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Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154-0.0296; R = 0.76; N = 2,948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio toward higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.
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Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic can spread through virus-containing aerosols ( ≤ 5 µm) and larger airborne droplets. Quantifying filtration efficiency of different kinds of masks and linings for aerosols that fall within the most penetrating particle size (80-400 nm) is critical to limiting viral transmission. The objective of our experiment was to compare the "real-world" filtering efficiency of different face masks for fine aerosols (350 nm) in laboratory simulations. Methods: We performed a simulated bench test that measured the filtering efficiency of N95 vs. N99 masks with elastomeric lining in relation to baseline ("background") aerosol generation. A mannequin head was placed within a chamber and was attached to an artificial lung simulator. Particles of known size (350 ± 6 nm aerodynamic diameter) were aerosolized into the chamber while simulating breathing at physiological settings of tidal volume, respiratory rate, and airflow. Particle counts were measured between the mannequin head and the lung simulator at the tracheal airway location. Results: Baseline particle counts without a filter (background) were 2,935 ± 555 (SD) cm-3, while the N95 (1348 ± 92 cm-3) and N99 mask with elastomeric lining (279 ± 164 cm-3; p <0.0001) exhibit lower counts due to filtration. Conclusion: The filtration efficiency of the N95 (54.1%) and N99 (90.5%) masks were lower than the filtration efficiency rating. N99 masks with elastomeric lining exhibit greater filtration efficiency than N95 masks without elastomeric lining and may be preferred to contain the spread of SARS-CoV-2 infection.
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This study focuses on the long-term aerosol and precipitation chemistry measurements from colocated monitoring sites in Southern Florida between 2013 and 2018. A positive matrix factorization (PMF) model identified six potential emission sources impacting the study area. The PMF model solution yielded the following source concentration profiles: (i) combustion; (ii) fresh sea salt; (iii) aged sea salt; (iv) secondary sulfate; (v) shipping emissions; and (vi) dust. Based on these results, concentration-weighted trajectory maps were developed to identify sources contributing to the PMF factors. Monthly mean precipitation pH values ranged from 4.98 to 5.58, being positively related to crustal species and negatively related to SO4 2-. Sea salt dominated wet deposition volume-weighted concentrations year-round without much variability in its mass fraction in contrast to stronger seasonal changes in PM2.5 composition where fresh sea salt was far less influential. The highest mean annual deposition fluxes were attributed to Cl-, NO3 -, SO4 2-, and Na+ between April and October. Nitrate is strongly correlated with dust constituents (unlike sea salt) in precipitation samples, indicative of efficient partitioning to dust. Interrelationships between precipitation chemistry and aerosol species based on long-term surface data provide insight into aerosol-cloud-precipitation interactions.
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The MONterey Aerosol Research Campaign (MONARC) in May-June 2019 featured 14 repeated identical flights off the California coast over the open ocean at the same time each flight day. The objective of this study is to use MONARC data along with machine learning analysis to evaluate relationships between both supermicrometer sea salt aerosol number (N>1) and volume (V>1) concentrations and wind speed, wind direction, sea surface temperature (SST), ambient temperature (Tamb), turbulent kinetic energy (TKE), relative humidity (RH), marine boundary layer (MBL) depth, and drizzle rate. Selected findings from this study include the following: (i) Near surface (<60 m) N>1 and V>1 concentration ranges were 0.1-4.6 cm-3 and 0.3-28.2 µm3 cm-3, respectively; (ii) four meteorological regimes were identified during MONARC with each resulting in different N>1 and V>1 concentrations and also varying horizontal and vertical profiles; (iii) the relative predictive strength of the MBL properties varies depending on predicting N>1 or V>1, with MBL depth being more highly ranked for predicting N>1 and with TKE being higher for predicting V>1; (iv) MBL depths >400 m (<200 m) often correspond to lower (higher) N>1 and V>1 concentrations; (v) enhanced drizzle rates coincide with reduced N>1 and V>1 concentrations; (vi) N>1 and V>1 concentrations exhibit an overall negative relationship with SST and RH and an overall positive relationship with Tamb; and (vii) wind speed and direction were relatively weak predictors of N>1 and V>1.
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Size-resolved aerosol samples were collected in Metro Manila between July 2018 and October 2019. Two Micro-Orifice Uniform Deposit Impactors (MOUDI) were deployed at Manila Observatory in Quezon City, Metro Manila with samples collected on a weekly basis for water-soluble speciation and mass quantification. Additional sets were collected for gravimetric and black carbon analysis, including during special events such as holidays. The unique aspect of the presented data is a year-long record with weekly frequency of size-resolved aerosol composition in a highly populated megacity where there is a lack of measurements. The data are suitable for research to understand the sources, evolution, and fate of atmospheric aerosols, as well as studies focusing on phenomena such as aerosol-cloud-precipitation-meteorology interactions, regional climate, boundary layer processes, and health effects. The dataset can be used to initialize, validate, and/or improve models and remote sensing algorithms.
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A particle-into-liquid sampler (PILS) was coupled to a flow-through conductivity cell to provide a continuous, nondestructive, online measurement in support of offline ion chromatography analysis. The conductivity measurement provides a rapid assessment of the total ion concentration augmenting slower batch-sample data from offline analysis and is developed primarily to assist airborne measurements, where fast time-response is essential. A conductivity model was developed for measured ions and excellent closure was derived for laboratory-generated aerosols (97% conductivity explained, R2 > 0.99). The PILS-conductivity measurement was extensively tested throughout the NASA Cloud, Aerosol and Monsoon Processes: Philippines Experiment (CAMP2Ex) during nineteen research flights. A diverse range of ambient aerosol was sampled from biomass burning, fresh and aged urban pollution, and marine sources. Ambient aerosol did not exhibit the same degree of closure as the laboratory aerosol, with measured ions only accountable for 43% of the conductivity. The remaining fraction of the conductivity was examined in combination with ion charge balance and found to provide additional supporting information for diagnosing and modeling particle acidity. An urban plume case study was used to demonstrate the utility of the measurement for supplementing compositional data and augmenting the temporal capability of the PILS.