ABSTRACT
Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R-2 value of more than 72% average in each year and decreased MAE value, which was better than other studies' deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O-3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O-3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.
ABSTRACT
In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O-3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O-3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0-50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants' (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O-3. PM10 was the primary pollutant, followed by O-3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O-3. This study provides useful information and a valuable reference for future research on air quality in northwest China.
ABSTRACT
A series of strict control measures were imposed in the Beijing-Tianjin-Hebei region in early 2020 to control the spread of COVID-19. These measures have led to a reduction of anthropogenic air pollutants, providing an opportunity to observe the contribution of human activities to local air pollution. In this study, the control period was divided into four stages:the before, early, middle, and later stages. Based on a variety of data including meteorological, traffic, and industrial manufacturing datasets, statistical methods were combined with spatial analysis to evaluate changes in air pollution and associated human impacts during each stage. In addition, suggestions are made for further regional air pollution control in the Beijing-Tianjin-Hebei area. Key results are as follows:â Overall, the AQI and the concentrations of six air pollutants, especially SO2, PM10, and NO2, were lower during control period than during the equivalent period in 2019 (reductions of 26.5%, 24.3%, and 16.9%, respectively). From the before to later stages, pollutants (except O3) showed a downward trend while O3 increased significantly during the before stage (by 76.2%) and the growth rate slowed during the middle and later stages; â¡During the prior stage, Beijing experienced two periods with heavy air pollution days as a result of the local accumulation of pollutants, secondary transformation, and regional transport. The concentration of PM2.5 in February was nearly 60% lower than in February 2014 under similar meteorological conditions in Beijing; ⢠Following an increase in traffic volume and industrial activity, changes in air pollutants tended to be stable or slightly increase during the middle and later stages of the control period. The grey relation coefficients between thermal radiation intensity anomalies and the main pollutants in heavy industrial cities were greater than 0.6, which means that the control of industrial emissions remains key to controlling air pollution.