CSIR-Central Institute of Mining and Fuel Research CSIR-CIMFR, previously known as Central Mining Research Institute and Central Fuel Research Institute, is based in Dhanbad, Jharkhand, India. It is a constituent laboratory of Council of Scientific & Industrial Research, an autonomous government body and India's largest research and development organisation. The establishment of CSIR-CIMFR was aimed to provide R&D inputs for the entire coal-energy chain from mining to consumption through integration of the core competencies of the two premier coal institutions of the country.CSIR-Central Institute of Mining and Fuel Research, Dhanbad, a constituent laboratory under the aegis of Council of Scientific and Industrial Research, New Delhi aims to provide research and declopment for the entire coal-energy chain encompassing exploration, mining and utilization. The laboratory also strives to develop mineral based industries to reach the targeted production for the country's energy security and growth with high standards of safety, economy and cleaner environment. In view of the National Missions recently declared by the Government of India, CIMFR has realigned its vision, missions and policies and also redefined targets for short and long term. This would promote rapid sustainable national techno-economic growth with equal emphasis on self-sustenance. CSIR-CIMFR is located in the town of Dhanbad, known as coal capital of India of Jharkhand state of India. It is strategically situated in the Damodar basin of Eastern part of the country which is endowed with rich coal deposits and hosts several large mineral based industries..
The present study is conducted to understand the rainwater chemistry at three different locations in India during the Southwest monsoon season from June to September 2018. The rainwater samples were analyzed for the pH, conductivity, along with the major anions (F−, Cl−, NO2−, NO3−, SO42−), and cations (Ca2+, Mg2+, NH4+, K+, Na+). The mean pH and conductivity shows a significant heterogeneity in rainwater samples with alkaline nature at Jaipur (7.09 and 43.46 µS/cm) and Varanasi (7.31 and 55.42 μ µS/cm) whereas acidic nature at Dhanbad (5.10 and 31.98 µS/cm). Moreover, the ionic compositions of rainwater were found to be drastically different at all the three stations, which are also reflected in the neutralization of rainwater. Neutralization factors of Ca2+, NH4+ and Mg2+ ions suggest that Ca2+ was the major neutralizing species in rainwater at Jaipur and Varanasi (but not at Dhanbad), with high neutralization factors of 3.10 and 2.65, respectively. The correlation among the measured ionic species indicates a significantly high correlation between SO42− and NO3−, Ca2+ and Mg2+ and Na+ and Cl− ions at all the three stations. Principal component analysis (PCA) was applied to identify the possible sources of rainwater constituents, explaining the crustal dust, biomass burning, fossil fuel combustion, agricultural emissions, and coal burning as possible sources of observed ions in rainwater. Further, the air mass back-trajectory clusters were also computed to estimate terrestrial influence on rainwater chemistry over these regions. Results suggest that the contribution from both local and regional sources significantly influenced monsoonal rainwater chemistry.
Large-scale coal mining in India produces vast overburden (OB) waste, contributing to land and environmental issues. This study is the first to evaluate OB from the Bhowra and Dhansar mines (Jharia Coalfield) as a sustainable alternative to conventional coarse aggregates in concrete. Five tons of OB were collected, processed, and subjected to comprehensive geotechnical, mineralogical, and mechanical characterization per Indian Standard methods. OB-based mixes (25%-100% replacement) demonstrated specific gravity (2.77-2.52), water absorption (1.02%-2.21%), and bulk density (1.61-1.38 kg/L), with aggregate crushing and impact values meeting IS:383-1970 guidelines. M-30 concrete with Bhowra OB retained a compressive strength of 48 MPa at 56 days for 100% replacement, suitable for construction use. Mineralogical analysis identified primarily quartz, kaolinite, gypsum, hematite, and calcite, with high SiO2 content and low hazardous metals. Microscopy and spectroscopic analyses confirmed mineral stability and absence of deleterious phases. The results show Bhowra OB is particularly effective at <= 50% replacement, providing strong mechanical performance and minimal strength loss. The findings present OB as a viable, environmentally responsible aggregate, potentially reducing the use of natural aggregates by 30%-50%, cutting mining waste by 5-10 Mt/year, and supporting circular economy goals in the construction industry.
Air pollution poses a significant public health risk, as pollutants, emitted from both natural and anthropogenic sources, can penetrate deep into the respiratory system, leading to a wide range of respiratory diseases. While numerous studies have examined the role of meteorological factors in modulating air quality, limited research has focussed specifically on their effectiveness in regions characterized by intensive mineral extraction activities, particularly coal mining zones where emission loads remain persistently high. In this context, the concentration levels of PM10, PM2.5, SO2, and NO2 were continuously monitored for one year using an automated ambient air quality monitoring system to investigate their seasonal behaviour and meteorological interactions. The recorded concentration ranged from 17.49 to 393.40 µg/m3 for PM10, 5.45 to 231.53 µg/m3 for PM2.5, 12.3 to 62.05 µg/m3 for NO2, and 11.59 to 182 µg/m3 for SO2. The pollutant concentrations peaked during winter and declined during summer and monsoon seasons. The trend analysis using Theil–Sen estimator revealed significant negative trends for all four pollutants PM10 (− 181.79 units), PM2.5 (− 106.11 units), SO2 (− 22.76 units), and NO2 (− 29.89 units). The linear regression analysis demonstrated a strong correlation between PM10 and PM2.5 (R2 = 0.89), whereas a weak association (R2 = 0.01) was observed between NO2 and SO2. Nonlinear regression further indicated temperature as a key influencing factor, showing a strong inverse relationship with NO2 (− 13.218) and a moderate negative impact on PM2.5 (− 1.517). Overall, the findings of this study underscore highlight the seasonal vulnerability of coal mining regions to pollutant accumulation and highlights the limitations of mechanisms under elevated emission scenarios. Furthermore, this study establishes temperature as a boundary-layer control variable and emphasizes that effective air-quality management in coal-mining regions must integrate real-time meteorological forecasting with emission scheduling for sustainable air-quality compliance.
Fracture connectivity (CL) is typically defined by the relative proportions of 'cross-cutting (X), abutting (Y), and isolated nodes (I) without accounting for their spatial distribution. To overcome this limitation, we propose a complementary parameter, S-connectivity (Sc), which integrates both node abundance and spatial distribution. Spatial distribution of nodes is captured using the statistical measure lacunarity. Scis defined as the product of CL and a weighting factor (kappa), where kappa is derived from the normalized lacunarity of the combined 'X' and 'Y' nodes (termed 'XY'-nodes). We evaluated Scusing synthetic fracture maps with identical CL values but different node distributions and the assumption that all fractures are open. Scsuc-cessfully distinguished between these maps. Numerical flow simulations further show that Sccorrelates positively with equivalent permeability, offering a slight improvement in accuracy compared to using CL alone. Analysis of natural fracture patterns from a fault damage zone confirmed the consistent positive relationship between Scand the simulated equivalent permeability. Overall, S-Connectivity offers a spatially informed and physically meaningful extension of node-based connectivity measures, enabling improved discrimination of connected fracture networks relevant to flow modeling and engineering analyses.
