National Institute of Hydrology

Spring water is one the important source of fresh water in the Himalayan region which is used for drinking, irrigation, and industrial purposes. With the unprecedential rise in the urbanization and industrialization, has posed severe threats to spring water resources. The water quality of springs has severely deteriorated owing to the changes in land use patterns. In the present study, the physiochemical parameters of the spring water of intermountain Doon valley, present at the eastern Himalayan region were assessed in order to simultaneously understand the hydrogeochemistry of the region and the factors controlling the ion chemistry of water. In total nine water samples were collected from three different water springs during three different seasons and were different water quality parameters were analyzed viz. temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), bicarbonate (HCO3⁻), sulphate (SO4²⁻), chloride (Cl⁻), nitrate (NO3⁻), and fluoride (F⁻) total hardness (TH), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺) and potassium (K⁺). The heavy metal analysis (for Cu, Pb, Ni, Cr and Cd) for all the three seasons was found to be below the BIS threshold for drinking water quality. The total coliform and E. coli tests were conducted and was found to above the BIS standard's permissible limits. The Water Quality Index (WQI) was evaluated to check the suitability of water for drinking purposes and as per WQI index water and was found to be under the good category for drinking purposes. The spring water have fallen under the excellent to good category for irrigation purposes. The high ratio of Ca²⁺ + Mg²⁺ / Na⁺ + K⁺ and a low ratio of Na⁺ + K⁺ /TZ⁺ indicated the dominance of carbonate weathering in the studied area. The piper trilinear diagram indicated the Ca²⁺-Mg²⁺-HCO3 and Ca²⁺-Mg²⁺–HCO3– -SO4²⁻ were dominant hydro-chemical facies in the study area. Graphical Abstract

The intricate relationship between groundwater and surface water has garnered significant attention due to the formidable challenges in model validation and calibration. These challenges mainly stem from the dynamic exchange at the soil–water interface, which becomes more intricate at smaller scales, posing significant obstacles for researchers. However, as investigations extend to larger scales, the complexity of these interactions intensifies, reaching unprecedented levels. To gain a deeper understanding of this critical coupling, it is essential to develop robust numerical models capable of accurate simulations. This comprehensive review explores two prominent coupling strategies within detailed domain models: the “fully-coupled” and “loosely-coupled” techniques, which are cornerstones of contemporary groundwater modeling efforts. Through careful analysis, we highlight the unique advantages and inherent limitations of each modeling approach. This review provides invaluable guidance for both researchers and practitioners alike, offering strategic insights for the development of groundwater flow models. It serves as a comprehensive resource, aiding in the formulation of effective strategies and considerations crucial for model crafting. By synthesizing key findings and methodologies, it empowers professionals to make informed decisions in their modeling endeavors. The ultimate goal is to develop models that accurately capture the complexities of real-world conditions governing surface and groundwater interactions, particularly at regional scales. By exploring the intricate landscape of coupling strategies, we pave the way for improved insights and more precise simulations in the field of groundwater dynamics.

Demarcation of the groundwater recharge prospective zones can be the foremost step in facilitating groundwater recharge in any terrain, as most nations have a major concern about unreasonable use of groundwater and declining the water table. To identify groundwater recharge zones in Haridwar district of Uttarakhand state in India, this study employs the integration of remote sensing data along with the Geographical Information System (GIS) and the Analytical Hierarchy Process (AHP) technique by incorporating remote sensing data acquired from different sources. Soil texture, slope, drainage density, land use/land cover (LULC), lithology, geomorphology, lineament density, topographic wetness index (TWI), and rainfall were analysed, and weights were assigned using the AHP technique to assess their impact on groundwater recharge. The study region has been divided into five possible groundwater recharge zones by using weighted overlay analysis: very high (0.82%), high (37.03%), moderate (40.22%), low (17.91%), and very low (4.02%). The verified groundwater recharge potential map for the study region has been validated with 30 existing bore wells. The efficacy of the method was confirmed by an Area Under Curve (AUC) calculated to be 71.08% with the evidence obtained, and the Receiver Operating Characteristic (ROC) curve is plotted. The findings facilitate the sustainable management of groundwater and the application of artificial recharge techniques in Haridwar.

The Himalayan rivers are particularly vulnerable to regional climate changes and anthropogenic influences, which can significantly alter both water quality and quantity, jeopardizing the fragile river ecosystems. This study investigates the hydrochemical characteristics of the Song River, a tributary of River Ganga focusing on non-point source (NPS) pollution, during the period June 2022 to November 2023. Monitoring of river discharge was carried out water samples were collected weekly during the monsoon (June to September), bi-weekly in the post-monsoon (October & November), and monthly during lean periods (December–May) from three monitoring stations. The study revealed that Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) eventually exceeded the criteria limits (3 mg/L for BOD and 10 mg/L for COD) prescribed by the Central Pollution Control Board (CPCB). The chemical composition of the river water at the monitoring stations revealed Ca²⁺and Mg²⁺ as the dominant cations, while HCO3⁻ and SO4²⁻ were identified as the major anions. Gibbs plot suggesting the dominance of rock weathering as the major natural controlling mechanism of chemical composition in the basin. Carbonation and sulphide oxidation are two proton-producing reactions controlling the chemical weathering processes. C-ratio plot suggested that dominance of carbonate dissolution in the tributary Suswa, while Song River showed dominance of sulphide oxidation, particularly in its upstream region. Spatial and temporal analysis identified nutrient pollution (NO₃⁻, NH₄⁺, PO₄³⁻), organic loads (BOD, COD), and other parameters (TSS, Cl⁻) as key contributors to water quality deterioration at the monitoring stations. A chemical mass balance (CMB) approach based on mass conservation has been applied for estimating the NPS pollution loads on the Song River. CMB calculations carried out for major cations such as (Na⁺, K⁺, Ca²⁺, Mg²⁺, and NH4⁺) and major anions (Cl⁻, SO4²⁻, HCO3⁻, NO3⁻, and PO4³⁻) across a 21 km stretch of the river, encompassing approximately 305 sq. km. and observed that the contribution of uncharacterised load was maximum in dry season and minimum during monsoon season. Seasonal variations in ion concentration and flux were strongly correlated with hydrological processes, highlighting the need for comprehensive monitoring and management of NPS pollution in the Song River to safeguard its water quality and downstream impacts on the Ganga River system.

Drug-induced autoimmune diseases are increasingly recognized although mechanistic insight into disease causation is lacking. Hydralazine exposure has been linked to autoimmune diseases, including anti-neutrophil cytoplasmic autoantibody (ANCA) vasculitis. Our hypothesis posits that hydralazine covalently binds to myeloperoxidase (MPO), triggering the autoimmune response in ANCA vasculitis. We in vitro observed formation of carbonyl derivatives on amine groups in the presence of acrolein. This facilitated the subsequent binding of hydralazine to heme-containing proteins, including MPO, via a Michael addition. Our studies demonstrated that carbonyl derivatives and hydrazone adducts induce conformational changes in the MPO heavy chain, potentially changing its immunogenicity. We identified hydrazone adducts on circulating MPO in patients with hydralazine-associated ANCA vasculitis. These patients exhibited elevated anti-MPO IgM levels, while anti-MPO IgG levels were comparable between hydralazine-associated and non-hydralazine-associated vasculitis patients. IgM isolated from hydralazine-associated MPO ANCA patients demonstrated a heightened affinity to hydralazine-modified MPO and activated neutrophil-like HL-60 cells. Hydralazine-modified MPO was pathogenic, as demonstrated by splenocyte transfer in a mouse model of ANCA vasculitis. Our findings unveil a mechanism of drug-induced autoimmunity wherein stepwise chemical modifications of MPO lead to conformational changes and hydrazone adduct formation producing a neoantigen to which pathogenic autoantibodies are generated.

Land and water are vital natural resources that underpin ecosystems, sustain life, and are essential for achieving sustainable development. However, these resources are increasingly threatened by climate change, population growth, urbanization, and pollution, necessitating advanced technological solutions for effective management. Google Earth Engine (GEE), a cloud computing-based geospatial platform, leverages Google’s vast computational infrastructure to provide open access to petabytes of data, enabling efficient and comprehensive analysis of land and water resources. While GEE has been widely applied in land and water resources (LWR) management, its potential remains underexplored, highlighting the need for further investigation. This study addresses this research gap by offering readers ‘one-stop overview’ of recent trends, key contributions, and future directions through a global scientometric analysis of GEE-enabled LWR research. A total of 507 articles published between 2010 and 2023 in high-impact journals, sourced from the Web of Science and Scopus databases, were analyzed across four key dimensions: descriptive, performance, keyword, and methodological analyses. The findings reveal a notable increase in GEE-enabled LWR research post-2019, with significant contributions originating from China, the USA, and India. Despite this growth, regions such as Africa, Central Asia, and Eastern Europe remain underrepresented. The co-authorship network also indicates limited collaboration, characterized by numerous isolated clusters. Prominent research themes identified include integrated water resource management, land use and land cover (LULC) change detection, agricultural monitoring, and sustainable development. Landsat data emerges as the most frequently utilized, appearing in 235 publications, while the Random Forest algorithm is the predominant method, employed in 27% of studies. Furthermore, the study underscores existing challenges and outlines future directions for advancing sustainable land and water resource management.

Groundwater is a vital natural resource for a healthy society and economy. The study of groundwater involves both geological and chemical aspects and its importance in characterizing the natural systems, understanding the contaminant sources involved, and their remedies.

The Pernote landslide event in the Ramban area on April 25, 2024, caused significant damage and displaced many residents. Preliminary investigations identified the landslide as a massive, complex debris slide and flow, primarily involving overburden materials such as mud, silt, clay, and rock fragments. The slide was characterized by several rotational slip planes and debris flow channels. The severity of the event was attributed to explicit geological conditions, including fault and thrust zones, loose consolidated and deformed rocks from the Murree Formation, and thick deposits of Quaternary sediments exceeding ~20 m. Heavy antecedent rainfall (100-175 mm) from April 20th to 24th saturated the debris and soil cover, triggering the landslide on the steep slopes (angle > 45°). The total displacement was approximately 40 m, with a depth of about ~12 m. The slide zone extended from the crown to the toe, reaching up to the River Chenab, covering approximately 1250 m. The Pernote landslide was not entirely unexpected, as early signs of movement-such as deep fissures, ground cracks, and bulges-were observed as early as 2021. Temporal analysis of high-resolution Google Earth images from 2012 to 2022 supports these observations, revealing signs like old landslide scars, ground cracks, and ongoing landslide activity. Additionally, during the past decade, significant changes in vegetation cover and a 19.2% increase in built-up areas were noted. These findings highlight the importance of monitoring early surface indications as warning signs for effective landslide mitigation, preparedness, and public awareness to prevent loss of life and infrastructure in future events.

Land Use and Land Cover (LULC) are used to map the natural features and human activities of a landscape for any given time frame. It is necessary to continuously monitor the changes in LULC for the effective management of natural resources to comprehend the various effects of climate change. The remote sensing techniques is used to explore, map, and monitor landscapes, thus helping to understand the diverse effects of natural and man-made features. This study focuses on using machine learning techniques, particularly supervised algorithms, to extract thematic information from multi spectral satellite images. The main objective of this work is to map the LULC of the Brahmani-Baitarani basin from India using the Random Forest algorithm on remote sensing data collected via Google Earth Engine (GEE). The LULC is classified into four categories: vegetative cover, water bodies, barren land, and urban land, utilizing Sentinel 2, Landsat 8, and Landsat 9 data along with dynamic world cover, and the European Space Agency dataset. This study aims to support decision-makers, planners, and remote sensing experts in accurately performing LULC classification in rapidly urbanizing areas.

Traditional flood models often address either coastal or inland flooding separately, making comprehensive flood prediction challenging. This study presents an integrated framework that combines coastal and inland hydrodynamics, including tides, storm-surges, waves, and river flooding, to improve flood prediction in deltaic regions. The framework links the HEC-RAS 2D model with the tightly coupled ADCIRC + SWAN model to simulate flood inundation extents. The Brahmani-Baitarani River delta in Odisha (India) and the Bay of Bengal serve as the study areas for simulating tides, surges, waves, and inland flooding. The ADCIRC + SWAN model was calibrated and validated for cyclones Fani (2019) and Yaas (2021), using wind and pressure data from Climate Forecast System Version 2 (CFSv2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5). Model performance in predicting water surface elevations was excellent, with Nash–Sutcliffe Efficiency (NSE) values of 0.97–0.99 and errors within 8–18 cm. Flood extent for cyclone Yaas, simulated with HEC-RAS 2D, was validated against Sentinel-1 SAR imagery which showed good agreement. The integrated ADCIRC-SWAN-HEC-RAS 2D framework provides a robust tool for predicting flood dynamics in deltaic regions, enabling better disaster management for areas prone to cyclonic storm tides and inland pluvial flooding.

Sulphur and oxygen stable isotopes of sulphate have been used to trace the sources of sulphur into aquatic systems. These isotopes have also been used to understand the transformation and fate of sulphur in the water bodies contaminated by AMD discharge from active and/or abandoned mines. Stable isotopes of oxygen in dissolved sulphate (δ18OSO4) and water (δ18OH2O) have helped to decipher the sulphide oxidation pathways and estimate their contributions. The present study is focused on analysing the composition of sulphur and oxygen stable isotopes of sulphate and oxygen stable isotope in AMD and Lunar-Lukha River water flowing through the coal mining area of the East Jaintia Hills District, Meghalaya, in order to decipher the sulphide oxidation pathways. The results showed that the sulphur stable isotope of sulphate (δ34SSO4) ranged between –12.5 and –8.0 ‰ (VCDT). The oxygen isotope of sulphate (δ18OSO4) ranged between 1.4 and 2.0 ‰ (VSMOW). The oxygen isotope of water (δ18OH2O) was distributed between –6.2 and –4.2 ‰ (VSMOW). Pyrite oxidation was found to be the dominant source of sulphate in the Lunar-Lukha Rivers. The results of the stoichiometric isotope balance model showed that 68–83 % of sulphate derived Fe3+ oxidation pathway, with a high portion of sulphate oxygen derived from water. The sulphite–water oxygen exchange model revealed the release of intermediate sulphoxyanions, suggesting the presence of an oxidation pathway of sulphide minerals to sulphate via sulphoxyanions. The results from this study will be helpful in defining effective remediation strategies to mitigate AMD impacts.

The primary objective of the study is to provide an analysis of Roof-Top Harvested Rainwater Filtration Systems (RTHRWFS), importantly emphasising the findings that will be pivotal for future innovation and development in filtration technologies for a promising and effective adaptation of RTHRWFS by the masses. The work addresses a gap in the global Roof-top Rainwater Harvesting (RTRWH) implementation using detailed Prismatic and Bibliometric Analysis. The study has thoroughly examined distinct technical approaches, the structural cost, and public consciousness hindering global acknowledgement of RTRWH systems. It discusses the influence of the composition of rainwater, roof materials, and implementation of the first flush on the concentration of roof runoff. Further, our study delves into the probable areas of the sustainability and pursuance of these practices, potential improvement in existing system designs, and possible proficiency gaps. The work presents a framework for evaluating the sustainability of roof-top harvested rainwater intending towards high (installation, operation and maintenance) cost, less public recognition, low filtration rate, liability towards microbiological contamination etc. It also points out the symbolic roadblocks pertinent to skills and technological implementation, hence curtailing the extensive adaption of these systems globally. Our paper reiterates the critical areas in filtration technologies, focusing on the evolution of competent, economic and sustainable filtration methodologies catering to the long-term storage needs and diverse demands along with ensuring public awareness related to contaminants primarily present in rainwater, reassuring embracement of filtration systems for better domestic and potable use.

This work investigated the role of operational conditions and typical functional microbes to maximize the nutrient removal efficiency of a pilot-scale sequencing batch reactor (SBR) system (100 m3/d) that treated municipal wastewater. The pilot system was operated in five phases, including start-up and four runs at variable cycle times (2.0, 1.5, 1.7, 2.0, and 3.0h) with an average readily biodegradable chemical oxygen demand (rbCOD) to chemical oxygen demand (COD) ratio of ∼15.3%. The best TN removal ‘ηmax’ of 75.6 ± 5.6% (TNinfluent= 27.5 ± 6.5 mg/L, TNeffluent ≤5.9 mg/L) and TP removal ‘pmax’ 77.9 ± 6.3% (TPinfluent= 3.8 ± 1.3 mg/L, TPeffluent ≤1.0 mg/L) along with the COD, biochemical oxygen demand (BOD), and total suspended solids (TSS) removal efficiencies of 87.3 ± 4.5%, 92.7 ± 2.8%, 92.0 ± 3.5%, respectively, were observed during run 3 (2h cycle) at settling/ total cycle times ratio (S/T) of 0.33 and recirculation/ total cycle times ratio (R/T) of 0.017 (6.4%), and operating DO of 0.5-2.5 mg/L. The denitrifying polyphosphate accumulating organisms ‘DPAOs’ of Burkholderia (17.0%), Rhodocyclales (6.1%), and Flavobacterium (8.7%) classes, and Nitrifiers of Nitrospira (5.4%) and Nitrosomonas (5.4%) classes were dominant in accomplishing simultaneous nitrification, denitrification, and phosphorus removal (SND-PR) in the pilot system.

In terms of farm fertilizer Consumption, India is the world’s second largest country after China. Reducing fertilizer use cannot be considered a viable option, since optimizing food output was the highest priority for the population’s food and nutritional protection. Nitrogen, Phosphorous and Potassium fertilizers are farm fertilizers and are in great demand in Indian agriculture. This study investigated the effect of rainfall dynamics on farm fertilizer transport for rainfed Cotton Crop in Semi-arid Region in the Raichur district of South India. The solute transport and reaction parameters were assessed at the depths of 50, 100 and 150 cm of soil column. Hydrus-1D and Hydrus-2D software has been used for modelling transport of fertilizer in vadose zone. Rainfall data from 2015 to 2020, Soil data, Crop data, Soil hydraulic parameters, and Solute data has been used for this study. Results suggested that solute transport for average actual precipitation, 20% decreased precipitation and 20% increased precipitation at 150 cm depth of soil column was 0.000109, 0.00000000424, 0.000371 mmol cm−3 for nitrogen fertilizer, 0, 0, 0.00000014 mmol−3 for phosphorous fertilizer and 0, 0, 0.0000000329 mmol−3 respectively for potassium fertilizer. From this, it can be inferred that increased precipitation caused solutes to move more quickly than usual. Therefore, it can be concluded that due to climate change, changes in rainfall patterns and increased rainfall intensity might lead to excessive leaching of agricultural fertilizers into the vadoze zone and groundwater.

Surface soil moisture (SSM) refers to the capacity of the top layer of soil to hold moisture. It is an essential part of the budget for surface water. Soil moisture monitoring is crucial to reduce the effects of precipitation deficits and determine the best ways to manage natural ecosystems in the face of climate change. The current study collected daily SSW data from MERRA-2 for the Tel River Basin in Odisha, India, from 2001 to 2020 with a spatial resolution of 0.5 • × 0.625 •. To forecast SSW time series (SSWTS) one step ahead, this study examines the reliability of three deep learning (DL) models: gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural network (simpleRNN). This study aims to address the following research questions: (1) How accurately can DL models predict SSWTS? (2) Which DL model-GRU, LSTM, or simpleRNN-is the most reliable for SSW forecasting? (3) How can the uncertainty in the predicted SSW be quantified and analyzed? Further, in an uncertainty investigation on SSW projected values, a Wilson score technique was employed to evaluate the uncertainty of the DL methods. GRU has outdone the other two models in forecasting monthly SSW with a 12-lookback timestep with a lower error for all the stations. The model appeared more accurate as it declined in gradient on larger sequencing samples. GRU's ability to remember significant prior knowledge, whereas discarding irrelevant data may assist in finding a novel, dependable solution for SSWTS forecasting.

Groundwater drought is caused by reduced recharge from deficient rainfall, affecting water availability and regional economic activities. A newly proposed modified groundwater drought index (MGWDI) was evaluated and compared with the conventional groundwater drought index (GWDI) using seasonal groundwater level data from the Shipra river basin in Central India. A new MGWDI demonstrated a stronger correlation with droughts derived from the standardized precipitation index (SPI) at a 9-month and 12-month timescale compared to the conventional GWDI. The new MGWDI has resolved the discrepancy of underestimating groundwater deficit in overexploited areas, as seen in the case of the conventional GWDI. The SPI-12 was found to be more effective than SPI-9 in the prediction of groundwater droughts. The SPI-12 for the month of November was found to have the strongest correlation with groundwater droughts, making it useful to predict such events. The frequency of meteorological droughts in the Shipra river basin occurs approximately once every five years; consequently, according to the new MGWDI, it leads to hydrological droughts almost every four years. The post-monsoon groundwater level data performed better at interpreting groundwater droughts in close correlation with meteorological drought indices as compared to the pre-monsoon groundwater level data. The new MGWDI is recommended for a more precise assessment, monitoring, and mitigation planning of groundwater droughts.

This study has checked 222 Rn concentrations in groundwater from 27 sites of Haridwar and Dehradun, Uttarakhand, using the Durridge RAD-7. Seasonal variability reveals that the activity of radon is at its peak during the monsoon. Among the samples, 96.29% exceeded the United States Environmental Protection Agency's Maximum Contaminant Level for radon during monsoon. The results indicated significant correlations between the concentration of radon and some factors like electrical conductivity and pH. The objective is to compare seasonal radon levels in groundwater, identify significant variations , and assess potential health risks by evaluating them against established safety guidelines.

Recent studies show variations in precipitation‐gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global‐scale effectiveness, but applying them to regional studies, particularly in small to medium‐sized sub‐basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based‐Minimum Error approach to assimilate different open‐source gridded precipitation data sets for forming an accurate precipitation product over small to medium‐sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km². The study utilizes nine open‐source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station‐wise, grid‐wise, and elevation‐wise analyses using statistical parameters, quantile‐quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash‐Sutcliffe Efficiency (NSE), Coefficient of determination (R²), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and R² values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub‐basins, especially in Himalayan regions with limited station data.

Improper and unscientific management of municipal solid waste (MSW) landfill sites has increasingly become a pressing environmental issue especially in the mountainous regions worldwide. In view of this, an attempt was made to assess the detrimental effects of MSW landfill on the natural water sources at Dharamshala, Himachal Pradesh. Further, the suitability of potential landfill site and dispersion of pollutant air masses were stipulated using Arc GIS and HYSPLIT model. The findings show a discernible increase in electrical conductivity (323–858 μS/cm) and total dissolved solids (1086–1144 mg/kg levels) during sampling period. The results exhibited a notable increasing trend in the mean concentrations of heavy metals viz. As (0.13 mg/kg and 0.10 mg/kg), Hg (0.52 mg/kg and 0.65 mg/kg), Pb (0.10 mg/kg and 0.06 mg/kg), Zn (30.40 mg/kg and 0.22 mg/kg), Cd (0.46 mg/kg and 0.04 mg/kg), Cr (0.10 mg/kg and 0.05 mg/kg), Ni (0.28 mg/kg and 0.10 mg/kg), Mn (24.40 mg/kg and 0.35 mg/kg) and Fe (1.81 mg/kg and 0.96 mg/kg) during monsoon and post monsoon. High HPI values were observed at the sampling location near to landfill drain (9060), followed by spring site (7338). However, most of sampling points consistently exceeding the critical HPI value, across all seasons, indicated a severe level of heavy metal pollution, where sampling sites near to landfill drain pose significant environmental health risks of 63%. An overwhelming 93% population in vicinity of MSW site expressed their concern that the current landfill site poses substantial threat to their health and livestock. Furthermore, the obtained forward trajectories showed the downhill dispersion of polluted air arising from solid waste burning. A continuous monitoring of landfill leachate dynamics, atmospheric pollutants due to burning of waste and their potential impact on regional climate followed by appropriate adaptation strategies will be a promising step towards a sustainable future for the Indian Himalayan Region (IHR).