ABSTRACT
The Tamil Nadu Air Pollution and Health Effects study (TAPHE-2) aims to evaluate the relationship between air pollution and birth outcome in a rural-urban cohort of 300 pregnant women. Due to COVID-19 related lockdowns, some TAPHE-2 activities were delayed;however, continuous indoor and outdoor air quality data were collected in and around Chennai, India. We report here the impact of graded COVID-19 lockdown on indoor particulate matter (PM2.5 and PM10) levels based on calibrated data from affordable real-time PM sensors called atmos™ and ambient PM levels from publicly available regulatory monitors. The study period was between 11 March and 30 June 2020 (i.e., 100 days of continuous monitoring), which coincided with four phases of a nationwide graded lockdown. Field calibration coefficients for the atmos PM were derived by collocating them with reference-grade PM monitors. The normalized root mean square error (NRMSE) of the atmos hourly PM2.5 (PM10) improved from 41% to 15% (33% to 18%) after applying the field calibration coefficients. Lockdowns resulted in significant reductions in indoor and ambient PM levels, with the highest reduction observed during lockdown phase 2 (L2) and phase 3 (L3). Reductions as high as 70%, 91%, and 62% were observed in ambient PM2.5, indoor PM2.5, and indoor PM10 relative to pre-lockdown levels (PL), respectively. The indoor PM2.5/PM10 ratio decreased during the lockdown, suggesting a decline in the fine mode dominance in PM10. The indoor-to-outdoor (I/O) ratios in PM2.5 marginally increased during L1, L2, and L3 phases compared to that of PL levels, suggesting an uneven reduction in indoor and ambient PM2.5 levels during the lockdown. © The Author's institution.
ABSTRACT
In this paper the data for dailyconfirmed new casesconcerning the rise and fall of the Covid-19 (aka, coronavirus) pandemic infection in India for the nine month period starting from the first March 2020 has been subjected to a non linear least square fitting analysis using Gaussian, Skewed-Gaussian, Moffat, andVoigt model functions.The fitting parameters determined by the Python software package LMFIT are then used to compare the predicted remission times of Covid-19pandemic during 2021. It is found that while the Gaussian, Skewed-Gaussian and Moffat models predictlowlevels byabout March/April 2021;Voigt and other models predict longertimes to reach samelow endemic levels.
ABSTRACT
Countries are working very hard to control the spread of new Covid-19 infections. When the number of cases comes down the people tend to relax controls quickly. This has resulted in the second wave of infections. European countries especially France, Italy, UK, and Germany have clearly exhibited this (bigger) second wave after noticing the low-level of infections in the earlier stages. US is now going through a third wave. In India while Delhi is ending its third wave, many states such as Haryana, Rajasthan and Madhya Pradesh have now begun showing clear signs of the second wave;the possibility of a second wave is also exhibited in the case of Karnataka state. We have analyzed these trends using Gaussian model combinations and point out the need to adhere to all safety norms for many more months as we feel that more waves are still possible but unpredictable at this time.