A comparative analysis of the causes of air pollution in three cities of the Danube region: implications for the implementation of the air quality directives

The causes of air pollution in three cities of the Danube region (Budapest, Sofia and Zagreb) were studied using datasets of measurements and modelling tools. The contributions from different activity sectors, including natural sources and their geographical origin were quantified. It was observed that most of the pollutants are emitted locally. However, the medium to long range transport may be also considerable. On the basis of the output of the source identification, a series of measures were proposed to deal wtih the pollution problem at local, national and international levels.JRC.H.2-Air and Climat

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Urban PM2.5 Atlas: Air Quality in European cities

Many European cities suffer from poor air quality and regularly exceed both the European standards prescribed by the Air Quality Directive and the guidelines recommended by the World Health Organization. This is particularly the case for fine particulate matter (PM10) for which both the daily and yearly average limit values are regularly exceeded in many cities and several regions in Europe. Similar conclusions hold for PM2.5 where few cities manage to keep concentrations below the levels recommended by the WHO. Actions have been proposed and taken at the international, national and urban scales to reduce air pollution. While they have undoubtedly resulted in an overall improvement of the air quality over the years, there are still problems which are localised in specific regions and many cities. A key issue is thus to determine at which scale to act in order to abate these remaining air pollution problems most effectively. Central to this for cities, is a quantitative assessment of the different origins of air pollution in the city (urban, regional, national and transboundary) to support the design of efficient and effective air quality plans, which are a legal obligation for countries and regions whenever exceedances occur. The “Screening for High Emission Reduction Potentials for Air quality” tool (SHERPA) has been developed by the Joint Research Centre to quantify the origins of air pollution in cities and regions. In this Atlas, both the spatial (urban, country…) and sectoral (transport, residential, agriculture…) contributions are quantified for 150 European urban areas in Europe, where many of the current exceedances to the air quality EU limit values and WHO guidelines are reported. There is a need to provide information to improve air quality policy governance, to support authorities in choosing the most efficient actions at the appropriate administrative level and scale. In particular, actions at the local level focusing on the urban scale and at national/international level needs to be carefully balanced. Key conclusions are: • For many cities, local actions at the city scale are an effective means of improving air quality in that city. The overall conclusion is that cities have a role to play by taking actions at their own scale. It is important to emphasise that the emissions in cities contribute significantly to country and EU overall PM concentrations, reinforcing the important role of cities in reducing the air pollution through a multilevel approach. • Impacts of abatement measures on air quality are city specific The impact of a given abatement measure on air quality differs from city to city, even for cities that are located in the same country. Actions taken at different scales or in different activity sectors therefore lead to impacts on air quality that are city-specific. The diversity of possible responses to abatement measures stresses the need to take into account these city-specific circumstances when designing air quality plans. Actions that are efficient in one city might not be efficient in others. • Sectoral measures addressing agriculture at country or EU scale would have a clear benefit on urban air quality. Although agricultural emissions are limited in the "city" as defined here, agriculture considerably impacts air quality in many EU cities. The extent of the impact of agriculture on air quality is indicative of the potential of EU- or country-wide measures addressing this sector. Moreover, other sectoral measures can have an important potential at the urban scale even though they are applied at EU or country scale. This is the case of road transport where the EURO norms are, in practice, most effective in the areas where traffic is most important, i.e. cities.JRC.C.5-Air and Climat

DeltaSA tool for source apportionment benchmarking, description and sensitivity analysis

DeltaSA is an R-package and a Java on-line tool developed at the EC-Joint Research Centre to assist and benchmark source apportionment applications. Its key functionalities support two critical tasks in this kind of studies: the assignment of a factor to a source in factor analytical models (source identification) and the model performance evaluation. The source identification is based on the similarity between a given factor and source chemical profiles from public databases. The model performance evaluation is based on statistical indicators used to compare model output with reference values generated in intercomparison exercises. The references values are calculated as the ensemble average of the results reported by participants that have passed a set of testing criteria based on chemical profiles and time series similarity. In this study, a sensitivity analysis of the model performance criteria is accomplished using the results of a synthetic dataset where “a priori” references are available. The consensus modulated standard deviation punc gives the best choice for the model performance evaluation when a conservative approach is adopted.JRC.C.5-Air and Climat

Model quality objectives based on measurement uncertainty. Part I: Ozone

Since models are increasingly used for policy support their evaluation is becoming an important issue. One of the possible evaluations is to compare model results to measurements. Statistical performance indicators then provide insight on model performance but do not tell whether model results have reached a sufficient level of quality for a given application. In a previous work Thunis et al. (2012, referred to as T2012) proposed a Model Quality Objective (MQO) based on the root mean square error between measured and modeled concentrations divided by the measurement uncertainty. In T2012 the measurement uncertainty was assumed to remain constant regardless of the concentration level. In the current work this assumption is overcome by quantifying all possible sources of uncertainty for the particular case of O3. Based on these uncertainty source quantifications, a simple relationship is proposed to formulate the measurement uncertainty which is then used to update the MQO and Model Performance Criteria (MPC) proposed in T2012 with more accurate values. The MQO and MPC calculated based on the European monitoring network AIRBASE data provide insight on the expected model results quality for a given application, depending on the geographical area and station type. These station specific MQOs and MPCs have the main advantage of relating expected model performances to the underlying measurement uncertainties.JRC.H.2-Air and Climat

Performance criteria to evaluate air quality modeling applications

A set of statistical indicators fit for air quality model evaluation is selected based on experience and literature: The Root Mean Square Error (RMSE), the bias (Bias), the standard Deviation (SD) and the correlation factor (R) are selected. Among these the RMSE is proposed as the key one for the description of the model skill. Model Performance Criteria (MPC) to investigate whether a model results are ‘good enough’ for a given application are calculated based on the observation uncertainty (U). The basic concept is to allow for model results a similar margin of tolerance (in terms of uncertainty) as for observations. U is pollutant, concentration level and station dependent, therefore the proposed MPC are normalized by U. Some existing composite diagrams are adapted to visualize model performance in terms of the proposed MPC and are illustrated in a real modeling application. The Target diagram, used to visualize the RMSE, is adapted with a new normalization on its axis, while complementary diagrams are proposed. In this first application the dependence of U on concentrations level and station is ignored, and an assumption on the pollutant dependent relative error is made. The advantages of this new approach are finally described.JRC.H.2-Air and Climat

SPECIEUROPE: The European data base for PM source profiles

AbstractA new database of atmospheric particulate matter emission source profiles in Europe (SPECIEUROPE) developed in the framework of the Forum for air quality modeling in Europe (FAIRMODE, Working Group 3) is accessible at the website http://source-apportionment.jrc.ec.europa.eu/Specieurope/index.aspx. It contains the chemical composition of particulate matter emission sources reported in the scientific literature and reports drafted by competent authorities. The first release of SPECIEUROPE consists of 151 measured (original), 13 composite (merging different subcategories of similar sources), 6 calculated (from stoichiometric composition) and 39 derived (results of source apportionment studies) profiles. Each profile is related to one or more source categories or subcategories. The sources with the highest PM relative mass toxic pollutants such as PAHs are fuel oil burning, ship emissions, coke burning and wood burning. Heavy metals are most abundant in metal processing activities while halogens are mostly present in fertilizer production, coal burning and metallurgic sector. Anhydrosugars are only measured in biomass and wood burning source categories, because are markers for these categories. The alkaline earth metals are mostly present in road dust, cement production, soil dust and sometimes coal burning. Source categories like traffic and industrial, which contain heterogeneous subcategories, show the greatest internal variability.The relationships between sources profiles were also explored using a cluster analysis approach based upon the Standardized Identity Distance (SID) indicator. The majority of profiles are allocated in 8 major clusters. Some of the clusters include profiles mainly from one source category (e.g. wood burning) while others, such as industrial source profiles, are more heterogeneous and spread over three different clusters

Impact of meteorology on air quality modeling over the Po valley in northern Italy

International audienceA series of sensitivity tests has been performed using both a mesoscale meteorological model (MM5) and a chemical transport model (CHIMERE) to better understand the reasons why all models underestimate particulate matter concentrations in the Po valley in winter. Different options are explored to nudge meteorological observations from regulatory networks into MM5 in order to improve model performances, especially during the low wind speed regimes frequently present in this area. The sensitivity of the CHIMERE modeled particulate matter concentrations to these different meteorological inputs are then evaluated for the January 2005 time period. A further analysis of the CHIMERE model results revealed the need of improving the parametrization of the in-cloud scavenging and vertical diffusivity schemes; such modifications are relevant especially when the model is applied under mist, fog and low stratus conditions, which frequently occur in the Po valley during winter. The sensitivity of modeled particulate matter concentrations to turbulence parameters, wind, temperature and cloud liquid water content in one of the most polluted and complex areas in Europe is finally discussed

Impact of meteorology on air quality modeling over the Po valley in northern Italy

A series of sensitivity tests has been made on both a meteorological model (MM5, Mesoscale Meteorological Model) and a chemical transport model (CHIMERE) to better understand the reasons for the underestimation of modeled particulate matter (PM) concentrations in the Po valley in winter. For the January 2005, time period chosen to perform the sensitivity tests, different techniques to nudge meteorological observations from regulatory networks into MM5 are explored to improve the model performance, especially the simulation of the frequent low wind regimes in this area. The sensitivity of the CHIMERE modeled PM concentrations to these different meteorological inputs are then evaluated. A further analysis of the CHIMERE model and in particular the in-cloud scavenging and vertical diffusion schemes reveals the need of improving some parameterizations when the model is applied in areas with frequent episodes of mist, fog and low stratus, as the Po valley in winter. Sensitivity of modeled PM to turbulence parameters, wind, temperature and cloud liquid water content both for surface and for vertical profiles in one of the most polluted and complex areas in Europe are discussed.JRC.H.2 - Air and Climat