Recognition models of cigarette smoking behavior by real-time indoor PM2.5 concentrations in public places

Abstract

BackgroundPublic places are frequently polluted by cigarette smoking, and there is a lack of accurate, real-time, and intelligent monitoring technology to identify smoking behavior. It is necessary to develop a tool to identify cigarette smoking behavior in public places for more efficient control of cigarette smoking and better indoor air quality. ObjectiveTo construct a model for recognizing cigarette smoking behavior based on real-time indoor concentrations of PM2.5 in public places. MethodsReal-time indoor PM2.5 concentrations were measured for at least 7 continuous days in 10 arbitrarily selected places (6 public service providers and and 4 office or other places) from Oct. to Nov. 2022 in Pudong New Area, Shanghai. Indoor nicotine concentrations were monitored with passive samplers simultaneously. Outdoor PM2.5 concentration data were obtained from three municipal environmental monitoring stations which were nearest to each monitoring point during the same period. Mann-Whitney U test was used to compare indoor and outdoor means of PM2.5 concentrations, and Spearman rank correlation was used to analyze indoor PM2.5 and nicotine concentrations. An interactive plot and a random forest model was applied to examine the association between video observation validated indoor smoking behavior and real-time indoor PM2.5 concentrations in an Internet cafe. ResultsThe average indoor PM2.5 concentration in the places providing public services [(97.5±149.3) µg·m−3] was significantly higher than that in office and other places [(19.8±12.2) µg·m−3] (P=0.011). The indoor/outdoor ratio (I/O ratio) of PM2.5 concentration in the public service providers ranged from 1.1 to 19.0. Furthermore, the indoor PM2.5 concentrations in the 10 public places were significantly correlated with the nicotine concentrations (rs=0.969, P<0.001). Among them, the top 3 highly polluted places were Internet cafes, chess and card rooms, and KTV. The results of random forest modeling showed that, for synchronous real-time PM2.5 concentration, the area under the curve (AUC) was 0.66, while for PM2.5 concentration at a lag of 4 min after the incidence of smoking behavior, the AUC increased to 0.72. ConclusionThe indoor PM2.5 concentrations in public places are highly correlated with smoking behavior. Based on real-time indoor PM2.5 monitoring, a preliminary recognition model for smoking behavior is constructed with acceptable accuracy, indicating its potential values applied in smoking control and management in public places.

Keywords

• public place
• fine particulate matter
• smoking behavior
• random forest model
• nicotine