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In this paper presents the assessment of source profile of nonmethane hydrocarbons (NMHCs) in the ambient air of Delhi. The samples were collected from five different urban sites using tedlar bags for aliphatic NMHCs and activated adsorption charcoal tubes for aromatic NMHCs during October 2014 to September 2015. Eleven aliphatic NMHCs propane, n-butane i-butene, i-butane, 1,3-butadiene, trans-2-butene, cis-2-pentene, n-pentane, n-hexane, heptane and four aromatic NMHCs benzene, toluene, o-xylene, p/m-xylene were identified in 112 urban ambient air samples. Samples were analysed using gas chromatography which is coupled with flame ionization detector (GC-FID). Pearson correlation coefficient (r) found to be = 0.5±0.2, shows significance level to have moderate among the NMHCs, indicates NMHCs in the urban ambient air have many sources profile mentioned in PCA result. Factor analysis(FA) and receptor model, i.e., Principal Component Analysis(PCA)/Absolute Principal Component Score (APCS) was used for identification of source profile distribution. PCA analysis after the varimax rotation have identified six possible source profile and explained about 70 % of the total dataset. The average % contribution of NMHCs emitted from vehicles was found to be 23%, whereas polymer manufacturing industries contributes 19% and from refinery operation/ gas station contribute 14%, and 13%, emitted from flare emissions and 10% from natural gas emissions. The secondary industrial process, including paints, body soaps and metal fabricator and processing was contributing 8%. Out of these remaining 13% was estimated as unidentified sources. These findings may be used by government authorities to formulate policies and strategies for improvement of urban air quality that can improve the health of urban communities.

In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information. The research addresses the growing interest in employing multiple autonomous robots for exploration tasks, particularly in scenarios such as Urban Search and Rescue (USAR) operations. Multiple robots offer advantages like increased task coverage, robustness, flexibility, and scalability. However, existing approaches often make assumptions such as search area, robot positioning, communication restrictions, and target information that may not hold in real-world situations. The hybrid algorithm leverages LF, known for its effectiveness in large space exploration with sparse targets, and incorporates inter-robot repulsion as a social component through PSO. This combination enhances area exploration efficiency. We redefine the local best and global best positions to suit scenarios without continuous target information. Experimental simulations in a controlled environment demonstrate the algorithm's effectiveness, showcasing improved area coverage compared to traditional methods. In the process of refining our approach and testing it in complex, obstacle-rich environments, the presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.