Exploring the spatiotemporal relationship between air pollution and meteorological conditions in Baden-Württemberg (Germany)

Abstract

Background: In the Epidemiological analysis of health data as the response variable for environmental stressors, a key question is to understand the interdependencies between environmental variables and which variables to include in the statistical model. Various meteorological (temperature, ultraviolet radiation, precipitation, and vapor pressure) and air pollution variables (O3, NO2, PM2.5, and PM10) are available at the daily level for Baden-Württemberg (Germany). This federal state covers both urban and rural areas. Methods: A spatial and temporal analysis of the internal relationships is performed using a) cross-correlations, both on the grand ensemble of data as well as with subsets, and b) the Local Indications of Spatial Association (LISA). Results: Meteorological and air pollution variables are strongly correlated between and among themselves, with specific seasonal and spatial features. For example, Nitrogen dioxide and Ozone are strongly interdependent, and the Pearson correlation coefficient varies with time. In January, there is a negative correlation of -0.84 whereas in April, the correlation coefficient is -0.47, in July 0.45, and in October -0.54. For Ozone and Nitrogen dioxide, a shift of the correlation direction as a function of temperature and UV radiation can be observed, confirmed by cross-correlation. Spatially, NO2, PM2.5, and PM10 concentrations are significantly higher in urban than rural regions. For O3, this effect is reversed. As confirmed also by LISA analysis, where distinct hot and cold spots of the different environmental stressors could be identified. In addition, a linear regression analysis suggests that PM10 variation is almost entirely explained by PM2.5 and vapor pressure by temperature. Conclusion: The results found are generally compatible with the expected dependencies. Thus, our investigation demonstrates that there are variables with similar temporal and spatial characteristics that should be adequately addressed in analyses of health and environmental stressors. Simplification strategies could e.g. discard redundant variables such as PM10 when PM2.5 is available. However, a reduction to one single variable is not helpful due to the complex relationships between meteorological and air pollution variables.