PMF Analysis of Wide-Range Particle Size Spectra Collected on a Major Highway

Abstract

Particle number concentration data have been collected on a very busy road in central London (Marylebone Road). Continuous size distributions from 15 nm to 10 μm diameter, collected over 21 days, were analyzed using positive matrix factorization which identified 10 factors, five of which were observed to make major contributions (greater than 8%) to either the total number or volume of particulate matter. The sources associated with each factor were identified from the size distribution, directional association, diurnal variation and their relationship to meteorological pollution and traffic volume variables. The factors related to the emissions on Marylebone Road accounted for 40.5% of particle volume and 71.9% of particle number. These comprised nucleation mode exhaust particles (3.6% of total volume and 27.4% of total number), solid mode exhaust particles (18.8% of total volume and 38.0% of total number), brake dust (13.7% of total volume and 1.7% of total number and resuspension (4.4% of total volume and 4.8% of total number). Another six factors were associated with the urban background accounting for 59.5% of total volume and 28.2% of total particle number count. The method is extremely successful at separating the components of on-road emissions including brake wear and resuspension.

Sources of sub-micrometre particles near a major international airport

“…For example, Charron and Harrison (2003) reported that particles in the range of 30-60 nm show a stronger association with light-duty traf- fic at a traffic hotspot in central London (Marylebone Rd); Janhäll et al (2004) reported an average particle size distribution peaking at 15-30 nm during morning peak high traffic intensity in the city of Göteborg (Sweden), which has a car fleet comparable to the UK; Ntziachristos et al (2007) found a sharp mode at 20-30 nm in sampling from engine exhausts. In addition, PMF factors with similar modal structures were found in other studies and were attributed to road traffic emissions: among others, Harrison et al (2011) linked a factor peaking at 20 nm to primary road traffic emissions near a major UK highway; Masiol et al (2016) measured PNSD in an international airport in northern Italy during summer and interpreted a factor with a clear mode at 35-40 nm as road traffic from the nearby city; Beddows et al (2015) and Vu et al (2016) found traffic factors with modal diameter at around 30 nm in an urban background site in London (North Kensington); Sowlat et al (2016) reported a factor peaking at 20-40 nm in number concentration and at around 30-40 nm in volume concentration in Los Angeles (US) and interpreted it as traffic tailpipe emissions. However, this factor lacks significant positive correlations with primary road traffic tracers (nitrogen oxides, eBC; Table 2), while other studies have reported weak positive correlations with such species Masiol et al, 2016;Vu et al, 2016;Sowlat et al, 2016).…”

“…It is recognised that road traffic contributes to a large range (30-200 nm) of PNSD in the urban atmosphere (e.g. Yue et al, 2008;Costabile et al, 2009;Harrison et al, 2011), which is compatible with these spectra. The directional analysis for the warm season shows increased levels when air masses move from the sectors more affected by traffic, i.e.…”

“…In this context, several source apportionment studies on PNSDs have attributed more than one factor to road traffic (e.g. Kasumba et al, 2009;Thimmaiah et al, 2009;Harrison et al, 2011;Liu et al, 2014;Al-Dabbous and Kumar, 2015;Vu et al, 2016;Sowlat et al, 2016). This result is not surprising in areas where heavy traffic is widespread, as particles may undergo condensation, agglomeration, evaporation and dilution processes, and, consequently, they may change modal characteristics in time and space.…”

The Policy Relevance of Wear Emissions from Road Transport, Now and in the Future—An International Workshop Report and Consensus Statement

“…This includes a large proportion of extra-urban highway traffic, where wear emissions are expected to be lower than in the urban environment with its frequent stop-and-go driving and cornering. Estimates of the contribution of nonexhaust particles to total vehicle-generated particulate matter are in the range of 35-55% (Harrison et al, 2001;Charron and Harrison, 2005;Harrison et al, 2011). Recently, Rexeis and Hausberger (2009) predicted, using a detailed emission model for the Austrian fleet, that the percentage of PM nonexhaust of the total PM emissions will increase from about 50% between 2005 and 2010 up to some 80-90% by 2020.…”

Identify the Odor Emission Sources in an Industrial Park by Integrated Data Mining Approach

“…To simulate the real situation of the atmosphere and to understand the exact environment problem, many researchers have developed air quality models and statistical methods recently. Using Receptor Model [5]- [7] and Source Model [8], [9] to evaluate the pollutant concentrations and emission. Source Model divides into three categories.…”