Abstract

Abstract. Measurements of airborne particles with aerodynamic diameter of 10 μm or less (PM10) and meteorological observations are available from 13 stations distributed throughout Switzerland and representing different site types. The effect of all available meteorological variables on PM10 concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM10 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 PM10 concentrations compared to Monday on average over 9 stations for the yearly data. The estimated effects of meteorological variables were removed from the measured PM10 values to obtain the PM10 variability and trends due to other factors and processes, mainly PM10 emissions and formation of secondary PM10 due to trace gas emissions. After applying this process, the PM10 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, PM10 trends in winter were more negative after the adjustment for meteorology and they ranged between −1.25 μg m−3 yr−1 and 0.07 μg m−3 yr−1. The adjusted trends for the other seasons ranged between −1.34 μg m−3 yr−1 and −0.26 μg m−3 yr−1 in spring, −1.40 μg m−3 yr−1 and −0.28 μg m−3 yr−1 in summer and −1.28 μg m−3 yr−1 and −0.11 μg m−3 yr−1 in autumn. The estimated trends of meteorologically adjusted PM10 were in general non-linear. The two urban street sites considered in the study, Bern and Lausanne, experienced the largest reduction in measured and adjusted PM10 concentrations. This indicates a verifiable effect of traffic emission reduction strategies implemented during the past two decades. The average adjusted yearly trends for rural, urban background and urban street stations were −0.37, −0.53 and −1.2 μg m−3 yr−1 respectively. The adjusted yearly trends for all stations range from −0.15 μg m−3 yr−1 to −1.2 μg m−3 yr−1 or −1.2% yr−1 to −3.3% yr−1.

Highlights

  • Airborne particles with aerodynamic diameter of 10 μm or less (PM10) have important negative consequences on human health (Zemp et al, 1999; Bayer-Oglesby et al, 2005)

  • Exponentiation of (1) yields a relationship between PM10 concentrations and the selected explanatory variables which are expressed as multiplicative factors, which contribute to an increase of PM10 if they are greater than 1 and a decrease of PM10 if they are less than 1

  • The trends of PM10 and the effect of meteorology were investigated for 13 air quality stations in Switzerland for the period between 1991 and 2008

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Summary

Introduction

Airborne particles with aerodynamic diameter of 10 μm or less (PM10) have important negative consequences on human health (Zemp et al, 1999; Bayer-Oglesby et al, 2005). PM10 measurements within the Swiss National Air Pollution Monitoring Network (NABEL) are available from 13 stations since 1991, providing a rare opportunity to study the PM10 trends during a time period with marked changes in air pollution levels in developed countries. PM10 concentrations used in this study were measured at 13 sites of the Swiss National Air Pollution Monitoring Network NABEL (Empa, 2010). The meteorological data measured include wind speed, wind direction, precipitation, global radiation, net radiation, air temperature and relative humidity. For each station and season, only those meteorological variables with less than 30% missing values were included in the data analysis procedure described below

Description of the statistical model
Model selection
Adjustment of PM10 concentrations for the effect of meteorology
Important explanatory variables and their relationship to PM10
PM10 trends before and after meteorological adjustment
Model validation
Conclusions
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