The contributions of fossil fuel (FF) and wood burning (WB) emissions to black carbon (BC) have been investigated in the recent past by analysis of multi-wavelength aethalometer data. This approach ...utilizes the stronger light absorption of WB aerosols in the near ultraviolet compared to the light absorption of aerosols from FF combustion. Here we present 2.5 years of seven-wavelength aethalometer data from one urban and two rural background sites in Switzerland measured from 2008–2010. The contribution of WB and FF to BC was directly determined from the aerosol absorption coefficients of FF and WB aerosols which were calculated by using confirmed Ångstrom exponents and aerosol light absorption cross-sections that were determined for all sites. Reasonable separation of total BC into contributions from FF and WB was achieved for all sites and seasons. The obtained WB contributions to BC are well correlated with measured concentrations of levoglucosan and potassium while FF contributions to BC correlate nicely with NOx. These findings support our approach and show that the applied source apportionment of BC is well applicable for long-term data sets. During winter, we found that BC from WB contributes on average 24–33 % to total BC at the considered measurement sites. This is a noticeable high fraction as the contribution of wood burning to the total final energy consumption is in Switzerland less than 4 %.
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).
In many large cities of Europe standard air quality limit values of particulate matter (PM) are exceeded. Emissions from road traffic and biomass burning are frequently reported to be the major ...causes. As a consequence of these exceedances a large number of air quality plans, most of them focusing on traffic emissions reductions, have been implemented in the last decade. In spite of this implementation, a number of cities did not record a decrease of PM levels. Thus, is the efficiency of air quality plans overestimated? Do the road traffic emissions contribute less than expected to ambient air PM levels in urban areas? Or do we need a more specific metric to evaluate the impact of the above emissions on the levels of urban aerosols? This study shows the results of the interpretation of the 2009 variability of levels of PM, Black Carbon (BC), aerosol number concentration (N) and a number of gaseous pollutants in seven selected urban areas covering road traffic, urban background, urban-industrial, and urban-shipping environments from southern, central and northern Europe. The results showed that variations of PM and N levels do not always reflect the variation of the impact of road traffic emissions on urban aerosols. However, BC levels vary proportionally with those of traffic related gaseous pollutants, such as CO, NO2 and NO. Due to this high correlation, one may suppose that monitoring the levels of these gaseous pollutants would be enough to extrapolate exposure to traffic-derived BC levels. However, the BC/CO, BC/NO2 and BC/NO ratios vary widely among the cities studied, as a function of distance to traffic emissions, vehicle fleet composition and the influence of other emission sources such as biomass burning. Thus, levels of BC should be measured at air quality monitoring sites. During morning traffic rush hours, a narrow variation in the N/BC ratio was evidenced, but a wide variation of this ratio was determined for the noon period. Although in central and northern Europe N and BC levels tend to vary simultaneously, not only during the traffic rush hours but also during the whole day, in urban background stations in southern Europe maximum N levels coinciding with minimum BC levels are recorded at midday in all seasons. These N maxima recorded in southern European urban background environments are attributed to midday nucleation episodes occurring when gaseous pollutants are diluted and maximum insolation and O3 levels occur. The occurrence of SO2 peaks may also contribute to the occurrence of midday nucleation bursts in specific industrial or shipping-influenced areas, although at several central European sites similar levels of SO2 are recorded without yielding nucleation episodes. Accordingly, it is clearly evidenced that N variability in different European urban environments is not equally influenced by the same emission sources and atmospheric processes. We conclude that N variability does not always reflect the impact of road traffic on air quality, whereas BC is a more consistent tracer of such an influence. However, N should be measured since ultrafine particles (<100 nm) may have large impacts on human health. The combination of PM10 and BC monitoring in urban areas potentially constitutes a useful approach for air quality monitoring. BC is mostly governed by vehicle exhaust emissions, while PM10 concentrations at these sites are also governed by non-exhaust particulate emissions resuspended by traffic, by midday atmospheric dilution and by other non-traffic emissions.
European publications dealing with source apportionment (SA) of atmospheric particulate matter (PM) between 1987 and 2007 were reviewed in the present work, with a focus on methods and results. The ...main goal of this meta-analysis was to provide a review of the most commonly used SA methods in Europe, their comparability and results, and to evaluate current trends and identify possible gaps of the methods and future research directions. Our analysis showed that studies throughout Europe agree on the identification of four main source types (
PM
10
and
PM
2.5
): a vehicular source (traced by carbon/Fe/Ba/Zn/Cu), a crustal source (Al/Si/Ca/Fe), a sea-salt source (Na/Cl/Mg), and a mixed industrial/fuel-oil combustion (
V
/
Ni
/
SO
4
2
-
) and a secondary aerosol
(
SO
4
2
-
/
NO
3
-
/
NH
4
+
)
source (the latter two probably representing the same source type). Their contributions to bulk PM levels varied widely at different monitoring sites, and showed clear spatial patterns in the cases of the crustal and sea-salt sources. Other specific sources such as biomass combustion or shipping emissions were rarely identified, even though they may contribute significantly to PM levels in specific locations.
Real-time measurements of non-refractory submicron aerosols (NR-PM1 ) were conducted within the greater Alpine region (Switzerland, Germany, Austria, France and Liechtenstein) during several ...week-long field campaigns in 2002-2009. This region represents one of the most important economic and recreational spaces in Europe. A large variety of sites was covered including urban backgrounds, motorways, rural, remote, and high-alpine stations, and also mobile on-road measurements were performed. Inorganic and organic aerosol (OA) fractions were determined by means of aerosol mass spectrometry (AMS). The data originating from 13 different field campaigns and the combined data have been utilized for providing an improved temporal and spatial data coverage. The average mass concentration of NR-PM1 for the different campaigns typically ranged between 10 and 30 μg m-3 . Overall, the organic portion was most abundant, ranging from 36% to 81% of NR-PM1 . Other main constituents comprised ammonium (5-15%), nitrate (8-36%), sulfate (3-26%), and chloride (0-5%). These latter anions were, on average, fully neutralized by ammonium. As a major result, time of the year (winter vs. summer) and location of the site (Alpine valleys vs. Plateau) could largely explain the variability in aerosol chemical composition for the different campaigns and were found to be better descriptors for aerosol composition than the type of site (urban, rural etc.). Thus, a reassessment of classifications of measurements sites might be considered in the future, possibly also for other regions of the world. The OA data was further analyzed using positive matrix factorization (PMF) and the multi-linear engine ME (factor analysis) separating the total OA into its underlying components, such as oxygenated (mostly secondary) organic aerosol (OOA), hydrocarbon-like and freshly emitted organic aerosol (HOA), as well as OA from biomass burning (BBOA). OOA was ubiquitous, ranged between 36% and 94% of OA, and could be separated into a low-volatility and a semi-volatile fraction (LV-OOA and SV-OOA) for all summer campaigns at low altitude sites. Wood combustion (BBOA) accounted for a considerable fraction during wintertime (17-49% OA), particularly in narrow Alpine valleys BBOA was often the most abundant OA component. HOA/OA ratios were comparatively low for all campaigns (6-16%) with the exception of on-road, mobile measurements (23%) in the Rhine Valley. The abundance of the aerosol components and the retrievability of SV-OOA and LV-OOA are discussed in the light of atmospheric chemistry and physics.
Nitrogen oxides (NOx = NO + NO2) in the atmosphere are often measured using instruments equipped with molybdenum converters. NO2 is catalytically converted to NO on a heated molybdenum surface and ...subsequently measured by chemiluminescence after reaction with ozone. The drawback of this technique is that other oxidized nitrogen compounds such as peroxyacetyl nitrate and nitric acid are also partly converted to NO. Thus such NO2 measurements are really surrogate NO2 measurements because the resultant values systematically overestimate the true value because of interferences of these compounds, especially when sampling photochemically aged air masses. However, molybdenum converters are widely used, and a dense network of surrogate NO2 measurements exists. As an alternative with far less interference, photolytic converters using ultraviolet light are nowadays applicable also for long‐term measurements. This work presents long‐term collocated NO2 measurements using molybdenum and photolytic converters at two rural sites in Switzerland. On a relative scale, the molybdenum converter instruments overestimate the NO2 concentrations most during spring/summer because of prevalent photochemistry. On a monthly basis, only 70–83% of the “surrogate” NO2 can be attributed to “real” NO2 at the non‐elevated site and even less (43–76%) at the elevated one. The observed interferences have to be taken into account for monitoring and regulatory issues and to be considered when using these data for ground‐truthing of satellite data or for validation of chemical transport models. Alternatively, an increased availability of artifact‐free data would also be beneficial for these issues.
The identification of atmospheric trace species measurements that are representative of well-mixed background air masses is required for monitoring atmospheric composition change at background sites. ...We present a statistical method based on robust local regression that is well suited for the selection of background measurements and the estimation of associated baseline curves. The bootstrap technique is applied to calculate the uncertainty in the resulting baseline curve. The non-parametric nature of the proposed approach makes it a very flexible data filtering method. Application to carbon monoxide (CO) measured from 1996 to 2009 at the high-alpine site Jungfraujoch (Switzerland, 3580 m a.s.l.), and to measurements of 1,1-difluoroethane (HFC-152a) from Jungfraujoch (2000 to 2009) and Mace Head (Ireland, 1995 to 2009) demonstrates the feasibility and usefulness of the proposed approach. The determined average annual change of CO at Jungfraujoch for the 1996 to 2009 period as estimated from filtered annual mean CO concentrations is −2.2 ± 1.1 ppb yr−1. For comparison, the linear trend of unfiltered CO measurements at Jungfraujoch for this time period is −2.9 ± 1.3 ppb yr−1.
In this study the sensitivity of the model performance of the chemistry transport model (CTM) LOTOS-EUROS to the description of the temporal variability of emissions was investigated. Currently the ...temporal release of anthropogenic emissions is described by European average diurnal, weekly and seasonal time profiles per sector. These default time profiles largely neglect the variation of emission strength with activity patterns, region, species, emission process and meteorology. The three sources dealt with in this study are combustion in energy and transformation industries (SNAP1), nonindustrial combustion (SNAP2) and road transport (SNAP7). First of all, the impact of neglecting the temporal emission profiles for these SNAP categories on simulated concentrations was explored. In a second step, we constructed more detailed emission time profiles for the three categories and quantified their impact on the model performance both separately as well as combined. The performance in comparison to observations for Germany was quantified for the pollutants NO2, SO2 and PM10 and compared to a simulation using the default LOTOS-EUROS emission time profiles. The LOTOS-EUROS simulations were performed for the year 2006 with a temporal resolution of 1 h and a horizontal resolution of approximately 25 × 25km2. In general the largest impact on the model performance was found when neglecting the default time profiles for the three categories. The daily average correlation coefficient for instance decreased by 0.04 (NO2), 0.11 (SO2) and 0.01 (PM10) at German urban background stations compared to the default simulation. A systematic increase in the correlation coefficient is found when using the new time profiles. The size of the increase depends on the source category, component and station. Using national profiles for road transport showed important improvements in the explained variability over the weekdays as well as the diurnal cycle for NO2. The largest impact of the SNAP1 and 2 profiles were found for SO2. When using all new time profiles simultaneously in one simulation, the daily average correlation coefficient increased by 0.05 (NO2), 0.07 (SO2) and 0.03 (PM10) at urban background stations in Germany. This exercise showed that to improve the performance of a CTM, a better representation of the distribution of anthropogenic emission in time is recommendable. This can be done by developing a dynamical emission model that takes into account regional specific factors and meteorology.
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