Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter

Abstract

Abstract. Trace element measurements in PM10–2.5, PM2.5–1.0 and PM1.0–0.3 aerosol were performed with 2 h time resolution at kerbside, urban background and rural sites during the ClearfLo winter 2012 campaign in London. The environment-dependent variability of emissions was characterized using the Multilinear Engine implementation of the positive matrix factorization model, conducted on data sets comprising all three sites but segregated by size. Combining the sites enabled separation of sources with high temporal covariance but significant spatial variability. Separation of sizes improved source resolution by preventing sources occurring in only a single size fraction from having too small a contribution for the model to resolve. Anchor profiles were retrieved internally by analysing data subsets, and these profiles were used in the analyses of the complete data sets of all sites for enhanced source apportionment. A total of nine different factors were resolved (notable elements in brackets): in PM10–2.5, brake wear (Cu, Zr, Sb, Ba), other traffic-related (Fe), resuspended dust (Si, Ca), sea/road salt (Cl), aged sea salt (Na, Mg) and industrial (Cr, Ni); in PM2.5–1.0, brake wear, other traffic-related, resuspended dust, sea/road salt, aged sea salt and S-rich (S); and in PM1.0–0.3, traffic-related (Fe, Cu, Zr, Sb, Ba), resuspended dust, sea/road salt, aged sea salt, reacted Cl (Cl), S-rich and solid fuel (K, Pb). Human activities enhance the kerb-to-rural concentration gradients of coarse aged sea salt, typically considered to have a natural source, by 1.7–2.2. These site-dependent concentration differences reflect the effect of local resuspension processes in London. The anthropogenically influenced factors traffic (brake wear and other traffic-related processes), dust and sea/road salt provide further kerb-to-rural concentration enhancements by direct source emissions by a factor of 3.5–12.7. The traffic and dust factors are mainly emitted in PM10–2.5 and show strong diurnal variations with concentrations up to 4 times higher during rush hour than during night-time. Regionally influenced S-rich and solid fuel factors, occurring primarily in PM1.0–0.3, have negligible resuspension influences, and concentrations are similar throughout the day and across the regions.

First long-term and near real-time measurement of trace elements in China's urban atmosphere: temporal variability, source apportionment and precipitation effect

“…Sofowote et al (2015). We noticed that a huge data set of hourly resolved trace metals had been reported in Jeong et al (2016) and Visser et al (2015a), but that no detailed information regarding the specific mass concentrations of trace elements was given.…”

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of southern China: source apportionment and health risk implications

“…A majority of PM source apportionment studies in the literature are documented at a lower temporal resolution, typically in 12-24 h integrated filter samples (Pant and Harrison, 2012;Belis et al, 2013). Only limited studies have applied PMF to determine the trace element emission sources at a high time (hourly) resolution (Gao et al, 2016;Dall'Osto et al, 2013;Pancras et al, 2013;Moreno et al, 2011;Crilley et al, 2017;Visser et al, 2015a;Ancelet et al, 2012Ancelet et al, , 2014. Increasing the time resolution of measurements can capture the impact of PM concentration and personal exposure from many intermittent sources such as biomass combustion or industry emission plumes.…”

Exploration of PM<sub>2.5</sub> sources on the regional scale in the Pearl River Delta based on ME-2 modeling

“…In recent years, ME-2, initiated and controlled via the Source Finder (SoFi) written by the Paul Scherrer Institute, was successfully developed to apportion the sources of organic aerosols (Canonaco et al, 2013). The novel ME-2 model has become a widely used and successful source analysis technique (e.g., Crippa et al, 2014;Fröhlich et al, 2015;Visser et al, 2015;Elser et al, 2016;Reyes-Villegas et al, 2016). The key challenges in running ME-2 are the construction of the appropriate constraint source profiles and the determination of factor numbers, and PMF could serve as the first step when using ME-2 for the determination of the a priori information needed.…”