In this study, we examined associations between the mortality risk and the 8 pollution types based on the combination of different concentration levels of three major pollutants (PM2.5, NO2, and O3). In our analysis, we found evidence that exposure to multi-pollutant types which several pollutants with high concentrations simultaneously was linked to a higher relative risk than exposure to single-pollutant types. The results from eight different areas collectively indicate that type 7 with higher of all three pollutants (PM2.5, O3, and NO2) and type 4 with higher PM2.5 and NO2 have a greater relative risk than other types. We also found regional differences: in most northern cities, the highest risk of death in the warm season is the multi-pollutant type, while the risk of single-pollutant type with high PM2.5 is highest in the cold season. In southeastern cities, the multi-pollutant type had a higher mortality effect in both seasons. In addition, the results also showed that the excess risk from simultaneous exposure to multiple pollutants was less than the sum of individual air pollutants effects, partially false conclusions would have been reached by ignoring the presence of interactions between air pollutants.
The difference in the mortality risk between each pollutant type modified by city and season is also observed. The results of the present study indicate that in most areas, the multi-pollutant type 7 and type 4 have a greater relative risk than other types. And the RR of type 1 is the highest in most northern cities in the cold season. These differences may be related to pollution conditions, pollutant composition, and indoor-outdoor activity patterns. In this study, the concentrations of O3 and NO2 in cities in economically developed areas such as Beijing and Guangzhou are higher, and the complex air pollution is more serious in these areas, which will lead to a higher health impact of multi-pollutant types in these areas than Kunming. At the same time, PM2.5 has greater seasonal and regional differences. Specifically, PM2.5 concentration in northern cities is much higher than that in southern cities in the cold season. Yan et al. (2019) found that evidence of the association between PM2.5 and the risk of cardiovascular death was higher during periods with high PM2.5 concentration than during periods with low PM2.5 concentration. This may partly explain the reason for the type with the highest RR is type 1 in the cold season in northern cities.
In our analysis, type 4 (PM2.5 and NO2 are both above the cut-off) appeared most frequently, and the concentration of PM2.5 and NO2 has a good consistency in this pollution type. On the other hand, the occurrence frequency of multi-pollutant types with opposite levels of PM2.5 and NO2 concentration (type 5 and type 6) is very low. In these 8 cities, the highest is less than 10% (Beijing type 5), and the lowest is only 1.5% (Guangdong type 6). This result is consistent with a robust positive correlation between PM2.5 and NO2. Types 4 and 7, which carry a greater risk of death, are among those with high concentrations of both PM2.5 and NO2. In fact, vehicle emissions are a major source of NO2, which is an important precursor of PM2.5 and has complex links to it. This suggests that in highly urbanized areas, the structure of emissions has an important impact on health.
On the other hand, studies show that the chemical composition of PM2.5 varies greatly by season and across China, and it may be different during periods of high and low pollution levels, which may affect toxicity (Bell et al., 2007; Dominici et al., 2006, 2010; Peng et al., 2009; Franklin et al., 2008; Cao et al., 2012). Generally, the period between mid-November and mid-March is the heating season in northern China, thus the use of coal-based heating adds a source of PM pollution. At this time, the contribution of fossil fuel burning to PM2.5 increases sharply in northern China (Zheng et al., 2005). Further, Cao et al. (2012) suggest that PM2.5 constituents from the combustion of fossil fuel may have a distinct impact on the health effects attributable to PM2.5. It has been proved that secondary inorganic species in PM2.5 (composed of secondary inorganic substances such as sulfate, nitrate, and ammonia) have a greater impact on cardiovascular mortality than other PM components (Huang et al., 2012; Son et al., 2012; Yan et al., 2019). These secondary inorganic substances are the main components of PM2.5 when PM2.5 concentration is higher than 150µg/m³ in Beijing (Ma et al., 2017), and this situation is more likely to occur in the heating season in northern China. These could be partly responsible for our result that type 1 (only PM2.5 higher than cut-off) usually is associated with the highest relative risk in most northern cities during the cold season.
In addition, different cities have different outdoor activities patterns and ventilation habits in different seasons due to each climate feature, which will affect indoor and outdoor exposure rates and thus affect health. The results of the present study indicate that the relative risk was higher during the warm season in northern and western cities, but higher during the cold season in southeastern cities, which may be related to the different exposure types of people living in cities with different climate features. The Severe cold in the cold season in the north and heatwave and heavy rain in the warm season in the south will reduce local people's exposure to pollutants outdoors. Also, the seasonal variation of ozone is obvious, and its concentration is much higher in the warm season than in the cold season. In the Pearl River Delta region, however, the cold season is cool and dry, with little temperature change, and people are more likely to go outside and open their windows for ventilation, thus exposing themselves to higher levels of air pollution. While the warm season is hot and humid, thus people often use air conditioning, which reduces the risk of exposure to ambient air pollution (Wong et al., 2001). This lifestyle will reduce the outdoor ozone exposure of people there. This may partly explain the reason why the difference between pollutant types with the highest relative risk between southern and north-central cities is only that the northern cities include ozone concentrations above the cut-off and the southern cities are below it.
We also found strong evidence for the health burden from simultaneous exposure to multiple pollutants was less than the sum of individual effects. This means that defining the health effects of multiple pollutants as the sum of the effects of each air pollutant can skew estimates. Some previous studies (Romieu et al., 2012; Li et al., 2017; Amini et al., 2019; Tang et al., 2021) of two-pollutant analyses have shown that the previously estimated effects were weaker and became no longer significant when adjusted for one pollution in two-pollutant models. This may arise from problems of interaction and multi-collinearity between PM2.5, NO2, and O3 (Romieu et al., 2012). Previous field and laboratory studies (He et al., 2014, Chen et al., 2016, Chu et al., 2019) confirmed that a high Correlation between PM2.5 and NO2 is caused by its chemical mechanism: NOx contributes to the formation of secondary PM2.5 by directly forming nitrate and by indirectly enhancing aerosol-phase oxidation (Russell et al., 1988). These chemical mechanisms issued in the key roles of NOx in the formation of secondary PM2.5, which explained why PM2.5 is highly related to NO2. In addition, the formation mechanism of ozone determines its complex correlation with the other two pollutants. First, the formation of ozone depends on a series of complicated photochemical reactions involving volatile organic compounds (VOCs) and nitrogen oxides (NOx) under light conditions.11.22. In other words, VOCs and NOx are the pathways of ozone formation and the precursors of ozone, but they are also the precursors of PM2.5 (Zhang et al., 2019; Godzinski et al., 2021; Dominici et al., 2010). These common precursors are the possible reason for the positive correlation between PM2.5 and O3 (Chu et al., 2020). On the other hand, as PM2.5 is produced by primary emission sources, however, will react with heterogeneous free radicals (e.g., HO2) needed to form ozone, consuming free radicals and thus reducing ozone formation (Shang et al., 2013; Chu et al., 2020). Also, the reduction of PM2.5 concentration always coincided with a rise in atmospheric visibility, thus increasing sunshine intensity, which is a favor to the formation of ozone (Zhang et al., 2019). This mechanism partly explains the inverse correlation between ozone concentration and PM2.5 concentration in some Chinese areas during the past few years (Chu et al., 2020). The difference between cities and seasons in terms of the occurrence frequency of different multi-pollutant types is also observed. In the northern and central cities with significantly higher PM2.5, the occurrence frequency of the multi-pollutant type (type 1,2,4,6) with opposite levels of PM2.5 and O3 concentration is much higher than that of the two cities with lower PM2.5 concentration in the south, which may be caused by the complex correlation between PM2.5 and O3 mentioned above. Chu et al. (2020) found that PM2.5 is negatively correlated with O3 in areas where PM2.5 concentration is greater than 50 µg M-3, but this negative correlation will weaken with the decrease of PM2.5 concentration. Specifically, PM2.5 and O3 are negatively correlated in North China but positively correlated in South China. This is consistent with our findings. These complex chemical mechanisms lead to complex interactions among pollutants with regional differences. The presence of these interactions partly explicates the inequality between the health effects estimated from simultaneous exposure to multiple pollutants and from the simple sum of individual exposure to single pollutant.
Current results suggest that policymakers should shift to a multi-pollutant approach to air quality and achieve greater public health protection through the regulation of multiple sources of air pollution and the overall mixture air pollution. Additionally, multi-pollution control should also conform to the features of local ambient air pollution, climate, and population activities. Specifically, for most cities in northern China, more strict measures should be taken to control the multi-pollutant of PM2.5, O3, and NO2 in the warm season, while more attention should be paid to the control of PM2.5 in the cold season. For southern cities, we should pay attention to multi-pollutant throughout the year and focus more on the coordinated control of PM2.5 and NO2 in the cold season.
Rather than following the type of previous air pollution health studies that looked at individual pollutants, this study is the first to consider air pollution as a mixture in China and calculate the impact of different pollutant types on mortality. This makes it outside of the very small set of the traditional two-pollutant model, in which the introduction of correlated pollutants can cause unstable. Furthermore, the research covers a wide range of 8 major cities in China, and these 8 cities are located in different regions with different climate, pollution levels, and economic development level features, which has strong regional representativeness. In summary, this study provides a reference for putting forward multi-pollutant control strategies for air quality following local conditions, to strengthen the ability of health-driven coordinated air pollution control in China.
There are still some limitations to the present study. Firstly, 8 cities with large climate differences were selected nationwide for research, which has regional representativeness to a certain extent, but its representativeness is still limited, and there may be some deviations in direct application to other cities. Secondly, as a time series analysis, this study inevitably has exposure errors. Since it is difficult to obtain the true exposure of individuals, observations from monitoring stations are used as proxies for population exposure, which leads to a certain degree of exposure error. Finally, due to data limitations, we did not classify the population by gender, age, economy, and education level, so we could not put forward more targeted health suggestions for vulnerable populations.