Cesunica Ivey

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.

Application of a Hybrid Chemical Transport-Receptor Model to Develop Region-Specific Source Profiles for PM2.5 Sources and to Assess Source Impact Changes in the United States

A novel, hybrid chemical transport/receptor model approach is used to develop spatial fields of daily source impacts. The spatial-hybrid (SH) method uses source impact fields obtained from CMAQ simulations, which are then adjusted to better match species-specific observations. Specifically, the SH method assimilates modeled 36-km source impact estimates from CMAQ and ground observations from the Chemical Speciation Network (CSN) to produce source impacts that better reflect observed data. New source profiles are generated using source impact results from the SH method. The new source profiles reflect modeled and observed concentrations and also reflect secondary formation processes that are captured by CMAQ. Results of the application of this method suggest that the default source profiles used in emissions inventories may be inconsistent with observations. In this work, we present SH source impact fields over the year 2006. These results are then used to develop updated source profiles for fine particulate matter sources for the contiguous U.S. The profiles characterize the composition of 22 particulate matter species, including major ions, carbon species, and 17 metals. Sources analyzed include fossil fuel combustion, mobile sources, sea salt, biogenic emissions, biomass burning, as well as livestock operations, agricultural activities, metals processing, and solvents. Source profiles are evaluated by comparing results for two locations Atlanta, GA, and St. Louis, MO to previous studies.

Source Impacts on and Cardiorespiratory Effects of Reactive Oxygen Species Generated by Water-Soluble PM2.5 Across the Eastern United States

It is hypothesized that PM2.5 with high oxidative potential (OP) can catalytically generate reactive oxygen species (ROS) in excess of the body’s antioxidant capacity, leading to oxidative stress. Therefore, two advanced methods for conducting source apportionment, along with field experiments characterizing air quality, are used to identify the sources of PM2.5 with high OP and relate them to acute health effects. The field study measured OP of ambient water-soluble PM2.5 using a dithiothreitol (DTT) assay at four sites across the Southeastern United States from June 2012 to June 2013. Source apportionment was performed on collocated speciated PM2.5 samples using the Chemical Mass Balance Method with ensemble-trained profiles in Atlanta, GA and CMAQ-DDM for Atlanta and all other measurement sites (Yorkville, GA, Centerville, AL, and Birmingham, AL). Source-OP relationships were investigated using least squares linear regression. The model for Atlanta, GA was applied to PM2.5 source impacts from 1998–2010 to estimate long-term trends in ambient PM2.5 OP for use in population-level acute epidemiologic studies. Biomass burning contributes the largest fraction to total historical OP in Atlanta, followed by light-duty gasoline vehicles and heavy-duty diesel vehicles (43, 22 and 17%, respectively). Results find significant associations between estimated OP and emergency department visits related to congestive heart failure and asthma/wheezing attacks, supporting the hypothesis that PM2.5 health effects are, in part, due to oxidative stress and that OP is a useful indicator of PM2.5 health impacts. Finally, controlling PM2.5 sources with high OP, like biomass burning, may help prevent acute health effects.

Air Quality Model-Based Methods for Estimating Human Exposures: A Review and Comparison

Determining estimates of human exposure is increasingly relying on the use of air quality models and satellite observations to provide spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive as they capture the emissions and meteorological linkages. Additionally they can provide source impact information and concentration fields for a range of species not currently provided from satellite-based observations (e.g., MODIS and MAIAC), and are not subject to cloud interference. Multiple methods based on air quality modeling (including using CMAQ and/or RLINE) with and without data fusion, have been developed and are being used in health studies as part of the EPA-funded Southeastern Center for Air Pollution and Epidemiology Clean Air Research Center. The methods include CMAQ-Data Fusion where concentrations fields are blended with observations to provide spatially and temporally complete pollutant concentrations fields of PM2.5, EC, CO, and NO2. To improve the spatial resolution, this method was extended to include RLINE fields for fine scale (250 m) exposure assessment. Another method was developed to estimate spatial exposure estimates of emissions source categories using CMAQ-derived source impacts for 16–32 sources, along with observations of individual PM species. Each of these approaches have individual strengths and weaknesses. The methods that use a data fusion approach to blend observations and air quality model fields are found to best capture the spatiotemporal trends in the observations, reducing the standard error in the exposure estimates. In the past, such methods were limited by the availability of air quality model fields over long periods, but such fields are becoming more routinely available from air quality forecasting activities.

Using Air Quality Model-Data Fusion Methods for Developing Air Pollutant Exposure Fields and Comparison with Satellite AOD-Derived Fields: Application over North Carolina, USA

A data fusion approach is developed to blend ground-based observations and simulated data from the Community Multiscale Air Quality (CMAQ) model. Spatiotemporal information and finer temporal scale variations have been captured by the resulting fields that are provided by both air quality modeling and observations. The approach is applied to daily PM2.5 total mass, five major particulate species (OC, EC, SO4 2−, NO3 −, and NH4 +), and three gaseous pollutants (CO, NOx, NO2) during 2006–2008 over North Carolina (USA). Applying the data fusion method significantly reduces biases in CMAQ fields to almost zero at monitor locations. The results show improvements in capturing spatial and temporal variability with observations, which is important to health and planning studies. The correlation for the cross-validation test decreased from 0.98 (no withholding) to 0.91 (10% random data withholding) when comparing modeled results to observations. If 10% monitor-based withholding is used, the correlation is 0.91 (random 10% of monitors withheld), and the correlation is 0.88 if spatially-specific withholding is used (10% of monitors withheld are grouped spatially). Results from a satellite-retrieved aerosol optical depth (AOD) method were compared with PM2.5 total mass concentration from data fusion, and the data-fusion fields have slightly less overall error; an R2 of 0.95 compared to 0.81 (AOD). Comparing results from an application of the Integrated Mobile Source Indicator method shows that the data fusion fields can be used to estimate mobile source impacts. Overall, the data fusion approach is attractive for providing spatiotemporal pollutant fields for speciated particulate pollutants, as the demand for accurate, fused, air quality model fields is growing.

Improved Spatiotemporal Source-Based Air Pollutant Mixture Characterization for Health Studies

The growing availability of spatially resolved health data sets (i.e., resident and county level patient records) requires spatially resolved exposure or air quality metrics to investigate the impact of air pollution on health outcomes. While daily air quality data are essential in time-series epidemiologic analysis, the spatial distribution of the observations is limited. Air pollution modeling (i.e., chemical transport modeling (CTM)) addresses this by producing spatially resolved air quality predictions using terrain, emissions and meteorology inputs. However, predicted concentrations may be biased. This work incorporates unique data fusion approaches to combine air quality observations from regulatory monitoring networks (OBS) with the output from a CTM (CMAQ) to generate spatially and temporally resolved gaseous and PM species concentrations. Species concentrations alone cannot directly identify emission sources or characterize pollutant mixtures, therefore source apportionment (SA) models are required to estimate source impacts. The focus of this work is a comparison of SA results for three U.S. regions with differing air pollution sources, St. Louis, Missouri; Atlanta, Georgia; and Dallas-Fort Worth, Texas.