Community kitchen tandoors (CKT)-a potential candidate for air pollution mitigation strategies?

Community kitchen tandoor (CKT) is a clay-based hollow cylindrical device commonly used in South Asian and Middle Eastern countries for baking flatbreads and cooking meat. These CKTs, generally fuelled by charcoal or wood, contribute significantly to the pollution loads in ambient air along with occupational exposure hazards. CKTs, being a part of the informal sector, lack emissions and safety guidelines. This study surveys 139 restaurants in CKT hotspots of New Delhi, India, to understand tandoor design and operational parameters and to assess PM2.5 and CO exposure concentrations at representative field restaurants. PM2.5 and CO exposure concentrations from traditional CKT was found to be several-folds higher than safe indoor air quality levels. Further, the traditional CKT was evaluated for different improved fuels (like briquettes and pellets) in the laboratory for PM2.5 and CO microenvironment concentrations. It was found that the fuel improvements in traditional CKT could not improve microenvironment concentrations to the desired levels; hence, an automated pellet-fed forced-draft improved tandoor with an improved combustion chamber design is demonstrated. The results of the laboratory trial of improved tandoor were compared with traditional tandoor (using pellets) and have shown 84% and 94% reductions in PM2.5 and CO concentrations, respectively, indicating significant benefits to the environment and health. We recommend implementing such improved CKT, on a large scale, combined with other identified control options, as a potential candidate under air pollution mitigation strategies in cities' action plans under National Clean Air Programme (NCAP).

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection.

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.

Impact assessment of clean cookstove intervention in Gujarat, India: a potential case for corporate social responsibility (CSR) funding.

Exposure to indoor air pollutants released from traditional cookstoves in rural Indian households is a matter of great concern. While there are various studies over several decades focused towards intervention strategies for reducing air pollutants, limited literature exists towards the identification of appropriate methodology for feasible intervention, adoption and usage of improved cookstoves (ICS). In the present study, PM2.5 and CO microenvironment concentrations are estimated in households using traditional and improved cookstove (NEERDHUR). The reduction in PM2.5 and CO microenvironment concentrations after the introduction of ICS was found to be 89-94% and 35-57%, respectively. Information-education-communication (IEC) activity was used as a tool to increase the adoption and usage rate in the ICS using households. The cost-benefit analysis was also performed to check the benefits of ICS use, and the benefit-cost ratio was found to be 3 to 4 times. Findings of the study suggest that, although the ICS intervention could significantly improve the indoor air quality, however, it fails to comply with the permissible safe limits; further focus on greener fuels and ventilation characteristics is suggested. The outcomes from the study can help decision-makers, corporate social responsibility fund mobilizers and policymakers for effective policy advocacy to design efforts by promoting clean cooking interventions and linking and mapping these with national missions and flagship programs.