Using Short‐Term Data to Quickly and Quantitatively Determine Formation Mechanisms of PM2.5 and Ozone

The short‐term exposure to high concentrations of fine particulate matter (PM2.5) and ozone increases the mortality burden in megacities. Quickly identifying formation mechanisms of PM2.5 and ozone is essential for the pollution control in the early stage. Min/max autocorrelation factor analysis (MAFA) is used to analyze hourly data of air pollutants and meteorological variables collected over 7‐day periods in high and low PM2.5 events at the Wuhan Supersite in 2015. The MAFA results determined the interactions of air pollutants and the influences of meteorological variables on the formation of major air pollutants. In the high PM2.5 event (winter), PM2.5 and secondary inorganic aerosols (SIAs) are major pollutants mainly affected by variations in temperature (TEMP), relative humidity (RH), planetary boundary layer height (PBLH), and wind speed (WS). The SIAs are produced by the oxidation of precursor pollutants which increases in daytime due to intense industrial and traffic activities. In the low PM2.5 event (summer), MAFA identified photochemical reaction of the carbon monoxide (CO), and formation of SIAs and ozone (O3). Serious O3 pollution occurs in high TEMP, high PBLH, and low RH conditions. The results can serve as a critical reference of quick decision making for local government agencies. Quickly identifying formation mechanisms of PM2.5 and ozone is essential for the pollution control in the early stage. MAFA using short‐term data identifies that formation of secondary inorganic aerosols under the certain meteorological conditions mainly contributes to high PM2.5 concentrations in winter. High ozone concentrations in summer are mainly attributed to photochemical reactions of CO and NOx in the daytime.