Revealing the driving effect of emissions and meteorology on PM 2.5 and O 3 trends through a new algorithmic model.

Quantifying the driving effect of each factor on atmospheric secondary pollutants is crucial for pollution prevention. We aim to establish a simple and accessible method to identify ozone (O ₃ ) and particulate matter (PM _2.5 ) 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 PM _2.5 concentration variance between the target and base year are respectively -4.74 and 0.20 μg/m ³ under same meteorological conditions, among which the contribution of the emissions driver and meteorological driver are respectively -5.81 and 1.07 μg/m ³ for O ₃ and respectively 0.55 and -0.35 μg/m ³ for PM _2.5 . Additionally, 84.45% of O ₃ variance is attributable to the emissions driver in terms of relative importance, which is 52.88% for PM _2.5 . 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 PM _2.5 .
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