Monsoon Season Research Articles

Prevalence of Common Ear Nose and Throat Diseases in a Tertiary Care Center during Monsoon Season in Nepal

Ear, nose and throat (ENT) disorders contribute significantly to global morbidity and mortality, with variations in disease patterns across different regions and seasons. Nepal’s diverse climate, particularly during the rainy monsoon season, influences the prevalence of certain ENT diseases. This study aimed to assess the pattern of ENT disorders during the rainy season (monsoon) at a tertiary hospital in Nepal. A descriptive study was conducted at Nepal Medical College Teaching Hospital from June to September 2024. A total of 1042 patients aged 1 year and above were included using purposive sampling. Data was collected through clinical examinations and investigations. Statistical analysis was performed using SPSS. Among the 1042 participants, 57.77% were female, and the mean age was 31.07±18.62 years. The most prevalent ENT diseases were throat disorders (44.82%), ear disorders (44.05%), and nose disorders (11.13%). Acute pharyngitis (12.38%), acute otitis media (9.50%), and allergic rhinitis (2.87%) were the most common conditions. The study revealed an increased prevalence of acute pharyngitis, tonsillitis, acute otitis media (AOM), and allergic rhinitis during the monsoon season. This highlights the importance of early diagnosis, treatment, and preventive strategies for reducing disease burden during this period.

An Integrated Machine Learning and Deep Learning Framework for River-Based Flood Early Warning System

The impact of global climatic conditions and unpredictable rainfall patterns has led to numerous natural disasters in Manipur, including floods and landslides. Developing a reliable flood forecasting and early warning system is crucial to prevent loss of life, property, infrastructure, and crops. This paper presents a machine learning and deep learning framework for flood prediction, comprising four modules: Data Collection, Data Manipulation, Model Implementation, and Early Warning and Flood Forecasting. By leveraging daily data from gauge stations and weather reports, the framework aims to predict river water levels accurately, particularly during the monsoon season. The objectives include developing a model that integrates K-Nearest Neighbor (KNN) with Long Short-Term Memory (LSTM) networks for predicting future water levels using multivariate time series data and providing an algorithm that offers early warnings for potential floods. The proposed framework employs a multivariate time-series approach using a hybrid KNN-LSTM method to predict river water levels. Missing data are imputed using the KNN algorithm with historical weather data, and an LSTM network predicts future water levels based on this information. This approach effectively models temporal dependencies across multiple gauge stations in the Imphal Valley, Manipur. The performance metrics for the proposed model are NSE score of 0.9393, MAPE score of 0.1076, and RMSE score of 1.1828, which are relatively better than those of the conventional approaches. This study presents a novel flood forecasting approach by integrating for imputing missing data with LSTM networks to capture temporal dependencies. The framework is applied to homogeneous regions with correlated time series, effectively modeling the complex dynamics of river water levels across multiple gauge stations.

Unveiling precipitation and its concentration over Himachal Pradesh, India.

Recently, precipitation concentration studies have gained much attention from geoscientists as they assist them in the recognition of rainfall heterogeneity (irregularity) and extreme events (flood, drought, etc.) over space and time, particularly in mountainous regions. Therefore, this study has attempted to explore the precipitation and its concentration over the north-western Himalayan state of Himachal Pradesh, India, from 1951 to 2020. For this, gridded rainfall data (0.25° × 0.25°) have been obtained from the India Meteorological Department (IMD), Pune, and daily and monthly concentrations have been assessed using concentration index (CI) and precipitation concentration index (PCI), respectively. The results have revealed a noticeable decrease in annual and monsoon precipitation amount, while a significant increase has been recorded in summer. Annually, the daily precipitation (CI) has been found highly concentrated at lower elevations and vice versa. Amongst the seasons, the monsoon season has recorded the lowest daily concentration (CI = 0.55) with a statistically significant increasing trend; however, the highest concentration (CI = 0.76) has been detected during the post-monsoon season but without a trend. Likewise, monthly concentration (PCI) has been found minimum during monsoon season (PCI = 9.7) with a significant positive trend, whereas its maximum value (PCI = 17.1) has been witnessed during summer with a noteworthy declining trend. The trends of CI and PCI indicate towards imminent chances of extreme precipitation events, especially during monsoon season. Finally, the results of this study will help risk reduction managers and policymakers in developing efficient flood and drought mitigation strategies over the Himalayan region.

Pacific Meridional Mode Implicated as a Prime Driver of Decadal Summer Temperature Variation over Taiwan

Abstract The Pacific meridional mode (PMM) is a climate phenomenon in the eastern Pacific, characterized by strong wind–sea surface temperature (SST) interactions. During its positive phase, an atypical north–south SST gradient develops in the northeastern subtropical Pacific, leading to significant global teleconnections, which refer to the mutual influence or correlation between atmospheric or oceanic changes in two distant regions of Earth. This study examines the relationship between historical summer temperature data for Taiwan (June–August) and PMM indices over a 59-yr period (1960–2018), revealing a significant correlation between the two. Regressed large-scale circulations indicate the eastward propagation of a wave train across the northern Pacific into East Asia, inducing anticyclonic circulations near southeastern China and Taiwan, accompanied by subsidence, which refers to the vertical downward movement of air masses in the atmosphere, typically associated with high pressure systems and dry weather conditions, that stabilize the atmosphere. This stabilization reduces the influence of seasonal southwesterly winds while increasing shortwave radiation at the surface, thereby raising surface temperatures in Taiwan. Our findings are further supported by linear baroclinic model (LBM) experiments featuring PMM-like heating forcing and by large-scale responses to PMM-related SST patterns simulated in CMIP6 historical runs. While these results suggest that the PMM could be a valuable tool for predicting Taiwan’s summer climate on decadal time scales, we also observe model uncertainties in simulating the teleconnection patterns of PMM-related SSTs in East Asia, including Taiwan’s summer temperature response. These findings underscore the need for further investigation into the complex interplay between PMM circulation responses, seasonal monsoons, and other components of climate systems simulated in climate models.

What Drives Interannual Rainfall Variability Over Northern Australia?

AbstractThe interannual variability of northern Australian (NA) rainfall is caused by local processes as well as remote teleconnections, many of them being interrelated. Their influence evolves throughout the wet season, from October through April. Using a stepwise linear regression and examining individual months, we identify the key drivers for rainfall variability over northwest and northeast Australia. Our research shows that the El Niño‐Southern Oscillation (ENSO), followed by local sea surface temperatures (SSTs), are the key sources of rainfall variability in October and November. More specifically, the Arafura and Coral Sea SSTs contribute to rainfall variability over northwest Australia, while the Coral Sea SSTs strongly impact on northeast Australian rainfall during these months. The combined ENSO and local SST indices explain up to 50% of variance in observed NA spring monthly mean rainfall. However, the SST influence from both seas breaks down with the onset of the Australian summer monsoon in late December, and by January, SST indices explain zero variance in rainfall. Instead, December to March rainfall variability is associated with a wind‐evaporation feedback, which is particularly strong over northwest Australia. The evaporation index is the only predictor that we investigated that can explain any variance in northwest Australian rainfall in January. While the more purely monsoonal northwest of Australia is dominated by variability internal to the monsoon system, rainfall variability in the northeast retains some influence from remote climate drivers throughout the monsoon season. Further research is needed to clarify the processes and timescale involved in the wind‐evaporation feedback.

Chandipura Virus in India: A Comprehensive Epidemiological Review.

The Chandipura virus (CHPV) is an emerging arbovirus in India, primarily transmitted by sandflies and associated with outbreaks of acute encephalitis in children. First identified in 1965 in Maharashtra, CHPV has since been linked to Acute Encephalitis Syndrome (AES) in several states, including Maharashtra, Gujarat, Telangana, and Madhya Pradesh. The virus predominantly affects children under 15 years, particularly in rural areas with poor housing conditions, and is most prevalent during the monsoon season when sandfly activities are high. Its clinical presentation includes a rapid-onset febrile illness progressing to encephalitis, often with a high case fatality rate. Diagnostic challenges arise due to similarities with other arboviral infections, complicating outbreak management. Current public health interventions emphasize vector control, environmental sanitation, and community awareness, though the lack of specific antiviral treatment and vaccines remains a significant barrier. This review highlights the need for improved surveillance, diagnostic tools, and vaccine development to mitigate the potential public health impact of CHPV in India. A One Health approach, integrating human, animal, and environmental health, is crucial for the effective management and prevention of CHPV outbreaks.

Anthropogenic influence on groundwater metal toxicity and risk to human health assessment in Umaria coalfield of Madhya Pradesh, India

Metal toxicity in groundwater surrounding coal mines is a major concern because it may pose a significant risk to human health of the local populace. The present study investigated Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, Sr, and Zn concentrations in the groundwater of Umaria coalfields during the post-monsoon season and pre-monsoon season. The study was carried out to investigate the spatial and temporal variation of the metals in the groundwater along with statistical source identification of the metals and assessment of human health risks due to intake of the metals through the groundwater. The metals of concern were Al, Fe and Mn, which exceeded the Indian drinking water quality standards in 26%, 38% and 12% of samples in the post-monsoon season and 38%, 40% and 14% of samples in the pre-monsoon season. A marked decrease in metal concentrations in the post-monsoon season was also observed, which may be attributed to the dilution effect associated with the heavy rainfall during the monsoon season. Principal component analysis used to identify contamination sources of the metals indicated geogenic attributes, coal mining activities and vehicular load as the sources of the metals in the groundwater. The human health risk assessment suggested considerable risk to the local populace using the groundwater for drinking purposes. The probable health risk, as suggested by the Hazard Index, depicted a higher risk to the child population as opposed to the adults. The Hazard Index for the child population was greater than unity in 60% and 76% of the samples in the post- and pre-monsoon seasons, respectively, suggesting a significant risk of metal exposure from groundwater intake. The study also suggested that ingestion was the primary exposure pathway and risk due to dermal exposure was trivial. The carcinogenic risk due to As and Cr were within the acceptable limits except for one location each for As and Cr. The present study suggests a potential non-carcinogenic human health risk due to groundwater intake; hence, the study area needs routine groundwater quality monitoring.

Growth and Yield Response of Mung Bean (Vigna radiata L.) to Different Rates of Phosphorous and Potassium Application

This study investigated the effects of phosphorus (P) and potassium (K) fertilization rates on the growth, yield attributes, and seed yield of mung bean (Vigna radiata L.) under pre-monsoon and monsoon seasons in Myanmar. Field experiments were conducted using a two-factor randomized complete block design with three replications, comprising four P levels (P0, P20, P40, P60: 0, 20, 40, 60 kg P ha-1) and four K levels (K0, K20, K40, K60: 0, 20, 40, 60 kg K ha-1). Results demonstrated that combined P and K application significantly enhanced mung bean productivity compared to single-nutrient treatments. During the pre-monsoon season, the optimal treatment was 60 kg P ha-1 with 40 kg K ha-1 (P₆₀K₄₀), yielding 1,126.87 kg ha-1, which was statistically comparable to P₆₀K₆₀. In the monsoon season, 60 kg P ha-1 with 40 kg K ha-1 (P₆₀K₄₀) produced the highest yield (1,561.17 kg ha-1), showing no significant difference from P₆₀K₂₀ or P₆₀K₆₀. In both seasons, the combined application of P and K fertilizers significantly enhanced yield and yield components, including pods per plant and seeds per pod. While P₆₀K₄₀ is recommended for pre-monsoon cultivation, reduced K input (P₆₀K₂₀) is sufficient during the monsoon, likely due to increased nutrient solubility under wetter conditions. The findings highlight the combined application of P and K in optimizing mung bean yields compared to the single application. Therefore, to optimize mung bean production, growers should consider the combined application of P and K fertilizers rather than single applications. However, to reach a precise conclusion and recommendation, more research work on mung bean should be conducted in different agro- ecological zones.

Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh

Abstract Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990–2015) and Chattogram (1999–2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions.

Statistical Analysis of Climate Variability and its Impacts on Tank Irrigation: A Case Study of the Agaramar Sub-Basin, India

Analyzing climatic behaviour at the relevant time scales is crucial in countries that rely on rain-fed agriculture. This study was conducted in the Thippasamudram tank within the Agaramar sub-basin, Vellore district. Historical rainfall and rainy-day data from 1981 to 2020 were acquired from the IMD data portal, Pune. This study used statistical analysis to detect trends in the temperature, rainfall, and rainy days at monthly, seasonal and annual timescales (1981-2020) at Thippasamudram village. The results revealed that the minimum temperature got significantly increased during the Southwest and the Northeast monsoon seasons, summer season and yearly timescales at rates of 0.017℃, 0.037℃, 0.024℃, and 0.018℃ respectively. The Mann Whitney Pettitt test revealed that the year 2001 was the change point on the annual scale. The highest positive temperature anomaly was recorded in the year 2012, at a rate of 0.86℃. From the Precipitation Concentration Index (PCI) and Rainfall Anomaly Index (RAI) results, the year 2012 was characterized as very dry and had an irregular distribution of rainfall. The tank filling levels were triangulated with the actual data and found below 50 %. This study found high variability in monthly and seasonal rainfall from the Coefficient of Variation (CV) statistical analysis. It showed a significant variation in seasonal and yearly rainy days exhibiting reduced variation. No significant annual precipitation trend was detected. The analysis revealed that the rainfed Thippasamudram tank experiences inconsistent filling due to erratic rainfall. The findings of this study enable policymakers and farmers to improve irrigation infrastructure and optimize irrigation scheduling, leading to enhanced efficiency.

Influence of Phosphorus and Boron Application on Yield and Yield Attributes of Mungbean (Vigna radiata L.)

The intensification of agricultural practices and restricted use of phosphorus and boron fertilizers have contributed to the depletion of soil nutrients, leading to deficiencies that hinder farmers' ability to reach their desired production targets. Therefore, this study aimed to assess the effects of phosphorus (P) and boron (B) applications on the yield and yield attributing characters of mungbean, along with determining the optimal nutrient doses for maximizing yields. Field experiments were conducted at the Soil and Water Science field of Yezin Agricultural University during the pre-monsoon and monsoon seasons of 2024. The experimental design followed a 4 x 3 factorial arrangement in a randomized complete block design (RCBD) with three replications. This included four levels of P (P0, P1, P2, P3: 0, 20, 40, and 60 kg P ha-1) and three levels of B (B0, B1, B2: 0, 0.5, and 1.0 kg B ha-1), along with a blanket dose of 20:40 kg N: K ha-1 as basal. There were twelve treatments in each block. The tested cultivar is yezin-15. The results revealed that both individual and combined applications of P and B significantly enhanced mungbean yield and its attributes. The highest yields were achieved with individual applications of 60 kg P ha-1 and 1.0 kg B ha-1 in both seasons. The combined treatment of P3B2 (60 kg P ha-1 + 1.0 kg B ha-1) yielded the best results for seed yield, plant height, number of pods plant-1, number of seeds pod-1, and total dry matter in both seasons. This treatment was statistically similar to P2B2 (40 kg P ha-1 + 1.0 kg B ha-1) and P3B1 (60 kg P ha-1 + 0.5 kg B ha-1). Thus, P2B2 is recommended as the optimal dose for increasing mungbean yield in similar agroecological zones. This research provides valuable insights for farmers aiming to optimize fertilizer use and improve crop yields in nutrient-depleted soils. Future studies could explore long-term effects and broader applicability across diverse environments to further refine nutrient management strategies for mungbean cultivation.

Climate warming modifies hydrological responses in the southern Western Ghats and the western coastal plains (India): Insights from CMIP6-VIC simulations.

Climate warming modifies hydrological responses in the southern Western Ghats and the western coastal plains (India): Insights from CMIP6-VIC simulations.

Generation of daily precipitation ensembles for Kosi Basin, India using Conditional Stochastic Weather Generator

ABSTRACT The present work demonstrates the generation of daily precipitation ensembles on the basis of daily precipitation for the Kosi Basin in India. The daily values of precipitation, along with conditional probability with respect to qualitative seasonal forecast, are used in an algorithm of Conditional Stochastic Weather Generator (CSWG), for generation of 100 precipitation ensembles at the basin and sub-basin level. For achieving this, CSWG algorithm was applied for lead time of 1 month on the three monsoon seasons of 2007, 2016 and 2018 which are having the highest predicted probabilities for the three categories of precipitation viz. above normal, normal and below normal, respectively. The PDF of seasonal precipitation shows expected shifts compared to historical precipitation. The historical mean lies below the 2007 ensemble median, near the 2016 median, and above the 2018 median from CSWG. These results highlight the high skill of the CSWG algorithm. Reliable long-lead forecasts support water management, agriculture, storage planning, and advance flood warnings.

Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud

The estimation of Forest above-ground biomass (AGB) is critical for comprehending forest ecosystems and promoting biodiversity restoration. The study was conducted to develop an effective approach to predict Forest Above-Ground Biomass (AGB) using Machine Learning, Image Classification, and GEE open-source fast processing system in the Similipal Tiger Reserve (STR), India. The study utilized six machine learning models and integrated various data sources, including Sentinel-1 and Sentinel-2 imagery, forest canopy height data from NASA’s GEDI Global Ecosystems Dynamics Investigation-LiDAR, geo-environmental Shuttle Radar Topography Mission (SRTM) data, and Climate Hazards Group Infrared precipitation with Station (CHIRPS) data. Sentinel-based optical and Synthetic Aperture Radar (SAR) signatures were also extracted before and after the monsoon season to evaluate AGB in a subtropical region. The Random forest-based Boruta method was used to examine the importance of multiple factors contributing to the prediction’s accuracy. In addition, the assessment of multicollinearity, which is accomplished by measuring the variance inflation factor (VIF), was carried out to address the issue of interrelatedness among variables that could affect the accuracy of the AGB mapping. The Random Forest model exhibited superior accuracy compared to other models, achieving an R2 of 0.71, MAE of 16.12Mg/ha, RMSD of 22.27Mg/ha, NRMSD of 0.212, and AUROC of 75%. The scatterplot analysis revealed a positive correlation between forest biomass and factors, such as forest canopy height, elevation, normalized differential vegetation index, normalized differential moisture index, and the ratio of VV/VH after the monsoon season. The VIF values ranged between 1.12 and 8.73, with VH_Jan_Mar having the maximum VIF and forest canopy height having the minimum VIF. As per the Boruta algorithm, 16 attributes were deemed important, while 14 attributes had less influence on AGB. The study presented a novel approach for estimating biomass in subtropical regions using remote sensing data set and machine learning models in Google platform. These results can be successfully used by the planners of STR for monitoring variation in AGB ensuring better habitat for wild animals.

Streamflow projections for the Jhelum River basin under climate change

High Asian mountain water resources, which serve as a lifeline for downstream communities, are vulnerable to warmer future climates. Following the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) 3b protocol, the future changes in the mean and extreme streamflow of the Jhelum River under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP3-7.0, and SSP5-8.5 were quantified. For this, successful calibration and validation of the eco-hydrological Soil and Water Integrated Model (SWIM) against observed streamflow for 1999–2004 and 1994–1999, respectively was completed. Then, the SWIM was forced with bias-adjusted ISIMIP3b historical (1985–2014) and future (2016–2100) datasets. Climate change analysis suggests a warming of 0.9 ºC, 5.7 ºC, and 7.5 ºC per century under SSP1-2.6, SSP3-7.0, and SSP5-8.5, whereas substantial wetting of 329 mm, 665 mm, and 1258 mm per century under SSP1-2.6, SSP3-7.0, and SSP5-8.5, relative to the historical period. Seasonal temperature changes reveal that during the monsoon temperature increase was more pronounced specifically 5.78 ºC whereas precipitation also increased markedly 20.84% during the monsoon season for the far future climate under the high-end warming scenario of SSP5-8.5. Subsequently, the mean ensemble changes suggest an increase in low flows (89% in February) but a decrease in high flows (37% in June) under all SSP scenarios, more pronounced under the high-end warming scenario of SSP5-8.5. Further, the 30-year return level will decrease for the near-future (2016–2043), mid-future (2044–2071) and far-future (2072–2100) climates under SSP1-2.6, ranging from −7.01% to −9.87%, under SSP3-7.0, ranging from − 7.22% to − 13.35%, and under SSP5-8.5, ranging from − 9.41% to − 26.60% for the ensemble of five models under consideration which imply a reduction in the likelihood or magnitude of extreme river flow events. The outcomes of this study will advance our knowledge of the discharge dynamics of the Jhelum River Basin under climate change, which will facilitate the better management of precious water resources.

A Study on Groundwater Quality in and Around the Pologround Industrial Area of Indore City, M.P. (India)

Safe and pure drinking water is the primary need of people, depends upon groundwater sources that have problems of hardness, calcium, nitrate, phosphate, fluoride, D.O., B.O.D., C.O.D. and Heavy metals copper and zinc and excess of MPN count. Different samples have been collected in premonsoon and past monsoon seasons during 2022-23. Different parameters of water has been analysed and evaluated to the suitability of drinking water for human consumption and public hygiene scenario and health impacts. Some of the parameters are observed within the prescribed limits of IS:10500 while others are beyond the limits of WHO and ISI.

Optimizing the planting density of Bt. cotton varieties for rainfed vertisols of central India

The field experiment was conducted during the monsoon seasons of 2019–20 and 2020–21 at ICAR-Central Institute for Cotton Research (CICR), Nagpur, Maharashtra. Bt cotton (Gossypium hirsutum L.) hybrids was introduced in India by the private seed industry in 2002. Hybrid seeds were expensive and its cultivation is input intensive, cotton breeders from the public sector R & D institutes released Bt cotton varieties in 2020 suitable for planting under low input situations. This study evaluated the production potential of four Bt varieties released and notified in India, from the ICAR-Central Institute for Cotton Research, Nagpur. The varieties, ICAR-CICR ‘PKV081- Bt’, ICAR-CICR ‘Suraj Bt’, ICAR-CICR ‘Rajat Bt’, and ICAR-CICR ‘GJHV 374 Bt’ were evaluated in a split plot design under rainfed conditions on a Vertisol, with four planting densities, viz. 0.37 lakh (90 cm × 30 cm), 0.55 lakh (90 cm × 20 cm), 0.74 lakh (90 cm × 15 cm) and 1.1 lakh plants/ha (90 cm × 10 cm). The results indicated that there were significant differences among varieties and plant densities with respect to seed cotton yield, lint yield, boll weight, days to first open flower and earliness index. ICAR-CICR ‘PKV081-Bt’ was the most productive variety, yielding 2,639 kg seed cotton/ha at 0.37 lakh plants/ha. The interaction (variety × plant density) was significant for seed cotton yield, lint yield and boll density. Concerning seed cotton yield, for varieties, ‘PKV 081 Bt’ and ‘Rajat Bt’, 0.37 lakh plants/ha was optimum whereas for ‘Suraj Bt’ and ‘GJHV 374 Bt’, 0.74 lakh plant/ha was optimum plant density. At densities of 0.74 and 1.1 lakh plant/ha, the plant height increased and the maturity was delayed. The study highlights the importance of selecting appropriate Bt varieties and optimizing planting density to increase the productivity of rained cotton on Vertisols of Central India.

Potential health risk estimation through deterministic and stochastic approach for the air pollutants found inside bottling beverage industries of Himachal Pradesh in India

The rapid advancement of global industrialization has significantly increased air pollution, particularly particulate matter (PM), black carbon (BC), and noise, impacting indoor air quality in urban areas and posing notable health risks. This study monitored total volatile organic compounds (TVOCs), noise, PM (PM10, PM2.5, PM1), and BC inside bottling and beverage industries during the monsoon season in Himachal Pradesh, India. The highest recorded values of parameters were 142 ppm (TVOC), 359 µg/m3 (PM10), 102 µg/m3 (PM2.5), 65 µg/m3 (PM1), 26 µg/m3 (BC), and 87 dB (noise). The health risk assessment revealed noncarcinogenic hazard ratios (HRs) ranging from 0.52 to 0.80, with the highest risk observed for males aged 50–60 and females aged 40–50. Carcinogenic risk attributed to BC was estimated between 5.8E−04 and 6.8E-04. The highest uncertainty (2.02%) was observed in the carcinogenic risk for BC among females aged 21–30. In comparison, the lowest uncertainty (0.36%) of risk was found in noncarcinogenic HR for PM2.5 among males of the same age group. While minimal variation for HR estimation (~ 0.87%) was found between deterministic and probabilistic approaches. Despite limitations inherent to its cross-sectional design, the study underscores significant occupational health risks due to indoor air pollution in industrial settings.Graphical

Analysing accident trends and safety factors in scrap-based steelmaking plants: a case study from Punjab, India (2017–2022)

This study identifies and analyses the primary elements contributing to accidents in scrap-based steelmaking plants in the Punjab state of India as a typical case study. The collected data for 2017–2022 from different scrap-based steelmaking plants were analysed utilizing Boston square matrix, Pareto chart, Ishikawa diagram and Poisson-distribution-based control chart analysis. The results revealed that fatal accidents contributed to around 13% of total accidents. The least fatal accidents are observed in the summer, around 17% of total accidents compared to the monsoon and winter seasons. The maximum contribution in non-fatal accidents is from lost-time injury, at around 85% of total non-fatal accidents. Moreover, compared to 2017, this study demonstrated a 78 and 45% decrease in non-fatal and fatal accidents in 2022. This decrease may be attributed to the efficacy and influence of regulatory measures, the acquisition of knowledge from past accidents and employee awareness in these scrap-based steelmaking facilities.

Development of Rockfall Early Warning System (REWS) for Prediction of Rockfall Hazard in Manikaran, NW Himalaya, India

ABSTRACT This study addresses the first rockfall early warning system (REWS) and the urgent need for monitoring and predicting rockfall hazards in the Parvati Valley, following a major landslide near the historic Manikaran Gurudwara in August 2015. The incident resulted in significant casualties and injuries, emphasizing the criticality of implementing preventive measures. The Gargi village, located approximately 1.5 km uphill from the Parvati River base, experiences recurring rockfalls, particularly during the monsoon season. Compounding the issue, the Manikaran-Bhuntar link road, a vital route to the Parbati Hydro Electric Project Stage-II by NHPC Limited, is situated within the runout zone of rockfall events. This research involves a comprehensive approach, including geological investigation, geomorphological zonation mapping, and a drone survey of the rockfall site upslope of Manikaran. Structural data from the rockfall site of the August, 2015 event was collected, and the active source area was identified on the southwest (SW) facing scarp. Anticipating future rockfall events, we have implemented an on-site wireless sensor network comprising tilt meters, crack meters, wireless nodes, piezometer, rain gauge, accelerometer, and gateway. The gateway wirelessly transmits real-time data to the Geotechnical Lab at the Department of Geology, Panjab University, Chandigarh, enabling early prediction of potential rockfall incidents. This integrated approach aims to predict the landscape evolution of the SW-facing scarp over the next 50 to 100 years, providing valuable insights into the potential rockfall hazard in Manikaran and the surrounding areas of Parvati Valley. The findings will contribute to proactive measures for safeguarding lives and infrastructure in the region.