3.1 Daily change in primary pollutant over China urban agglomerations in 2015-2019
To elucidate the change trend of primary pollutants under the 13th Five-Year Plan, we calculated the daily primary pollutants in 2015 and 2019 based on formula (1) and formula (2). Such diurnal comparisons can reduce the effects of seasonal weather to some extent. From the 19 UAs (224 prefecture-level cities), the heat diagram of the daily change transfer matrix of primary pollutants from 2015 to 2019 is shown in Figure 2, including six primary pollutants and clean day conditions.
From the sum of the diagonal numbers, 37% of the primary pollutants had no shift during the 13th Five-Year Plan period. PM2.5, PM10 and O3 were the main primary pollutants, especially PM2.5. More primary pollutants were diverted to ozone pollution, indicating that the proportion of O3 as the primary pollutant is gradually increasing. In addition, the proportion of clean air has increased significantly, which shows that pollution control has been effectively reflected during the 13th Five-Year Plan period. However, the proportion of NO2 before and after metastasis was approximately the same, with approximately 5% NO2 pollution. This may imply that the governance of NO2 pollution was rendered nonsignificant. It is noteworthy that ozone pollution in China has become an increasingly prominent task in recent years. Similar to Xiao’s 16 research on ozone pollution, they argue that present-day ozone levels in major Chinese cities are comparable to or even higher than the 1980 levels in the United States. Taken together, ozone and PM2.5 have become the top two air pollution pollutants in China.
3.2 Monthly distribution of primary pollutants over China’s urban agglomerations in 2019
To further explore the spatiotemporal distribution of the primary pollutants across the UAs, we obtained the most primary pollutants per month by dividing the number of days with the most pollutants by the number of cities in each urban agglomeration from the 2019 data. In Figure 3, the urban agglomeration location is plotted on the abscissa, and the monthly variance of the primary pollutant is plotted on the ordinate. As shown in Figure 3, PM2.5 appears as dark green, PM10 appears as light green, O3 appears as orange, NO2 appears as yellow, and clean days appear as dark blue. The main pollutants in the 19 UAs are PM2.5, PM10 and O3. NO2, as the primary pollutant, only appeared in the HBOY urban agglomeration in January. CO and SO2 did not become major pollutants. Clean days (where AQI<50) occur mainly in South and Southwest China during summer. As the primary pollutant, PM2.5 is mainly concentrated in December, January and February, that is, it occurs in winter. As the primary pollutant, PM10 is mainly concentrated in October-November and March-April, that is, autumn and spring. As the primary pollutant, O3 is mainly concentrated from May to September, that is, summer.
From the perspective of UAs, monthly primary pollutants in different UAs are different. The O3 pollution in the PRD and BG, as representatives of UAs in southern China, is mainly concentrated from August to November. In addition to human factors, this may be associated with lower latitudes, higher temperatures and stronger solar radiation. The northern, northwestern, northeastern, central and eastern UAs have similar monthly primary pollutant distributions, with PM2.5, PM10 and O3 pollution distributed in the 12 months of 2019. Nearsurface O3 is mainly produced by the complex photochemical reactions between nitrogen oxides, volatile organic compounds, carbon monoxide, methane and other precursors. O3 is a major pollutant in most UAs from May to September, which may be the result of the comprehensive action of anthropogenic factors and meteorological conditions.
3.3 Annual patterns of PM2.5, PM10 and O3 concentrations over China’s urban agglomerations in 2015−2019
As shown in Figure 4, unary linear regression was used to extract the time variation trend of PM2.5, PM10 and O3 concentrations in each urban agglomeration. The slope value depicted a decreasing trend in the annual mean PM2.5 and PM10 during 2015–2019, suggesting that the emissions of particulate matter were effectively controlled.
In terms of urban agglomerations, the average annual PM2.5 and PM10 concentrations significantly decreased, mostly in Harbin-Changjiang (HC) and mid-southern Liaoning (MSL) in the northeast, while there was little difference in annual ozone concentration. In contrast, the annual mean ozone pollution displayed the greatest enhancement, while PM2.5 and PM10 showed a minimal decrease in the Taiyuan-Jinzhong (TJ) urban agglomeration in northern China. At the same time, by checking the annual average AQI in 2019, we showed that the Central Plains urban agglomeration has the highest annual average AQI, and the particulate matter pollution and ozone are both high, suggesting that the control effect is not obvious in the central region. We found that the annual average AQI showed a gradually decreasing trend outward, taking the CP urban agglomeration as the center. In addition, the UAs in the southern and southwestern regions showed both good annual average AQI and low fine particulate pollution while high ozone concentration pollution. The average annual AQI, fine particulate matter and ozone pollution in the TNS are also at a high level in 19 UAs. This is consistent with the level of the monthly primary pollutants mentioned above.
The World Health Organization (WHO) published new Global Air Quality Guidelines (AQGs), which recommend new air quality levels on 22 September 2021. As shown in Table 2 and Figure 5, almost half of the UAs have reached the second-level national standards of China and the first-stage interim target of the World Health Organization.
Table 2
Recommended AQG levels and interim targets
Pollutants
|
Averaging time
|
GB3095-2012
|
AQG 2021
|
AQG
|
Level-1
|
Level-2
|
IT-1
|
IT-2
|
IT-3
|
IT-4
|
PM2.5(µg/m3)
|
Annual
|
15
|
35
|
35
|
25
|
15
|
10
|
5
|
PM10(µg/m3)
|
Annual
|
40
|
70
|
70
|
50
|
30
|
20
|
10
|
3.4 Hourly variations in PM2.5、PM10 and O3
As seen from the above analysis, the spatial distribution of changes and the concentrations of air pollutants shared a location similarity between adjacent UAs. Furthermore, to profile the diurnal changes in pollutant concentrations during the 13th Five-Year Plan, the hourly variations were calculated in seven directions. The results are shown in Fig. 6. From 2015 to 2019, the rates of O3 concentration in hours significantly increased compared to particulate matter (PM) pollution, which was consistent with the average annual variation trends above. Overall, diurnal trends in O3 concentration at UAs in all directions also showed a single peak, with the largest increments concentrating between 13:00 and 16:00, due to more frequent heat waves in recent times29 and the fact that higher temperature, lower relative humidity, and stronger solar radiation at 16:00 favor more secondary pollutant production 8,30. However, the peak O3 pollution spatially varied, exhibiting a relatively earlier trend in the southeastern region than in the northwestern region because of the Earth's rotation, making the eastern area receive direct sunlight first. The distance between the whiskers of the box plot in the southern city is the largest compared with the others, indicating that ground-level ozone concentration variations presented great dissimilarity in southern cities.
For PM pollution, there was a significant downward trend in all UAs. PM2.5 reduction is more obvious in rush hours (10:00 and 22:00), which reveals the response to aggressive actions by the government to restrict motor vehicles 30. In addition, we noticed that the ‘coal-to-gas and coal-to-electricity’ transformation has substantially improved air pollution, particularly in northern China. It seems clear from these figures that the changing rate of PM2.5 in northern regions was significantly higher than that of PM10.
3.5 Sector emission source impacts on primary pollutants
Due to the availability of data, statistics on industry emissions are limited to 2015-2017. The average annual emission value of agricultural sources was too small to provide statistical significance. Fig. 7 only shows the contribution of four sector sources (industry, power, civil and transportation) to pollutants in 19 UAs from 2015 to 2017, where the dotted line represents the average of total emissions. As a result, near-surface ozone pollution is a secondary pollutant produced by the photochemical reaction of a series of precursors under the action of solar radiation. In particular, VOCs and NOx are important precursors for ozone pollution. Therefore, we explore the emissions of these two important precursors in different urban agglomerations.
Sector pollution sources directly cause increases in PM2.5, PM10, nitrogen oxides, volatile organic compounds and other pollutants in the atmosphere. As seen from Figure 7(a), PM2.5 is mainly derived from industrial sources, and the main high value areas are TJ, YRD and SP UAs. Meanwhile, in the three UAs, the total contribution of anthropogenic emission sources to particulate pollution declined the fastest, which is largely related to government intervention, industrial transformation and environmental governance. Approximately 50–75% of PM2.5 in the northern UAs comes from industrial sources, while PM2.5 in the southwestern UAs mainly comes from civil sources. This result is similar to the analysis of PM2.5 sources in BTH in 2013 by Li et al. Regarding PM10, industrial sources have the greatest influence on PM10 in all UAs (Figure 7(b)). There is no significant regional difference in the impact of traffic sources on PM2.5 and PM10. The contribution of traffic sources to PM2.5 in 19 UAs is 5-7% on average, and PM10 tends to 0. By using the receptor model method, 31found that the contribution of gasoline dust to total suspended particulate matter (TSP) in Changzhou was less than 1%, confirming that exhaust gas was not the main contribution source of PM2.5. For the total amount of particulate matter emissions, the total amount of PM2.5 pollution caused by anthropogenic emission sources is approximately 2.5 times that of PM10.
Among the 19 urban agglomerations, NOx and VOCs mainly come from industrial and traffic sources, with less contribution from civil and power sources (Figure 7(c), Figure 7(d)). During the study period, the NOx emissions of most UAs decreased, but the CY, CG and CC UAs in the southwest, WSS in the eastern and BG urban agglomerations all showed an increasing trend of NOx. This is mainly concentrated in the increase in industrial sources, which can also be seen from the increase in industrial production in the south. The contribution rate of power sources to VOCs in urban agglomerations is low and can be ignored.
From the perspective of time series changes, the contribution rate of anthropogenic emission sources to particulate pollution is decreasing, but for gas pollutants, nearly half of UAs show an increasing trend of VOCs and NOX. In addition, VOCs high value areas are mainly concentrated in the YRD, PRD, BTH and other state-level urban agglomerations.