Guy Brasseur

Typhoons are major synoptic systems linked to ozone (O3) pollution in the Pearl River Delta, South China, particularly in autumn and summer. Although previous studies have identified various mechanisms of typhoon-induced O3 pollution, analyses of meteorological conditions or overall process contributions alone do not fully explain how typhoons affect O3 pollution through specific atmospheric processes, particularly transport and chemistry. Here, we combine O3 mass budget analysis, source apportionment, precursor-transport sensitivity simulations and chemical diagnostics based on WRF-CMAQ models to investigate the responses of these processes to typhoons in autumn and summer. Typhoons enhance O3 transport through both vertical exchange and horizontal transport, with increased transport of O3 derived from upwind emissions in both seasons and background O3 mainly in autumn. Transported precursors also contribute considerably to local O3 pollution (>10 µg m-3), which is further enhanced under typhoon influence, primarily driven by increased contributions from acyl peroxy nitrates (APNs) and NOx. Local photochemistry exhibits more complex responses: typhoons accelerate O3 production in autumn but suppress it in summer, reduce HOx cycling efficiency while enhancing O3 production efficiency, alter VOC contributions to OH reactivity, and generally shift O3 production toward more NOx-limited conditions. These findings reveal that typhoons influence regional O3 pollution through multiple transport pathways, transported precursor contributions and different aspects of photochemistry, providing further process-level evidence for understanding the mechanisms underlying typhoon-induced O3 pollution.

Canadian permafrost wildfires, characterized by surface flaming and deep organic soil smoldering, emit complex suites of volatile organic compounds (VOCs). These emissions include air toxics that pose substantial public health risks and fuel secondary pollution, yet remain understudied. We quantified VOC emissions from the record‐setting 2023 wildfires using three inventories (GFED, GFAS, QFED) and assessed air quality impacts via GEOS‐Chem simulations and in situ observations. VOC emissions amount to ∼15 ± 8 TgC, just ∼2% of inorganic carbon. Despite this small fraction, their impacts are significant: simulated concentrations increase by 2–5 μg m ⁻³ for benzene and 5–35 μg m ⁻³ for formaldehyde and acetaldehyde during the fire season. Organic aerosols account for ∼60%–80% of the persistent record‐high PM _2.5 during the season. These findings highlight the distinct role of high‐latitude permafrost wildfires in driving regional reactive organic carbon (ROC) pollution and provide a framework to address interdisciplinary Earth science challenges.

Organic aerosol (OA) constitutes a major fraction of tropospheric submicron particulate matter, with primary (POA) and secondary (SOA) components exhibiting different physicochemical properties and health impacts. The POA and SOA budgets are however highly uncertain, and the results from different model studies are confusing because of more or less consideration of the volatility distributions of organic precursor emissions and inconsistent attributions of model tracers in model-observation comparisons. Here we develop an OA simulation framework in GEOS-Chem that resolves the full volatility spectrum of organic precursor emissions from anthropogenic sources and open biomass burning. The model reasonably reproduces the observed OC, POA, and SOA concentrations from comprehensive surface, shipborne, and airborne datasets, providing a consistent global validation. The model simulations suggest greater POA (0.5 Tg) and SOA burdens (2.0 Tg) and potentially stronger and more widespread impacts of the OA components on air quality, health, and radiation than previous estimates, led by both of the emission updates and the revised OA scheme. The simulated global SOA production is about 106 Tg in 2018, 46 % of which is contributed by open biomass burning. The results demonstrate distinct regional variations in the dominant source types and population exposure distributions of POA and SOA, highlighting the needs for SOA mitigation, multi-sector control measures, and clean energy replacements for long-term health-oriented air quality improvements globally. The model results are sensitive to the emissions and wet-deposition parameterization, calling for more measurement constraints on local emission factors and the deposition fluxes of OA and its components.

Air quality forecasts are essential to support decision‐making in urban agglomerations, where millions of people are exposed to high levels of pollution. However, these forecasts are often limited by uncertainties in anthropogenic emissions, especially in large urban agglomerations where no dedicated operational system exists. In this study, we present an observation‐based emission scaling approach aimed at improving operational forecasts. This method derives scaling factors (SF) from the ratio of observed‐to‐modeled concentrations over the previous week and applies them to anthropogenic emissions in the forecast model, assuming that in large urban agglomerations, biases between observed and modeled concentrations are primarily driven by uncertainties in anthropogenic emission inventories. The method also derives SF for anthropogenic volatile organic compound emissions based on modeled daytime biases under the assumption of a NOx‐saturated regime. We implement this method in a chemistry‐transport model using a global anthropogenic emission inventory and apply it to São Paulo for two distinct periods (February–April 2023 and July–September 2024). The results show that forecasts of CO, , and concentrations are significantly improved within a few weeks. For and , improvements are more limited by the influence of secondary aerosol formation and by pollution transport from outside of the agglomeration. Overall, our results demonstrate that observation‐based emission scaling provides an efficient and transferable methodological approach for improving operational air quality forecasts in urban agglomerations without requiring model‐specific developments.

Organic aerosol (OA) constitutes a major fraction of tropospheric submicron particulate matter, with primary (POA) and secondary (SOA) components exhibiting different physicochemical properties and health impacts. The POA and SOA budgets are however highly uncertain, and the results from different model studies are confusing because of more or less consideration of the volatility distributions of organic precursor emissions and inconsistent attributions of model tracers in model-observation comparisons. Here we develop an OA simulation framework in GEOS-Chem that resolves the full volatility spectrum of organic precursor emissions from anthropogenic sources and open biomass burning. The model reasonably reproduces the observed OC, POA, and SOA concentrations from comprehensive surface, shipborne, and airborne datasets, providing a consistent global validation. The model simulations suggest greater POA (0.5 Tg) and SOA burdens (2.0 Tg) and potentially stronger and more widespread impacts of the OA components on air quality, health, and radiation than previous estimates, led by both of the emission updates and the revised OA scheme. The simulated global SOA production is about 106 Tg in 2018, 46 % of which is contributed by open biomass burning. The results demonstrate distinct regional variations in the dominant source types and population exposure distributions of POA and SOA, highlighting the needs for SOA mitigation, multi-sector control measures, and clean energy replacements for long-term health-oriented air quality improvements globally. The model results are sensitive to the emissions and wet-deposition parameterization, calling for more measurement constraints on local emission factors and the deposition fluxes of OA and its components.

Organic aerosol (OA) constitutes a major fraction of tropospheric submicron particulate matter, with primary (POA) and secondary (SOA) components exhibiting different physicochemical properties and health impacts. The POA and SOA budgets are however highly uncertain, and the results from different model studies are confusing because of more or less consideration of the volatility distributions of organic precursor emissions and inconsistent attributions of model tracers in model-observation comparisons. Here we develop an OA simulation framework in GEOS-Chem that resolves the full volatility spectrum of organic precursor emissions from anthropogenic sources and open biomass burning. The model reasonably reproduces the observed OC, POA, and SOA concentrations from comprehensive surface, shipborne, and airborne datasets, providing a consistent global validation. The model simulations suggest greater POA (0.5 Tg) and SOA burdens (2.0 Tg) and potentially stronger and more widespread impacts of the OA components on air quality, health, and radiation than previous estimates, led by both of the emission updates and the revised OA scheme. The simulated global SOA production is about 106 Tg in 2018, 46 % of which is contributed by open biomass burning. The results demonstrate distinct regional variations in the dominant source types and population exposure distributions of POA and SOA, highlighting the needs for SOA mitigation, multi-sector control measures, and clean energy replacements for long-term health-oriented air quality improvements globally. The model results are sensitive to the emissions and wet-deposition parameterization, calling for more measurement constraints on local emission factors and the deposition fluxes of OA and its components.

The QUALARIA Project main objective is the development of a state-of-the-art Artificial Intelligence (AI) model, able to provide accurate predictions of air pollutant concentrations, air quality indexes and health risks in suburban scale (< 100 m). The inference capacity in high spatial resolution is enabled by the incorporation of a low-cost air pollutant sensor (Bernard Air Analysers, BAAs) observational network, spread in a miscellaneous urban configuration. In this way, the project pursuits overcome the limitation of current info by traditional methodologies, which provide regional scale estimates (for example, 40 km from the dynamic model Copernicus Atmosphere Monitoring Service/European Centre for Medium-Range Weather Forecasts (CAMS/ECMWF)), restricting the effective decision making in the urban context. The final product is an online dashboard with air pollution predictions and high-performance health indicators, allowing the implementation of risk quantification and actions for mitigation in cities. The features and predicted indicators of the dashboard are designed together with national and international stakeholders from the public and private sectors. The target audience are decision makers, public policy makers, urban planners, public health, climate and environment institutions, and air quality non-governmental organizations. A pilot is being implemented for the Metropolitan Area of São Paulo (MASP), with the intention of scaling to all Brazil and other countries later. The AI model is being trained by the air pollutant observational networks and BAAs deployed in 28 MASP spots, in addition to info of surface physical features derivative from satellite imagery, air pollutant prediction from CAMS/ECMWF, stationary and mobile sources, building height, populational density, simulations from the model Weather Research and Forecasting with Chemistry, among other inputs.