In coastal environments, sea breeze recirculation has been found to be an important mesoscale meteorological phenomenon that causes high ozone episodes, yet the identification of this small‐scale circulation pattern remains difficult. In this study, a new method was developed to automatically identify sea breeze recirculation in Houston, TX, by applying K‐Means clustering algorithm to surface winds measurements at near‐coast sites during the DISCOVER‐AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign period from August to October 2013. The key to the clustering algorithm is seven features derived from site‐based surface winds on each day, including zonal (U) and meridional (V) winds in the morning and afternoon, 24‐hr transport direction (θ) and the recirculation factor, which is the ratio of net transport distance (L) to wind run distance (S). For comparison, the same clustering was applied to San Antonio, TX, a noncoastal city yet within the synoptic‐scale distance from Houston. Four clusters were obtained for each region, including three synoptic patterns common to both regions and one mesoscale pattern that differs by region, classified as Stagnation and Sea Breeze Cluster for San Antonio and Houston, respectively. The clustering outputs were verified by wind profiler data in Houston. By linking the wind clusters with surface and aircraft ozone measurements, we revealed a clear connection between circulation patterns and daily ozone variability showing that maximum daily average 8‐hr (MDA8) ozone levels and spatial distributions differed by cluster type (e.g., the highest ozone found in the Stagnation/Sea Breeze Cluster and the lowest ozone in the Southerly Cluster). This automatable method of sea breeze identification we developed can be potentially applied to other coastal cities because it has low data requirement and no ad hoc location‐specific adjustments.

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1–1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model–OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γ_{d_OMI}) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γ_{d_OMI} the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought–isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O₃ in high-isoprene regions and a 1–3 ppbv decrease in O₃ in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Drought is an extreme weather and climate event that has been shown to cause the worsening of ozone (O₃) air pollution. Using 15‐year (2005–2019) surface O₃ observations and weekly US Drought Monitor (USDM) indices, this study estimated that summertime US‐mean surface O₃ increased by 1.47 ppb per USDM level. It is revealed that O₃ responses to drought display a spatial east‐west variation: higher O₃ enhancement in the Southeast (2.24 ppb/USDM), and no significant change or even a decrease in the west (e.g., −0.06 ppb/USDM in California). The diurnal changes of O₃ with drought also show an opposite pattern between the Southeast and California. Formaldehyde (HCHO) and nitrogen dioxide (NO₂) column, two satellite‐based O₃ precursors proxies, show an increasing rate of 0.41 × 10¹⁵ molec/cm²/USDM and 0.03 × 10¹⁵ molec/cm²/USDM in the Southeast, respectively, while these rates are not statistically significant in California. We explained this spatial discrepancy from the perspective of O₃ chemistry by applying a zero‐dimensional model at the sites with long‐term observations in California and Georgia. Isoprene concentrations decreased by ∼37% under exceptional drought in California causing a reduction of O₃ production (PO₃) by ∼23.7% during daytime. On the contrary, isoprene increased by ∼41% in Georgia inducing a consequent increase of PO₃ by ∼33.4% which accounts for more than half of the O₃ enhancement. This study reveals the key role of biogenic isoprene on ozone chemistry under drought conditions.

The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R>0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (<50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.

Mechanisms for high offshore ozone (O₃) events in the Houston area have not been systematically examined due to limited O₃ measurements over water. In this study, we used the datasets collected by three boats deployed in Galveston Bay and the Gulf of Mexico during the Tracking Aerosol Convection Interactions ExpeRiment – Air Quality (TRACER-AQ) field campaign period (September 2021), in combination with the Weather Research and Forecasting (WRF) coupled Comprehensive Air quality Model with Extensions (CAMx) modeling system (WRF–CAMx), to investigate the reasons for high offshore O₃. The model can capture the spatiotemporal variability in the daytime (10:00–18:00 central daylight time, CDT) O₃ for the three boats (R > 0.7) but tends to overestimate O₃ by ∼ 10 ppb on clean days and underestimate O₃ by ∼ 3 ppb during high-O₃ events. The process analysis tool in CAMx identifies O₃ chemistry as the major process leading to high-O₃ concentrations. The region-wide increase in the long-lived volatile organic compounds (VOCs) through advection transits O₃ formation to be more sensitive to NO_x, leading to more O₃ production under a NO_x-limited regime. In addition, the VOC-limited O₃ formation is also boosted along western Galveston Bay and the Gulf Coast under high-NO_x conditions brought by the northeasterly winds from the Houston Ship Channel. Two case studies illustrate that high offshore O₃ events can develop under both large- and mesoscale circulations, indicating both the regional and local emissions need to be stringently controlled. Wind conditions are demonstrated to be important meteorological factors in such events, so they must be well represented in photochemical models to forecast air quality over the urban coastal regions accurately.