Emissions Modeling with MOVES and EMFAC to Assess the Potential for a Transportation Project to Create Particulate Matter Hot Spots

Abstract

In particulate matter (PM) nonattainment and maintenance areas, quantitative hot-spot analyses are required to assess air quality impacts of transportation projects that are identified as projects of local air quality concern (POAQC). In its 2006 rulemaking, the U.S. Environmental Protection Agency identified sample projects that would likely be POAQCs, including a new highway project with annual average daily traffic (AADT) greater than 125,000 and at least 8% diesel truck traffic. The objective of this study was to identify project characteristics that could reasonably exclude the project from consideration as a POAQC. Scenario analyses were performed for a hypothetical project that featured a new freeway with four mixed-flow lanes and baseline traffic activity of 125,000 AADT and 8% diesel truck traffic. The MO Vehicle Emission Simulator and the Emission FACtors models were used to quantify PM10 and PM2.5 emissions for a 2006 analysis and to evaluate the impact of fleet turnover and truck percentages on project-level emissions from 2006 to 2035. Fleet turnover effects sharply reduce project-level PM2.5 emissions over time. For an analysis year of 2015, impacts from a highway project with 125,000 AADT and 8% trucks are approximately 50% less than impacts from such a project in 2006. In contrast, fleet turnover effects do not substantially reduce PM10 emissions, since re-entrained road dust emissions and tire wear and brake wear emissions increasingly dominate project-level inventories over time, and these emissions vary little by analysis year.

The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

“…However, MOVES exhaust emissions estimates for 2006 were about 70% higher than the EMFAC estimates. On the other hand, EMFAC brake wear estimates were 2.5 times higher than the MOVES estimates [12].…”

“…To assess air quality impacts of transportation projects and determine which projects should be identified as a Project of Air Quality Concern (POAQC), Reid et al (2017) used both the MOVES and the Emission Factors model (EMFAC) to quantify PM10 and PM2.5 emissions in an analysis conducted for the year 2006. Their goal was to evaluate the impact of fleet turn-over and truck use percentages on future project-level emissions [12]. Comparing the two models, they concluded that tire wear and re-entrained road dust emissions from both models were roughly equal.…”

“…Besides, it benefits from the studies mentioned above to develop a working model of particulate matter for California. In particular, monthly PM2.5 data is generated [12], monthly observed meteorological data is acquired [13,18], spatial analysis is utilized to improve low data resolution [15], and eventually, the ANN approach is used to develop an accurate model for particulate matter spatial distribution based on the meteorological variables [14,16,17]. The work's novelty is an in-depth study of the impact of spatial resolution and the use of model generated particulate matter.…”

The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

“…To assess air quality impacts of transportation projects and determine which projects should be identified as a Project of Air Quality Concern (POAQC), Reid et al (2017) used both the MOVES and the Emission Factors model (EMFAC) to quantify PM10 and PM2.5 emissions in an analysis conducted for the year 2006. Their goal was to evaluate the impact of fleet turn-over and truck use percentages on future project-level emissions [13]. Comparing the two models, they concluded that tire wear and re-entrained road dust emissions from both models were roughly equal.…”

“…Additionally, it builds upon the studies mentioned above to develop a working model of particulate matter for California. In particular, monthly PM2.5 data are generated [13], monthly observed meteorological data are acquired [14,19], spatial analysis is utilized to improve low data resolution [16], and eventually, the ANN approach is used to develop an accurate model for particulate matter spatial distribution based on the meteorological variables [15,17,18]. The work's novelty is an in-depth study of the impact of spatial resolution and the use of model-generated particulate matter.…”