This study demonstrates significant intra-urban spatial variability in ambient levels of benzene, total BTEX, and formaldehyde across New York City monitoring sites, with the widest range in concentrations found in total BTEX. Within the season, we observed limited temporal variability for benzene and BTEX while formaldehyde levels increased with increasing average temperatures. Land-use regression models explained 65%, 70%, and 83% of the total variability of benzene, BTEX, and formaldehyde, respectively with temporal terms and spatial variables representing traffic density, solvent-use industries and built space. The provisional models built with the modeling subset were found to predict concentrations well, predicting 62% to 68% of monitored values at validation sites.
Average benzene and BTEX levels were higher than those measured at rooftop regulatory monitors during the study period, reflecting closer proximity of NYCCAS monitoring sites to traffic sources. Prior NYC-based monitoring studies of air toxics showed higher ambient levels of benzene and BTEX at residential sites mainly in the Bronx and Northern Manhattan than levels reported here [13, 36]. This is likely explained by overall decreases in concentrations in NYC and nationwide over the past decade as well as relatively higher levels of traffic related pollutants in Northern Manhattan and the Bronx compared to the city overall [14, 37]. Associations of benzene and BTEX concentrations with high traffic density are consistent with prior monitoring studies [23, 38, 39].
We found that variables specific to traffic congestion and volume best explained the spatial variability of benzene, with traffic volume indicated through total road lengths around monitoring sites and indicators of traffic density and congestion represented by traffic signal density. These variables were consistent with known sources of benzene in NYC, where gasoline vehicles are, collectively, the predominant source . Prior LUR models for benzene have shown similar results, although some included additional terms related to petroleum usage, proximity to point sources, and population density [16, 21–23]. The association of benzene concentrations with traffic within 400 meters of monitoring locations is consistent with observations that increased benzene levels near roadways decay to background within around 300 meters . In contrast to many prior LUR studies, we chose to address temporal variation by using raw unadjusted concentrations as the dependent variable and the reference site mean as a covariate with the spatial covariates in the model. The advantage of this approach over a model in which temporally adjusted values are regressed onto spatial covariates is that, in estimating the slope for emission source terms, it adjusts for city-wide temporal variation due to meteorology while explicitly accounting for error in estimating the temporal term.
The correlates of spatial variability in total BTEX we observed in New York City are also consistent with known local emission sources including traffic and solvent usage  and with prior studies linking higher BTEX concentrations to traffic as well as distance to VOC emitting point sources [20, 21, 41]. Likely due to limited geographic distribution throughout the city, we did not find associations with large point sources reported in the National Emissions Inventory  and Toxics Release Inventory  or petroleum storage facilities. We did however find associations with density of nearby facilities too small to require Title V permits, but permitted by the City to use solvents in industries known to produce BTEX compounds such as spray booths, graphics industries, and auto body and detailing shops. These facilities are distributed throughout many neighborhoods, although more concentrated in industrial areas. An important limitation of our data is the lack of detailed information on solvent type and quantity at these smaller permitted facilities. Additional sampling near different types of facilities and improved emissions data or proxies could help elucidate these patterns in future work.
Formaldehyde measurements showed less spatial variability than benzene and total BTEX, compatible with findings from prior intra-urban analyses of data from national monitoring networks . We found more temporal variability in formaldehyde with levels increasing with higher average temperatures. These findings are consistent with studies indicating higher temperature and longer daylight hours increase photochemical formation of secondary formaldehyde and levels peak during warm months and mid-day periods [43–45]. To our knowledge there have been no published LUR models for formaldehyde. The predictors of spatial variation found are consistent with known sources of local primary ambient formaldehyde with higher levels found in areas of increased traffic emissions and interior built space indicating increased fuel combustion related to space and water heating.
This study indicates that LUR modeling can be applied successfully to predicting benzene, BTEX, and formaldehyde levels for use in exposure assessment and epidemiological research in complex urban environments like New York City. Prior VOC and aldehyde exposure assessments have applied modeled data from EPA’s National Air Toxics Assessment (NATA) [3, 46–48], regulatory monitoring data [49, 50], and combinations of fixed site and personal monitoring [13, 41]. While NATA modeling is useful in estimating relative concentrations in regional scale assessments, in fine scale, urban analyses, estimates are subject to limited spatial resolution of area and mobile sources in the National Emissions Inventory . Similarly, using few central-site regulatory monitors for exposure classification limits the ability to assess near source concentration gradients, such as near roadways . Prior air toxics assessments conducted in New York City using fixed site and personal monitoring have provided important data on indoor, outdoor, and personal exposures among cohorts in specific neighborhoods [13, 36] but have not offered comprehensive assessments across the City.
City-wide average temporally adjusted springtime measurements of benzene correspond to concentrations between EPA’s 1 in 105 and 106 lifetime cancer risk benchmarks . Average formaldehyde levels in this study correspond to concentrations above the EPA 1 in 105 lifetime cancer risk benchmark . While risk benchmarks are based on continuous exposures experienced over a lifetime, these springtime results suggest HAPs may contribute meaningfully to cancer and other health risks among large populations of New Yorkers who reside in close proximity to traffic and other local emission sources.
An important limitation to these results is that data was collected during a single spring season. Pollutant concentrations observed may differ in other seasons, particularly for formaldehyde where differences in photochemical activity will affect secondary formation. However, spatial variation should be consistent throughout the year as patterns in source density overall remain relatively unchanged over short time periods. As with all LUR studies, limited data on specific emitters of VOC compounds adds uncertainty to model estimates and likely attenuates associations between observed concentrations and source indicators.
These findings, and those from prior saturation sampling and land-use regression studies conducted in New York City (Clougherty et al. submitted 2012, [19, 37]), indicate many of the neighborhoods impacted by high levels of PM2.5 and NO2 exposure may also experience high levels of benzene, BTEX and formaldehyde. High traffic density contributes to higher levels of both criteria and toxic pollutants evaluated here while areas of high building density are associated with high PM2.5 and formaldehyde levels. Because most studies of intra-urban spatial variation in air pollution exposures have focused on criteria pollutants, characterizing spatial patterns of exposure to common urban air toxics will be valuable in elucidating the health effects of individual pollutants in common pollutant mixtures  as well as development of emissions reduction strategies that maximize health benefits.