Pre-monsoon Season Research Articles

Pharmaceutical Active Compounds Contamination and Seasonal-Based Ecotoxicity Assessment of the Yamuna River for the Delhi stretch

Yamuna River is the only river in Delhi. This results in heavy reliance on Yamuna to meet Delhi Water demands. This has prompted several research works covering physicochemical, biological, heavy metal concentration, emerging pollutant occurrence, and their risk assessment. This study investigated occurrence, seasonal variation (pre-monsoon, monsoon, and post-monsoon), and risk assessment posed by 7 PhACs at five sampling locations along a 22 km stretch of Yamuna River in Delhi. The samples were collected and analysed in pre-monsoon, monsoon, and post-monsoon seasons. The seven PhACs, comprised of 2 antibiotics (Ciprofloxacin; CIP, sulfamethoxazole; SMZ), 2 NSAIDs (Paracetamol; PCM, Ketoprofen; KPF), 1 anxiety control (Lorazepam, LOR), 1 anticonvulsant (Carbamazepine; CBZ) and 1 statin (Fluvastatin; FUT). The PhACs range of occurrence across three seasons was PCM 75-589 ng L-1, KPF 31-238 ng L-1, CBZ 11-192 ng L-1, LOR 62-462 ng L-1, CIP 48-192 ng L-1, SMZ 192-1534 ng L-1, and FUT 0-421 ng L-1. The seasonal occurrence was in the order of post-monsoon> pre-monsoon> monsoon. PCM, CBZ FUT posed a negligible ecotoxicological risk, LOR posed a low-medium risk, and ketoprofen, ciprofloxacin, and sulfamethoxazole posed high risks. Based on the risk index, all seasons have high ecological risk at every sample point. Discharges of untreated sewage and insufficient and inefficient treated wastewater are the primary contributors of PhACs in the Yamuna River. This study concludes that existing WWTPs need drastic upgrades. Policies and measures should also be developed to prevent untreated wastewater from reaching the Yamuna River. This necessitates further studies to investigate processes suitable for installing and treating wastewater along the longitudinal section of the drain and assess their technical feasibility.

Assimilation of INSAT-3D Imager Retrieved Aerosol Optical Depth for Enhanced Dust Storm Prediction Using WRF-Chem Model

Abstract Frequent dust storms during the pre-monsoon season in northwest India significantly impact weather, air quality, and health, necessitating accurate predictions. This study demonstrates the first-time assimilation of aerosol optical depth (AOD) data from INSAT-3D into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), alongside previously assimilated AOD data from MODIS AQUA. Focusing on an extreme dust storm over the Indo-Gangetic Plain (IGP) from June 12-17, 2018, identical experiments were conducted with and without AOD assimilation. The Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) method was utilized for data assimilation, with simulations performed for 144 hours, adjusting for uncertainties in the model's background error covariance using different inflation factors. The model's performance was validated using the ECMWF Atmospheric Composition Reanalysis 4 (EAC4) product. The assimilated AOD analysis significantly improved bias, root mean square difference (RMSD), and correlation from [0.47, 0.71, 0.40] to [0.07, 0.38, 0.80]. The 24-hour AOD forecast improved by approximately 56% and 30% with INSAT-3D and MODIS AOD assimilation, respectively. AOD assimilation directly impacted cloud properties and radiative forcing, enhancing the 24-hour forecast of net shortwave downward flux (SWD) by approximately 51 W/m² and 24 W/m² for INSAT-3D and MODIS, respectively. These changes also affected vertical levels up to 5 km, modifying the rainwater mixing ratio (QRAIN) and surface rainfall forecasts. This study shows that integrating INSAT-3D AOD data provides substantial improvements in aerosol forecasts, crucial for making reliable and accurate forecasts of severe dust storms and air pollution episodes over the Indian region.

Influence of seasonal variation on spatial distribution of PM2.5 concentration using low-cost sensors.

Fine particulate matter (PM2.5) is one of the major airborne pollutants in urban environments and is associated with severe health impacts. In this study, a dense network of low-cost sensor (LCS) is used to cover large spatial area and detect ambient PM2.5 concentration in Guwahati city. The measurements were conducted at multiple sites in different seasons between July 2022 and June 2023. Seasonal variability significantly influences regional meteorology, aerosol optical depth (AOD), and PM2.5 concentration. The seasonal average PM2.5 concentration was highest during winter (113.05µgm -3), followed by post-monsoon (56.11µgm-3), then pre-monsoon (46.60µgm-3), and least for monsoon (32.36µgm-3) season. The elevated PM2.5 concentrations may be attributed to environmental conditions (low ambient temperature, calm wind, and low planetary boundary layer height) that resulted in the least dispersion of PM2.5. The concentration-weighted trajectory (CWT) analysis identifies the effect of regional (Indo-Gangetic Plain and northeast region) and transboundary (Bay of Bengal, Bangladesh, and northwest Asian countries) transported air masses on urban air quality. Post-monsoon and winter season has a high influence on long-range transported aerosols, whereas the monsoon and pre-monsoon seasons are affected by ocean and land air masses. Changes in surrounding activities and meteorology influence spatial distribution of PM2.5 particles. Elevated PM2.5 concentrations were recorded at in-city and outskirt sites because of the nearby activities (industry and traffic) and build-up area. In meteorology, wind significantly affects spatial dispersion of PM2.5 concentration to the sites located in upwind and downwind directions.

Emissions of methane from coal fields, thermal power plants, and wetlands and their implications for atmospheric methane across the south Asian region

Abstract. Atmospheric methane (CH4) is a potent climate change agent responsible for a fraction of global warming. The present study investigated the spatiotemporal variability of atmospheric-column-averaged CH4 (XCH4) concentrations using data from the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument on board the Sentinel-5 Precursor (S5P/TROPOMI) from 2009 to 2022 over the south Asian region. During the study period, the long-term trends in XCH4 increased from 1700 to 1950 ppb, with an annual growth rate of 8.76 ppb yr−1. Among all natural and anthropogenic sources of CH4, the rate of increase in XCH4 was higher over the coal site at about 10.15 ± 0.55 ppb yr−1 (Paschim Bardhaman) followed by Mundra Ultra Mega Power Project at about 9.72 ± 0.41 ppb yr−1. Most of the wetlands exhibit an annual trend of XCH4 of more than 9.50 ppb yr−1, with a minimum rate of 8.72 ± 0.3 ppb yr−1 over Wular Lake. The WetCHARTs-based emissions of CH4 from the wetlands were minimal during the winter and pre-monsoon seasons. Maximum CH4 emissions were reported during the monsoon, with a maximum value of 23.62 ± 3.66 mg m−2 per month over the Sundarbans Wetland. For the 15 Indian agroclimatic zones, significant high emissions of CH4 were observed over the Middle Gangetic Plain, Trans-Gangetic Plain, Upper Gangetic Plain, Eastern Coastal Plains, Lower Gangetic Plain, and East Gangetic Plain. Further, the bottom-up anthropogenic CH4 emissions data are mapped against the XCH4 concentrations, and a high correlation was found in the Indo-Gangetic Plain region, indicating the hotspots of anthropogenic CH4.

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

The role of antecedent southwest summer monsoon rainfall on the occurrence of premonsoon heat waves over India in the present global warming era

Global warming has significantly increased the risk of heat waves (HWs) globally, with India being particularly vulnerable during the summer months (March-June; MAMJ). This study investigated the critical relationship between Indian summer monsoon rainfall (ISMR) and the occurrence of premonsoon HWs in subsequent years across the Indian subcontinent. It has been hypothesized that droughts during the ISMR could lead to more frequent HWs in the following MAMJ period. Using the Indian Meteorological Department's (IMD) gridded observed surface air daily maximum temperature (Tmax) dataset for the period 1951–2023, we analyzed the climatic patterns, interannual variability (IAV), and coefficient of variation (CV) of Tmax across India. The analysis compares two distinct periods: 1951–1999 (P1) and 2000–2023 (P2), with focus on Tmax trends and HW duration, distinguishing between short-duration HWs (SHWs, 2 days) and long-duration HWs (LHWs, 5 days or more). A key purpose of this study is to examine the relationship between the preceding all India summer monsoon rainfall (AISMR) and the occurance of various types of HW in the subsequent premonsoon season. In particular extreme AISMR events, such as droughts or excess rainfall, influence HW occurrence. The findings reveal a significant rise in Tmax across many regions of India during the MAMJ period, with the highest temperatures (> 37 °C) observed in northwestern, central, and eastern coastal areas. Northern India, particularly the Himalayan region, exhibits a greater interannual variability in Tmax, with June showing the most pronounced fluctuations. The study also highlights an increase in the frequency and intensity of HWs, especially in central and southern India, with the Chandigarh-Haryana-Delhi region recording the highest occurrences. A critical finding is the strong inverse relationship between the AISMR and conditions in the subsequent premonsoon season. Specifically, drought in the antecedent AISMR results in reduced soil moisture, which is strongly associated with higher premonsoon Tmax and an increased frequency of extreme heat events across India, particularly in regions prone to severe heat during this season. Drought conditions during AISMR are closely linked to higher HW frequencies in the following summer, especially in the central, northeast-central, and east-coastal regions. The frequencies of HW days, SHWs, and LHWs are significantly greater in years following AISMR droughts than in those following excess rainfall, indicating that drought years are more likely to lead to widespread HW activity. Despite the overall warming trends, some regions, such as the Indo-Gangetic Plain and parts of the Himalayan region, show cooling trends, although these trends are less widespread. The onset of the monsoon in June tends to reduce the intensity and spatial extent of warming, particularly in the central and eastern coastal regions, although significant HW trends persist in northwestern India and along the east coast. This study underscores the crucial role of AISMR in influencing HW events across India and highlights the need for adaptive strategies that account for the interactions between monsoon rainfall and HW risk, providing valuable insights for mitigating the impacts of HWs in the context of global warming.

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.

Retrieval of Parameters of Constrained Gamma Raindrop Size Distribution in South China

Abstract Raindrop size distributions (RSDs) retrieved from polarimetric radar observations have proven to be effective for understanding precipitation microphysics. However, the current RSD retrieval scheme from polarimetric radar observations (using radar reflectivity ZHH and differential reflectivity ZDR) in South China has been established for monsoon precipitation only. Using abundant RSD observations from 2D video disdrometers (2DVDs), this study constructs the high-precision shape–slope (μ–Λ) relationships of the constrained gamma (C-G) model for subgroups of ZHH and ZDR for not only monsoon but also premonsoon, typhoon, and dry seasons. The current RSD retrieval is only applicable for precipitation with rain rate: R > 5.0 mm h−1, and total raindrop number concentration: Nt > 1000 m−3. This study expanded the applicability of rain rate and number concentration with R > 0.5 mm h−1 and Nt > 100 m−3. The new retrieval scheme was tested in four typical rainfall cases and provided better accuracy in estimating mass-weighted mean diameters and normalized intercept parameters. The new scheme was also better suited for analyzing precipitation microphysics in South China, which helps to gain a better understanding of microphysics in various types of precipitation in this region.

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.

Ensemble versus deterministic lightning forecast performance at a convective scale over Indian region

The present study quantifies the improvement achieved in lightning forecast skill of the NCMRWF regional ensemble prediction system (NEPS-R) compared to its deterministic counterpart (CNTL). The lightning forecasts over study regions of East and Northeast India (ENEI) and Peninsular India (PI) during the pre-monsoon season and Central-East and Northeast India (CENEI) during the monsoon season have been verified using lightning observations from the Indian Institute of Tropical Meteorology (IITM) Lightning Detection Network (LDN). The persisting systematic negative bias in deterministic and EPS-based forecasts of the ensemble mean (EnsMean) and ensemble maximum (EnsMax) indicate the lack of spread among the members, supported by the low values of ensemble spread over all the study regions. EnsMean has the lowest RMSE, with a decrease in error ranging from 0.8 % to 2.18 % compared to CNTL. Categorical skill scores indicate that the EPS-based forecasts (EnsMean and EnsMax) are more skillful than the deterministic forecast at all thresholds and lead times. Further, Fractions Skill Score (FSS) establishes the superiority of the ensemble forecasts over the deterministic forecasts, where for threshold >1, EnsMean is skillful at comparatively smaller neighborhood sizes (ENEI and PI ∼68 km; CENEI ∼36 km for day-1) than CNTL (ENEI-116 km; PI-196 km; CENEI-68 km). EnsMax at higher thresholds (>5 and >10) is skillful at lesser neighborhood sizes ranging from 116 to 276 km compared to CNTL (>401 km) for day-1. Hence, skillful re-scaled EPS forecasts based on FSS could provide better guidance for the forecasters. The Continuous Ranked Probability Score of EPS forecasts is lower by around 9 % than the Mean Absolute Error of CNTL forecasts, and the ROC of EPS shows better discrimination of events and non-events compared to CNTL. These highlight the merits of using an EPS over a deterministic system for forecasting a field of high spatial variability, like lightning, and thereby, the use of vast computational resources to run a convective scale EPS is justified.

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.