An Empirical Study of the Impact of Freeway Traffic

47 There is clear evidence of the adverse health impacts of traffic-related ultrafine particulate matter. As 48 more commuters are spending a significant portion of their daily routine inside vehicles it is increasingly 49 relevant to study exposure levels to harmful pollutants. This study is the first research effort to 50 simultaneously link detailed traffic data, traffic video analysis, and in-vehicle ultrafine particulate (UFP) 51 exposure data. The objective is to empirically test relationships between traffic characteristics and UFP 52 exposure concentrations. We also study the impact of vehicle shell effects including windows, ventilation, 53 and air conditioning on UFP levels. The results of statistical tests and analysis show that the vehicle shell 54 is the most important factor for in-vehicle UFP exposure concentrations. Closing the external air intake 55 vent is more than twice as effective as rolling up the windows alone – showing that there are steps 56 individual travelers can take to reduce their exposure. Surprisingly, traffic variables have little significant 57 impact on UFP exposure concentrations. Traffic density is the most significant traffic variable, suggesting 58 that inter-vehicle spacing is more important than changing emissions rates in congestion. Finally, 59 qualitative analysis suggests that heterogeneity in the vehicle fleet is the other major factor influencing 60 variations in exposure concentrations. The results of this research have important implications for 61 exposure modeling and potential exposure mitigation strategies. 62 INTRODUCTION 63 Motor vehicle emissions are a known contributor to urban air quality problems [1]. They also 64 have been shown to lead to negative health outcomes for people with long-term exposures, especially to 65 fine particulate matter [2]. These concerns raise interest in strategies to mitigate the health impacts of 66 traffic-related pollution – either by reducing vehicle emissions or reducing human exposure to emissions. 67 Traffic congestion, in particular, has been cited as a cause of human health problems [3]. 68 Congestion mitigation in general is often cited as an air quality improvement strategy [4]. But the full 69 effects of congestion mitigation on motor vehicle emissions and air quality are not well quantified [5–7]. 70 There is even less research regarding the impacts of congestion and congestion mitigation on human 71 exposure to traffic-related pollution. 72 The objective of this research is to quantify relationships between freeway traffic characteristics 73 and air quality/exposure for motorists. This will help illuminate potential exposure mitigation strategies 74 by identifying the primary influencing factors. We also aim to identify gaps or misconceptions in our 75 knowledge about the traffic congestion-exposure relationship, which will help to guide future research in 76 this area. 77 This paper presents results from an ongoing empirical study of traffic conditions and in-road or 78 near-road pollution exposures in Portland, Oregon. We first discuss the background literature and state79 of-knowledge regarding the traffic congestion-exposure relationship. We then describe the data collection 80 method. Results are presented next, followed by conclusions and a discussion of future work. 81 BACKGROUND AND LITERATURE 82 Ultrafine particles (with diameter <0.1μm) are a main component, in terms of particle number, of 83 motor vehicle emissions. Gasoline and diesel engines produce a significant number of particles in the 84 ultrafine size range, with the majority of particle number for gasoline engine exhaust ranging from 2085 60nm and for diesel engine exhaust from 20-130nm [8], [9]. While changes in fuel composition and 86 modern engine technology have led to reductions in vehicle emissions of particles with larger diameters 87 and mass concentration, UFP emissions measured by particle number concentration (PNC) have remained 88 unchanged or even increased [10]. 89 Bigazzi, Kendrick, and Figliozzi 3 In-roadway concentrations of UFPs are elevated compared to ambient conditions. PNCs are 90 significantly higher adjacent to freeways and can remain significantly greater than background 91 concentrations at distances of 300m away [11–15]. During times of heavy congestion, UFP 92 concentrations have been found to be elevated above background to a region of impact beyond 300m 93 [16]. Evaluation of on-road, in-vehicle particle concentrations has recently begun with a small number of 94 studies [17], [18]. Particle concentrations have been found to vary widely by location or roadway and to 95 be affected by specific vehicular traffic sources like truck traffic density [19]. Due to roadway 96 concentrations many times higher than ambient conditions time spent in a vehicle can contribute a large 97 fraction of total exposure [17], [18], [20]. 98 99 Health Impacts 100 Epidemiological evidence shows associations between adverse health effects for populations 101 living in close proximity to traffic-related pollution compared to those living further away. Long-term 102 exposure to traffic-related particulate matter has been associated with pulmonary risks such as asthma 103 development, reduced lung function and growth, increased hospital visits, pulmonary mortality, and a 104 higher prevalence of adverse respiratory symptoms [21]. In a thorough, critical review of epidemiology 105 and toxicology studies involving particulate vehicular emissions, Grahame and Schlesinger [22] found 106 that epidemiology studies with accurate exposure measurement methods show consistent associations 107 between vehicle particulate matter and cardiovascular morbidity and mortality including long-term risks 108 for ischemic heart disease and acute myocardial infarction. 109 Toxicological studies have shown specific mechanisms by which traffic-related UFP and diesel 110 exhaust particles may cause adverse health responses. The small sizes allow for deep deposition into the 111 lung to the alveolar region, pulmonary interstitial spaces, mitochondria cell level, and passage into the 112 circulatory system [23–25]. Macrophages and other respiratory clearance mechanisms are not effective 113 for UFPs, leaving the respiratory system vulnerable to exposure. The high number and presence of UFPs 114 in the lungs can also cause mechanical damage leading to inflammation and oxidative stress both of 115 which can be precursors to cardiopulmonary health risks. Studies using in-vitro, in-vivo and human panel 116 designs involving particle numbers and diesel exhaust exposures have shown significant results of 117 adverse health impacts, supporting a causal relationship between traffic-related particulate matter and 118 adverse cardiovascular impacts [22]. 119 Short-term exposures, as would be experienced while commuting in traffic, have also begun to 120 show negative health effects tied to traffic-related particulates. The National Human Activity Pattern 121 Survey found an average of 95 minutes per day is spent in-vehicle [26]. Various studies exposing healthy 122 humans to diesel exhaust for approximately a 60-minute exposure found adverse health responses of 123 inflammation and oxidative stress hours after the exposure occurred [27–29]. Time spent in traffic with 124 the use of a car was the most common source of exposure significantly associated with the onset of a first 125 myocardial infarction (MI) (heart attack) [30]. The time spent commuting in the roadway environment 126 with elevated PNCs has direct effects on the blood stream and respiratory system of humans suggesting 127 the need to mitigate in-vehicle exposures to traffic-related particulate matter. 128 129 Factors Affecting UFP Exposure in the Transportation Environment 130 PNCs in the transportation environment are reduced by atmospheric dispersion and dilution 131 through enhanced Brownian coagulation leading to particle size growth [31] or condensation/evaporation 132 to alter particle size, lowering number concentrations [32]. The roadway environment is not homogenous, 133 Bigazzi, Kendrick, and Figliozzi 4 and characteristics of the roadway and immediate surroundings will affect how much dispersion or 134 dilution can take place. 135 Driving behavior and individual human receptor factors may also affect exposure. The close 136 proximity of a vehicle to undiluted emissions from other vehicles can elevate in-vehicle exposure [33]. 137 Respiration rate and/or previous health conditions of the driver would affect volumes of pollutants 138 inhaled, absorption, uptake levels, and total exposure levels. Additionally, the seal of the individual 139 vehicle and ventilation types could create different barrier levels changing exposure levels [34]. A recent 140 study of in-vehicle exposure found lower UFP concentrations with the ventilation system set to 141 recirculation and the ventilation fan on high [17]. 142 143 Traffic Congestion and In-Vehicle Exposure Relationship 144 In-vehicle exposure assessment studies have traditionally focused on comparing exposure 145 concentrations across travel modes (car, bike, bus, taxi, rail) and types of routes [35]. The impacts of 146 changing traffic conditions on in-vehicle exposure, however, are still not quantified. Real-world data are 147 important to understand the relationships between traffic conditions and in-vehicle exposure due to 148 heterogeneity of the roadway environment. Mobile platform measurements of roadway concentrations 149 have begun to increase in recent years in order to better understand spatial and temporal gradients of air 150 quality in urban areas [19]. 151 While on-road concentrations and in-vehicle concentrations of traffic-related pollution are 152 beginning to be better characterized using real-world data measurement techniques and mobile 153 monitoring, no study has used simultaneous real-world traffic data and pollutant exposure data (outside of 154 video recordings only [18]). This study combines in-vehicle and outside-vehicle UFP measurements with 155 simultaneous traffic data gathered at various levels of traffic congestion. Measurements are used to 156 quantify relationships between freeway traffic congestion characteristics and UFP exposure 157 concentrations for motorists. 158 DATA COLLECTION 159 The data collection effort was designed to empirically test relationships between traffic conditions 160 and UFP concentrations. Using probe vehicles in the traffic stream and embedded roadway traffic sensors, 161 we collected concurrent traffic and air quality data on six non-contiguous days during the summer and fall 162 of 2010.Probe vehicle were driven on a 6.4-mile stretch of OR-217, an urban freeway in the Portland, 163 Oregon metropolitan area. 164 On each day of data collection, a single probe vehicle equipped with air quality instruments, two 165 GPS (Global Positioning System) receivers, and a forward-facing video camera was driven continuously 166 on the freeway for a period of approximately three hours. Simultaneous data were also gathered from 167 vehicle detectors along the freeway and from stationary air quality and meteorological monitoring 168 stations. Three different probe vehicles were used over the six days of data collection (all passenger 169 sedans). 170 In total, 94 trips were executed, where a “trip” consists of the probe vehicle traveling the 6.4-mile 171 corridor in a single direction. These trips constitute 15.4 hours of data, or 55,543 second-by-second data 172 points. The probe vehicle trips were executed in loops, alternating southbound (SB) and northbound (NB) 173 travel directions. Five of the data collection days were on weekdays (Tuesdays and Thursdays), and one 174 was on a Sunday (to capture lighter traffic conditions). On the weekdays, the data collection periods 175 covered varying time spans before, during, and after the evening traffic peak period. 176 177 Bigazzi, Kendrick, and Figliozzi 5 The simultaneous data collected were: 178  Forward-facing digital video recordings from the probe vehicle 179  GPS-based speed and position for the probe vehicle (1 second intervals) 180  In-vehicle UFP concentrations on both the driver’s and passenger’s sides (1 second intervals) 181  Outside-vehicle UFP concentrations (1 second intervals) 182  Traffic data for each lane (vehicle count and speed) from inductive dual-loop detectors (20 183 second intervals) 184  Meteorology from a nearby weather station (10 minute intervals) 185  Air quality from regional air quality monitoring stations (Hourly and daily aggregations) 186  Road grade and geometry 187 UFP data were collected on all days but because only two UFP monitors were available, either two in188 vehicle monitors or one in-vehicle and one outside-vehicle monitor were used. The 6 data collection days 189 are summarized in Table 1. The weather and air quality data in Table 1 are averaged over the data 190 collection period, with the exception of PM2.5 (particulate matter <2.5 microns) and AQI (Air Quality 191 Index) which are daily averages. The data sources are described in more detail below. 192 193 Table 1. Data Collection Summary June 10, 2010 August 31, 2010 September 2, 2010 September 7, 2010 October 12, 2010 October 17, 2010 Day of Week Thursday Tuesday Thursday Tuesday Tuesday Sunday Hours 15:00–18:32 14:48–18:02 14:42–17:50 14:27–18:18 15:50–19:18 17:45–20:00 # of Trips 7 SB, 7 NB 7 SB, 7 NB 8 SB, 8 NB 8 SB, 8 NB 9 SB, 9 NB 8 SB, 8 NB Probe Vehicle 1999 Pontiac Grand Prix 2010 Toyota Prius Hybrid 2010 Toyota Prius Hybrid 2007 Honda Civic Hybrid 2007 Honda Civic Hybrid 2007 Honda Civic Hybrid OR-217 Traffic Volume (veh/day) 103,259 99,456 103,905 97,678 97,186 72,205 Temperature * (°F) 54 60 81 62 65 54 Wind Speed * (mph) 0.6 1.4 7.3 0.7 0.5 1.2 Wind Gusts * (mph) 4.1 5.7 16.2 3.9 1.5 5.6 Relative Humidity * (%) 97 93 37 80 42 57 Hourly Precip. (in) 0.02 0.01 0.00 0.06 0.00 0.00 Nitrogen Oxides * (ppb) 13.8 10.9 8.87 13.4 20.2 15.6 Ozone * (ppm) 19.4 21.6 41.8 20.6 14.6 13.4 Carbon Monoxide * (ppm) 0.42 0.30 0.22 0.27 0.35 0.39 PM2.5 + (μg/m) 2.6 2.8 3.0 3.6 5.6 7.2 AQI + 8 9 10 12 18 23 * averaged over data collection period; + averaged over entire day