A comparison between concentrations calculated with CAR II 5.0 and concentrations measured via the air quality network

Lokale overheden gebruiken het model CAR II (Calculation of Air pollution from Road traffic) voor de berekening van de luchtkwaliteit in verkeersbelaste situaties. Uit onderzoek van het RIVM blijkt dat de berekende stikstofdioxide en fijn stof concentraties redelijk goed overeen komen met metingen. De berekende concentraties zijn gemiddeld enkele microgrammen per kubieke meter hoger respectievelijk lager dan de gemeten stikstofdioxide en fijn stof concentraties. Op basis van door gemeenten zelf aangeleverde verkeersgegevens zijn voor de jaren 2003, 2004 en 2005 berekeningen met CAR II, versie 5.0, uitgevoerd in de straten waar meetstations van het van het Landelijk Meetnet Luchtkwaliteit van het RIVM staan. De resultaten van de berekeningen voor stikstofdioxide en fijn stof zijn vervolgens vergeleken met metingen in de straten over dezelfde periode. De berekende stikstofdioxide concentraties zijn gemiddeld ruim twee microgram per kubieke meter hoger dan de gemeten concentraties. De berekende fijn stof concentraties zijn gemiddeld iets lager dan de gemeten concentraties. Er is voor fijn stof een grotere spreiding in de geconstateerde verschillen dan voor stikstofdioxide het geval is. De resultaten van CAR II voldoen voor de jaargemiddelde concentratie van zowel stikstofdioxide als fijn stof aan de in de wet gestelde eisen voor de nauwkeurigheid van de bepaling van jaargemiddelde concentraties. In hoeverre de resultaten van de uitgevoerde vergelijking een garantie zijn voor de betrouwbaarheid en/of de toepasbaarheid van CAR voor toekomstige jaren is moeilijk te zeggen. Het is niet onmogelijk dat verschillende bouwstenen in de huidige CAR onvolkomenheden bevatten die elkaar nu (deels) compenseren.Dutch municipalities use the model CAR II (Calculation of Air pollution from Road traffic) to estimate local air quality in streets with traffic. RIVM has shown that the concentrations of nitrogen dioxide and particulate matter, as calculated by CAR II, version 5.0, are in fairly good agreement with measurements. Calculated concentrations are, on average, a few micrograms per cubic metre higher (for nitrogen dioxide) or lower (for particulate matter) compared to measurements. Based on traffic information provided by municipalities, calculations were performed using CAR II, version 5.0, for the years 2003, 2004 and 2005 for those streets where a measuring station of the national air quality measuring network of the RIVM is located. The results of the calculations were compared with measurements during the same period. Calculated nitrogen dioxide concentrations are on average just over two micrograms per cubic metre higher than measured concentrations. For particulate matter the calculated concentrations are just below measured levels. Furthermore, the spread in differences between measured and calculated concentrations is larger for particulate matter. With respect to accuracy of the calculations, the yearly averaged concentrations calculated by CAR II comply with legal regulations. It will be difficult to determine to what extent the results of the present study can be used to indicate the reliability and applicability of the CAR model in future. It is also quite possible that several modules of CAR contain errors, which, at present, counteract each other.VROM-LM

A comparison of atmospheric transport models for traffic emissions

De in Nederland gebruikte rekenmodellen voor luchtkwaliteit langs snelwegen en stadswegen geven wisselende resultaten. Voor berekening van jaargemiddelde concentraties van stikstofdioxide en fijn stof zijn de verschillen tussen modellen beperkt. Bij de berekening van meer specifieke aspecten van luchtkwaliteit, bijvoorbeeld het aantal overschrijdingsdagen, kunnen de verschillen tussen modelresultaten aanzienlijk groter zijn. Atmosferische verspreidingsmodellen berekenen luchtkwaliteit als gevolg van emissies door het verkeer. In Nederland is een aantal van deze verspreidingsmodellen in gebruik bij overwegend commerciele bureaus. Zij voeren berekeningen uit in opdracht van overheid en bedrijfsleven. De rekenmodellen die deze bureaus gebruiken werken volgens uiteenlopende rekenmethoden. Om vast te stellen in hoeverre de resultaten van de verschillende modellen met elkaar overeenstemmen, zijn in dit onderzoek zes in Nederland gebruikte rekenmodellen met elkaar vergeleken. Bij de berekening van de jaargemiddelde concentratie van stikstofdioxide en fijn stof langs snel- en stadswegen, liggen de resultaten van de modellen binnen een marge van 10-15% rond het gemiddelde van de modellen. Bij de berekening van het aantal overschrijdingsdagen voor fijn stof langs snelwegen liggen de verschillende modelresultaten echter in een bandbreedte van 30% rond het gemiddelde. Voor een typische stadswegsituatie is die bandbreedte 50%.This report presents the results of an intercomparison study of six atmospheric transport models for traffic situations in the Netherlands. A number of test cases were defined in consultation with the model owners in which the input parameters to the models, such as emissions, meteorological conditions and road characteristics. Two base cases were defined: one for roadways and one for urban roads, along with a number of variants on these two base cases. The variants, for example, consisted of different meteorological conditions, different background concentrations and the presence of a noise barrier. The model owners calculated - for both base cases and variants - the annual concentration of NO2 and PM10 and the number of days in which the daily threshold of 50 ug/m3 of PM10 is exceeded. For the roadway test cases annual levels of NO2 and PM10 calculated by the different models are within about 10% of the average of all model results. The large difference found in the number of exceedence days was caused by the difference in the methods used to derive the exceedence days. When using one standard method, as in the Dutch regulations, this difference fell to within 30%. Because of technical reasons only the "street canyon" variant was considered for urban roads. In this variant the model results were between 10% and 15% for NO2 and PM10, respectively, of the average of all models. If the contribution of the road alone is considered (i.e. comparing the concentrations without the prescribed background concentration), the models differ by a factor of 2 to 3 and the number of exceedence days for PM10 by a factor of 2.VROM-DG

Progress of the National Air Quality Cooperation Programme (NSL)

Om de luchtkwaliteit in Nederland te verbeteren is het Nationaal Samenwerkingsprogramma Luchtkwaliteit (NSL) opgezet. In dit programma werken de Rijksoverheid en decentrale overheden samen om te zorgen dat Nederland overal tijdig aan de grenswaarden voor fijnstof (2011) en stikstofdioxide (2015) zal voldoen. Om de voortgang te volgen is bij het NSL een monitoringsprogramma opgezet. Centraal onderdeel daarvan is een rekeninstrument waarvoor de overheden de brongegevens aanleveren. De daaruitvolgende rekenresultaten zijn vervolgens door het Bureau Monitoring (samenwerkingsverband RIVM en InfoMil) samengevoegd in voorliggende voortgangsrapportage. De prognoses voor 2011 en 2015 laten zien dat voor een groot deel van Nederland de resultaten onder de Europese grenswaarden voor PM10 (fijnstof) en NO2 liggen. Op een aantal plekken zijn er wel nieuwe of grotere overschrijdingen van de PM10- en NO2-grenswaarden zichtbaar. Bij de fijnstof (PM10) overschrijdingen gaat het hoofdzakelijk om locaties bij veehouderijen en een aantal industriele gebieden. Vooral nabij veehouderijen is op een aantal plekken nog sprake van grote overschrijdingen die lastig voor medio 2011 op te lossen zijn. De huidige prognose voor de concentraties stikstofdioxide in 2015 laat een minder gunstige ontwikkeling zien ten opzichte van wat is berekend in de vaststelling van het NSL. Dit komt voor een belangrijk deel door tegenvallende verkeersemissies wat heeft geleid tot een aantal nieuwe overschrijdingen. De nu in de prognoses berekende concentraties liggen op veel locaties net onder de grenswaarde. Met veel concentraties net onder de grenswaarde neemt het aantal overschrijdingen snel toe bij een tegenvaller in een van de gemaakte aannamen. In combinatie met een grote en deels onbekende onzekerheid in de rekenresultaten vormt dit een risico voor het behalen van de doelstelling van het NSL.The NSL has been put in place to improve air quality in the Netherlands and to ensure that the Netherlands meets the date of compliance with the EU limit values for particulate matter and nitrogen dioxide. Local, regional and national authorities work together within the framework of this programme to ensure that these goals are met. A monitoring programme, centred around a specially designed assessment tool, has been set up to monitor the progress. This tool uses data that the participating authorities are required to provide as part of the annual monitoring cycle. The results of the tool have been bundled by the Bureau Monitoring into this progress report. The prognosis for 2011 and 2015, based on the results obtained using the assessment tool, are that the concentrations of PM10 and NO2 fall below the EU limit values in most parts of the Netherlands. However, exceedances of the limit values do occur at specific locations. For PM10, these exceedances mostly occur close to a number of industrial sites and stock farms. Particularly high exceedances in the vicinity of these stock farms will make it difficult to meet the limit values by mid 2011 at these locations. The prognostications for NO2 show a less favourable decline in NO2 concentrations than was modelled at the establishment of the NSL. This is mostly due to the decline in traffic emissions falling short of expectations, resulting in new exceedances. At many locations, the calculated concentrations in the prognostications fall just under the limit value and, consequently, there will be a large increase in the number of exceedances when one or more of the premises become less favourable. This possibility, together with the large and partially unknown uncertainty in the calculation results, add up to a risk for not meeting the limit values by the date of compliance.VRO

Stable isotope tagging of epitopes: a highly selective strategy for the identification of major histocompatibility complex class I-associated peptides induced upon viral infection.

Identification of peptides presented in major histocompatibility complex (MHC) class I molecules after viral infection is of strategic importance for vaccine development. Until recently, mass spectrometric identification of virus-induced peptides was based on comparative analysis of peptide pools isolated from uninfected and virus-infected cells. Here we report on a powerful strategy aiming at the rapid, unambiguous identification of naturally processed MHC class I-associated peptides, which are induced by viral infection. The methodology, stable isotope tagging of epitopes (SITE), is based on metabolic labeling of endogenously synthesized proteins during infection. This is accomplished by culturing virus-infected cells with stable isotope-labeled amino acids that are expected to be anchor residues (i.e. residues of the peptide that have amino acid side chains that bind into pockets lining the peptide-binding groove of the MHC class I molecule) for the human leukocyte antigen allele of interest. Subsequently these cells are mixed with an equal number of non-infected cells, which are cultured in normal medium. Finally peptides are acid-eluted from immunoprecipitated MHC molecules and subjected to two-dimensional nanoscale LC-MS analysis. Virus-induced peptides are identified through computer-assisted detection of characteristic, binomially distributed ratios of labeled and unlabeled molecules. Using this approach we identified novel measles virus and respiratory syncytial virus epitopes as well as infection-induced self-peptides in several cell types, showing that SITE is a unique and versatile method for unequivocal identification of disease-related MHC class I epitopes

A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management

Evaluation of receptor and chemical transport models for PM10 source apportionment

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models

Identification of emulsifier potato peptides by bioinformatics: application to omega-3 delivery emulsions and release from potato industry side streams

We are grateful for the financial support from Innovation Fund Denmark (Grant nr: 7045-00021B, PROVIDE project). We also acknowledge K.M.C. amba (Brande, Denmark) and A.K.V. amba (Langholt, Denmark) for providing the potato samples used in this study.In this work, we developed a novel approach combining bioinformatics, testing of functionality and bottom-up proteomics to obtain peptide emulsifiers from potato side-streams. This is a significant advancement in the process to obtain emulsifier peptides and it is applicable to any type of protein. Our results indicated that structure at the interface is the major determining factor of the emulsifying activity of peptide emulsifiers. Fish oil-in-water emulsions with high physical stability were stabilized with peptides to be predicted to have facial amphiphilicity: (i) peptides with predominantly α-helix conformation at the interface and having 18–29 amino acids, and (ii) peptides with predominantly β-strand conformation at the interface and having 13–15 amino acids. In addition, high physically stable emulsions were obtained with peptides that were predicted to have axial hydrophobic/hydrophilic regions. Peptides containing the sequence FCLKVGV showed high in vitro antioxidant activity and led to emulsions with high oxidative stability. Peptide-level proteomics data and sequence analysis revealed the feasibility to obtain the potent emulsifier peptides found in this study (e.g. γ-1) by trypsin-based hydrolysis of different side streams in the potato industry.Innovation Fund Denmark 7045-00021