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- Author or Editor: Jose L. Jimenez x
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Abstract
Measurements of aerosol size distribution, chemical composition, and cloud condensation nuclei (CCN) concentration were performed during the Chemical Emission, Loss, Transformation, and Interactions with Canopies (CELTIC) field program at Duke Forest in North Carolina. A kinetic model of the cloud activation of ambient aerosol in the chamber of the CCN instrument was used to perform an aerosol–CCN closure study. This study advances prior investigations by employing a novel fitting algorithm that was used to integrate scanning mobility particle sizer (SMPS) measurements of aerosol number size distribution and aerosol mass spectrometer (AMS) measurements of the mass size distribution for sulfate, nitrate, ammonium, and organics into a single, coherent description of the ambient aerosol in the size range critical to aerosol activation (around 100-nm diameter). Three lognormal aerosol size modes, each with a unique internally mixed composition, were used as input into the kinetic model. For the two smaller size modes, which control CCN number concentration, organic aerosol mass fractions for the defined cases were between 58% and 77%. This study is also unique in that the water vapor accommodation coefficient was estimated based on comparing the initial timing for CCN activation in the instrument chamber with the activation predicted by the kinetic model. The kinetic model overestimated measured CCN concentrations, especially under polluted conditions. Prior studies have attributed a positive model bias to an incomplete understanding of the aerosol composition, especially the role of organics in the activation process. This study shows that including measured organic mass fractions with an assumed organic aerosol speciation profile (pinic acid, fulvic acid, and levoglucosan) and an assumed organic aerosol solubility of 0.02 kg kg−1 still resulted in a significant model positive bias for polluted case study periods. The slope and y intercept for the CCN predicted versus CCN observed regression was found to be 1.9 and −180 cm−3, respectively. The overprediction generally does not exceed uncertainty limits but is indicative that a bias exists in the measurements or application of model. From this study, uncertainties in the particle number and mass size distributions as the cause for the model bias can be ruled out. The authors are also confident that the model is including the effects of growth kinetics on predicted activated number. However, one cannot rule out uncertainties associated with poorly characterized CCN measurement biases, uncertainties in assumed organic solubility, and uncertainties in aerosol mixing state. Sensitivity simulations suggest that assuming either an insoluble organic fraction or external aerosol mixing were both sufficient to reconcile the model bias.
Abstract
Measurements of aerosol size distribution, chemical composition, and cloud condensation nuclei (CCN) concentration were performed during the Chemical Emission, Loss, Transformation, and Interactions with Canopies (CELTIC) field program at Duke Forest in North Carolina. A kinetic model of the cloud activation of ambient aerosol in the chamber of the CCN instrument was used to perform an aerosol–CCN closure study. This study advances prior investigations by employing a novel fitting algorithm that was used to integrate scanning mobility particle sizer (SMPS) measurements of aerosol number size distribution and aerosol mass spectrometer (AMS) measurements of the mass size distribution for sulfate, nitrate, ammonium, and organics into a single, coherent description of the ambient aerosol in the size range critical to aerosol activation (around 100-nm diameter). Three lognormal aerosol size modes, each with a unique internally mixed composition, were used as input into the kinetic model. For the two smaller size modes, which control CCN number concentration, organic aerosol mass fractions for the defined cases were between 58% and 77%. This study is also unique in that the water vapor accommodation coefficient was estimated based on comparing the initial timing for CCN activation in the instrument chamber with the activation predicted by the kinetic model. The kinetic model overestimated measured CCN concentrations, especially under polluted conditions. Prior studies have attributed a positive model bias to an incomplete understanding of the aerosol composition, especially the role of organics in the activation process. This study shows that including measured organic mass fractions with an assumed organic aerosol speciation profile (pinic acid, fulvic acid, and levoglucosan) and an assumed organic aerosol solubility of 0.02 kg kg−1 still resulted in a significant model positive bias for polluted case study periods. The slope and y intercept for the CCN predicted versus CCN observed regression was found to be 1.9 and −180 cm−3, respectively. The overprediction generally does not exceed uncertainty limits but is indicative that a bias exists in the measurements or application of model. From this study, uncertainties in the particle number and mass size distributions as the cause for the model bias can be ruled out. The authors are also confident that the model is including the effects of growth kinetics on predicted activated number. However, one cannot rule out uncertainties associated with poorly characterized CCN measurement biases, uncertainties in assumed organic solubility, and uncertainties in aerosol mixing state. Sensitivity simulations suggest that assuming either an insoluble organic fraction or external aerosol mixing were both sufficient to reconcile the model bias.
Abstract
The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.
Abstract
The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.
Abstract
The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.
Abstract
The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.
