GIS-Based Estimation of Exposure to Particulate Matter and NO(2) in an Urban Area: Stochastic versus Dispersion Modeling

Stochastic modeling was used to predict nitrogen dioxide and fine particles [particles collected with an upper 50% cut point of 2.5 μm aerodynamic diameter (PM(2.5))] levels at 1,669 addresses of the participants of two ongoing birth cohort studies conducted in Munich, Germany. Alternatively, the Gaussian multisource dispersion model IMMIS(net/em) was used to estimate the annual mean values for NO(2) and total suspended particles (TSP) for the 40 measurement sites and for all study subjects. The aim of this study was to compare the measured NO(2) and PM(2.5) levels with the levels predicted by the two modeling approaches (for the 40 measurement sites) and to compare the results of the stochastic and dispersion modeling for all study infants (1,669 sites). NO(2) and PM(2.5) concentrations obtained by the stochastic models were in the same range as the measured concentrations, whereas the NO(2) and TSP levels estimated by dispersion modeling were higher than the measured values. However, the correlation between stochastic- and dispersion-modeled concentrations was strong for both pollutants: At the 40 measurement sites, for NO(2), r = 0.83, and for PM, r = 0.79; at the 1,669 cohort sites, for NO(2), r = 0.83 and for PM, r = 0.79. Both models yield similar results regarding exposure estimate of the study cohort to traffic-related air pollution, when classified into tertiles; that is, 70% of the study subjects were classified into the same category. In conclusion, despite different assumptions and procedures used for the stochastic and dispersion modeling, both models yield similar results regarding exposure estimation of the study cohort to traffic-related air pollutants

Particle number size distributions in urban air before and after volatilisation

Aerosol particle number size distributions (size range 0.003–10 μm) in the urban atmosphere of Augsburg (Germany) were examined with respect to the governing anthropogenic sources and meteorological factors. The two-year average particle number concentration between November 2004 and November 2006 was 12 200 cm−3, i.e. similar to previous observations in other European cities. A seasonal analysis yielded twice the total particle number concentrations in winter as compared to summer as consequence of more frequent inversion situations and enhanced particulate emissions. The diurnal variations of particle number were shaped by a remarkable maximum in the morning during the peak traffic hours. After a mid-day decrease along with the onset of vertical mixing, an evening concentration maximum could frequently be observed, suggesting a re-stratification of the urban atmosphere. Overall, the mixed layer height turned out to be the most influential meteorological parameter on the particle size distribution. Its influence was even greater than that of the geographical origin of the prevailing synoptic-scale air mass. Size distributions below 0.8 μm were also measured downstream of a thermodenuder (temperature: 300 °C), allowing to retrieve the volume concentration of non-volatile compounds. The balance of particle number upstream and downstream of the thermodenuder suggests that practically all particles >12 nm contain a non-volatile core while additional nucleation of particles smaller than 6 nm could be observed after the thermodenuder as an interfering artifact of the method. The good correlation between the non-volatile volume concentration and an independent measurement of the aerosol absorption coefficient (R2=0.9) suggests a close correspondence of the refractory and light-absorbing particle fractions. Using the "summation method", an average diameter ratio of particles before and after volatilisation could be determined as a function of particle size. The results indicated that particles >60 nm contain a significantly higher fraction of non-volatile compounds, most likely black carbon, than particles <60 nm. The results are relevant for future health-related studies in that they explore the size distribution and time-dependent behaviour of the refractory component of the urban aerosol over an extended time period

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health

Organic molecular markers and signature from wood combustion particles in winter ambient aerosols: aerosol mass spectrometer (AMS) and high time-resolved GC-MS measurements in Augsburg, Germany

The impact of wood combustion on ambient aerosols was investigated in Augsburg, Germany during a winter measurement campaign of a six-week period. Special attention was paid to the high time resolution observations of wood combustion with different mass spectrometric methods. Here we present and compare the results from an Aerodyne aerosol mass spectrometer (AMS) and gas chromatographic &ndash; mass spectrometric (GC-MS) analysed PM₁ filters on an hourly basis. This includes source apportionment of the AMS derived organic matter (OM) using positive matrix factorisation (PMF) and analysis of levoglucosan as wood combustion marker, respectively. <br><br> During the measurement period nitrate and OM mass are the main contributors to the defined submicron particle mass of AMS and Aethalometer with 28% and 35%, respectively. Wood combustion organic aerosol (WCOA) contributes to OM with 23% on average and 27% in the evening and night time. Conclusively, wood combustion has a strong influence on the organic matter and overall aerosol composition. Levoglucosan accounts for 14% of WCOA mass with a higher percentage in comparison to other studies. The ratio between the mass of levoglucosan and organic carbon amounts to 0.06. <br><br> This study is unique in that it provides a one-hour time resolution comparison between the wood combustion results of the AMS and the GC-MS analysed filter method at a PM₁ particle size range. The comparison of the concentration variation with time of the PMF WCOA factor, levoglucosan estimated by the AMS data and the levoglucosan measured by GC-MS is highly correlated (<i>R</i>² = 0.84), and a detailed discussion on the contributors to the wood combustion marker ion at mass-to-charge ratio 60 is given. At the end, both estimations, the WCOA factor and the levoglucosan concentration estimated by AMS data, allow to observe the variation with time of wood combustion emissions (gradient correlation with GC-MS levoglucosan of <i>R</i>² = 0.84). In the case of WCOA, it provides the estimated magnitude of wood combustion emission. Quantitative estimation of the levoglucosan concentration from the AMS data is problematic due to its overestimation in comparison to the levoglucosan measured by the GC-MS

Traffic-related air pollution and respiratory symptoms among asthmatic children, resident in Mexico City: the EVA cohort study

<p>Abstract</p> <p>Background</p> <p>Taffic-related air pollution has been related to adverse respiratory outcomes; however, there is still uncertainty concerning the type of vehicle emission causing most deleterious effects.</p> <p>Methods</p> <p>A panel study was conducted among 147 asthmatic and 50 healthy children, who were followed up for an average of 22 weeks. Incidence density of coughing, wheezing and breathing difficulty was assessed by referring to daily records of symptoms and child's medication. The association between exposure to pollutants and occurrence of symptoms was evaluated using mixed-effect models with binary response and poisson regression.</p> <p>Results</p> <p>Wheezing was found to relate significantly to air pollutants: an increase of 17.4 μg/m³(IQR) of PM_2.5(24-h average) was associated with an 8.8% increase (95% CI: 2.4% to 15.5%); an increase of 34 ppb (IQR) of NO₂(1-h maximum) was associated with an 9.1% increase (95% CI: 2.3% to16.4%) and an increase of 48 ppb (IQR) in O₃levels (1 hr maximum) to an increase of 10% (95% CI: 3.2% to 17.3%). Diesel-fueled motor vehicles were significantly associated with wheezing and bronchodilator use (IRR = 1.29; 95% CI: 1.03 to 1.62, and IRR = 1.32; 95% CI: 0.99 to 1.77, respectively, for an increase of 130 vehicles hourly, above the 24-hour average).</p> <p>Conclusion</p> <p>Respiratory symptoms in asthmatic children were significantly associated with exposure to traffic exhaust, especially from natural gas and diesel-fueled vehicles.</p