and Mann-Whitney tests compare concentrations of shrimp farm sediment and not directly impacted sediment, showing non-significant differences for most elements. pH and salinity, crucial for shrimp health, exhibit higher values in shrimp farm sediment. Alkali and alkaline earth metals, including K and Na, show no significant differences. Factor and cluster analyses suggest that certain elements, mainly radionuclides, are influenced by sediment variability. Hazard indices for naturally occurring radionuclides indicate negligible risk to both human and non-human biota, reinforcing the absence of adverse effects from shrimp farming activities. This study provides a comprehensive analysis of the environmental impacts of shrimp farming, emphasizing the importance of monitoring geochemical parameters for coastal environmental management.

Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003-2022) in Nepal.

Understanding the vegetation dynamics and their drivers in Nepal has significant scientific reference value for implementing sustainable ecological policies. This study provides a comprehensive analysis of the spatio-temporal variations in vegetation cover in Nepal from 2003 to 2022 using MODIS NDVI data and explores the effects of climatic factors and anthropogenic activities on vegetation. Mann-Kendall test was used to assess the significant trend in NDVI and was integrated with the Hurst exponent to predict future trends. The driving factors of NDVI dynamics were analyzed using Pearson's correlation, partial derivative, and residual analysis methods. The results indicate that over the last 20 years, Nepal has experienced an increasing trend in NDVI at 0.0013 year-1, with 80% of the surface area (vegetation cover) showing an increasing vegetation trend (~ 28% with a significant increase in vegetation). Temperature influenced vegetation dynamics in the higher elevation areas, while precipitation and human interventions influenced the lower elevation areas. The Hurst exponent analysis predicts an improvement in the vegetation cover (greening) for a larger area compared to vegetation degradation (browning). A significantly increased area of NDVI residuals indicates a positive anthropogenic influence on vegetation cover. Anthropogenic activities have a higher relative contribution to NDVI variation followed by temperature and then precipitation. The results of residual trend and Hurst analysis in different regions of Nepal help identify degraded areas, both in the present and future. This information can assist relevant authorities in implementing appropriate policies for a sustainable ecological environment.

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 µg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.