Research Projects

Ambient inhalable fine particulate matter (PM₂.₅) is considered a menace of atmospheric pollution because of its complex composition, consisting of harmful components, both chemical and biological. Induction of oxidative stress owing to reactive oxygen species (ROS) is a common toxic condition triggered by inhalation of PM₂.₅. Indigenous ability of PM to evoke oxidative damage resulting from elevated presence of ROS with concomitant reduction in the levels of antioxidants generally indicates its oxidative potential (OP). In the Indian context, there is limited understanding on the toxicity due to OP induced by fine particulates considering chemical and especially biological constituents of PM₂.₅ in different cities with dense population, distinctive urban settlements having diverse livelihood and various emission sources. Moreover, concerning poor air quality in the Kolkata metropolitan city of India, recent studies on evaluating the impacts of chemical and biological constituents of PM₂.₅ on its toxicity through the measurement of oxidative potential (OP) based on oxidative stress-mediated toxicological endpoints are very limited. Based on the facts above, the current work would focus on the modeling of the overall ambient PM₂.₅ toxicity vis-à-vis chemical and biological (bioaerosol) characteristics using artificial neural networks (ANN) (AI modeling tool) for the prediction of hazards and exposure risks through the establishment of a cause-effect relationship. Further, a toxicity scale will be established based on the modeling outcome to augment the health-based air quality index scale, thereby indicating the need to trigger an emergency response system (ERS) for providing alerts of taking timely actions for better air quality management. Thus, the proposed work would establish a trained model to predict the overall toxicity of ambient aerosol with given chemical and biological characteristics. The novelty of the proposed study: The quantification of bioaerosol load in PM₂.₅ involving a metagenomics approach through the analysis of their genetic material is yet to be reported in the Indian scenario and will be targeted in the proposed work. Additionally, no model including both chemical and bioaerosol composition of PM₂.₅ has been implemented until now to establish a toxicity-based air quality index for effective air pollution management. This research gap will also be addressed in the present study. The objective of the proposed research is aligned to fulfil India’s National Missions, such as the Swachh Bharat Mission, Smart Cities Mission, and National Clean Air Programme (NCAP), targeting non-attainment urban areas of India for air quality improvement and a healthier environment. The research objective also aligns with United Nations’ Sustainable Development Goals such as SDG 3: Good Health and Wellbeing, SDG 11: Sustainable cities and communities, and SDG 15: Life on Land.

PM2.5,Bioaerosol,Oxidative potential,In vitro cell-based assay,Toxicity-based AQI,Artificial Neural Network