Recognition models of cigarette smoking behavior by real-time indoor PM_2.5 concentrations in public places

Background Public 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.

Objective To construct a model for recognizing cigarette smoking behavior based on real-time indoor concentrations of PM_2.5 in public places.

Methods Real-time indoor PM_2.5 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 PM_2.5 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 PM_2.5 concentrations, and Spearman rank correlation was used to analyze indoor PM_2.5 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 PM_2.5 concentrations in an Internet cafe.

Results The average indoor PM_2.5 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 PM_2.5 concentration in the public service providers ranged from 1.1 to 19.0. Furthermore, the indoor PM_2.5 concentrations in the 10 public places were significantly correlated with the nicotine concentrations (r_s=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 PM_2.5 concentration, the area under the curve (AUC) was 0.66, while for PM_2.5 concentration at a lag of 4 min after the incidence of smoking behavior, the AUC increased to 0.72.

Conclusion The indoor PM_2.5 concentrations in public places are highly correlated with smoking behavior. Based on real-time indoor PM_2.5 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.