The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM 2.5 ) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NO x ). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NO x emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM 2.5 , the differences were 0.1-0.25 g/m 3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM 2.5 NAAQS of 15 g/m 3 . Spatial variations in the ozone and PM 2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM 2.5 levels are dependent on ambient levels of anthropogenic emissions.

The spatial variations of volatile organic compounds (VOCs) were characterized in the Village of Waterfront South neighborhood (WFS), a “hot spot” for air toxics in Camden, NJ. This was accomplished by conducting “spatial saturation sampling” for 11 VOCs using 3500 OVM passive samplers at 22 sites in WFS and 16 sites in Copewood/Davis Streets (CDS) neighborhood, an urban reference area located ∼1000 m east of the WFS. Sampling durations were 24 and 48 h. For all 3 sampling campaigns (2 in summer and 1 in winter), the spatial variations and median concentrations of toluene, ethylbenzene, and xylenes (TEX) were found significantly higher (p < 0.05) in WFS than in CDS, where the spatial distributions of these compounds were relatively uniform. The highest concentrations of methyl tert-butyl ether (MTBE) (maximum of 159 μg m−3) were always found at one site close to a car scrapping facility in WFS during each sampling campaign. The spatial variation of benzene in WFS was found to be marginally higher (p = 0.057) than in CDS during one sampling campaign, but similar in the other two sampling periods. The results obtained from the analyses of correlation among all species and the proximity of sampling site to source indicated that local stationary sources in WFS have significant impact on MTBE and BTEX air pollution in WFS, and both mobile sources and some of the stationary sources in WFS contributed to the ambient levels of these species measured in CDS. The homogenous spatial distributions (%RSD < 24%) and low concentrations of chloroform (0.02–0.23 μg m−3) and carbon tetrachloride (0.45–0.51 μg m−3) indicated no significant local sources in the study areas. Further, results showed that the sampling at the fixed monitoring site may under- or over-estimate air pollutant levels in a “hot spot” area, suggesting that the “spatial saturation sampling” is necessary for conducting accurate assessment of air pollution and personal exposure in a community with a high density of sources.

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.