ABSTRACT
A widely used analytical model to quantitatively assess airborne infection risk is the Wells-Riley model based on the assumption of complete air mixing in a single zone. This study aimed to extend the Wells-Riley model so that the infection risk can be calculated in spaces where complete mixing is not present. This is done by evaluating the time-dependent distribution of infectious quanta in each zone and by solving the coupled system of differential equations based on the zonal quanta concentrations. In conclusion, this study shows that using the Wells-Riley model based on the assumption of completely mixing air may overestimate the long-range airborne infection risk compared to some high-efficiency ventilation systems such as displacement ventilation, but also underestimate the infection risk in a room heated with warm air supplied from the ceiling. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
A widely used analytical model to quantitatively assess airborne infection risk is the Wells-Riley model which is limited to complete air mixing in a single zone. However, this assumption tends not to be feasible (or reality) for many situations. This study aimed to extend the Wells-Riley model so that the infection risk can be calculated in spaces where complete mixing is not present. Some more advanced ventilation concepts create either two horizontally divided air zones in spaces as displacement ventilation or the space may be divided into two vertical zones by downward plane jet as in protective-zone ventilation systems. This is done by evaluating the time-dependent distribution of infectious quanta in each zone and by solving the coupled system of differential equations based on the zonal quanta concentrations. This model introduces a novel approach by estimating the interzonal mixing factor based on previous experimental data for three types of ventilation systems: incomplete mixing ventilation, displacement ventilation, and protective zone ventilation. The modeling approach is applied to a room with one infected and one susceptible person present. The results show that using the Wells-Riley model based on the assumption of completely air mixing may considerably overestimate or underestimate the long-range airborne infection risk in rooms where air distribution is different than complete mixing, such as displacement ventilation, protected zone ventilation, warm air supplied from the ceiling, etc. Therefore, in spaces with non-uniform air distribution, a zonal modeling approach should be preferred in analytical models compared to the conventional single-zone Wells-Riley models when assessing long-range airborne transmission risk of infectious respiratory diseases.
ABSTRACT
The study aims to examine the major atmospheric air pollutants such as NO2, CO, O3, PM2.5, PM10, and SO2 to assess the overall air quality using air quality zonal modeling of 15 major cities of China before and after the COVID-19 pandemic period. The spatio-temporal changes in NO2 and other atmospheric pollutants exhibited enormous reduction due to the imposition of a nationwide lockdown. The present study used a 10-day as well as 60-day tropospheric column time-average map of NO2 with spatial resolution 0.25 × 0.25° obtained from the Global Modeling and Assimilation Office, NASA. The air quality zonal model was employed to assess the total NO2 load and its change during the pandemic period for each specific region. Ground surface monitoring data for CO, NO2, O3, PM10, PM2.5, and SO2 including Air Quality Index (AQI) were collected from the Ministry of Environmental Protection of China (MEPC). The results from both datasets demonstrated that NO2 has drastically dropped in all the major cities across China. The concentration of CO, PM10, PM2.5, and SO2 demonstrated a decreasing trend whereas the concentration of O3 increased substantially in all cities after the lockdown effect as observed from real-time monitoring data. Because of the complete shutdown of all industrial activities and vehicular movements, the atmosphere experienced a lower concentration of major pollutants that improves the overall air quality. The regulation of anthropogenic activities due to the COVID-19 pandemic has not only contained the spread of the virus but also facilitated the improvement of the overall air quality. Guangzhou (43%), Harbin (42%), Jinan (33%), and Chengdu (32%) have experienced maximum air quality improving rates, whereas Anshan (7%), Lanzhou (17%), and Xian (25%) exhibited less improved AQI among 15 cities of China during the study period. The government needs to establish an environmental policy framework involving central, provincial, and local governments with stringent laws for environmental protection. [ FROM AUTHOR] Copyright of Atmosphere is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
This study presents the transmission of SARS-CoV-2 in the main types of public transport vehicles and stations to comparatively assess the relative theoretical risk of infection of travelers. The presented approach benchmarks different measures to reduce potential exposure in public transport and compares the relative risk between different means of transport and situations encountered. Hence, a profound base for the selection of measures by operators, travelers and staff is provided. Zonal modeling is used as the simulation method to estimate the exposure to passengers in the immediate vicinity as well as farther away from the infected person. The level of exposure to passengers depends on parameters such as the duration of stay and travel profile, as well as the ventilation situation and the wearing of different types of masks. The effectiveness of technical and behavioral measures to minimize the infection risk is comparatively evaluated. Putting on FFP2 (N95) masks and refraining from loud speech decreases the inhaled viral load by over 99%. The results show that technical measures, such as filtering the recirculated air, primarily benefit passengers who are a few rows away from the infected person by reducing exposure 84–91%, whereas near-field exposure is only reduced by 30–69%. An exception is exposure in streetcars, which in the near-field is 17% higher due to the reduced air volume caused by the filter. Thus, it can be confirmed that the prevailing measures in public transport protect passengers from a high theoretical infection risk. At stations, the high airflows and the large air volume result in very low exposures (negligible compared to the remaining means of transport) provided that distance between travelers is kept. The comparison of typical means of transport indicates that the inhaled quanta dose depends primarily on the duration of stay in the vehicles and only secondarily on the ventilation of the vehicles. Due to the zonal modeling approach, it can also be shown that the position of infected person relative to the other passengers is decisive in assessing the risk of infection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
The complete lockdown due to COVID-19 pandemic has contributed to the improvement of air quality across the countries particularly in developing countries including India. This study aims to assess the air quality by monitoring major atmospheric pollutants such as AOD, CO, PM2.5, NO2, O3 and SO2 in 15 major cities of India using Air Quality Zonal Modeling. The study is based on two different data sources; (a) grid data (MODIS- Terra, MERRA-2, OMI and AIRS, Global Modeling and Assimilation Office, NASA) and (b) ground monitoring station data provided by Central Pollution Control Board (CPCB) / State Pollution Control Board (SPCB). The remotely sensed data demonstrated that the concentration of PM2.5 has declined by 14%, about 30% of NO2 in million-plus cities, 2.06% CO, SO2 within the range of 5 to 60%, whereas the concentration of O3 has increased by 1 to 3% in majority of cities compared with pre lockdown. On the other hand, CPCB/SPCB data showed more than 40% decrease in PM2.5 and 47% decrease in PM10 in north Indian cities, more than 35% decrease in NO2 in metropolitan cities, more than 85% decrease in SO2 in Chennai and Nagpur and more than 17% increase in O3 in five cities amid 43 days pandemic lockdown. The restrictions of anthropogenic activities have substantial effect on the emission of primary atmospheric pollutants.