Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China

Le, Tianhao; Wang, Yuan; Liü, Lang; Yang, Jiani; Yung, Yuk L.; Li, Guohui; Seinfeld, John H.

doi:10.1126/science.abb7431

Cited by 672 publications

(710 citation statements)

References 38 publications

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“…The daily mean PM 2.5 concentrations at the urban site were 168 and 222 μg m −3 during these two events (Figure S3), when the corresponding PAN concentrations exceeded 4 ppb (Figure 1). The two haze events associated with high PAN levels suggest unfavorable meteorological conditions during the lockdown period (Le et al, 2020). Figure 3 compares differences in temperature, sea level pressure, and horizontal winds at 850 hPa between the lockdown period and preperiod (LKD‐PRE) over the NCP region from GEOS‐FP reanalysis data.…”

Section: Resultsmentioning

confidence: 99%

Markedly Enhanced Levels of Peroxyacetyl Nitrate (PAN) During COVID‐19 in Beijing

Qiu

et al. 2020

Geophysical Research Letters

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High levels of secondary air pollutants during COVID-19 in China have aroused great concern. In Beijing, measured daily mean peroxyacetyl nitrate (PAN) concentrations reached 4 ppb over the lockdown period (24 January to 15 February), whose averages were 2-3 times that before lockdown (1-23 January). The lockdown PAN levels also reached a high historical record based on our long-term measurements (2016-2019). Unlike ozone and PM 2.5 , PAN formation depends on less complex photochemistry between NO x and volatile organic compounds (VOCs), providing a novel approach to investigate the wintertime photochemistry during COVID-19. The GEOS-Chem simulations suggest a markedly enhanced photochemistry by a factor of 2 during the lockdown. Change of meteorology featuring with anomalous wind convergence under higher temperatures is the main reason for enhanced photochemical formation of PAN, while chemically nonlinear feedbacks also play a role. Our results suggest implementing targeted VOC emission controls in the context of increasing photochemical pollution over this complex polluted region. Plain Language Summary Outbreaks of the COVID-19 pandemic caused immediate implementation of lockdown policy in China, which drastically decreased emissions of primary air pollutants. Peroxyacetyl nitrate (PAN), as an important photochemical product, is controlled by reactions between NO x and volatile organic compounds (VOCs) that were reduced substantially due to the lockdown. However, observed PAN levels in Beijing during the lockdown were markedly enhanced and were even much higher than the concentrations during the same periods in 2016-2019. Modeling results prove that this increase in PAN is driven by enhanced photochemistry, resulting from anomalous wind convergence under higher temperature and enhanced radical level in response to NO x reduction. Our results suggest the necessity of reducing VOC emissions in controlling photochemical pollution even in the wintertime over China.

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Section: Resultsmentioning

confidence: 99%

Markedly Enhanced Levels of Peroxyacetyl Nitrate (PAN) During COVID‐19 in Beijing

Qiu

et al. 2020

Geophysical Research Letters

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“…High production efficiency of nitrate was also observed in Shanghai (Chang et al, 2020). The contrast between Lanzhou and Eastern China on aerosol formation could be due to adverse meteorological conditions in eastern China, such as low PBLH and calm wind (Le et al, 2020; Wang et al, 2020), but also aerosol precursors. For example, both anthropogenic and biogenic emissions in eastern China are important sources and can promote the production of secondary aerosol, in contrast, anthropogenic emission (fossil fuel) is the dominated source in western China, such as in Lanzhou (Hu, Wang, et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

COVID‐19 Impact on the Concentration and Composition of Submicron Particulate Matter in a Typical City of Northwest China

Zhang

et al. 2020

Geophysical Research Letters

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In this study, we evaluated the variations of air quality in Lanzhou, a typical city in Northwestern China impacted by the COVID-19 lockdown. The mass concentration and chemical composition of non-refractory submicron particulate matter (NR-PM 1) were determined by a high-resolution aerosol mass spectrometer during January-March 2020. The concentration of NR-PM 1 dropped by 50% from before to during control period. The five aerosol components (sulfate, nitrate, ammonium, chloride, and organic aerosol [OA]) all decreased during the control period with the biggest decrease observed for secondary inorganic species (70% of the total reduction). Though the mass concentration of OA decreased during the control period, its source emissions varied differently. OA from coal and biomass burning remained stable from before to during control period, while traffic and cooking related emissions were reduced by 25% and 50%, respectively. The low concentration during the control period was attributed to the lower production rate for secondary aerosols.

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“…Various scientists reported decreased air pollutant levels, comparing pre- and post COVID-19 air pollution levels using different methods and scales ( Chauhan and Singh 2020 ; Dantas et al, 2020 ; Le et al, 2020 ; Li et al, 2020 ; Muhammad et al, 2020 ; Nakada and Urban, 2020 ; Sharma et al, 2020 ; Shehzad et al, 2020 ; Tobías et al, 2020 ). However, the available air pollution studies related to the COVID-19 situation are based on satellite images, air quality modeling and generally comparing lockdown period data with monthly means over the past few years.…”

Section: Introductionmentioning

confidence: 99%

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Tadano

Potgieter‐Vermaak

Kachba

et al. 2021

Environmental Pollution

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Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O 3 , NO 2 , NO, PM 2.5 , and PM 10 , for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

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Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China

Cited by 672 publications

References 38 publications

Markedly Enhanced Levels of Peroxyacetyl Nitrate (PAN) During COVID‐19 in Beijing

Markedly Enhanced Levels of Peroxyacetyl Nitrate (PAN) During COVID‐19 in Beijing

COVID‐19 Impact on the Concentration and Composition of Submicron Particulate Matter in a Typical City of Northwest China

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

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