A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds.

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing.

Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources.

Exposure to fine particulate matter (PM2.5) from indoor and outdoor sources is a leading environmental contributor to global disease burden. In response, we established under the auspices of the UNEP/SETAC Life Cycle Initiative a coupled indoor-outdoor emission-to-exposure framework to provide a set of consistent primary PM2.5 aggregated exposure factors. We followed a matrix-based mass balance approach for quantifying exposure from indoor and ground-level urban and rural outdoor sources using an effective indoor-outdoor population intake fraction and a system of archetypes to represent different levels of spatial detail. Emission-to-exposure archetypes range from global indoor and outdoor averages, via archetypal urban and indoor settings, to 3646 real-world cities in 16 parametrized subcontinental regions. Population intake fractions from urban and rural outdoor sources are lowest in Northern regions and Oceania and highest in Southeast Asia with population-weighted means across 3646 cities and 16 subcontinental regions of, respectively, 39 ppm (95% confidence interval: 4.3-160 ppm) and 2 ppm (95% confidence interval: 0.2-6.3 ppm). Intake fractions from residential and occupational indoor sources range from 470 ppm to 62 000 ppm, mainly as a function of air exchange rate and occupancy. Indoor exposure typically contributes 80-90% to overall exposure from outdoor sources. Our framework facilitates improvements in air pollution reduction strategies and life cycle impact assessments.

Surface tension prevails over solute effect in organic-influenced cloud droplet activation.

The spontaneous growth of cloud condensation nuclei (CCN) into cloud droplets under supersaturated water vapour conditions is described by classic Köhler theory. This spontaneous activation of CCN depends on the interplay between the Raoult effect, whereby activation potential increases with decreasing water activity or increasing solute concentration, and the Kelvin effect, whereby activation potential decreases with decreasing droplet size or increases with decreasing surface tension, which is sensitive to surfactants. Surface tension lowering caused by organic surfactants, which diminishes the Kelvin effect, is expected to be negated by a concomitant reduction in the Raoult effect, driven by the displacement of surfactant molecules from the droplet bulk to the droplet-vapour interface. Here we present observational and theoretical evidence illustrating that, in ambient air, surface tension lowering can prevail over the reduction in the Raoult effect, leading to substantial increases in cloud droplet concentrations. We suggest that consideration of liquid-liquid phase separation, leading to complete or partial engulfing of a hygroscopic particle core by a hydrophobic organic-rich phase, can explain the lack of concomitant reduction of the Raoult effect, while maintaining substantial lowering of surface tension, even for partial surface coverage. Apart from the importance of particle size and composition in droplet activation, we show by observation and modelling that incorporation of phase-separation effects into activation thermodynamics can lead to a CCN number concentration that is up to ten times what is predicted by climate models, changing the properties of clouds. An adequate representation of the CCN activation process is essential to the prediction of clouds in climate models, and given the effect of clouds on the Earth's energy balance, improved prediction of aerosol-cloud-climate interactions is likely to result in improved assessments of future climate change.

Aerosol liquid water driven by anthropogenic nitrate: implications for lifetimes of water-soluble organic gases and potential for secondary organic aerosol formation.

Aerosol liquid water (ALW) influences aerosol radiative properties and the partitioning of gas-phase water-soluble organic compounds (WSOCg) to the condensed phase. A recent modeling study drew attention to the anthropogenic nature of ALW in the southeastern United States, where predicted ALW is driven by regional sulfate. Herein, we demonstrate that ALW in the Po Valley, Italy, is also anthropogenic but is driven by locally formed nitrate, illustrating regional differences in the aerosol components responsible for ALW. We present field evidence for the influence of controllable ALW on the lifetimes and atmospheric budgets of reactive organic gases and note the role of ALW in the formation of secondary organic aerosol (SOA). Nitrate is expected to increase in importance due to increased emissions of nitrate precursors, as well as policies aimed at reducing sulfur emissions. We argue that the impacts of increased particulate nitrate in future climate and air quality scenarios may be under predicted because they do not account for the increased potential for SOA formation in nitrate-derived ALW, nor do they account for the impacts of this ALW on reactive gas budgets and gas-phase photochemistry.

Toward refined estimates of ambient PM2.5 exposure: Evaluation of a physical outdoor-to-indoor transport model.

Because people spend the majority of their time indoors, the variable efficiency with which ambient PM2.5 penetrates and persists indoors is a source of error in epidemiologic studies that use PM2.5 concentrations measured at central-site monitors as surrogates for ambient PM2.5 exposure. To reduce this error, practical methods to model indoor concentrations of ambient PM2.5 are needed. Toward this goal, we evaluated and refined an outdoor-to-indoor transport model using measured indoor and outdoor PM2.5 species concentrations and air exchange rates from the Relationships of Indoor, Outdoor, and Personal Air Study. Herein, we present model evaluation results, discuss what data are most critical to prediction of residential exposures at the individual-subject and populations levels, and make recommendations for the application of the model in epidemiologic studies. This paper demonstrates that not accounting for certain human activities (air conditioning and heating use, opening windows) leads to bias in predicted residential PM2.5 exposures at the individual-subject level, but not the population level. The analyses presented also provide quantitative evidence that shifts in the gas-particle partitioning of ambient organics with outdoor-to-indoor transport contribute significantly to variability in indoor ambient organic carbon concentrations and suggest that methods to account for these shifts will further improve the accuracy of outdoor-to-indoor transport models.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations.

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species.

Previous studies have reported an increased risk of myocardial infarction (MI) associated with acute increases in PM concentration. Recently, we reported that MI/fine particle (PM2.5) associations may be limited to transmural infarctions. In this study, we retained data on hospital discharges with a primary diagnosis of acute myocardial infarction (using International Classification of Diseases ninth Revision [ICD-9] codes), for those admitted January 1, 2004 to December 31, 2006, who were ≥ 18 years of age, and were residents of New Jersey at the time of their MI. We excluded MI with a diagnosis of a previous MI and MI coded as a subendocardial infarction, leaving n = 1563 transmural infarctions available for analysis. We coupled these health data with PM2.5 species concentrations predicted by the Community Multiscale Air Quality chemical transport model, ambient PM2.5 concentrations, and used the same case-crossover methods to evaluate whether the relative odds of transmural MI associated with increased PM2.5 concentration is modified by the PM2.5 composition/mixture (i.e., mass fractions of sulfate, nitrate, elemental carbon, organic carbon, and ammonium). We found the largest relative odds estimates on the days with the highest tertile of sulfate mass fraction (OR = 1.13; 95% CI = 1.00, 1.27), nitrate mass fraction (OR = 1.18; 95% CI = 0.98, 1.35), and ammonium mass fraction (OR = 1.13; 95% CI = 1.00 1.28), and the lowest tertile of EC mass fraction (OR = 1.17; 95% CI = 1.03, 1.34). Air pollution mixtures on these days were enhanced in pollutants formed through atmospheric chemistry (i.e., secondary PM2.5) and depleted in primary pollutants (e.g., EC). When mixtures were laden with secondary PM species (sulfate, nitrate, and/or organics), we observed larger relative odds of myocardial infarction associated with increased PM2.5 concentrations. Further work is needed to confirm these findings and examine which secondary PM2.5 component(s) is/are responsible for an acute MI response.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction.

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.

Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data.

Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM(2.5)). We describe and compare different ambient PM(2.5) exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM(2.5) for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM(2.5) mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM(2.5) mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.

Variability in the fraction of ambient fine particulate matter found indoors and observed heterogeneity in health effect estimates.

Exposure to ambient (outdoor-generated) fine particulate matter (PM(2.5)) occurs predominantly indoors. The variable efficiency with which ambient PM(2.5) penetrates and persists indoors is a source of exposure error in air pollution epidemiology and could contribute to observed temporal and spatial heterogeneity in health effect estimates. We used a mass balance approach to model F for several scenarios across which heterogeneity in effect estimates has been observed: with geographic location of residence, residential roadway proximity, socioeconomic status, and central air-conditioning use. We found F is higher in close proximity to primary combustion sources (e.g. proximity to traffic) and for lower income homes. F is lower when PM(2.5) is enriched in nitrate and with central air-conditioning use. As a result, exposure error resulting from variability in F will be greatest when these factors have high temporal and/or spatial variability. The circumstances for which F is lower in our calculations correspond to circumstances for which lower effect estimates have been observed in epidemiological studies and higher F values correspond to higher effect estimates. Our results suggest that variability in exposure misclassification resulting from variability in F is a possible contributor to heterogeneity in PM-mediated health effect estimates.