Uwe Schlink

A rigorous inter-comparison of ground-level ozone predictions

Novel statistical approaches to prediction have recently been shown to perform well in several scientific fields but have not, until now, been comprehensively evaluated for predicting air pollution. In this paper we report on a model inter-comparison exercise in which 15 different statistical techniques for ozone forecasting were applied to ten data sets representing different meteorological and emission conditions throughout Europe. We also attempt to compare the performance of the statistical techniques with a deterministic chemical trajectory model. Likewise, our exercise includes comparisons of sites, performance indices, forecasting horizons, etc. The comparative evaluation of forecasting performance (benchmarking) produced 1340 yearly time series of daily predictions and the results are described in terms of predefined performance indices. Through analysing associations between the performance indices, we found that the success index is of outstanding significance. For models that are excellent in predicting threshold exceedances and have a high success index, we also observe high performance in the overall goodness of fit. The 8-h average ozone concentration forecast accuracy was found to be superior to the 1-h mean ozone concentration forecast, which makes the former very significant for operational forecasting. The best forecasts were achieved for sites located in rural and suburban areas in Central Europe unaffected by extreme emissions (e.g. from industries). Our results demonstrate that a particular technique is often excellent in some respects but poor in others. For most situations, we recommend neural network and generalised additive models as the best compromise, as these can handle nonlinear associations and can be easily adapted to site specific conditions. In contrast, nonlinear modelling of the dynamical development of univariate ozone time-series was not profitable.

Statistical models to assess the health effects and to forecast ground-level ozone

By means of statistical approaches we attempt to bridge both aspects of the ground-level ozone problem: assessment of health effects and forecasting and warning. Disagreement has been highlighted in the literature recently regarding the adverse health effects of tropospheric ozone pollution. Based on a panel study of children in Leipzig we identified a non-linear (quadratic) concentration-response relationship between ozone and respiratory symptoms. Our results indicate that using ozone as a linear covariate might be a misspecification of the model, which might explain non-uniform results of several field studies in health effects of ozone. We conclude that there is urgent demand for forecasting episodes of high ozone that may help susceptible persons to avoid high exposure. Novel approaches to statistical modelling and data mining are helpful tools in operational smog forecasting. We present a rigorous assessment of the performance of 15 different statistical techniques in an inter-comparison study based on data sets from 10 European regions. To evaluate the results of the inter-comparison exercise we suggest an integrated assessment procedure, which takes the unbalanced study design into consideration. This procedure is based on estimating a statistical model for the performance indices depending on predefined factors, such as site, forecasting technique, forecasting horizon, etc. We find that the best predictions can be achieved for sites located in rural and suburban areas in Central Europe. For application in operational air pollution forecasting we may recommend neural network and generalised additive models, which can handle non-linear associations between atmospheric variables. As an example we demonstrate the application of a Generalised Additive Model (GAM). GAMs are based on smoothing splines for the covariates, i.e., meteorological parameters and concentrations of other pollutants. Finally, it transpired that respiratory symptoms are associated with the daily maximum of the 8-h average ozone concentration, which in turn is best predicted by means of non-linear statistical models. The new air quality directive of the European Commission (Directive 2002/3/EC) accounts for the special relevance of the 8h mean ozone concentration.

Increased asthma and respiratory symptoms in children exposed to petrochemical pollution.

BACKGROUND Epidemiologic studies show statistical associations between levels of air pollutants and respiratory outcomes. OBJECTIVE We sought to determine the effects of exposure to petrochemical pollution on the respiratory health of children. METHODS Children aged 6 to 12 years living close to the petrochemical plants in La Plata, Argentina (n = 282), were compared with those living in a region with exposure to heavy traffic (n = 270) or in 2 relatively nonpolluted areas (n = 639). Parents answered a validated questionnaire providing health and demographic data. A random sample (n = 181) had lung function measured. Particulate matter and outdoor and indoor volatile organic compound levels were measured during 4-week study periods and reported as overall means for each study area. RESULTS Children living near the petrochemical plant had more asthma (24.8% vs 10.1% to 11.5%), more asthma exacerbations (6.7 vs 2.9-3.6 per year), more respiratory symptoms (current wheeze, dyspnea, nocturnal cough, and rhinitis), and lower lung function (>13% decrease in FEV(1) percent predicted) than those living in other regions. Length of residence in the area was a significant risk factor, but age, sex, body mass index, proximity to busy roads and other nonpetrochemical industries, length of breast-feeding, and socioeconomic and demographic characteristics of children or their families were not. CONCLUSION Exposure to particulate matter and volatile organic compounds arising from petrochemical plants but not from high traffic density was associated ith worse respiratory health in children.

Modelling SO2 concentration at a point with statistical approaches

In this paper, the results obtained by inter-comparing several statistical techniques for modelling SO2 concentration at a point such as neural networks, fuzzy logic, generalised additive techniques and other recently proposed statistical approaches are reported. The results of the inter-comparison are the fruits of collaboration between some of the partners of the APPETISE project funded under the Framework V Information Societies and Technologies (IST) programme. Two different cases for study were selected: the Siracusa industrial area, in Italy, where the pollution is dominated by industrial emissions and the Belfast urban area, in the UK, where domestic heating makes an important contribution. The different kinds of pollution (industrial/urban) and different locations of the areas considered make the results more general and interesting. In order to make the inter-comparison more objective, all the modellers considered the same datasets. Missing data in the original time series was filled by using appropriate techniques. The inter-comparison work was carried out on a rigorous basis according to the performance indices recommended by the European Topic Centre on Air and Climate Change (ETC/ACC). The targets for the implemented prediction models were defined according to the EC normative relating to limit values for sulphur dioxide. According to this normative, three different kinds of targets were considered namely daily mean values, daily maximum values and hourly mean values. The inter-compared models were tested on real cases of poor air quality. In the paper, the inter-compared techniques are ranked in terms of their capability to predict critical episodes. A ranking in terms of their predictability of the three different targets considered is also proposed. Several key issues are illustrated and discussed such as the role of input variable selection, the use of meteorological data, and the use of interpolated time series. Moreover, a novel approach referred to as the technique of balancing the training pattern set, which was successfully applied to improve the capability of ANN models to predict exceedences is introduced. The results show that there is no single modelling approach, which generates optimum results in terms of the full range of performance indices considered. In view of the implementation of a warning system for air quality control, approaches that are able to work better in the prediction of critical episodes must be preferred. Therefore, the artificial neural network prediction models can be recommended for this purpose. The best forecasts were achieved for daily averages of SO2 while daily maximum and hourly mean values are difficult to predict with acceptable accuracy.

Respiratory effects of indoor particles in young children are size dependent

BACKGROUND Extensive epidemiological studies have provided evidence of an association between elevated outdoor particulate air pollution and adverse health effects. However, while people typically spend majority of time indoors, there is limited knowledge on airborne indoor particles and on the correlation between the concentrations of indoor particles and health effects. Even insights into the influence of differently sized indoor particles on human health are still rare. OBJECTIVE The association between differentially sized indoor air particles and the development of respiratory diseases was studied for three year aged children. METHODS Short-term measurements of particle mass and number concentrations were carried out in childrens rooms. Information on possible particle sources (smoking habits, type of heating, and traffic) and respiratory outcomes were obtained from questionnaires. Measured indoor particle concentrations were correlated with possible sources of indoor particles and with respiratory health impacts. RESULTS Daily smoking, smoking more than 5 cigarettes per day at home and traffic density in front of the window of childrens room were found to be related to indoor exposure by particles of different diameters. High indoor particle exposures were associated with an increased risk for the development of obstructive bronchitis and in some extent of non-obstructive bronchitis. The strongest impact was observed for the mass concentration of particles <1 μm and the number concentration of particles >0.5 μm. The risk increases still remain significant if tested for stability changing the number of adjustment variables or omitting randomly selected cases, respectively. CONCLUSION Our results show significant associations between indoor particle concentrations and the risks for respiratory diseases in young children. The applied short-term measurements can help to assess the health risks of indoor particles with different sizes within epidemiological studies.

Sub-micrometer particulate air pollution and cardiovascular mortality in Beijing, China

BACKGROUND While the link between particulate matter and cardiovascular mortality is well established, it is not fully investigated and understood which properties of the aerosol might be responsible for the health effects, especially in polluted mega-city areas. OBJECTIVES Our goal was to explore the association between daily cardiovascular mortality and different particle metrics in the sub-micrometer range in Beijing, China. METHODS We obtained daily counts of cause-specific cardiovascular deaths in the Beijing urban area for the period March 2004 to August 2005. Concurrently, continuous measurements of particle number size distributions were performed. Particle number concentrations (NC) between 0.003 μm and 0.8 μm were converted to particle mass and surface area concentrations assuming spherical particles. Semi-parametric Poisson regression models adjusting for trend, seasonality, day of the week, and meteorology were used to estimate immediate, delayed and cumulative particle effects. Additionally, effect modification by air mass origin was investigated. RESULTS We observed associations between daily cardiovascular mortality and particle NC for a 2-days delay. Moreover, nearly all particle metrics showed 2-days delayed associations with ischemic heart disease mortality. The strongest association was found for particle NC in the size range 0.03-0.1 μm (7.1% increase in daily mortality with a 95%-confidence interval of 2.9%-11.5%, per an increase of 6250 particles/cm3). Results for surface and mass concentrations with a lag of two days indicated effect modification by air mass origin, whereas effects of particle NC were not modified. CONCLUSIONS Results show an elevated risk of cardiovascular mortality in Beijing from short-term exposure to particulate air pollution in the sub-micrometer range. Results also indicate that locally produced smaller particles and regionally transported particles may exhibit different effects in Beijing.

Size-Segregated Particle Number Concentrations and Respiratory Emergency Room Visits in Beijing, China

Background The link between concentrations of particulate matter (PM) and respiratory morbidity has been investigated in numerous studies. Objectives The aim of this study was to analyze the role of different particle size fractions with respect to respiratory health in Beijing, China. Methods Data on particle size distributions from 3 nm to 1 μm; PM10 (PM ≤ 10 μm), nitrogen dioxide (NO2), and sulfur dioxide concentrations; and meteorologic variables were collected daily from March 2004 to December 2006. Concurrently, daily counts of emergency room visits (ERV) for respiratory diseases were obtained from the Peking University Third Hospital. We estimated pollutant effects in single- and two-pollutant generalized additive models, controlling for meteorologic and other time-varying covariates. Time-delayed associations were estimated using polynomial distributed lag, cumulative effects, and single lag models. Results Associations of respiratory ERV with NO2 concentrations and 100–1,000 nm particle number or surface area concentrations were of similar magnitude—that is, approximately 5% increase in respiratory ERV with an interquartile range increase in air pollution concentration. In general, particles < 50 nm were not positively associated with ERV, whereas particles 50–100 nm were adversely associated with respiratory ERV, both being fractions of ultrafine particles. Effect estimates from two-pollutant models were most consistent for NO2. Conclusions Present levels of air pollution in Beijing were adversely associated with respiratory ERV. NO2 concentrations seemed to be a better surrogate for evaluating overall respiratory health effects of ambient air pollution than PM10 or particle number concentrations in Beijing.

Maternal immune status in pregnancy is related to offspring's immune responses and atopy risk

To cite this article: Herberth G, Hinz D, Röder S, Schlink U, Sack U, Diez U, Borte M, Lehmann I. Maternal immune status in pregnancy is related to offspring’s immune responses and atopy risk. Allergy 2011; 66: 1065–1074.

Quantile regression of indoor air concentrations of volatile organic compounds (VOC).

There are many factors determining the concentration of volatile organic compounds (VOCs) in indoor air. On the basis of 601 population-based measurements we develop an explicit exposure model that includes factors, such as renovation, furniture, flat size, smoking, and education level of the occupants. As a novel method for the evaluation of concentrations of indoor air pollutants we use quantile regression, which has the advantages of robustness against non-Gaussian distributions (and outliers) and can adjust for unbalanced frequencies of observations. The applied bi- and multivariate quantile regressions provide (1) the VOC burden that is representative for the population of Leipzig, Germany, and (2) an inter-comparison of the effects of the studied factors and their levels. As a result, we find strong evidence for factors of general impact on most VOC components, such as the season, flooring, the type of the room, and the size of the apartment. Other impact factors are very specific to the VOC components. For example, wooden flooring (parquet) and new furniture increase the concentration of terpenes as well as the modifying factors high education and sampling in the childs room. Smokers ventilate their flats in an extent that in general reduces the VOC concentrations, except for benzene (contained in tobacco smoke), which is still higher in smoking than in non-smoking flats. Very often dampness is associated with an increased VOC burden in indoor air. An investigation of mixtures emphasises a high burden of co-occurring terpenes in very small and very large apartments.

T cell reactivity in neonates from an East and a West German city – results of the LISA study

Background: Within an ongoing birth cohort study (LISA) the cytokine production of cord blood T cells was compared between neonates from Leipzig (East Germany) and Munich (West Germany). The aim of this study was to analyse regional differences and influencing factors of the immune status.