Ruaraidh Dobson1, Sean Semple2. 1. Institute for Social Marketing, University of Stirling, United Kingdom. Electronic address: r.p.dobson@stir.ac.uk. 2. Institute for Social Marketing, University of Stirling, United Kingdom.
Abstract
BACKGROUND: Second-hand smoke (SHS) at home is a target for public health interventions, such as air quality feedback interventions using low-cost particle monitors. However, these monitors also detect fine particles generated from non-SHS sources. The Dylos DC1700 reports particle counts in the coarse and fine size ranges. As tobacco smoke produces far more fine particles than coarse ones, and tobacco is generally the greatest source of particulate pollution in a smoking home, the ratio of coarse to fine particles may provide a useful method to identify the presence of SHS in homes. METHODS: An algorithm was developed to differentiate smoking from smoke-free homes. Particle concentration data from 116 smoking homes and 25 non-smoking homes were used to test this algorithm. RESULTS: The algorithm correctly classified the smoking status of 135 of the 141 homes (96%), comparing favourably with a test of mean mass concentration. CONCLUSIONS: Applying this algorithm to Dylos particle count measurements may help identify the presence of SHS in homes or other indoor environments. Future research should adapt it to detect individual smoking periods within a 24 h or longer measurement period.
BACKGROUND: Second-hand smoke (SHS) at home is a target for public health interventions, such as air quality feedback interventions using low-cost particle monitors. However, these monitors also detect fine particles generated from non-SHS sources. The Dylos DC1700 reports particle counts in the coarse and fine size ranges. As tobacco smoke produces far more fine particles than coarse ones, and tobacco is generally the greatest source of particulate pollution in a smoking home, the ratio of coarse to fine particles may provide a useful method to identify the presence of SHS in homes. METHODS: An algorithm was developed to differentiate smoking from smoke-free homes. Particle concentration data from 116 smoking homes and 25 non-smoking homes were used to test this algorithm. RESULTS: The algorithm correctly classified the smoking status of 135 of the 141 homes (96%), comparing favourably with a test of mean mass concentration. CONCLUSIONS: Applying this algorithm to Dylos particle count measurements may help identify the presence of SHS in homes or other indoor environments. Future research should adapt it to detect individual smoking periods within a 24 h or longer measurement period.
Authors: Arti Singh; Gabriel Okello; Sean Semple; Fiona Dobbie; Tarja I Kinnunen; Kwabena F Lartey; Divine D Logo; Linda Bauld; Sampson T Ankrah; Ann McNeill; Ellis Owusu-Dabo Journal: Tob Induc Dis Date: 2020-05-20 Impact factor: 2.600