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.

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.

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.

Emerging pollutant in surface water bodies: a review on monitoring, analysis, mitigation measures and removal technologies of micro-plastics.

Water bodies play a crucial role in supporting life, maintaining the environment, and preserving the ecology for the people of India. However, in recent decades, human activities have led to various alterations in aquatic environments, resulting in environmental degradation through pollution. The safety of utilizing surface water sources for drinking and other purposes has come under intense scrutiny due to rapid population growth and industrial expansion. Surface water pollution due to micro-plastics (MPs) (plastics < 5 mm in size) is one of the emerging pollutants in metropolitan cities of developing countries because of its utmost resilience and synthetic nature. Recent studies on the surface water bodies (river, pond, Lake etc.) portrait the correlation between the MPs level with different parameters of pollution such as specific conductivity, total phosphate, and biological oxygen demand. Fibers represent the predominant form of MPs discovered in surface water bodies, exhibiting fluctuations across seasons. Consequently, present study prioritizes understanding the adaptation, prevalence, attributes, fluctuations, and spatial dispersion of MPs in both sediment and surface water environments. Furthermore, the study aims to identify existing gaps in the current understanding and underscore opportunities for future investigation. From the present study, it has been reported that, the concentration of MPs in the range of 0.2-45.2 items/L at the Xisha Islands in the south China sea, whereas in India it was found in the range of 96 items/L in water samples and 259 items/kg in sediment samples. This would certainly assist the urban planners in achieving sustainable development goals to mitigate the increasing amount of emergent pollutant load.

Spatio-temporal variation in water quality due to the anthropogenic impact in Rudrasagar Lake, a Ramsar site in India.

Rudrasagar Lake, a vital habitat for diverse flora and fauna, supports over 2000 households to sustain their daily livelihoods. The current study attempts to examine the impact of human activities on spatio-temporal variation in the water quality of the study area. The study integrates extensive field surveys, sample processing, and statistical analysis to assess the recent status of wetland health. Latin Square Matrix (LSM) was employed to select the sampling sites while the Inverse Distance Weighting (IDW) interpolation technique was used for spatial variation mapping. Modified Weighted Arithmetic Water Quality Index (MWAWQI) and Comprehensive Pollution Index (CPI) were utilized for assessing seasonal variation water quality and pollution loads, respectively. The results showed that dissolved oxygen (DO) was strongly influenced by the tributaries, and recreational activities have substantially influenced the highest concentrations of biochemical oxygen demand (BOD), and total suspended solids (TSS). The central portion of the lake is particularly susceptible to pollution from extensive fishing and recreational activities while peripheral sites are strongly influenced by agricultural run-offs, seepages from brick industries, and municipal wastes characterized by high concentrations of pH, total hardness (TH), oxidation-reduction potential (ORP). The findings reveal remarkable spatio-temporal fluctuations and highlight the areas within the lake susceptible to anthropogenic activities. The study proposed a sustainable management model to ameliorate anthropogenic threats. Moreover, the study contributes to the scientific understanding of the challenges and ensures the long-term viability of wetland health as a vital ecological and socio-economic resource.

Temporal and spatial evolution simulation and attribution analysis of vegetation photosynthesis over the past 21 years based on satellite SIF data: a case study from Asia.

Photosynthesis in vegetation is one of the key processes in maintaining regional ecological balance and climate stability, and it is of significant importance for understanding the health of regional ecosystems and addressing climate change. Based on 2001-2021 Global OCO-2 Solar-Induced Fluorescence (GOSIF) dataset, this study analyzed spatiotemporal variations in Asian vegetation photosynthesis and its response to climate and human activities. Results show the following: (1) From 2001 to 2021, the overall photosynthetic activity of vegetation in the Asian region has shown an upward trend, exhibiting a stable distribution pattern with higher values in the eastern and southern regions and lower values in the central, western, and northern regions. In specific regions such as the Turgen Plateau in northwestern Kazakhstan, Cambodia, Laos, and northeastern Syria, photosynthesis significantly declined. (2) Meteorological factors influencing photosynthesis exhibit differences based on latitude and vertical zones. In low-latitude regions, temperature is the primary driver, while in mid-latitude areas, solar radiation and precipitation are crucial. High-latitude regions are primarily influenced by temperature, and high-altitude areas depend on precipitation and solar radiation. (3) Human activities (56.44%) have a slightly greater impact on the dynamics of Asian vegetation photosynthesis compared to climate change (43.56%). This research deepens our comprehension of the mechanisms behind the fluctuations in Asian vegetation photosynthesis, offering valuable perspectives for initiatives in environmental conservation, sustainability, and climate research.

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.

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.

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.

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.

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.

Creation of an international laboratory network towards global microplastics monitoring harmonisation.

Infrastructure is often a limiting factor in microplastics research impacting the production of scientific outputs and monitoring data. International projects are therefore required to promote collaboration and development of national and regional scientific hubs. The Commonwealth Litter Programme and the Ocean Country Partnership Programme were developed to support Global South countries to take actions on plastics entering the oceans. An international laboratory network was developed to provide the infrastructure and in country capacity to conduct the collection and processing of microplastics in environmental samples. The laboratory network was also extended to include a network developed by the University of East Anglia, UK. All the laboratories were provided with similar equipment for the collection, processing and analysis of microplastics in environmental samples. Harmonised protocols and training were also provided in country during laboratory setup to ensure comparability of quality-controlled outputs between laboratories. Such large networks are needed to produce comparable baseline and monitoring assessments.

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.

Spatial and temporal-trend assessment of desertification-sensitive land using the desertification sensitivity index in the provincial Ninh Thuan, Vietnam.

Desertification is a specific land-degrading process, reducing soil productivity and potentially threatening global food security. Therefore, spatially and temporally identifying and mapping desertification-sensitive areas is essential for better management. The current study aimed to (1) assess spatial areas sensitive to desertification and (2) examine the changing tendency of the desertification-sensitive areas over the past 25 years in the provincial Ninh Thuan. The desertification sensitivity index (DSI) was computed based on the Medalus model using 10 quantitative parameters, grouped into the soil, climate, and vegetation quality indexes, computed for the years 1996, 2005, 2010, and 2016. GIS was used to map desertification-sensitive areas associated with five DSI classes. Results showed that classes II and III had the highest area percentage, followed by classes IV and V, and class I. The classes most sensitive to desertification (classes IV and V) covered around 13 to 17%, and classes II and III were 25 to 32% of the total study area, respectively. The coastal areas located in the southeastern parts were more sensitive to desertification than the other parts. Over the four examined periods, the areas of classes IV and V increased while those of classes II and I decreased. These indicated that the study province tended to increase in its desertification sensitivity with a severe increase in the coastal areas over the past 25 years. The key factors involved in these changes could be related the human activities and climate variation, which could be more serious in southeastern areas than in the other areas.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.