Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network.

Glacier area fraction at high altitude mountains is a serious worry in today's time triggered by climate change. The current information on this natural resource is very important for the survival of humanity as it affects the water, food, and energy security of people dependent on it. Due to its problematic accessibility and tough environmental condition, ground monitoring is quite challenging. This study investigates the impact of environmental parameters and pollutants on glacier area fraction over the Eastern Himalaya region and its prediction through random forest (RF), multilayer perceptron (MLP), radial basis function analysis (RBFN), and response surface methodology (RSM) models. The data are obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA's data archive portal ( https://giovanni.gsfc.nasa.gov ). The collinearity of independent variables reveals that all selected input parameters are highly correlated with R2 value > 0.9. The RSM and RF model provided valuable insight of the predictor's significance in addition to their capability to predict the response. The model performance was evaluated in terms of R2 value and the error matrices. The model's R2 value was found to be 0.843, 0.839, 0.838, and 0.743 for MLP, RBFN, RF, and RSM respectively. Although, the neural network model R2 values are the highest, but the most reliable and suitable model is RF as the error matrices for this model are much lower than others. This study encourages the investigation of the hybridization of these models for more accurate prediction.

Extreme climate index estimation and projection in association with enviro-meteorological parameters using random forest-ARIMA hybrid model over the Vidarbha region, India.

This study aims to estimate and analyse extreme climate indices such as standardised precipitation index (SPI) coupled with enviro-met (air pollutants and meteorological) parameters over the Vidarbha region from 1980 to 2019. Seasonal SPI, also known as the draught index, is derived from rainfall data using the R language. An attempt is made to determine the best combination of enviro-met on SPI using the random forest (RF) models. The study region is divided into four zones to assess the microclimatic impact on the forecast model. Three sets of data combinations, viz., meteorological and air pollution parameters, are applied for SPI prediction using RF. The autoregressive integrated moving average (ARIMA) model is also used for a future scenario projection. It is observed from the projection results that the drought severity is enhancing with time. The drought severity scale from 1980 to 1989 is found to be between - 1 and 1, but the scale increases from 1990 to 2019 (- 3). From 1990 to 2019, SPI's negative (-) scale has become more prominent in all Vidarbha regions. These trends are indicative of drought severity and will have a significant impact on both life and property.

Assessment of meteorological parameters on air pollution variability over Delhi.

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

Interaction of aerosol with meteorological parameters and its effect on the cash crop in the Vidarbha region of Maharashtra, India.

Regional weather variability depends on various meteorological variables such as temperature and rainfall. The current research focuses on the variability and trends in annual aerosol optical depth (AOD), temperature (T), and rainfall (RF) in 11 Vidarbha districts. The annual trend analysis of AOD, T, and R is determined using the non-parametric Sen slope and Mann-Kendall (MK) test at a 5% significant level from 1980 to 2019. Annual T and AOD indicate a substantial increase in this study, whereas rainfall shows a non-significant trend (MK, test) over the study period. According to Sen's slope trends, the relatively high rainfall area (Chandrapur = 1.273 and Garchiroli = 4.06) got positive trends, but Gondia and Bhandara districts have negative (Sen's slope = - 2.79 and - 2.56) trends. The moderate rainfall areas are showing a less negative Sen slope (Wardha = - 0.21, Washim = - 1.13 and Yavatmal = - 2.75), whereas Nagpur districts' Sen's slope shows a positive value (Sens's slope = 0.72). The assured rainfall area districts show Sen's slope trends are positive (Akola = 0.45, Amravati = 1.17 and Buldana = 0.42). Sen's slope trend indicates rising rainfall, whereas negative trends indicate decreasing rainfall in the time series. This study has also looked at the effect of RF, AOD, and T on the last two decades' cash crop production (2000-2019) for Vidarbha districts. The relationship between rainfall departure (DRF) and cash crop yield has also been highlighted. Five cash crops, such as cotton (Ct), total cereals (TCrl), total oilseeds (TOsd), total pulses (TPS), and sugarcane (Sc), are selected for the present study.

Assessment of occupational exposure to diesel particulate matter through evaluation of 1-nitropyrene and 1-aminopyrene in surface coal miners, India.

DPM (diesel particulate matter) is ubiquitously present in the mining environment and is known for mutagenicity and carcinogenicity to humans. However, its health effects in surface coal mines are not well studied, particularly in India. In this study, DPM exposure and corresponding exposure biomarkers were investigated in four different surface coal mines in Central India. To document and evaluate the DPM exposure in surface coal miners, we characterized 1-NP (1-nitropyrene) in the mining environment as surrogate for DPM using Sioutas Cascade Impactor. Exposure biomarkers were analyzed by collecting post work shift (8-h work shift) urine samples and determining the concentrations of 1-aminopyrene (1-AP) as a metabolite of 1-NP and 8-hydroxydeoxyguanosine (8OHdG) as DNA damage marker. We observed high concentration of 1-NP (7.13-52.46 ng/m3) in all the mines compared with the earlier reported values. The average creatinine corrected 1-AP and 8OHdG levels ranged 0.07-0.43 [Formula: see text]g/g and 32.47-64.16 [Formula: see text]g/g, respectively, in different mines. We found 1-AP in majority of the mine workers' urine (55.53%) and its level was higher than that reported for general environmental exposure in earlier studies. Thus, the study finding indicates occupational exposure to DPM in all the four mines. However, the association between 1-NP level and exposure biomarkers (1-AP and 8OHdG) was inconsistent, which may be due to individual physiological variations. The data on exposure levels in this study will help to understand the epidemiological risk assessment of DPM in surface coal miners. Further biomonitoring and cohort study are needed to exactly quantify the occupational health impacts caused by DPM among coal miners.

Co-combustion of distillery sludge and coal for application in boiler and subsequent utilization of the generated bottom ash.

Combustion stands as one of the essential methods in resource recovery for disposal of distillery sludge. In this study, sludge along with coal has been considered an option for co-combustion in the grate furnace aiming for further application as a boiler fuel. Detailed analysis was carried out to verify the feasibility of co-combustion of sludge with coal. Distillery sludge was blended with coal as a mixed fuel at co-combustion ratios of 20%, 30%, and 40% in grate furnace. The results of the analysis indicated that the combustion with 40% sludge mixed coal is suitable for application as a fuel in boiler. According to the chemical composition of bottom ash, weight loss from 460 to 800°C indicated the presence of C-C and C-H. Also, EDX and XRD analyses of mixed fuel was carried out to determine the mineralogical composition. The presence of quartz (SiO2), mullite (3Al2O32SiO2), and hematite (Fe2O3) present in the ash can be used as mineral additives in cement industries. The study also provided a promising approach towards diverting combustion bottom ash from landfills for its utilization in various industries which can be a possible cost-effective solution.

The reciprocal relation between lightning and pollution and their impact over Kolkata, India.

Aerosol loading in the atmosphere can cause increased lightning flashes, and those lightning flashes produce NOX , which reacts in sun light to produce surface ozone. The present study deals with the effect of surface pollutants on premonsoon (April-May) lightning activity over the station Kolkata (22.65° N, 88.45° E). Seven-year (2004-2010) premonsoon thunderstorms data are taken for the study. Different parameters like aerosol optical depth and cloud top temperature from the Moderate Resolution Imaging Spectroradiometer satellite products along with lightning flash data from Tropical Rainfall Measuring Mission's (TRMM) Lightning Imaging Sensor are analyzed. Some surface pollution parameters like suspended particulate matter, particulate matter 10, nitrogen oxides (NOX), and surface ozone (O3) data during the same period are taken account for clear understanding of their association with lightning activity. Heights of convective condensation level and lifting condensation level are collected from radiosonde observations to anticipate about cloud base. It is found that increased surface pollution in a near storm environment is related to increased lightning flash rate, which results in increased surface NOX and consequently increased surface ozone concentration over the station Kolkata.