Noninvasive integrative approach applied to children in the context of recent air pollution exposure demonstrates association between fractional exhaled nitric oxide (FeNO) and urinary CC16.

Exposure to the air pollutant particulate matter (PM) is associated with increased risks of respiratory diseases and enhancement of airway inflammation in children. In the context of large scale air pollution studies, it can be challenging to measure fractional exhaled nitric oxide (FeNO) as indicator of lung inflammation. Urinary CC16 (U-CC16) is a potential biomarker of increased lung permeability and toxicity, increasing following short-term PM2.5 exposure. The single nucleotide polymorphism (SNP) CC16 G38A (rs3741240) affects CC16 levels and respiratory health. Our study aimed at assessing the use of U-CC16 (incl. CC16 G38A from saliva) as potential alternative for FeNO by investigating their mutual correlation in children exposed to PM. Samples from a small-scale study conducted in 42 children from urban (n = 19) and rural (n = 23) schools examined at two time points, were analysed. When considering recent (lag1) low level exposure to PM2.5 as air pollution measurement, we found that U-CC16 was positively associated with FeNO (ß = 0.23; 95% CI [-0.01; 0.47]; p = 0.06) in an adjusted analysis using a linear mixed effects model. Further, we observed a positive association between PM2.5 and FeNO (ß = 0.56; 95% CI [0.02; 1.09]; p = 0.04) and higher FeNO in urban school children as compared to rural school children (ß = 0.72; 95% CI [0.12; 1.31]; p = 0.02). Although more investigations are needed, our results suggest that inflammatory responses evidenced by increased FeNO are accompanied by potential increased lung epithelium permeability and injury, evidenced by increased U-CC16. In future large scale studies, where FeNO measurement is less feasible, the integrated analysis of U-CC16 and CC16 G38A, using noninvasive samples, might be a suitable alternative to assess the impact of air pollution exposure on the respiratory health of children, which is critical for policy development at population level.

Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children.

Air pollution exposure can lead to exacerbation of respiratory disorders in children. Using sensitive biomarkers helps to assess the impact of air pollution on children's respiratory health and combining protein, genetic and epigenetic biomarkers gives insights on their interrelatedness. Most studies do not contain such an integrated approach and investigate these biomarkers individually in blood, although its collection in children is challenging. Our study aimed at assessing the feasibility of conducting future integrated larger-scale studies evaluating respiratory health risks of air pollution episodes in children, based on a qualitative analysis of the technical and logistic aspects of a small-scale field study involving 42 children. This included the preparation, collection and storage of non-invasive samples (urine, saliva), the measurement of general and respiratory health parameters and the measurement of specific biomarkers (genetic, protein, epigenetic) of respiratory health and air pollution exposure. Bottlenecks were identified and modifications were proposed to expand this integrated study to a higher number of children, time points and locations. This would allow for non-invasive assessment of the impact of air pollution exposure on the respiratory health of children in future larger-scale studies, which is critical for the development of policies or measures at the population level.

Using Large-Scale NO2 Data from Citizen Science for Air-Quality Compliance and Policy Support.

Citizen science projects that monitor air quality have recently drastically expanded in scale. Projects involving thousands of citizens generate spatially dense data sets using low-cost passive samplers for nitrogen dioxide (NO2), which complement data from the sparse reference network operated by environmental agencies. However, there is a critical bottleneck in using these citizen-derived data sets for air-quality policy. The monitoring effort typically lasts only a few weeks, while long-term air-quality guidelines are based on annual-averaged concentrations that are not affected by seasonal fluctuations in air quality. Here, we describe a statistical model approach to reliably transform passive sampler NO2 data from multiweek averages to annual-averaged values. The predictive model is trained with data from reference stations that are limited in number but provide full temporal coverage and is subsequently applied to the one-off data set recorded by the spatially extensive network of passive samplers. We verify the assumptions underlying the model procedure and demonstrate that model uncertainty complies with the EU-quality objectives for air-quality monitoring. Our approach allows a considerable cost optimization of passive sampler campaigns and removes a critical bottleneck for citizen-derived data to be used for compliance checking and air-quality policy use.

Sources of the PM10 aerosol in Flanders, Belgium, and re-assessment of the contribution from wood burning.

From 30 June 2011 to 2 July 2012 PM10 aerosol samples were simultaneously taken every 4th day at four urban background sites in Flanders, Belgium. The sites were in Antwerpen, Gent, Brugge, and Oostende. The PM10 mass concentration was determined by weighing; organic and elemental carbon (OC and EC) were measured by thermal-optical analysis, the wood burning tracers levoglucosan, mannosan and galactosan were determined by gas chromatography/mass spectrometry, 8 water-soluble ions were measured by ion chromatography, and 15 elements were determined by a combination of inductively coupled plasma atomic emission spectrometry and mass spectrometry. The multi-species dataset was subjected to receptor modeling by PMF. The 10 retained factors (with their overall average percentage contributions to the experimental PM10 mass) were wood burning (9.5%), secondary nitrate (24%), secondary sulfate (12.6%), sea salt (10.0%), aged sea salt (19.2%), crustal matter (9.7%), non-ferrous metals (1.81%), traffic (10.3%), non-exhaust traffic (0.52%), and heavy oil burning (3.0%). The average contributions of wood smoke for the four sites were quite substantial in winter and ranged from 12.5 to 20% for the PM10 mass and from 47 to 64% for PM10 OC. Wood burning appeared to be also a notable source of As, Cd, and Pb. The contribution from wood burning to the PM10 mass and OC was also assessed by making use of levoglucosan as single marker compound and the conversion factors of Schmidl et al. (2008), as done in our previous study on wood burning in Flanders (Maenhaut et al., 2012). However, the apportionments were much lower than those deduced from PMF. It seems that the conversion factors of Schmidl et al. (2008) may not be applicable to wood burning in Flanders. From scatter plots of the PMF-derived wood smoke OC and PM versus levoglucosan, we arrived at conversion factors of 9.7 and 22.6, respectively.

Assessment of the contribution from wood burning to the PM10 aerosol in Flanders, Belgium.

From February 2010 to February 2011 PM10 aerosol samples were simultaneously taken every 4th day at 7 monitoring sites in Flanders, Belgium. Two of the sites (i.e., Borgerhout and Gent) were urban background sites; one (i.e., Mechelen) a suburban background site, and the other four (i.e., Hamme, Lier, Retie, and Houtem) rural background sites, whereby Hamme and Lier were expected to be particularly impacted by biomass burning. The samplings were done for 24h and 47-mm diameter Pallflex® Tissuquartz™ 2500 QAT-UP filters were used. After sampling the PM10 mass concentration was determined by weighing; organic and elemental carbon (OC and EC) were measured by thermal-optical transmission analysis and the wood burning tracers levoglucosan, mannosan, and galactosan were determined by means of gas chromatography/mass spectrometry. The atmospheric concentrations of levoglucosan and the other two monosaccharide anhydrides showed a very clear seasonal variation at each site, with highest levels in winter, followed by autumn, spring, and summer. The levoglucosan levels for 5 of our 7 sites (i.e., Retie, Lier, Mechelen, Borgerhout, and Gent) were very highly correlated with each other (all between site correlation coefficients r>0.9, except for one value of 0.86) and the levels in the parallel samples of these 5 sites were similar, indicating that wood burning at these 5 sites was a regional phenomenon and that it was taking place in many individual houses on similar occasions (e.g., on cold days, weekends or holidays). The levoglucosan levels at Houtem and the correlation coefficients of the 5 sites with Houtem were lower, which is explained by the fact that the latter site is at less than 20 km from the North Sea so that the air there is often diluted by rather clean westerly maritime air. A peculiar behavior was seen for Hamme, with on many occasions very high levoglucosan levels, which was attributed to the fact that there is wood burning going on in several houses nearby this site. From our levoglucosan/mannosan ratios we derived the relative contributions of softwood and hardwood burning, thereby following the same approach as used by Schmidl et al. (Atmos Environ 2008;38:126-41). It was found that softwood burning accounted, on average, for about 70%, and there was little variation in this percentage with site or with season. The levoglucosan data were used to assess the contribution of wood burning to the OC and to the PM10 mass, again following the approach of Schmidl et al. (2008). The annual average contributions of wood burning OC to the PM10 OC were in the range of 20-25% for 6 of our 7 sites and 36% for Hamme; the averages for summer were 2.0-3.9% for the 6 sites and 14.5% for Hamme; the corresponding data for winter were 36-43% and 60%. As to the contribution from wood burning to the PM10 mass, the annual averages were in the range of 4.8-6.3% for 6 of our 7 sites and 13.3% at Hamme; the averages for summer were 0.51-1.14% for the 6 sites and 5.0% for Hamme; the corresponding data for winter were 8.6-11.3% and 22%. Our finding that wood burning is an important contributor to the OC and the PM10 mass, especially in winter, is in line with published data from various other sites in other European countries.

Testosterone metabolism in the estuarine mysid Neomysis integer (Crustacea; Mysidacea) following tributyltin exposure.

Current evidence suggests that the biocide tributyltin (TBT) causes the development of imposex, a state of pseudohermaphrodism in which females exhibit functional secondary male characteristics, by altering the biotransformation or elimination of testosterone. Imposex in gastropods following TBT exposure is the most complete example of the effects of an endocrine disrupter on marine invertebrates. Previous studies have demonstrated that the estuarine mysid Neomysis integer converts testosterone into multiple polar and nonpolar metabolites resulting from both phase I and phase II biotransformations. In this study, the effects of TBT chloride (TBTCl) on the phase I and II testosterone metabolism of N. integer were evaluated. The TBTCl was highly toxic to N. integer (96-h median lethal concentration [LC50] of 164 ng/L). To assess the effects on testosterone metabolism, mysids were exposed for 96 h to different concentrations of TBTCl (control, 10, 100, and 1,000 ng/L), and testosterone elimination as polar hydroxylated, nonpolar oxido-reduced, and glucose- and sulfate-conjugated metabolites was examined. The TBTCl differentially affected testosterone metabolism. The effect of TBTCl on phase I metabolism was unclear and has been shown to vary among species, likely depending on the inducibility or presence of certain P450 isozyme families. Reductase activity and metabolic androgenization were induced in the 10-ng/L treatment, whereas higher concentrations resulted in a reduction of sulfate conjugation. The exact mechanisms underlying TBT-induced imposex and alterations in the steroid metabolism need to be further elucidated.