In
Section 3.2.1, it is shown that the OCC in the lower troposphere (OCC
LT) in the eight major urban agglomerations shows an increasing trend year-by-year.
Figure 8 further shows the trend of the annual average OCC
LT for the whole land area of China from 2005 to 2020, which shows that the overall OCC
LT for the whole region of China is still exhibiting an increasing trend, with an overall concentration increase of 2%, and an average annual increase of 0.13 DU. The lower tropospheric ozone has a significant impact on human production and life, and the growth of plants and animals, so it is important to investigate the causes of the increase in order to combat ozone pollution. Studies have shown that ozone formation in the atmosphere is closely related to the ratio of VOCs to NO
x. While VOCs have an obvious linear relationship with HCHO, hydrocarbons (RH) are oxidised by organic peroxyl radicals (RO
2) and OH generated in the first stage of oxidation, which react with NO
x to produce HCHO or higher carbonyl groups. The subsequent reaction eventually produces HCHO [
27,
28], which can indirectly reflect the accumulation of VOCs through the properties of HCHO [
29]. At the same time, due to China’s rapid development resulting in consistently high NO
x concentrations in China, the increase in VOCs has been the main driver of soaring ozone pollution across China due to the positive impact of rising HCHO concentrations on OCCs in the context of the Chinese government’s vigorous NO
x abatement policies and tight restrictions on NO
x emissions [
30,
31]. A large proportion of biogenic VOCs (BVOCs), which are ozone-generating precursors, originate from vegetation and other biogenic emissions [
4,
32]. Whereas BVOCs are more reactive in photochemical reactions, ozone is more sensitive to changes in BVOCs emissions and BVOCs contribute relatively more to ozone formation, making them an important precursor to tropospheric ozone production [
33]. The analysis of vegetation changes in Chinese regions over many years based on satellite remote sensing data can help us to analyse the causes of low-level ozone growth; temperature is the most important meteorological factor for ground-level ozone concentrations across China, and ozone production is positively correlated with sensitivity to increased temperature, with temperature being one of the main causes of ozone production, and areas where near-surface ozone increases significantly often being accompanied by severe near-surface warming [
34,
35]. In terms of the direction of influence, temperature has a persistent positive effect on ground-level ozone concentrations in most cities [
36]. At the same time, warming will not only increase the reaction rate, but also increase the natural emission of VOCs from natural sources, which contributes to ozone production [
37]. In addition, it has been pointed out that under certain weather systems, the rise in PBLH will lead to the transport of ozone from the upper atmosphere to the near-surface layer [
38], which will facilitate the vertical exchange of ozone-producing precursors within the boundary layer [
39], and eventually lead to an increase in ozone concentration. Furthermore, studies have shown that the positive correlation between the PBLH and the concentration of near-surface ozone is even greater than that of ozone precursors, especially when high ozone concentrations occur [
40,
41]. The above studies show that the effect of PBLH on ozone concentration is opposite to that of particulate matter [
42]. In summary, in this paper, four factors, namely ozone-producing precursors (HCHO, NO
2), normalised difference vegetation index (NDVI), air temperature, and PBLH, were selected to carry out a causal analysis of the increase in the OCC
LT.
4.1. Ozone Transport in the 3–5.8 km Altitude Layer
Figure 9 shows the spatial distribution of the OCC slope in the 0–3 km and 3–5.8 km altitude layers in China from 2005 to 2020. Note that due to the influence of altitude, only a few areas of the QTP have valid pixels, so the slope of most areas is 0. Except for the QTP, the OCC
LT mainly shows an increase, while that at the 3–5.8 km layer mainly shows a decrease. The spatial distribution of the two shows an obvious inverse distribution, so the 3–5.8 km layer most likely contributes to the increase in the OCC. To determine this possibility, an analysis of whether the 3–5.8 km altitude layer transmits ozone upwards is required. In this paper, the slope of all altitude layers of the OMI ozone profile product for 2005–2020 was calculated. The slope of the OCC at each altitude layer and 3–5.8 km was correlated, and the corresponding correlation coefficient results are shown in
Table 4. Because the number of effective pixels in the 0–3 km altitude layer is smaller due to the altitude of the QTP, the total sample size of the 0–3 km altitude layer is smaller than that of the other altitude layers. The table shows that although there is a high correlation between several altitude layers and the 3–5.8 km OCC slope, a positive correlation coefficient only represents a homogeneous change in slope, i.e., the OCC is decreasing in all these altitude layers, and if the 3–5.8 km altitude layer has a direct influence on these altitude layers, the correlation coefficient should be negative. There are four altitudes with negative correlation coefficients: 24–26.7 km, 21–24 km, 18.8–21 km, and 0–3 km. The correlation coefficient for 0–3 km is −0.891, indicating that there is an obvious negative correlation between the 3–5.8 km and 0–3 km OCCs. The correlation coefficients for the rest of the height layers are all greater than −0.3, and the negative correlation is relatively weak. The above analysis shows that the 3–5.8 km altitude layer contributes significantly to the increase in OCC
LT, i.e., there is a transfer of ozone from the 3–5.8 km altitude layer to the 0–3 km altitude layer.
However, as can be seen from the mean year-on-year changes in OCCs at different altitudes in the troposphere for each region in
Table 4, the decrease in OCCs at altitudes of 3–5.8 km is not sufficient to offset the increase at altitudes of 0–3 km, so there are still other external factors influencing OCC
LT. Next, analysis will be carried out in relation to ozone precursors, vegetation cover, meteorological conditions, and other factors.
4.2. Precursor Effects
The annual trend of HCHO and NO
2 tropospheric variation over China from 2005 to 2020 is shown in
Figure 10. As seen from
Figure 10a, the growth trend of HCHO during 2005–2016 is obvious. The average slope is 0.072, and the maximum value reaches 0.72, with 82.7% of the pixels showing positive growth. The high HCHO slope area is mainly in eastern China, including BTH, MSL, YRD, COC, and SCB. In the south-central region of QTP and Yunnan Province, although the increase in HCHO concentration is obvious, it is not conducive to ozone production because of the lower temperature and higher level of water vapour. From
Figure 10b, it can be seen that the trend of NO
2 changes in the east and west of China during 2005–2020 differed significantly; the background value of NO
2 in western China increased slowly, with its slope being between 0 and 0.1 overall, and between 0.1 and 0.2 in a few areas; southern and central-eastern China showed a significant decreasing trend, with the PRD, YRD, COC, and BTH areas decreasing most significantly, exhibiting slope indices greater than −0.6, and up to −0.93. SCB, MLS, and UCS also have a more obvious reduction, which is related to the country’s recent introduction of relevant energy-saving and emission-reduction measures. In a comprehensive analysis, comparing the spatial distribution trends of HCHO slope growth areas with
Figure 6a, there is an obvious agreement between the two, especially in MLS, BTH, YRD, COC, and SCB, with a trend correlation up to r = 0.8, which indicates that the continuous growth of HCHO is an important driver of the rising OCC
LT in China. At the same time, the rapid decline in NO
2 is also in high agreement with the spatial distribution trend of 0–3 km ozone. The areas where NO
2 declines significantly are also the areas where the distribution of high value ozone areas is concentrated, most significantly in PRD, YRD, COC, and BTH, while the areas where NO
2 changes insignificantly or increases also have relatively low ozone concentrations. Previous studies have shown that when atmospheric NO
x is at high concentrations, the reduction in NO
x emissions will reduce the NO
x titration and the reduction of ozone by NO
x will also increase the oxidation of VOCs to produce ozone and OH, resulting in higher ozone concentrations [
43,
44].
Figure 10a,b shows that the increasing HCHO level and decreasing NO
2 level in China over the years have disrupted the mutual balance of ozone production and depletion by atmospheric VOCs and NO
x, and the “titration” of ozone by NO has weakened, resulting in a shift from VOC-limited to VOC-driven ozone generation [
45,
46,
47]. This has resulted in more conversion of VOCs to ozone, leading to a continued increase in tropospheric near-surface ozone concentrations, suggesting that NO
x and VOCs should be controlled in tandem, rather than one or the other.
4.3. Vegetation Growth Effects
Monthly mean values of the NDVI for 2004–2020 were obtained based on the MYD13Q1 product and compared with the OCC
LT, the results of which are shown in
Figure 11. Over the years, NDVI has been on a fluctuating upward trend in most regions of China, while NDVI and OCC
LT show a cyclical variation with obvious consistency in all regions except QTP. During March to July of each year, vegetation grows rapidly with increasing temperature, and also releases a large amount of BVOCs, including monoterpenes and isoprenes [
48], which also provide good conditions for photochemical reactions to generate ozone [
49]. From July to August, when vegetation is flourishing and growing more slowly, the release of BVOCs decreases and ozone concentrations begin to decrease; from September to February, as vegetation gradually dies, NDVI drops rapidly to lower values, and along with the rapid drop in temperature, ozone concentrations also begin to decrease rapidly to their lowest value of the year [
50,
51]. At the same time, although OCC
LT increases with NDVI, it also shows a significant lag, with peak NDVI lagging behind peak OCC
LT by approximately one month in most areas, especially in BTH, MLS, COC, SCB, and UCS. If the peak NDVI is used as a marker of plant maturity, the stage of NDVI increase can correspond to the growing phase of plants, which in turn indicates that plants drive OCC
LT in these areas more strongly during the growing phase than during maturity. In the PRD region there is a 2-month difference in the occurrence of the two peaks, with the peak PRD OCC
LT occurring in April and the peak NDVI occurring in July, suggesting that vegetation growth in this region is a weak driver of ozone. In the northern part of the YRD, as a major wheat-rice-growing area, the cultivation cycle of cash crops has a strong influence on NDVI [
52], and the maturation of cash crops for harvesting causes the NDVI during May–June in YRD to undergo a period of rapid decline, and the rapid decrease in vegetation is accompanied by a significant decrease in OCC
LT, so the relationship between changes in OCC
LT and vegetation growth is linear. At QTP, due to the low vegetation cover related to the climate, there is no significant relationship between OCC
LT and vegetation growth trends.
By comparing the correlation between NDVI and OCC
LT among different regions, as shown in
Table 5, it can be seen from the fitted relationship equations in most regions that NDVI and ozone changes show a clear linear correlation. The consistency between OCC
LT and NDVI trends is highest in UCS, with an r as high as 0.87, which indicates that changes in OCC
LT in this region are significantly influenced by vegetation growth. Followed by MLS and BTH, the r exceeds 0.7, indicating that plant growth is an important influencing factor driving the increase in OCC
LT in this region. In SCB and YRD, the growth of vegetation cover also has some influence, while there is no significant correlation between the two in the PRD and QTP regions.
4.5. PBLH Effects
In this study, the monthly and annual average values of PBLH over China were calculated using the ERA-5 meteorological reanalysis data. Then, the correlation between PBLH and OCC
LT was analysed using the averaged results.
Figure 14 is a scatter plot of the monthly mean values of OCC
LT and PBLH in China from 2005 to 2020, and the r of the two is 0.902, which demonstrates a high positive correlation. Similar to other studies, the results illustrate the contribution of elevated boundary layer height to OCC
LT.
Figure 15 shows the annual average spatial distribution of PBLH from 2005 to 2020. The slope of the PBLH in the Chinese region ranges from −2 to 2. The PBLH in most regions show an increasing or slightly decreasing trend, and only some regions in QTP (Qinghai) exhibit a significant decrease. The regions with increasing PBLH also correspond to the regions with significantly increasing or higher OCC
LT shown in
Figure 6a, i.e., the four regions of MSL, BTH, COC, and YRD. For the other regions, although there is a small decrease in the PBLH, the increase in temperature (
Figure 13) still eventually leads to an increase in the OCC
LT that is smaller than that of the above four regions. Among the meteorological driving factors leading to OCC
LT growth, the effect of temperature is greater than that of PBLH. However, once the PBLH and temperature in a certain region are increased simultaneously, the growth of OCC
LT will be further enhanced.