Hybrid Modeling Approach to Estimate Exposures of Hazardous Air Pollutants (HAPs) for the National Air Toxics Assessment (NATA)

Abstract

A hybrid air quality model has been developed and applied to estimate annual concentrations of 40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the 2011 calendar year National Air Toxics Assessment (NATA). By combining a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive HAPs are accommodated across local to regional spatial scales, through a multiplicative technique designed to improve mass conservation relative to previous additive methods. The broad scope of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial domain is precedent setting within the air toxics community. The hybrid design exhibits improved performance relative to the stand alone CTM and dispersion model. However, model performance varies widely across pollutant categories and quantifiably definitive performance assessments are hampered by a limited observation base and challenged by the multiple physical and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP concentration and cancer risk drivers, characterized by strong regional signals associated with naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile source sector emissions. The multiple pollutant emission characteristics of combustion dominated source sectors creates largely similar concentration patterns across the majority of HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to nonreactive HAPs across the CONUS.

A Model-to-Monitor Evaluation of 2011 National-Scale Air Toxics Assessment (NATA)

“…The maximum concentrations in AQS were much higher than those modeled by NATA for all HAPs except for toluene, bromomethane, and methyl isobutyl ketone. This could be explained by the inability of dispersion models to simulate extreme concentrations [38,51].…”

“…Average concentrations measured from monitors within a census tract might be affected by extrema due to nearby short-term strong emissions, which could not be captured by the census-tract averages in NATA. Similarly, the National Emission Inventory, on which NATA estimates were based, might have missed local emission sources [51]. Lack of stable estimates of meteorological conditions and photochemical reactions is another factor leading to disagreement.…”

“…For example, unstable estimates on wind conditions and secondary formation of chemicals were major weakness of the NATA model [38]. The uncertainty in monitored measurement due to insufficient and unbalanced geographic coverage of monitoring sites also contributed to the discrepancies between monitored and modeled estimates [35,51]. These factors warrant future improvements in both monitoring adequacy (technology, frequency and coverage) and modeling parameterization.…”

“…Previous studies have applied a number of model-to-monitor comparison metrics and methods, including biases and root mean square error [50,51,53,54], Kendall rank correlation [38], ratios [33,34,35], regressions [50], and even complex metrics [55]. There has been no commonly accepted criterion for the metric; for example, EPA uses a relative bias of within ±30% and median ratio of 0.5–2 for agreement.…”

Quantifying Urban Spatial Variations of Anthropogenic VOC Concentrations and Source Contributions with a Mobile Sampling Platform

“…Often these sampling campaigns are limited in spatial and/or seasonal coverage [24]. Spatial modeling tools such as the EPA National Air Toxics Assessment (NATA) [26,27,28], chemical transport models including the Community Multiscale Air Quality (CMAQ) model [29], and land-use regression [13,25,30,31,32] have all been used to investigate intra-city variations in BTEX exposure [25,29]. Overall, spatial patterns of VOCs have received less attention than pollutants such as PM 2.5 and NO 2 , so less is known about urban patterns of VOC exposure and the sources driving those spatial patterns.…”