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ONLINE AIR QUALITY MAPPING OF TRAFFIC EMISSIONS IN NEAR REAL-TIME (extended abstract)
Citation:
Arunachalam, S., B. Naess, C. Seppanen, Vladilen Isakov, AND T. Barzyk. ONLINE AIR QUALITY MAPPING OF TRAFFIC EMISSIONS IN NEAR REAL-TIME (extended abstract). HARMO19 International Conference, Bruges, BELGIUM, June 03 - 06, 2019.
Impact/Purpose:
The Kansas City Transportation and Local-scale Air Quality Study (KC-TRAQS) produced a rich dataset from both traditional measurement techniques and emerging measurement technologies. This big dataset with differing time and spatial resolutions requires innovative data analysis approaches to interpret these data. This paper describes the application of dispersion modeling with real-time traffic activity from Google as an illustrative example of mapping of real-time near road air quality in urban areas.
Description:
The presentation describes an approach for hyperlocal-scale mapping of real-time near road air quality in urban areas. We developed C-REAL, a web-based modeling system to study air pollution exposures due to various sources at a community or city-scale. The power of such web-based, easy-to-use tools is the ability to make these assessments at a rapid pace, and to assess impacts of scenarios on-demand. The tool utilizes meteorology from the National Oceanic and Atmospheric Administration (NOAA’s) High Resolution Rapid Refresh (HRRR) model, traffic activity from Google’s Waze, and emissions factors for NOx and primary PM2.5 from MOVES-2014 as a function of road type, vehicle type, temperature and speed. The dispersion algorithms are based on C-LINE modeling system (Barzyk et al 2015), augmented with near real-time traffic activity and meteorological inputs. In this presentation, we demonstrate an illustrative example of application of the tool in Kansas City, KS in support of the Kansas City TRansportation and Local-scale Air Quality Study (KC-TRAQS). The modeling was centered on Kansas City, KS downtown and included a high-resolution receptor network with 40-meter uniform grid for the entire domain. Every 15 minutes, Google’s Waze data are downloaded and used as inputs for automated model simulations to assess near-road air quality in near real-time. The results are available continuously for every hour (current, two hours prior and two hours in the future) through the web-based interface. We demonstrate the utility of using real-time traffic data and findings from our comparisons with the KC-TRAQS measurements. Furthermore, this presentation discusses the data / infrastructure needs and potential connections that can be made to additional datasets such as more realistic fleet characterization or actual speeds for road segments from Google’s real-time traffic database, and to plan possible interventions for mitigating high exposures. We will also discuss possible extensions of this tool for other cities in the U.S. and in the world
