Direct thermal enhancement dominates over emission-mediated pathways in heatwave-induced O₃ and SOA increases across China

Heatwaves are major drivers of ozone (O₃) and secondary organic aerosol (SOA) pollution. High temperatures directly accelerate photochemical reaction rates and concurrently enhance emissions of biogenic volatile organic compounds (BVOCs) and soil nitric oxide (SNO). However, the individual contributions of these direct and emission-mediated pathways to pollution formation remain poorly constrained. This study explicitly quantifies the distinct roles of these two pathways during heatwave events in China. Results show that high temperatures drive over 80 % (normalized) of the O₃ and SOA increases nationally, primarily through favorable weather conditions and enhanced atmospheric oxidation capacity. The O₃-temperature dependence is strongest in the Yangtze River Delta (0.66 ppb °C⁻¹) and Pearl River Delta (0.95 ppb °C⁻¹). Furthermore, high-temperature-induced BVOC emissions significantly exacerbate O₃ in VOC-limited regions like the North China Plain. These findings underscore the importance of climate mitigation by illustrating its critical role in alleviating temperature-driven secondary pollution.

1 Introduction

The Chinese government has implemented stringent emission control policies since 2013, achieving substantial reductions in primary pollutants sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) (Weng et al., 2023; Zheng et al., 2017; Zhang et al., 2019; Zhao et al., 2018). However, there were still heavy pollution events driven by secondary pollutants such as ozone (O₃) and secondary organic aerosol (SOA) (Shao et al., 2023; Wang et al., 2019; An et al., 2019; Wang et al., 2021b). In 2022, the observed O₃ concentration across 337 cities in China reached an average of 68 ppb, a 5.8 % increase from 2021, with even higher levels in major city clusters. For instance, the Chengdu Plain experienced 17 consecutive days of O₃ pollution in August 2022 (Wang et al., 2024). Similarly, widespread severe haze events were also largely driven by secondary components like SOA (Zhang et al., 2024; Huang et al., 2014; Duan et al., 2020; Lin et al., 2024; Huang et al., 2020, 2021). These trends raise growing concern, as exposure to elevated surface O₃ and SOA poses serious threats to human respiratory health, crop yields and climate (Huang et al., 2018; Li et al., 2022; Hu and Guo, 2020; Li et al., 2020; Oh et al., 2023).

Secondary pollutant concentrations are directly or indirectly influenced by meteorological conditions, which affect their formation, transport, and removal processes (Mousavinezhad et al., 2021; Bai et al., 2022). Regarding direct effects, high temperatures could accelerate O₃ formation rates including those involving hydrogen oxide radicals (HO_x) (Eremenko et al., 2008; Filleul et al., 2006; Otero et al., 2016; Meng et al., 2023). Synoptic patterns, such as high-pressure systems and low wind speeds further exacerbate O₃ accumulation by suppressing vertical and horizontal diffusion (Liu and Wang, 2020). These impacts are even more pronounced in urban areas due to the urban heat island effect, which intensifies local warming (Oke, 1982; Conti et al., 2005; Luo and Lau, 2017; Sun et al., 2014). An et al. (2023) reported the slope between maximum daily 8 h average (MDA8) O₃ and daily maximum temperature (T_max) was 0.49 ppb °C⁻¹ steeper in urban areas than in rural areas, resulting in heightened O₃ pollution.

As for the indirect effects, rising temperatures enhance emissions of biogenic volatile organic compounds (BVOCs) and soil NO (SNO), key precursors that drive secondary pollutant production (Guenther et al., 2012; Chang et al., 2009; Seco et al., 2022; Huang et al., 2023; Li et al., 2024). Based on measurements and model simulations, Zhang et al. (2023) found that high temperature drove an 18 % yr⁻¹ isoprene increase at a suburban Hong Kong site, leading to a 0.44 ppb h⁻¹ yr⁻¹ O₃ elevation. Besides, modeling studies also reported a 20 % increase in summertime biogenic SOA over North Europe under a 2.5 °C temperature rise (Megaritis et al., 2013). Beyond BVOCs, Oikawa et al. (2015) proved a positive correlation between SNO and temperature, which in turn aggravated secondary pollution. Although the impact of high temperatures on air quality has been widely recognized, the relative contributions of direct effects versus indirect emission-mediated pathways (e.g., via BVOCs and SNO) are still largely unknown.

This study quantifies the direct and indirect contributions of high temperatures to O₃ and SOA concentrations during the record-breaking 2022 summer heatwave in China, using coupled Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) modeling. The year was defined by an unprecedented summer of extreme heat, which had its most severe impact across the Yangtze River Basin (YRB) (Lu et al., 2023). Results show that ∼ 80 % of the increases in O₃ and SOA are attributed to the direct impacts of high-temperature meteorology. These findings imply that effective mitigation of secondary pollution calls for integrated controls targeting both anthropogenic emissions and climate-driven warming.

2 Materials and Methods

2.1 Model Configuration

CMAQ version 5.4 with a modified SAPRC-11 photochemical mechanism was applied with a horizontal resolution of 36 km × 36 km. The WRF version 4.2.2 was utilized to generate meteorology inputs, driven by initial and boundary conditions from National Centers for Environmental Prediction (NCEP) Final Reanalysis Data (FNL) with a high resolution of 0.25 ° × 0.25 ° (https://rda.ucar.edu/datasets/ds083.2/, last access: April 2024). Anthropogenic emissions were projected for 2021 and 2022 by adjusting the 2020 Multi-resolution Emission Inventory for China (MEIC) (http://www.meicmodel.org, last access: April 2024) with updated traffic and industrial emission data (Zheng et al., 2018) (Sect. S1 in the Supplement). Biogenic emissions including BVOC and SNO were generated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 (Guenther et al., 2012). Based on the MEGAN estimates, the relative proportions of isoprene, terpenes, and sesquiterpenes are highly consistent between the 2021 baseline and 2022 heatwave conditions (Fig. S1 in the Supplement). They account for 81.8 %, 15.5 %, and 2.6 % of the total BVOC emissions in 2021, and 82.8 %, 14.7 %, and 2.5 % in 2022, respectively. Overall, the simulation was acceptable for the analysis compared with the observations, and a detailed model validation is presented in Sect. 3.1.

2.2 CMAQ O₃ Process Analysis

The process analysis tools embedded in the CMAQ model were applied in the following analysis. The Integrated Process Rate (IPR) analysis was employed to quantify the contributions of key physical and chemical processes to O₃ formation and to identify those most responsible for its increase (Byun and Schere, 2006). Seven processes in the IPR contributed to O₃ formation, including horizontal diffusion (HDIF), horizontal advection (HADV), vertical diffusion (VDIF), vertical advection (ZADV), gas-phase chemistry (CHEM), dry deposition (DDEP), and cloud processes (CLDS). In this study, the sum of VDIF and ZADV was classified as vertical transport (VTRA). The sum of HADV and HDIF was classified as horizontal transport (HTRA).

2.3 CMAQ Simulation Scenarios

The summer of 2022 was selected for its record-high temperatures, while the summer of 2021 served as a baseline representing typical meteorological conditions (Lu et al., 2023). Two base cases, BASE21 and BASE22, were configured with default settings for 2021 and 2022 (Table 1). To isolate the effects of meteorology, BVOC, and SNO emissions, three sensitivity cases MET22, BVOC22, and SNO22 were conducted. These cases simulated 2022 using 2021 meteorology (MET22), 2021 BVOC emissions (BVOC22), and 2021 SNO emissions (SNO22), respectively. These simulations allowed the total difference between the two years (BASE22 – BASE21, termed “22-21”) to be partitioned into three components: (1) the direct meteorological effect (BASE22 – MET22, “MET”), (2) the indirect effect from climate-driven BVOC emissions (BASE22 – BVOC22, “BVOC”), and (3) the indirect effect from SNO emissions (BASE22 – SNO22, “SNO”). Four major Chinese city clusters including the North China Plain (NCP), the Yangtze River Delta (YRD), the Chongqing-Sichuan region (CY), and the Pearl River Delta (PRD) were selected for detailed analysis (Fig. 1a).

Figure 1 The observed and simulated differences in temperature and MDA8 O₃ between summer 2022 and 2021. (a) Major urban clusters in China. (b) Observed differences in temperature (T) and MDA8 O₃ between summer 2022 and 2021 (2022–2021). (c) Simulated and observed temperature differences between summer 2022 and 2021 (BASE22–BASE21, dots: observed difference). (d) Same as (c), but for MDA8 O₃.

Table 1 Simulation experiments design.

2.4 Response of Secondary Pollutants to Temperature and Emission Changes

To better quantify the response of secondary pollutants to meteorology and biogenic emissions, a best-fit slope method was applied. This method, previously applied to the O₃-T_max relationship, was extended to SOA and biogenic emissions in this study (Wang et al., 2021a; Ma et al., 2019). Linear regression slope (k) and correlation coefficient (r) between secondary pollutants (MDA8 O₃ and SOA) and T_max, daily average temperature (T), BVOC and SNO emission rates were calculated, respectively. The equations are shown below (Su et al., 2012):

$$\begin{array}{}\text{(1)}& {\displaystyle }k={\displaystyle \frac{\sum _{i=\mathrm{1}}^n{x}_i{y}_i-n\stackrel{\mathrm{‾}}{x}\stackrel{\mathrm{‾}}{y}}{\sum _{i=\mathrm{1}}^n{x}_i^{\mathrm{2}}-n{\stackrel{\mathrm{‾}}{x}}^{\mathrm{2}}}}\text{(2)}& {\displaystyle }r={\displaystyle \frac{\sum _{i=\mathrm{1}}^n\left(x_i-\stackrel{\mathrm{‾}}{x}\right)\left(y_i-\stackrel{\mathrm{‾}}{y}\right)}{\sqrt{\sum _{i=\mathrm{1}}^n{\left(x_i-\stackrel{\mathrm{‾}}{x}\right)}^{\mathrm{2}}}\sqrt{\sum _{i=\mathrm{1}}^n{\left(y_i-\stackrel{\mathrm{‾}}{y}\right)}^{\mathrm{2}}}}}\end{array}$$

where n is the total number of grids in the area, and x represents T_max, average temperature, BVOC and SNO emission rates, respectively. y denotes the concentrations of MDA8 O₃ and SOA. $\stackrel{\mathrm{‾}}{x}$ and $\stackrel{\mathrm{‾}}{y}$ are the average of x and y. The sensitivity of MDA8 O₃ is expressed in ppb °C⁻¹ for temperature and ppb s mol⁻¹ for emissions. The sensitivity of SOA is expressed in µg m⁻³ °C⁻¹ for temperature and µg s (m⁻³ mol⁻¹) for emissions. Because the calculated sensitivities to temperature and emissions possess incomparable units, these regression slopes were not normalized.

3 Results and Discussions

3.1 Model Evaluation

The simulation of meteorological conditions and pollutants was generally acceptable for the following analysis. Simulated 2 m temperature (T₂), relative humidity (RH), 10 m wind speed (WS), and 10 m wind direction (WD) were compared with observations (Table S1 in the Supplement). The benchmarks were provided by Emery et al. (2017). For T₂, despite the gross error (GE) being slightly higher than the benchmark (≤ 2), the mean bias (MB) values ranging from −0.2 to 0.0 K fully met the benchmark (≤ 0.5). WS was slightly overestimated, indicated by positive MB values of 0.6 to 0.8 m s⁻¹, while the GE (1.4 to 1.6) and root mean square error (RMSE, 1.8 to 2.1) well met the benchmarks (≤ 2.0). Additionally, CMAQ simulations were compared with hourly observations from China National Environmental Monitoring Centre (CNMEC). Table S2 showed the model performance statistics of O₃-1 h, PM_2.5, and NO₂. Simulated O₃ and PM_2.5 concentrations fell within the recommended standards. Specifically, although O₃-1 h was slightly overestimated, its mean normalized bias (MNB) values ranged from 0.08 to 0.15 and successfully met the ≤ ± 0.15 benchmark. Similarly, while PM_2.5 was underestimated, the corresponding MFB values ranged from −0.17 to 0.06, fully satisfying the ≤ ± 0.6 benchmark. Overall, the WRF and CMAQ model performance in this study was acceptable and comparable with previous studies, giving robust results for the subsequent process attribution analysis (Ji et al., 2024; Lyu et al., 2023).

However, a direct evaluation of SOA is currently unfeasible due to the absence of a nationwide monitoring network in China. Furthermore, previous studies reported that air quality models generally underestimate SOA concentrations because of missing precursor emissions, such as semivolatile/intermediate VOCs, and unrepresented multigenerational oxidation pathways (Zhao et al., 2022; Li et al., 2026). Consequently, the actual amplification of SOA caused by high temperatures is likely more severe than the model estimates.

3.2 Heatwave Increased Biogenic Emissions and O₃ Pollution

The temperature in China was higher in the summer of 2022 than in 2021 based on observations (Fig. 1b and c), with an overall increase of ∼ 1 °C. In particular, the warming was most pronounced in the YRB, with the CY region experiencing an increase of over 4 °C in August. In contrast, the temperatures slightly decreased in the PRD region, consistent with previous studies (Yuan et al., 2023). Model simulations also reproduced the observed high-temperature pattern in summer 2022 (Fig. S2). Besides, these elevated temperatures were accompanied by an increase in planetary boundary layer (PBL) (∼ 300 m) and reductions in RH (−15 %) and soil moisture (SM) (−10 %), comparable with previous studies (Ding et al., 2021; García-García et al., 2023).

Figure 2 (a) The differences in simulated BVOC emission rates between BASE22 and BASE21 (BASE22–BASE21, units: mol s⁻¹). (b) Same as (a) but for SNO (units: µmol s⁻¹). (c) Differences of BVOC and SNO emission rates between summer 2022 and 2021 across different regions based on the MEGAN model.

Correspondingly, the observed MDA8 O₃ concentration in summer 2022 was higher than that in 2021 (Fig. 1d). The most notable increase of O₃ was also found in the YRD and CY regions, with an increase of more than 10 ppb in July and August, aligned with the greatest warming (Fig. 1b and d), indicating that the occurrence of secondary pollution such as O₃ was closely linked to high temperatures (Chen et al., 2024). Along with the high temperatures, simulated biogenic emission rates also increased during summer 2022 (Fig. 2). Notably, BVOC and SNO emissions increased in the YRD and CY regions, coinciding with the areas of most pronounced warming. In July, the BVOC emission rate in the YRD increased by 3 mol s⁻¹, whereas the SNO emission rate in NCP saw the most significant increase of over 1 µmol s⁻¹ in June. To understand how high temperatures affect secondary pollution, the following analysis separated their influence into direct meteorological effects and indirect effects mediated by biogenic emissions.

3.3 Dominant Role of High-Temperature Meteorology in Secondary Pollutants

Model results showed that the MET22 case (direct meteorological impacts of high temperatures) contributed the most to secondary pollution increases (Fig. 3a and b). For O₃, the MET22 case accounted for over 80 % of the total increase and exhibited a spatial pattern consistent with the BASE2022 case. The most pronounced increases were concentrated in the YRD and CY regions. For SOA, the BVOC22 case also made a substantial contribution, particularly in the CY region. In contrast, the SNO22 case contributed the least to secondary pollutant formation (mostly <3 % for SOA) and even caused a slight SOA reduction in the CY region.

Figure 3 (a) Simulated differences in O₃ concentrations for BASE22–BASE21 (labeled “B”), BASE22–MET22 (labeled “M”), BASE22-BVOC22 (labeled “V”) and BASE22-SNO22 (labeled “N”). These scenarios represent the total differences between 2021 and 2022 (B), the contribution of meteorology (M), weather-driven BVOC (V) and SNO (N) emission changes, respectively (Units: ppb). (b) Same as (a) but for SOA (units: µg m⁻³).

IPR analysis of the total difference between summers 2022 and 2021 identified enhanced chemical production (CHEM) and horizontal transport (HTRA) were the primary processes responsible for the O₃ rise (Fig. S3). Over the YRD and CY regions, where O₃ increased markedly, both CHEM and HTRA increased by more than 1.0 ppb h⁻¹ in 2022. Furthermore, more than 75 % of the increases in these two processes were attributed to the MET22 case. High-temperature conditions were typically accompanied by intensified solar radiation, which accelerated chemical reaction rates and further enhanced the CHEM process (Tian et al., 2024). Such conditions were often associated with persistent high-pressure systems that favor stable and intensified synoptic-scale flows, subsequently promoting O₃ advection and its net influx into downwind areas (Tian et al., 2024).

3.4 Diurnal Patterns in Secondary Pollutants Driven by Atmospheric Oxidation Capacity

Diurnal variations of secondary pollutants (O₃ and SOA) across regions exhibiting significant increases (NCP, YRD, and CY) were also examined (Figs. 4 and S4). In general, the MET22 case contributed significantly to secondary pollutants during both daytime and nighttime compared to other cases. Notably, its contribution to O₃ varied regionally. The MET22 simulation showed a clear daytime contribution in the YRD and CY, whereas its contribution in the NCP was more pronounced at night. For SOA, the MET22 contribution was nearly equal between day and night across these regions.

Figure 4 Diurnal differences in hourly (a) O₃, (b) daytime HO_x, and nighttime NO₃ concentrations in the NCP, YRD, and CY regions, respectively. Daytime is defined as 08:00 to 20:00 and nighttime as 20:00 to 08:00 Beijing Time (Zhu et al., 2020; Wang et al., 2022). The differences shown are BASE22–BASE21 (labelled “B”) for the total change between 2021 and 2022, BASE22–MET22 (labelled “M”) for the contribution of meteorology; BASE22–BVOC22 (labelled “V”) for weather-driven BVOC emission changes; and BASE22–SNO22 (labelled “N”) for weather-driven SNO emission changes.

To understand this regional divergence in the diurnal MET22 response, the atmospheric oxidation capacity (AOC) was also discussed. In this study, daytime AOC is indicated by hydrogen oxide radical (HO_x = OH + HO₂) concentrations, while nighttime AOC was indicated by nitrate radical (NO₃) concentrations (Sillman, 1995; Zhu et al., 2021; Zhu et al., 2023; Liu et al., 2012). In general, the AOC enhancement driven by the MET22 case accounted for over 90 % of the total increase in AOC, which was closely linked to the formation of secondary pollutants (Fig. 4) (Coates et al., 2016; Xing et al., 2017). For O₃, the daytime increase in the MET22 was primarily driven by enhanced HO_x concentrations, which showed net increases of 4.5 and 2.7 ppt in the YRD and CY regions, respectively (Liu et al., 2022). While daytime chemistry dominated under high-temperature conditions, concurrent high-pressure system promoted stable atmospheric conditions at night (Nasong et al., 2025). This stability led to nocturnal surface cooling, which facilitated the formation of a temperature inversion layer. This inversion inhibited the vertical diffusion of O₃, resulting in its accumulation at ground level and a subsequent increase in NO₃ concentration. This mechanism explained the larger contribution of the MET22 case to nocturnal O₃ elevation. For SOA, both daytime formation via thermally enhanced emissions and OH oxidation and nighttime formation through NO₃/O₃ oxidation were promoted under high temperatures. Consequently, SOA contribution from the MET22 remained similar throughout the diurnal cycle.

3.5 NCP O₃ Showed Heightened Sensitivity to BVOC Emissions

In addition to the MET22 case, the BVOC22 case (weather-driven changes in BVOC emissions) also played an important role in O₃ elevation, particularly in the NCP region (28 %). Although the increase in BVOC emissions was small there (+0.15 mol s⁻¹), the resulting O₃ increase was greater than in other regions (Fig. 5). This could be explained by the VOC-limited regime in the NCP, which makes O₃ highly responsive to VOC emission changes (Fig. S5). The best-fit slope method quantifies this sensitivity at 4.03 ppb s mol⁻¹ in the NCP (Fig. 6), which was an 8-fold enhancement over the national mean and a 5-to-6-fold increase compared to the YRD. These findings highlight the urgent need for enhanced VOC emission regulations within the NCP.

Figure 5 Changes in MDA8 O₃ and SOA are shown for BASE22–BASE21 (labelled “B”, total 2021–2022 change), BASE22–MET22 (“M”, meteorological contribution), BASE22–BVOC22 (“V”, weather-driven BVOC emission changes), and BASE22–SNO22 (“N”, weather-driven SNO emission changes) across major urban clusters. Units are ppb and µg m⁻³ for O₃ and SOA, respectively.

Figure 6 Sensitivity of (a) MDA8 O₃ and (b) SOA to T_max, daily average temperature (T), BVOC emissions (B), and SNO emissions (N) across major urban clusters. MDA8 O₃ sensitivity is in ppb °C⁻¹ to temperature and ppb s mole⁻¹ to emissions. SOA sensitivity is in µg m⁻³ °C⁻¹ to temperature and µg s (m⁻³ mol⁻¹) to emissions. Values are not normalized due to differing units. Color scales are row-specific, ranging from minimum (white) to maximum (dark red).

Moreover, the impact of the BVOC22 on NCP O₃ exhibits a diurnal reversal, from negative in the daytime to positive at night (Fig. 4). During the day, an increase in BVOCs enhanced the oxidation of NO to NO₂, and further facilitated the formation of O₃. However, the concurrently increased NO from NO₂ photolysis also intensified the O₃ loss via titration (O₃ + NO). When the titration process became slightly dominant, this could lead to a net negative daytime contribution to O₃. Conversely, at night, the suppressed PBL height limits vertical mixing and traps pollutants (Fig. S6). Within this stable layer, increased BVOCs promoted NO₃ radical chemistry, which is verified by the net decrease of nighttime NO₃ concentrations (Fig. 4b). This process consumed NO and thus reduced the nocturnal O₃ titration, facilitating residual O₃ accumulation.

These estimated impacts of biogenic emissions in the NCP likely represent a conservative estimate. Specifically, coarse vegetation data and simplified fertilizer parameterizations lead the MEGAN model to underestimate urban BVOCs and SNO emissions, respectively (Zhang et al., 2025; Oikawa et al., 2015; Huang et al., 2023). As the NCP features dense urban clusters interspersed with intensive agricultural lands, the actual emissions of both precursors and their subsequent contributions to secondary pollution during the heatwave are expected to exceed the current simulations.

3.6 Sensitivity Patterns of Secondary Pollutants to Temperatures and Emissions

Based on best-slope analysis, O₃ in urban areas was more sensitive to temperature changes (including both T_max and T) compared to the national average level (+50 % to 290 %, Fig. 6). Especially in the YRD and PRD, the sensitivity of MDA8 O₃ to temperature reached 0.66 and 0.95 ppb °C⁻¹, respectively. This was partly due to the urban heat island (UHI) effect, which elevated daytime temperatures and favored pollutant accumulation. This effect could be further intensified during heatwaves (Clinton and Gong, 2013; Delcloo et al., 2018; Wang et al., 2018).

Notably, despite a slight decrease in summer O₃ and temperature in the PRD in 2022, the region exhibited the highest O₃-temperature sensitivity, which heightened local risks and necessitated targeted mitigation. As defined in Sect. 2.4, this positive slope represents the spatial linear regression across PRD grids rather than interannual temperature changes. This spatial variability heightens local risks and necessitates targeted mitigation. However, the correlation between O₃ and temperature was higher in the YRD than in the PRD (Table S3). This was because precursor emissions in the PRD were generally lower and affected by multiple factors such as sea-land breezes, which may weaken the temperature-O₃ linkage. NCP O₃ showed remarkable sensitivity to emission changes. During heatwaves, both BVOCs and SNO emissions increased. This may accelerate radical-initiated oxidation cycles and enhance OH regeneration, resulting in amplified O₃.

For SOA, the sensitivity to temperature showed pronounced regional variability, with the highest values found in the YRD and CY regions (0.28 to 0.30 µg m⁻³ °C⁻¹, Fig. 6), roughly twice the national average. This heightened sensitivity in the CY region is primarily driven by weather-driven biogenic emissions (the BVOC22 scenario), which contribute more significantly to SOA formation than the direct meteorological impact (MET22) (Fig. 5). In the extensively vegetated CY region, high temperatures markedly increase the emissions of BVOC, such as isoprene and monoterpenes (Cao et al., 2022; Guenther et al., 2012). Simultaneously, the enhanced AOC during heatwaves (as discussed in Sect. 3.4) rapidly oxidizes these precursors into low-volatility products, facilitating SOA yields. Alongside BVOCs, SOA is also highly sensitive to SNO emissions in the YRD and CY areas (0.63 and 0.58 µg m⁻³ °C⁻¹, respectively, Fig. 6). However, the SNO22 scenario reports that reducing SNO emissions only leads to a slight decrease in overall SOA concentrations (Fig. 5). Total NO_x emissions in these regions are still dominated by anthropogenic sources (Huang et al., 2023). Although heat-induced SNO provides additional NO_x to promote BVOC oxidation, fluctuations in SNO emissions have a limited impact on the total SOA concentration.

In contrast, temperature and SNO have minimal influence on SOA in the PRD. The limited contribution of heat induced SNO is largely constrained by low precursor emissions resulting from strict regional controls (Ou et al., 2016). Furthermore, elevated temperatures increase the saturation vapor pressure of semi-volatile organic compounds, driving their thermodynamic partitioning back into the gas phase and thereby suppressing SOA yields (Donahue et al., 2006). In the NCP, SOA is highly sensitive to BVOC emissions. Driven by high NO_x emissions and AOC alongside lower overall vegetation coverage, additional biogenic precursors in the NCP are rapidly oxidized into SOA, leading to this pronounced sensitivity.

4 Conclusions

The 2022 summer heatwave clearly demonstrates that high temperatures drive secondary pollution through multiple pathways. This study separates the direct meteorological effects from the indirect effects mediated by biogenic emissions. Results show that direct meteorological conditions account for over 80 % of the O₃ increase, particularly in the YRD and CY regions. This dominance reflects two linked processes. First, process analysis identifies enhanced chemical production as a primary factor, consistent with temperature-accelerated reaction kinetics. Second, horizontal transport also increased by more than 1.0 ppb h⁻¹ in these regions, indicating that the persistent high-pressure systems accompanying heatwaves not only stagnate local air but also facilitate synoptic-scale transport of O₃ to downwind areas. Together, these mechanisms explain why the spatial pattern of O₃ increase closely matches the pattern of greatest warming.

Diurnal responses reveal additional complexity. In the YRD and CY, the direct meteorological contribution to O₃ is largest during the daytime, driven by elevated HO_x radicals. In the NCP, however, the contribution is more pronounced at night. This regional difference arises from nocturnal surface cooling under stable high-pressure conditions, which forms a temperature inversion that traps O₃ near the ground. The trapped O₃ then reacts with increased NO₃ radicals, creating a distinct nighttime accumulation pathway. For SOA, the direct meteorological contribution is nearly equal between day and night across all regions, suggesting that both daytime OH oxidation and nighttime NO₃/O₃ oxidation are promoted under high temperatures. These patterns imply that heatwaves intensify atmospheric processing throughout the diurnal cycle, not only during peak photochemical hours.

Indirect effects driven by BVOCs, while smaller overall, are critically important in specific regions. In the NCP, a modest increase in BVOC emissions (0.15 mol s⁻¹) produces an O₃ response eight times the national average. This heightened sensitivity is explained by the VOC-limited chemical regime of the NCP, where O₃ formation is highly responsive to VOC changes. The BVOC effect also exhibits a diurnal reversal: negative during the day and positive at night. Daytime BVOC increases enhance NO-to-NO₂ conversion and O₃ formation, but the concurrent increase in NO from NO₂ photolysis also strengthens O₃ loss via titration. When titration slightly dominates, the net daytime contribution becomes negative. At night, increased BVOCs promote NO₃ chemistry, which consumes NO and reduces nocturnal O₃ titration, allowing residual O₃ to accumulate. This mechanism highlights that BVOC impacts are not simply additive but interact with background chemistry in ways that vary across the diurnal cycle.

Regional sensitivity analysis provides a quantitative basis for differentiated control strategies. O₃ in urban areas is 50 %–290 % more sensitive to temperature than the national average, partly due to urban heat island effects. The YRD and CY show the highest SOA sensitivity to both temperature and soil NO_x emissions, indicating that coordinated precursor reductions are needed in these regions. In the PRD, O₃ shows the highest temperature sensitivity despite lower emissions, suggesting that mitigating the urban heat island itself may be necessary. In the NCP, the extreme O₃ sensitivity to BVOC emissions points to an urgent need for aggressive VOC controls. These region-specific sensitivities, derived directly from the model simulations, offer a practical framework for designing climate-informed air quality policies.

While scenario analysis successfully isolates the key drivers of the 2022 anomaly, using the summer of 2021 as the sole baseline cannot completely separate the heatwave from background interannual weather variability. Nevertheless, this selection is justified by the highly comparable anthropogenic emissions between 2021 and 2022 resulting from continuous national emission control policies, which minimizes the interference of emission fluctuations. Furthermore, although 2021 also experienced high temperatures, the 2022 heatwave remains an unprecedented extreme event across China. The atmospheric conditions in 2021 therefore provide a relatively moderate control case to highlight the impacts of this rare extremity. Future studies should use a multi-year baseline to better separate localized extreme impacts from background climate variability.