Evaluation of the cancer risk from PAHs by inhalation:Are current methods fit for purpose?

There is ample evidence from occupational studies that exposure to a mixture of Polycyclic Aromatic Hydrocarbons (PAHs) is causally associated with an increased incidence of lung cancers. In both occupational atmospheres and ambient air, PAHs are present as a mixture of many compounds, but the composition of the mixture in ambient air differs from that in the occupational atmosphere, and varies in time and space in ambient air. Estimates of cancer risk for PAH mixtures are based upon unit risks which derive from extrapolation of occupational exposure data or animal model data, and in the case of the WHO use one compound, benzo[a]pyrene as a marker for the entire mixture, irrespective of composition. The U.S. EPA has used an animal exposure study to derive a unit risk for inhalation exposure to benzo[a]pyrene alone, and there have been a number of rankings of relative carcinogenic potency for other PAHs which many studies have used to calculate a cancer risk from the PAHs mixture, frequently incorrectly by adding the estimated relative risks of individual compounds, and applying the total “B[a]P equivalent” to the WHO unit risk, which already applies to the entire mixture. Such studies are often based upon data solely for the historic US EPA group of 16 compounds which do not include many of the apparently more potent carcinogens. There are no data for human cancer risk of individual PAHs, and conflicting evidence of additivity of PAH carcinogenicity in mixtures. This paper finds large divergences between risk estimates deriving from the WHO and U.S. EPA methods, as well as considerable sensitivity to the mixture composition, and assumed PAH relative potencies. Of the two methods, the WHO approach appears more likely to provide reliable risk estimates, but recently proposed mixture-based approaches using in vitro toxicity data may offer some advantages.peer-reviewe

Comparison of machine learning approaches with a general linear model to predict personal exposure to benzene

Machine learning techniques (MLTs) offer great power in analyzing complex data sets and have not previously been applied to non-occupational pollutant exposure. MLT models that can predict personal exposure to benzene have been developed and compared with a standard model using a linear regression approach (GLM). The models were tested against independent data sets obtained from three personal exposure measurement campaigns. A correlation-based feature subset (CFS) selection algorithm identified a reduced attribute set, with common attributes grouped under the use of paints in homes, upholstery materials, space heating, and environmental tobacco smoke as the attributes suitable to predict the personal exposure to benzene. Personal exposure was categorized as low, medium, and high, and for big data sets, both the GLM and MLTs show high variability in performance to correctly classify greater than 90 percentile concentrations, but the MLT models have a higher score when accounting for divergence of incorrectly classified cases. Overall, the MLTs perform at least as well as the GLM and avoid the need to input microenvironment concentrations

Model Development and Validation of Personal Exposure to Volatile Organic Compound Concentrations

Background: Direct measurement of exposure to volatile organic compounds (VOCs) via personal monitoring is the most accurate exposure assessment method available. However, its wide-scale application to evaluating exposures at the population level is prohibitive in terms of both cost and time. Consequently, indirect measurements via a combination of microenvironment concentrations and personal activity diaries represent a potentially useful alternative. Objective: The aim of this study was to optimize a model of personal exposures (PEs) based on microenvironment concentrations and time/activity diaries and to compare modeled with measured exposures in an independent data set. Materials: VOC PEs and a range of microenvironment concentrations were collected with active samplers and sorbent tubes. Data were supplemented with information collected through questionnaires. Seven models were tested to predict PE to VOCs in 75% (n = 370) of the measured PE data set, whereas the other 25% (n = 120) was used for validation purposes. Results: The best model able to predict PE with independence of measurements was based upon stratified microenvironment concentrations, lifestyle factors, and individual-level activities. The proposed model accounts for 40–85% of the variance for individual VOCs and was validated for almost all VOCs, showing normalized mean bias and mean fractional bias below 25% and predicting 60% of the values within a factor of 2. Conclusions: The models proposed identify the most important non-weather-related variables for VOC exposures; highlight the effect of personal activities, use of solvents, and exposure to environmental tobacco smoke on PE levels; and may assist in the development of specific models for other locations.peer-reviewe

Validation of an optimised microwave-assisted acid digestion method for trace and ultra-trace elements in indoor PM2.5 by ICP-MS analysis

Three microwave-assisted digestion procedures, followed by analysis of digestates employing inductively coupled mass spectrometry (ICP-MS) were evaluated for use in the determination of elements at trace and ultra-trace levels in PM2.5 samples. Digestion procedure 1 used 2.5 mL HNO3 (65%) at 200 ◦C. Procedure 2, consisted of a two-stage digestion step at 200 ◦C with 2.5 mL HNO3 (65%) and 3 μL HF (48%) followed by 24 μL H3BO3 (5%). A 10-fold increase in the amounts of HF and H3BO3 was used for procedure 3. The addition of HF/H3BO3 was carried out to aid the dissolution of silicate matrices and refractory compounds. The digestions were carried out using PTFE ultra-trace inserts which increased the sample throughput threefold. The addition of small quantities of HF resulted in the effective solubilisation of Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Sr, Cd, Sb and Pb. The optimal method using HNO3/HF/H3BO3 digestion as in procedure 3 showed recovery efficiency greater than 70% for all elements. The validated method was applied to quantify the elemental content of indoor and outdoor PM2.5 (with samples <0.5 mg) at an urban background site in Malta.peer-reviewe

The seventh national communication of Malta under the United Nations framework convention on climate change

This is the fourth time that Malta is submitting a National Communication under the United Nations Framework Convention on Climate Change (UNFCCC), following the submission of a First National Communication in 2004 and a Second National Communication in 2010. This is also the second time that Malta is submitting such a Communication since its accession to Annex I status under the Convention, the first two submissions having been made as a non-Annex I Party. Emission reduction or limitation commitments applicable to Malta Malta’s status under the Convention up to the time it applied for accession to Annex I, and with that accession being conditional to not taking on quantified emission limitation or reduction targets for the first commitment period of the Kyoto Protocol, meant that until 2012 Malta was not subject to an economy-wide greenhouse gas related obligation under the Protocol. This however did not mean that Malta had no obligations to limit or reduce emissions from anthropogenic activities taking place in the country. In line with, Malta will be contributing its fair share of the EU’s unconditional commitment under the Convention to reduce emissions by 20% below 1990 levels by 2020. This is in line with the target inscribed in the amendments to the Kyoto Protocol (the Doha Amendments), that will be jointly fulfilling the second commitment period with the other Union member states; therefore, emissions from the aforementioned power plants remain subject to compliance with EU Emissions Trading Scheme provisions, while the Effort-Sharing Decision target is the principal emissions mitigation obligation that the country has until 2020, for all other greenhouse gas emissions. The major point sources of greenhouse gas emissions in Malta, namely the electricity generation plants have been, since of 2005, subject to the EU Emissions Trading Scheme, whereby they are required to surrender allowances in respect of emissions of carbon dioxide. Emissions of greenhouse gases not covered by the EU Emissions Trading Scheme, are subject to an overall limit under the so-called Effort-Sharing Decision. Under this decision, Malta must limit such greenhouse gases to not more than 5% over emission levels in 2005, by 2020. The EU is already looking towards the longer-term future, with the 2030 climate and energy framework providing for a 40% domestic reduction target for 2030. Legislative implementation of this goal is currently under discussion at EU level.peer-reviewe