Climate change and increases in the frequency and severity of climate-driven wildfires, particularly in the western United States, has serious ramifications for enhanced downwind reactive nitrogen (Nr) emissions, deposition, and critical load exceedances. Here we present a multi-decadal (2002 - 2021), harmonized model-data-driven study using the George Mason University North American Chemical Reanalysis (NACR) system and simulations including both "with-fire" and "without-fire" conditions to quantify the change in trends of fire activity and source contributions to total Nr emissions and deposition over the U.S. Our results show that fire activity has increased substantially in the western U.S., especially in the west-northwest U.S. for wildfires, and that this increase is associated with positive annual near-surface temperature and vapor pressure deficit anomalies compared to the period average. Major results and implications of this work are increasing trends in the contribution of climate-driven wildfires to higher Nr emissions, deposition, and critical load exceedances of up to 20-40% due to fires in the western U.S. There are also smaller increases (<5 %) in Nr deposition trends for the eastern U.S., which are related to greater occurrence and reporting of agricultural and prescribed burns.

Airborne dust exerts myriad effects on climate, weather, human health and safety. In this study, 35-year records (1988-2022) of ground aerosol observations were analyzed to identify windblown dust events from 18 Interagency Monitoring of Protected Visual Environments (IMPROVE) stations over the western United States (US). Over 45% of dust events were recorded at two sites in the Chihuahuan Desert. The intensity of dust events, indicated by regional average concentration of PM10 (particulate matter with diameter <= 10 mu m) during these events, decreased by -0.24 mu g/m(3) per year compared to -0.10/m(3) per year on non-dust days. However, the number and frequency of both severe dust events (24-hr PM10 > 40 mu g/m(3)) and moderate dust events (30 mu g/m(3) < PM10 <= 40 mu g/m(3)) have increased with moderate events increasing faster than severe events. The variability of dust event frequency was strongly associated with that of Pacific Decadal Oscillation (PDO) and El Ni & ntilde;o Southern Oscillation (ENSO). The increasing wind speed has a stronger effect on the dust activity in the Northwest, whereas in the Southwest, the decreasing soil moisture has a larger impact on the increase in dust event frequency. This work provides the latest dust climatology, along with the underlying climate drivers and synoptic indicators that control the long-term variations of dust events over the western US. Plain LanguageSummary Dust impactsmany aspectsof the societyand environment.Theproductionof dust from arid and semiaridareas is highlysensitiveto climate.Understandinghow dust changesand respondsto climateis importantto assessthe potentialrisks climatechangeposes on societythroughthelens of dust events.This study reportsthe long-termtrend of dust activityin the westernUS. We found that thefrequencyof dust eventshas been increasingwhile the averageconcentrationof particulatematterduringtheseeventshas been decreasing.Thesechangesare drivenby large-scaleclimateand local weatherconditions

Acute exposure to surface nitrogen dioxide (NO 2 ) poses substantial global health risks. Using machine learning, we generated the first global, daily, 1-km-resolution, gap-free surface NO 2 data from satellite observations for 2018–2022. Surface NO 2 shows strong day-to-day variability, with fluctuations reaching 73% of the global mean. We identify pronounced pollution hotspots and large global inequalities in acute NO 2 exposure: although only 28% of inhabited land exceeds the WHO daily guideline (25 μg m -3 ) at least once per year, these exceedances disproportionately affect more than 61% of the global population and nearly all megacities (98%). When exceedances are aggregated over 7-day and 30-day windows, 77% and 56% of megacities remain exposed. The decline in NO 2 -affected areas outpaces reductions in population exposure, highlighting the challenge of mitigating impacts in densely populated urban centers. Acute NO 2 exposure caused approximately 576,000 (95% CI: 473,000–678,000) premature deaths globally in 2019. COVID-19 strictest lockdowns in 2020 temporarily reduced NO 2 levels, but rebounds occurred in 91% of countries by 2022. These results reveal the widespread and under-recognized burden of acute NO 2 exposure and emphasize the need for high-resolution global monitoring to support effective pollution control.

Recent advances in high-resolution urban climate and air quality modeling have enabled more explicit representation of fine-scale meteorology and pollutant transport within cities. However, the emission data used to drive these models have not kept pace. This mismatch can introduce structural errors and uncertainties that propagate into simulated pollutant concentrations, chemical regimes, and population exposure, particularly in neighborhoods characterized by steep emission gradients and nonlinear chemistry. These limitations are especially pronounced during extreme and compound heat–air pollution events, when emissions can deviate substantially from climatological patterns, and in settings such as the wildland–urban interface, where conventional inventories often omit or simplify key sources. Recent developments in emission source mapping, such as source-resolving emission estimation, improved chemical speciation, integration of indoor and biogenic emissions, and high-resolution observational constraints, demonstrate the feasibility of more process-informed emission inventories. We argue that high-resolution urban emission inventories should be treated as critical scientific infrastructure for next-generation air quality and climate applications. We outline key requirements for such inventories, including spatial and temporal fidelity, sectoral and chemical details, and transparent uncertainty characterization. We also propose a roadmap centered on benchmarking, reproducible data pipelines, uncertainty quantification, and timely incorporation of emerging emission sources. Without parallel progress in emission datasets, the growing capabilities of urban atmospheric models will remain fundamentally constrained, limiting their reliability for scientific understanding and decision support.

The 2024 revision of the National Ambient Air Quality Standard for particulate matter less than 2.5 μm diameter (PM2.5) to 9 μg/m3 by the US Environmental Protection Agency (EPA) has motivated an assessment of whether reducing Electric Generating Units (EGU) emissions is a potential strategy for bringing nonattainment counties into compliance. We assessed coal power plants' contributions to PM2.5 using a chemical transport model. We identified the contribution of specific coal EGUs to PM2.5 concentrations using a reduced complexity air quality model and demonstrated health benefits of emissions reductions at facilities that would need to shutter to attain the new standard. In 2023, 9 nonattainment counties could have met the standard by eliminating SO2 emissions at 94 facilities (9% of US EGU capacity). Retiring these EGUs would avoid stack emissions of 500,000 tons of SO2, 304,000 tons of NOx, and 485 million tons of CO2, along with approximately 1,170 premature deaths per year (95% confidence interval: 1,060-1,280) among elderly people. Reducing coal power plant emissions continues to provide an avenue to meet U.S. air quality standards and improve public health.

Abstract Dust and dust storms are known to negatively affect human health, safety, and welfare. Most dust‐related studies have previously focused on the physical processes of dust initiation, transport, and deposition at various temporal and spatial scales. In recent years, more scholars have attempted to link the physical processes of dust to human health, safety, and welfare. The idea for this special collection, named “Dust and dust storms, from physical processes to human health, safety, and welfare” was proposed by members of the Dust Alliance for North America (DANA), a partnership of scientists and practitioners with the purpose of accelerating the transition of dust‐related research into service. Papers in this special issue covered a wide range of topics, including the physical processes of dust particles and dust events, and their subsequent impacts on human health, transportation safety, and welfare, with various temporal and spatial scales. These papers were jointly published in three American Geophysical Union (AGU) journals, namely, GeoHealth, Earth's Future, and Journal of Geophysical Research (JGR)‐Atmospheres. The special collection opened in May 2023 and closed in June 2025. Eventually, a total of 24 papers were published as part of this collection, including five papers in GeoHealth, two in Earth's Future, and 17 in the JGR‐Atmospheres. Collectively, these papers report the latest interdisciplinary efforts by the research community to understand the physical processes of dust and its societal effects.

Compound heat and ozone (O 3 ) pollution events are widespread and pose significant health risks in urban areas. In the Southern Great Plains (SGP), where heat waves are becoming more frequent and intense, these compound events represent a growing environmental and public health concern, underscoring the need for improved process-based understanding to support effective risk assessment and mitigation. This study investigates the three severe O 3 pollution episodes recorded in Oklahoma City (OKC) since 2014—19 July 2018 (106 ppb), 15 June 2021 (93 ppb), and 6-7 August 2024 (86 ppb)—with a particular focus on the June 2021 event that coincided with a pronounced heat wave. Using high-resolution WRF-Chem simulations, we examine the formation mechanisms and identify key meteorological and emission-related contributors to the episode. The episode developed under atypical summer conditions over the SGP, characterized by a frontal passage coincided with the evolution of a tropical storm over the Atlantic Ocean. TROPOMI-based satellite observations reveal substantial power plant emissions in addition to urban emissions in the Tulsa region. Under persistent northeasterly winds, southwestward transport of pollutants from Tulsa on 14–15 June supplied additional pollutants to OKC. Meanwhile, the weak-wind zone over OKC on 15 June, ahead of the advancing front, further favored pollutant accumulation and O 3 production. WRF-Chem sensitivity experiments illustrate that total NO x emissions from the Tulsa area contributed ~4–15 ppbv of surface O 3 in OKC on 15 June 2021, while power plants alone accounted for ~2–6 ppbv, with contributions varying by location.

The National Air Quality Forecast Capability (NAQFC) provides numerical forecasting guidance of air quality up to 72 hr ahead over the United States. In this study, we evaluate the changes in its prediction skills by updating the Community Multiscale Air Quality (CMAQ) modeling system from version 5.3.1 to 5.4 and utilizing a high-resolution 1-km anthropogenic emission data set. The baseline and several sensitivity simulations with emission and science updates in CMAQv5.4 were conducted and evaluated. The results show improved prediction performance among model configurations over the contiguous United States (CONUS) in terms of correlation coefficient, normalized mean bias (NMB), and normalized mean error for fine particulate matter (PM2.5) with NMBs changing from -12.1% to minimum of -6.4% and maximum daily 8 hr average O3 (MDA8 O3) with an NMB changing from 4.6% to best of 0.6%. Evaluation over CONUS and along the FIREX-AQ field campaign flight paths suggests that current NAQFC predictions can be improved by using high-resolution anthropogenic emissions, adjustments to wildfire emissions, and newer versions of CMAQ. This work facilitates future developments of NAQFC by providing detailed information on the factors that contribute to model biases.

Criteria air pollutants (CAPs) and greenhouse gases (GHGs) are often co-emitted atmospheric chemicals that bear intertwined consequences on human health and the environment. This work evaluates performance of the newly developed air quality model that concurrently simulates CAPs and GHGs over triple-nested domains in Greater Boston. The model satisfactorily reproduces ozone with normalized mean bias (NMBs) within ±15% and normalized mean errors (NMEs) within 25% in most months and reduced NMBs and NMEs at finer grid resolutions. NMBs and NMEs of fine particulate matter (PM 2.5 ) can reach beyond benchmarks due to uncertainties in meteorology, emissions, chemical boundary conditions, and physical schemes for urban processes. Uncertainties in carbon dioxide simulations are mainly due to lack of reliable emission inventory and observations. To identify major sources of uncertainty, sensitivity simulations are carried out to investigate the impacts of planetary boundary layer (PBL) schemes, primary particle emission, urban canopy models (UCMs) and chemical boundary conditions. Compared to Yonsei University (YSU) PBL scheme used in the baseline, using Mellor, Yamada, Nakanishi and Niino Level 3 (MYNN3) reduces biases and errors for most meteorological and chemical parameters in January 2023, but not necessarily in July 2023. Using multilayer UCMs yields better prediction of PM 2.5 diurnal variations at urban sites but worse results at non-urban sites. Adjusting primary particle emissions and chemical boundary conditions improves model performance in January and July 2023. This work demonstrates that addressing uncertainties of UCM-PBL coupling and input data quality would benefit future model simulations at fine scales in urban areas.

In June 2020, the tropical Atlantic and the Caribbean Basin were affected by a series of African dust outbreaks unprecedented in size and intensity. These events, informally named "Godzilla," coincided with CALIMA, a large field campaign, offering a rare opportunity to assess the impact of African dust on air quality in the Greater Caribbean Basin. Network measurements of respirable particles (i.e., PM10 and PM2.5) showed that dust significantly degraded regional air qual-ity and increased the risk to public health in the Caribbean, the southern United States, northern South America, and Central America. CALIMA examined the meteorological context of Godzilla dust events over North Africa and how these conditions might relate to the greatly increased dust emissions and enhanced transport to the Americas. Godzilla was linked to strong pressure anomalies over West Africa, resulting in a large-scale geostrophic wind anomaly at 700 hPa over North Africa. We used surface-based and columnar measurements to test the performance of two frequently used aerosol forecast models: the NASA Goddard Earth Observing System (GEOS) and Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) models. The models showed some skills but differed substantially between their forecasts, suggesting large uncertainties in these forecasts that are critical for issuing early warnings of health-threatening dust events. Our results demonstrate the value of an integrated approach in characterizing the spatial and temporal variability of African dust transport and assessing its impact on regional air quality. Future studies are needed to improve models and to track the long-term changes in dust transport from Africa under a changing climate. SIGNIFICANCE STATEMENT: Every year, vast quantities of African dust are transported across the Atlantic to the Caribbean Basin. During these events, respirable dust concentrations often exceed the air quality standards established by the United States EPA and the World Health Organization. We discuss the record-breaking June 2020 "Godzilla" dust events in terms of measurements made during a large-scale surface-based field campaign (CALIMA). During CALIMA, we made aerosol measurements at sites throughout the Caribbean Basin. We used satellite and aerosol model products to interpret the data and understand the meteorological processes that affect dust emissions in Africa and the subsequent transport to the Americas. Models could provide advanced warnings of health-threatening dust events, thereby enabling public health officials to issue health risk alerts.

Estimating tropospheric ozone (O 3 ) production from observations is challenging but possible given the close coupling of O 3 with formaldehyde (HCHO) and nitrogen dioxide (NO 2 ), two remotely sensed air pollutants. The previous reliance on once‐daily satellite overpasses highlights the need to study diurnal changes and surface‐column relationships. Using surface observations, Pandora spectrometer retrievals, and a high‐resolution (1.33 km) air quality model (WRF‐CMAQ), we characterize diurnal patterns of HCHO and NO 2 at seven locations along an upwind‐downwind pathway through New York City during June–August 2018. Diurnal patterns of limited surface HCHO measurements suggest biogenic emission influence, while a bimodal surface NO 2 pattern indicates the impact of local anthropogenic nitrogen oxides emissions. Details of these patterns vary by site: an afternoon NO 2 spike at New Haven (CT) indicates traffic emissions, while a delayed daily HCHO peak at Westport (CT) relative to other sites likely reflects sea breeze dynamics. Peak column concentrations generally lag surface peaks by about four hours, occurring at 9–10 a.m. for morning NO 2 (from Pandora and WRF‐CMAQ) and around 4 p.m. for midday HCHO (from WRF‐CMAQ). TROPOMI overpass time at 1:30 p.m. misses peak column HCHO and NO 2 concentrations. A box model (F0AM) constrained with site‐level observations and WRF‐CMAQ fields indicates 1–9 ppb hr −1 higher noontime local O 3 production rates on three sets of paired high‐ versus mid‐to‐low‐O 3 days. F0AM sensitivity analyses on these six days suggest a predominantly transitional O 3 formation regime at urban and downwind sites, differing at some sites from the NO x ‐saturated regime diagnosed for summertime average conditions via the weekday‐weekend effect.

Recognizing the uncertainties associated with fire emission, a crucial factor influencing the fire aerosol prediction, we have initiated studies to improve fire emission for subseasonal to seasonal (S2S) forecasts. Two global aerosol/chemistry forecast models are currently under development and have been fully coupled with the Unified Forecast System (UFS), encompassing ocean, sea ice, wave and land surface components for S2S forecasts at NOAA. One is UFS-Aerosols: the second-generation UFS coupled aerosol system, which embeds NASA’s 2nd-generation GOCART model in a National Unified Operational Prediction Capability (NUOPC) infrastructure, has been collaboratively developed by NOAA and NASA since 2021. It is planned to be implemented into the Global Ensemble Forecast System (GEFS) v13.0 for ensemble prototype 5 (EP5) experiments early this year. The other one is UFS-Chem: an innovative community model of chemistry online coupled with UFS, developed collaboratively between NOAA Oceanic and Atmospheric Research (OAR) laboratories and NCAR. The aerosol component implemented into UFS-Chem is based on the current operational GEFS-Aerosols v12.3 and utilizes the Common Community Physics Package (CCPP) infrastructure with updates to wet deposition, dust and fire emission, etc. Both these two global aerosols forecast models include the direct and semi-direct radiative feedback from online aerosols prediction. Various global fire emission data, as well as their ensemble product, are employed to quantify the uncertainties associated with fire aerosol prediction. The capabilities of UFS-Aerosols and UFS-Chem in medium-range and S2S predictions of fire aerosol are assessed and compared using observations from reanalysis data, ground-based measurements, and satellite data. Additionally, preliminary blending and machine learning methods have been developed to predict fire emission and improve the S2S prediction.

Wind erosion and resulting dust negatively impact the environment and society, but there has been no comprehensive assessment of costs to the United States since the 1990s. Climate and society have changed greatly since then, including changing dustiness, spiking Valley fever infections and increased renewable energy use. By adopting published estimates and calculating emerging costs, we estimate that wind erosion and dust in the United States cost $154.4 billion annually (2017 value). This estimate quadruples the previous assessment and is higher than most other US weather and climate disasters. We also discovered many costs associated with wind erosion that are not accounted for. Our estimate, while conservative, reveals that the economic burden of wind erosion is substantial and investment in dust mitigation could yield large economic benefits. Wind erosion and dust transport present challenges to human wellbeing, health and infrastructure. Last estimated for the United States in the 1990s, present economic costs of wind erosion across the United States have nearly quadrupled, and an updated assessment of vulnerable systems is required.

The presence of dense forest canopies significantly alters the near-field dynamical, physical, and chemical environment, with implications for atmospheric composition and air quality variables such as boundary layer ozone (O3). Observations show profound vertical gradients in O3 concentration beneath forest canopies; however, most chemical transport models (CTMs) used in the operational and research community, such as the Community Multiscale Air Quality (CMAQ) model, cannot account for such effects due to inadequate canopy representation and lack of sub-canopy processes. To address this knowledge gap, we implemented detailed forest canopy processes – including in-canopy photolysis attenuation and turbulence – into the CMAQv5.3.1 model, driven by the Global Forecast System and enhanced with high-resolution vegetation datasets. Simulations were conducted for August 2019 over the contiguous US. The canopy-aware model shows substantial improvement, with mean O3 bias reduced from +0.70 ppb (Base) to −0.10 ppb (Canopy), and fractional bias from +9.71 % to +6.37 %. Monthly mean O3 in the lowest model layer (∼ 0–40 m) decreased by up to 9 ppb in dense forests, especially in the East. Process analysis reveals a 75.2 % drop in first-layer O3 chemical production, with daily surface production declining from 673 to 167 ppb d−1, driven by suppressed photolysis and vertical mixing. This enhances NOx titration and reduces O3 formation under darker, stable conditions. The results highlight the critical role of canopy processes in atmospheric chemistry and demonstrate the importance of incorporating realistic vegetation-atmosphere interactions in CTMs to improve air quality forecasts and health-relevant exposure assessments.

Abstract The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality prediction system (AQPS) within the Unified Forecast System (UFS) framework to improve representations of wildfire emissions and their impacts on air quality predictions. This innovative system integrates the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model as a column chemistry model with the UFS-based atmospheric model, operating in an online mode. The calculation of wildfire gas and particulate emissions relies on satellite-derived fire products, high-resolution Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). A period in June and July 2023 with Quebec Canadian wildfires, which severely impacted air quality in the United States (US), was chosen as a case study to assess the predictive capability of the UFS-AQM system. The UFS-AQM predictions of fine particulate (PM2.5) and ozone (O3) were evaluated against AirNow observations from June 15 to July 14, 2023. The results indicate a substantial improvement in PM2.5 predictions when compared to the previous operational forecast. Meanwhile, the system demonstrates a strong ability of predicting O3 exceedance events during the dissipation phase of the wildfire. Furthermore, the online system shows more realistic predictions of aerosol optical depth (AOD) as compared to the previous operational forecast and satellite retrieval data. Finally, this study outlines a plan for further advancing a comprehensive regional AQPS at NOAA.

Accurate and efficient retrieval of atmospheric chemical concentrations across space and time is crucial for weather prediction and health assessments. However, existing model-measurement fusion methods suffer from limitations due to imbalanced samples from ground measurements or less effective assimilation of satellite data along with numerical modeling. To address these limitations, this study introduces a novel Deep-learning Measurement-Model Fusion method (DeepMMF) constrained by physical and chemical laws inferred from numerical chemical transport models (CTM). This method is applied to NO₂ species over the Continental United States (CONUS) domain for the years 2019 and 2020. By pre-training with abundant CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting weighting loss and prior emissions, the retrieved spatiotemporally continuous surface NO₂ concentrations present consistent values and daily variations with observations (NMB reduced from -0.3 to -0.1 compared to original CTM simulation). Importantly, the corresponding emissions have been simultaneously adjusted, showing good agreement with changes reported in the national emission inventory (NEI) between 2019 and 2020. Interpretation analysis suggests that the DeepMMF model effectively identifies the importance of satellite data at the regional level and ground measurements at the city level, which is scientifically sound. It exhibits consistent prediction of ground measurements while successfully avoiding the sample imbalance problem that leads to overestimation (up to +100%) of downwind/rural concentrations compared to other existing methods. These results demonstrate the great potential of DeepMMF in data assimilation and retrieval studies for other pollutants and regions, to better support weather forecasting and heatlh studies.

Wildfires are a major natural source of atmospheric aerosols, leading to air quality degradation and adverse human health effects. Accurate prediction of air quality effects from wildfires remains challenging due to uncertainties in fire emission estimates. To enhance the accuracy of fire emissions used in air quality forecast models, we developed a method that utilizes satellite aerosol optical depth (AOD) observations and air quality simulations to calculate dynamic emission scaling factors and improve wildfire air quality forecasts. TROPOMI (TROPOspheric Monitoring Instrument) UV Aerosol Index (UVAI) data are employed to fill AOD gaps under thick smoke using two approaches: a regression model and an artificial intelligence model. The scaling factor method was applied to NOAA blended Global Biomass Burning Emissions Product. The emission scaling factors exhibited significant variability across different fire points, highlighting the need for point‐specific scaling factors. On average, scaling factors were less than 1.0 (indicating emission overestimation) during the initial stages of fire events but exceeded 1.0 (suggesting underestimation) after 7 days of fire duration. An inverse relationship between scaling factors and fire radiative power (FRP) was observed, with emission underestimation for low‐intensity fires (FRP <5 MW) and substantial overestimation for high‐intensity fires (FRP >500 MW). The improved fire emissions were employed in the air quality model for the 2020 US Gigafire event. Utilizing emission scaling factors reduced model bias, increased the correlation and hit rate of PM 2.5 exceedance prediction, demonstrating the potential of using emission scaling factors for improving air quality forecasting during wildfire events.

The air quality forecasting system is an essential tool widely used by environmental managers to mitigate adverse health effects of air pollutants. This work presents the latest development of the next-generation regional air quality model (AQM) forecast system within the Unified Forecast System (UFS) framework in the National Oceanic and Atmospheric Administration (NOAA). The UFS air quality model incorporates the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model as its main chemistry component. In this system, CMAQ is integrated as a column model to solve gas and aerosol chemistry, while the transport of chemical species is processed by UFS. The current AQM version 7 (AQMv7) is coupled with an earlier version of CMAQ (version 5.2.1). Here we describe the development of the updated AQMv7 by coupling to a “state-of-the-science” CMAQ version 5.4. The updates include improvements in gas and aerosol chemistry, dry deposition processes, and structural changes to the input/output (I/O) interface, enhancing both computational efficiency and representation of air–surface exchange processes. A simulation was conducted for the period of June–August 2023 to assess the effects of these updates on the forecast performance of ozone (O3) and fine particulate matter (PM2.5), two major air pollutants over the continental United States (CONUS). The results show that the updated model demonstrates an enhanced capability in simulating O3 over the CONUS by reducing the positive bias, leading to a reduction in the mean bias by 3 %–5 % and 8 %–12 % for hourly and the maximum daily 8 h average O3, respectively. Spatially, the updated model lowers the positive bias of hourly O3 in most of the 10 EPA regions, particularly within the central and northwest areas, while amplifying the O3 underestimation over the sites with negative bias. Similarly, the updates induce uniformly lower fine particulate matter (PM2.5) concentrations across the CONUS domain, reducing the positive bias at some sites over the northeast in August and central Great Plains. The updated model does not improve model performance for PM2.5 in the vicinity and downwind of fire emission sources, where AQMv7 shows the highest negative bias, thus indicating a focal point of model uncertainty and needed improvement. Despite these challenges, the study highlights the importance of the ongoing refinements for reliable air quality predictions from the UFS-AQM model, which is a planned future update to NOAA's current operational air quality forecast system.

Coccidioidomycosis (Valley fever, VF) is a climate-sensitive infectious disease caused by inhaling soil-dwelling fungus Coccidioides, mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies-precipitation and air temperature-that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers-including SM, ST, and wind speed from the North American Land Data Assimilation Phase 2 (NLDAS-2), and PM10-based dusty-day counts-in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi-year soil hydrothermal cycles-alternating wet-dry and cool-hot periods along with concurrent dry, dusty conditions-significantly influenced incidence. Notably, no antecedent dry-cool seasons were linked to increased incidence, indicating moisture and/or heat are prerequisites for fungal growth and dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS-2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi-year soil indicators-with up to 3-year lead times-into early-warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.