Creating locally-resolved mobile-source emissions inputs for air quality modeling in support of an exposure study in Detroit, Michigan, USA.

Michelle Snyder,Neil Davis,Alejandro Valencia,Adel Hanna,Brian Naess,Saravanan Arunachalam,Kevin Talgo,Mohammad Omary,Vlad Isakov,Rich Cook

doi:10.3390/ijerph111212739

Abstract

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants.

Highlights

A recent World Health Organization report [1] linked more than seven million deaths in 2012 to air pollution, making it the largest single environmental health risk
In the NEXUS study, we found that local measurements showed slightly different annual average daily traffic (AADT) counts for some roadways compared to the traffic counts obtained from the travel demand model, a model typically used to estimate vehicle travel on roadways in an urban area
We used local measurements of traffic to modify our estimation of emissions and examined the resulting impacts on the exposure estimates for the same locations and examine the implications of using local measurements to augment the methodology for the application in the NEXUS health study

Summary

Introduction

A recent World Health Organization report [1] linked more than seven million deaths in 2012 to air pollution, making it the largest single environmental health risk. With the large number of people exposed to high levels of air pollution in urban areas, there is an increasing need to have highly resolved spatiotemporal modeling tools to capture high exposure levels for health studies, and to link sources of emissions with their adverse health effects. Air quality models have traditionally provided critical information for air quality management decisions, but recently have been used to provide exposure estimates for air pollution health effects studies. Dispersion models can be challenging to implement, in studies addressing near-road impacts, due to the detailed inputs needed to estimate traffic-related emissions [2]. The methodology and example case study presented here provide information on some of the data needed to model traffic-related pollutant impacts accurately in a large, diverse urban area

Methods

Results

Conclusion