Due to the pandemic of coronavirus disease 2019 in China, almost all avoidable activities in China are prohibited since Wuhan announced lockdown on January 23, 2020. With reduced activities, severe air pollution events still occurred in the North China Plain, causing discussions regarding why severe air pollution was not avoided. The Community Multi-scale Air Quality model was applied during January 01 to February 12, 2020 to study PM2.5 changes under emission reduction scenarios. The estimated emission reduction case (Case 3) better reproduced PM2.5. Compared with the case without emission change (Case 1), Case 3 predicted that PM2.5 concentrations decreased by up to 20% with absolute decreases of 5.35, 6.37, 9.23, 10.25, 10.30, 12.14, 12.75, 14.41, 18.00 and 30.79 μg/m3 in Guangzhou, Shanghai, Beijing, Shijiazhuang, Tianjin, Jinan, Taiyuan, Xi'an, Zhengzhou, Wuhan, respectively. In high-pollution days with PM2.5 greater than 75 μg/m3, the reductions of PM2.5 in Case 3 were 7.78, 9.51, 11.38, 13.42, 13.64, 14.15, 14.42, 16.95 and 22.08 μg/m3 in Shanghai, Jinan, Shijiazhuang, Beijing, Taiyuan, Xi'an, Tianjin, Zhengzhou and Wuhan, respectively. The reductions in emissions of PM2.5 precursors were ~2 times of that in concentrations, indicating that meteorology was unfavorable during simulation episode. A further analysis shows that benefits of emission reductions were overwhelmed by adverse meteorology and severe air pollution events were not avoided. This study highlights that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable. More efforts should be made to completely avoid severe air pollution.

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Air quality is concerned with pollutants in both the gas phase and solid or liquid phases. The latter are referred to as aerosols, which are multifaceted agents affecting air quality, weather and climate through many mechanisms. Unlike gas pollutants, aerosols interact strongly with meteorological variables with the strongest interactions taking place in the planetary boundary layer (PBL). The PBL hosting the bulk of aerosols in the lower atmosphere is affected by aerosol radiative effects. Both aerosol scattering and absorption reduce the amount of solar radiation reaching the ground and thus reduce the sensible heat fluxes that drive the diurnal evolution of the PBL. Moreover, aerosols can increase atmospheric stability by inducing a temperature inversion as a result of both scattering and absorption of solar radiation, which suppresses dispersion of pollutants and leads to further increases in aerosol concentration in the lower PBL. Such positive feedback is especially strong during severe pollution events. Knowledge of the PBL is thus crucial for understanding the interactions between air pollution and meteorology. A key question is how the diurnal evolution of the PBL interacts with aerosols, especially in vertical directions, and affects air quality. We review the major advances in aerosol measurements, PBL processes and their interactions with each other through complex feedback mechanisms, and highlight the priorities for future studies.

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Aerosol and boundary-layer interactions and impact on air quality

Air pollution in China: Status and spatiotemporal variations

. In 2013, China's government published the Air Pollution Prevention and
Control Action Plan (APPCAP) with a specific target for Beijing, which aims
to reduce annual mean PM 2.5 concentrations in Beijing to
60  µ g m −3 in 2017. During 2013–2017, the air quality in
Beijing was significantly improved following the implementation of various
emission control measures locally and regionally, with the annual mean
PM 2.5 concentration decreasing from 89.5  µ g m −3 in 2013
to 58  µ g m −3 in 2017. As meteorological conditions were more
favourable to the reduction of air pollution in 2017 than in 2013 and 2016,
the real effectiveness of emission control measures on the improvement of air
quality in Beijing has frequently been questioned. In this work, by combining a detailed bottom-up emission inventory over
Beijing, the MEIC regional emission inventory and the WRF-CMAQ (Weather
Research and Forecasting Model and Community Multiscale Air Quality) model, we
attribute the improvement in Beijing's PM 2.5 air quality in 2017
(compared to 2013 and 2016) to the following factors: changes in
meteorological conditions, reduction of emissions from surrounding regions,
and seven specific categories of local emission control measures in Beijing.
We collect and summarize data related to 32 detailed control measures
implemented during 2013–2017, quantify the emission reductions associated
with each measure using the bottom-up local emission inventory in 2013,
aggregate the measures into seven categories, and conduct a series of CMAQ
simulations to quantify the contribution of different factors to the
PM 2.5 changes. We found that, although changes in meteorological conditions partly explain
the improved PM 2.5 air quality in Beijing in 2017 compared to 2013 (3.8  µ g m −3 ,
12.1 % of total), the rapid decrease in
PM 2.5 concentrations in Beijing during 2013–2017 was dominated by local
(20.6  µ g m −3 , 65.4 %) and regional (7.1  µ g m −3 ,
22.5 %) emission reductions. The seven categories of
emission control measures, i.e. coal-fired boiler control,
clean fuels in the residential sector, optimize industrial structure,
fugitive dust control, vehicle emission control,
improved end-of-pipe control, and integrated treatment of VOCs,
reduced the PM 2.5 
concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2  µ g m −3 ,
respectively, during 2013–2017. We also found
that changes in meteorological conditions could explain roughly 30 % of
total reduction in PM 2.5 concentration during 2016–2017 with more
prominent contribution in winter months (November and December). If the
meteorological conditions in 2017 had remained the same as those in 2016,
the annual mean PM 2.5 concentrations would have increased from
58 to 63  µ g m −3 , exceeding the
target established in the APPCAP. Despite the remarkable impacts from
meteorological condition changes, local and regional emission reductions
still played major roles in the PM 2.5 decrease in Beijing during
2016–2017, and clean fuels in the residential sector, coal-fired boiler control,
and optimize industrial structure were the three most effective local measures
(contributing reductions of 2.1, 1.9, and 1.5  µ g m −3 ,
respectively). Our study confirms the effectiveness of clean air actions in
Beijing and its surrounding regions and reveals that a new generation of
control measures and strengthened regional joint emission control measures
should be implemented for continued air quality improvement in Beijing
because the major emitting sources have changed since the implementation of
the clean air actions.

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Dominant role of emission reduction in PM 2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis

. A 5-year Clean Air Action Plan was implemented in 2013 to reduce air
pollutant emissions and improve ambient air quality in Beijing. Assessment
of this action plan is an essential part of the decision-making process to
review its efficacy and to develop new policies. Both
statistical and chemical transport modelling have been previously applied to
assess the efficacy of this action plan. However, inherent uncertainties in
these methods mean that new and independent methods are required to support
the assessment process. Here, we applied a machine-learning-based random
forest technique to quantify the effectiveness of Beijing's action plan by
decoupling the impact of meteorology on ambient air quality. Our results
demonstrate that meteorological conditions have an important impact on the
year-to-year variations in ambient air quality. Further analyses show that
the PM 2.5 mass concentration would have broken the target of the plan
(2017 annual PM 2.5 µ g m −3 ) were it not for the
meteorological conditions in winter 2017 favouring the dispersion of air
pollutants. However, over the whole period (2013–2017), the primary
emission controls required by the action plan have led to significant
reductions in PM 2.5 , PM 10 , NO2 , SO2 , and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM 2.5 and SO2 is largely attributable to a reduction in coal
combustion. Our results indicate that the action plan has been highly
effective in reducing the primary pollution emissions and improving air
quality in Beijing. The action plan offers a successful example for
developing air quality policies in other regions of China and other
developing countries.

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Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

. In the 2015 winter month of December, northern China witnessed the most severe air pollution phenomena since the 2013 winter haze events occurred. This triggered the first-ever red alert in the air pollution control history of Beijing, with an instantaneous fine particulate matter (PM2. 5) concentration over 1 mg m−3. Air quality observations reveal large temporal–spatial variations in PM2. 5 concentrations over the Beijing–Tianjin–Hebei (Jing-Jin-Ji) area between 2014 and 2015. Compared to 2014, the PM2. 5 concentrations over the area decreased significantly in all months except November and December of 2015, with an increase of 36 % in December. Analysis shows that the PM2. 5 concentrations are significantly correlated with the local meteorological parameters in the Jing-Jin-Ji area such as the stable conditions, relative humidity (RH), and wind field. A comparison of two month simulations (December 2014 and 2015) with the same emission data was performed to explore and quantify the meteorological impacts on the PM2. 5 over the Jing-Jin-Ji area. Observation and modeling results show that the worsening meteorological conditions are the main reasons behind this unusual increase of air pollutant concentrations and that the emission control measures taken during this period of time have contributed to mitigate the air pollution ( ∼  9 %) in the region. This work provides a scientific insight into the emission control measures vs. the meteorology impacts for the period.

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Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China's Jing-Jin-Ji area

The diurnal variations in ozone concentrations in the summer were studied using the temperature, wind speed and direction, total cloud cover, and solar radiation intensity data collected in Langfang, China. The ratio of volatile organic compounds to nitrogen oxides (VOCs/NOx) and the EKMA curve were studied to analyze the sensitivity of ozone formation. The results showed that:① The ozone generation rate and ozone concentrations were positively correlated with the solar radiation intensity with Pearson correlation coefficients of 0.61 and 0.48, respectively. Both the ozone generation rate and the solar radiation intensity reached their peak at about 12:00, while the ozone concentration reached its peak at about 16:00, which lagged behind the peak of the solar radiation intensity by 4 h. ② The correlation coefficient between the ozone generation rate and the temperature was 0.44, between the ozone concentration and temperature was 0.68. The ozone generation rate and ozone concentrations were inversely correlated with total cloud cover with correlation coefficients of -0.24 and -0.45, respectively. ③ The ozone concentrations in Langfang were high when the winds were from the west, south, or southeast. ④ The ozone concentrations in Langfang were more sensitive to VOCs than to NOx; thus, they can be reduced efficiently by controlling the VOCs emissions.

Wei Liu

Papers

Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China's Jing-Jin-Ji area

Influence Factors and Sensitivity of Ozone Formation in Langfang in the Summer