Elsevier

Atmospheric Environment

Volume 71, June 2013, Pages 364-375
Atmospheric Environment

Fast response sequential measurements and modelling of nanoparticles inside and outside a car cabin

https://doi.org/10.1016/j.atmosenv.2013.02.028Get rights and content

Abstract

Commuters are regularly exposed to short-term peak concentration of traffic produced nanoparticles (i.e. particles <300 nm in size). Studies indicate that these exposures pose adverse health effects (i.e. cardiovascular). This study aims to obtain particle number concentrations (PNCs) and distributions (PNDs) inside and outside a car cabin whilst driving on a road in Guildford, a typical UK town. Other objectives are to: (i) investigate the influences of particle transformation processes on particle number and size distributions in the cabin, (ii) correlate PNCs inside the cabin to those measured outside, and (iii) predict PNCs in the cabin based on those outside the cabin using a semi-empirical model. A fast response differential mobility spectrometer (DMS50) was employed in conjunction with an automatic switching system to measure PNCs and PNDs in the 5–560 nm range at multiple locations inside and outside the cabin at 10 Hz sampling rate over 10 s sequential intervals. Two separate sets of measurements were made at: (i) four seats in the car cabin during ∼700 min of driving, and (ii) two points, one the driver seat and the other near the ventilation air intake outside the cabin, during ∼500 min of driving. Results of the four-point measurements indicated that average PNCs at all for locations were nearly identical (i.e. 3.96, 3.85, 3.82 and 4.00 × 104 cm−3). The modest difference (∼0.1%) revealed a well-mixed distribution of nanoparticles in the car cabin. Similar magnitude and shapes of PNDs at all four sampling locations suggested that transformation processes (e.g. nucleation, coagulation, condensation) have minimal effect on particles in the cabin. Two-point measurements indicated that on average, PNCs inside the cabin were about 72% of those measured outside. Time scale analysis indicated that dilution was the fastest and dominant process in the cabin, governing the variations of PNCs in time. A semi-empirical model was proposed to predict PNCs inside the cabin as a function of those measured outside. Performance evaluation of the model against multiple statistical measures was within the recommended guidelines for atmospheric dispersion modelling. Trip average PNCs obtained using the model demonstrate a reasonably good correlation (i.e. R2 = 0.97) with measured values.

Highlights

► Pseudo-simultaneous measurements at all 4 seats in car, and inside–outside taken. ► Identical PNCs at all 4 seats indicated car cabin air is well-mixed. ► Ratio of in-cabin to outside PNCs is not uniform for different particle sizes. ► Time scale analysis highlights dilution as a dominating process. ► A proposed semi-empirical model predicted inside cabin PNC adequately well.

Introduction

Vehicle emissions are generally the major source of atmospheric nanoparticle pollution in urban areas and consequently make a very significant contribution to the associated adverse health effects (Bos et al., 2013; Donaldson et al., 2005; Hofmann, 2011; Oberdorster, 2000). The scale of such emissions can be estimated from the total number of road vehicles in operation worldwide, a figure put at more than 1 billion in 2010 (Sousanis, 2011). Road users are one of the most exposed groups and recent research by the authors (Joodatnia et al., 2013) demonstrated that freshly emitted nanoparticles comprised more than 99% of particle number concentrations (PNCs) inside a car cabin during journeys on typical UK urban roads. We continue that focus in this paper and investigate the relationship between nanoparticle pollution inside a car cabin and that prevailing outside, and the physical behaviour of particles within a cabin. We are referring nanoparticles as those below 300 nm here to represent the major population of PNCs.

A number of recent studies attempt to characterise passenger exposure to PNCs during commuting. In general, higher PNCs are reported in car cabins (4.9 × 104 cm−3) compared with other transport modes such as buses (4.2 × 104 cm−3) or cycles (3.4 × 104 cm−3) (Int Panis et al., 2010; Knibbs et al., 2011; Knibbs and de Dear, 2010; Wang and Oliver Gao, 2011). Knibbs et al. (2011) highlight that the key determinants (e.g. ventilation system, routes, traffic parameters, meteorological conditions) should be taken into account prior to ranking different transport modes in respect to exposure level. Joodatnia et al. (2013) conducted car cabin measurements in a typical UK town (Guildford). They found that the close proximity to the tail pipe of the preceding vehicle, in slow moving and congested traffic conditions, was the dominant traffic parameter responsible for high PNC levels in the cabin. One second averaged PNC measurements were found to be up to two order of magnitude greater than hourly average values in the car cabin (Joodatnia et al., 2013).

A number of recent studies have also addressed the correlation between PNCs in a car cabin and those measured outside, as summarised in Table 1. The flux rate of nanoparticles into the car cabin is highly influenced by the air exchange rate (AE) (Fruin et al., 2011; Hudda et al., 2012). Hudda et al. (2012) identified dominant factors which influence AE and the ratio of PNCs in the car cabin to those measured outside the cabin; the latter is the so-called penetration factor (I/O). Regression analysis of 116 vehicles under different driving speeds and ventilation settings indicated that AE is the dominant factor affecting I/O (Hudda et al., 2012). General consensus is that AE increases when windows are kept open compared to closed windows conditions with the ventilation on. Fruin et al. (2011) measured AE under recirculation fan setting for 63 vehicles and found that AE increased at higher travelling speeds. However, this effect was more significant for older vehicles compared to newer ones (Fruin et al., 2011). This is possibly due to reduction of sealing efficiency of doors and windows in older cars, which causes them to be less air tight (Fruin et al., 2011; Knibbs et al., 2009). Zhu et al. (2007) measured PNCs in a cabin of a Volkswagen Jetta (model year 2000) on a Los Angeles freeway and reported I/O ∼0.8 under the ‘outside’ air intake fan setting (see Table 1). Zhu et al. (2007) show that the I/O decreases (i.e. ∼0.4) in newer cars (e.g. Audi A4, model year 2004) under the same ventilation conditions. Generally, higher penetration of nanoparticles into the cabin of old cars is experienced compared to newer cars (Tartakovsky et al., 2013; Zhu et al., 2007). Zhu et al. (2007) conclude that vehicle age plays a significant role in commuter protection to nanoparticles in the car cabin. Knibbs et al. (2010) measured PNCs outside and in the car cabin during trips in a tunnel in Sydney, and found the lowest I/O (0.84) for filter fitted vehicles, with the ventilation set to intake outside air into the car cabin. They also showed that the filtration efficiency was improved and I/O reduced further to 0.66 when lower fan settings were employed, see Table 1. Substantial reduction in I/O (i.e. to 0.08–0.47) was observed when the recirculation ventilation setting was employed (Knibbs et al., 2010). Knibbs et al. (2010) state that newer cars with built-in air filters generally offer greater passenger protection to external nanoparticles. A significant reduction in penetration factor is usually observed in driving modes with windows closed and fan set to recirculation. Despite attempts to identify influential factors on I/O, the general assumption is that I/O is constant for all size ranges and no quantitative methods of estimating I/O for different particle sizes have yet been reported in the literature.

Particles emitted from road vehicles undergo a series of complex transformation processes which are constantly competing against each other on different time scales (Ketzel and Berkowicz, 2004). Carpentieri and Kumar (2011) indicate that nucleation is the fastest (∼10−7–10−8 s) particle transformation process during the first stage of dispersion near the tail pipe. Except this initial dispersion stage, for almost all concentration levels near kerbsides in urban environments, the fastest process is dilution, ∼10−1–10−2 s (Ketzel and Berkowicz, 2004). Previous works have evaluated the time scales of particle transformation processes at different urban scales (i.e. street, vehicle wake), but similar studies do not currently exist for vehicle cabins. Therefore, measurements at high sampling frequencies (e.g. 1 s or faster) are essential to obtain a realistic insight of PNC levels, PNDs and transformation processes in car cabins. Such understanding will also provide an opportunity to study short-term personal exposure in car cabins.

In response to these research gaps, a fast response differential mobility spectrometer (Cambustion DMS50) was deployed in conjunction with an automated switching system to measure PNCs and PNDs at multiple points in and outside a car cabin. Measurements represent the driver and passenger seats, and in front of the bonnet outside the car cabin. This study analyses PNC distributions at four points in the car cabin. The study also assesses effects of transformation processes (i.e. coagulation, dry deposition) on PNCs and PNDs in the car cabin using fast response (500 ms) measurements. Furthermore, a quantitative method of estimating I/O for different particle size and a semi-empirical model was proposed to link PNCs in the car cabin to those measured outside.

Section snippets

Study design and route

Measurements were conducted on car journeys during May 2012 in Guildford town centre. Guildford is a typical UK town with about 137,200 inhabitants (OFNS, 2011). Guildford-Borough (2008) has reported a much higher car ownership (more than two cars per household) than the national level (∼1.1). As in previous measurements in Guildford (Joodatnia et al., 2013), measurements were made on a 2.7 km long route that connects Guildford town centre to the University of Surrey (Fig. 1a). The maximum

Results and discussion

In order to ensure the quality of the data collected, sensitivity levels of the DMS50 were assessed by comparing the lowest level of PNDs that the instrument is capable to detect with the minimum PND measured along the route. PNDs for background (minimum) PNCs were found to be well above the lowest level of PNDs that the DMS50 is capable to detect for particle diameters above 7 nm. Further details of instrument signal-to-noise ratio are discussed in our recent study (Joodatnia et al., 2013).

Conclusions and future work

Measurements of particles in the 5–560 nm size range were conducted using a fast response differential mobility spectrometer (DMS50) in conjunction with an automated solenoid switching system. Measurement were conducted at 10 Hz sampling rate over sequential 10 s intervals (i) at four points in the car cabin, and (ii) at two points, one the driver seat, and the other near the ventilation air intake outside the cabin. The four point and two point measurements were conducted during ∼700 and

Acknowledgements

This work is supported by the EPSRC DTA Grant. Thanks also to Surrey University for instrument grant and to Dr. Paul Hayden and Mr. Alistair Reynolds for their help during the experimental campaigns.

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