Pre-monsoon Season Research Articles

Impacts of 3DVAR data assimilation on the near surface weather variables and predictability of rainfall over the mid-hills of central Nepal Himalaya during Pre-Monsoon Season

Impacts of 3DVAR data assimilation on the near surface weather variables and predictability of rainfall over the mid-hills of central Nepal Himalaya during Pre-Monsoon Season

Seasonal occurrence and ecological risk assessment of polycyclic aromatic hydrocarbons in the sediments and water in the left-bank canals of Indus River, Pakistan.

This study investigated a pressing environmental concern: the presence, distribution, sources, and ecological implications of sixteen polycyclic aromatic hydrocarbons (PAHs) in the left-bank canals of Kotri barrage-Akram, Pinyari, and Phuleli of the Indus River in Pakistan. These vital waterways, crucial for industrial, domestic, and agricultural activities, are experiencing contamination threats from anthropogenic sources, particularly PAHs. The study collected three water and two sediment samples from each canal in pre-monsoon and post-monsoon seasons. Then the EPA's liquid-liquid extraction method and gas chromatography determined the concentrations of PAHs. The findings of this study reveal alarming contamination levels, with pre-monsoon concentrations ranging from 22.256 to 836.455ng/L in water and 1,459.941 to 43,179.243ng/g in sediments. The post-monsoon concentrations ranged from 60.352 to 5663.058ng/L in water and 2976.770 to 15,238.335ng/g in sediments. The diagnostic ratios and principal component analysis (PCA) identified multiple sources of contamination, including industrial and domestic wastewater discharge, solid waste burning, vehicular emissions, biomass combustion, and petroleum residues. Furthermore, the assessment of the toxic equivalency factor (TEF) underscored the heightened carcinogenic potential of certain PAHs, notably benzo(a)pyrene and benzo(a)anthracene. Thus, the high levels of PAH contamination pose severe health risks to both human populations and aquatic ecosystems, emphasizing the urgency of addressing this issue. Stricter regulations governing industrial and domestic waste discharge, advocacy for cleaner fuel technologies, and the implementation of effective waste management practices must be initiated as crucial strategies in safeguarding the environmental integrity of the left-bank canals and the health of the surrounding communities.

Assessment of Dry Microburst Index over India derived from INSAT-3DR satellite

Dry microbursts can generate severe meteorological conditions including turbulence and strong winds even in the absence of precipitation. Present study evaluate the performance of Indian geostationary satellite, INSAT-3DR in capturing Dry Microburst Index (DMI) and validated against the radiosonde dataset. Data is validated across 14 selected stations across the India for 3 year (2020–2022). However, radiosonde data is very limited but spatial and temporal resolution of INSAT-3DR is good to analyse and predict the atmospheric phenomena. Different statistics have been used to validate INSAT-3DR against radiosonde observation. A Taylor plot confirm strong correlation and low RMSE between INSAT-3DR and radiosonde data. Spatial distribution depicts annual mean DMI values, it is influence by diurnal variation, regional weather pattern, and seasonal factors. Seasonal analysis indicates lower DMI during winter (5–45) due to reduced instability and moisture, while post-monsoon season witness increased DMI owing to warmer, humid conditions. The pre-monsoon season shows rising DMI as temperature increase. Study also analyses the co-occurrence of thunderstorm during DMI events, revealing a Probability of Detection (POD) of 0.75 for the INSAT-3DR DMI product, indicating 75% correct identification of thunderstorms. However, the False Alarm Rate (FAR) suggest false alarms occurred in approximately 55.2% of cases. Overall, study underscores the importance of considering local factors and conditions in interpreting INSAT-3DR satellite-based DMI data. Understanding and accurately predicting dry microbursts are crucial for enhancing aviation safety and improving the resilience of infrastructure in regions prone to these phenomena.

Spatial distribution, source apportionment, and health risks assessment of trace elements in pre- and post-monsoon soils in the coal-mining region of North Karanpura basin, India

Coal mining activities in the North Karanpura basin have significantly increased the trace element (TE) concentrations in the soil, resulting in soil pollution and potential health risks. To assess this, 113 soil samples, along with coal, shale, and overburden rocks, were collected from open-cast mining areas during pre-monsoon (Pre-M) and post-monsoon (Post-M) seasons. Seasonal analysis revealed higher TE concentrations in the Post-M period, especially in the SE direction, followed by NE and NW, likely due to surface runoff and deposition, demonstrating temporal variability in TE distribution which corroborated from the spatial distribution maps. Positive matrix factorization (PMF) model identified four factors: mixed sources (F1Pre-M: 37.6 %; F4Post-M: 28.9 %), coal-fired emissions (F2Pre-M: 20.5 %; F3Post-M: 26.0 %), overburden rocks (F3Pre-M: 25.5 %; F2Post-M: 16.7 %), and agricultural and lithogenic origin (F4Pre-M: 16.4 %) during the Pre-M period, attributed to coal mining. Post-M sources were similar, but agricultural and lithogenic origins were replaced by atmospheric deposition (F1Post-M: 28.4 %), enhanced by monsoon effects. Carcinogenic risk assessment revealed that As, Cr, and Ni exceeded acceptable levels for children via ingestion, though adults remained within safe limits. Inhalation and dermal contact were also considered, but ingestion posed the highest risk. The hazard index (HI) via ingestion showed that children had an HI of 1.6 in Pre-M, increasing to 2.66 in Post-M, highlighting their potential vulnerability to non-carcinogenic risks, while adults stayed within safe limits. The expansion of mining areas in the study region led to decrease in vegetative areas which could affect agriculture and local communities, raising a comprehensive environmental and public health issues. These results underline the need for implementing effective biannual soil monitoring and mitigation strategies, such as phytoremediation, bioremediation, rock dust remediation, chemical amendments and improved waste management, to reduce TE contamination.

Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought–Heat Extremes in Tropical South America

Abstract Observational evidence has demonstrated that heat extremes consistently coincide with droughts in tropical South America. However, the underlying causes and associated physical mechanisms remain relatively unexplored. In this study, we employ numerical experiments using Community Earth System Model, version 2 (CESM2), with prescribed oceanic forcing to assess how extreme soil temperature and moisture affect subsequent hydrometeorological conditions during the premonsoon season in tropical South America, a season when land can play an important role in regional climate. The results reveal a different diurnal pattern in the strength of the surface air temperature response, with a stronger response at night to soil temperature anomalies and a stronger response at noon to soil moisture anomalies. Elevated initial soil temperature or reduced initial soil moisture tends to yield warmer and drier hydrometeorological conditions and increase the occurrence of both drought and heat extremes, with stronger and longer-lasting responses to soil moisture anomalies (than soil temperature anomalies). The simulated responses to initial soil moisture anomalies alone closely resemble those in the experiment with combined soil temperature and moisture anomalies. These findings underscore the dominant role of soil moisture feedback in shaping compound drought–heat extremes, in regions where evaporation plays a significant role, highlighting its importance in the modeling and prediction of hydrometeorological extremes.

Seasonal vertical accretion in different hydro-geomorphic environments of Indian Sundarban: Assessing the role of mangroves in mitigating sea level rise impacts

The Indian Sundarban, renowned for its unique hydro-geomorphic diversity, faces rising threats from sea level rise, necessitating an examination of the spatio-temporal variation of vertical accretion and the role played by mangroves in mitigating these impacts. As such, fifteen transects were chosen across buffer and transition zones of Indian Sundarban, each equipped with 4–6 sedimentation pins, to gauge accretion rates in premonsoon, monsoon, and postmonsoon season during period March 2020 to March 2022, considering diverse hydro-geomorphic attributes. The vertical accretion rate exhibits a curvilinear pattern, ascending from the lower seafront zone towards the middle of the estuarine region, reaching a peak, and subsequently declining towards the upper estuarine zones. Specifically, in the seafront zone, the yearly mean accretion (focusing solely on accretion and excluding subsidence) ranges between 5 and 10 mm/year, while in the middle estuarine region, it increases to an average of 38.1 ± 9.44 mm/year. However, in the upper zone, the yearly mean accretion decreases slightly to approximately 17.71 ± 9.54 mm/year. Similar patterns are observed when considering the distance from the riverfront to the interior of the mangrove zones. Seasonal analysis demonstrates that the postmonsoon period exhibits the highest accretion, followed by the premonsoon season, while the monsoon tends to be erosional in nature. Mangrove zonation, and subsequent distribution of root height, root diameter, root density, tree density, tree basal area, inundation frequency, current speed, variation in suspended sediment concentration were identified as influential factors affecting vertical accretion rates. Based on multiple regression analysis, root density, inundation frequency, and tree density demonstrated moderate-positive correlations with vertical accretion, whereas distance from the riverfront, root height, and root diameter displayed negative correlations. This study enhances our understanding of the intricate processes behind vertical accretion in the Sundarban, offering valuable insights for future research and advancing discussions on the broader implications of climate change in coastal regions.

Insights into the seasonal variation, distribution, composition and dynamics of microplastics in the Ganga River ecosystem of Varanasi City, Uttar Pradesh, India.

The current study explores the seasonal dynamics of microplastic (MP) pollution in the Ganga River of Varanasi City, Uttar Pradesh, India, focusing on water and sediment samples collected during pre-monsoon and post-monsoon periods. The analysis shows significant variations in MP occurrence, shape dynamics, color distribution, and size composition across diverse sampling sites. During the pre-monsoon season, MP concentrations ranged from 17 to 36 particles/L in water samples and 160 to 312 particles/kg in sediment, indicating a moderate to high level of contamination. Post-monsoon sampling showed higher MP concentrations at most sites, indicating the influence of seasonal hydrological changes on MP distribution. Shifts in MP shape dynamics were observed between seasons, with films, foams, fragments, and filaments showing variable distributions. Similarly, color variations in MPs exhibited site-specific patterns, with white, brown, blue, and other colors being predominant. These findings highlight the diverse sources and compositions of MPs in the river ecosystem, highlighting the complexity of MP pollution dynamics. Polymer-type distributions further elucidated the composition of MPs, with notable contributions from polyethylene terephthalate, rayon, polyester, and polyvinyl chloride. PCA analysis revealed significant shifts in particle size and shape distribution between pre-monsoon and post-monsoon periods in both water and sediment samples, with post-monsoon samples showing an increase in larger particles and filaments. These changes highlighted key factors driving the variance in microplastic contamination across different sites. The prevalence of these polymers features diverse sources of MP pollution, including textiles, packaging materials, and industrial waste. Ongoing monitoring and research are crucial to understanding its sources, distribution, and impact on river ecosystems, essential for protecting aquatic biodiversity and human health.

A geospatial investigation of cloud-to-ground lightning strikes during pre-monsoon and post-monsoon seasons in Dakshina Kannada district during the years 2021 to 2023

A geospatial investigation of cloud-to-ground lightning strikes during pre-monsoon and post-monsoon seasons in Dakshina Kannada district during the years 2021 to 2023

A Report on the Tabanus Species Tabanus rufofrater in Mithun (Bos frontalis)

Background: Mithun (Bos frontalis) experience infestations from various ectoparasites, including several types of flies that cause painful bites, stress, blood loss and transmit a range of microorganisms. In the Mithun-inhabited forested regions of Phek, Nagaland, the Tabanus fly is particularly prevalent. Methods: Researchers studied Tabanus flies infesting free-range mithun in Phek District, Nagaland. The flies were collected and processed at the ICAR-NRC on Mithun in Nagaland. Both morphological (using stereoscopic microscopes) and molecular methods (employing ribosomal and mitochondrial gene markers) were utilized for identification. Additionally, DNA barcoding was done at Eurofins Genomics India Pvt. Ltd. to further ascertain the flies’ identities. Climate attributes (Temperature, Rainfall and Humidity) was recorded during the study. Result: Through morphological and molecular investigations, it was conclusively determined to be Tabanus rufofrater. The highest occurrence of Tabanus rufofrater encounters takes place between May and August, aligning with the pre-monsoon and monsoon seasons, before gradually declining thereafter. The prevalence of Tabanus rufofrater attacks peaks from May to August, coinciding with the pre-monsoon and monsoon periods and subsequently decreases.

Satellite derived air pollution climatology over India and its neighboring regions: Spatio-temporal trends and insights

This study examines the spatial and temporal variations of key air pollutants, including Nitrogen Dioxide (NO₂), Carbon Monoxide (CO), Tropospheric Ozone (O₃), Sulfur Dioxide (SO₂), Aerosol Optical Depth (AOD), and Formaldehyde (HCHO) over India and neighboring regions, using satellite data from 2005 to 2022. Peak NO₂ (∼11–13 × 101⁵molecules/cm2) occurs in pre-monsoon and winter, mainly over urban centers, thermal plants, and crop-burning areas, emphasizing anthropogenic emissions. High CO levels (2.0–2.3 × 101⁸molecules/cm2) during summer dominate in Indo-Gangetic Plains (IGP), Eastern India, and Northeastern region, linked to the incomplete combustion of organic matter (e.g., natural forest fires and biofuel-based household cooking). Stable CO trends reflect effective government policies promoting cleaner fuels. Tropospheric O₃ shows a spatial gradient, with higher values in northern and coastal regions influenced by sunlight, humidity, and precursor emissions. Seasonal peaks in summer are governed by solar intensity, while the December peak over the northern region is likely due to dynamical transport from O₃-rich areas. SO₂ hotspots in IGP, central and eastern India, especially during winter (∼1.3–1.9 × 101⁵molecules/cm2), are attributed to local anthropogenic emissions and fossil fuel combustion such as coal. Long-term trends reveal rising SO₂ levels. AOD hotspots occur over IGP, Central and Southern India, with particularly high values during the pre-monsoon (0.5–0.6) and winter seasons. HCHO shows hotspots in the IGP, west coast, eastern, and northeastern regions, peaking in summer (7.5–8.5 × 101⁵molecules/cm2) due to biogenic VOC emissions. This study underscores the need for integrated policies to mitigate emissions, improve air quality, and protect public health.

Assessing ecological risks and microplastic pollution in a tropical coastal ecosystem: Effects of rainfall variability in Southeast India

Microplastic pollution poses a significant threat to coastal ecosystems, prompting an in-depth investigation into its seasonal fluctuations and ecological impacts. This study examines microplastics in Cuddalore’s coastal waters and sediments across four seasons. Results show varying microplastic concentrations, peaking during the monsoon and pre-monsoon seasons, likely influenced by rainfall variability. Fibers dominate in both water (69–100 %) and sediment samples (66–92 %), with polyethylene and polyethylene terephthalate as the predominant polymer types in water. Sediments exhibit a more diverse polymer distribution, with polypropylene and polyethylene terephthalate being prevalent. Elevated polymer hazard index and potential ecological risk index raise concerns, reaching values of 108930 and 1125, respectively. This underscores the urgency of implementing effective management strategies to safeguard tropical coastal ecosystems from microplastic pollution. This study highlights the complex interplay between seasonal dynamics, rainfall variability, polymer types, and pollution levels, emphasizing the need for proactive environmental management measures.

Composited sea surface responses to tropical cyclones in the Bay of Bengal during 2003–2020

Tropical cyclones (TCs) in the Bay of Bengal (BoB) caused significant changes in the sea surface as they passed through the sea. To quantitatively study the differences in oceanic responses caused by TCs during pre-monsoon and post-monsoon seasons in the BoB from 2003 to 2020 and the varying importance of TC intensity and translation speed in modulating oceanic responses, the oceanic environmental parameters affected by TCs were composited and analyzed. The spatial distributions show that the responses of sea surface temperature (SST) rather than chlorophyll-a (Chl-a) concentrations and sea surface salinity (SSS) coincided with the maximum response centers of the wind power index (WPi). All the temporal changes in SST, Chl-a, and SSS indicated that the changes induced by TCs began approximately 2–3 days before TC passage, continued to increase during cyclone passage, and then slowly returned to the initial state after at least 8 days. Compared with TC intensity, TC translation speed generally played a less important role in influencing the ocean responses of SST, Chl-a and SSS. This result is somewhat different from previous results in which the response of Chl-a was correlated with TC translation speed rather than TC intensity in the northern Indian Ocean. In addition, the SSS response was positively proportional (R = 0.886) to the TC translation speed in the BoB, which was different from that in the Northwest Pacific. The differences in the responses during pre-monsoon and post-monsoon seasons were mainly in terms of quantity, as all the composited results were similar. In addition, they were more pronounced in their responses but less significant in their correlations during pre-monsoon.

Assessment of groundwater quality for irrigation purpose in aquifers of the Upper Brahmaputra floodplains of Assam, India.

Groundwater is an essential natural resource for mankind. Due to various geogenic and anthropogenic causes, groundwater quality has raised serious concern over the years. In this study, groundwater quality was evaluated for its suitability for irrigation in the Jorhat and Golaghat Districts of Assam, India. A total of 100 groundwater samples were collected from shallow aquifers (< 35m) from different locations during the pre-monsoon season (March-April 2022). Groundwater in the study area is slightly alkaline in nature (mean pH value of 7.44). The average cations and anions chemistry are in the order of Na+ > Ca2+ > Mg2+ > K+ and HCO3- > Cl- > SO42- > CO32-, respectively. Ca-Mg-HCO3 followed by Na-Ca-HCO3-Cl are the primary water types in the study area. Pearson's correlation matrix showed a positive correlation between TDS and EC (r = 0.78) and sodium showed a positive correlation with TDS and bicarbonate (r = 0.62 and r = 0.65), respectively. Gibbs plot indicated that rock-water interaction is the dominant factor that controls the chemistry of the groundwater of the area. Irrigation parameters like Sodium Absorption Ratio (SAR), Permeability Index (PI), Magnesium Absorption Ratio (MAR), Kelly's Ratio (KR), and Irrigation Water Quality Index (IWQI) indicated that groundwater is overall suitable for irrigation. USSL diagram illustrated that most of the samples fell into the C2 (medium salinity) and S1 (low sodium hazard) categories. Wilcox plot showed the samples fell in excellent to good categories indicating fitness of groundwater for irrigation in the area.

Environmental and human health risk associated with heavy metals in sediment of a tropical river in India

ABSTRACT Heavy metals persist in the sediment and can induce toxicological effects on living organisms. For analysing the risk of heavy metals, geochemical fractionation is beneficial. These geochemical fractions or speciations help to determine the heavy metals availability, mobility, toxicity, enrichment, and various risks. Here, we show how heavy metal contamination of sediment poses risks to both the ecosystem and human beings. We found significant Zn, Co and Cd contamination by analysing the geoaccumulation index. We also observed higher geoaccumulation index values for the pre-monsoon season than the post-monsoon one. Furthermore, pollution load index indicated gradual contamination of heavy metals in the sediments. This study's risk assessment and modified risk assessment codes showed low or no risk, but contamination factors indicated moderate to high risk. Our analysis of the effective range median mean quotient revealed some sampling stations as medium to high-priority sites. Similarly, probable effect level mean quotient designated some sampling stations as medium-low priority sites. We anticipated a moderate to significant mortality effect on the organisms from toxicity unit index values. Potential ecological risk index indicated a very high ecological risk on the Mahananda River. Furthermore, we predicted non-carcinogenic health hazards and carcinogenic health risks in the river's sediment.

Observational evidence of the association between aerosol properties and lightning characteristics in the Indian subcontinent

Abstract Lightning is a leading cause of natural disaster-related mortality in the Indian Subcontinent. Currently, there is a lack of observational evidence supporting the role of aerosol composition in modulating lightning intensity and trends in this region. In this study, we analyzed satellite and reanalysis datasets to examine the association between aerosol properties and lightning intensity over three contrasting geographic regions of Indian subcontinent during the premonsoon season (1998-2022). By comparing lightning and non-lightning days, we found a 0.1 unit increase in total aerosol optical depth specifically on lightning days in all three study areas. Additionally, we observed that the aerosol composition of a region influences convective activity. The presence of dust alongside fine aerosols acts as efficient ice nuclei, thereby stimulating lightning activity in all three regions. Therefore, considering aerosol composition together with local meteorology is essential for accurately predicting lightning flash rates in a region.

District wise spatiotemporal analysis of precipitation trend during 1900-2022 in Bihar state, India

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900–2022. Major findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter, and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post-monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas, and extreme eastern districts like Purnia, Katihar, Araria, and Supaul had negative trend.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Assessment of plankton diversity and physico-chemical characteristics for sustainable fish production in a tropical reservoir in South India

Plankton forms an important component of the aquatic ecosystem and act as indicators of fish diversity, pollution levels and environmental disturbances. They form an important natural fish food resource and play a vital role in the trophic structure of the ecosystem. In the present study a systematic assessment of the community distribution and diversity of plankton was conducted in the Karapuzha Reservoir in South India. Thirty six genera of phytoplankton belonging to 4 classes viz, Chlorophyceae, Bacillariophyceae, Dinophyceae and Myxophyceae were identified. Eleven genera of zooplankton belonging to four classes; Protozoa, Rotifera, Cladocera and Copepoda were also recorded. The Jaccard index of similarity showed that pre-monsoon and monsoon seasons shared a similar plankton composition. Diversity indices revealed the highest values during monsoon, while the Dominance index indicated the highest species dominance during post-monsoon (0.199) followed by pre-monsoon (0.1445) and monsoon (0.1417). Water quality parameters were found to have a significant correlation with plankton abundance in the reservoir. ShannonWeiner index indicated that the Karapuzha Reservoir ecosystem is moderately polluted. Seasonal variations in plankton composition showed the highest species abundance during the pre-monsoon followed by post-monsoon and monsoon seasons. Keywords: Diversity index, Plankton abundance, Planktivorous fish, Reservoir ecosystem, Seasonal variation

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.