Assessing the nonlinearity of wintertime PM2.5 formation in response to precursor emission changes in North China with the adjoint method

Abstract

While China’s clean air actions implemented since 2013 have been effective in mitigating PM _2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM _2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM _2.5 formation to emission changes. Here we apply emission-concentration relationships for PM _2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM _2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM _2.5 in Beijing by 17.9 μ g m ^–3 , and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μ g m ^–3 because of greater reductions of SO _2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM _2.5 by 3.4 μ g m ^–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO _x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μ g m ^–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

Keywords

• air pollution
• source attribution
• fine particulate matter
• nonlinear chemistry
• adjoint sensitivity
• COVID-19 lockdown