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The San Diego−Tijuana border region has experienced rapid urbanization and industrial growth with unmitigated environmental consequences. For nearly a century, the Tijuana River (TR) has carried untreated sewage and industrial waste into the United States, contributing to a long-recognized and ongoing water pollution crisis. However, the impact of this pollution on air quality has been almost entirely overlooked until very recently. Analysis of low-cost air sensor data reveals that gases released from the polluted TR directly contribute to the formation of fine aerosols, increasing PM1 (particulate matter <1 μm) concentrations, particularly when river flow is high and atmospheric dispersion is low. Analysis of PM1 size distributions revealed the enhancement of smaller particle fractions, and persistently high PM1-to-PM2.5 ratios (≥0.56 ± 0.15) showed that submicrometer particles constitute the majority of fine PM mass. Combined with recent evidence of elevated gas-phase emissions from the polluted TR, these results point to secondary aerosol formationdriven by the chemical transformations of river-emitted gaseous precursorsas a major source of PM1. Concentrations peaked near a turbulent riverine hotspot, particularly at night when intensified flow and stable conditions promoted secondary aerosol formation. These findings identify a previously unrecognized source of urban air pollution, showing that the river-to-air transfer of particulate precursors can perpetuate poor air quality and heighten environmental justice and public health concerns. 

Oceanic emissions of dimethyl sulfide (DMS) have long been known to influence aerosol particle composition, cloud condensation nuclei (CCN) concentration, and Earth’s radiative budget. However, the impact of oceanic emissions of methanethiol (MeSH), a sulfur compound produced by the same oceanic precursor as DMS, has been relatively less explored. The gas-phase oxidation of MeSH has a higher eNective yield of SO2 and a shorter oxidative lifetime compared to DMS, highlighting the relevance of this pathway for the modeled representation of particle formation, growth, and CCN abundance in the marine atmosphere. Here, we use the global chemical transport model GEOS- Chem to explore possible scenarios representative of specific environmental conditions and MeSH emission schemes based on previous experimental studies. We further implement and test previously reported chemical mechanisms for MeSH oxidation, along with additional improvements, highlighting key uncertainties and sensitivities for regional and global sulfur budgets. We place our results in the context of recent modeling updates to DMS chemistry and cloud processing, which further impact SO2 production in the marine atmosphere in parallel with MeSH oxidation. Within the overall marine sulfur budget, our findings highlight that MeSH plays a significant role in SO2 production in the marine atmosphere, contributing to regional surface layer concentration increases of up to 40−60%. These results point to the importance of MeSH for eNorts aimed at improving the modeled representation of sulfur spatiotemporal patterns relevant to air quality predictions and climate impact assessments. 

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process ( $r$ process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate $r$ -process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power. 

Abstract. Dimethyl sulfide (DMS) is primarily emitted by marine phytoplankton and oxidized in the atmosphere to form methanesulfonic acid (MSA) and sulfate aerosols. Ice cores in regions affected by anthropogenic pollution show an industrial-era decline in MSA, which has previously been interpreted as indicating a decline in phytoplankton abundance. However, a simultaneous increase in DMS-derived sulfate (bioSO4) in a Greenland ice core suggests that pollution-driven oxidant changes caused the decline in MSA by influencing the relative production of MSA versus bioSO4. Here we use GEOS-Chem, a global chemical transport model, and a zero-dimensional box model over three time periods (preindustrial era, peak North Atlantic NOx pollution, and 21st century) to investigate the chemical drivers of industrial-era changes in MSA and bioSO4, and we examine whether four DMS oxidation mechanisms reproduce trends and seasonality in observations. We find that box model and GEOS-Chem simulations can only partially reproduce ice core trends in MSA and bioSO4 and that wide variation in model results reflects sensitivity to DMS oxidation mechanism and oxidant concentrations. Our simulations support the hypothesized increase in DMS oxidation by the nitrate radical over the industrial era, which increases bioSO4 production, but competing factors such as oxidation by BrO result in increased MSA production in some simulations, which is inconsistent with observations. To improve understanding of DMS oxidation, future work should investigate aqueous-phase chemistry, which produces 82 %–99 % of MSA and bioSO4 in our simulations, and constrain atmospheric oxidant concentrations, including the nitrate radical, hydroxyl radical, and reactive halogens. 

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event. 

Abstract. Marine emissions of dimethyl sulfide (DMS) and the subsequent formation of its oxidation products methanesulfonic acid (MSA) and sulfuric acid (H2SO4) are well-known natural precursors of atmospheric aerosols, contributing to particle mass and cloud formation over ocean and coastal regions. Despite a long-recognized and well-studied role in the marine troposphere, DMS oxidation chemistry remains a work in progress within many current air quality and climate models, with recent advances exploring heterogeneous chemistry and uncovering previously unknown intermediate species. With the identification of additional DMS oxidation pathways and intermediate species that influence the eventual fate of DMS, it is important to understand the impact of these pathways on the overall sulfate aerosol budget and aerosol size distribution. In this work, we update and evaluate the DMS oxidation mechanism of the chemical transport model GEOS-Chem by implementing expanded DMS oxidation pathways in the model. These updates include gas- and aqueous-phase reactions, the formation of the intermediates dimethyl sulfoxide (DMSO) and methanesulfinic acid (MSIA), and cloud loss and aerosol uptake of the recently quantified intermediate hydroperoxymethyl thioformate (HPMTF). We find that this updated mechanism collectively decreases the global mean surface-layer gas-phase sulfur dioxide (SO2) mixing ratio by 40 % and enhances the sulfate aerosol (SO42-) mixing ratio by 17 %. We further perform sensitivity analyses exploring the contribution of cloud loss and aerosol uptake of HPMTF to the overall sulfur budget. Comparing modeled concentrations to available observations, we find improved biases relative to previous studies. To quantify the impacts of these chemistry updates on global particle size distributions and the mass concentration, we use the TwO-Moment Aerosol Sectional (TOMAS) aerosol microphysics module coupled to GEOS-Chem and find that changes in particle formation and growth affect the size distribution of aerosol. With this new DMS-oxidation scheme, the global annual mean surface-layer number concentration of particles with diameters smaller than 80 nm decreases by 16.8 %, with cloud loss processes related to HPMTF being mostly responsible for this reduction. However, the global annual mean number of particles larger than 80 nm (corresponding to particles capable of acting as cloud condensation nuclei, CCN) increases by 3.8 %, suggesting that the new scheme promotes seasonal particle growth to these sizes. 

Abstract In situ observations and output from a numerical model are utilized to examine three dust outbreaks that occurred in the northwestern Sonoran Desert. Via analysis of these events, it is shown that trapped waves generated in the lee of an upwind mountain range produced high surface wind speeds along the desert floor and the observed dust storms. Based on analysis of observational and model output, general characteristics of dust outbreaks generated by trapped waves are suggested, including dust-layer depths and concentrations that are dependent upon wave phase and height above the surface, emission and transport associated with the presence of a low-level jet, and wave-generated high wind speeds and thus emission that occurs far downwind of the wave source. Trapped lee waves are ubiquitous in Earth’s atmosphere and thus it is likely that the meteorological aspects of the dust storms examined here are also relevant to understanding dust in other regions. These dust outbreaks occurred near the Salton Sea, an endorheic inland body of water that is rapidly drying due to changes in water-use management. As such, these findings are also relevant in terms of understanding how future changes in size of the Salton Sea will impact dust storms and air quality there. Significance Statement Dust storms are ubiquitous in Earth’s atmosphere, yet the physical processes underlying dust emission and subsequent transport are not always understood, in part due to the wide variety of meteorological processes that can generate high winds and dust. Here we use in situ measurements and numerical modeling to demonstrate that vertically trapped atmospheric waves generated by air flowing over a mountain are one such mechanism that can produce dust storms. We suggest several features of these dust outbreaks that are specific to their production by trapped waves. As the study area is a region undergoing rapid environmental change, these results are relevant in terms of predicting future dust there.