Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by ?29.5%, +31.2%, and ?7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ?14.1% decrease in NO2, ?6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: ?15.4%, +24.6%, and ?9.7% for NO2, O3, and PM2.5, respectively.

/pdf/spring-festival-and-covid-19-lockdown-disentangling-pm-4tij5ju883.pdf

Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities

. The lockdown measures taken to prevent a rapid spreading
of the coronavirus in Europe in spring 2020 led to large emission
reductions, particularly in road traffic and aviation. Atmospheric
concentrations of NO 2 and PM 2.5 were mostly reduced when
compared to observations taken for the same time period in previous years;
however, concentration reductions may not only be caused by emission
reductions but also by specific weather situations. In order to identify the role of emission reductions and the meteorological
situation for air quality improvements in central Europe, the meteorology
chemistry transport model system COSMO-CLM/CMAQ was applied to Europe for
the period 1 January to 30 June 2020. Emission data for 2020 were
extrapolated from most recent reported emission data, and lockdown adjustment
factors were computed from reported activity data changes, e.g. Google
mobility reports. Meteorological factors were investigated through
additional simulations with meteorological data from previous years. The results showed that lockdown effects varied significantly among
countries and were most prominent for NO 2 concentrations in urban areas with 2-week-average reductions up to 55 % in the second half of March. Ozone concentrations were less strongly influenced (up to ± 15 %) and
showed both increasing and decreasing concentrations due to lockdown
measures. This depended strongly on the meteorological situation and on the
NO x /  VOC emission ratio. PM 2.5 revealed 2 %–12 % reductions of 2-week-average concentrations in March and April, which is much less than
a different weather situation could cause. Unusually low PM 2.5 
concentrations as observed in northern central Europe were only marginally
caused by lockdown effects. The lockdown can be seen as a big experiment about air quality improvements
that can be achieved through drastic traffic emission reductions. From this
investigation, it can be concluded that NO 2 concentrations can be
largely reduced, but effects on annual average values are small when the
measures last only a few weeks. Secondary pollutants like ozone and
PM 2.5 depend more strongly on weather conditions and show a limited
response to emission changes in single sectors.

/pdf/the-role-of-emission-reductions-and-the-meteorological-1h1fs129c3.pdf

The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe

Deterioration of surface ozone (O3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O3 trend and identify the key meteorological factors affecting O3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O3 time series, we found that variation of O3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O3 pollution since 2015. Since then, the favorable effects of meteorology on O3 pollution tended to be weaker. Temperature was the most important factor affecting O3 level, followed by air humidity in O3 pollution season. In the midday of summer days, O3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O3 forecasting. 

Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021.

With the emergence of the COVID-19 pandemic, several governments imposed severe restrictions on socio-economic activities, putting most of the world population into a general lockdown in March 2020. Although scattered, studies on this topic worldwide have rapidly emerged in the literature. Hence, this systematic review aimed to identify and discuss the scientifically validated literature that evaluated the impact of the COVID-19 pandemic and associated restrictions on air quality. Thus, a total of 114 studies that quantified the impact of the COVID-19 pandemic on air quality through monitoring were selected from three databases. The most evaluated countries were India and China; all the studies intended to evaluate the impact of the pandemic on air quality, mainly concerning PM10, PM2.5, NO2, O3, CO, and SO2. Most of them focused on the 1st lockdown, comparing with the pre- and post-lockdown periods and usually in urban areas. Many studies conducted a descriptive analysis, while others complemented it with more advanced statistical analysis. Although using different methodologies, some studies reported a temporary air quality improvement during the lockdown. More studies are still needed, comparing different lockdown and lifting periods and, in other areas, for a definition of better-targeted policies to reduce air pollution.

https://www.mdpi.com/1660-4601/19/4/1950/pdf?version=1644482905

Impact of COVID-19 Pandemic on Air Quality: A Systematic Review

We analyzed NO2 and O3 data from 32 U.S. non-attainment areas (NAAs) for 1995–2020. Since 1995, all regions have shown steady reductions in NO2 and the weekend-weekday pattern indicates that the O3 production regime in most NAAs has transitioned to a NOx-limited regime, while a few NAAs remain NOx-saturated. In the eastern U.S., all NAAs have made steady progress toward meeting the current (70 ppb) O3 standard, but this is less true in midwestern and western NAAs, with most showing little improvement in peak O3 concentrations since about 2010. Due to COVID-19 restrictions, NO2 concentrations were substantially reduced in 2020. In the eastern NAAs, we see significant reductions in both NO2 and peak O3 concentrations. In the midwestern U.S., results were more variable, with both higher and lower O3 values in 2020. In the western U.S. (WUS), we see variable reductions in NO2 but substantial increases in O3 at most sites, due to the influence from huge wildland fires. The recent pattern over the past decade shows that the large amount of wildland fires has a strong influence on the policy-relevant O3 metric in the WUS, and this is making it more difficult for these regions to meet the O3 standard. 

NO <sub>x</sub> and O <sub>3</sub> Trends at U.S. Non‐Attainment Areas for 1995–2020: Influence of COVID‐19 Reductions and Wildland Fires on Policy‐Relevant Concentrations

The authors of this study truly appreciate the funding provided by Nazarbayev University
through the Collaborative Research Grant (grant number: 091019CRP2104). MAT, MN, EA, and
NE are the members of the Chemical and Aerosol Research Team (CART) and also the
Environment and Resource Efficiency Cluster (EREC) at Nazarbayev University, and they would
like to acknowledge the resources provided by CART and EREC to conduct this research. For India,
except for Delhi, daily and hourly averaged air quality data were obtained from the Central
Pollution Control Board (CPCB) CCR, OpenAQ or Air quality historical data platform, and location
of stations were obtained from the OpenAQ website. CPCB is gratefully acknowledged for making
the data available. Further, the CPCB data were consolidated and curated by Mr Prem
Maheshwarkar, Earth and Environmental Sciences, IISER Bhopal whose contribution is also
acknowledged. In Chile, the contributions made by Mr. Roberto Martinez, Head of the Planning
and the Standards, Department of the Air Quality and Climate Change Division, Ministry for the
Environment to provide data to this study, are greatly appreciated. In Brazil, CETESB
(Environmental Company of the State of Sao Paulo) is acknowledged for its contribution to data
preparation. In Cyprus, the authors would like to thank the Department of Labor Inspection of
the Ministry of Environment of the Republic of Cyprus for providing the observational data for
their monitoring network. For New Zealand, authors acknowledge the Environment Canterbury
as the source for the Christchurch data. For Spain, Barcelona, the authors would like to thank
"Department of Environmental Quality, Generalitat de Catalunya" as the data source. Turkish
Ministry of Environment and Urbanization and Turkish State of Meteorological Services provided
air quality and meteorological data, respectively, that are greatly appreciated. In the Netherlands,
data were obtained from the National Institute for Public Health and the Environment (RIVM)
and contained additional data from the GGD Amsterdam (provided by Dave de Jonge) and the
DCMR Environmental Protection Agency (provided by Ed van der Gaag) and National Institute for
Public Health and the Environment (RIVM) that are greatly appreciated. The authors would like
to thank Tehran Air Quality Control Company for providing the data for the city of Tehran.

/pdf/global-air-quality-and-covid-19-pandemic-do-we-breathe-2x7ic5aepe.pdf

Mohadeseh Kazemitabar

Papers

Global air quality and covid-19 pandemic: Do we breathe cleaner air?