Revealing the driving effect of emissions and meteorology on PM2.5 and O3 trends through a new algorithmic model

Quantifying the driving effect of each factor on atmospheric secondary pollutants is crucial for pollution pre-vention. We aim to establish a simple and accessible method to identify ozone (O-3) and particulate matter (PM2.5) concentration trends induced by emissions and meteorology. The method comprises five main steps, which involve matrix construction and mutual calculations, and the whole process is demonstrated and verified by employing long-term monitoring data. With regard to the case study, O-3 and PM2.5 concentration variance between the target and base year are respectively-4.74 and 0.20 mu g/m(3) under same meteorological conditions, among which the contribution of the emissions driver and meteorological driver are respectively-5.81 and 1.07 mu g/m(3) for O-3 and respectively 0.55 and-0.35 mu g/m(3) for PM2.5. Additionally, 84.45% of O-3 variance is attrib-utable to the emissions driver in terms of relative importance, which is 52.88% for PM2.5. The meteorological driver is further separated into atmospheric secondary reaction and regional transport. The results reveal that ongoing prevention policy for O-3 is effective; however, it needs to be further optimized for PM2.5.