近些年随着我国对大气污染的有效治理，PM2.5浓度显著下降，但仍与国家二级浓度标准有较大的差距，与此同时，大气O3污染问题逐渐凸显，PM2.5和O3协同控制成为持续改善空气质量的关键。如何协同减排前体物以同时削减PM2.5与O3是急需回答的关键科学难题。因此，迫切需要厘清PM2.5与O3对前体物排放的协同响应关系。研究基于大气化学反应机制与统计方法，建立了PM2.5与O3浓度对大气污染物排放的协同响应关系模型（pf-RSM），量化了PM2.5与O3浓度对前体物排放的非线性响应。结合排放清单-空气质量响应的等效排放率方法及多区域响应模拟技术，建立了京津冀及周边“2+26”城市PM2.5和O3对多区域、多部门、多污染物排放非线性响应模型（pf-ERSM）。留一法校验、外部验证和等值线验证表明，研究建立的pf-ERSM模型可以较好地表征在全局多因子协同调控下前体物排放与PM2.5和O3的响应关系。研究基于PM2.5和O3对NOx与VOCs排放的协同响应关系，提出了实现PM2.5和O3浓度双降的VOCs/NOx减排比值(VNr)指标。对京津冀及周边“2+26”城市的分析表明，秋冬季需协同控制VOCs和NOx (VNr：0.36~1.19)，夏季控制NOx对于浓度下降较为有利(VNr：0~1.24)；太原、淄博、唐山、邯郸等少数城市对区域VOCs减排需求较高(VNr：0.67~1.6)。对本地及周边协同减排的分析表明，“2+26”城市的NO3-浓度48%~87%来自区域传输，周边NOx控制对O3浓度减少有利，区域联合控制对减少NO3-和O3均很重要。此外，解析了部门排放对PM2.5与O3的协同贡献，工业部门由于排放较高、且具有合适的VOCs/NOx排放比值(0.93~3.14)，其优先减排对PM2.5与O3协同控制较为有利；民用部门减排对控制冬季PM2.5污染更为重要。基于pf-ERSM技术，研究建立了基于成本最小的协同减排策略优化方法，解决了多变量共同作用下的减排途径的非线性约束优化问题，确定了以PM2.5与O3浓度为目标的多污染物协同减排需求。为使年均PM2.5达到国家二级标准的需求，各城市NOx、SO2、NH3、VOCs、一次PM2.5需分别减排17%~79%、5%~62%、0%~62%、5%~63%、28%~82%，对应情景下各城市O3浓度也有所降低。
In recent years, with the effective control of air pollution in China, the PM2.5 concentration has decreased significantly, but it is still far from the national air quality standard. At the same time, O3 concentration is increasing. Thus, the coordinated control of PM2.5 and O3 becomes the key to continuous improvement of air quality. How to reduce emissions of precursors to reduce PM2.5 and O3 at the same time is a key question in science. Therefore, it is important to clarify the synergistic responsive relationship between PM2.5 and O3 to precursor emissions.Based on the atmospheric chemical mechanism and statistical methods, a Response Surface Model with Polynomial Functions (pf-RSM) of PM2.5 and O3 concentrations to atmospheric pollutant emissions was established, and the nonlinearity response of PM2.5 and O3 concentrations to precursor emissions was quantified. Based on the equivalent emission method used to distinguish the impact of each emission sectors, as well as the multi-regional RSM method, we established the non-linear response relationship between PM2.5 and O3 in Beijing-Tianjin-Hebei and surrounding cities (2+26 cities) to the emission of multi-region, multi-sector, and multi-pollutant (pf-ERSM). The reliability of the prediction results was verified by leave-one-out verification, out-of-sample verification, and isopleth verification. The pf-ERSM technology can be used to characterize the “emission-concentration” response relationship for PM2.5 and O3 under a wide range of simutanous change of multiple emission factors.This study analyzed the response of PM2.5 and O3 concentration according to the simutanous change of NOx and VOCs for 2+26 cities. The “VNr” was defined to indicate the reduction ratio of VOCs/NOx to avoid the increase of PM2.5 or O3 concentration. Their spatial and temporal variations in 2+26 cities were analyzed. VOCs needs to be controlled in autumn-winter and the VNr shows between 0.36~1.19. In summer, NOx control is more effective for the concentration reduction (VNr: 0~1.24). A few cities such as Taiyuan, Zibo, Tangshan, and Handan have higher demand for regional VOCs emission control (VNr: 0.67~1.6). Secondly, the non-linear characteristics of PM2.5 and O3 at different regional scales were identified. About 48%~87% of NO3- concentration came from regional transport. Regional NOx control was beneficial to the reduction of O3 concentration. Regional joint control is important to solve PM2.5 and O3 pollution. In addition, the contributions of sector emissions were analyzed. Industrial sector has large emissions and the appropriate VOCs/NOx emission ratio (0.93~3.14), thus the control of industrial emission is more beneficial to the coordinated control of PM2.5 and O3. Domestic emission reduction is more important to control PM2.5 pollution in winter.Least-cost control strategy optimization method was established and used to calculate the requirements of multi-pollutant emission reduction to meet the designed target of PM2.5 and O3 concentrations. Optimization results show that it is recommended to first strengthen VOCs emission reductions to avoid the potential risk of O3, and then with more strengthened NOx emission control to achieve substainal concentration reduction. Based on cost optimization, a multi-region and multi-precursor emission reduction strategy that meets the PM2.5 national standard was obtained. O3 concentration shows decrease as well.
