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 20km 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.
► A one-year study with 7 sampling sites and a total of 644 daily PM10 samples. ► PM10 mass, elemental carbon, organic carbon (OC), and 3 anhydrosugars determined. ► Wood burning in Flanders appears to be a regional scale phenomenon. ► Wood burning is an important contributor to the PM10 OC and PM10 mass in winter. ► Less wood burning would help in complying with the European air quality legislation.
The ability of a complex model chain to simulate elemental carbon (EC) concentrations was examined. The results of the model chain were compared to EC concentration measurements made at several ...locations, every sixth day. Two measurement campaigns were taken into account, one in 2006–2007 and one in 2008–2009. The model results compare very well for both periods, with an
R
2 of 0.74, a bias of 0.02 μg m
−3 and a RMSE of 0.32 μg m
−3. Sensitivity analyses to different meteorology inputs and changing emissions from year to year were performed. The differences between the two measurement periods were also investigated. It is shown that somewhat more than half of these differences is due to meteorology. However, emission changes also play an important role.
► The ability of a model chain to simulate elemental carbon was examined. ► The model simulations were made at very high resolutions. ► The results of the model chain were compared to EC concentration measurements. ► The model results compare very well for both periods:
R
2 = 0.74; Bias = 0.02 μg m
−3. ► Sensitivity analyses to different meteorology inputs and changing emissions.
The results of
Chemkar PM10, the first large scale chemical characterisation project of PM10 in Flanders are presented. Between September 2006 and September 2007 a total of 365 PM10 samples were ...collected by sampling every sixth day at six different sites in Flanders (Belgium) varying in character from urban background to rural. Samples were analysed for a series of elements, elemental and organic carbon,
13C/
12C- and
15N/
14N-isotopic ratios, and water-soluble ions. At three sites extra sampling was carried out to determine PAHs by means of a novel technique that uses sorption tubes consisting of polydimethylsiloxane (PDMS) foam, PDMS particles and a TENAX TA bed. Results showed that the most important fractions were secondary inorganic ions (nitrate, sulphate and ammonium): 41% (12.6
μg
m
−3), organic matter: 20% (6.1
μg
m
−3), crustal matter: 14% (4.3
μg
m
−3), sea salt: 8% (2.4
μg
m
−3) and elemental carbon: 4% (1.2
μg
m
−3). Although the general composition profile was rather similar for the six sites, substantial differences could be observed for some main components and for several trace metals such as chromium, copper, zinc, arsenic and lead. Although the average sum of the “PAH 16” was quite variable between the three sites (between 132 and 248
ng
m
−3) the average sum of the “PAH 7” was between 7 and 9
ng
m
−3 for the three sites. The largest relative differences between sites were found for elemental carbon and crustal matter, thereby indicating that there is some potential for local measures to reduce the concentrations of particulate matter by a few μg
m
−3. Both for carbon and nitrogen isotopic ratios significant differences in time were observed. Isotopic differences in location could only be detected for carbon.
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.
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•A one-year study with 4 urban background sites and a total of 372 daily PM10 samples•First application of Positive Matrix Factorisation on aerosol data sets from Belgium•Comparison of two approaches for estimating the contribution from wood burning•Wood burning appears to be a notable source of As, Cd, and Pb•The impact from wood burning in Flanders is larger than previously thought
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.
Nitro-organic compounds were determined in a one-year set of atmospheric PM10 filter samples that were collected at a rural background site in Hamme, Belgium. In an earlier study, it was found that ...the site was substantially impacted by wood burning, making the filter samples appropriate for further investigations on wood burning indicators. In total, four groups of nitro-aromatic compounds (with molecular weights (MWs) of 139, 155, 169, and 183), α-pinene-related nitrooxy-organosulfates (MW 295), and the resin acid dehydroabietic acid (DHAA, MW 300) were quantified using liquid chromatography combined with negative ion electrospray ionization mass spectrometry. The annual mean concentrations were 0.94, 6.0, 7.7, 4.8, 7.8, and 1.76 ng m−3 for the sum of the nitrophenols (MW 139), 4-nitrocatechol (MW 155), the sums of the methyl-nitrocatechols (MW 169), of the dimethyl-nitrocatechols (MW 183), and of the α-pinene-related nitrooxy-organosulfates (MW 295), and DHAA (MW 300), respectively. 4-nitrocatechol, the sum of the methyl-nitrocatechols, and the sum of the dimethyl-nitrocatechols were substantially correlated with levoglucosan (r-values of 0.71, 0.66, and 0.65, respectively), consistent with their proposed origin from biomass burning. The nitro-aromatic compounds were also observed during the summer months, indicating a non-negligible usage of wood burning for domestic purposes at the site. The α-pinene-related nitrooxy-organosulfates (MW 295) were detected in high concentrations during the winter period, but they were poorly correlated with the biomass burning tracers. All of the targeted species showed a clear seasonal variation with highest concentrations in winter, followed by autumn, spring, and summer. Based on the DHAA measurements, it is suggested that burning of softwood is likely an important source for the formation of all the nitro-organic compounds measured.
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•Nitro-aromatic compounds, α-pinene related organosulfates and a resin acid measured.•Compounds measured in PM10 every 4th day over a full year at a rural site.•Strong seasonality for most compounds, highest levels in winter, lowest in summer.•Organosulfates pronounced in summer, but highest levels in winter.•Nitro-aromatic compounds and the resin acid associated with wood burning.
Recently, we described the cellular energy allocation (CEA) methodology to asses the effects of abiotic stress on the energy metabolism of the estuarine crustacean
Neomysis integer (Crustacea: ...Mysidacea) J. Exp. Mar. Biol. Ecol. 279 (2002) 61. This short-term assay is based on the biochemical assessment of changes in the energy reserves (total carbohydrate, protein and lipid content) and the energy consumption (electron transport activity), and has been shown to be predictive of effects at the population level in daphnids J. Aquat. Ecosyst. Stress Recovery 6 (1997) 43. In the present study, the CEA methodology was evaluated using adult
N. integer exposed for 96 h to the antifoulant tributyltinchloride (TBTCl). From a range-finding experiment with juvenile
N. integer, a 96-h LC50 of 164 ng TBTCl/l was calculated. The energy metabolism of
N. integer, as summarized by the CEA, was significantly altered by TBTCl exposure. Mysids exposed to 10, 100 and 1000 ng TBTCl/l consumed less energy and had lower respiration rates (in 10 and 1000 ng TBTCl/l treatments) than the control, resulting in a lower CEA. These changes at the cellular level occurred at environmentally relevant concentrations of the toxicant TBTCl which were an order of magnitude lower than reported effect concentrations for scope for growth in other marine invertebrates.
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 (β = 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 (β = 0.56; 95% CI 0.02; 1.09; p = 0.04) and higher FeNO in urban school children as compared to rural school children (β = 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.
•Use of noninvasive integrative approach in children exposed to low levels of particulate matter.•Respiratory health biomarker CC16 and SNP CC16 G38A were measured in urine and saliva, respectively.•Positive association found between FeNO and urinary CC16 in children recently exposed to PM2.5•Use of U-CC16 proposed as potential alternative of FeNO when no possibility to measure FeNO.
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.
In this study we studied the effects of particulate matter samples (PM) through gene expression analysis in a routine air quality monitoring campaign by the Flemish Environment Agency (VMM, Belgium). ...We selected a human hepatoma (HepG2) multiple endpoint reporter assay for targeted stress related endpoint screening. Organic extracts of air samples (total suspended particles, TSP) were collected during one year in an industrial, urban and background location in Flanders, Belgium. Simultaneously, meteorological conditions (temperature, wind speed and precipitation) and particulate matter size ≤ 10 μM (PM10), organic (OC), elemental (EC) and total (TC) carbon were monitored and air samples were collected for chemical analysis (11 PAHs). Correlations between the induction of the different stress genes and the chemical pollutants were analysed.
Exposure of HepG2 cells to daily air equivalents (20 m3) of organic TSP extracts revealed the dominant induction of the xenobiotic response element (Xre) and phase I (Cyp1A1) and phase II (GstYa) biotransformation enzymes. Additional effects were the induction of c-Fos, a proto-oncogen and Gadd45, a marker for cell cycle disturbance and responsive to genotoxic compounds. Inductions of other relevant pathways, such as sequestration of heavy metals, retinoids response, protein misfolding and increased cAMP levels were measured occasionally.
A significant correlation was found between the genes Cyp1A1 (a typical marker for presence of PAHs and dioxin like compounds), c-Fos, Gadd45, (responsive to DNA damaging compounds) and the amount of PM10 and elemental carbon (EC) whereas no correlation was found between these genes and total PAHs content. This may suggest that the observed induction of Cyp1A1 and DNA damage related genes was provoked (partially) by other particle bound compounds (e.g. pesticides, PCBs, brominated flame retardants, dioxins, …), than PAHs. The contribution of particle bound compounds, other than PAHs might be important to take into account in risk evaluation of air pollution.
► Successful inclusion of effect monitoring in current air quality monitoring campaigns. ► Exposure to daily equivalents of air sample induced several stress related pathways. ► PM bound compounds, other than PAHs have a contribution to the induced pathways.
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