Predicting long-term average concentrations of traffic-related air pollutants using GIS-based information

Hochadel, Matthias; Heinrich, Joachim; Gehring, Ulrike; Morgenstern, Verena; Kuhlbusch, Thomas A. J.; Link, Elke; Wichmann, Heinz‐Erich; Krämer, Ursula

doi:10.1016/j.atmosenv.2005.09.067

Cited by 120 publications

(87 citation statements)

References 22 publications

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“…Similar to a study conducted in North-Rhine Westphalia, Germany, 22 we found that the combination of smaller and larger spatial scales forms the basis of good prediction models. As our study region comprises rural and urban areas, a largescale predictor (2500-5000 m) is necessary, and variables in those buffers turned out to be predictive.…”

Section: Discussionsupporting

confidence: 65%

Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children

et al. 2006

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Objectives: To estimate long-term exposure to traffic-related air pollutants on an individual basis and to assess adverse health effects using a combination of air pollution measurement data, data from geographical information systems (GIS) and questionnaire data. Methods: 40 measurement sites in the city of Munich, Germany were selected at which to collect particulate matter with a 50% cut-off aerodynamic diameter of 2.5 mm (PM 2.5 ) and to measure PM 2.5 absorbance and nitrogen dioxide (NO 2 ). A pool of GIS variables (information about street length, household and population density and land use) was collected for the Munich metropolitan area and was used in multiple linear regression models to predict traffic-related air pollutants. These models were also applied to the birth addresses of two birth cohorts (German Infant Nutritional Intervention Study (GINI) and Influence of Life-style factors on the development of the Immune System and Allergies in East and West Germany (LISA)) in the Munich metropolitan area. Associations between air pollution concentrations at birth address and 1-year and 2-year incidences of respiratory symptoms were analysed. Results: The following means for the estimated exposures to PM 2.5 , PM 2.5 absorbance and NO 2 were obtained: 12.8 mg/m 3 , 1.7610 25 m 21 and 35.3 mg/m 3 , respectively. Adjusted odds ratios (ORs) for wheezing, cough without infection, dry cough at night, bronchial asthma, bronchitis and respiratory infections indicated positive associations with traffic-related air pollutants. After controlling for individual confounders, significant associations were found between the pollutant PM 2.5 and sneezing, runny/stuffed nose during the first year of life (OR 1.16, 95% confidence interval 1.01 to 1.34) Similar effects were observed for the second year of life. These findings are similar to those from our previous analysis that were restricted to a subcohort in Munich city. The extended study also showed significant effects for sneezing, running/stuffed nose. Additionally, significant associations were found between NO 2 and dry cough at night (or bronchitis) during the first year of life. The variable ''living close to major roads'' (,50 m), which was not analysed for the previous inner city cohort with birth addresses in the city of Munich, turned out to increase the risk of wheezing and asthmatic/spastic/obstructive bronchitis. Conclusions: Effects on asthma and hay fever are subject to confirmation at older ages, when these outcomes can be more validly assessed.

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Section: Discussionsupporting

confidence: 65%

Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children

et al. 2006

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“…Such results highlight the efficiency of GIS tools to process secondary information and demonstrate the relevance of a detailed correlation analysis, as attested by other mapping studies (Hochadel et al, 2006;Ross et al, 2006;Sahsuvaroglu et al, 2006;Smith et al, 2006). Without any treatment (i.e.…”

Section: Looking For Explanatory Variablessupporting

confidence: 74%

Using different spatial scale measurements in a geostatistically based approach for mapping atmospheric nitrogen dioxide concentrations. Application to the French Centre region

Malherbe

Cardenas

Colin³

et al. 2008

Environmetrics

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SUMMARYPassive sampling surveys followed by geostatistical data analysis have become a common and efficient approach for mapping background concentrations at regional and urban scales. Traffic-related pollution is also a matter of concern as regards population exposure but since it acts at shorter spatiotemporal scales, it is usually not integrated in the same maps. However, to provide more comprehensive information to the authorities and the public, the agencies responsible for air quality monitoring are searching for innovative ways of representing background and roadside concentrations together. A methodology based on geostatistics and the examination of the relationships between seasonal nitrogen dioxide concentrations and auxiliary variables is proposed in this study. It is applied to data collected in the French Centre region.

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“…LUR has frequently been used for air pollution exposure assessment, and modeling of small-scale spatial variation of air pollutants in urban areas using different predictor variables [113][114][115]. There is no standard method for conducting LUR, but some explanations about the general approach can be found in the literature (e.g., [98,[116][117][118]. In LUR, a statistical relationship between air pollutants and some urban characteristics (e.g., land use characteristics, traffic intensity, and population density) is established [39,119].…”

Section: Spatial Prediction (Spatial Distribution) Of Pm 10 In Urban mentioning

confidence: 99%

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

2016

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PM 10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM 10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM 10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM 10 prediction. A review of the spatial predictions of PM 10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM 10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM 10 , only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ď 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.

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Predicting long-term average concentrations of traffic-related air pollutants using GIS-based information

Cited by 120 publications

References 22 publications

Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children

Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children

Using different spatial scale measurements in a geostatistically based approach for mapping atmospheric nitrogen dioxide concentrations. Application to the French Centre region

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

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