A novel spatiotemporal convolutional long short-term neural network for air pollution prediction.

Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China.

Spatiotemporal trends in PM2.5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China.

Control of both PM2.5 and O3 in Beijing-Tianjin-Hebei and the surrounding areas

. The fine particles (PM 2.5 ) in China have decreased significantly in
recent years as a result of the implementation of Chinese Clean Air Action Plan
since 2013, while the O3 pollution is getting worse, especially in
megacities such as Beijing and Shanghai. Better understanding of the elevated
 O3 pollution in Chinese megacities and its response to emission change
is important for developing an effective emission control strategy in
the future. In this study, we analyze the significant increasing trend of daily
maximum O3 concentration from 2006 to 2015 in the megacity Shanghai
with the variability of 0.8–1.3 ppbv yr −1 . It could likely be attributed to
the notable reduction in NOx concentrations with the decreasing rate of
1.86–2.15 ppbv yr −1 accompanied by the small change in VOCs during
the same period by excluding the weak trends of meteorological impacts on
local dispersion (wind speed), regional transport (wind direction), and
 O3 photolysis (solar radiation). It is further illustrated by using a
state-of-the-art regional chemical and dynamical model (WRF-Chem) to explore the
 O3 variation response to the reduction in NOx emissions in
Shanghai. The control experiment conducted for September of 2009 shows
excellent performance for O3 and NOx simulations, including both
the spatial distribution pattern and the day-by-day variation through
comparison with six in situ measurements from the MIRAGE-Shanghai field campaign.
Sensitivity experiments with 30 % reduction in NOx emissions from 2009
to 2015 in Shanghai estimated by Shanghai Environmental Monitoring Center
shows that the calculated O3 concentrations exhibit obvious enhancement
by 4–7 ppbv in urban zones with increasing variability of 0.96–1.06 ppbv yr −1 , which is consistent with the observed O3 trend as a
result of the strong VOC-limited condition for O3 production. The large
reduction in NOx combined with less change in VOCs in the past 10
years promotes the O3 production in Shanghai to move towards
an NOx -limited regime. Further analysis of the WRF-Chem experiments and
 O3 isopleth diagram suggests that the O3 production downtown
is still under a VOC-limited regime after 2015 despite the remarkable
 NOx reduction, while it moves to the transition regime between
 NOx -limited and VOC-limited in sub-urban zones. Supposing the
insignificant VOC variation persists, the O3 concentration downtown
would keep increasing until 2020 with the further 20 % reduction in
 NOx emission after 2015 estimated by Shanghai Clean Air Action Plan.
The O3 production in Shanghai will switch from a VOC-limited to
an NOx -limited regime after 2020 except for downtown area, which is likely
close to the transition regime. As a result the O3 concentration will
decrease by 2–3 ppbv in sub-urban zones and by more than 4 ppbv in rural areas as a response to a 20 % reduction in NOx emission after 2020, whereas it is not
sensitive to both NOx and VOC changes downtown. This result reveals
that the control strategy of O3 pollution is a very complex process
and needs to be carefully studied.

/pdf/measurement-and-model-analyses-of-the-ozone-variation-during-4xmvgmucw6.pdf

Ji Zhou

Papers

Numerical air quality forecasting over eastern China: An operational application of WRF-Chem