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 .
The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. ...Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).
A hygroscopicity tandem differential mobility analyzer (HTDMA) was used to measure the water uptake (hygroscopicity) of secondary organic aerosol (SOA) formed during the chemical and photochemical ...oxidation of several organic precursors in a smog chamber. Electron ionization mass spectra of the non-refractory submicron aerosol were simultaneously determined with an aerosol mass spectrometer (AMS), and correlations between the two different signals were investigated. SOA hygroscopicity was found to strongly correlate with the relative abundance of the ion signal m/z 44 expressed as a fraction of total organic signal (f44). m/z 44 is due mostly to the ion fragment CO2+ for all types of SOA systems studied, and has been previously shown to strongly correlate with organic O/C for ambient and chamber OA. The analysis was also performed on ambient OA from two field experiments at the remote site Jungfraujoch, and the megacity Mexico City, where similar results were found. A simple empirical linear relation between the hygroscopicity of OA at subsaturated RH, as given by the hygroscopic growth factor (GF) or "ϰorg" parameter, and f44 was determined and is given by ϰorg = 2.2 × f44 − 0.13. This approximation can be further verified and refined as the database for AMS and HTDMA measurements is constantly being expanded around the world. The use of this approximation could introduce an important simplification in the parameterization of hygroscopicity of OA in atmospheric models, since f44 is correlated with the photochemical age of an air mass.
Biogenic volatile organic compounds (BVOC) emitted from vegetation are important for the formation of secondary pollutants such as ozone and secondary organic aerosols (SOA) in the atmosphere. ...Therefore, BVOC emission are an important input for air quality models. To model these emissions with high spatial resolution, the accuracy of the underlying vegetation inventory is crucial. We present a BVOC emission model that accommodates different vegetation inventories and uses satellite-based measurements of greenness instead of pre-defined vegetation periods. This approach to seasonality implicitly treats effects caused by water or nutrient availability, altitude and latitude on a plant stand. Additionally, we test the influence of proposed seasonal variability in enzyme activity on BVOC emissions. In its present setup, the emission model calculates hourly emissions of isoprene, monoterpenes, sesquiterpenes and the oxygenated volatile organic compounds (OVOC) methanol, formaldehyde, formic acid, ethanol, acetaldehyde, acetone and acetic acid. In this study, emissions based on three different vegetation inventories are compared with each other and diurnal and seasonal variations in Europe are investigated for the year 2006. Two of these vegetation inventories require information on tree-cover as an input. We compare three different land-cover inventories (USGS GLCC, GLC2000 and Globcover 2.2) with respect to tree-cover. The often-used USGS GLCC land-cover inventory leads to a severe reduction of BVOC emissions due to a potential miss-attribution of broad-leaved trees and reduced tree-cover compared to the two other land-cover inventories. To account for uncertainties in the land-cover classification, we introduce land-cover correction factors for each relevant land-use category to adjust the tree-cover. The results are very sensitive to these factors within the plausible range. For June 2006, total monthly BVOC emissions decreased up to -27% with minimal and increased up to +71% with maximal factors, while in January 2006, the changes in monthly BVOC emissions were -54 and +56% with minimal and maximal factors, respectively. The new seasonality approach leads to a reduction in the annual emissions compared with non-adjusted data. The strongest reduction occurs in OVOC (up to -32%), the weakest in isoprene (as little as -19%). If also enzyme seasonality is taken into account, however, isoprene reacts with the steepest decrease of annual emissions, which are reduced by -44% to -49%, annual emissions of monoterpenes reduce between -30 and -35%. The sensitivity of the model to changes in temperature depends on the climatic zone but not on the vegetation inventory. The sensitivity is higher for temperature increases of 3 K (+31% to +64%) than decreases by the same amount (-20 to -35%). The climatic zones "Cold except summer" and "arid" are most sensitive to temperature changes in January for isoprene and monoterpenes, respectively, while in June, "polar" is most sensitive to temperature for both isoprene and monoterpenes. Our model predicts the oxygenated volatile organic compounds to be the most abundant fraction of the annual European emissions (3571-5328 Gg yr super(-1)), followed by monoterpenes (2964-4124 Gg yr super(-1)), isoprene (1450-2650 Gg yr super(-1)) and sesquiterpenes (150-257 Gg yr super(-1)). We find regions with high isoprene emissions (most notably the Iberian Peninsula), but overall, oxygenated VOC dominate with 43-45% (depending on the vegetation inventory) contribution to the total annual BVOC emissions in Europe. Isoprene contributes between 18-21%, monoterpenes 33-36% and sesquiterpenes contribute 1-2%. We compare the concentrations of biogenic species simulated by an air quality model with measurements of isoprene and monoterpenes in Hohenpeissenberg (Germany) for both summer and winter. The agreement between observed and modelled concentrations is better in summer than in winter. This can partly be explained with the difficulty to model weather conditions in winter accurately, but also with the increased anthropogenic influence on the concentrations of BVOC compounds in winter. Our results suggest that land-cover inventories used to derive tree-cover must be chosen with care. Also, uncertainties in the classification of land-cover pixels must be taken into account and remain high. This problem must be addressed together with the remote sensing community. Our new approach using a greenness index for addressing seasonality of vegetation can be implemented easily in existing models. The importance of OVOC for air quality should be more deeply \mbox{addressed} by future studies, especially in smog chambers. Also, the fate of BVOC from the dominant region of the Iberian Peninsula should be studied more in detail.
This paper describes aerosol modelling in Europe with a focus on Switzerland during summer and winter periods. We modelled PM2.5 (particles smaller than 2.5 μm in aerodynamic diameter) for one summer ...and two winter periods in years 2006 and 2007 using the CAMx air quality model. The meteorological fields were obtained from MM5 simulations. The modelled wind speeds during some low-wind periods, however, had to be calibrated with measurements to use realistic input for the air quality model. The detailed AMS (aerosol mass spectrometer) measurements at specific locations were used to evaluate the model results. In addition to the base case simulations, we carried out sensitivity tests with modified aerosol precursor emissions, air temperature and deposition. Aerosol concentrations in winter 2006 were twice as high as those in winter 2007, however, the chemical compositions were similar. CAMx could reproduce the relative composition of aerosols very well both in the winter and summer periods. Absolute concentrations of aerosol species were underestimated by about 20 %. Both measurements and model results suggest that organic aerosol (30–38 %) and particulate nitrate (30–36 %) are the main aerosol components in winter. In summer, organic aerosol dominates the aerosol composition (55–57 %) and is mainly of secondary origin. The contribution of biogenic volatile organic compound (BVOC) emissions to the formation of secondary organic aerosol (SOA) was predicted to be very large (>95 %) in Switzerland. The main contributors to the modelled SOA concentrations were oxidation products of monoterpenes and sesquiterpenes as well as oligomerization of oxidized compounds. The fraction of primary organic aerosol (POA) derived from measurements was lower than the model predictions indicating the importance of volatility of POA, which has not yet been taken into account in CAMx. Sensitivity tests with reduced NOx and NH3 emissions suggest that aerosol formation is more sensitive to ammonia emissions in winter in a large part of Europe. In Switzerland however, aerosol formation is predicted to be NOx-sensitive. In summer, effects of NOx and NH3 emission reductions on aerosol concentrations are predicted to be lower mostly due to lower ammonium nitrate concentrations. In general, the sensitivity to NH3 emissions is weaker in summer due to higher NH3 emissions.
We developed a method to generate two-week NO2 concentration maps with a high spatial resolution (10 m by 10 m) for the city of Zurich, Switzerland, based on statistical modelling. Our models utilize ...data from a dense passive diffusion sampler network consisting of 49 sites that measured 14-day mean NO2 concentrations in the year 2008. The regression analysis is based on Generalized Additive Models (GAMs) and a stepwise forward selection algorithm that leads to models relying on a small number of explanatory variables (2–3). The explanatory variables included in the regression analysis are spatially resolved information on traffic and heating systems related NOX and NO2 emissions, respectively, sky view factors, and topography (elevation). Deviance explained of the 26 models ranges from 0.66 to 0.79. 81% of the modelled and 77% of the predicted NO2 concentrations, respectively, deviate less than 25% from the observations.
The modelling approach outlined in this paper augments the value of point measurements obtained from urban routine passive diffusion sampler networks by providing spatially resolved concentration fields. The derived maps allow a detailed assessment of NO2 levels in cities and can be used in applications such as public health protection.
•We generated highly resolved two-week NO2 maps for the city of Zurich.•The used statistical models rely on NO2 passive sampler data and geoinformation.•Traffic activity is the most important predictor for our NO2 models in Zurich.•The statistical modelling augments the value of pointwise passive sampler data.•We analysed the NO2 concentration field w.r.t. annual limit value exceedances.
The aim of the study is to investigate the existence of a weekly cycle of coarse mode (PM
10–PM
2.5) atmospheric particles, to compare this weekly cycle to the weekly cycle of PM
2.5 and to compare ...the strength of the coarse mode weekly cycle in different seasons and different wind speed, wind direction and precipitation conditions. In addition, an estimate of the contribution of traffic to the total ambient coarse mode particulate matter in Zurich, Switzerland is provided by estimating the weekly cycle of coarse mode traffic emissions and by comparing it to the weekly cycle of ambient concentrations. The coarse mode data used in the study are the result of simultaneous daily measurements of PM
10 and PM
2.5 at seven sites located in Switzerland. The measurements cover a period of 7–12 years for six stations and 3 years for one station. It is found that a coarse mode weekly cycle is present in various types of urban and rural stations. Ambient concentrations on weekdays are higher than on Sundays by a factor of 1.53 on average over all urban and suburban sites and by a factor of 1.32 on average over all rural sites. Moreover, the relative increase of coarse mode ambient concentrations on weekdays compared to Sundays was larger than the relative increase of PM
2.5 concentrations by a factor of 2.7 on average over all urban and suburban sites, whereas no considerable difference was found at the rural sites. A calculation of coarse mode traffic emissions for an urban scenario was carried out using traffic-induced dust resuspension and brake wear emission factors for light and heavy duty vehicles and traffic counts from Zurich, Switzerland. It is shown that coarse mode emissions on weekdays are greater than on Sundays by a factor of 2.0. The contribution of traffic to coarse mode urban ambient concentrations was estimated to be 53% (34%–78%) on Sundays and 70% (57%–86%) on weekdays. It is deemed however that these numbers are somewhat overestimating the traffic contribution because of lack of information on construction activities around the considered sites.
► A weekly cycle of PM
coarse (PM
10–PM
2.5) is found at urban, rural and rural elevated sites. ► The weekly cycle of PM
coarse is similar to the weekly cycle of PM
2.5. ► Weekday/weekend concentration ratios are higher for PM
coarse than for PM
2.5. ► Traffic emissions contribute an estimated 34%–78% to PM
coarse on Sundays and 57%–86% on weekdays.
Measurements of airborne particles with aerodynamic diameter of 10 mu m or less (PM sub(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 sub(10) concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM sub(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 sub(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 sub(10) values to obtain the PM sub(10) variability and trends due to other factors and processes, mainly PM sub(10) emissions and formation of secondary PM sub(10) due to trace gas emissions. After applying this process, the PM sub(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 sub(10) trends in winter were more negative after the adjustment for meteorology and they ranged between -1.25 mu g m super(-3) yr super(-1) and 0.07 mu g m super(-3) yr super(-1). The adjusted trends for the other seasons ranged between -1.34 mu g m super(-3) yr super(-1) and -0.26 mu g m super(-3) yr super(-1) in spring, -1.40 mu g m super(-3) yr super(-1) and -0.28 mu g m super(-3) yr super(-1) in summer and -1.28 mu g m super(-3) yr super(-1) and -0.11 mu g m super(-3) yr super(-1) in autumn. The estimated trends of meteorologically adjusted PM sub(10) 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 PM sub(10) 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 mu g m super(-3) yr super(-1) respectively. The adjusted yearly trends for all stations range from -0.15 mu g m super(-3) yr super(-1) to -1.2 mu g m super(-3) yr super(-1) or -1.2% yr super(-1) to -3.3% yr super(-1).
The trends and variability of PM.sub.10, PM.sub.2.5 and PM.sub.coarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were ...investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM.sub.2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PM.sub.coarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM.sub.2.5 except for one station, which exhibits a stationary point. Considering PM.sub.coarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM.sub.2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM.sub.10 and PM.sub.2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are -0.4 μg m.sup.-3 yr.sup.-1 for PM.sub.10 and PM.sub.2.5 size fractions. PM.sub.coarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PM.sub.coarse in addition to PM.sub.2.5 can result in a faster decrease of PM.sub.10 in the future. The trends of the 90th quantile of PM.sub.10 and PM.sub.2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (-0.6 μg m.sup.-3 yr.sup.-1).
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