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Assessing ozone dynamics during the 2023 summer STAQS field campaign using synergistic observations and model simulations

Oct 2026 · Atmospheric Chemistry and Physics · 0 citations · 83 references

Abstract

NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite sensor provides high temporal and spatial resolution measurements critical for monitoring air quality. During the Synergistic TEMPO Air Quality Science (STAQS) component of the 2023 AGES+ campaign, extensive surface, airborne, and remote-sensing observations were collected over the New York City/Long Island Sound region, enabling comprehensive investigation of ozone and its precursors, including nitrogen dioxide (NO 2 ) and formaldehyde (HCHO). Evaluating TEMPO (version 3) NO 2 and HCHO column retrievals against Pandora and GEO-CAPE Airborne Simulator (GCAS) observations, based on the limited number of coincident flight days available, shows TEMPO can capture urban-suburban pollution gradients and exhibits biases comparable to previous satellite validation studies with strong NO 2 column correlations ( R ≈0.79–0.81), though sharp transitions between high and low emission regions remain challenging. The high-resolution (1.33 km × 1.33 km) WRF-Chem simulation reproduces the major spatiotemporal patterns of surface ozone and NO 2 ( R ≈0.56–0.73), supporting its use to fill observational gaps. Integrating TEMPO, WRF-Chem, in situ measurements, and ozone and wind lidar observations, we characterize the spatiotemporal ozone dynamics under different pollution regimes. High-pollution days involve early urban precursor accumulation and sea-breeze-driven coastal recirculation of pollutant-rich air. Moderate days exhibit localized enhancements driven by transport, such as downwind plume transport, while low-pollution days show efficient dispersion and limited ozone formation. This multi-platform framework highlights the importance of resolving fine-scale variability in coastal and transition zones and illustrates TEMPO's potential for improving ozone forecasting and mitigation in complex environments.

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