Freshwater macrophytes thrive in tropical and subtropical climates, providing valuable feed ingredients for a variety of aquatic and terrestrial animals while also influencing hydrology, sediment dynamics, and biogeochemical cycles. The purpose of this review is to assess the viability of freshwater macrophytes as a sustainable source of minerals for agriculture. To provide a comprehensive understanding of the topic, over 120 studies published across diverse geographic regions with diverse climates, precipitations and topography, were reviewed, with a focus on different species and methodologies. Key findings show that anthropogenic introductions of exotic freshwater macrophytes pose significant ecological risks due to their rapid growth in new habitats, which can disrupt local ecosystems. However, the biomass derived from these plants has a wide range of applications, including animal feed, biofuel, and ceramics, as well as biological markers and environmental remediators. Furthermore, fresh tissues, dried matter, manure, ensilage, and biochar are examples of in-situ applications of freshwater macrophytes that have shown to have major benefits for agricultural practices. These applications promote healthy plant growth, raise crop yields, lessen the need for chemical fertilizers, and decrease crop disease rates. The review also covers these organisms’ ability to produce antibacterial compounds, remove heavy metals and other pollutants from soil and water, and enhance soil quality and biodiversity in general. The results collectively highlight the significance of freshwater macrophytes in fostering ecological balance and resource efficiency, as well as their potential to support environmentally friendly farming methods and environmental conservation.

Extreme air pollution poses global health and environmental threats, necessitating robust policy interventions. This study first analyses the surface mass concentration of major aerosols (such as black carbon, organic carbon, dust, sea salts, and sulphates) to estimate global PM2.5 concentrations from 1980 to 2023. The developed model-estimated PM2.5 database was validated against data from 526 cities worldwide, showing strong accuracy, with RMSE, r, and R2 values of 7.47 μg/m³, 0.87, and 0.75, respectively. The motivation arises from the need to understand whether recent pollution increases are driven by rising emissions or natural variability, given the significant impacts on life and property. To assess both short-and long-term pollution trends, magnitudes, and risks, we proposed twelve novel extreme pollution indices, which comprehensively characterize the spatial and temporal variations in pollution. The highest PM2.5 concentrations were observed in regions near the Saharan Desert, reaching up to 90,000 μg/m³. However, significant PM2.5TOT (total pollution) concentrations were also found in the Indo-Gangetic Plain (IGP) and eastern China, ranging from 20,000 to 40,000 μg/m³. Persistent pollution burdens North Africa for approximately 350 days annually, while the IGP and eastern China experience extreme pollution for over 200 days yearly. Other pollution indices highlight the intensity and frequency of pollution in regions such as North Africa, IGP, Eastern Russia, Western USA, and Eastern China, revealing critical regional air quality challenges. Our analysis identifies cities in low-income and middle-income countries, such as New Delhi, Lahore, Dhaka, and Dammam, as being at extreme risk scores above 90 out of 100. Meanwhile, cities like Ghaziabad, Chongqing, Kolkata, Mumbai, and East London fall into the high-risk category, scoring between 60 and 80. Conversely, most cities in the EU, USA, and Canada are at very low risk, a result of the effective implementation of strategic air pollution norms and policies. The study promotes a phased approach for low- and middle-income regions, emphasizing achievable air quality standards, low-cost monitoring, targeted interventions, urban greening, public awareness, and innovative financing for improvements.

The present study aims to identify potential locations for small-scale hydroelectric power (HEP) stations in hilly regions for the purpose of generating renewable energy. A rainfall-runoff (R-R) model of the Beas River catchment was established using the MIKE 11 NAM to estimate the available discharge. The model was calibrated and validated over the period of June-2015-May-2018 and June-2018-May-2020, respectively, using daily observed discharge data at the Pandoh Dam site. The model exhibited good performance with a coefficient of determination (R2) of 0.82 during calibration and 0.70 during validation and a water balance of -0.01% and -18%, respectively. However, Lmax, CK1, CK2 and CQOF are found most sensitive parameters during the calibration. Further, thirteen major streams of order five or higher were selected for the assessment of hydropower potential, resulting in the identification of 131 potential run-of-river (ROR) hydropower sites. The hydropower potential at two proposed sites, Bhang SHEP (9 MW) and Raison SHEP (18 MW), was estimated to be 11 and 15 MW, respectively, using 90% dependable flow. The results demonstrate the effectiveness of using Digital Elevation Model (DEM) and Geographic Information System (GIS) techniques for determining hydropower potential in ungauged basins in the Himalayas.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics of the Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the data were subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann–Kendall test and Sen’s slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03 days/year and decrease in consecutive wet days (CWD) at 0.02 days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06

This study focuses on understanding how aerosols are transported over long distances, especially during extreme events. Leveraging the integrated vapour transport (IVT) based atmospheric river (AR) algorithm to integrated aerosol transport (IAT) to detect the aerosol atmospheric rivers (AARs) for key aerosol species such as black carbon (BC), organic carbon (OC), dust (DU), sea salt (SS), and sulphate (SU). The present study also assesses the occurrence, intensity, and societal impacts of AARs globally during 2015–2022 on a spatiotemporal resolution of 1.5° × 1.5° and 6 h, respectively. The detection algorithm found a total number of 128,261 AARs found globally for key aerosol species. However, the availability of BC, OC, and SU AARs is most common and intense in densely populated areas like the Indus-Brahmaputra-Ganga (IBG) plains ( 15–20 AAR days/year), Eastern China ( 25–40 AAR days/year), and Japan ( 20–30 AAR days/year), where human activities including agriculture burning contribute to their formation. DU AARs, on the other hand, are more prevalent in Northern Africa ( 15 AAR days/year), the Gulf ( 5–10 AAR days/year), the USA, and the Amazon rainforests. SS AARs share similar characteristics with atmospheric rivers and are more intense in higher latitudes and over the oceans ( 30–40 AAR days/year). The study also validates its findings by analysing recent extreme events involving BC and DU worldwide. The potential applications of specific AARs could assist us in identifying the causes of snow darkening, reducing snow cover area, and accelerating melting rate. Moreover, AARs could aid in quantifying the health risks associated with severe air pollution.

Assessment and modelling of hydro-sedimentological flows of a high-altitude river system is a critical step for developing and managing sustainable water resource projects and best management practices (BMPs) in the downslope regions of the Indian Himalayan Region (IHR). A field study was carried out to measure the hydraulic parameters such as water pressure, water flow rate, and stage of the 6th order glacier-fed river to quantify hydro-sedimentological flows using area-velocity and vacuum filtration method for 3 successive years during 2018–2020. Further, a process-based hydrological model: Soil and Water Assessment Tool (SWAT), is used to simulate the hydro-sedimentological flows. The statistical indices such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percentage bias (PBIAS) attain higher values during both calibration and validation periods. The snowmelt and rainfall contributions to the total streamflow range from 17–35

The current study presents a comprehensive assessment of hydrological models, including the Snowmelt Runoff Model (SRM), MIKE HYDRO RIVER NAM model, and Soil and Water Assessment Tool (SWAT), for simulating runoff dynamics in the Beas River Basin (BRB). Utilizing data spanning seven years from 2014 to 2020, the models underwent rigorous calibration and validation processes to evaluate their performance under varying hydrological conditions. The SRM model exhibited commendable performance, with high correlation coefficients (R²) of 0.85 during calibration and 0.82 during validation, indicating strong agreement between observed and simulated runoff volumes. Similarly, the MIKE HYDRO RIVER NAM model demonstrated satisfactory performance, albeit with a slightly higher root mean square error (RMSE), indicating a reasonable fit between observed and simulated data. In contrast, the SWAT model exhibited relatively lower performance metrics, particularly regarding R² and Nash-Sutcliffe Efficiency (NSE) values, suggesting limitations in accurately capturing runoff dynamics, especially during peak flow events. Comparison of model performance highlighted the superior capability of the SRM and MIKE HYDRO RIVER NAM models in simulating runoff dynamics, attributed to their robust representation of hydrological processes and comprehensive consideration of relevant parameters. Analysis of water resource management in the BRB emphasized the importance of understanding flow dynamics, particularly seasonal variations in water availability, for effective water resource management. Overall, this study underscores the significance of accurate hydrological modelling for informed decision-making in water resource management and highlights the potential of the SRM and MIKE HYDRO RIVER NAM models for such applications.

A large population depends on runoff from Himalayan rivers. They provide enough water for drinking, domestic, industrial, and irrigation. Also, these rivers have a high hydropower potential. A lack of in-depth studies has made it difficult to understand how these rivers respond hydrologically to climate change and, thus, impact the environment. In this paper, modelling the Alkhnanda river system using the Soil and Water Assessment Tool (SWAT) has been conducted to understand the hydrological response and assess its water balance components. The result shows that the basin’s water yield and Evapotranspiration (ET) vary from 58-63% and 34-39% of precipitation, respectively. The amount of lateral runoff contributed by snowmelt to the Alkhnanda River ranged from 20-24%. SFTMP, TLAPS, SMTMP, CN2, SMFMX, and GW_DELAY is found most sensitive at the significance level less than 0.05, shows the contribution of the snowmelt is significant in streamflow while delay in the groundwater will affect the contribution of surface runoff and groundwater in the streamflow. Based on the results, it is highly recommended that the study area’s spatial and temporal hydro-meteorological data be strengthened to improve modelling.

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural-urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28-30 dB and Mean Square Error below 10 mu g/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

The study explores extreme aerosol transport (EAT) events using atmospheric river (ARs) dynamics to identify aerosol atmospheric rivers (AARs). This provides insight into their significance in mitigating aerosol pollution and strengthening resilience within Earth system Boundaries (ESBs). AARs are narrow and long regions with high concentrations of various aerosols, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphate (SU), transported over long distances. Leveraging MERRA-2 re-analysis datasets, this study detects the AARs by applying various boundary conditions and develops a Spatio-Temporal AAR Availability Prediction Model (ST-AARAPM) based on a convolutional autoencoder. The model predicts AAR availability for the next t + 5-time frames using Stochastic Gradient Descent (SGD) optimization, minimizing Mean Squared Error (MSE) loss with a Rectified Linear Unit. Model performance is evaluated using metrics such as Structural Similarity Index (SSIM), Root Mean Squared Error (RMSE), Peak Signal Noise Ratio (PSNR), and MSE. From 2015 to 2022, the study identified 128,261 AARs worldwide with at least 8 AARs present at any given time frame. However, the model evaluation indicates satisfactory results, with SSIM, PSNR, RMSE, and MSE ranging from 0.88 to 0.96, 67.60 to 78.50 dB, 0.0656 to 0.1552, and 0.0043 to 0.0247, respectively. The findings highlight the effectiveness of ST-AARAPM in forecasting AAR availability and enhancing resilience in hotspot regions with significant aerosol loading, including the Indo-Gangetic plains, Eastern China, Japan, Northern Africa, Eastern USA, and South America. The study offers a fresh approach to tackling the effects of severe aerosol pollution via AARs within ESB’s. It stresses the need for policies to curb emissions, encourage sustainable production, and embrace clean energy. It calls on vulnerable systems to shift to cleaner technologies for resilience against aerosol pollution.

This study focuses on the hydro-sedimentological characterization and modeling of the Dhauliganga River in Uttarakhand, India. Field data collected from 2018-2020, including stage, velocity, and suspended sediment concentration (SSC), showed notable variations influenced by melting snow, glaciers, and precipitation. Challenges in accurately modeling rivers with a topography and sparse gauging stations were addressed using artificial neural networks (ANN). The calibrated models precisely predicted stage-discharge and sediment-discharge relationships, demonstrating the effectiveness of machine learning, particularly ANN-based modeling, in such challenging terrains. The model's performance was assessed using coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). During the calibration phase, the model exhibited notable performance with R2 values of 0.96 for discharge and 0.63 for SSC, accompanied by low RMSE values of 5.29 cu m s-1 for discharge and 0.61 g for SSC. Subsequently, in the prediction phase, the model maintained its robustness, achieving R2 values of 0.97 for discharge and 0.63 for SSC, along with RMSE values of 5.67 cu m s-1 for discharge and 0.68 g for SSC. The study also found a strong agreement between water flow estimates derived from traditional methods, ANN, and actual measurements. The suspended sediment load, influenced by both water flow and SSC, varied annually, potentially modifying aquatic habitats through sediment deposition, and altering aquatic communities. These findings offer crucial insights into the hydro-sedimentological dynamics of the studied river, providing valuable applications for sustainable water-resource management in challenging terrains and addressing environmental concerns related to sedimentation, water quality, and aquatic ecosystem. Machine learning was used to predict water flow and sediment levels in the Dhauliganga River, Uttarakhand. By analyzing data from 2018 to 2020, we showed how artificial neural networks can accurately model river dynamics despite challenging terrains. These findings will help improve water management and address environmental issues like sedimentation and water quality. image

Floods are recurrent global catastrophes causing substantial disruptions to human life, extensive land degradation, and economic losses. This study aims to identify flood-triggering watershed features and employ a Multi-Criteria Decision-Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) model to delineate flood-prone zones. Weights for various flood-influencing factors (slope, rainfall, drainage density, land-use/land-cover, geology, elevation, and soil) were derived using a 7 × 7 AHP decision matrix, reflecting their relative importance. A Consistency Ratio (CR) of 0.089 (within acceptable limits) confirms the validity of the assigned weights. The analysis identified approximately 128.51 km2 as highly vulnerable to flooding, particularly encompassing the entire stretch of riverbanks within the watershed. Historically, snow avalanches and flash floods have been the primary water-related disasters in the region, posing significant threats to critical infrastructure. In this context, this model-based approach facilitates the proactive identification of susceptible areas, thereby promoting improved flood risk mitigation and response strategies.

Accurate streamflow data and its appropriate treatment are of paramount importance for water resource management. With the growing role of computational models such as soil and water assessment tool (SWAT) and artificial neural network (ANN) in hydrological assessments, we conducted an evaluation of the accuracy of these models for the streamflow simulation of Sindh River. In this study, we utilized monthly time-series data to assess the accuracy of the ANN and SWAT models. A comparative analysis based on prediction accuracy was conducted, and the results indicated that the ANN model demonstrated excellent performance in forecasting peak flow, whereas the SWAT model showed better outcomes when simulating low-flow values. Our findings reveal that the SWAT model will achive the highest Nash–Sutcliffe efficiency (NSE) and coefficient of determination ( R 2 ) values during calibration and validation stages. The study showed that ANN-based modeling is time and computationally efficient, does not require extensive studies, is not limited by the nature or quantity of inputs, and produces results equivalent to those generated by process-based models. On the contrary, process-based models necessitate the collection of comprehensive data, as well as regular field inspections and monitoring. Therefore, our study has the potential to be utilized in the management of freshwater resources in the Indian Himalayan Region (IHR).

Studying geo-morphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools is crucial to routing runoff and remaining hydrological processes. A geo-spatial model and principal component analysis (PCA) approach are used in this study to prioritize sub-watersheds of the upper Beas river up to Pandoh dam. Dendritic drainage patterns throughout its sub-watersheds characterized the 6 th -order Beas river. The sub-watersheds show a lithological uniformity that indicates that the entire watershed has structurally impermeable materials at both surface and sub-surface levels. Moreover, the aerial and relief aspects of the sub-watershed indicate fine drainage textures, steep slopes, immediate peak flows, a hydrograph with multiple peaks, and a low concentration time. In other words, the sub-watershed may not be able to manage flash floods during the storm period. Surface runoff and sediment production rates (SPR) were estimated in the present study ranged from 3.576–5.240 sq. km-cm/sq.km and 0.101–0.234 ha-m/100sq.km/year, respectively. Finally, the study concluded that the sub-watersheds in the upper regions produced high runoff and sediments, usually carried into the mainstream. Further, the PCA technique was applied to find the redundant morphometric parameters and then the same results were utilized to determine the effective way to prioritize the watershed. The present study will serve as a basis for developing appropriate policies and practices for peak flooding and promoting the sustainability of the watershed.