A revised estimate of copper emissions from road transport in UNECE-Europe and its impact on predicted copper concentrations

Comparisons of measured and model-predicted atmospheric copper concentrations show a severe underestimation of the observed concentrations by the models. This underestimation may be (partly) due to underestimated emissions of copper to air. Since the phase out of asbestos brake lining material, the composition of brake lining material has changed and may contain up to ∼15% copper. This makes brake wear from vehicles potentially an important source of atmospheric (particulate) copper concentrations. In this paper, we reassess the copper emissions due to exhaust emissions and brake wear from road transport. Overall, our reassessments result in an estimate of total copper emission to air in UNECE-Europe of 4.0–5.5 ktonnes yr⁻¹, which is substantially higher than the previous estimate of 2.8 ktonnes yr⁻¹. Copper concentrations over Europe are calculated with the LOTOS-EUROS model using the revised emission data as model input. The results show that the revised emission estimates are a major step towards gap closure of predicted versus observed copper concentrations in ambient air. Brake wear emissions may be responsible for 50–75% of the total copper emissions to air for most of Western Europe. The hypothesis that road transport is an important source of copper emissions is tested and confirmed by (1) reviewing available literature data of chemically speciated PM data from road tunnel studies and (2) the gradient observed in copper concentrations from ambient PM monitoring going from rural sites to street stations. The literature review and observational data suggest that the majority of the emitted PM10 brake wear particles is in the PM2.5–10 size range. The results of this study indicate that modification of brake lining composition is an important mitigation option to reduce copper exposure of the population in Western Europe.     

Validation of a hybrid forecasting system for the ozone concentrations over the Paris area

A simplified hybrid statistical-deterministic chemistry-transport model, is used in real time for the prediction of ozone in the area of Paris during Summer 1999. We present here a statistical validation of this experiment. We distinguish the forecasts in the urban area from forecasts in the pollution plume downwind of the city. The validation of model forecasts, up to 3 days ahead, is performed against ground based observations within and up to 50 km outside of Paris. In the urban area, ozone levels are fairly well forecast, with correlation coefficients between forecast and observations ranging between 0.7 and 0.8 and root mean square errors in the range 15–20 μg m⁻³ at short lead times. While the bias of urban forecast is very low, the largest peaks are somehow underestimated. The ozone plume amplitude is generally well reproduced, even at long lead times (root mean square errors of about 20–30 μg m⁻³), while the direction of the plume is only captured at short lead times (about 70% of the time). The model has difficulties in forecasting the direction of the plume under stagnant weather conditions. We estimate the model ability to forecast concentrations above 180 μg m⁻³, which are of practical relevance to air quality managers. It is found that about 60% of these events are well forecast, even at long lead times, while the exact monitoring station where the exceedance is observed can only be forecast at short lead times. Finally, we found that about half of the forecast error is due to the error in the estimation of the boundary conditions, which are forecast by a simple linear regression model here.     

A full year evaluation of the CALIOPE-EU air quality modeling system over Europe for 2004

The CALIOPE-EU high-resolution air quality modeling system, namely WRF-ARW/HERMES-EMEP/CMAQ/BSC-DREAM8b, is developed and applied to Europe (12 km × 12 km, 1 h). The model performances are tested in terms of air quality levels and dynamics reproducibility on a yearly basis. The present work describes a quantitative evaluation of gas phase species (O₃, NO₂ and SO₂) and particulate matter (PM2.5 and PM10) against ground-based measurements from the EMEP (European Monitoring and Evaluation Programme) network for the year 2004. The evaluation is based on statistics. Simulated O₃ achieves satisfactory performances for both daily mean and daily maximum concentrations, especially in summer, with annual mean correlations of 0.66 and 0.69, respectively. Mean normalized errors are comprised within the recommendations proposed by the United States Environmental Protection Agency (US-EPA). The general trends and daily variations of primary pollutants (NO₂ and SO₂) are satisfactory. Daily mean concentrations of NO₂ correlate well with observations (annual correlation r = 0.67) but tend to be underestimated. For SO₂, mean concentrations are well simulated (mean bias = 0.5 μg m^?3) with relatively high annual mean correlation (r = 0.60), although peaks are generally overestimated. The dynamics of PM2.5 and PM10 is well reproduced (0.49 < r < 0.62), but mean concentrations remain systematically underestimated. Deficiencies in particulate matter source characterization are discussed. Also, the spatially distributed statistics and the general patterns for each pollutant over Europe are examined. The model performances are compared with other European studies. While O₃ statistics generally remain lower than those obtained by the other considered studies, statistics for NO₂, SO₂, PM2.5 and PM10 present higher scores than most models.     

Aerosol transport model evaluation of an extreme smoke episode in Southeast Asia

Biomass burning is one of many sources of particulate pollution in Southeast Asia, but its irregular spatial and temporal patterns mean that large episodes can cause acute air quality problems in urban areas. Fires in Sumatra and Borneo during September and October 2006 contributed to 24-h mean PM₁₀ concentrations above 150 μg m^?3 at multiple locations in Singapore and Malaysia over several days. We use the FLAMBE model of biomass burning emissions and the NAAPS model of aerosol transport and evolution to simulate these events, and compare our simulation results to 24-h average PM₁₀ measurements from 54 stations in Singapore and Malaysia. The model simulation, including the FLAMBE smoke source as well as dust, sulfate, and sea salt aerosol species, was able to explain 50% or more of the variance in 24-h PM₁₀ observations at 29 of 54 sites. Simulation results indicated that biomass burning smoke contributed to nearly all of the extreme PM₁₀ observations during September–November 2006, but the exact contribution of smoke was unclear because the model severely underestimated total smoke emissions. Using regression analysis at each site, the bias in the smoke aerosol flux was determined to be a factor of between 2.5 and 10, and an overall factor of 3.5 was estimated. After application of this factor, the simulated smoke aerosol concentration averaged 20% of observed PM₁₀, and 40% of PM₁₀ for days with 24-h average concentrations above 150 μg m^?3. These results suggest that aerosol transport models can aid analysis of severe pollution events in Southeast Asia, but that improvements are needed in models of biomass burning smoke emissions.     

Disaggregation of nation-wide dynamic population exposure estimates in The Netherlands: Applications of activity-based transport models

Traditional exposure studies that link concentrations with population data do not always take into account the temporal and spatial variations in both concentrations and population density. In this paper we present an integrated model chain for the determination of nation-wide exposure estimates that incorporates temporally and spatially resolved information about people's location and activities (obtained from an activity-based transport model) and about ambient pollutant concentrations (obtained from a dispersion model). To the best of our knowledge, it is the first time that such an integrated exercise was successfully carried out in a fully operational modus for all models under consideration. The evaluation of population level exposure in The Netherlands to NO₂ at different time-periods, locations, for different subpopulations (gender, socio-economic status) and during different activities (residential, work, transport, shopping) is chosen as a case-study to point out the new features of this methodology. Results demonstrate that, by neglecting people's travel behaviour, total average exposure to NO₂ will be underestimated by 4% and hourly exposure results can be underestimated by more than 30%. A more detailed exposure analysis reveals the intra-day variations in exposure estimates and the presence of large exposure differences between different activities (traffic > work > shopping > home) and between subpopulations (men > women, low socio-economic class > high socio-economic class). This kind of exposure analysis, disaggregated by activities or by subpopulations, per time of day, provides useful insight and information for scientific and policy purposes. It demonstrates that policy measures, aimed at reducing the overall (average) exposure concentration of the population may impact in a different way depending on the time of day or the subgroup considered. From a scientific point of view, this new approach can be used to reduce exposure misclassification.     

Origin of the exceedances of the European daily PM limit value in regional background areas of Spain

The origin of the daily exceedances of 50 μg PM10 m⁻³ (daily limit value or DLV of the EU air quality directive) and of an arbitrary daily value (DV) 35 μg PM2.5 m⁻³ recorded in 2001–2003 in 13 regional background stations of the Iberian Peninsula were interpreted. This was carried out by means of back-trajectory analysis, available PM model outputs, satellite data and meteorological maps. This allows the detection of high PM episodes on a regional scale and the study of their seasonal and geographical variability.The number of exceedances of the PM10 DLV ranged in 2001–2003 from 6 to 41 depending on the monitoring site. For the selected PM2.5 DV, the range of daily exceedances was 0–10 in the study period.The majority of the PM10 (>70% in most stations) and PM2.5 (17–55% in most stations) exceedances in regional background monitoring stations are caused by African dust outbreaks. These exceedances were less frequent in winter than in summer due to: (a) the frequent long range transport of dust in the warm seasons over Iberia, (b) the re-suspension associated with convective atmospheric dynamics, and (c) the relative low rainfall favouring re-suspension and high residence time of PM. Moreover, a regional contribution of secondary aerosols derived from the efficient photochemical transformation of gaseous precursors may coincide with African transport in summer.Episodes with lack of advective conditions caused 2–29% and 20–50% of the PM10 and PM2.5 exceedances. These occurred mainly in summer due to poor renovation of air masses, increased convective re-suspension, dispersion of pollutants towards rural areas and regional re-circulation and aging of air masses which result in the proliferation of secondary inorganic species.Long-range transport of PM from continental Europe caused exceedances (9–40% and 18–38% of the PM10 and PM2.5 exceedances, respectively), only in northern Iberia because, as the European air masses evolve towards the south, the pollutants suffer dispersion/dilution. Local exceedances are associated with the advection of the clean Atlantic air masses, which cannot increase PM levels to a great extent without the influence of a local source of PM. The proportion of local exceedances of PM10 and PM2.5 ranged 6–33% and 17–40%, respectively.     

An analysis of seasonal surface dust aerosol concentrations in the western US (2001–2004): Observations and model predictions

Long-term surface observations indicate that soil dust represents over 30% of the annual fine (particle diameter less than 2.5 μm) particulate mass in many areas of the western US; in spring and summer, it represents an even larger fraction. There are numerous dust-producing playas in the western US, but surface dust aerosol concentrations in this region are also influenced by dust of Asian origin. This study examines the seasonality of surface soil dust concentrations at 15 western US sites using observations from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network from 2001 to 2004. Average soil concentrations in particulate matter less than 10 μm in diameter (PM₁₀) were lowest in winter and peaked during the summer months at these sites; however, episodic higher-concentration events (>10 μg m⁻³) occurred in the spring, the time of maximum Asian dust transport to the western US. Simulated surface dust concentrations from the Navy Aerosol Analysis and Prediction System (NAAPS) suggested that long-range transport from Asia dominates surface dust concentrations in the western US in the spring, and that, although some long-range transport does occur throughout the year (1–2 μg m⁻³), locally generated dust plays a larger role in the region in summer and fall. However, NAAPS simulated some anomalously high concentrations (>50 μg m⁻³) of local dust in the fall and winter months over portions of the western US. Differences between modeled and observed dust concentrations were attributed to overestimation of total observed soil dust concentrations by the assumptions used to convert IMPROVE measurements into PM₁₀ soil concentrations, lack of inhibition of model dust production in snow-covered regions, and lack of seasonal agricultural sources in the model.     

The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy

The sensitivity of the CHIMERE model to emission reduction scenarios on particulate matter PM2.5 and ozone (O₃) in Northern Italy is studied. The emissions of NO_x, PM2.5 SO₂, VOC or NH₃ were reduced by 50% for different source sectors for the Lombardy region, together with 5 additional scenarios to estimate the effect of local measures on improving the air quality for the Po valley area. Firstly, we evaluate the model performance by comparing calculated surface aerosol concentrations for the standard case (no emission reductions) with observations for January and June 2005. Calculated monthly mean PM10 concentrations are in general underestimated. For June, modelled PM10 concentrations slightly overestimate the measurements. Calculated monthly mean SO₄, NO₃^?, NH₄⁺ concentrations are in good agreement with the observations for January and June. Secondly, the model sensitivity of emission reduction scenarios on PM2.5 and O₃ calculated concentrations for the Po valley area is evaluated. The most effective scenarios to abate PM2.5 concentration are based on the SNAP2 (non-industrial combustion plants) and SNAP7 (road traffic) sectors, for which the NO_x and PM2.5 emissions are reduced by 50%. The number of days that the 2015 PM2.5 limit value of 25 μg m^?3 in Milan is exceeded by reducing primary PM2.5 and NO_x emissions for SNAP2 and 7 by 50%, does not change in January when compared to the standard case for the Milan area. It appears that 40% of the PM2.5 concentration in the greater Milan area is caused by the emissions surrounding the Lombardy region and from the model boundary conditions.This study also showed that a more effective pollutant reduction (emissions) per ton of pollutant reduced (concentrations) for the greater Milan area is obtained by reducing the primary PM2.5 emissions for SNAP7 by 50%. The most effective scenario on PM2.5 decrease for which precursor emissions are reduced is achieved by reducing SO₂ emissions by 50% for SNAP7.Our study showed that during summer time, the largest reductions in O₃ concentrations are achieved for SNAP7 emission reductions, when volatile organic compounds (VOCs) are reduced by 50%.     

Simulating PM concentration during a winter episode in a subtropical valley: Sensitivity simulations and evaluation methods

We investigated a two-week episode with high PM concentrations in California Central Valley during the Christmas–New Year of 2000–2001 using a modeling system that consists of a computationally efficient, 3-D photochemical–microphysical transport model, a mesoscale meteorological model, emission models, and an evaluation package. One hundred simulations were conducted with fine resolutions and observational constraints, to reproduce spatial and temporal features of observed PM concentrations and to understand the formation mechanism of the episode. Simulated PM concentrations consist of secondary inorganic components, mainly ammonium nitrate, and total carbon in areas with elevated concentrations in the accumulation mode, and consist of mainly dust and sea salt in the coarse mode. Simulated oxidants and nitrate were significantly elevated over the valley, and the latter showed much less amplitude than the former. Simulated PM concentrations were evaluated with observations systematically with spatially and temporally paired method, a more restrictive multivariate method (NMFROC), and a more flexible “gradient evaluation” method. The paired evaluation shows that high correlation coefficient (R = ～0.8) and low fractional error (FE = ～0.1) could be achieved at stations with elevated 24-h concentration of PM in the accumulation mode in some simulations. The NMFROC method was used to extract useful information from seemingly failed simulations. A “gradient evaluation” method is introduced here to extract additional information from simulations. We found that emission reductions of NO_x and AVOC showed similar effects on percentage basis in different areas, and both are more effective than reducing NH₃ for abating elevated concentrations of accumulation mode PM in California Central Valley during the winter episode.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Sea salt concentrations across the European continent

The oceans are a major source for particles that play an important role in many atmospheric processes. In Europe sea salt may contribute significantly to particulate matter concentrations. We have compiled sodium concentration data as a tracer for sea salt for 89 sites in Europe to provide more insight in the distribution of sea salt across Europe. The annual average sea salt concentrations above land were estimated to range between 0.3 and almost 13 μg m^?3. Maximum concentrations are found at the Irish coast. At coastal sites along the Atlantic and North Sea coast concentrations tend to be around 5 μg m^?3. More inland locations up to about 300 km away from the coast tend to show concentrations between 2 and 5 μg m^?3, whereas sites further away from the coast are characterized by lower concentrations. An analysis of the representativity of the data with respect to a long term average showed that the long average is associated with a standard deviation of around 15%. The compilation of observations provides an improved overview of sea salt concentrations in Europe as well as an improved basis for model validation. Verification of the results of the LOTOS-EUROS model learned that the model represents well the spatial variability of the observed sea salt concentrations very well. However, the absolute concentrations are significantly overestimated due to large uncertainties in the emission and dry deposition parameterizations. Using the high explained variability in the gradients across Europe, the bias-corrected modelled distribution serves as a best estimate of the sea salt distribution across Europe for 2005.     

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations during ETEX 2

In this paper the meteorological processes responsible for transporting tracer during the second ETEX (European Tracer EXperiment) release are determined using the UK Met Office Unified Model (UM). The UM predicted distribution of tracer is also compared with observations from the ETEX campaign. The dominant meteorological process is a warm conveyor belt which transports large amounts of tracer away from the surface up to a height of 4 km over a 36 h period. Convection is also an important process, transporting tracer to heights of up to 8 km. Potential sources of error when using an operational numerical weather prediction model to forecast air quality are also investigated. These potential sources of error include model dynamics, model resolution and model physics. In the UM a semi-Lagrangian monotonic advection scheme is used with cubic polynomial interpolation. This can predict unrealistic negative values of tracer which are subsequently set to zero, and hence results in an overprediction of tracer concentrations. In order to conserve mass in the UM tracer simulations it was necessary to include a flux corrected transport method. Model resolution can also affect the accuracy of predicted tracer distributions. Low resolution simulations (50 km grid length) were unable to resolve a change in wind direction observed during ETEX 2, this led to an error in the transport direction and hence an error in tracer distribution. High resolution simulations (12 km grid length) captured the change in wind direction and hence produced a tracer distribution that compared better with the observations. The representation of convective mixing was found to have a large effect on the vertical transport of tracer. Turning off the convective mixing parameterisation in the UM significantly reduced the vertical transport of tracer. Finally, air quality forecasts were found to be sensitive to the timing of synoptic scale features. Errors in the position of the cold front relative to the tracer release location of only 1 h resulted in changes in the predicted tracer concentrations that were of the same order of magnitude as the absolute tracer concentrations.     

Operational ozone forecasts for the region of Copenhagen by the Danish Meteorological Institute

The Danish Meteorological Institute (DMI) has developed an operational forecasting system for ozone concentrations in the Atmospheric Boundary Layer; this system is called the Danish Atmospheric Chemistry FOrecasting System (DACFOS). At specific sites where real-time ozone concentration measurements are available, a statistical after-treatment of DACFOS’ results adjusts the next 48 h ozone forecasts. This post-processing of DACFOS’ forecasts is based on an adaptive linear regression model using an optimal state estimator algorithm. The regression analysis uses different linear combinations of meteorological parameters (such as temperature, wind speed, surface heat flux and atmospheric boundary layer height) supplied by the Numerical Weather Prediction model DMI-HIRLAM. Several regressions have been tested for six monitoring stations in Denmark and in England, and four of the linear combinations have been selected to be employed in an automatic forecasting system. A statistical study comparing observations and forecasts shows that this system yields higher correlation coefficients as well as smaller biases and RMSE values than DACFOS; the present post-processing thus improves DACFOS’ forecasts. This system has been operational since June 1998 at the DMI's monitoring station in the north of Copenhagen, for which a new ozone forecast is presented every 6 h on the DMI's internet public homepage.     

PM10 regional transport pathways in Thessaloniki,Greece

In this study, the most dominant regional transport pathways for the city of Thessaloniki, Greece were identified and linked to air quality issues with respect to particulate matter (PM). Using air mass trajectories, cluster analysis techniques and PM10 measurements of a background-urban station of the greater Thessaloniki area during 2001–2004, it was found that north-eastern and southern flows were the most frequent in appearance with high potential to influence the city of Thessaloniki, especially when coinciding with biomass burning or Saharan dust events correspondingly. These incidents appeared to occur mostly during summer adding to a PM10 monthly mean up to 10 μg m^?3. High concentrations of surface PM10 related to north-eastern flows were in most cases accompanied with high aerosol columnar optical depths implying that particulate matter transport from the North-East was multi-layered. South-southwesterly flows originating from N. Africa, though less frequent, seemed to affect decisively Thessaloniki's aerosol budget especially during transition seasons. These flows were related with an increase of the monthly PM10 average up to 20–30 μg m^?3 for the time period studied. Finally, northerly flows were found to transport rather clean air masses that did not seem to contribute to the air quality deterioration of the city.     

A method to determine the effect of mineral dust aerosols on air quality

Natural mineral dust storms (DS) from the Arabo-African region blow over the Mediterranean, reach Israel, and add to the anthropogenic particulate pollution. The effects of mineral dust on air quality in Israel were investigated using only PM10 and PM2.5 automatic measurements. The method does not require any other inputs such as satellite observations, model back-trajectories, dust forecast models, or mineralogical analyses. The method employs an automatic algorithm with three thresholds: the half-hour PM10 average must be above 100, this level is maintained for at least 3 h, and the maximum concentration recorded is above 180 μg m^?3. The algorithm was designed for Israel, but can be adapted for other locations.The contribution of DS caused PM10 values to exceed the Israeli annual standard of 60 μg m^?3 year^?1 in 6 of the 12 years examined. The DS contribution to PM10 annual average ranged from 9.4% to 29.5%. The level recommended by WHO, 20 μg m^?3 year^?1, was exceeded every year even without the DS contribution. The number of days in which the daily Israeli standard (150 μg m^?3) was exceeded during the 12 years was 6–20 days per year. The number of days in which the daily standard was exceeded shows an increasing trend of 7 days per decade.PM2.5 in Israel is in the range 40–56% of PM10. PM2.5 values were over the recommended standard with and without DS. The contribution of DS to annual average of PM2.5 ranged from 3.6% to 19.1%.The automatic algorithm was calibrated with a list of Dust Storms identified by visual means supported by mineralogical analysis. Mineralogical analyses of single particles were performed using Environmental Scanning Electron Microscope (ESEM). Two representative samples are given. The main difference is that the particles of the Saudi-Arabian storm had much more palygorskite, while the North-African storm had more sea-salt and organic particles. The mineral composition differences indicate that analysis can differentiate between sources.     

Revised estimates of construction activity and emissions: Effects on ozone and elemental carbon concentrations in southern California

Emissions from diesel-powered construction equipment are an important source of nitrogen oxides (NO_x) and particulate matter (PM). A new emission inventory for construction equipment emissions is developed based on surveys of diesel fuel use; the revised inventory is compared to current emission inventories. California's OFFROAD model estimates are 4.5 and 3.1 times greater, for NO_x and PM respectively, than the fuel-based estimates developed here. The most relevant uncertainties are the overall amount of construction activity/diesel fuel use, exhaust emission factors for PM and NO_x, and the spatial allocation of emissions to county level and finer spatial scales. Construction permit data were used in this study to estimate spatial distributions of emissions; the resulting distribution is well correlated with population growth. An air quality model was used to assess the impacts of revised emission estimates. Increases of up to 15 ppb in predicted peak ozone concentrations were found in southern California. Elemental carbon and fine particle mass concentrations were in better agreement with observations using revised emission estimates, whereas negative bias in predictions of ambient NO_x concentrations increased.     

Simulating industrial emissions using atmospheric dispersion modeling system: model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM10) and sulfur dioxide (SO2)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NOx, CO, PM10 and SO2, respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.     

Spatial surrogates for the disaggregation of CORINAIR emission inventories

CORINAIR atmospheric emission inventories are frequently used input data for air quality models with a domain situated in Europe. In CORINAIR emission inventories, sources are broken down over 11 major source categories. This paper presents spatial surrogates for the disaggregation of CORINAIR atmospheric emission inventories for input of air pollutants and particulate matter to grid or polygon based air quality model domains inside Europe. The basis for the disaggregation model was the CLC2000 land cover data to which statistical weights were added. Weights were population census data for residential emissions, employment statistics for agricultural and industrial area emissions, livestock statistics for ammonia emissions and annual aircraft movements for emissions realized by air transport. Additional road and off-road network information was used to disaggregate emissions realized by traffic. A comparison of top down produced emission estimates with spatially resolved national emission data for The Netherlands and the United Kingdom gave confidence in the present spatial surrogates as a tool for the top down production of atmospheric emission maps. Explained variance at a spatial resolution of 5 km was >70% for CO, NMVOC and NO_x, >60% for PM10 and almost 50% for SO₂.     

Impact of mineral components and selected trace metals on ambient PM10 concentrations

PM10 levels of the mineral components Si, Al, Fe, Ca, Mg and some trace metals were measured at three different sites in the urban area of Vienna (Austria). Observed trace metal concentrations varied between less than 0.1 ng m^?3 (Cd) and approximately 200 ng m^?3 (Zn), mineral components showed enhanced concentrations ranging from 0.01 μg m^?3 (Ca) to 16.3 μg m^?3 (Si). The contribution of the respective mineral oxides to PM10 mass concentrations accounted on average for 26.4 ± 16% (n = 1090) of the PM10 mass, with enhanced rates in spring and autumn (monthly averages of up to 40%) and decreased contributions in the cold season (monthly averages below 10%). The atmospheric occurrence of Al, Ti and Sr could be assigned to crustal sources, whereas for the elements Ba, Ca, Fe, Mg, Mn and V an increased contribution of non-crustal origin was observed. PM10 levels of As, Cd, Co, Cr, Cu, Ni, Pb, Sb, Sn and Zn were predominantly derived from man-made emissions. Intersite comparison indicated that urban PM10 mass concentrations and PM10 levels of As, Pb and Zn were predominantly influenced from the transport of aerosols from outside into the city, whereas for the elements Ba, Mg, Ca, Cu and Fe a distinctly increased impact of local emissions was observed. The contribution of these urban emissions to total PM10 concentrations was estimated by calculating the so-called “urban impact”, which was found to be 32.7 ± 18% (n = 392) in the case of PM10 mass concentrations. The investigated elements accounted on average for 31.3 ± 19% (n = 392) of the observed PM10 mass increase. The mean values for the “urban impacts” of individual elements varied between 25.5% (As) and 77.0% (Ba).