Heather A. Holmes

The near-neutral atmospheric surface layer: turbulence and non-stationarity.

The neutrally stable atmospheric surface layer is used as a physical model of a very high Reynolds number, canonical turbulent boundary layer. Challenges and limitations with this model are addressed in detail, including the inherent thermal stratification, surface roughness and non-stationarity of the atmosphere. Concurrent hot-wire and sonic anemometry data acquired in Utahs western desert provide insight to Reynolds number trends in the axial velocity statistics and spectra.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

Bayesian-based ensemble source apportionment of PM2.5.

A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties. Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM2.5). The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model. The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM2.5 data set. For each day in a long-term PM2.5 data set, 10 source profiles are sampled from these distributions and used in a CMB application, resulting in 10 SA results for each day. This formulation results in a distribution of daily source impacts rather than a single value. The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty, respectively. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R(2) = 0.66) and water-soluble potassium (R(2) = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S. EPA). In addition to providing results that are more consistent with independent observations and known emission sources being present, the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results.

Air Pollution and Preterm Birth in the U.S. State of Georgia (2002–2006): Associations with Concentrations of 11 Ambient Air Pollutants Estimated by Combining Community Multiscale Air Quality Model (CMAQ) Simulations with Stationary Monitor Measurements

Background: Previous epidemiologic studies suggest associations between preterm birth and ambient air pollution. Objective: We investigated associations between 11 ambient air pollutants, estimated by combining Community Multiscale Air Quality model (CMAQ) simulations with measurements from stationary monitors, and risk of preterm birth (< 37 weeks of gestation) in the U.S. state of Georgia. Methods: Birth records for singleton births ≥ 27 weeks of gestation with complete covariate information and estimated dates of conception between 1 January 2002 and 28 February 2006 were obtained from the Office of Health Indicators for Planning, Georgia Department of Public Health (n = 511,658 births). Daily pollutant concentrations at 12-km resolution were estimated for 11 ambient air pollutants. We used logistic regression with county-level fixed effects to estimate associations between preterm birth and average pollutant concentrations during the first and second trimester. Discrete-time survival models were used to estimate third-trimester and total pregnancy associations. Effect modification was investigated by maternal education, race, census tract poverty level, and county-level urbanicity. Results: Trimester-specific and total pregnancy associations (p < 0.05) were observed for several pollutants. All the traffic-related pollutants (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon) were associated with preterm birth [e.g., odds ratios for interquartile range increases in carbon monoxide during the first, second, and third trimesters and total pregnancy were 1.005 (95% CI: 1.001, 1.009), 1.007 (95% CI: 1.002, 1.011), 1.010 (95% CI: 1.006, 1.014), and 1.011 (95% CI: 1.006, 1.017)]. Associations tended to be higher for mothers with low educational attainment and African American mothers. Conclusion: Several ambient air pollutants were associated with preterm birth; associations were observed in all exposure windows. Citation: Hao H, Chang HH, Holmes HA, Mulholland JA, Klein M, Darrow LA, Strickland MJ. 2016. Air pollution and preterm birth in the U.S. state of Georgia (2002–2006): associations with concentrations of 11 ambient air pollutants estimated by combining Community Multiscale Air Quality Model (CMAQ) simulations with stationary monitor measurements. Environ Health Perspect 124:875–880; http://dx.doi.org/10.1289/ehp.1409651

A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

Community Multi-Scale Air Quality (CMAQ) estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority of PM2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is −0.28 (OC), 0.11 (NO3), 0.05 (NH4), and −0.08 (SO4). The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO3, NH4, and SO4 after applying the secondary bias correction. Ten-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.

Development of PM2.5 source profiles using a hybrid chemical transport-receptor modeling approach

Laboratory-based or in situ PM2.5 source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.

Temporally and Spatially Resolved Air Pollution in Georgia Using Fused Ambient Monitor Data and Chemical Transport Model Results

Health data geo-coded with residential coordinates are being used to investigate the relationship between ambient air quality and pediatric emergency department visits in the State of Georgia over the time period 2000–2010. Two types of ambient air quality data – observed concentrations from ambient monitors and predicted concentrations from a chemical transport model (CMAQ) – are being fused to provide spatially resolved daily metrics of five air pollutant gases (CO, NO2, NOx, SO2 and O3) and seven airborne particulate matter measures (PM10, PM2.5, and PM2.5 constituents SO4 2−, NO3 −, NH4 +, EC, OC). The observational data provide reliable temporal trends at and near monitors, but limited spatial information due to the sparse monitoring network; CMAQ data, on the other hand, provide rich spatial information but less reliable temporal information. Four data fusion techniques were applied to provide daily spatial fields of ambient air pollutant concentrations, with data withholding used to evaluate model performance. Two of the data fusion methods were combined to provide results that minimized bias and maximized correlation over time and space with withheld data. Results vary widely across pollutants. These results provide health researchers with complete temporal and spatial air pollutant fields, as well as with temporal and spatial error estimate fields that can be incorporated into health risk models. Future work will apply these methods to five cities for use in ongoing air pollution health studies and to examine strategies for incorporating land use regression variables for spatial downscaling of data fusion results.

Use of Air Quality Modeling Results in Health Effects Research

The most recent Global Burden of Disease study (Lim SS et al, Lancet 380(9859):2224–2260, 2012), for example, finds that combined exposure to ambient and indoor air pollution is one of the top five risks worldwide. Of particular concern is particulate matter (PM). Health researchers are now trying to assess how this mixture of air pollutants links to various health outcomes and how to tie the mixture components and health outcomes back to sources. This process involves the use of air quality models. As part of an EPA Clean Air Research Center, the Southeastern Center for Air Pollution and Epidemiology (SCAPE), a variety of air quality models are being developed and applied to provide enhanced temporal and spatial resolution of pollutant concentrations for use in epidemiologic analysis. Air quality models that are being further developed and used as part of the center include Bayesian-based ensemble methods and hybrid chemical transport-chemical mass balance modeling. The hybrid method uses knowledge of the emissions, modeling and measurement uncertainties, and can provide spatially and temporally complete pollutant fields.

Spatial and Temporal Extension of a Novel Hybrid Source Apportionment Model

Exposure assessment and development of control strategies are limited by the air pollutants measured and the spatial and temporal resolution of the observations. Air quality modeling can provide more comprehensive estimates of the temporal and spatial variation of pollutant concentrations, however with significant uncertainties. Source apportionment, which can be conducted as part of the air quality modeling, provides estimates of the impacts of sources on the mixtures of pollutants and contains surrogate estimates for pollutants that are not measured. This study details results using a novel spatiotemporal hybrid source apportionment method employed with interpolation techniques to quantify the impact of 33 PM2.5 source categories. The hybrid model, which aims to reduce estimating uncertainties, adjusts original source impact estimates from a chemical transport model at monitoring sites to closely reflect observed ambient concentrations of measured PM2.5 species. Daily source impacts are calculated for the contiguous U.S. Two interpolation methods are used to generate the data needed for spatiotemporal hybrid source apportionment. Hybrid adjustment factors are spatially interpolated using kriging, and daily observations are calculated by temporally interpolating available monitoring data. Methods are evaluated by comparing daily simulated concentrations—generated by reconstruction of source impact results—to observed species concentrations from monitors independent of model development. Results also elucidate U.S. regions with relatively higher impacts from specific sources. Monitoring data in this study originated from the Chemical Speciation Network (CSN), EPA-funded supersites, and the Southeastern Aerosol Research Characterization (SEARCH) Network. Results are to be used in health impact assessments.

Investigation of time-resolved atmospheric conditions and indoor/outdoor particulate matter concentrations in homes with gas and biomass cook stoves in Nogales, Sonora, Mexico.

This paper reports findings from a case study designed to investigate indoor and outdoor air quality in homes near the United States–Mexico border. During the field study, size-resolved continuous particulate matter (PM) concentrations were measured in six homes, while outdoor PM was simultaneously monitored at the same location in Nogales, Sonora, Mexico, during March 14–30, 2009. The purpose of the experiment was to compare PM in homes using different fuels for cooking, gas versus biomass, and to obtain a spatial distribution of outdoor PM in a region where local sources vary significantly (e.g., highway, border crossing, unpaved roads, industry). Continuous PM data were collected every 6 seconds using a valve switching system to sample indoor and outdoor air at each home location. This paper presents the indoor PM data from each home, including the relationship between indoor and outdoor PM. The meteorological conditions associated with elevated ambient PM events in the region are also discussed. Results indicate that indoor air pollution has a strong dependence on cooking fuel, with gas stoves having hourly averaged median PM3 concentrations in the range of 134 to 157 μg m−3 and biomass stoves 163 to 504 μg m−3. Outdoor PM also indicates a large spatial heterogeneity due to the presence of microscale sources and meteorological influences (median PM3: 130 to 770 μg m−3). The former is evident in the median and range of daytime PM values (median PM3: 250 μg m−3, maximum: 9411 μg m−3), while the meteorological influences appear to be dominant during nighttime periods (median PM3: 251 μg m−3, maximum: 10,846 μg m−3). The atmospheric stability is quantified for three nighttime temperature inversion episodes, which were associated with an order of magnitude increase in PM10 at the regulatory monitor in Nogales, AZ (maximum increase: 12 to 474 μg m−3). Implications: Regulatory air quality standards are based on outdoor ambient air measurements. However, a large fraction of time is typically spent indoors where a variety of activities including cooking, heating, tobacco smoking, and cleaning can lead to elevated PM concentrations. This study investigates the influence of meteorology, outdoor PM, and indoor activities on indoor air pollution (IAP) levels in the United States–Mexico border region. Results indicate that cooking fuel type and meteorology greatly influence the IAP in homes, with biomass fuel use causing the largest increase in PM concentration.