Smoke-related PM2.5 is the primary air quality concern during the summer in Alaska, yet accurate forecasting remains a major challenge. In this study, we use machine learning (ML) techniques to improve smoke forecasts from NOAA's HRRR-Smoke model. We find that the model underestimates surface PM2.5 by a factor of up to five during the wildfire season in Alaska. We evaluated Random Forest (RF), one-dimensional, and two-dimensional convolutional neural network (CNN1D and CNN2D) models. Among them, CNN1D performed the best, reducing the underestimation factor to two or less. Analysis of the relationships between key predictors, such as Surface and Vertical Smoke and calibrated PurpleAir observations, suggests that errors in the vertical distribution of smoke are a primary source of underestimating bias. Atmospheric sounding data further show that the HRRR-Smoke model fails to capture daytime temperature inversion layers during wildfire events. This bias is likely caused by missing fire radiative power (FRP) detections under heavy smoke or cloudy conditions, which leads to low smoke concentrations and under-represented radiation feedback necessary to maintain near-surface inversion. Although accurately representing physical and chemical processes in models remains highly challenging, our results demonstrate ML offers an effective approach to improving daily surface PM2.5 forecasts in wildfire-prone regions like Alaska.

The Goddard Earth Observing System Composition Forecast (GEOS-CF) is a global atmospheric constituent forecasting system created and operated by the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center. In alignment with the NASA Earth Science to Action Strategy, GEOS-CF forecasts support a variety of research and practical applications, including NASA satellite missions, aircraft campaigns and instrument teams, local and regional air quality forecasters, and public and occupational health experts investigating air pollutant exposure. In alignment with the NASA Open-Source Science Initiative, GEOS-CF supports several data access methods, thereby serving a range of users with varying technical capabilities. This paper surveys several case studies of applications making use of GEOS-CF, investigating how the data were accessed and examining the perceived benefits and limitations of GEOS-CF in each case. The global coverage and data availability of multiconstituent information at a relatively low data latency was broadly perceived as the strength of GEOS-CF, while cited weaknesses included the relatively coarse spatial resolution for local-scale applications, lack of outputs of interest to specific applications, and uncertain data quality for regions and outputs with limited validation observations. Our goal is to present these examples and lessons learned to the broader community both as illustrations of NASA's Earth Science to Action Strategy and Open-Source Science Initiative and to inform future developments of GEOS-CF and similar systems, with the aim of improving the free and open provision of actionable Earth science information for societal benefit.

Light absorption by brown carbon (BrC) represents a major uncertainty in assessing the climatic effects of carbonaceous aerosols. Using 38,622 PM2.5 samples collected from the U.S. Chemical Speciation Network (2016-2018) and analyzed by a multiwavelength thermal/optical analyzer (TOA), we applied an enhanced spectral/mass balance receptor model to quantify black carbon (BC), BrC, and nonabsorbing white carbon (WtC) while allowing BrC optical properties to vary across samples. The model achieved excellent fits (r(2) > 0.98) and revealed a wide range of BrC absorption & Aring;ngstrom exponent (AAE(405-635 nm) = 2.13 +/- 0.74) and mass absorption efficiency (MAE(532 nm) = 2.03 +/- 0.35 m(2) g(-1)). An inverse AAE-MAE relationship was found, with strongly to moderately absorbing BrC being the most prevalent BrC classes. Seasonal patterns showed higher "organic brownness" (i.e., higher BrC mass fraction in organic carbon regardless of BrC class) but lower MAE in winter and the opposite in summer, reflecting the bleaching evolution of BrC with photochemical aging. BrC abundance also influenced the reconciliation between BC- and TOA-derived elemental carbon, likely through altered thermal-optical carbon analysis splits. This study provides the first nationwide characterization of BrC optical variability from national network data, establishing a scalable framework toward long-term monitoring of organic aerosol absorption within existing regulatory programs.

In the polar regions, the extreme cold and dark atmosphere of the winter season imposes stringent conditions on the planetary boundary layer (PBL) leading to limited vertical atmospheric mixing and increasing the severity of air pollution episodes. Understanding the physical and chemical transformations affecting air pollution critically depends on our ability to accurately describe the dynamics of the PBL. This requires an adequate combination of modeling, ground-based observations, and vertical profile measurement systems to assess emission dispersion and transport in stratified environments. We present an overview of the meteorological conditions and PBL observations collected during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fairbanks, Alaska, in winter 2022. Surface and vertical profile observations of radiation, turbulence, dynamics, and atmospheric composition were collected to account for surface and elevated emissions. The study area is the Tanana Valley in the interior of Alaska, which experiences persistent synoptic anticyclonic conditions resulting in stagnant flow and limited ventilation, exacerbating air pollution levels. These conditions are interspersed with periodic transits of cyclonic air masses influencing the PBL through radiative forcing that erodes low-level temperature inversion layers and mixes local air masses into the free troposphere. This paper highlights the research objectives, experimental findings, and first results linking meteorological conditions with the observed structure and composition of the PBL under the challenging experimental conditions of the Alaskan winters. It also highlights the need for integrated surface and profiling observations of PBL dynamics and composition, which are critical for advancing across-scale modeling and enhancing predictive capabilities for air pollution episodes locally and throughout the Arctic air shed. SIGNIFICANCE STATEMENT: This article provides an overview of the meteorology during the Alaskan Layered Pollution and Chemical Analysis (ALPACA)-2022 winter field experiment. It also presents observations designed to better understand the physical processes influencing the planetary boundary layer (PBL) and their role in wintertime Arctic air pollution. It describes the synoptic meteorological conditions throughout the experiment and the diverse instrumental platforms used to study the dynamics and composition of the Arctic polluted PBL for the first time. The article emphasizes the importance of multi-instrumental platforms to improve understanding of the PBL composition and dynamics in the context of Arctic air pollution. This is particularly relevant in conditions with limited photochemistry and in areas experiencing very cold, stable environments that exacerbate pollution levels. The observations are used to improve meteorological and air quality model simulations.

The temperature sensitivity of fine particulate matter (PM2.5) critically influences air quality and human health under a warming climate, yet models struggle to accurately reproduce observed sensitivities. This study improves the representation of PM2.5-temperature relationships in the chemical transport model GEOS-Chem through targeted improvements and analyses of the underlying drivers based on simulations across the contiguous US (2000-2022). Our simulations reveal that chemical production processes, particularly isoprene secondary organic aerosol (SOA) and sulfate formation, determine the magnitude of PM2.5 sensitivity in the eastern US. In the western US, primary emissions drive the increasing PM2.5-temperature sensitivity. Transport processes contribute to interannual variability in PM2.5 sensitivity across all regions. We quantified the contributions from individual temperature-sensitive processes for the first time. Sulfate concentration plays a pivotal role in modulating the sensitivity of isoprene SOA due to its direct influence on isoprene SOA formation. Furthermore, the increased SO2 emissions on warm days dictates both the magnitude and variability of sulfate sensitivity in the eastern and central US. In the western US, however, sulfate sensitivity is primarily controlled by the temperature response of hydroxyl radicals (center dot OH). These findings highlight the impact of anthropogenic emission reductions on declining PM2.5-temperature sensitivity in the eastern US, improve our understanding of climate-driven air quality changes, and underscore the importance of accurately representing temperature-dependent processes in future air quality projections.

Organic compounds were measured in both the gas and particle phases in Fairbanks, Alaska, using a real-time, high-resolution proton transfer reaction-time of flight mass spectrometer (PTR-ToF MS) during a wintertime campaign. The organic aerosol (OA) was dominated by semi-volatile organic compounds (SVOCs), followed by compounds in the low-volatile bin (LVOCs). Due to the persistently cold conditions, both heavy and highly oxygenated compounds showed a limited shift in partitioning with temperature change. In contrast, some semi-volatile compounds, such as methoxy phenols from wood combustion, presented some partitioning to the particle phase at lower temperatures. Laboratory studies or theoretical efforts rarely explore gas-particle partitioning at extremely low temperatures, and thus, their applicability under complex meteorological conditions remains to be assessed. A comparison of the observed and estimated volatilities at temperatures from 5 to -33 °C revealed a clear disagreement, with higher estimated volatility for light molecules (m/z below 120) and lower volatilities for heavier compounds (m/z above 300) with respect to the observed ones. Our findings from the Fairbanks winter campaign stress the need to extend the breadth of environmentally relevant conditions under which phase partitioning of organic compounds is generally explored.

Formaldehyde (HCHO) serves as an important proxy for emissions of volatile organic compounds (VOCs) and their subsequent photochemistry affecting air quality and climate. Understanding HCHO diurnal variability is essential to accurately represent emissions, chemistry, and planetary boundary layer (PBL) mixing in chemical transport models (CTMs). Here we compare HCHO diurnal variations from Pandora Global Network (PGN), GEOS-CF (0.25°x0.25°) and GEOS-Chem (2°x2.5°) CTMs at 55 sites, to characterize the HCHO diurnal patterns in urban and rural sites over North America (NA), Europe (EU) and East Asia (AS) in 2021-2022 summers. We find that HCHO total column (HCHOcol) from GEOS-CF model shows a comparable stronger diurnal variability (quantified by relative amplitude) with that from PGN measurements, which is lower in GEOS-Chem (10-200% bias in late afternoon). While models and PGN show comparable HCHOcol at rural sites (e.g., ChapelHillNC and DearbornMI), PGN shows significantly higher (a factor of 2 - 3) local noon HCHOcol in some urban areas (e.g., Busan and Bangkok), suggesting missing Volatile Organic Compounds (VOCs) emissions in the models. We further examine the relationship between HCHOcol and HCHO near-surface concentration (HCHOsurf). While both model and PGN show a linear relationship (p

The diurnal cycle of Formaldehyde (HCHO) provide critical insights into tropospheric photochemistry. Here we use tropospheric HCHO retrievals from the Pandonia Global Network (PGN), combined with NASA's GEOS Composition Forecast (GEOS-CF) model to understand the diurnal variation of summertime HCHO across the contiguous US. While PGN HCHO tropospheric column (HCHOtrop) shows a weak diurnal cycle in most regions, a distinct midday peak is found at the urban sites of Southern US (mainly in Houston), likely driven by highly reactive VOC emissions. For the vertical profile of HCHO mixing ratio within the Planetary Boundary Layer (PBL), PGN shows a significant decrease from 0.5 to 2 km while GEOS-CF exhibits an excessively well-mixed vertical shape. This discrepancy in HCHO profile leads to an overestimated HCHOtrop in GEOS-CF in Northeast Coastal US and Southeast US. These findings offer valuable insights for interpreting geostationary satellite observations and understanding model biases in surface ozone (O-3). Plain Language Summary We use observations from a global spectrometer network to investigate the HCHO diurnal cycle in five regions of the contiguous United States during summertime and to evaluate performance of a chemical transport model that influences HCHO products from geostationary satellites. We find that HCHO diurnal cycle is weak in most regions but is strong in Southern US (around Houston). Model well reproduces the HCHO column in Western US, but significantly overestimates it in Eastern US, likely because model overestimates HCHO concentration in 0.5-2 km above the surface in Eastern US.

Wildfire activity is increasing globally due to climate change, with implications for air quality and public health. Fine particulate matter (PM _2.5 ) from wildfire smoke contributes to cardiorespiratory morbidity and mortality, adverse birth outcomes, mental health stressors, and disruptions to food security and traditional livelihoods. However, quantifying health risks remains difficult due to sparse monitoring, challenges in isolating wildfire-specific pollution, and limited long-term exposure assessments. We developed a historical air quality dataset for Alaska using a hybrid approach that integrates GEOS-Chem atmospheric modeling with ground-based data to estimate daily wildfire-attributable PM _2.5 at a 0.625° × 0.5° resolution from 2003 to 2020. We aggregated these estimates by census tract and derived metrics to quantify long-term wildfire smoke exposure, then combined these estimates with social vulnerability data to identify populations disproportionately affected. Alaskans experienced an average of 3.5 million person-days of moderate and >800 000 person-days of dense smoke exposure annually. In years when over 2 million acres burned, 86%–98% of census tracts recorded at least 1 d of moderate smoke, and up to 73% experienced dense smoke. Northern Interior Alaska had over 300 cumulative days of poor air quality (∼10% of summer days) over the 18 year period, with smoke waves lasting as long as 43 d. Tracts identified as having high smoke exposure and high smoke vulnerability were generally in rural Interior Alaska; however, urban tracts in Interior and Southcentral were also identified. High-exposure census tracts had statistically greater proportions of housing cost-burdened residents and women of childbearing age. This study highlights the need to move beyond traditional fire metrics and adopt measures that better capture the full scope of human exposure. Our approach provides a framework for assessing health risks and integrating public health into climate adaptation and fire management especially in wildfire-prone regions where observations are sparse.

Reactions of dissolved sulfur dioxide (SO2) with aldehydes form particulate hydroxyalkylsulfonates, including hydroxymethanesulfonate (HMS), hydroxyethanesulfonate (HES), dihydroxyethanesulfonate (DHES), and hydroxyacetylmethanesulfonate (HAMS). Recent work has shown enhanced production of HMS within aqueous particles at low temperatures; however, little is known about the presence of HES, DHES, and HAMS. During Jan.-Feb. 2022 in Fairbanks, Alaska, these hydroxyalkylsulfonates were identified and quantified within 340,877 individual aerosol particles using single-particle mass spectrometry. HMS was identified within 27 +/- 3% of the particle number concentration in the size range of 0.1-1.0 mu m. The primary source of the majority (similar to 94%, by number) of individual HMS-containing particles was identified as residential heating (wood and oil combustion). The average HMS mass fraction within these HMS-containing particles was 7.0%. The number fraction of HMS-containing particles increased with particle diameter and plateaued at similar to 500 nm, consistent with aqueous-phase processing. Therefore, locally emitted residential heating combustion particles accumulated water, promoting aqueous-phase reactions and secondary organosulfur formation. Notably, HES, DHES, and HAMS accounted for 48 +/- 14% of the average 0.1-1.0 mu m S(IV) mass. Together, the identified hydroxyalkylsulfonates, including HMS, comprised similar to 90% of the measured S(IV) mass, with inorganic S(IV) estimated at similar to 10%. Therefore, focusing measurements and modeling solely on sulfate and/or HMS may underestimate organic S(IV) and misrepresent the sulfur budget. We utilize single-particle and bulk aerosol measurements to identify and quantify often misidentified organosulfur species formed in low-temperature, aqueous aerosol during urban winter.

Fairbanks, Alaska, is a sub-Arctic city that frequently suffers from the non-attainment of national air quality standards in the wintertime due to the coincidence of weak atmospheric dispersion and increased local emissions. As part of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign, we deployed a Chemical Analysis of Aerosol Online (CHARON) inlet coupled with a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF MS) and an Aerodyne high-resolution aerosol mass spectrometer (AMS) to measure organic aerosol (OA) and non-refractory submicron particulate matter (NR-PM1), respectively. We deployed a positive matrix factorization (PMF) analysis for the source identification of NR-PM1. The AMS analysis identified three primary factors: biomass burning, hydrocarbon-like, and cooking factors, which together accounted for 28 %, 38 %, and 11 % of the total OA, respectively. Additionally, a combined organic and inorganic PMF analysis revealed two further factors: one enriched in nitrates and another rich in sulfates of organic and inorganic origin. The PTRCHARON factorization could identify four primary sources from residential heating: one from oil combustion and three from wood combustion, categorized as low temperature, softwood, and hardwood. Collectively, all residential heating factors accounted for 79 % of the total OA. Cooking and road transport were also recognized as primary contributors to the overall emission profile provided by PTRCHARON. All PMF analyses could apportion a single oxygenated secondary organic factor. These results demonstrate the complementarity of the two instruments and their ability to describe the complex chemical composition of PM1 and related sources. This work further demonstrates the capability of PTRCHARON to provide both qualitative and quantitative information, offering a comprehensive understanding of the OA sources. Such insights into the sources of submicron aerosols can ultimately assist environmental regulators and citizens in improving the air quality in Fairbanks and in rapidly urbanizing regional sub-Arctic areas.

Lagrangian tracer simulations are deployed to investigate processes influencing vertical and horizontal dispersion of anthropogenic pollution in Fairbanks, Alaska, during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) 2022 field campaign. Simulated concentrations of carbon monoxide (CO), sulfur dioxide ( S O 2 ), and nitrogen oxides ( N O x ), including surface and elevated sources, are the highest at the surface under very cold stable conditions. Pollution enhancements above the surface (50-300 m) are mainly attributed to elevated power plant emissions. Both surface and elevated sources contribute to Fairbanks' regional pollution that is transported downwind, primarily to the south-west, and may contribute to wintertime Arctic haze. Inclusion of a novel power plant plume rise treatment that considers the presence of surface and elevated temperature inversion layers leads to improved agreement with observed CO and N O x plumes, with discrepancies attributed to, for example, displacement of plumes by modelled winds. At the surface, model results show that observed CO variability is largely driven by meteorology and, to a lesser extent, by emissions, although simulated tracers are sensitive to modelled vertical dispersion. Modelled underestimation of surface N O x during very cold polluted conditions is considerably improved following the inclusion of substantial increases in diesel vehicle N O x emissions at cold temperatures (e.g. a factor of 6 at -30°C). In contrast, overestimation of surface S O 2 is attributed mainly to model deficiencies in vertical dispersion of elevated (5-18 m) space heating emissions. This study highlights the need for improvements to local wintertime Arctic anthropogenic surface and elevated emissions and improved simulation of Arctic stable boundary layers.

Sulfate comprises an average of 20% of the ambient PM2.5 mass during the winter months in Fairbanks, as indicated by 24-hour average filter measurements. During ALPACA 2022 field campaign (Jan 15th-Feb28th of 2022), we deployed two aerosol mass spectrometers (AMS) and one aerosol chemical speciation monitor (ACSM) at three urban sites, combined with Scanning Mobility Particle Sizer (SMPS), to examine the evolution of aerosol composition and size distribution at a sub-hourly time scale. During an intense pollution episode (ambient temperature is between -25 and -35 °C), all three instruments (two AMS and one ACSM) exhibit a sharp increase in sulfate mass within a matter of hours, while organic aerosols, black carbon and SO2 concentrations remain relatively stable. This notable increase in sulfate mass contributes to approximately half of the observed change in ambient PM2.5. The abrupt rise in sulfate mass is concurrent with a substantial increase in particle number density within the accumulation mode (100-1000 nm), suggesting the secondary formation of sulfate onto pre-existing aerosols. We further investigate possible mechanisms and have ruled out the possible role of cloud chemistry and transition metal ion. The rapid formation of sulfate seems to be linked to the ambient level of nitrogen oxides and, possibly, sunlight. Further investigation is underway to elucidate the intricate connections underlying this rapid sulfate formation.

Subarctic cities notoriously experience severe winter pollution episodes with fine particle (PM2.5) concentrations above 35 mu g m-3, the US Environmental Protection Agency (EPA) 24 h standard. While winter sources of primary particles in Fairbanks, Alaska, have been studied, the chemistry driving secondary particle formation is elusive. Biomass burning is a major source of wintertime primary particles, making the PM2.5 rich in light-absorbing brown carbon (BrC). When BrC absorbs sunlight, it produces photooxidants - reactive species potentially important for secondary sulfate and secondary organic aerosol formation - yet photooxidant measurements in high-latitude PM2.5 remain scarce. During the winter of 2022 Alaskan Layered Pollution And Chemical Analysis (ALPACA) field campaign in Fairbanks, we collected PM filters, extracted the filters into water, and exposed the extracts to simulated sunlight to characterize the production of three photooxidants: oxidizing triplet excited states of BrC, singlet molecular oxygen, and hydroxyl radical. Next, we used our measurements to model photooxidant production in highly concentrated aerosol liquid water. While conventional wisdom indicates photochemistry is limited during high-latitude winters, we find that BrC photochemistry is significant: we predict high triplet and singlet oxygen daytime particle concentrations up to 2x10-12 and 3x10-11 M, respectively, with moderate hydroxyl radical concentrations up to 5x10-15 M. Although our modeling predicts that triplets account for 0.4 %-10 % of daytime secondary sulfate formation, particle photochemistry cumulatively dominates, generating 76 % of daytime secondary sulfate formation, largely due to in-particle hydrogen peroxide, which contributes 25 %-54 %. Finally, we estimate triplet production rates year-round, revealing the highest rates in late winter when Fairbanks experiences severe pollution and in summer when wildfires generate BrC.

Satellite data have long been recognized as valuable for air quality applications. These applications are in a stage of rapid growth: new geostationary satellites provide hourly or sub-hourly data; improvements in algorithms convert measured wavelengths into retrievals of atmospheric constituents; advances in machine learning support improved estimates of near-surface pollution; and growing interest among air quality managers has led to a range of new satellite data applications. Considering mainly activities in the United States under the Clean Air Act, we discuss proven applications relevant to air quality management, including: informing epidemiological studies and health risk assessments for setting regulatory standards; evaluating regulatory models; constraining emissions inventories; supporting Exceptional Event Demonstrations through tracking wildfire plumes and other sources; characterizing emission patterns and ozone-forming chemistry for State Implementation Plans; improving air quality forecasting; and tracking long-term trends to evaluate regulatory impact. Air quality professionals are increasingly using satellite data for these and related analyses, but barriers remain. This review provides a summary of satellite products used in applications for air quality and related health assessments; progress in using satellite observations for deriving surface-level air quality information across scales; and their use in air quality management.Implications: The review covers advancements in satellite data for air quality applications over the last 15 years. Success with satellite applications, especially for PM2.5 and NO2, include use in health risk assessment, constraining emissions inventories, and supporting tracking short- and long-term trends with regulatory relevance. Solutions co-developed between researchers and practitioners show promise for continued improvements in the use and value of satellite data for air quality applications.