Identification of tobacco smoke components in indoor breathable particles by SEM–EDS

Tobacco smoke is one of the greatest sources of indoor particles, which has been linked with serious health effects. Consequently, there has been a widespread interest in analysing tobacco related indoor particulate matter (PM). Nevertheless, the majority of performed studies focused on bulk chemical composition of tobacco related PM, but the knowledge of individual tobacco smoke particles is still limited. Therefore, more information on PM should be provided, namely concerning morphological and chemical characterisation of individual particles. Aiming to further understand the impact of tobacco smoke on human health, this work studied the influence of tobacco smoke on chemical and morphological characteristics of PM10 and PM2.5, collected at one site influenced by smoking and at one reference (non-smoking) site. Chemical and morphological characteristics of 4000 individual particles were determined by Scanning Electron Microscopy (SEM) combined with X-ray microanalysis (by Energy Dispersive Spectrometer – EDS). Cluster analysis (CA) was used to classify different particle groups that occurred in PM, aiming the identification of the respective emission sources. The results showed that tobacco smoke influenced the characteristics of both fine and coarse particles, this influence being stronger for fine fraction. The abundance of particles associated with tobacco smoke was 27% and 5% for PM2.5 and PM2.5–10, respectively; as expected, those particles were not identified in PM2.5 and PM2.5–10 of the reference (non-smoking) site. The results showed that at both sites PM was also influenced by outdoor sources. For PM2.5–10, outdoor particles essentially originated from natural sources accounting for 35% and 15% at the smoking and reference sites, respectively. For PM2.5, outdoor particles account for 38% and 29% at the smoking and reference sites, respectively; these particles showed considerable contribution (13% and 17%) from anthropogenic sources (mainly from traffic emissions). In general SEM–EDS showed to be a useful technique to complement characterisation of PM2.5 and PM2.5–10, and to identify the respective emission sources.