3.1.1. Annual Variation of PM2.5 and O3 Concentrations

As shown in Figure 2, the five–year time series of O3 and PM2.5 shows that, in general, PM2.5 shows a high concentration in winter and a low concentration in summer in these five

years, which is basically consistent with the results of other studies [30], mainly because there is a heating period in northeast China, which generally reaches about five months, so it shows a trend of high PM2.5 in roughly late October–early April [31,32]. The overall trend of PM2.5 is high. For O3, concentrations are higher in the late spring, throughout the summer, and early autumn in the three major cities in the northeast than in the winter, that is, they are higher in May–September, mainly because of the high temperature in summer and low temperature in winter, and O3 has a certain sensitivity to temperature [33]. It is also noteworthy that in winter, all three cities experience pollution events with different degrees of high concentrations, with Harbin having the highest frequency, with varying degrees of high PM2.5 concentration days (>300 μg/m3) in each of the years 2016–2020, Changchun in 2020, and Shenyang without, with Harbin reaching a maximum daily average concentration of 487.8 μg/m3, while Changchun reached a maximum of 508.4 μg/m3, which was the highest value among the three cities, and Shenyang was 266.9 μg/m3.

**Figure 2.** Five-year time series of PM2.5 and O3 concentrations in the capital cities of northeast China ((**a**) Changchun; (**b**) Shenyang; (**c**) Harbin. Red and blue lines are O3 and PM2.5 concentrations, respectively).

3.1.2. Relationship between Meteorological Factors and PM2.5 and O3

The distribution of PM2.5 and O3 in 2016–2020 in the three provincial capitals in each wind direction is shown in Figure 3. The dominant wind direction in Changchun and Harbin is southwest followed by northwest, and the dominant wind direction in Shenyang is southward followed by northeast. The results show that one of the influential factors causing pollution in the northeast region may be the transfer of pollutants from other surrounding areas to the downwind region due to atmospheric flow, while higher wind speeds on clean days may also serve to disperse pollutants, which may also reduce pollutant concentrations in the northeast region [20].

**Figure 3.** Distribution of PM2.5 and O3 concentrations in Changchun, Shenyang, and Harbin in each wind direction.

As shown in Figure 4, the correlations between PM2.5 and O3 and other meteorological factors were most significant in the studied cities. PM2.5 was positively correlated with atmospheric pressure and relative humidity and negatively correlated with temperature and wind speed in all cities. The highest correlation coefficients of PM2.5 with atmospheric pressure and temperature were in Harbin, −0.36 and 0.28, respectively, and the highest correlation coefficients with relative humidity and wind speed were in Shenyang, 0.1 and −0.17, respectively. It is worth noting that PM2.5 was not significantly correlated with wind direction, but O3 was negatively correlated with wind direction; this result indicates that in the large cities in the northeast region, PM2.5 from regional transmission PM2.5 concentrations do not account for a high proportion, mostly from local sources, while O3 is influenced by regional transport, which leads to an increase in local O3 concentrations.

**Figure 4.** Correlation between PM2.5 and O3 and each meteorological factor in the capital cities of northeast China (*p* < 0.05).

O3 is positively correlated with temperature and wind speed in each city and negatively correlated with atmospheric pressure and relative humidity. The highest correlation coefficients with temperature and atmospheric pressure were in Shenyang with 0.61 and −0.49, respectively; relative humidity and wind direction in Harbin with −0.47 and −0.046, respectively; and wind speed in Changchun with 0.46. Notably, the correlation results of O3 and PM2.5 with each meteorological factor were the opposite, especially with wind direction and wind speed. This indicates that external sources contribute more O3, while PM2.5 is more from local sources.
