Influence of meteorology on PM<sub>10</sub> trends and variability in Switzerland from 1991 to 2008

Abstract

Abstract. Measurements of airborne particles with aerodynamic diameter of 10 µm or less (PM 10 ) and meteorological observations are available from 13 stations distributed throughout Switzerland and representing different site types. The effect of all available meteorological variables on PM 10 concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM 10 concentrations in winter, autumn and spring were wind gust, the precipitation rate of the previous day, the precipitation rate of the current day and the boundary layer depth. In summer, the most important variables were wind gust, Julian day and afternoon temperature. In addition, temperature was important in winter. A "weekend effect" was identified due to the selection of variable "day of the week" for some stations. Thursday contributes to an increase of 13% whereas Sunday contributes to a reduction of 12% of PM 10 concentrations compared to Monday on average over 9 stations for the yearly data. The estimated effects of meteorological variables were removed from the measured PM 10 values to obtain the PM 10 variability and trends due to other factors and processes, mainly PM 10 emissions and formation of secondary PM 10 due to trace gas emissions. After applying this process, the PM 10 variability was much lower, especially in winter where the ratio of adjusted over measured mean squared error was 0.27 on average over all considered sites. Moreover, PM 10 trends in winter were more negative after the adjustment for meteorology and they ranged between −1.25 µg m

Analysis of exceedances in the daily PM<sub>10</sub> mass concentration (50 μg m<sup>−3</sup>) at a roadside station in Leipzig, Germany

“…Since the year 2000, however, the PM 10 concentrations in Germany seem to stagnate, and feature some mere inter-annual fluctuations (UBA, 2009). A similar stagnation of PM 10 since 2000 has also been reported for other areas in Europe, such as the UK , Switzerland (Barmpadimos et al, 2011), Belgium, Czech Republic, Italy and Norway. This is in notable contrast to the continued reductions in PM emissions.…”

Random forest meteorological normalisation models for Swiss PM<sub>10</sub> trend analysis

“…A method to control or take into account meteorology effects on pollutant concentrations involves the development and use of predictive statistical models (Lou Thompson et al, 2001;Carslaw et al, 2006;Beevers et al, 2009;Carslaw and Priestman, 2015;Fuller and Carslaw, 2017). Such models attempt to use a number of explanatory variables such as surface measurements of wind behaviour, atmospheric temperature, and pressure to explain the variability of pollutant concentrations.…”

Random forest meteorological normalisation models for Swiss PM<sub>10</sub> trend analysis

“…When the six site type trends were aggregated together, the stronger decreasing trend for traffic sites was clear with an average trend of -0.77 µg m ( Figure 5). Barmpadimos et al (2011) also reported trends based on site type but their site type definitions were not the same as used in this study so they should not be directly compared. The higher first four points in the rural panel of Figure 5 was caused by 5 the aggregated time series only containing the Magadino-Cadenazzo monitoring site at the very beginning of analysis period.…”