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Kumar RP, Singh R, Kumar P, Kumar R, Nahid S, Singh SK, Nijjar CS. Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India. ENVIRONMENTAL RESEARCH 2024; 255:119141. [PMID: 38754606 DOI: 10.1016/j.envres.2024.119141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/12/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024]
Abstract
The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.
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Affiliation(s)
- Ram Pravesh Kumar
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India.
| | - Ranjit Singh
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Pradeep Kumar
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India; Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Ritesh Kumar
- Haryana Space Applications Centre (HARSAC), Citizen Resources Information Department, Govt. of Haryana-125004, India
| | - Shadman Nahid
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Sudhir Kumar Singh
- K. Banerjee Centre of Atmospheric & Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj-211002, India
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Sahu V, Khan MA, Madguni OD. Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:810. [PMID: 39141225 DOI: 10.1007/s10661-024-12960-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/01/2024] [Indexed: 08/15/2024]
Abstract
Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22 years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.
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Affiliation(s)
- Vibha Sahu
- Discipline of Forest Ecology and Environment, Indian Institute of Forest Management, Nehru Nagar, Bhopal, Madhya Pradesh, 462003, India
| | - Mohd Amin Khan
- School of Humanities and Social Sciences, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh, 452020, India.
| | - Omprakash D Madguni
- Faculty of Ecosystem and Environment Management, Indian Institute of Forest Management, Nehru Nagar, Bhopal, Madhya Pradesh, 462003, India
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Banerjee A, Kang S, Meadows ME, Sajjad W, Bahadur A, Ul Moazzam MF, Xia Z, Mango J, Das B, Kirsten KL. Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India. ENVIRONMENTAL RESEARCH 2024; 250:118450. [PMID: 38360167 DOI: 10.1016/j.envres.2024.118450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/07/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024]
Abstract
Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.
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Affiliation(s)
- Abhishek Banerjee
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China.
| | - Shichang Kang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Michael E Meadows
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China; Department of Environmental and Geographical Science, University of Cape Town, Cape Town, 7701, South Africa
| | - Wasim Sajjad
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China
| | - Ali Bahadur
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China
| | - Muhammad Farhan Ul Moazzam
- Department of Civil Engineering, College of Ocean Science, Jeju National University, 102 Jejudaehakro, Jeju, 63243, Republic of Korea; Department of Environmental Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - Zilong Xia
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Joseph Mango
- Department of Transportation and Geotechnical Engineering, University of Dar es Salaam, P.O. Box 35131, Dar es Salaam, Tanzania
| | - Bappa Das
- Department of Geography, Goalpara College, P.O. & Dist, Goalpara, (Assam), 783101, India
| | - Kelly L Kirsten
- School of Energy, Construction and Environment, Coventry University, Coventry, CV1 2LT, United Kingdom
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Feng L, Khalil U, Aslam B, Ghaffar B, Tariq A, Jamil A, Farhan M, Aslam M, Soufan W. Evaluation of soil texture classification from orthodox interpolation and machine learning techniques. ENVIRONMENTAL RESEARCH 2024; 246:118075. [PMID: 38159666 DOI: 10.1016/j.envres.2023.118075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.
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Affiliation(s)
- Lei Feng
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Umer Khalil
- ITC Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands
| | - Bilal Aslam
- Department of Earth Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Bushra Ghaffar
- Department of Environmental Science, Faculty of Sciences, International Islamic University, Islamabad, Pakistan
| | - Aqil Tariq
- Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, 39762-9690, USA.
| | - Ahsan Jamil
- Department of Plant and Environmental Sciences, New Mexico State University, 3170S Espina Str., Las Cruces, NM, 88003, USA
| | - Muhammad Farhan
- School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China
| | - Muhammad Aslam
- Department of Computer Science, Aberystwyth University, UK
| | - Walid Soufan
- Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia
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Yadav N, Wu J, Banerjee A, Pathak S, Garg RD, Yao S. Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India. ENVIRONMENTAL RESEARCH 2024; 244:117962. [PMID: 38123049 DOI: 10.1016/j.envres.2023.117962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/10/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023]
Abstract
The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.
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Affiliation(s)
- Nilesh Yadav
- Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
| | - Jianping Wu
- Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China.
| | - Abhishek Banerjee
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Donggang, West RD. 318, Lanzhou, 730000, China
| | - Shray Pathak
- Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India
| | - R D Garg
- Geomatics Engineering Group, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - Shenjun Yao
- Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China
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Ayyamperumal R, Banerjee A, Zhang Z, Nazir N, Li F, Zhang C, Huang X. Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 909:168470. [PMID: 37951269 DOI: 10.1016/j.scitotenv.2023.168470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/31/2023] [Accepted: 11/08/2023] [Indexed: 11/13/2023]
Abstract
Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.
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Affiliation(s)
- Ramamoorthy Ayyamperumal
- MOE Key Laboratory of Mineral Resources in Western China, College of Earth Sciences, Lanzhou University, Lanzhou, Gansu Province 730000, China; MOE Key Laboratory of Western China's Environmental System, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Abhishek Banerjee
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou 730000, China.
| | - Zhenhua Zhang
- Institute of Green Finance, Lanzhou University, Lanzhou 730000, China
| | - Nusrat Nazir
- MOE Key Laboratory of Mineral Resources in Western China, College of Earth Sciences, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Fengjie Li
- School of History and Culture, Lanzhou University-, Lanzhou 73000, China
| | - Chengjun Zhang
- MOE Key Laboratory of Mineral Resources in Western China, College of Earth Sciences, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Xiaozhong Huang
- MOE Key Laboratory of Western China's Environmental System, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
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Zheng X, Sarwar A, Islam F, Majid A, Tariq A, Ali M, Gulzar S, Khan MI, Sardar Ali MA, Israr M, Jamil A, Aslam M, Soufan W. Rainwater harvesting for agriculture development using multi-influence factor and fuzzy overlay techniques. ENVIRONMENTAL RESEARCH 2023; 238:117189. [PMID: 37742752 DOI: 10.1016/j.envres.2023.117189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 09/26/2023]
Abstract
Rainwater harvesting (RWH) is an essential technique for enhancing agricultural development, particularly in regions facing water scarcity or unreliable rainfall patterns. Water shortage, however, is one of the key causes of low crop production especially in mountainous regions like the Khyber Pakhtunkhwa province where most rainwater is lost by runoff. Therefore, rainwater harvesting could be a suitable to make better use of runoff and increase crop production. The study focuses on selecting suitable rainwater harvesting sites in District Karak to enhance agriculture by utilizing multi-influence factor (MIF) and fuzzy overlay techniques. We considered seven factors, i.e., land use land cover (LULC), slope, geology, soil, rainfall, lineament, drainage density, to create a ranking system to understand its application in site selection analysis. The results were combined into one overlay process to produce a rainwater harvesting suitability map. The weighted overlay analysis of the MIF model results reveals that 167.96 km2 area has a very high potential for rainwater harvesting, 874.17 km2 has a high potential, 1182.92 km2 has a moderate and 354.50 km2 has a poor potential for rainwater harvesting. The fuzzy overlay analysis revealed that 257.53 km2 has a very high potential for rainwater harvesting, 896.56 km2 area is classified as high, 1018.30 km2 moderate, and 407.7 km2 has poor potential for rainwater harvesting. The findings of this research work will help the policymakers and decision-makers construct various rainwater harvesting structures in the study area to overcome the water shortage problems.
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Affiliation(s)
- Xiangtian Zheng
- Nanjing Institute of Technology, No.1 Hongjing Avenue, Jiangning Science Park, YKJ202118, Nanjing, 211167, China; School of Geography and Marine Science, Nanjing University, Xianlin Avenue No.163, Nanjing, 210023, China
| | - Abid Sarwar
- GIS Lab, Directorate General Soil & Water Conservation, Peshawar, Pakistan
| | - Fakhrul Islam
- Department of Geology, Khushal Khan Khattak University, Karak, Khyber Pakhtunkhwa, Pakistan
| | - Abdul Majid
- GIS Lab, Directorate General Soil & Water Conservation, Peshawar, Pakistan
| | - Aqil Tariq
- Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, 39762-9690, USA.
| | - Muhammad Ali
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, Pakistan
| | - Shazia Gulzar
- GIS Lab, Directorate General Soil & Water Conservation, Peshawar, Pakistan
| | | | | | - Muhammad Israr
- Agriculture Department Khyber Pakhtunkhwa Peshawar Pakistan, Pakistan
| | - Ahsan Jamil
- Department of Plant and Environmental Sciences, New Mexico State University, 3170S Espina Str., Las Cruces, NM, 88003, USA
| | - Muhammad Aslam
- Department of Computer Science, Aberystwyth University, UK
| | - Walid Soufan
- Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
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Gani A, Pathak S, Hussain A, Ahmed S, Singh R, Khevariya A, Banerjee A, Ayyamperumal R, Bahadur A. Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique. Mol Biotechnol 2023:10.1007/s12033-023-00864-2. [PMID: 37730900 DOI: 10.1007/s12033-023-00864-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 08/10/2023] [Indexed: 09/22/2023]
Abstract
The Ganges (Ganga) river contributes significant water resources for the ecology and economy, but it frequently encounters severe deterioration due to cumulative impact from upstream natural and anthropogenic variables. Knowledge and understanding of the dynamic behavior of such networks remain a significant challenge, particularly in the context of rising environmental pressures, such as climate change and industrialization, as well as constraints in both process and data understanding across geographies. An interdisciplinary approach is required to be developed to investigate the hydrogeochemical dynamics and anthropogenic sources influencing water quality in major river systems. The present study has been carried out to evaluate the characterization of river water quality in terms of the physico-chemical & bacteriological parameters. Also, the development of a water quality index (WQI) for Domestic (drinking) and Spiritual (bathing) usage is a part of the study. The water quality index has been developed using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI). The river's water quality index score in the present study lies in the range of 38.32 to 79.82, indicating the quality of water from fair to poor for drinking purposes. The highest water quality index value of 79.82 has been observed at Guru Kashnik Ghat, while the lowest WQI value of 38.32 has been observed at Har ki Pauri for drinking purposes. However, the water quality score for bathing purposes ranged from 71.04 to 91.22 thus signifying the quality of the water from fair to good for bathing purposes. The highest water quality index value of 91.22 has been assessed at Guru Kashnik Ghat, while the lowest WQI value of 71.04 has been assessed at Bhimgoda Barrage. The developed water indices assessment in the present study will be beneficial for society to provide a benchmark for the control of water pollution in River Ganga. These findings will support policymakers and stakeholders in addressing water quality issues in a more efficient and effective manner. The study also emphasizes the requirement for ongoing water quality monitoring and evaluation in order to guarantee the long-term well-being of the river and its ecosystems.
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Affiliation(s)
- Abdul Gani
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
| | - Shray Pathak
- Department of Civil Engineering, Indian Institute of Technology, Ropar, Punjab, 140001, India
| | - Athar Hussain
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
| | - Salman Ahmed
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
| | - Rajesh Singh
- Environment Hydrology Division, National Institute of Hydrology, Haridwar, Roorkee, Uttarakhand, India
| | - Abhishek Khevariya
- Department of Civil Engineering, Gautam Buddha University, Greater Noida, Uttar Pradesh, India
| | - Abhishek Banerjee
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China.
| | - Ramamoorthy Ayyamperumal
- Key Laboratory of Mineral Resources in Western China, College of Earth Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Ali Bahadur
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
- Key Laboratory of Extreme Environmental Microbial Resources and Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
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