Department of Chemistry, University of Girona, Campus Montilivi s/n, 17071 Girona, Spain; Institut de Química Computacional i Catàlisi (IQCC), University of Girona, Campus Montilivi s/n, 17071 Girona, Spain.
Department of Neurology, Dr Josep Trueta University Hospital, Girona, Spain; Cerebrovascular Unit, Girona Biomedical Research Institute (IdIBGi), Girona, Spain.
Sci Total Environ. 2014 Aug 15;490:899-904. doi: 10.1016/j.scitotenv.2014.05.093. Epub 2014 Jun 5.
The use of biomarkers permits the detection of smoking having taken place in an environment. However, no single biomarker is able to differentiate clearly between different types of environments. Multivariate classification models have helped us to differentiate between outdoors, non-smoking indoors, well ventilated smoking indoors, and smoking environments without good air exchange. We found that the variables that enabled us to classify environments most accurately were indoor temperature, 2,5-dimethylfuran and ethyltoluene. A successful prediction rate of 86.5% was obtained by applying both direct fitting and cross validation discriminant (leave-one-out) analyses. Our results show that although a good air exchange ratio decreases the levels of volatile organic compounds in indoor air due to tobacco smoke, significant contamination still remains.
生物标志物的使用可以检测到在环境中发生的吸烟情况。然而，没有单一的生物标志物能够清楚地区分不同类型的环境。多元分类模型帮助我们区分了室外、非吸烟室内、通风良好的吸烟室内和通风不良的吸烟环境。我们发现，能够最准确地对环境进行分类的变量是室内温度、2,5-二甲基呋喃和乙基甲苯。通过应用直接拟合和交叉验证判别（留一法）分析，我们获得了 86.5%的成功预测率。我们的结果表明，尽管良好的空气交换率会由于烟草烟雾而降低室内空气中挥发性有机化合物的水平，但仍然存在显著的污染。
