Interventions for improving indoor and outdoor air quality in and around schools

Students spend nearly one third of their typical day in the school environment, where they may be exposed to harmful air pollutants. A consolidated knowledge base of interventions to reduce this exposure is required for making informed decisions on their implementation and wider uptake. We attempt to fill this knowledge gap by synthesising the existing scientific literature on different school-based air pollution exposure interventions, their efficiency, suitability, and limitations. We assessed technological (air purifiers, HVAC - Heating Ventilation and Air Conditioning etc.), behavioural, physical barriers, structural, school-commute and policy and regulatory interventions. Studies suggest that the removal efficiency of air purifiers for PM2.5, PM10, PM1 and BC can be up to 57 %, 34 %, 70 % and 58 %, respectively, depending on the air purification technology compared with control levels in classroom. The HVAC system combined with high efficiency filters has BC, PM10 and PM2.5 removal efficiency up to 97 %, 34 % and 30 %, respectively. Citizen science campaigns are effective in reducing the indoor air pollutants' exposure up to 94 %. The concentration of PM10, NO2, O3, BC and PNC can be reduced by up to 60 %, 59 %, 16 %, 63 % and 77 %, respectively as compared to control conditions, by installing green infrastructure (GI) as a physical barrier. School commute interventions can reduce NO2 concentration by up to 23 %. The in-cabin concentration reduction of up to 77 % for PM2.5, 43 % for PNC, 89 % for BC, 74 % for PM10 and 75 % for NO2, along with 94 % reduction in tailpipe emission of total particles, can be achieved using clean fuels and retrofits. No stand-alone method is found as the absolute solution for controlling pollutants exposure, their combined application can be effective in most of the scenarios. More research is needed on assessing combined interventions, and their operational synchronisation for getting the optimum results.Copyright © 2022 The Authors

A review on automatic covid-19 lung lesions detection from tomographical image

Corona Virus Disease-2019 commonly known as COVID-19 which has been defined by the Novel Corona Virus. It is a family of severe acute respiratory syndrome corona virus2 (SARS-CoV-2) and was primarily detected during respiratory outbreak. It was first reported to the World Health Organization on December 31, 2019. On January 30, 2020, the World Health Organization announced the COVID-19 eruption a global health emergency. As of 30-July-2020 17 million confirmed cases have been reported in the world and 16,32,422 cases in India. It is required to identify the infection with high precision rate but there are lots of deficiency in the diagnosing system that may resulted false alarm rate. Initially it could be detected through throat saliva but now it can also be identified thought the impairment in lungs from computerized tomographical imaging technique. This paper reviewed various researches over COVID-19 diagnosis approach as well as the syndrome in respiratory organs. © 2021 IEEE.