Read more about each presentation below
A. Dust, Weather, and Climate
A1. To Be Announced (invited talk)
Yan Feng
A2.Revisiting the resolution dependence of dust emissions: Insights from a storm-resolving model
Tong Ying, SIO; Amato Evan, SIO; Danny M. Leung, NCAR; Falko Judt, NCAR
Typical global climate models struggle to accurately simulate dust emissions, partly because their coarse horizontal resolution cannot represent important processes controlling surface winds. We combine global simulations from the Model for Prediction Across Scales (MPAS), spanning grid spacings from 240 to 3.75 km, with a process-based offline dust emission scheme to investigate how dust emissions change as horizontal resolution increases from conventional climate-model scales to storm-resolving scales.
Increasing resolution from 240 to 30 km approximately doubles global dust emission. Further increase from 30 to 3.75 km produces little additional change but substantially redistributes emission among major dust-source regions. These regional shifts are especially pronounced in the southern Sahara and Sahel, where the kilometer-scale simulation explicitly represents most convective precipitation. A set of spatial-averaging experiments is performed, showing that fine-scale spatial variability, rather than changes in coarse-scale mean conditions alone, primarily controls the resolution dependence of dust emission. The combined spatial variability in friction velocity and soil moisture contributes approximately 38% of global dust emission in the 3.75 km simulation. These results show that increasing resolution does not simply amplify dust emission: reaching storm-resolving scales changes both the processes and source regions represented in the simulated global dust cycle.
A3.Extreme Heat and Dust Emissions in North Africa: From the 2020 “Godzilla” Event to a Multi-Year Perspective
Ibrahim Tella, Bing Pu, Qinjian Jin, Danny M. Leung, Nathaniel Brunsell,
Extreme heat and dust storms are often treated as separate hazards, yet their interactions may amplify dust-generating conditions in arid regions. We investigate this relationship over North Africa by combining a detailed case study of the June 2020 “Godzilla” dust event with preliminary evidence from a multi-year analysis. Observations and numerical simulation results show that heat-wave conditions during the 2020 event dried the land surface, steepened lower-tropospheric temperature gradients, shifted the Inter-Tropical Discontinuity northward, and favored the formation of mesoscale convective systems, and haboob-generating winds. In sensitivity experiments with reduced surface heating, mesoscale convective activity weakened substantially, and dust uplift potential decreased, supporting the importance of land surface heating in mesoscale dust emission processes. Extending beyond this event, composites of temperature and aerosol extreme events indicate that extreme temperature events frequently precede or overlap extreme dust events across key North African dust-source regions. Preliminary causality tests further identify a predominantly temperature-to-dust relationship, with the strongest signal occurring at short lags. Our results suggest that extreme heat could precondition the surface and atmosphere for enhanced dust uplift, highlighting the need to treat heat and dust in dust-prone regions as interconnected hazards under a warming climate.
A4.Why Do Dust-Precipitation Relationships Differ across the Contiguous United StatesFollowing High-Dust Conditions?
Guoqing Gong, Adeyemi A Adebiyi
Mineral dust is a major component of atmospheric aerosol and can influence precipitation through both radiative effects and aerosol–cloud interactions.These influences, however, are strongly modulated by the meteorological background. Precipitation can also reduce dust loading through wet scavenging, further complicating the interpretation of observed dust–precipitation relationships. Whether these competing effects produce regionally distinct dust–precipitation relationships over the contiguous United States, particularly for heavy precipitation, remains unclear. Here we combine satellite observations, reanalysis datasets and CMIP6 historical simulations to examine regional differences in one-week accumulated precipitation following high- and low-dust conditions over the contiguous United States during summer over the past two decades. Preliminary analysis indicates that the precipitation products yield consistent but heterogeneous spatial patterns, whereby relative to low-dust conditions, high-dust conditions are followed by generally increased precipitation across the northern United States, but by decreased precipitation over the Rocky Mountains, the interior Southwest, and the Southeast coast. Early results further suggest that these dust–precipitation relationships depend largely on the meteorological background of each region, with stronger influence on heavy accumulated precipitation. In contrast to the observational products,CMIP6 models did not exhibit the observed dust–precipitation relationships, highlighting limitations arising from model uncertainty and the representation of associated dust–precipitation processes.
A5.Evaluating Model Dust in the Cold-cloud Regime Using Airborne and Satellite Observations
Flor Vanessa Maciel, Dr. Jasper F. Kok, Dr. Karl Froyd, Xia Li
Dust is an efficient ice nucleating particle (INP) and can aid the formation of mixed-phase and cirrus clouds in the cold-cloud regime (< 0°C). Both the size and concentration of dust can influence the microphysical properties of these clouds and can have an impact on the Earth's radiation budget. However, it remains unclear how well global climate models (GCMs) capture dust in the cold-cloud regime.
We address this by comparing airborne observations against a dust climatology, five GCM simulations, and two reanalysis products. We found that models systematically overestimated dust concentrations, especially at finer particle diameters, and that this overestimation increases with altitude. We also evaluate airborne dust measurements derived from two different instruments (PALMS and SAGA) against dust mass concentrations derived from CALIPSO satellite extinction retrievals.
Overall, misrepresentations in model dust could contribute to biases in cold-cloud processes, which could lead to inaccuracies in GCM climate projections. Model overestimation of dust in the cold-cloud regime could also cause inaccurate predictions of the effects of climate intervention methods, such as mixed-phase or cirrus cloud thinning. These findings underscore the need for simulated dust concentrations in the cold-cloud regime to be further constrained with observations.
B. Dust Observations, Forecasting, and Physical Processes
B1. Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM): Overview, Dust Storms, and Monsoon Science from the First Summer
Allison Aikemn (invited speaker) - Virtual
The Southwest (SW) U.S. is a rapidly growing energy-rich infrastructure region that includes the Phoenix metropolitan statistical area (MSA). The Phoenix MSA is located in the Sonoran Desert, a semi-arid dryland, that experiences significant impacts to the Earth system due to dust’s role at the land-atmosphere interface and in atmospheric processes, including interactions with clouds, radiation, and precipitation. Dust aerosols play an important role in the SW U.S. desert-urban interface that is characterized by low annual precipitation, high temperatures, dry conditions, and strong winds. The conditions result in seasonally-dependent impacts to visibility and infrastructure due to dust events, including large scale ones, “haboobs”, that have particulate matter (PM) concentrations exceeding milligrams per meter cubed, an order of magnitude higher than the standard scale for PM. Specifically, during the North American Monsoon (NAM), thunderstorms around Phoenix can create haboobs that lift large quantities of dust from the land surface. During this presentation, we will present an overview of a new field campaign that is collecting observations within the Phoenix MSA as well as results from some of the large-scale dust storms that were sampled during the First Summer Intensive Operational Period (IOP) in 2026 where >10 categorized storms were observed.
B2. Integrating Satellite, Model, and Ground Measurement Data through Machine Learning to Estimate Surface PMcoarse Concentrations in the United States
Jennifer McGinnies, Haihui Zhu, Patrick Reddy, Yawen Guan, Jeffrey R. Pierce
PMcoarse (particulate matter with diameters >2.5 µm and <10 µm) is a prevalent but understudied pollutant in the contiguous US (CONUS). Due to limited observations and its high spatiotemporal variability, reliable CONUS-wide PMcoarse estimates are limited. The current ground monitoring network does not capture the full spatiotemporal variability of hourly PMcoarse, with ordinary kriging of observations resulting in an R2 of 0.2 when evaluated through leave-one-out cross validation (LOOCV). Similarly, focusing on one of the main components of PMcoarse, mineral dust, a longwave-based geostationary satellite dust detection method misses ~90% of dust events captured by hourly surface observations. Producing better estimates of PMcoarse will require integrating multiple data products, since no single product captures the full picture. To address this, we have applied gradient boosted decision trees to blend together satellite data, meteorological model output, land type, and surface measurements to estimate hourly PMcoarse concentrations away from monitors. Through this work, we have found that the machine learning model trained on hourly PMcoarse observations increased the R2 value to 0.50 for hourly and 0.65 for daily PMcoarse observations when evaluated through LOOCV.
B3. Beyond magnitude: ecological controls on wind-erosion hotspots in southwestern US drylands
Shubham Tiwari, Nicholas P. Webb, Sarah E. McCord
Wind erosion from drylands is highly uneven: a relatively small number of sites can account for much of the sediment transported across a region. We asked what ecological conditions characterize these wind-erosion hotspots and whether that context can help improve where mitigation is targeted. We linked standardized AIM, LMF and NRI rangeland monitoring data with the Aeolian EROsion (AERO) model to estimate horizontal sediment transport at 13,199 plots across five ecoregions of the southwestern United States. Within the monitoring sample, the top 5% of plots accounted for 72% of modeled flux, while extreme plots (Q > 100 g m⁻¹ d⁻¹) made up only 2.2% of plots but accounted for 54%. Hotspot plots generally had lower perennial grass cover and occurred in more arid long-term climate settings, while 12-month drought indices did not distinguish hotspots overall. Some vegetation relationships also varied among ecoregions. Rather than treating every high-flux site the same, we use these results to outline a management triage that combines transport magnitude with ecological condition, ecoregion context and possible natural constraints. This provides a more realistic basis for prioritizing dust-source mitigation using monitoring data that already exist.
B4. An improved dust emission modeling with multiple endmember spectral mixture analysis
Junran Li
Traditional dust emission schemes typically rely on photosynthetic vegetation (PV) alone when parameterizing key surface properties. Here, we integrate five endmembers from the multiple endmember spectral mixture analysis (MESMA), namely PV, non-photosynthetic vegetation (NPV), bare soil (BS), dark surface (DA), and ice/snow (IS), into a well-established dust emission scheme to simultaneously constrain surface roughness and the erodible fraction, avoiding the common reliance on PV alone as in traditional dust emission schemes. By evaluating three severe dust events in East Asia in 2021, 2023, and 2024, the MESMA-constrained configurations reduce total dust emissions by 22.2% to 49.7% and show substantially improved agreement with station observations. By enabling a physically realistic characterization of dryland surface heterogeneity through multiple endmembers, this approach substantially alleviates the systematic overestimation that is typical when using PV-based parameterizations.
B5. Estimating Current and Future Dust Exposure from the Shrinking Salton Sea
Xuan Liu, William C. Porter, Shu-Hua Chen, Jasper F. Kok, Gregory S. Okin, Adeyemi Adebiyi, Lynne Xu, Amato T. Evan
Dust is a major contributor to airborne particulate matter (PM) in arid and semi-arid regions, where dry lakebeds and disturbed soils can be important emission sources. The ongoing shrinkage of California's Salton Sea is expanding exposed playa, creating a growing regional dust source that may be enriched with contaminants from historical agricultural and industrial activities. However, the contribution of Salton Sea playa dust to regional PM10 exposure and its change with playa expansion remain unclear. We use the Air Force Weather Agency (AFWA) dust emission scheme within the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to simulate dust emissions across the Salton Basin. We improve model inputs with high-resolution surface and meteorological datasets and optimize model configuration using a 3 km–1 km nested domain and meteorological nudging. We modify the emission parameterization by replacing the sandblasting efficiency equation and incorporating dynamic bare soil fraction and vegetation drag partitioning. Model performance is evaluated against meteorological and aerosol observations. Source tagging quantifies contributions from individual dust sources. Initial results indicate that communities southeast of the Salton Sea experience the highest population-weighted daily PM10 exposure from playa dust, which is estimated to increase substantially under the 2029 equilibrium playa extent scenario.
B6. Integration of QuantAQ Particle Sensors in the South Coast AQMD Real-Time Air Quality Index map
Nico Schulte, Emily Bian, Wilton Mui, Brandon Feenstra, William Porter, Scott A. Epstein
South Coast Air Quality Management District (AQMD) currently uses consumer-grade sensor network data from PurpleAir and Clarity to enhance the spatial resolution and accuracy of PM2.5 data used in real-time Air Quality Index (AQI) maps. Several areas of the South Coast AQMD jurisdiction can significantly benefit from improved spatial resolution of real-time PM10 data such as the Coachella Valley (CV) where windblown dust often results in unhealthy PM10 concentrations. Due to coarse PM detection challenges, consumer-grade PM sensors often fail to measure PM10 accurately. With advancements in PM10 sensing technology, data collected allows for improved AQI predictions in areas between regulatory monitors.
This study 1) develops and validates a semi-physical model to correct QuantAQ MODULAIR-PM PM2.5 and PM10 data at collocated sites in the CV area to improve agreement with regulatory measurements, especially during high winds, 2) explains how we developed a new QuantAQ sensor network with seven measurement locations in CV and 3) integrates QuantAQ PM2.5 and PM10 sensor data in the South Coast AQMD AQI map. Preliminary results on the spatial variation of PM10 in CV are discussed. Empirical orthogonal function analysis indicates that a major dust source is likely in the northwestern CV.
B7. Dust Emission Potential and Chemical Composition from Recently Exposed Salton Sea Playa and Adjacent Desert Landforms in Southern California, USA
Hank Dicke, Yohannes Yimam, Brian Schmid, Josh Esquivel, William Castello
Over a decade of research with the Portable In-Situ Wind ERosion Lab (PI-SWERL), a small wind tunnel, have quantified the ability of Salton Sea playa and surrounding desert areas in Southern California, USA to produce dust. Emission flux potentials measured monthly between Fall and Spring were parameterized by surface characteristics and meteorological conditions to investigate dust emission variability and controls on PM10. Drivers found to have a significant impact (P < 0.001) on playa emission rates include crust type, loose surface sand, and soil moisture condition; while geomorphic landform was found to be a control on desert emission rates. Parameterized emission rates provide a basis for comparison of relative dust contributions by surface type and serve as inputs to landscape scale dust models. To further contextualize the potential dust emissions, a PI-SWERL attachment was used to collect PM10 onto filters for X-ray fluorescence analysis. In-situ assessments of dust composition indicate the relative abundance of specific elements between landforms and facilitate comparisons with regional and local ambient dust and surface sediments. Considering relative emission rates alongside composition can be used to evaluate the implication of dust produced by the Salton Sea playa and surrounding areas as the Sea continues to recede.
C. Dust Impacts on Air Quality, Safety, and Health
C1. TBA (invited talk)
Jill Johnston — Virtual
C2. The impact of mineral dust exposure on birth and pregnancy outcomes in the Salton Sea region of Southern California
Wenxin Lu, Christina Chambers, Gretchen Bandoli, Rebecca Baer, Amato Evan, William C. Porter, Alexandra Heaney
Mineral dust is an increasingly prevalent and chemically distinct component of particulate matter (PM) that may threaten maternal and infant health, particularly in highly-exposed, climate-vulnerable communities. However, evidence on prenatal dust exposure and adverse birth outcomes remains limited because quantitative assessment of individual-level dust exposures has only recently become feasible. We used data from the Study of Outcomes in Mothers and Infants birth cohort. For births from 2005 to 2021 in the Imperial and Coachella Valleys (n = 126,336), we estimated residential exposures to both coarse dust (PM10-2.5) and fine dust (dust-specific PM2.5), from US Environmental Protection Agency monitoring data and the SatPM2.5 database, respectively. We found that a 1-µg/m3 higher prenatal coarse dust was associated with 1.4% [95% CI 1.1% to 1.8%] higher adjusted odds of preterm birth (PTB), 2.7% [2.1% to 3.4%] for induced PTB, 1.3% [0.6% to 1.9%] for spontaneous PTB, 0.5% [0.2% to 0.8%] for large for gestational age, and 0.6% [0.1% to 1.1%] for preeclampsia. A 1-µg/m3 higher prenatal fine dust exposure was also associated with 16.8% [3.6% to 31.7%] higher odds of spontaneous PTB. Associations were stronger in communities with higher social vulnerability.
C3. From Dust Science to Dust Management: An Evidence-Based Framework for Great Salt Lake
Kevin Perry, John Lin, Paul Brooks, Alberto Garcia, Sara Grineski, Derek Mallia, D. Kip Solomon
Declining Great Salt Lake levels have exposed more than 800 mi² of lakebed, but exposed playa is not synonymous with active dust sources. Approximately 70 mi² are currently identified as dust hotspots, creating an emerging air-quality and public-health challenge for Utah. We integrated field measurements of lakebed erodibility, atmospheric transport modeling, population exposure, health impacts, and engineered dust-control options to provide a quantitative basis for management decisions. Transport simulations included 26 dust events over six years across 11 northern Utah counties and incorporated spatially varying lakebed erodibility. Modeled exposure varies substantially across communities and declines as lake elevation rises. At 4,192 ft, Great Salt Lake dust is associated with approximately $116 million in annual health damages; estimated damages decline to $73 million at the minimum healthy elevation of 4,198 ft. Engineered controls are feasible, but projected 50-year costs for currently identified hotspots range from $3.2 billion to more than $31 billion, with water availability constraining many approaches. These findings support a weight-of-the-evidence framework integrating source attribution, exposure, health risk, engineering feasibility, and water requirements to guide targeted mitigation while recognizing lake-level stabilization as a long-term source-reduction strategy.
C4. A Bayesian Framework for Spatio-Temporal Modeling of Environmental Exposure and Health Effects Related to PM₁₀, Dust, Wildfires, and Haze Events in Fresno County, California, USA
Estrella Herrera, Kimberly Valle, Flavio Herrera Miramontes, Asa Bradman
The San Joaquin Valley of California consistently ranks among the regions with the worst air quality in the United States, routinely exceeding the U.S. Environmental Protection Agency's National Ambient Air Quality Standards (NAAQS) for particulate matter (PM). This study examines the short-term effects of ambient PM₁₀ and its principal sources — windblown soil dust, wildfire smoke, and meteorological haze — on emergency department (ED) visits for respiratory illness across ZIP codes in Fresno County, the highest-value agricultural county in the nation by production value. We linked daily exposure data from regulatory air-quality monitors and meteorological stations with individual health records from Valley Community Healthcare and the Fresno County Department of Public Health for April 2, 2022 through December 31, 2024. A spatio-temporal conditional autoregressive (ST-CAR) model estimated associations between three exposure conditions—windblown dust events, haze days, and wildfire smoke—and respiratory ED visits, accounting for spatial dependence and temporal trends. Point estimates were consistently above the null for every respiratory outcome — all respiratory illness (RR 1.05, 95% CrI 0.99–1.07), respiratory infection (1.06, 0.99–1.08), asthma (1.10, 0.98–1.15), and other chronic respiratory conditions (1.09, 0.96–1.13) — though all 95% credible intervals included RR = 1.0. Taken together, these results suggest a possible increase in respiratory ED visits following acute exposure to dust, wildfire smoke, and haze, with posterior uncertainty that does not exclude no effect. We will re-estimate these models with an additional year of exposure, which should narrow the credible intervals and clarify whether the observed associations persist. If confirmed, these associations would support targeted public health and air-quality interventions in agricultural regions facing recurrent dust and PM exposure, where environmental-justice concerns are acute.
C5. Seasonal and Temporal Dynamics of Airborne Fungal Communities in the San Joaquin Valley
Adeola Fagbayibo, Estrella Herrera Molina, Jesica Calderon, Asa Bradman, Katrina Hoyer
Dust aerosols transport biological particles, including bacteria, fungi, and other microorganisms that may influence air quality, ecosystem processes, and respiratory health. The San Joaquin Valley (SJV) experiences frequent dust events shaped by agricultural activity, urban development, drought, wind, and land-use change. However, the seasonal and temporal dynamics of airborne microbial communities across the region remain incompletely understood. In this study, we characterize the SJV aerobiome by sequencing the bacterial 16S rRNA gene and fungal internal transcribed spacer (ITS) regions. Air samples were collected across multiple locations and sampling periods to capture seasonal and temporal variation in airborne microbial composition. We evaluated microbial richness, diversity, taxonomic composition, and community turnover, and examined relationships between aerobiome structures. This work provides an integrated assessment of bacterial and fungal communities associated with airborne dust in an agriculturally intensive and environmentally heterogeneous region. We characterized and identified microbial taxa and community profiles associated with specific seasons, sampling conditions, and potential dust-influenced periods. These findings will improve understanding of how environmental conditions and human activities shape the SJV aerobiome and may help identify microbial indicators relevant to dust exposure and respiratory health. The study also establishes a framework for integrating sequencing, quantitative microbial measurements, and community-based monitoring.
C6. Mineral Dust and Health: An Overlooked Risk in the U.S.
Alexandra Heaney, Wenxin Lu, Minghao Qiu, Adeyemi Adebiyi, Jennifer Burney, Carlos F. Gould
Mineral dust is an intensifying hazard in the western U.S., where dust storm frequency rose roughly 240% from the 1990s to the 2000s amid increasing drought, wildfires, and shrinking terminal lakes. International studies link dust exposure to mortality, cardiorespiratory disease, and adverse birth outcomes, yet the U.S. is nearly absent from that evidence base. We argue this is largely a data problem. Rigorous dust-health epidemiology requires dust concentration data that no current product delivers: specificity to mineral dust (i.e. not all-source particulate matter[PM]), near-surface rather than column concentrations, both fine and coarse size fractions, and spatial and temporal resolution fine enough to capture sharp gradients and brief, intense events. Here, we quantify how well a widely used satellite-derived dust product (van Donkelaar SatPM2.5) reproduces ground-based speciation monitors (IMPROVE and CSN) across the contiguous United States, and find systematic disagreement that widens with dust loading—worst exactly where exposures, and the health risks they carry, are highest. As dust rises, the satellite captures less of the temporal variability that monitors record: the ratio of satellite to monitor temporal variability falls from 0.97 at the least-dusty sites to 0.74 at the dustiest. The satellite-derived data also renders dust more spatially uniform, with errors largest at the extreme peaks. When monitored dust reaches or exceeds 8 µg/m³, the satellite estimates under 3 µg/m³, and it captures only 10% of the most intense dust periods versus 62% of moderate ones. We present priorities for closing this gap: a national dust climatology across both size fractions, source-resolved attribution, compositional data, and operational forecasting. Together, these would give U.S. researchers and health agencies what they now lack — the ability to quantify the health burden of dust exposure, identify populations at greatest risk, and forecast exposure in time to protect communities that bear the highest exposures.
C7. A Statewide Daily Dust-Informed PM₁₀ Exposure Surface for California: Machine-Learning Estimation and External Validation, 2006–2019
Saud Alomair, Michael Jerrett
Dust exposure is difficult to characterize continuously at the spatial and temporal scales required for population health research. We developed a daily, statewide dust-informed PM₁₀ exposure surface for California using population-weighted ZIP-code centroids and integrated meteorological, modeled air-quality, satellite, land-surface, atmospheric, and dust-indicator predictors for 2006–2019. Random forest, gradient boosting, and deep-learning models were combined using a stacked ensemble and applied to 1,797 population-weighted centroids. Because no statewide gold standard for dust exposure exists, external validity was evaluated against independently derived IMPROVE measured soil dust. The ensemble achieved a pooled Spearman correlation of 0.646 with measured dust and discriminated high-dust days with an AUC of 0.837 against an independent composition-based dust classification. Agreement remained strongest at close spatial separation, with Spearman correlation reaching 0.670 within 5 km of IMPROVE sites, and declined progressively with increasing monitor-to-centroid distance. Statewide predictions identified persistent exposure hotspots in the southern San Joaquin Valley and Imperial Valley/Salton Sea region. The resulting surface provides a population-relevant, externally evaluated framework for studying spatial and temporal variation in dust-related PM₁₀ exposure and supports subsequent epidemiologic analysis.
E. Dust and Agriculture, and Others
E1. TBA (invited talk)
Speaker TBA
E2. Agricultural dust emissions and their radiative forcing from the past to the present
Yang Shi, Xiaohong Liu, Chenglai Wu, Zheng Lu, Kai Zhang, Po-Lun Ma
Dust emissions related to agricultural activities is not represented in most global climate models and its radiative impact remains unassessed. Here, we develop a new and physically based method to parameterize agricultural dust (AD) based on the DOE’s Energy Exascale Earth System Model version 1 (E3SMv1). This method relates AD emission to the crop land use fraction in the E3SMv1 land component. Major AD sources simulated by our parameterization include those over Central America, the Sahel, North India, and North China. The annual averaged AD emission is 567 Tg yr-1 in present-day, which contributes to 13.3% of total dust emission. Model evaluation against satellite and ground-based observations shows that the new parameterization can represent AD emissions and global dust cycle reasonably well. We find that the total dust emission increases by 13.1% from 1850 to 2000 due to the cropland land use fraction changes. This induces a direct effective radiative forcing of -0.041 W m-2 at top of the atmosphere. This dust-induced cooling exceeds 10% of the total anthropogenic aerosol direct effective radiative forcing estimated by IPCC AR6. Our findings indicate an important role of anthropogenic dust in global climate change, which should be included in future climate change assessments.
E3. The Contribution of Agricultural and Other Anthropogenic Dust Emissions to Ambient Coarse Particulate Matter in the U.S.
Haihui Zhu, Jennifer McGinnis, Moumita Chanda, James L. Crooks, Karl M. Seltzer, Alison Eyth, Jude Bayham, Alexander Maas, Jeffrey R. Pierce
Dust emitted from anthropogenic activities contributes to ambient particulate matter (PM), which is associated with millions of premature deaths globally each year. This study leverages ground-based monitoring data across the contiguous United States (CONUS) and the GEOS-Chem chemical transport model to evaluate biases in anthropogenic dust emissions provided by the National Emissions Inventory. Results indicate that the simulated coarse-mode PM concentrations are underestimated by about 46% with a mean bias of -3.23 μg m-3 for 2019, compared to ground-based aerosol measurements. The bias is stronger where there is more cropland, fallow land, and denser populations near monitors. Sensitivity tests using GEOS-Chem suggest that discrepancies between observed and simulated PM concentrations are primarily driven by uncertainties in anthropogenic dust emissions rather than dry deposition or natural dust sources. Our analysis further reveals that the biases are more pronounced for agricultural dust emissions rather than non-agricultural anthropogenic dust emissions. A CONUS-wide mean bias factor of 7.37 was identified for agricultural dust emissions, compared to 1.66 for non-agricultural anthropogenic dust emissions. Improved representation of anthropogenic dust emissions, through process-based emission schemes or data-driven approaches, is desired for better model representation of coarse PM concentration and its associated health impacts.
E4. From Crust to Dust: Characterizing the Physical, Chemical, and Mineralogical Properties of Potential Dust Sources Around the Salton Sea
Rebecca Lybrand, Zachary Brown, Cassie Collins, Amato Evan, Rayna Goodman, Jocelyn Hillhouse, Xuan Liu, Maddie Morris, Kylee Nielsen, Lorraine Lucas-Scott
The generation of dust from desert soils and the sediments of the receding Salton Sea is of major environmental concern given the impacts of dust on human health, nearby communities, social infrastructure, and the surrounding desert ecosystems. Our objective was to assess the physicochemical and mineralogical properties of the surface crusts, exposed playa lakebed sediments, and soils from the Salton Sea and the neighboring desert landscapes to better understand the characteristics and potential sources of windblown dust in the area. Our team conducted field soil descriptions in combination with site sampling and laboratory analyses for 20+ locations around the Salton Sea. Overall, we found the surface crusts, soils, and sediments to exhibit neutral to alkaline soil pH (~7.5 to 8.5), moderate to high electrical conductivity values (1.5 dS/m to >10 dS/m), and contrasting soil textures ranging from fine-grained surface crusts to black coarse sands and silty clay to clayey mud in the subsurface. The surface crusts also varied in composition and morphology as evidenced by the presence of soft to hard white salt crusts in addition to darker silt-clay surface crusts. Our project is employing a field-lab geochemical analysis approach to advance knowledge of the surface crusts, sediments, and soils that may serve as potential sources of dust around the Salton Sea and in the surrounding communities.
F.Policy Response to Dust Storms
F1. Groundwater, Fallowing, and Dust in the San Joaquin Valley
Andrew Ayres (Invited speaker), University of Nevada, Reno
This (oral) presentation will present a summary of recent work studying the air quality risks posed by major upcoming land transitions in California's San Joaquin Valley (SJV). Implementation of the Sustainable Groundwater Management Act (SGMA) is expected to result in over 500,000 acres of irrigated agricultural land coming out of irrigated production in the SJV, and concerns persist that uncoordinated land transitions may result in significant fugitive dust generation and negative impacts to local communities and the valley economy. The presentation will relate the conclusions of a 2022 Public Policy Institute of California report on land transitions and dust risk, discuss policy and management responses, including the economic characteristics of some proposed cropping strategies, and summarize relevant empirical findings from recent analysis of land management and particulates in the Valley. Opportunities for federal, state, and local actors to positively influence SGMA outcomes will be discussed.