Impacts of travel activity and urbanicity on exposures to ambient oxides of nitrogen and on exposure disparities

Abstract

Daily exposures to ambient oxides of nitrogen were estimated here for residents of Hillsborough County, FL. The 2009 National Household Travel Survey provided geocoded data on fixed activity locations during each person-day sampled. Routes between activity locations were calculated from transportation network data, assuming the quickest travel path. To estimate daily exposure concentrations for each person-day, the exposure locations were matched with diurnally and spatially varying ambient pollutant concentrations derived from CALPUFF dispersion model results. The social distribution of exposures was analyzed by comparing frequency distributions of grouped daily exposure concentrations and by regression modeling. To investigate exposure error, the activity-based exposure estimates were also compared with estimates derived using residence location alone. The mean daily activity-based exposure concentration for the study sample was 17 μg/m3, with values for individual person-day records ranging from 7.0 to 43 μg/m3. The highest mean exposure concentrations were found for the following groups: black (20 μg/m3), below poverty (18 μg/m3), and urban residence location (22 μg/m3). Urban versus rural residence was associated with the largest increase in exposure concentration in the regression (8.3 μg/m3). Time in nonresidential activities, including travel, was associated with an increase of 0.2 μg/m3 per hour. Time spent travelling and at nonresidential locations contributed an average of 6 and 24 %, respectively, to the daily estimate. A mean error of 3.6 %, with range from −64 to 58 %, was found to result from using residence location alone. Exposure error was highest for those who travel most, but lowest for the sociodemographic subgroups with higher mean exposure concentrations (including blacks and those from below poverty households). This work indicates the importance of urbanicity to social disparities in activity-based air pollution exposures. It also suggests that exposure error due to using residence location may be smaller for more exposed groups.

Use of an exposure model to explore the impact of residential proximity to a highway on exposures to air pollutants of an ambient origin

“…The best of these performed fairly well against results from personal passive samplers (r=0.60-0.62). Further research in Hillsborough County, FL, USA, utilizing data generated by the CALPUFF dispersion model, eventuated in an urban residential group mean exposure of 22 μg/m 3 and individual exposures up to 43 μg/m 3 (Gurram et al 2014). The collective results from these NO x studies are reasonably comparable to our group means of 25-60 μg/m 3 (Table 2, Appendix 7).…”

“…Despite all of the limitations mentioned herein, our results do not deviate wildly from the ranges found in previous modeling studies (Gurram et al 2014;Hannam et al 2013;Zidek et al 2005). Finally, a larger population sample would have likely strengthened statistical associations between occupational groups at the interaction effect level and improved estimates of potential exposure benefits when moving further away from the highway.…”

Inequalities in exposure to the air pollutants PM2.5 and NO2 in Australia

“…A significant body of international quantitative Environmental Justice research has found that vulnerable subpopulations-particularly low-income communities and ethnic minorities-are exposed to higher concentrations of air pollution compared to the rest of the population (Chakraborty et al 2011, Taylor 2011, Hajat et al 2015, Holifield et al 2017. A small number of studies have examined age as well, with some evidence of disparities in exposure to air pollution between age groups, although these findings are not consistent across studies (Clark et al 2014, Gurram et al 2015, Clark et al 2017, Gurram et al 2019.…”

The Multi-Agent Transport Simulation MATSim

“…These make it an interesting test case from an air pollution perspective. Source: Gurram et al (2015) 90.3 Modeling Framework Figure 90.2 depicts the modeling framework used to simulate urban population activity and transportation network performance for the study region. An activity-based model (ABM) of travel demand (DaySim) is coupled with MATSim, here applied as a dynamic tra c assignment (DTA) model.…”

“…The resulting pollutant concentrations will be combined with the diurnal locations of individuals (obtained from the ABM and MATSim) to estimate individual-level exposure to tra c-related pollutants, such as nitrogen oxides. Such individual-level exposure measures will be utilized to estimate demographic group-level exposures for assessment of inequality in exposure to tra c-related air pollution, as we have done previously using travel survey data (Gurram et al, 2015). The model system described above will be used to obtain estimates of population exposure, for alternative scenarios of urban land-use design and transport policies.…”