Particulate matter from tobacco versus diesel car exhaust: an educational perspective: Figure 1

Invernizzi, Giovanni; Ruprecht, Ario; Mazza, Roberto; Rossetti, Edoardo; Sasco, Annie J.; Nardini, S; Boffi, Roberto

doi:10.1136/tc.2003.005975

Cited by 73 publications

(52 citation statements)

References 12 publications

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“…A recent survey in Australia showed that more than half of the long-haul truck drivers are exposed to ETS (Si et al 2016). The exposure to respirable suspended particles PM 2.5 originating from cigarette smoke may even be higher than that from the exhaust of a diesel heavy-duty truck, as recent studies demonstrated (Invernizzi et al 2004;De Marco et al 2016).…”

Section: Residual Confounding By Smokingmentioning

confidence: 99%

A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk

Möhner

Wendt

2017

Critical Reviews in Toxicology

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In 2012, a working group of the International Agency for Research on Cancer classified diesel exhaust (DE) as a human carcinogen (Group 1). This decision was primarily based on the findings of the Diesel Exhaust in Miners Study (DEMS). The disparity between the results of various methodological approaches applied to the DEMS led to several critical commentaries. An expert panel was subsequently set up by the Health Effects Institute to evaluate the DEMS results, together with a large study in the trucking industry. The panel concluded that both studies provided a useful basis for quantitative risk assessments (QRAs) of DE exposure. However, the results of both studies were non-definitive as the studies suffer from several methodological shortcomings. We conducted a critical review of the studies used by the International Agency for Research on Cancer (IARC) working group to evaluate the relationship between DE and lung cancer. The aim was to assess whether the available studies support the statement of a causal relationship and, secondarily if they could be used for QRA. Our review highlights several methodological flaws in the studies, amongst them overadjustment bias, selection bias, and confounding bias. The conclusion from our review is that the currently published studies provide little evidence for a definite causal link between DE exposure and lung cancer risk. Based on two studies in miners, the DEMS and the German Potash Miners study, QRA may be conducted. However, the DEMS data should be reanalyzed in advance to avoid bias that affects the presently published risk estimates.

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Section: Residual Confounding By Smokingmentioning

confidence: 99%

A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk

Möhner

Wendt

2017

Critical Reviews in Toxicology

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“…A brief report found that cigarette smoke caused ten times more particulate matter concentrations in an indoor environment than an idling eco-diesel truck engine. 7 It is evident that smoking and tobacco use are not only a great threat to general public health, but to the health of the environment as well.…”

Section: Health Effects Of Particulate Mattermentioning

confidence: 99%

“…Similar methods are described elsewhere. 7 These smoke samples were subsequently titrated with a strong acid or strong base.…”

Section: Cigarette Samplingmentioning

confidence: 99%

Development and Initial Application of Low-Molecular Weight Organic Acids and Inorganic Ionic Species in Aerosol Particulate Matter via Ion Chromatography

Karpinski

2015

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Air pollution, especially particulate matter, is a widespread environmental and public health risk. In this work, a method has been developed to separate, resolve, and quantitate monovalent anionic and cationic organic acids in aerosol particulate matter through ion chromatography (IC). The standard samples were analyzed on a Dionex ICS-5000 Ion Chromatography System with suppressed conductivity detection, and calibration curves for all analytes were created. A preliminary tobacco smoke sample was also analyzed. This method may be applied to the analysis of atmospheric particulate matter, as well as tobacco smoke particulate matter, in order to determine and quantitate primary anions and cations.

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“…Table 3 shows that there are certainly a lot of activities and conditions which could trigger IAP, such as air fresheners, combustion appliances, water damage, cigarettes, fire and combustion equipment [10,12,13]. However, for the purpose of this study, the sources of IAP are limited to five conditions only, because they are commonly present in the indoor environment: ambient air, combustion activity, the presence of chemical products, the presence of food and beverages, and the presence of fragrances [17,[37][38][39]. The first condition of sources of indoor air pollution is the ambient air.…”

Section: Sources Of Indoor Air Pollutionmentioning

confidence: 99%

Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

et al. 2017

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Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as 'unknown', because the system cannot recognize the input of the new pattern.

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Particulate matter from tobacco versus diesel car exhaust: an educational perspective: Figure 1

Cited by 73 publications

References 12 publications

A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk

A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk

Development and Initial Application of Low-Molecular Weight Organic Acids and Inorganic Ionic Species in Aerosol Particulate Matter via Ion Chromatography

Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

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