Regional impacts of ultrafine particle emissions from the surface of the Great Lakes

Quantifying the impacts of aerosols on climate requires a detailed knowledge of both the anthropogenic and the natural contributions to the aerosol population. Recent work has suggested a previously unrecognized natural source of ultrafine particles resulting from breaking waves at the surface of large freshwater lakes. This work is the first modeling study to investigate the potential for this newly discovered source to affect the aerosol number concentrations on regional scales. Using the WRF-Chem modeling framework, the impacts of wind-driven aerosol production from the surface of the Great Lakes were studied for a July 2004 test case. Simulations were performed for a base case with no lake surface emissions, a case with lake surface emissions included, and a default case wherein large freshwater lakes emit marine particles as if they were oceans. Results indicate that the lake surface emissions can enhance the surface-level aerosol number concentration by ~20% over the remote northern Great Lakes and by ~5% over other parts of the Great Lakes. These results were highly sensitive to the new particle formation (i.e., nucleation) parameterization within WRF-Chem; when the new particle formation process was deactivated, surface-layer enhancements from the lake emissions increased to as much as 200%. The results reported here have significant uncertainties associated with the lake emission parameterization and the way ultrafine particles are modeled within WRF-Chem. Nevertheless, the magnitudes of the impacts found in this study suggest that further study to quantify the emissions of ultrafine particles from the surface of the Great Lakes is merited

Contributions of wood smoke and vehicle emissions to ambient concentrations of volatile organic compounds and particulate matter during the Yakima wintertime nitrate study

A multiple linear regression (MLR) chemical mass balance model was applied to data collected during an air quality field experiment in Yakima, WA, during January 2013 to determine the relative contribution of residential wood combustion (RWC) and vehicle emissions to ambient pollutant levels. Acetonitrile was used as a chemical tracer for wood burning and nitrogen oxides (NOx) as a chemical tracer for mobile sources. RWC was found to be a substantial source of gas phase air toxics in wintertime. The MLR model found RWC primarily responsible for emissions of formaldehyde (73%), acetaldehyde (69%), and black carbon (55%) and mobile sources primarily responsible for emissions of carbon monoxide (CO; 83%), toluene (81%), C2-alkylbenzenes (81%), and benzene (64%). When compared with the Environmental Protection Agency’s 2011 winter emission inventory, the MLR results suggest that the contribution of RWC to CO emissions was underestimated in the inventory by a factor of 2. Emission ratios to NOx from the MLR model agreed to within 25% with wintertime emission ratios predicted from the Motor Vehicle Emissions Simulator (MOVES) 2010b emission model for Yakima County for all pollutants modeled except for CO, C2-alkylbenzenes, and black carbon. The MLR model results suggest that MOVES was overpredicting mobile source emissions of CO relative to NOx by a factor of 1.33 and black carbon relative to NOx by about a factor of 3

Thermodynamic characterization of Mexico City aerosol during MILAGRO 2006

International audienceFast measurements of aerosol and gas-phase constituents coupled with the ISORROPIA-II thermodynamic equilibrium model are used to study the partitioning of semivolatile inorganic species and phase state of Mexico City aerosol sampled at the T1 site during the MILAGRO 2006 campaign. Overall, predicted semivolatile partitioning agrees well with measurements. PM2.5 is insensitive to changes in ammonia but is to acidic semivolatile species. Semi-volatile partitioning equilibrates on a timescale between 6 and 20 min. When the aerosol sulfate-to-nitrate molar ratio is less than 1, predictions improve substantially if the aerosol is assumed to follow the deliquescent phase diagram. Treating crustal species as "equivalent sodium" (rather than explicitly) in the thermodynamic equilibrium calculations introduces important biases in predicted aerosol water uptake, nitrate and ammonium; neglecting crustals further increases errors dramatically. This suggests that explicitly considering crustals in the thermodynamic calculations are required to accurately predict the partitioning and phase state of aerosols

Characterization of ambient aerosol from measurements of cloud condensation nuclei during the 2003 Atmospheric Radiation Measurement Aerosol Intensive Observational Period at the Southern Great Plains site in Oklahoma

Measurements were made by a new cloud condensation nuclei (CCN) instrument (CCNC3) during the Atmospheric Radiation Measurement (ARM) Program's Aerosol Intensive Observational Period (IOP) in May 2003 in Lamont, Oklahoma. An inverse aerosol/CCN closure study is undertaken, in which the predicted number concentration of particles available for activation (N_P) at the CCNC3 operating supersaturations is compared to that observed (N_O). N_P is based on Köhler Theory, with assumed and inferred aerosol composition and mixing state, and the airborne aerosol size distribution measured by the Caltech Dual Automatic Classified Aerosol Detector (DACAD). An initial comparison of N_O and N_P, assuming the ambient aerosol is pure ammonium sulfate ((NH_4)_2SO_4), results in closure ratios (N_P/N_O) ranging from 1.18 to 3.68 over the duration of the IOP, indicating that the aerosol is less hygroscopic than (NH_4)_2SO_4. N_P and N_O are found to agree when the modeled aerosol population has characteristics of an external mixture of particles, in which insoluble material is preferentially distributed among particles with small diameters (<50 nm) and purely insoluble particles are present over a range of diameters. The classification of sampled air masses by closure ratio and aerosol size distribution is discussed in depth. Inverse aerosol/CCN closure analysis can be a valuable means of inferring aerosol composition and mixing state when direct measurements are not available, especially when surface measurements of aerosol composition and mixing state are not sufficient to predict CCN concentrations at altitude, as was the case under the stratified aerosol layer conditions encountered during the IOP

Evaluation of an entraining droplet activation parameterization using in situ cloud data

This study investigates the ability of a droplet activation parameterization (which considers the effects of entrainment and mixing) to reproduce observed cloud droplet number concentration (CDNC) in ambient clouds. Predictions of the parameterization are compared against cloud averages of CDNC from ambient cumulus and stratocumulus clouds sampled during CRYSTAL‐FACE (Key West, Florida, July 2002) and CSTRIPE (Monterey, California, July 2003), respectively. The entrainment parameters required by the parameterization are derived from the observed liquid water content profiles. For the cumulus clouds considered in the study, CDNC is overpredicted by 45% with the adiabatic parameterization. When entrainment is accounted for, the predicted CDNC agrees within 3.5%. Cloud‐averaged CDNC for stratocumulus clouds is well captured when entrainment is not considered. In all cases considered, the entraining parameterization compared favorably against a statistical correlation developed from observations to treat entrainment effects on droplet number. These results suggest that including entrainment effects in the calculation of CDNC, as presented here, could address important overprediction biases associated with using adiabatic CDNC to represent cloud‐scale average values

Quantification of biogenic volatile organic compounds with a flame ionization detector using the effective carbon number concept

Biogenic volatile organic compounds (BVOCs) are emitted into the atmosphere by plants and include isoprene, monoterpenes, sesquiterpenes, and their oxygenated derivatives. These BVOCs are among the principal factors influencing the oxidative capacity of the atmosphere in forested regions. BVOC emission rates are often measured by collecting samples onto adsorptive cartridges in the field and then transporting these samples to the laboratory for chromatographic analysis. One of the most commonly used detectors in chromatographic analysis is the flame ionization detector (FID). For quantitative analysis with an FID, relative response factors may be estimated using the effective carbon number (ECN) concept. The purpose of this study was to determine the ECN for a variety of terpenoid compounds to enable improved quantification of BVOC measurements. A dynamic dilution system was developed to make quantitative gas standards of VOCs with mixing ratios from 20&amp;ndash;55 ppb. For each experiment using this system, one terpene standard was co-injected with an internal reference, n-octane, and analyzed via an automated cryofocusing system interfaced to a gas chromatograph flame ionization detector and mass spectrometer (GC/MS/FID). The ECNs of 16 compounds (14 BVOCs) were evaluated with this approach, with each test compound analyzed at least three times. The difference between the actual carbon number and measured ECN ranged from −24% to −2%. The difference between theoretical ECN and measured ECN ranged from −22% to 9%. Measured ECN values were within 10% of theoretical ECN values for most terpenoid compounds

Development of a regional-scale pollen emission and transport modeling framework for investigating the impact of climate change on allergic airway disease

Exposure to bioaerosol allergens such as pollen can cause exacerbations of allergenic airway disease (AAD) in sensitive populations, and thus cause serious public health problems. Assessing these health impacts by linking the airborne pollen levels, concentrations of respirable allergenic material, and human allergenic response under current and future climate conditions is a key step toward developing preventive and adaptive actions. To that end, a regional-scale pollen emission and transport modeling framework was developed that treats allergenic pollens as non-reactive tracers within the coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF/CMAQ) modeling system. The <b>S</b>imulator of the <b>T</b>iming <b>a</b>nd <b>M</b>agnitude of <b>P</b>ollen <b>S</b>eason (STaMPS) model was used to generate a daily pollen pool that can then be emitted into the atmosphere by wind. The STaMPS is driven by species-specific meteorological (temperature and/or precipitation) threshold conditions and is designed to be flexible with respect to its representation of vegetation species and plant functional types (PFTs). The hourly pollen emission flux was parameterized by considering the pollen pool, friction velocity, and wind threshold values. The dry deposition velocity of each species of pollen was estimated based on pollen grain size and density. An evaluation of the pollen modeling framework was conducted for southern California (USA) for the period from March to June 2010. This period coincided with observations by the University of Southern California's Children's Health Study (CHS), which included O₃, PM_2.5, and pollen count, as well as measurements of exhaled nitric oxide in study participants. Two nesting domains with horizontal resolutions of 12 and 4 km were constructed, and six representative allergenic pollen genera were included: birch tree, walnut tree, mulberry tree, olive tree, oak tree, and brome grasses. Under the current parameterization scheme, the modeling framework tends to underestimate walnut and peak oak pollen concentrations, and tends to overestimate grass pollen concentrations. The model shows reasonable agreement with observed birch, olive, and mulberry tree pollen concentrations. Sensitivity studies suggest that the estimation of the pollen pool is a major source of uncertainty for simulated pollen concentrations. Achieving agreement between emission modeling and observed pattern of pollen releases is the key for successful pollen concentration simulations