http://nopr.niscpr.res.in/handle/123456789/36 2026-08-19T06:07:33Z 2026-08-19T06:07:33Z Vyawahare, Rahul Varghese Georgea, Kariampallil Gargava, Prashant http://nopr.niscpr.res.in/handle/123456789/68284 2026-08-13T11:50:34Z 2026-04-01T00:00:00Z

Title: Evaluating line source emission models for Indian urban environments Authors: Vyawahare, Rahul; Varghese Georgea, Kariampallil; Gargava, Prashant Abstract: Vehicular emissions have constituted a primary source of urban air pollution, with profound implications for public health and environmental quality, especially in rapidly developing countries like India. This review has chronicled the progression of mobile source emission inventory methodologies, which have transitioned from simplified Excel-based bottom-up approaches to sophisticated tools such as MOBILE, MOVES, COPERT, and IVE. Early methods have primarily utilized static emission factors combined with limited vehicle activity and road length data, resulting in poor spatial and temporal resolution and inconsistent emission estimates. Advanced inventory models have improved accuracy by incorporating modal driving cycles, vehicle-specific power calculations, and detailed fleet characteristics; however, their efficacy in developing country contexts has often been hampered by the unavailability of comprehensive local inputs, including fuel composition, vehicle kilometres travelled, and fleet age distributions. This review has highlighted significant deficiencies in model validation, emission factor localization, and integration of real-world traffic dynamics within existing emission inventories. Current inventories have largely neglected to disaggregate vehicle kilometres travelled by road type or to capture heterogeneous traffic behaviour, leading to biased spatial emission distributions and compromised policy relevance. The review has identified the Indian Vehicle Emission (IVE) model as a promising example that has improved context specificity through detailed driving cycle and terrain sensitivity, complementing established models like MOVES and COPERT. To enhance emission inventory reliability for rapidly urbanizing Indian cities, the adoption of a modular, India-specific emission framework has become essential. Such a framework should incorporate real-time vehicle counts, stratified vehicle activity by road classification, and heterogeneous traffic patterns. Implementing an India-tailored emission inventory model will greatly support urban air quality management and policy formulation, aligning with national objectives such as the National Clean Air Programme (NCAP). It will provide policymakers with precise, actionable insights to target emission hotspots, design effective interventions, and ultimately safeguard urban environmental health Page(s): 143-159

2026-04-01T00:00:00Z Birwal, Amit Ali, Ariba Patel, Kamlesh http://nopr.niscpr.res.in/handle/123456789/68283 2026-08-13T08:37:39Z 2026-04-01T00:00:00Z

Title: Design and experimental validation of a hexagonal split-ring resonator sensor for microwave dielectric characterization of liquids Authors: Birwal, Amit; Ali, Ariba; Patel, Kamlesh Abstract: This research work has presented a compact microwave sensor based on a hexagonal split-ring resonator (H-SRR) useful for dielectric (ε) characterization of liquid samples. The proposed microwave sensor has consisted of three concentric hexagonal rings, which are etched on a low-cost FR4 substrate with a two-port resonant structure. The sensing principle depends upon the perturbation of the resonant characteristics when a liquid sample has been introduced into the high electric-field region made of H-SRR of the resonator. The sensor has been designed using 3D full-wave electromagnetic simulation and has been fabricated on a single-layer FR4 substrate. Experimental validation has been carried out using a low-cost LibreVNA and a commercial Rohde & Schwarz vector network analyzer. In the measurement results, noticeable shifts in the resonant frequency of the measured S-parameter responses have been observed for different liquid samples. A MATLAB-based matrix method has been used to estimate the complex permittivity (ε′−jε′′) from measured S-parameters data. The extracted dielectric properties have shown good agreement with reported literature values, thereby demonstrating the effectiveness of the proposed sensing approach. Page(s): 160-168

2026-04-01T00:00:00Z Jyothika Rani, Appana H Badami, Shashidhar Purushottam Gheware, Prasad Rajendra Donadkar, Rujuta http://nopr.niscpr.res.in/handle/123456789/68282 2026-08-13T07:24:17Z 2026-04-01T00:00:00Z

Title: Comparative performance evaluation of diesel, Amoora rohituka and Calophyllum inophyllum biodiesel-ethanol blends in a compression ignition engine Authors: Jyothika Rani, Appana; H Badami, Shashidhar; Purushottam Gheware, Prasad; Rajendra Donadkar, Rujuta Abstract: The depletion of fossil fuels and increasing environmental concerns have motivated the exploration of biodiesel and ethanol as renewable alternative fuels for diesel engines. The effects of renewable alternative fuels such as Amoora rohituka (Amoora) biodiesel, and Calophyllum inophyllum (Surahonne) biodiesel and ethanol-blended diesel on performance parameters of a single-cylinder, four-stroke CI engine operating at a constant speed of 1500 rpm have been investigated. The fuel blends used for the tests have included pure diesel (D100), diesel-ethanol blends (D95E5, D92E8, D90E10, D88E12), pure biodiesels, and biodiesel-ethanol blends (B95E5, B92E8, B90E10, B88E12). The performance parameters such as Brake Horse Power (BHP), Brake-Specific Fuel Consumption (BSFC), Brake Thermal Efficiency (BTE), and heat balance have been calculated, and results have been compared. The results have revealed that D95E5 has achieved the highest BHP (4.796 kW), while D88E12 has exhibited the maximum BTE (15.26%). BSFC has been found to be the lowest for D95E5 (0.502 kg/kW/hr) and highest for Amoora B100 (0.814 kg/kW/hr) and the heat balance analysis has indicated lower unaccounted heat losses for diesel-ethanol blends as compared to biodiesels. Overall, ethanol-diesel blends have demonstrated superior performance in terms of engine performance parameters, whereas biodiesel-ethanol blends have shown prominence with optimized combinations of fuel blends. Page(s): 169-175

2026-04-01T00:00:00Z Bannigidad, Parashuram Chingali, Sagar Gurubasavaraj, Prabhuodeyara http://nopr.niscpr.res.in/handle/123456789/68281 2026-08-13T07:20:30Z 2026-04-01T00:00:00Z

Title: Structural analysis and classification of silver–graphene oxide (Ag-GO) nanocomposites using SAM-based segmentation and machine learning models Authors: Bannigidad, Parashuram; Chingali, Sagar; Gurubasavaraj, Prabhuodeyara Abstract: Nanocomposites have acquired significant interest in antimicrobial, catalytic, and electronic applications due to their synergistic properties. The morphology and structural arrangements of nanocomposites at the micro and nanoscale have been recognized as strong determinants of these functional properties. This study has addressed the Structural Activity Relationship (SAR) of Silver–Graphene Oxide (Ag–GO) nanocomposites by employing a methodology to segment and classify the nanocomposites. Scanning Electron Microscopy (SEM) images have been used to segment nanocomposites using the Segment Anything Model (SAM) and to extract structural features such as area, perimeter, aspect ratio, eccentricity, and circularity, enabling the classification of nanocomposites using various machine learning models. Supervised machine learning techniques, namely Random Forest, Logistic Regression, Decision Tree, K-Nearest Neighbours (KNN) and XGBoost, have been employed for nanocomposite classification. This study has addressed a critical gap by incorporating automated segmentation and robust classification within a transparent performance evaluation framework, where XGBoost has demonstrated the highest classification accuracy of 97% for shape and 95% for size, outperforming other models in identifying and categorizing diverse morphological patterns within Ag–GO nanocomposites. Page(s): 176-183

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