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.

Relationships of ozone formation sensitivity with precursors emissions,meteorology and land use types,in Guangdong-Hong Kong-Macao Greater Bay Area,China

Due to the influences of precursors emissions,meteorology,geography and other factors,ozone formation sensitivity(OFS)is generally spatially and temporally heterogeneous.This study characterized detailed spatial and temporal variations of OFS in Guangdong-Hong Kong-Macao Greater Bay Area(GBA)from 2012 to 2016 based on OMI satellite data,and analyzed the relationships of OFS with precursors emissions,meteorology and land use types(LUTs).From 2012 to 2016,the OFS tended to be NOx-limited in GBA,with the value of FNR(HCHO/NO 2)increasing from 2.04 to 2.22.According to the total annual emission statistics of precursors,NOx emissions decreased by 33.1%and VOCs emissions increased by 35.2%from 2012 to 2016,directly resulting in OFS tending to be NO x-limited.The Grey Relation Analysis results show that total column water(TCW),surface net solar radiation(SSR),air temperature at 2 m(T2)and surface pressure(SP)are the top four meteorological factors with the greatest influences on OFS.There are significant positive correlations between FNR and T2,SSR,TCW,and significant negative correlations between FNR and SP.In GBA,the OFS tends to be NO x-limited regime in wet season(higher T2,SSR,TCW and lower SP)and VOCs-limited regime in dry season(lower T2,SSR,TCW and higher SP).The FNR displays obvious gradient variations on different LUTs,with the highest in“Rural areas”,second in“Suburban areas”and lowest in“Urban areas”.

Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity

Quantification of the nonlinearities between ambient ozone(O3)and the emissions of nitrogen oxides(NOx)and volatile organic compound(VOC)is a prerequisite for an effective O3 control strategy.An Enhanced polynomial functions Response Surface Model(Epf-RSM)with the capability to analyze O3-NOx-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model(ERSM)system.The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O3 concentrations to precursor emission changes.Several comparisons between Epf-RSM and pf-ERSM(polynomial functions based ERSM)were performed using out-of-sample validation,together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams.The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to overfitting in the margin areas and high biases in the transition areas.The O3 concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results.The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O3 formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January,April,and October,while more NOx-sensitive in July.