Modeling of Personal Exposures to Ambient Air Toxics in Camden, New Jersey: An Evaluation Study

Abstract

This study presents the Individual Based Exposure Modeling (IBEM) application of MENTOR (Modeling ENvironment for TOtal Risk studies) in a hot spot area, where there are concentrated local sources on the scale of tens to hundreds of meters, and an urban reference area in Camden, NJ, to characterize the ambient concentrations and personal exposures to benzene and toluene from local ambient sources. The emission-based ambient concentrations in the two neighborhoods were first estimated through atmospheric dispersion modeling. Subsequently, the calculated and measured ambient concentrations of benzene and toluene were separately combined with the time-activity diaries completed by the subjects as inputs to MENTOR/IBEM for estimating personal exposures resulting from ambient sources. The modeling results were then compared with the actual personal measurements collected from over 100 individuals in the field study to identify the gaps in modeling personal exposures in a hot spot. The modeled ambient concentrations of benzene and toluene were generally in agreement with the neighborhood measurements within a factor of 2, but were underestimated at the high-end percentiles. The major local contributors to the benzene ambient levels are from mobile sources, whereas mobile and stationary (point and area) sources contribute to the toluene ambient levels in the study area. This finding can be used as guidance for developing better air toxic emission inventories for characterizing, through modeling, the ambient concentrations of air toxics in the study area. The estimated percentage contributions of personal exposures from ambient sources were generally higher in the hot spot area than the urban reference area in Camden, NJ, for benzene and toluene. This finding demonstrates the hot spot characteristics of stronger local ambient source impacts on personal exposures. Non-ambient sources were also found as significant contributors to personal exposures to benzene and toluene for the population studied.

Comparison of stationary and personal air sampling with an air dispersion model for children’s ambient exposure to manganese

“…Evaluations of the performance of AERMOD relative to other air dispersion estimation techniques indicted that AERMOD provides location-specific exposure risk estimates with higher accuracy, simulates pollutant dispersion at or under 50 km accurately, and tends to generate lower concentration estimates for point sources at the point of maximum air concentration. [21][22][23] The Communities Actively Researching Exposure Study (CARES) in Marietta, OH provided an opportunity to gain a greater understanding of the link between concentrations of ambient air Mn and children's exposure. 24 Marietta is home to the longest operating ferromanganese refinery in the nation, Eramet Marietta Incorporated (EMI).…”

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

“…At state and local levels, Lupo and Symanski [34] found 1996 NATA underestimated 8 out 15 HAPs and 1999 NATA underestimated 18 out of 27 HAPs in Texas. Wang et al [50] found general agreement for benzene and toluene concentrations modeled by 1999 NATA in Camden, New Jersey. Logue et al [33] reported that the 2002 NATA underestimated 32 out of 49 HAPs measured at 7 sites in and around Pittsburgh, Pennsylvania.…”

“…It was notable that previous studies found good agreement for benzene; however, our results showed moderate agreement, possibly due to different comparison methods. These findings indicate that model-to-monitor agreement was inconsistent by region and chemical, and under-prediction was more frequent [35,36,50].…”

“…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.…”

“…It is symmetrical, bounded and dimensionless [47]. An FB between −0.67 and +0.67 indicates acceptable agreement, and values of −2 and +2 indicate extreme underestimation and overestimation, respectively [47,50]. An FB was calculated when ΔM = |C NATA − C AQS | was quantifiable.…”