Assessing the nonlinearity and cost effectiveness of PM2.5 in response to emission changes in North China with the adjoint method

While great efforts have been made to China’s clean air actions since 2013 and effectively mitigated PM2.5 pollution, the emission-concentration relationships and cost-effectiveness may have changed substantially and are poorly constrained. 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. At the same time, strengthened emissions standards and elimination of outdated industrial capacities have replaced the existing technologies and increased the cost of pollution control. 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, and further evaluate the marginal cost and benefit of possible technological alternatives. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM_2.5 in Beijing by 10.6 μg m^-3, and the 2013-2017 emission controls resulted in a larger decrease of 54.2 μg m^-3 because of greater reductions of SO₂ and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM_2.5 by 4.7 μg m^-3 in lockdown, which was mainly attributed to enhanced sensitivity of aerosol nitrateto NO_x emissions, but enhanced the efficiency of 2013-2017 emission controls by 12.5 μg m^-3 due to the weakened heterogeneous reaction of sulfate. For further PM_2.5 mitigation, emission reductions in ammonia by urea substitution and primary PM_2.5 with electrostatic precipitator have high PM_2.5 reduction potential and cost effectiveness. Such chemical nonlinearity and cost optimization are important to estimate and consider when designing or assessing air pollution control strategies.