Modeling Framework of Population Exposure to Traffic-related PM2.5 and Environmental Equity Analysis: Case Study in Atlanta, GA.

This project has been expanded to estimate the population exposure to traffic-related fine particular matter of 2.5 μm or smaller (PM2.5) at an individual/household level, with the input of travel paths, demographic information, and concentration profiles. The extension focuses on additional uncertainty analysis associated with temporal spatial aggregation of input data through the modeling framework and expanded equity analysis based on household-level demographic data in the Metro Atlanta area.

The exposure to fine particular matter of 2.5 μm or smaller (PM2.5) has been widely connected with adverse impacts on human health, but exposure estimation is often limited by a lack of high-resolution modeling framework, which is of crucial importance for quantifying inhaled particulate mass and in conducting environmental equity analysis. The existing models of population exposure are mostly based on coarse input data (area-wide concentration, for instance), and are not sensitive to reflect the change of travel paths. Although there are a few exposure models that provide detailed outputs, these models have not been comprehensively integrated to a complete modeling framework, quantifying both off- and in-vehicle inhalation.

In this work, a modeling framework of population exposure to PM2.5 with high spatiotemporal resolution is proposed and applied to the region of Atlanta, GA for environmental equity analysis. The research is conducted with the following steps: First, the output database of Atlanta Regional Commission’s (ARC) Activity-Based Model 2015 (ABM15) is retrieved, and a refined path retention algorithm is proposed based on previous study (Zhao, et al., 2019) to generate individual travel paths and population temporospatial distribution. The high-resolution concentration profiles by AERMOD is also retrieved from the previous study (Kim, et al., 2019). Second, the travel activity data is integrated with MOVES-Matrix (Guensler et al., 2017) to obtain the individual PM2.5 emission for comparison with the inhalation. Third, the exposure modeling will be conducted based upon the quantification of both off-vehicle and in-vehicle inhaled mass, derived from detailed spatial and temporal attributions of concentration and travel activity input.  Analysis will also include an assessment of temporospatial uncertainty analysis. The outputs will be aggregated into household/demographic group levels and environmental equity will be evaluated across the demographic groups for exposure to traffic-related PM2.5. The products of this research provide exposure/emission analysis at an individual level, with an appropriate number of input data. The proposed modeling framework can be used to link concentration profiles and population exposure for analysis such as environmental equity issues, and the output dataset also benefits the decision-making in the Atlanta region.


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