Underestimated benefits of NOx control in reducing SNA and O_(3)based on missing heterogeneous HONO sources

Substantial NOx emission mitigation is crucial for the synergistic reduction of particulate matter and ozone(O_(3))pollution in China.The traditional air quality model does not consider heterogeneous HONO chemistry,leading to uncertainties in estimating the benefits of NOx control.Previous studies have shown that the parameterization of heterogeneous HONO formation increases both the simulated value of sulfate–nitrate–ammonium(SNA)and that of O_(3),thus adding the heterogeneous reactions of HONO into air quality models inevitably leads to changes in the estimated benefits of NOx abatement.Here we investigated the changes in SNA and O_(3)concentrations from NOx emission reduction before and after adding heterogeneous HONO reactions in the Community Multi-Scale Air Quality(CMAQ)model.Including heterogeneous HONO reactions in the simulation improved the benefits of NOx reduction in terms of SNA control in winter.With 80%NOx reduction,the reduction in SNA increased from 36.9%without considering heterogeneous HONO reactions to 42.8%with heterogeneous HONO chemistry.The reduction in the maximum daily 8h average(MDA8)O_(3)in summer caused by NOx reduction increased slightly from 4.7%to 5.2%after adding heterogeneous HONO reactions.The results in this study highlight the enhanced effectiveness of NOx controls for the reduction of SNA and O_(3)after considering heterogeneous HONO formation in a complex chemical ambient,demonstrating the importance of NOx controls in reducing PM2.5 and O_(3)pollution in China.

Accurate prediction of air quality response to emissions for effective control policy design

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3and PM2.5,which are formed by multiple precursors under complex physical and chemical processes;this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions.Different from the traditional sensitivity analysis and source apportionment methods,the reduced form models(RFMs)based on chemical transport models(CTMs)are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost.Here we review recent approaches used in RFMs and compare their structures,advantages and disadvantages,performance and applications.In general,RFMs are classified into three types including(1)sensitivity-based models,(2)models with simplified chemistry and physical processes,and(3)statistical models,with considerable differences in principles,characteristics and application ranges.The prediction of nonlinear responses by RFMs enables more in-depth analysis,not only in terms of real-time prediction of concentrations and quantification of human exposure,health impacts and economic damage,but also in optimizing control policies.Notably,data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management.In future studies,improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses.Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes,which pose great challenges to the quantification of response relationships.Additionally,with rising requirements for fine-scale air quality management,improving the performance of urban-scale simulations is worth considering.In short,accurate predictions of the response of air quality to emissions,though challenging,holds great promise for the present as well as for future scenarios.

Evaluation of the influence of El Niño–Southern Oscillation on air quality in southern China from long-term historical observations

Previous studies demonstrated that the El Niño–Southern Oscillation(ENSO)could modulate regional climate thus influencing air quality in the low-middle latitude regions like southern China.However,such influence has not been well evaluated at a long-term historical scale.To filling the gap,this study investigated two-decade(2002 to 2020)aerosol concentration and particle size in southern China during the whole dynamic development of ENSO phases.Results suggest strong positive correlations between aerosol optical depth(AOD)and ENSO phases,as low AOD occurred during El Niño while high AOD occurred during La Niña event.Such correlations are mainly attributed to the variation of atmospheric circulation and precipitation during corresponding ENSO phase.Analysis of the angstrom exponent(AE)anomalies further confirmed the circulation pattern,as negative AE anomalies is pronounced in El Niño indicating the enhanced transport of sea salt aerosols from the South China Sea,while the La Niña event exhibits positive AE anomalies which can be attributed to the enhanced import of northern fine anthropogenic aerosols.This study further quantified the AOD variation attributed to changes in ENSO phases and anthropogenic emissions.Results suggest that the long-term AOD variation from 2002 to 2020 in southern China is mostly driven(by 64.2%)by the change of anthropogenic emissions from 2002 to 2020.However,the ENSO presents dominant influence(70.5%)on year-to-year variations of AOD during 2002–2020,implying the importance of ENSO on varying aerosol concentration in a short-term period.

Understand the local and regional contributions on air pollution from the view of human health impacts

The source-receptor matrix of PM_(2.5)concentration from local and regional sources in the Beijing-Tianjin-Hebei(BTH)and surrounding provinces has been created in previous studies.However,because the spatial distribution of concentration does not necessarily match with that of the population,such concentration-based source-receptor matrix may not fully reflect the importance of pollutant control effectiveness in reducing the PM_(2.5)-related health impacts.To demonstrate that,we study the source-receptor matrix of the PM_(2.5)-related deaths instead,with inclusion of the spatial correlations between the concentrations and the population.The advanced source apportionment numerical model combined with the integrated exposure-response functions is used for BTH and surrounding regions in 2017.We observed that the relative contribution to PM_(2.5)-related deaths of local emissions was 0.75%to 20.77%larger than that of PM_(2.5)concentrations.Such results address the importance of local emissions control for reducing health impacts of PM_(2.5)particularly for local residents.Contribution of regional transport to PM_(2.5)-related deaths in rural area was 22%larger than that in urban area due to the spatial pattern of regional transport which was more related to the rural population.This resulted in an environmental inequality in the sense that people staying in rural area with access to less educational resources are subjected to higher impacts from regional transport as compared with their more resourceful and knowledgeable urban compatriots.An unexpected benefit from the multi-regional joint controls is suggested for its effectiveness in reducing the regional transport of PM_(2.5)pollution thus mitigating the associated environmental inequality.