Use of real-time monitors to evaluate the potential exposure of secondhand electronic cigarette particulate matter inside vehicles

Advancing low-cost air quality monitor calibration with machine learning methods

Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, these monitors require calibration and validation in their specific environment using expensive reference instruments, which are often scarce and costly. This study proposes machine-learning calibration methods that utilize a single high-cost instrument as an active reference to improve the accuracy of large networks of low-cost monitors. Three machine learning models-linear regression, random forest, and Gradient Boosting Regression (GBR)-were employed. The proposed approach was tested in a controlled chamber under two conditions: environmental simulations with salt- and dust-based aerosols and occupational settings using three electronic cigarette (ECIG) brands. The study involved thirty low-cost GeoAir2 monitors, divided into ten groups of three. Initially, all groups were collocated with a high-cost monitor using Aerosol A to develop prediction and regression models. These models, along with intrinsic error measurements from one group, were then applied to improve data accuracy for the remaining groups using Aerosol B. The results demonstrated substantial improvements in accuracy, with r2 values ranging from 0.91 to 1.00 and RMSE reductions of up to 88 %, depending on the model and aerosol type. GBR consistently provided the highest accuracy and performance, particularly for complex, nonlinear patterns, while linear regression offered a faster, computationally efficient alternative suitable for less demanding scenarios. Random forest models performed moderately well, balancing accuracy and complexity. These methods provide a scalable and cost-effective solution for deploying networked low-cost sensors. Further research is needed to validate these findings in outdoor environments with meteorological and spatial influences, and indoor occupational settings where humidity effects may play a role.

Characteristics of second-hand exposure to aerosols from e-cigarettes: A literature review since 2010

In recent years, the use of electronic vaping products (also named e-cigarettes) has increased due to their appealing flavors and nicotine delivery without the combustion of tobacco. Although the hazardous substances emitted by e-cigarettes are largely found to be much lower than combustible cigarettes, second-hand exposure to e-cigarette aerosols is not completely benign for bystanders. This work reviewed and synthesized findings on the second-hand exposure of aerosols from e-cigarettes and compared the results with those of the combustible cigarettes. In this review, different results were integrated based upon sampling locations such as residences, vehicles, offices, public places, and experimental exposure chambers. In addition, the factors that influence the second-hand exposure levels were identified by objectively reviewing and integrating the impacts of combustible cigarettes and e-cigarettes on the environment. It is a challenge to compare the literature data directly to assess the effect of smoking/vaping on the indoor environment. The room volume, indoor air exchange rate, puffing duration, and puffing numbers should be considered, which are important factors in determining the degree of pollution. Therefore, it is necessary to calculate the "emission rate" to normalize the concentration of pollutants emitted under various experimental conditions and make the results comparable. This review aims to increase the awareness regarding the harmful effects of the second-hand exposure to aerosols coming from the use of cigarettes and e-cigarettes, identify knowledge gaps, and provide a scientific basis for future policy interventions with regard to the regulation of smoking and vaping.