Hybrid Modeling Approach to Estimate Exposures of Hazardous Air

Article

A hybrid modeling approach to estimate exposures of hazardous air pollutants (HAPs) for the National Air Toxics Assessment (NATA) Richard D. Scheffe, Madeleine Strum, James Thurman, Sharon B Phillips, Alison Eyth, Steve Fudge, Mark Morris, Ted Palma, and Richard Cook Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04752 • Publication Date (Web): 25 Oct 2016 Downloaded from http://pubs.acs.org on October 26, 2016

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Environmental Science & Technology

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A hybrid modeling approach to estimate exposures

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of hazardous air pollutants (HAPs) for the National

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Air Toxics Assessment (NATA)

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Richard D. Scheffe1,*, Madeleine Strum1, Sharon B. Phillips1, James Thurman1 , Alison Eyth1,

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Steve Fudge2, Mark Morris1, Ted Palma1, Richard Cook3

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* Corresponding author email: [email protected]; Phone: 919-541-4650; Fax: 919-541-

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4511

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1

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Durham,

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NC 27711, United States 3

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EC/R Incorporated, Chapel Hill, NC 27514, United States

U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Ann Arbor,

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MI 48105, United States

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KEYWORDS

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Hazardous Air Pollutants

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Hybrid Modeling

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Air Toxics Risk

ACS Paragon Plus Environment

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ABSTRACT

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A hybrid air quality model has been developed and applied to estimate annual concentrations of

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40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the

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2011 calendar year National Air Toxics Assessment (NATA).

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transport model (CTM) with a Gaussian dispersion model, both reactive and non-reactive HAPs

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are accommodated across local to regional spatial scales, through a multiplicative technique

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designed to improve mass conservation relative to previous additive methods. The broad scope

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of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial

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domain is

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improved performance relative to the stand alone CTM and dispersion model. However, model

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performance varies widely across pollutant categories and quantifiably definitive performance

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assessments are hampered by a limited observation base and challenged by the multiple physical

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and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP

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concentration and cancer risk drivers, characterized by strong regional signals associated with

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naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile

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source sector emissions.

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dominated source sectors creates largely similar concentration patterns across the majority of

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HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to non-

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reactive HAPs across the CONUS.

By combining a chemical

precedent setting within the air toxics community. The hybrid design exhibits

The multiple pollutant emission characteristics of combustion

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INTRODUCTION

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The 1990 Clean Air Act Amendments (CAA) list 187 hazardous air pollutants (HAPs) known

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to cause or suspected of causing cancer as well as respiratory, neurological, reproductive and

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other serious health effects. HAPs include a variety of volatile (e.g., formaldehyde, benzene,

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1,3-butadiene) and semi-volatile (e.g., naphthalene, PAH congeners) organic compounds (VOCs

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and SVOCs) and metals (e.g., arsenic, nickel, hexavalent chromium). These air toxics are

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emitted by mobile sources (e.g., cars, trucks and construction equipment); large or major

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stationary sources (e.g., chemical manufacturing, refineries and power plants); smaller, or area,

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sources (e.g., gas stations and dry cleaners); and natural processes (biogenic VOC releases and

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wildfires). Markedly different air quality management approaches are applied to air toxics

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relative to the National Ambient Air Quality Standards (NAAQS) criteria pollutants (ozone,

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particulate matter, nitrogen dioxide, sulfur dioxide, carbon monoxide and lead), despite similar

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emission sources. The NAAQS set defined ambient air target concentration levels based on

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human and environmental welfare risk assessments, and emission strategies are developed to

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meet those targets. In contrast, air toxics emitted from stationary sources are regulated by

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emissions targets through the National Emissions Standards for Hazardous Air Pollutants

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(NESHAPS)1 authorized by section 112d of the CAA. Also, mobile source toxics are regulated

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based on technological feasibility (under Section 202(l) of the CAA), and also reduced by


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controls on hydrocarbons and particulate matter (for example, Tier 3 and 2007 heavy duty

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vehicle standards), and the Mobile Sources Air Toxics (MSAT)2 rules. Consequently, regulatory

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modeling to characterize air toxics concentrations is limited to individual source sector Risk and

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Technology Reviews (RTRs) which attempt to quantify residual risk after implementation of the

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NESHAPS Maximum Available Control Technology (MACT) rules.

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National Air Toxics Assessment (NATA) complements RTR modeling by providing a highly

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spatially resolved national view of air toxics concentrations and risk patterns of 178 HAPs, not

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achievable through a limited observations network.

The non–regulatory

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Here we describe the air quality modeling approach and results driving the 2011 calendar

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year NATA which estimates HAPs inhalation risks associated with cancer incidences and

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noncancer respiratory effects. NATA modeling provides census tract level risk values driven by

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annual concentration estimates based on EPA’s triannually released national emissions inventory

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(NEI). NATAs have been developed for calendar years 1996, 1999, 2002, and 2005 to provide a

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screening of potential high risk areas across the U.S. that warrant further analysis.3

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The NATA presents a comprehensive multiple-pollutant challenge to estimate HAPs with

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a range of reactive and thermodynamic properties influencing exposures across local to regional

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spatial scales.

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previous urban scale studies that integrate a variety of modeling platforms and observational

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data.4-10 Collectively, those urban scale applications expanded the range of exposures and

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reduced exposure misclassification, relative to observation based exposure studies. This national

This national scale hybrid modeling application stands apart from several


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scale application transforms that scope of exposures into a true multiple pollutant, multiple

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spatial scale application over a vast geographic domain.

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METHODS

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Structure and overview. The 2011 NATA air quality model, referred to herein as the hybrid

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method, utilizes the fine spatial scale and direct source attribution features of the AERMOD

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dispersion model11-12 and the full treatment of chemistry and transport afforded by the

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Community Multiscale Air Quality (CMAQ) model, version 5.02 with the Carbon Bond 05

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(CB05) chemical mechanism13.

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Eulerian air quality model designed to simulate the formation and fate of gaseous and particulate

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species, including ozone, oxidant precursors, primary and secondary PM concentrations and

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sulfur, nitrogen and mercury deposition over urban and regional spatial scales. In this

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application, AERMOD treats all species as chemically non-reactive and excludes deposition.

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AERMOD receptor locations are based on the centroids of populated census blocks, monitoring

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site positions and five evenly distributed points within each 12 km horizontal CMAQ grid

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(Figure S1) resulting in a minimum of 5 receptors to cells with over 10,000 receptors and 6.5

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million receptors nationwide.

CMAQ is a comprehensive, three-dimensional grid-based

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Equation 1 is used to calculate 2011 annual average air concentrations at receptor locations,

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which are constrained by forcing the average of within grid cell receptor concentrations to equal

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the CMAQ surface level grid value with AERMOD providing sub-grid scale spatial texture:

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C = AERMODREC × (CMAQPNFB /AERMODGRIDAVG) + CMAQSEC

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CMAQPBIOGENICS

CMAQPFIRES + (1)

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Where;

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C = concentration at a receptor,

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AERMODREC = the AERMOD receptor concentration,

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CMAQPNFB = CMAQ grid cell concentration contributed by primary emissions, excluding fires

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and biogenics,

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AERMODGRIDAVG = the average of all AERMOD results within a CMAQ grid, calculated

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through surface interpolation of all AERMOD receptor locations across CMAQ grid cells to

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eliminate concentration discontinuities,

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CMAQSEC = the CMAQ grid cell contribution from atmospheric reactions, and

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CMAQPFIRES and CMAQPBIOGENICS = CMAQ contribution from primary emissions of fires and

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biogenics, respectively, which are not incorporated in AERMOD.

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CMAQ separately tracks primary and secondary contributions, enabling the AERMOD

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estimate at each receptor location to be normalized to the non-fire, non-biogenic CMAQ primary

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contribution. By anchoring concentration averages to CMAQ, mass conservation is improved,

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which can be violated in direct additive model combinations or when observations are directly

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added to model results, a procedure used in previous NATA iterations. For example, the most

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recent 2005 NATA was susceptible to duplicate counting as an added “background”

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concentration based on ambient observations was added to AERMOD estimates. Diagnosing


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model behavior based on paired model to measurement values can be compromised by the dual

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use of observations and largely inconsistent model inputs driving AERMOD and CMAQ.

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Therefore, the receptor average mass allocation constraint to CMAQ grid values imposed by

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equation (1) is appropriate when combining results from vastly different model architectures as it

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minimizes potential double counting. The emissions and meteorological data sets used to drive

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CMAQ were processed further to generate AERMOD inputs consistent with CMAQ. This

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hybrid approach, which builds on earlier area-specific applications to Philadelphia, PA6 and

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Detroit, MI9, reflects an evolution of national scale HAPs modeling intended to optimize

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characterization of non-reactive and reactive species across multiple spatial scales.

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Meteorological data processing: The gridded meteorological data for 2011 at the 12 km

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continental U.S. scale domain was derived from version 3.4 of the Weather Research and

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Forecasting Model (WRF)14. The WRF model was initialized using the 12km North American

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Model (12NAM) analysis product, initialized by all available observations15, provided by

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National Climatic Data Center (NCDC). Where 12NAM data was unavailable, the 40km Eta

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Data Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmospheric

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Research (NCAR) was used. Landuse and land cover data were based on the 2006 National

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Land Cover Database16. WRF meteorological outputs were processed using the Meteorology-

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Chemistry Interface Processor (MCIP) package, version 4.1.3, to derive hourly specific inputs to

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CMAQ: horizontal wind components (i.e., speed and direction), temperature, moisture, vertical

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diffusion rates, and rainfall rates for each grid cell in each vertical layer17. CMAQ resolved the

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vertical atmosphere with 25 layers, preserving greater resolution in the planetary boundary layer

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(PBL). The meteorological inputs to AERMOD were based on the underlying prognostic data,


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i.e. 12 km WRF data, that was used in CMAQ. The AERMOD meteorological inputs were

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created using the Mesoscale Model Interface Program18 (MMIF). MMIF was used to extract

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data at every 4th WRF grid cell, as well as over 700 WRF grid cells containing National Weather

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Service (NWS) surface stations. Each AERMOD simulation of a source calculated the air

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quality impacts of that source at receptors out to 50 km from the source. The closest WRF grid

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cell that was extracted from MMIF was used for the respective AERMOD simulation. All

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receptors received impacts from sources within 50 km and cumulative concentrations were

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calculated by adding concentrations from all modeled sources impacting the receptor. Isakov et

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al.6 demonstrated successful use of meteorological variables derived from prognostic modeling

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to drive dispersion models, motivated primarily to address spatial gaps in meteorological

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monitoring. Additionally, MMIF outputs have been shown to compare favorably well against

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observed meteorological data when used in AERMOD19.

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Emissions Processing:

The 2011 National Emissions Inventory (NEI) provided the root

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emissions data for CMAQ and AERMOD20.

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broad categories (emissions input resolution down to over 800 source classification codes is

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retained – Text S1) with similar spatial and temporal delineation: major point sources, non-point

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sources (excluding transportation, fires and biogenics), on-road mobile, and non-road mobile

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(including locomotive, aviation and commercial marine vessels). Fires (combined wild,

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prescribed and agricultural) and biogenic emissions are handled only through CMAQ. NEI data,

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provided as specific point and aggregated county level annual estimates, are processed to hourly

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values distributed over 12 km horizontal grids through the Sparse Matrix Operator Kernel

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Emissions (SMOKE) modeling system21. Biogenic emissions were generated by version 3.60

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the Biogenic Emissions Inventory System (BEIS)22. Hourly temporal allocations were developed

Emissions to AERMOD are grouped into four


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for AERMOD, consistent with CMAQ, a departure from previous NATAs that were based on

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annual average inputs.

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typically were allocated spatially to population census tract resolution using a variety of

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surrogates (e.g., land use classifications, population)19.

Non-point source, on-road and non-road emissions for AERMOD

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Initial and Boundary Conditions (IC/BCs): The CMAQ lateral boundary and initial species

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concentrations for benzene, formaldehyde and acetaldehyde were generated by a 2011 year GEOS-Chem

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simulation23. Due to the scarcity of observations suitable for establishing BCs and the

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extended calendar year simulation, zero value IC/BCs were used for the remaining hybrid

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model HAPs.

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Treatment of species. The hybrid model was applied to 40 (Table S1) high risk HAPs among

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a total of 178 HAPs included in the 2011 NATA. The remaining 138 HAPs (Exhibit B-119) were

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modeled just with AERMOD. Although this application focuses on HAPS, it reflects the second

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major national scale U.S. EPA application of the multiple pollutant version of CMAQ, following

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a national assessment of increased ethanol use associated with renewable fuels24. The

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atmospheric chemistry treatments in chemical transport models (CTMs) such as CMAQ are

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based on gas phase reaction processes optimized to characterize ozone, linked with a variety of

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heterogeneous and thermodynamic processes to accommodate particulate matter (PM) formation.

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Consequently, the inclusion of explicit HAP species in current chemical mechanisms is

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predicated by its relative importance in ozone chemistry. Formaldehyde and acetaldehyde are

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explicit species that generate significant amounts of peroxy radicals leading to enhanced ozone

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production and secondary particulate matter formation, and also are high risk HAPs which

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exemplify multiple pollutant linkages driven by atmospheric processes.

HAP species not


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incorporated explicitly in the chemical mechanism are added as non-reactive tracers (e.g., several

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halogenates and napthalene). Other important and reactive HAPs, including benzene, toluene,

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xylene isomers, 1,3 butadiene and acrolein, were added to the CB05 chemical mechanism in

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order to explicitly track their decay through reactions with time-varying radicals and oxidants.

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Since the impact of these species on the chemistry and radical cycling in the mechanism is

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already accounted for in CB05 lumped model species, this explicit representation consisted of

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decay without modification of the radicals or oxidants, and in the case of 1,3-butadiene,

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production of secondary acrolein. Future applications of the hybrid model will increase the

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number of hybrid species as part of a continuous CMAQ development program, which will add

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explicit treatment of napthalene, benzene, acrolein, 1,3 butadiene, xylenes, and toluene.

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Additional non-reactive tracers to be added include carbon tetrachloride, ethylbenzene,

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chloroprene and methyl chloride. AERMOD treats all HAPs as nonreactive and was applied to

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the remaining HAPs not incorporated within CMAQ using previous methodology to develop the

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complete 2011 NATA19.

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Source Attribution: Estimates of the source contributions associated with primary emissions

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were generated by the following ratio technique normalized to CMAQ concentrations for the

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AERMOD source groups:

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[CREC,J] = AERMODREC,J × [CMAQPNFB]/[AERMODGRIDAVG]

(2)

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where [CREC,J] = the contribution to concentration at a receptor from source category J,

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excluding secondary formation. This ratio approach provides an estimate of primary emission


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contributions only. Primary emission contributions from biogenics and fires were processed

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through CMAQ and all contributions from secondary formation processes were aggregated into

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CMAQSEC, resulting in all within grid receptors receiving identical contributions from fires,

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biogenics and secondary contributions.

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Risk Calculations: Cancer and noncancer risks are estimated by relating annual exposure

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concentration estimates to relevant health toxicity values.

Exposure concentrations were

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generated by the Hazardous Air Pollutant Exposure Model19 (HAPEM), which modified hybrid

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concentrations to account for movement of individuals across indoor, outdoor and commuting

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environments. Differences between exposure and ambient concentrations were relatively minor

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for most species. The toxicity values (Table S1) are quantitative expressions used to estimate the

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likelihood of adverse health effects given an .estimated level and duration of exposure. Because

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NATA is focused on long-term exposures, the toxicity values used are based on the results of

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chronic dose-response studies when such data are available. Cancer risk is estimated from

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inhalation exposure concentration using a cancer unit risk estimate (URE), which is the upper-

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bound excess lifetime cancer risk estimated to result from continuous exposure to an agent at a

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concentration of 1 µg/m3. Noncancer risk (hazard) for a pollutant is benchmarked to a Reference

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Concentration (RfC), which is an estimate of a continuous inhalation exposure that is thought to

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be without an appreciable risk of deleterious health effects over a lifetime. The resulting

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noncancer metrics for each pollutant (the hazard quotient) are aggregated into an index if the

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pollutants act by similar toxic modes of action or affect the same target organ. Only non-cancer

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respiratory effects are reported here, which is the most significant for inhalation exposure to the

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pollutants included in NATA.


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Model Evaluation and Air Quality Observations: Annual average comparisons of paired

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model estimates and observations for a subset of HAPs were developed using 2011 ambient

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observations obtained from Phase 9 of EPA’s Air Toxics Monitoring Archive25, derived largely

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from EPA’s Air Quality System (AQS). State and local agencies and Tribes (SLTs) are required

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to submit data generated from EPA’s National Air Toxics Trends (NATTS) network and the

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Urban Air Toxics Monitoring Program (UATMP) to the AQS. The UATMP provides centralized

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laboratory analytical support for air toxics networks (Figure S2) 26-27, which are described in the

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supporting materials (Text S2).

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Results and Discussion

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Model Evaluation

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We focus on a core group of 8 HAPs (benzene, formaldehyde, 1,3 butadiene, acetaldehyde,

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acrolein, nickel, arsenic and napthalene) among the 40 hybrid species, that aggregated represent over 90%

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of the national HAPs associated cancer and noncancer risk and reflect the variety of chemical and

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physical properties across the complete set of 178 HAPs. Paired average annual model to monitor

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site comparisons are presented for the hybrid model along with stand-alone CMAQ and

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AERMOD for several HAPs. Acrolein was excluded given the well-known and substantial

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sampling artifact issues associated with acrolein formation inside canisters and stability of

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calibration standards28. Inclusion of all three model results is intended to demonstrate the

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merged attributes of the hybrid model.

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Significant scatter occurs across all pollutants with hybrid model normalized mean error

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(NME) ranging from 38 (acetaldehyde) to 62% (1,3 butadiene) for VOCs (Figure S3, Table S2).


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Secondarily formed species, formaldehyde and acetaldehyde, have less NME, which is associated

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with relatively smooth spatial gradients reducing spatial distribution errors. CMAQ alone and

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the hybrid model exhibit similar NME (acetaldehyde 36%, 38%; formaldehyde 40%, 39%) and

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NMB (acetaldehyde 17%, 21%; formaldehyde -37%, -33%) for secondarily formed VOCs. This

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convergence of performance between CMAQ and the hybrid is expected for pollutants exhibiting

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relatively low spatial variability. The low NMB of -6% for benzene might appear encouraging

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from a risk perspective based on long term averaging. However, the low bias masks the

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canceling of positive and negative errors. Because we have relatively high confidence in the

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accuracy and precision of benzene observations relative to other HAPs, there is a sound

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infrastructure for further diagnosing of model behavior to truly improve the performance of

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relatively conservative species. Over 95% of benzene observations exceed minimum detection

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limits (MDLs)27 and there is no evidence of substantial sampling artifacts. AERMOD under

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estimates are expected for secondarily formed HAPs (e.g., -81 NMB for acetaldehyde) given the

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exclusion of atmospheric chemistry. Error and bias statistics between the hybrid model and

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CMAQ were considerably more aligned relative to either model compared to AERMOD. The

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hybrid model exhibited improved NMB relative to CMAQ for non-secondarily formed VOCs -

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benzene (CMAQ:hybrid -27%: -6%) and 1,3 butadiene (CMAQ:hybrid -49%:-20%). Benzene

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appeared to exhibit over-predictions in the Northeast while no clear pattern of regional biases

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were exhibited for carbonyls, an analysis hampered by the limited number of monitoring stations

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(Figure S4). Very minor differences in performance statistics across the three modeling

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approaches were exhibited for non-reactive species (napthalene, arsenic and nickel).

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Formaldehyde under-predictions of nearly 30% are consistent with other modeling

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studies29-31. Biogenically generated isoprene is the principal VOC precursor for formaldehyde.

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BEIS generates less isoprene relative to the Model of Emissions of Gases and Aerosols from

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Nature (MEGAN)32; however, MEGAN typically exhibits a strong positive bias for isoprene

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leading to over estimates of formaldehyde30,33. Biogenic emissions characterization has been an

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active research area for three decades, largely in support of ozone and particulate matter

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modeling and, looking forward, perhaps reinvigorated by formaldehyde and acetaldehyde being

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significant air toxics risk drivers.

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Very high NMEs and NMBs for toluene and metals and order of magnitude and greater

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underpredictions of halogenated VOCs (Table S2) are consistent with the generally poor

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performance exhibited by non-secondarily formed species. Poor performance illustrates the

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difficulty of characterizing HAPs that rely on a voluntary emissions reporting program lacking

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the rigor associated with criteria pollutant emissions, compounded by species that push the

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detection limits of current analysis methods with concentrations often below MDLs27.

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This evaluation is broad in the scope of pollutant and geographic coverage relative to

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previous NATAs and related air toxics model evaluations, which collectively lack any

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standardized evaluation approach. For example, an analysis34 for the 1996 NATA in Baltimore,

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MD demonstrated good comparisons based on the ratio of means, but their focus was on

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demonstrating discrepancies between indoor and outdoor exposures. Lupo and Symanski35

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reported ratios of model to monitor means ranging from 0.5 to 2.0 for 48% of paired

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comparisons based on the earlier 1996 and 1999 NATAs throughout Texas urban areas, and


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noted that concordance analysis suggested poorer model performance relative to a means

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comparison. Logue et al.36 produced a rigorous area-specific evaluation of the 2005 NATA in

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Pittsburgh, PA with a relatively rich observation base enabling comparison of nearly 50 HAPs.

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Their findings illustrated a wide range of error across different HAPs, but concentrations for the

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highest risk HAPs typically were within a factor of 2 of the observations. George et al.37

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concluded that the NATA 2002 simulation exhibited good performance for benzene based

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largely on a means evaluation for the Detroit metropolitan area using 2004-2007 Detroit

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Exposure and Aerosol Research Study (DEARS) observations. Kimbrough et al.38 reported

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NATA 2005 under predictions of formaldehyde, acetaldehyde and benzene, but not for 1,3

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butadiene using 2008-2009 observations in Las Vegas. Vennam et al.39 applied CMAQ with 4

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km resolution to an airport in Rhode Island for the 2005 calendar year and reported NME from

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36-70% for eight VOC HAPs and napthalene, with modest error improvement (5-20%) relative

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to 36 km grid resolution. Stroud et al.40 applied the Unified Regional Air-quality Modelling

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System (AURAMS – nested 45 and 22.5 km grid cells) for 2006 over Canada for six VOCs and

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reported NMB values of -15% and 26% for formaldehyde and acetaldehyde, respectively, and

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generally better performance for carbonyls relative to primary VOC species - findings

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directionally consistent with the hybrid model.

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These model evaluation results demonstrate expected behavior of the hybrid model and

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its two modeling components, reinforcing the original model design objectives to capture

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concentration patterns of multiple HAPs across a variety of spatial scales. This diversity of

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HAPs and spatial scales combined with a limited observation network challenges model

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evaluation efforts. Carbonyls are compromised by a limited observations network with


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sampling and analysis artifacts and evolving model process formulations that have not been

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designed to explicitly address the intermediate reaction sets, of great significance to HAPs,

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leading to ozone and secondary particulate matter formation. However, the limited spatial

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variability of carbonyls, relative to non-reactive HAPs, results from atmospheric dynamics

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somewhat analogous to those influencing ozone, perhaps setting a hypothetical reference frame

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for model performance, contingent on improved observations and atmospheric process

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characterizations. The relatively low bias in benzene comparisons may be a reasonable

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performance goal for non-reactive HAPs accompanied by a reliable observation base.

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Nevertheless, the high NME (50%) for benzene is a concern for such a high volume, ubiquitous

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pollutant that benefits from a nationally consistent approach for generating transportation related

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emissions. Arguably, the hybrid model has greater skill in capturing formaldehyde patterns as

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much of the NME (39%) is associated with under-predictions. The poor performance of metals

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and halogenated VOCs (Table S2) reflects the relative small investments in HAP emissions

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inventories and ambient monitoring networks, limited by a lack of regulatory drivers. Unlike

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ozone and PM precursors, HAP emissions reporting for State agencies is voluntary, resulting in

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inconsistent methodologies. Relatedly, the robustness of HAP observations also is.

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As HAP modeling has transitioned from a dispersion model centered framework to

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greater reliance on CTMs, the evaluation methods also reflect those practices associated with

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CTMs. There are no accepted benchmarks for HAP model evaluation, in contrast to ozone

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model performance where error and bias for paired (monitoring site and hourly resolution)

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comparisons typically are within 35 and 15%, respectively41-42. Such criteria are not realistic for

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HAPs which are more analogous to ozone precursors, which also lack accepted performance


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criteria. In contrast, a common assertion of “a factor of 2 agreement seems reasonable” often

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accompanies air toxics modeling studies. In addition, aggregation to annual average estimates

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associated with long term chronic health effects dampens errors that would arise from paired

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temporal comparisons, but also compromises diagnostic power to improve model processes and

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inputs.

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Concentration Patterns of Selected HAPs

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Pronounced national and regional scale patterns emerge for several high concentration

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gases.

Acetaldehyde and formaldehyde, generated largely through secondary formation

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processes, exhibit the highest annual average concentrations ranging from 0.5

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aggregated across NOAA climate regions43 for cancer related HAPs (Table S3; Figures 1 and

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S5).

365

concentrations of the remaining cancer benchmarked HAPs by one or more orders of magnitude.

366

Concentrations of toluene and xylene isomers, both lacking a cancer benchmark, are comparable

367

to carbonyl levels.

to 2 µg/m3

Aggregated benzene concentrations approach 1 µg/m3, exceeding the average


17


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368 369 370 371

Figure 1. Annual average 2011 concentration estimates (µg/m3) of reactive HAPs generated by

372

the hybrid model for formaldehyde, acetaldehyde, 1,3 butadiene and acrolein, clockwise from

373

upper left panel, respectively.

374

All of these HAPs exhibit elevated levels in populated areas, indicative of similar source

375

signatures influenced strongly by onroad vehicles, as well as numerous combustion based

376

sources. However, the elevated southeast U.S. acetaldehyde and formaldehyde signals (Figures

377

1- 2) illustrate the combined influences of a rich source of biogenic precursors (isoprene),

378

atmospheric chemistry and transport. The regional nature of these HAPS governed largely by

379

chemistry and transport provides a smoothing of spatial gradients with relatively low spatial


18

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380

coefficients of variation (Cv’s of 0.31 and 0.30 for formaldehyde and acetaldehyde), in contrast

381

to metals (Table S3) which have stronger local scale signals associated with point sources. Yu

382

and Stuart10 reported similar diffuse patterns of secondarily formed HAPs relative to primary

383

species using a hybrid modeling approach

384

estimates for the Tampa, FL area. Benzene is widely distributed across all regions and exhibits

385

less spatial variability compared to metals, although the influence of direct emissions results in

386

greater variability compared to secondarily formed species. 1,3 butadiene undergoes decay, but

387

is not formed secondarily, therefore exhibiting spatial variability (Cv = 0.81) similar to

388

conservative species benzene (Cv = 0.61) and naphthalene (Cv = 0.82).

389

reported similar relative patterns of spatial variability based on near roadway measurements of

390

benzene, 1,3 butadiene, acetaldehyde and formaldehyde.

that combined CMAQ and dispersion model

Kimbrough et al.38

391 392

Figure 2. Normalized concentrations, calculated as the ratio of the regional to national average,

393

of key HAPs by NOAA climate region43 to illustrate pollutant variability across regions. NOAA

394

has defined nine climatically consistent regions across the CONUS.


19


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395

Acrolein is formed secondarily, but is handled simplistically in CMAQ v5.02 through 1,3

396

butadiene decay. Elevated acrolein in northern CA and a widespread region of acrolein in the

397

southeast (Figure 1) is associated with high agricultural fire emissions which are introduced into

398

the lowest CMAQ vertical layer. Emissions from wildfires and prescribed fires are allocated to

399

multiple vertical layers based on plume rise calculations, which increases dilution and transport,

400

perhaps explaining the enhanced effect of northern CA acrolein relative to larger. Because all

401

three carbonyls also are products of industrial, residential and transportation combustion sources,

402

their concentration patterns reflect an underlying structure based on anthropogenic source

403

distributions with regional enhancements associated with natural sources and fires.

404 405

Although metal HAPs exhibit greater spatial variability, their broad (Figures 1, 2 and S5)

406

and fine spatial scale (Figure 3) concentration patterns resemble non-reactive VOCs reflecting

407

the multiple pollutant attributes of ubiquitous combustion processes. Noted exceptions such as

408

elevated Ar and Ni associated with gold mining in Carlin, NV and manufacturing in southeast

409

Idaho (Figure S5), respectively, emerge from scattered metals extraction and processing

410

facilities.

411 412

Figure 3. Annual average concentration distributions of arsenic (left) and benzene(right) in the

413

New York city area illustrating similar distribution patterns (with different concentration ranges)


20

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414

across these two HAPs, and the spatial resolution of the hybrid receptors at the census tract level

415

along with the CMAQ 12 km grid lines.

416 417 418

Risk and Source Attribution

419 420

Pollutant Risk. Formaldehyde, acetaldehyde and benzene represent 77% of total cancer

421

risk nationally, with over half the national risk associated with formaldehyde (Figure 4).

422

Formaldehyde dominates HAPs risk as it is the highest risk driver in over 99% of all U.S. census

423

tracts (Figure S6). Although not a hybrid model HAP, carbon tetrachloride (CCl4) is carried

424

along in the risk reports given the significant 8% contribution. CCl4 concentrations underlying

425

risk estimates are based on observations, justified by spatially homogeneous measurements,

426

centered about 0.6 µg/m3, reflecting a 30 year residence time and elimination of most CCl4

427

emission sources. Naphthalene and 1,3 butadiene also are significant national cancer risk

428

drivers.


21


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429 430

Figure 4. Summary of relative cancer (left) and non-cancer (right) risk results with source

431

attribution (bottom). Note that all source categories reflect primary emission contributions only

432

and clearly understate the contributions from source categories, such as onroad and biogenetics,

433

contributing to secondarily formed HAPs.

434

fraction of the specified HAP to the aggregate risk of all HAPs. CCl4 (top left) is a non-hybrid

435

HAP that is delineated from the non-hybrid category because of its relatively high contribution to

436

risk. CCl4 will be treated as a hybrid HAP in future NATAs. “Other” represents collections of

437

hybrid HAPs which individually contribute less than 1% and 0.1 % to national cancer (top left)

438

and non-cancer risk (top right), respectively, and source groupings contributing less than 0.3%

439

to cancer risk (bottom left).

Pollutant risk percentages are calculated as the

440


22

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441 442 443

Although formaldehyde dominates HAPs risk, other HAPs pose significant cancer risks

444

from a local scale perspective. Chloroprene, for example, emitted by a single facility in southeast

445

Louisiana generates the highest census tract risk (~800 in a million) in the nation. Of a total 178

446

HAPs considered in NATA, only 13 HAPs are high risk cancer drivers for any particular census

447

tract (Figure S6). Aside from benzene, formaldehyde and napthalene, these HAPs generally are

448

associated with localized point source facilities. In contrast, HAPs such as chloroprene and coke

449

oven emissions are modeled just with AERMOD as non-hybrid HAPs as they are limited to

450

specific point source facilities.

451 452

Noncancer risk also is driven by carbonyls with acrolein the dominant driver for 70% of

453

the risk across the U.S. (Figure 4). As noted above, acrolein will be treated explicitly in future

454

chemical mechanisms employed by the hybrid model. In addition, theses high non-cancer risk

455

results should promote research into and deployment of reliable measurement technology

456

enabling ground truthing of this important HAP. Diesel PM, based on direct particulate

457

emissions from diesel engines, accounts for nearly 5% of the noncancer related risk. Diesel

458

exhaust is a known carcinogen that currently is not included in EPA’s NATA cancer risk results.

459 460

Source attribution. The hybrid model provides limited capability to characterize

461

emissions sector contributions to concentration and risk as each AERMOD simulation is linked

462

to a specific source group and CMAQ separately tracks secondary production of carbonyls

463

(formaldehyde, acetaldehyde and acrolein) from primary contributions. The combination of high


23


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464

concentrations, relatively high toxicity and carbonyl production through atmospheric

465

transformations leads to 47% of the cancer risk interpreted as “secondary” (Figure 4).

466

Anthropogenic contributors to secondary production are spread across many combustion based

467

sectors that emit VOC and inorganic (oxides of nitrogen and carbon monoxide) precursors for

468

carbonyl formation. Consequently, the 18% contribution of onroad sources noted in Figure 4 is

469

associated with primary emissions only, thus understating actual source attribution to sectors that

470

emit organic and inorganic carbonyl precursors.

471 472

Emissions strategy implications

473 474

Further source attribution delineation on secondary carbonyl production can be inferred

475

through instrumented CTM techniques such as direct decoupled method (DDM) and adjoint

476

methods, integrated source apportionment tracking, as well as through brute force sector zero-out

477

simulations; techniques that have been widely applied for ozone and particulate matter.

478

Dunker44 and Dunker et al.45 applied a path-integral method imbedded in the Comprehensive Air

479

Quality Model with Extensions (CAMx) to demonstrate that roughly 50 to 75% of formaldehyde

480

would naturally be derived in urban and rural locations, respectively44-45. The anthropogenic

481

increment in rural and urban locations was limited by NOx and VOC emissions, respectively,

482

partly reflecting the strong primary formaldehyde emissions in urban locations. Carlton and

483

Baker30 estimated that nearly 50% of biogenically derived secondary organic aerosol (SOA) is

484

controllable based on a series of CMAQ zero-out simulations, and reasoned that the strict

485

classification of a biogenic or anthropogenic contribution to SOA is insufficient. The same

486

reasoning is applicable to carbonyls in which the root material may be of biogenic (or fire based)


24

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487

origin. Clearly there are analogies to ozone where “background” ozone has increased in relative

488

importance as the ozone NAAQS has tightened over time. However, two important differences

489

exist: (1) trans-continental transport of carbonyls, and their precursors, are probably insignificant

490

and (2) the long term chronic exposures associated with HAPs elevates the importance of a

491

stable pool of HAPs relative to acute based criteria pollutant exposures where peak

492

concentrations often drive risk.

493 494

For many years air toxics was viewed as a local scale or “hot spot” exposure issue.

495

However, this assessment demonstrates a dominant regional air toxics risk component. The

496

majority of HAPs are combustion derived products emitted collectively through various source

497

sectors, illustrated through the similar concentration patterns across pollutant groups (VOCs,

498

SVOCs and metals; Figures 1, 3 and S5). Consequently, HAPs fit well within a multiple

499

pollutant air management framework where the primary focus on ozone and fine particulate

500

matter abatement strategies likely impart directionally positive impacts on reducing HAPs

501

emissions. Nevertheless, the relevance of local scale exposures remains, as the occurrence of

502

anomalous high exposures to individual pollutants is augmented by the numerous HAPs of

503

concern.

504 505

The hybrid model is unique in its ability to characterize gradients of air pollutants with a

506

wide range of chemical and physical attributes across regional to local scales, reflecting progress

507

in developing a multiple pollutant air quality modeling platform. The findings reported here

508

indicating the importance of carbonyls as national risk drivers challenge perceptions that air

509

toxics is largely a local scale exposure phenomenon. Overall exposure characterization, and


25


Page 26 of 31

510

consequent management, is not markedly different than that associated with strong regional

511

nature of ozone and fine particulate matter. Fortunately, the infrastructure developed to address

512

those NAAQS is well suited to handle the most dominant HAPs risk drivers.

513 514

Limitations and Recommendations

515 516

Limitations of the current hybrid approach clearly in addressing the multiple pollutant,

517

multiple spatial scale challenges are driven by basic data concerns. The scarcity of monitoring

518

sites, with observations challengings MDLs, combined with an evolving and voluntary emissions

519

system, challenges diagnosing the most efficient course for improvement.

520

capturing regional level exposures of high risk secondarily formed HAPs is encouraging.

521

Despite this success, added attention to characterizing carbonyls is warranted as they drive

522

national level cancer risk estimates.

523

modeling may reside in addressing local scale characterizations of less ubiquitous HAPs that

524

pose significant risks in local areas. The high spatial resolution afforded by AERMOD reveals

525

relatively minor differences in the spatial distribution of concentrations across pollutant groups

526

(Figures 1 and 3), suggesting potential efficiencies limiting the use of dispersion modeling to key

527

locations with unique source configurations. Because of these local scale concerns, a national

528

level modeling assessment at best can indicate areas requiring further analysis. Progress can be

529

catalyzed by (1) conducting focused urban and local scale field campaigns to evaluate emissions,

530

(2) requiring SLTs to report HAP emissions, (3) supporting satellite and ground based

531

technologies to improve the carbonyl observational data bases and (4) expanding the integration

The success in

Nevertheless, the most challenging aspect of HAPs


26

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532

and coordination of HAPs emissions and modeling with criteria pollutants to optimize

533

infrastructure leveraging.

534

Acknowledgments.

535

The authors greatly appreciate the effort and insights from the anonymous reviewers that lead

536

to a greatly improved manuscript.

537

management and resource support, Karen Wesson for support in the early model development

538

stage, Chris Misenis for meteorological data processing and Halil Cakir for software support.

539 540

ASSOCIATED CONTENT

541

Supporting Information Available.

542

Figures S1- S6 and Tables S1-S3 provide additional information on model and risk results and

543

model evaluation statistics. Descriptions of monitoring methods are included. This material is

544

available free of charge via the Internet at http://pubs.acs.org.

545

AUTHOR INFORMATION

546

Corresponding Author

547

We also thank Tyler Fox and Marc Houyoux for

*E-mail: [email protected]; Phone: 919-541-4650; Fax: 919-541- 4511

548

Author Contributions

549

The manuscript was written through contributions of all authors. All authors have given approval

550

to the final version of the manuscript.

551


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552

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Hybrid Modeling Approach to Estimate Exposures of Hazardous Air

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