Figure 4 compares the spatial distributions of the period-mean WRF-Chem carbon and energy fluxes with SMAP L4C and FLUXCOM products which contain observation information, and Table 4 summarizes WRF-Chem and observation-derived flux results by three LULC groups. The observation-derived products indicate the highest GPP and EF over croplands. Without the DA, the Noah-MP-related cases outperformed the Noah-related P1_W case on simulating EF, especially over shrub/grassland and cropland regions. This indicates that, from Noah to Noah-MP, the multiple updates in LSM physics related to rs, irrigation, and CH are beneficial. Larger GPP and EF values are found in CLM_D than in Noah_D; most of these larger values match better with the SMAP L4C and FLUXCOM data. The DA led to increased EF over shrub/grasslands in all model cases as well as over croplands in the Noah_D case, bringing the model results closer to the FLUXCOM data. The EF values were unfavorably reduced by the DA in the CLM_D and P1_W cases over croplands and in all model cases over forests, reflecting the challenges of satellite SM DA over regions with dense vegetation and/or affected by human activities, which have also been reported and discussed in previous studies (e.g., Huang et al., 2021). For the Noah_D and CLM_D cases, this may also be due to the possibly degraded vegetation performance discussed in Sect. 3.1. The modeled GPP in the CLM_D cases was lowered by the DA overall, which helped reduce the model–SMAP L4C discrepancies over forests and croplands. In the Noah_D case, GPP was improved by the DA over forests and (slightly) over shrub/grasslands. Based on the evaluation statistics, for this case, the CLM-type β factor scheme is shown to be slightly superior to the Noah type. Note that the quality of the SMAP L4C and FLUXCOM products may also be strongly LULC-dependent; e.g., it has been known that the uncertainty of SMAP L4C data is generally larger for highly productive plant functional types (Kimball et al., 2021). Such evaluation, therefore, has demonstrated the critical role of LULC type in understanding the model performance of carbon and energy fluxes and its responses to satellite SM DA.

Additional datasets were also utilized to help understand terrestrial carbon uptake, including satellite SIF and ACT-America aircraft OCS, as well as its vertical gradients (Fig. S3). Consistent with the SMAP L4C- and WRF-Chem-based results, the largest SIF values are shown over croplands, especially maize and soybean fields in Illinois and Indiana, 2–3 times as high as those over shrub/grasslands in the South Central Plains. All these datasets suggest moderate to high terrestrial carbon uptake around the Lower Mississippi croplands and the forests and croplands near the Texas–Oklahoma border, which is supported by the large OCS drawdowns (i.e., the free tropospheric-near surface gradients far exceeded 60 pptv) along with other trace gas measurements taken on board the B-200 and C-130 aircraft.

In general, the modeled EF fields as well as their directions of changes due to the DA resemble those of latent heat flux and relative humidity (RH), which are opposite to those of sensible heat and surface temperatures (Figs. 5 and S4). The model reproduced the observed spatiotemporal variability of 2 m air temperature (T2) and RH, as well as FLUXCOM latent and sensible heat fluxes, well overall. The diagnostic 2 m weather fields and their responses to the DA strongly correlate with the model's surface-level results. The Noah-MP-related cases reacted more strongly to the DA than the Noah-related cases, with the responses in the CLM_D case larger than in the Noah_D case except for dry, sandy regions, which can be attributed to combined effects of the CH and stomatal resistance schemes used. It is important to note that diagnostic temperature and humidity variables are represented differently in Noah and Noah-MP and thus are not directly comparable. Specifically, in Noah, T2 is an explicit function of surface temperature, air density, specific heat of dry air at constant pressure, and 2 m surface exchange coefficient for heat, and 2 m specific humidity is a function of surface specific humidity, upward moisture flux at the surface, air density, and 2 m surface exchange coefficient for moisture, whereas in Noah-MP, they are expressed as functions of temperatures and water vapor for vegetated land and bare soil being weighted by their respective fractions. We therefore focus on quantitatively evaluating and intercomparing prognostic model weather variables (i.e., the model-level air temperature and humidity) against ACT-America aircraft observations (Fig. 6). For air temperature, at all altitudes and near the surface (i.e., ≥800 hPa), the CLM_D case responded most strongly to the DA, and the DA-enabled CLM_D case outperformed the Noah_D and P1_W cases. This performance is qualitatively consistent with the model's sensible heat performance referring to the FLUXCOM data. As for humidity, despite the most significant DA improvements in CLM_D, the Noah-MP-related cases did not perform as well as the Noah-related cases, which is also found in the model's latent heat performance in comparison with the FLUXCOM data. However, note that the model's humidity performance is more strongly related to that of rs and vd in the Noah-MP-based cases via the direct impacts of humidity on rs calculations (Eq. 2). The solar radiation fields from all model cases, which play vital roles in controlling the land–atmosphere exchanges of water and trace gases, do not differ remarkably, and their responses to the DA are negligible (e.g., Fig. 5g–l). This indicates that the DA impacts on the modeled surface fluxes resulted primarily from the changes in the modeled SM, humidity, and canopy and/or surface temperatures, as well as vegetation fields. In many cases, these primary contributing factors to the DA impacts are interdependent, and their relative contributions vary by location and time.

Figure 7 presents the period-mean, daily-averaged vd and dry-deposition flux Ft (i.e., vd multiplied by concentration at the surface level, Wesely, 1989) for O3 from all model cases, along with their responses to the SMAP DA. The daytime averages of these fields have similar spatial gradients but of larger magnitudes (not shown in figures). Table 5 summarizes for three LULC groups the daily- and daytime-averaged results. The modeled stomatal–mesophyll and cuticular conductances, as well as their diurnal variability, are indicated in Fig. 8. All model cases produced lower vd and Ft values over shrub/grasslands than over forests and croplands, qualitatively consistent with results from many existing model- and measurement-based studies (e.g., Val Martin et al., 2014; Hardacre et al., 2015; Silva and Heald, 2018; Lin et al., 2019). The results from Noah_W and P1_W, both of which are based on the same scheme (Wesely), are generally similar, with minor differences largely attributed to different surface temperature fields (Figs. 5 and S4). The WRF-Chem-modeled vd and Ft fluxes were more strongly affected by the upgrade from the Wesely to the dynamic scheme; i.e., with the updated scheme, they show enhanced magnitudes, stronger spatial variability, and more intensive responses to the DA, especially over forests and croplands. These results can be mainly explained by the fact that the stomatal–mesophyll and cuticular resistances in the dynamic scheme are sensitive to more environmental and biophysical variables, accounting for both the direct and indirect (i.e., via influencing the weather fields and plants' physiology) effects of SM on vd. vd from the Noah_D and CLM_D cases, as well as its major term stomatal–mesophyll conductance, shows strong correlations with the modeled GPP, latent heat, and EF fields, which have been discussed in earlier sections. Comparing the cases that implemented the CLM- and Noah-type β schemes, O3-related fluxes resulting from the former configuration are of notably larger magnitude, spatial variability, and absolute changes due to the DA. The SM impacts on the modeled vd and Ft were further quantified using linear regression analyses between the relative changes in the modeled O3 fluxes due to the DA versus those in column-averaged SM initial conditions. All regression models yielded low p values (i.e., ≪0.01), suggesting good Δvd–ΔSM and ΔFt–ΔSM relationships. The regression slopes, all with standard errors of <0.01 %, are summarized in barplots (Fig. 9) by three LULC groups for all model cases in Table 1. For all LULC groups, the slopes based on the two cases that implemented the dynamic scheme are 2–3 times larger than those from the two cases using the Wesely scheme, and the slopes differ most strongly among the cases over forests and croplands. The low r values (<0.5) associated with several regression models reflect the stronger nonlinear relationships between the changes in the studied O3 fluxes and SM. These results emphasize the importance of better understanding and representing in models the SM control on plants' stomatal behaviors which regulate the land–atmosphere exchanges of water, energy, and trace gases. The earlier evaluation of the period-mean GPP and EF across the domain has demonstrated some advantages of using the CLM-type β scheme and that the DA more effectively improved the model performance in sparsely vegetated shrub/grassland regions. These conclusions are likely also applicable to the modeled O3 dry-deposition process, particularly its stomatal–mesophyll pathway.

In all no-DA and DA cases, the diurnal variability of O3-related surface fluxes shows clear LULC dependency. Over the shrub/grassland and forests/croplands regions, the daytime-averaged vd values are 24 %–31 % and 35 %–50 % higher than the 24 h mean, respectively, while the daytime-averaged Ft results are 40 %–50 % and 42 %–63 % higher than the 24 h mean, respectively (Table 5). Such vd diurnal cycles are a result of the strongest diurnal variability in stomatal–mesophyll conductance (i.e., its daytime mean values are approximately twice as high as the 24 h mean for all LULC types) being balanced out by weak diurnal variability associated with other vd terms. As the most diurnally variable vd component, stomatal–mesophyll conductance, on average, contributes less substantially to vd for shrub/grassland areas (24 h/daytime: up to ∼30 %/40 %) than for forests/croplands (24 h/daytime: up to ∼50 %/66 %), which helps explain the weaker diurnal variability in the modeled vd over shrub/grasslands. The stronger diurnal cycles in Ft than in vd reflect the impacts of higher daytime O3 surface concentrations used in the Ft calculations. The DA did not dominantly intensify or dampen the diurnal cycles of these fluxes for any given grouped LULC type. Whether the DA improved the estimated diurnal cycles of fluxes for various LULC types remains to be evaluated, which can benefit from independent observation-constrained flux products of broad spatial coverage and subdaily variability.

A detailed analysis was then conducted at two forest CASTNET sites with different soil types and hydrological regimes. The modeled vd and Ft from various cases are compared with the operational MLM-based calculations produced at a Florida site SUM156 and a Virginia site PED108 (Figs. 10a, b, e, f and S5; Table 6), where many, most, or all MLM assumptions apply. The dominant soil types at these sites are sand and loam, and the column-averaged SM values from various model cases are approximately 0.15 and 0.20 m3 m-3, respectively. These various datasets show that stomatal–mesophyll conductance, vd, and Ft sharply increase soon after sunrise, reaching their daily maxima in the late morning or early afternoon. The slight declines in fluxes around midday based on some simulations can result from the water and heat stresses which cause stomata closures (Fig. 10c and d). The water stress starts to get relieved from the mid-afternoon at the SUM156 site under the influences of convective precipitation, whereas it persists throughout the afternoon at the PED108 site (Fig. 10g and h). This helps shape the slightly different afternoon flux dynamics at these two locations. Without the DA, at both sites, the highest daytime fluxes were produced from the CLM_D case, followed by the Noah_D and Noah_W cases, which are 2–3 times as high as the MLM-estimated cases. The fluxes from all WRF-Chem cases during the nighttime are close, up to >80 % lower than their daytime maxima, contributed mostly by ra, rb, and non-stomatal rc pathways as stomatal–mesophyll conductance is shown to be negligible (Fig. 10c and d). Despite the uncertainty possibly introduced by the limitations of the Monin–Obukhov similarity theory, our nighttime vd results are close to flux observations at European forest sites during both dry and wet periods in the past decades (Lin et al., 2020). They are, however, dramatically higher than the MLM-based results that are nearly zero. Wu et al. (2018) compared vd observations with single-point model calculations based on the operational MLM, Wesely, and Noah-Gas Exchange Model photosynthesis-based scheme, at a Canadian mixed forest site dominated by sand-like soil. Their diverse model results are qualitatively consistent with our findings at the SUM156 and PED108 sites. The remarkably lower vd values from the operational MLM calculations can be partially attributed to the MLM's simplified approaches of calculating ra and rb using wind speed and direction, as well as the empirical approach of calculating rs which is subject to errors in the season- and LULC-dependent ri. The possible uncertainty in MLM vd can also be explained by the lack of continuous, accurate model input data. Specifically, the factual data such as plant and canopy attributes used in the MLM calculations are outdated, which, according to the CASTNET database, represent the conditions in the 2000s; and based on the little day-by-day variability found in the MLM vd data during the study period which contrasts with our WRF-Chem results (Fig. S5), it is likely that many but not all of these are filled historical average vd values due to the lack of meteorological measurements that are needed in the MLM calculation. Additionally, based on the surface heterogeneity within the WRF-Chem grids that these sites fall in, representation errors are estimated to be pronounced when comparing the point-scale MLM fluxes with our 12 km WRF-Chem results.

Within the respective ranges of the modeled SM at these two sites, β factors based on the CLM-type scheme are both larger than those based on the Noah-type β scheme (referring to Niu et al., 2011, Fig. 3), which helps explain the higher and more variable model fluxes from the CLM_D case than the Noah_D case without the DA. At SUM156, despite the strongest SM decrease (∼0.04 m3 m-3) by the DA in case CLM_D, the modeled fluxes responded least strongly to the DA, in part due to the flattened CLM-type SM–β curves in contrast to the linear Noah-type SM–β function for sand within the 0.12–0.16 m3 m-3 SM range. At PED108, the modeled SM values from all model cases were lowered by the DA by ∼0.02 m3 m-3. The stronger reactions of fluxes (i.e., vd, Ft, and their stomatal–mesophyll portions) to the DA from the CLM_D case than those from the Noah_D case can be partially explained by the steep CLM-type SM–β curve versus the linear Noah-type SM–β relationship for loam within the 0.18–0.22 m3 m-3 SM range. Our case studies at these two sites with the same type of LULC emphasize the importance of soil type and hydrological regimes for understanding SM controls on dry deposition, which was often omitted or discussed little in previous dry-deposition studies. It is noted that the effectiveness of SM DA in improving the accuracy of land surface states and fluxes at point scale is dependent on the representativeness of the assimilated satellite SM data for these sites, which is expected to increase with the resolutions of the model and the assimilated satellite land product.

Figure 11 illustrates the impacts of the choice of dry-deposition scheme and SM DA on WRF-Chem-modeled surface MDA8 O3. During the study period, several warmer- and drier-than-normal Atlantic states experienced high MDA8 at times (i.e., ≥60 ppbv, which can negatively affect lung function and, at ≥70 ppbv, cause respiratory symptoms and other adverse effects; Fleming et al., 2018, and references therein). Numerous populated urban centers reside in these areas. The levels of MDA8 are shown to be much lower (i.e., <40 ppbv) over the southern part of the domain, including several major urban/suburban regions such as the Texas Triangle, which was frequently influenced by passing cold fronts and tropical systems from the Gulf of Mexico.

All model cases reproduced the observed MDA8 spatial patterns (Fig. 12a) moderately well. Referring to observations at AQS and CASTNET sites, their domain-wide mean root-mean-square errors (RMSEs) all fall within 6–8.5 ppbv (Fig. 12b). We first intercompare the MDA8 levels from all no-DA cases. Positive and negative differences between the results from Noah_W and P1_W, both of which implemented the Wesely scheme, are almost equally distributed across the domain, with the MDA8 from the former case associated with negligibly lower RMSEs (i.e., <0.02 ppbv on average) referring to AQS and CASTNET observations (Figs. 11k and 12b). The differences between these two cases are largely due to the impact of the chosen LSM on the model's meteorological fields, particularly temperatures, which affected the simulations of various O3-related processes including dry deposition. As Figs. 11i and j and 12b show, replacing Wesely with the dynamic dry-deposition scheme considerably lowered the calculated MDA8 levels in majority of the model grids, as well as their associated RMSEs (i.e., by >0.5 ppbv on average) relative to surface observations. These reductions in MDA8 are of comparable magnitude with those due to updating anthropogenic emissions from the National Emission Inventory 2014 to 2016 beta (Huang et al., 2021). Comparing the implementations of the CLM- and Noah-type β schemes, the former led to stronger reductions in the modeled MDA8 fields and their associated uncertainty. These results reflect the impacts of the faster O3 removal via dry deposition in the dynamic-scheme-related cases, as well as the different model meteorology. Our findings are qualitatively consistent with the conclusions from several global-scale modeling experiments that compared the Wesely and dynamic schemes (e.g., Val Martin et al., 2014; Lin et al., 2019).

In all model cases, the DA reduced surface and subsurface SM in many of the grids, leading to enhanced MDA8 (Fig. 11e–h). The responses of the period-mean MDA8 to the DA from the Noah_W and P1_W cases are mostly within ±4 ppbv. When the dynamic dry-deposition scheme was applied, the modeled MDA8 responded several times more strongly to the DA (i.e., by up to 6 and 8 ppbv in the Noah_D and CLM_D cases, respectively), especially over nonurban regions, where surface MDA8 is on average several parts per billion by volume (ppbv) lower than in urban grids. In urban grids where population densities are ∼25 times higher than in nonurban grids (Fig. 1c), the DA impacts on MDA8 reach 3–4 ppbv in places, under the controls of the local-to-regional circulation patterns (Fig. 13a and e). As the no-DA cases are positively biased against surface observations in many places, corresponding to the DA-induced surface O3 changes, the overall model performance of MDA8 was not improved, or much degraded, by the DA. Over limited areas such as the South Central Plains, the modeled MDA8 decreased due to the DA by up to >2 ppbv, corresponding to improved performance. The no-DA and DA results based on different LSMs and dry-deposition schemes confirm that drier soil conditions exacerbate O3 air pollution, which, together with heat stress, threatens human health. Such O3–SM relationships have also been demonstrated by Falk and Søvde Haslerud (2019) and Anav et al. (2018) using other chemical transport models and multiplicative dry-deposition schemes. Our Noah_W- and P1_W-related results indicate the influences of SM on air quality via its feedbacks to weather; and results from the Noah_D and CLM_D cases provide valuable information regarding both the indirect (i.e., via adjusting vegetation phenology and weather conditions) and direct SM effects on O3. The complex SM impacts on O3 dry-deposition as well as surface O3 concentrations based on the coupled photosynthesis–rs calculations rely heavily on the application of water stress function (β scheme), soil properties, and hydrological regime. The WRF-Chem results from this case indicate that, to more accurately simulate MDA8, improving land DA must be combined with strong efforts to identify other sources of uncertainty in O3 modeling (e.g., emissions, chemistry, and extra-regional pollution contributions) and reduce their negative impacts on model performance.

Both O3 flux- and concentration-based metrics have been applied to assess O3 impacts on vegetation as well as the associated economic loss. Estimating the plants' stomatal O3 uptake Fs is the basis for constructing flux-based O3 impact assessments. Figure 14 illustrates the period-mean daytime Fs fields based on all WRF-Chem no-DA cases as well as their responses to the SM DA. Box-and-whisker plots in Fig. 13b and f summarize these results by three LULC groups. The averaged Fs values for all three LULC groups exceed their respective critical levels (i.e., 1 nmol m-2 s-1 for forest and grasslands and 3 nmol m-2 s-1 for crops). As a major contributor to O3 dry-deposition flux during the daytime, Fs fields appear to be closely correlated in space and time with the surface humidity and flux fields (e.g., GPP, latent heat, and EF, as well as vd), which differ distinctly from the surface O3 concentration fields. For example, Fs hotspots are shown over some low O3 concentration areas including the humid, Lower Mississippi River regions, and the lowest Fs values occur in certain high O3 concentration regions strongly affected by urban pollution (e.g., Georgia) and pollution transport from upwind US states and/or the stratosphere (e.g., western Kansas and Oklahoma, as discussed in Huang et al., 2021). The changes in Fs and surface O3 concentrations due to the DA show opposite directions; i.e., drier soil enhances surface O3 concentrations, whereas it slows down the plants' stomatal O3 uptake (Figs. 11e–h and 14e–h). This comparison highlights how the choice of O3 metrics can affect the assessment of O3 vegetation impacts under the changing climate. As emphasized by Mills et al. (2018b) and Ronan et al. (2020), flux-based metrics have evident advantages over concentration-based metrics. To conduct reliable impact assessments using these flux-based metrics, accurate information on stomatal and non-stomatal fluxes as well as the various environmental and biophysical variables that they are sensitive to becomes increasingly important.

An assessment of O3 vegetation impacts was conducted based on the results from various model cases and different metrics, namely PODy (where y is the LULC-dependent critical level) and AOT40. For this demonstration, the 13 d model results were linearly extrapolated to approximately 3 months. This also assumed similar DA adjustments to SM dynamics (driven by factors such as clouds/radiation, rainfall, and irrigation for cropland-dominant regions) at the seasonal timescale. Based on the seasonal variability of surface O3 and surface fluxes in the study region in 2016 (Fig. S6), the linearly scaled PODy and AOT40 values are underestimated overall referring to the 2016 peak AOT40 and surface fluxes occurring during April–May–June and June–July–August, respectively. These overall underestimations may be invalid if the (sub)seasonal variability of surface O3 and surface fluxes of other years was referred to. We therefore focus on discussing the results qualitatively and highlighting their implications for O3 impact assessments using long-term records. Statistics of the derived PODy and AOT40 fields are summarized by O3-sensitive LULC and crop types in Fig. 13c, d, g, and h. Figures 15 and 12c and d present the estimated AOT40 fields and the evaluation of them, as well as their responses to the SM DA for cropland-dominant grids. The highs and lows in AOT40-related results are found over maize- and wheat-dominant fields, respectively. Among the three focused LULC types, the highest and lowest PODy values are estimated for forests and grasslands, respectively. Largely driven by daytime peak O3 concentrations, the spatial variability and biases (referring to AQS and CASTNET observations) of the model-derived AOT40 fields, as well as their responses to the DA, match those of the MDA8-based results (Fig. 11). In contrast, the spatial variability of PODy and Fs aligns well, and so do their responses to the DA. Both PODy and AOT40 reacted several times more intensively in the cases that implemented the dynamic dry-deposition scheme, especially the CLM_D case.

For selected LULC and crop types, the WRF-Chem-derived PODy and AOT40 fields were used together with dose–response functions in literature to evaluate the RBL/RYL due to O3 exposure and uptake. As reported in Fig. 13c and g, with the SM DA enabled, the mean RBLs based on Noah_D- and CLM_D-derived PODy are 0.05–0.08, 0.01–0.02, and 0.04 for deciduous forest, grasslands, and wheat, respectively, which are >33 % lower than the Noah_W- and P1_W-based RBL estimates. It is shown that, in response to the DA which lowered SM in many places, the Noah_W- and P1_W-based RBL estimates did not drop as strongly as the Noah_D- and CLM_D-based ones and even increased by 0.01 for grasslands and wheat. For wheat, one of the most O3-sensitive crops, the estimated RYL values based on the PODy and AOT40 approaches differ by up to a factor of 2–3, and the DA had contrasting effects on these estimates (Fig. 13c, d, g, h). The PODy- and AOT40-based RYL values differ more significantly when the model-derived PODy and AOT40 fields came from the Noah_D and CLM_D cases. Using the model-derived AOT40 and different AOT40 dose–response functions (Mills et al., 2007, 2018b, Table 3), the estimated RYLs and their changes due to the DA are nonnegligible (Fig. 13d and h). Our estimated RBL/RYL results for various LULC and crop types mostly fall within the ranges reported in previous studies which applied model-derived O3 metrics and dose–response functions (e.g., Avnery et al., 2011; Mills et al., 2007, 2018b). Our results emphasize that the selected O3 impact assessment metrics for various LULC/crop types and their matching dose–response functions, as well as the model results used to derive the chosen O3 metrics which are sensitive to dry-deposition schemes and SM, all introduce uncertainty to the estimated O3 impacts on vegetation. The widely used dose–response functions are considered appropriate for studying North America and Europe, but they may not be applicable to other regions (Emberson et al., 2009). Therefore, updating and developing dose–response relationships for a larger number of vegetation types in different regions of the world are needed, which may require new experiments to be conducted. Yue and Unger (2014) and Lombardozzi et al. (2015), as well as follow-on investigations, parameterized the O3 impacts on several types of vegetation using the relationships between cumulative O3 uptake and O3 damage factors for photosynthesis and conductance from empirical and experimental studies. Based on multidecadal model simulations, they reported <20 % changes of biomass, GPP, and energy fluxes due to O3, which are roughly consistent with our RBL/RYL results in Fig. 13. Such approaches that dynamically assess the impacts of O3 along with other factors (e.g., non-O3 pollutants and environmental stresses), as highlighted in Emberson et al. (2018), will be considered in future work.

We note that, revising the dry-deposition scheme and constraining the modeled SM fields with observations would not only better be combined with adding O3 impacts on vegetation but also multi-stress impacts on biogenic emissions. Considering O3-induced vegetation injuries would more evidently affect longer-term climate simulations via feedbacks to biomass, surface fluxes, weather, and weather-driven emissions. As for biogenic emissions, Fig. S7 shows SM anomalies during the study period determined by our Noah-MP modeling system as well as drought stress activity factor γd estimated from β of a multiyear, independent CLM (version 4.5) simulation by Jiang et al. (2018). Based on this analysis, we estimate that, depending on soil type, hydrological regime, and β configurations, omitting the direct impacts of water stress on biogenic emissions may have introduced larger uncertainty (i.e., >30 %) to biogenic emission and O3 modeling over several states experiencing drier-than-normal conditions, particularly South Carolina, Georgia, and Alabama. Quantitatively understanding the interplay between these processes and O3 pollution levels is recommended for more accurate air quality modeling and O3 impact assessments.