Sam Erik Walker

• Chetna, NN; Dhaka, Surendra K.; Walker, Sam-Erik; Rawat, Vikas & Singh, Narendra (2024). Decoding temporal patterns and trends of PM<inf>10</inf> pollution over Delhi: a multi-year analysis (2015–2022). Environmental Monitoring & Assessment. ISSN 0167-6369. 196(6). doi: 10.1007/s10661-024-12638-7.
• Chetna, NN; Dhaka, Surendra K.; Walker, Sam-Erik; Rawat, Vikas & Singh, Narendra (2024). Long-term meteorology-adjusted and unadjusted trends of PM<inf>2.5</inf> using the AirGAM model over Delhi, 2007–2022. Atmospheric Environment: X. ISSN 2590-1621. 22. doi: 10.1016/j.aeaoa.2024.100255.
• Walker, Sam-Erik; Solberg, Sverre; Schneider, Philipp & Guerreiro, Cristina (2023). The AirGAM 2022r1 air quality trend and prediction model. Geoscientific Model Development. ISSN 1991-959X. 16(2), p. 573–595. doi: 10.5194/gmd-16-573-2023. Full text in Research Archive Show summary

  This paper presents the AirGAM 2022r1 model – an air quality trend and prediction model developed at the Norwegian Institute for Air Research (NILU) in cooperation with the European Environment Agency (EEA) over 2017–2021. AirGAM is based on nonlinear regression GAMs – generalised additive models – capable of estimating trends in daily measured pollutant concentrations at air quality monitoring stations, discounting for the effects of trends and time variations in corresponding meteorological data. The model has been developed primarily for the compounds NO2, O3, PM10, and PM2.5. Meteorological input data consist of temperature, wind speed and direction, planetary boundary layer height, relative and absolute humidity, cloud cover, and precipitation over the period considered. The exact set of meteorological variables used in the model depends on the compound selected for analysis. In addition to meteorological variables introduced in the model as covariates, i.e. explanatory variables for the concentration levels, the model also incorporates time variables such as the day of the week, day of the year, and overall time, which is related to the model's trend term. The trend analysis is performed at each station separately. Thus, the model only considers the temporal features of concentrations and meteorology at a station, rather than any spatial correlations or dependencies between stations. AirGAM is implemented using the R language for statistical computing and, in particular, the GAM package mgcv. In the model, meteorological and time covariates are represented and estimated as smooth nonlinear functions of the corresponding variables. Thus, the trend term is defined and estimated as a smooth nonlinear function of time over the period selected for analysis. Once fitted to training data, the model may be used as a prediction tool capable of predicting air pollutant concentrations for new sets of meteorological and time data which are not in the training set – e.g. for cross-validation or forecasting purposes. The model does not explicitly use emissions or background concentrations – these are sought to be implicitly represented through the estimated nonlinear relations between meteorology, time, and concentrations. In addition to meteorology-adjusted trends, the program also produces unadjusted trends – i.e. trends based on the same regression set-up but only including the time covariates. Both types of trends can be output in the same run, making it possible to compare them. Ideally, the meteorology-adjusted trend will show the trend in concentration mainly due to changes in emissions or physicochemical processes not induced by changes in meteorology. AirGAM has been developed and tested primarily in trend studies based on measurement data hosted by the EEA, including the AirBase data (before 2013) and the Air Quality e-Reporting (AQER) data from 2013 and onwards. Still, the model is general and could be applied in other regions with other input data. The EEA data provide daily or hourly surface measurements at individual monitoring stations in Europe. For input meteorological data, we extract time series from the gridded meteorological re-analysis (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for each monitoring station. The paper presents results with the model for all AirBase/AQER stations in Europe from the latest EEA trend study for 2005–2019.

• Solberg, Sverre; Walker, Sam-Erik; Schneider, Philipp & Guerreiro, Cristina (2021). Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe. Atmosphere. ISSN 2073-4433. 12. doi: 10.3390/atmos12020131. Full text in Research Archive Show summary

  In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

• Hamer, Paul David; Walker, Sam-Erik; Sousa Santos, Gabriela; Vogt, Matthias; Vo, Dam Thanh & Lopez-Aparicio, Susana [Show all 9 contributors for this article] (2020). The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions. Geoscientific Model Development. ISSN 1991-959X. 13, p. 4323–4353. doi: 10.5194/gmd-13-4323-2020. Full text in Research Archive Show summary

  This paper describes the Eulerian urban dispersion model EPISODE. EPISODE was developed to address a need for an urban air quality model in support of policy, planning, and air quality management in the Nordic, specifically Norwegian, setting. It can be used for the calculation of a variety of airborne pollutant concentrations, but we focus here on the implementation and application of the model for NO2 pollution. EPISODE consists of an Eulerian 3D grid model with embedded sub-grid dispersion models (e.g. a Gaussian plume model) for dispersion of pollution from line (i.e. roads) and point sources (e.g. chimney stacks). It considers the atmospheric processes advection, diffusion, and an NO2 photochemistry represented using the photostationary steady-state approximation for NO2. EPISODE calculates hourly air concentrations representative of the grids and at receptor points. The latter allow EPISODE to estimate concentrations representative of the levels experienced by the population and to estimate their exposure. This methodological framework makes it suitable for simulating NO2 concentrations at fine-scale resolution (<100 m) in Nordic environments. The model can be run in an offline nested mode using output concentrations from a global or regional chemical transport model and forced by meteorology from an external numerical weather prediction model; it also can be driven by meteorological observations. We give a full description of the overall model function and its individual components. We then present a case study for six Norwegian cities whereby we simulate NO2 pollution for the entire year of 2015. The model is evaluated against in situ observations for the entire year and for specific episodes of enhanced pollution during winter. We evaluate the model performance using the FAIRMODE DELTA Tool that utilises traditional statistical metrics, e.g. root mean square error (RMSE), Pearson correlation R, and bias, along with some specialised tests for air quality model evaluation. We find that EPISODE attains the DELTA Tool model quality objective in all of the stations we evaluate against. Further, the other statistical evaluations show adequate model performance but that the model scores greatly improved correlations during winter and autumn compared to the summer. We attribute this to the use of the photostationary steady-state scheme for NO2, which should perform best in the absence of local ozone photochemical production. Oslo does not comply with the NO2 annual limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes with the highest NO2 concentrations, which lead to the occurrence of exceedances of the AQD hourly limit for NO2, occur primarily in the winter and autumn in Oslo, so this strongly supports the use of EPISODE for application to these wintertime events. Overall, we conclude that the model is suitable for an assessment of annual mean NO2 concentrations and also for the study of hourly NO2 concentrations in the Nordic winter and autumn environment. Further, in this work we conclude that it is suitable for a range of policy applications specific to NO2 that include pollution episode analysis, evaluation of seasonal statistics, policy and planning support, and air quality management. Lastly, we identify a series of model developments specifically designed to address the limitations of the current model assumptions. Part 2 of this two-part paper discusses the CityChem extension to EPISODE, which includes a number of implementations such as a more comprehensive photochemical scheme suitable for describing more chemical species and a more diverse range of photochemical environments, as well as a more advanced treatment of the sub-grid dispersion.

• Hamer, Paul David; Walker, Sam-Erik; Sousa Santos, Gabriela; Vogt, Matthias; Vo, Dam Thanh & Lopez-Aparicio, Susana [Show all 8 contributors for this article] (2020). A presentation of the EPISODE urban scale air quality model and its application to Nordic winter conditions. In Moussiopoulos, Nicolas; Sokhi, Ranjeet S.; Tsegas, George; Fragkou, Evangelia; Chourdakis, Eleftherios & Pipilis, Ioannis (Ed.), Proceedings of Abstracts. 12th International Conference on Air Quality Science and Application. Online Conference. The Air quality Conference. ISSN 9781527258297. p. 70–70.
• Karl, Matthias; Walker, Sam-Erik; Solberg, Sverre & Ramacher, Martin O. P. (2019). The Eulerian urban dispersion model EPISODE – Part 2: Extensions to the source dispersion and photochemistry for EPISODE–CityChem v1.2 and its application to the city of Hamburg. Geoscientific Model Development. ISSN 1991-959X. 12, p. 3357–3399. doi: 10.5194/gmd-12-3357-2019. Full text in Research Archive Show summary

  This paper describes the CityChem extension of the Eulerian urban dispersion model EPISODE. The development of the CityChem extension was driven by the need to apply the model in largely populated urban areas with highly complex pollution sources of particulate matter and various gaseous pollutants. The CityChem extension offers a more advanced treatment of the photochemistry in urban areas and entails specific developments within the sub-grid components for a more accurate representation of dispersion in proximity to urban emission sources. Photochemistry on the Eulerian grid is computed using a numerical chemistry solver. Photochemistry in the sub-grid components is solved with a compact reaction scheme, replacing the photo-stationary-state assumption. The simplified street canyon model (SSCM) is used in the line source sub-grid model to calculate pollutant dispersion in street canyons. The WMPP (WORM Meteorological Pre-Processor) is used in the point source sub-grid model to calculate the wind speed at plume height. The EPISODE–CityChem model integrates the CityChem extension in EPISODE, with the capability of simulating the photochemistry and dispersion of multiple reactive pollutants within urban areas. The main focus of the model is the simulation of the complex atmospheric chemistry involved in the photochemical production of ozone in urban areas. The ability of EPISODE–CityChem to reproduce the temporal variation of major regulated pollutants at air quality monitoring stations in Hamburg, Germany, was compared to that of the standard EPISODE model and the TAPM (The Air Pollution Model) air quality model using identical meteorological fields and emissions. EPISODE–CityChem performs better than EPISODE and TAPM for the prediction of hourly NO2 concentrations at the traffic stations, which is attributable to the street canyon model. Observed levels of annual mean ozone at the five urban background stations in Hamburg are captured by the model within ±15 %. A performance analysis with the FAIRMODE DELTA tool for air quality in Hamburg showed that EPISODE–CityChem fulfils the model performance objectives for NO2 (hourly), O3 (daily max. of the 8 h running mean) and PM10 (daily mean) set forth in the Air Quality Directive, qualifying the model for use in policy applications. Envisaged applications of the EPISODE–CityChem model are urban air quality studies, emission control scenarios in relation to traffic restrictions and the source attribution of sector-specific emissions to observed levels of air pollutants at urban monitoring stations.

• Walker, Sam-Erik; Hermansen, Gudmund Horn & Hjort, Nils Lid (2015). Model selection and verification for ensemble based probabilistic forecasting of air pollution in Oslo, Norway. In Samaniego, Francisco J. (Eds.), Proceedings of the 60th World Statistics Congress of the International Statistical Institute, ISI2015. The International Statistical Institute. ISSN 978-90-73592-35-3. p. 3131–3136. Show summary

  In this paper, we discuss building time series models for forecasting of air pollution during wintertime conditions in Oslo, Norway, using ensembles of air pollution model data. Since such ensembles becomes increasingly available as part of regular air quality forecast modelling, it is important to build properly calibrated statistical models utilising such data. In particular, we focus on model selection using the Akaike and Bayesian information criteria, and verification of the forecasts using Probability Integral Transform (PIT) histograms and Brier scores. Three time series models are considered, using ensemble mean values as a primary covariate in a linear regression setting explaining observations, and modelling the residual errors as an autoregressive process, using either a constant variance; a timevarying (heteroscedastic) variance only depending on the ensemble variances; or as a combination of both. We show that for the limited, although representative, data analysed, the model incorporating both terms, seems to have an edge according to the model selection criteria and forecast verification tools used. Finally, we briefly discuss the possibility of introducing more focused model selection criteria for these types of models and data.

• Karl, Matthias; Svendby, Tove Marit; Walker, Sam-Erik; Velken, Anna von Streng; Castell Balaguer, Nuria & Solberg, Sverre (2015). Modelling atmospheric oxidation of 2-aminoethanol (MEA) emitted from post-combustion capture using WRF-Chem. Science of the Total Environment. ISSN 0048-9697. 527-528, p. 185–202. doi: 10.1016/j.scitotenv.2015.04.108. Full text in Research Archive
• Karl, Matthias; Castell, Nuria; Simpson, David; Solberg, Sverre; Starrfelt, Jostein & Svendby, Tove Marit [Show all 8 contributors for this article] (2014). Uncertainties in assessing the environmental impact of amine emissions from a CO2 capture plant. Atmospheric Chemistry and Physics (ACP). ISSN 1680-7316. 14(16), p. 8533–8557. doi: 10.5194/acp-14-8533-2014. Full text in Research Archive Show summary

  In this study, a new model framework that couples the atmospheric chemistry transport model system WRF-EMEP and the multimedia fugacity level III model was used to assess the environmental impact of amine emissions to air from post-combustion carbon dioxide capture. The modelling framework was applied to a typical carbon capture plant artificially placed at Mongstad, west coast of Norway. WRF-EMEP enables a detailed treatment of amine chemistry in addition to atmospheric transport and deposition. Deposition fluxes of WRF-EMEP simulations were used as input to the fugacity model in order to derive concentrations of nitramines and nitrosamine in lake water. Predicted concentrations of nitramines and nitrosamines in ground-level air and drinking water were found to be highly sensitive to the description of amine chemistry, especially of the night time chemistry with the nitrate (NO3) radical. Sensitivity analysis of the fugacity model indicates that catchment characteristics and chemical degradation rates in soil and water are among the important factors controlling the fate of these compounds in lake water. The study shows that realistic emission of commonly used amines result in levels of the sum of nitrosamines and nitramines in ground-level air (0.6–10 pgm−3) and drinking water (0.04–0.25 ngL−1) below the current safety guideline for human health enforced by the Norwegian Environmental Directorate. The modelling framework developed in this study can be used to evaluate possible environmental impacts of emissions of amines from post-combustion capture in other regions of the world.

• Vanem, Erik & Walker, Sam-Erik (2013). Time Series Analysis of Significant Wave Height Data for Identification of Trends in the Ocean Wave Climate. In ASME, conference (Eds.), Proceedings of ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2013). Volume 2A: Structures, Safety and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 978-0-7918-5532-4. doi: 10.1115/omae2013-10024.
• Vanem, Erik & Walker, Sam-Erik (2013). Identifying trends in the ocean wave climate by time series analyses of significant wave height data. Ocean Engineering. ISSN 0029-8018. 61, p. 148–160. doi: 10.1016/j.oceaneng.2012.12.042.
• Walker, Sam-Erik (2012). WORM: A new open road line source model for low wind speed conditions. International Journal of Environment and Pollution. ISSN 0957-4352. 47(1/2/3/4), p. 348–357.
• Denby, Bruce; Dudek, Agnes V; Walker, Sam-Erik; Costa, Ana Margarida; Monteiro, Alexandra & van den Elshout, Sef [Show all 7 contributors for this article] (2011). Towards uncertainty mapping in air-quality modelling and assessment. International Journal of Environment and Pollution. ISSN 0957-4352. 44(1-4), p. 14–23. doi: 10.1504/IJEP.2011.038398.
• Slini, Theodora ; Walker, Sam-Erik & Moussiopoulos, Nicolas (2011). Data assimilation within the Air4EU project: the Athens case. International Journal of Environment and Pollution. ISSN 0957-4352. 44(1-4), p. 298–306. doi: 10.1504/IJEP.2011.038430.
• Walker, Sam-Erik (2011). WORM: A new open road line source model for low wind speed conditions. International Journal of Environment and Pollution. ISSN 0957-4352. 47(1-4), p. 348–357.
• Berger, Janne; Walker, Sam-Erik; Denby, Bruce; Berkowicz, Ruwim; Løfstrøm, Per & Ketzel, Matthias [Show all 9 contributors for this article] (2010). Evaluation and inter-comparison of open road line source models currently in use in the Nordic countries. Boreal Environment Research. ISSN 1239-6095. 15(3), p. 319–334. Full text in Research Archive
• Madsen, Christian; Gehring, Ulrike; Walker, Sam-Erik; Brunekreef, Bert; Stigum, Hein & Næss, Øyvind Erik [Show all 7 contributors for this article] (2010). Ambient air pollution exposure, residential mobility and term birth weight in Oslo, Norway. Environmental Research. ISSN 0013-9351. 110(4), p. 363–371. doi: 10.1016/j.envres.2010.02.005.
• Oftedal, Bente Margaret; Walker, Sam-Erik; Gram, Frederick; McInnes, Harold & Nafstad, Per (2009). Modelling long-term averages of local ambient air pollution in Oslo, Norway: evaluation of nitrogen dioxide, PM10 and PM2.5. International Journal of Environment and Pollution. ISSN 0957-4352. 36(1-3), p. 110–126.
• Oftedal, Bente Margaret; Brunekreef, Bert; Nystad, Wenche; Madsen, Christian; Walker, Sam-Erik & Nafstad, Per (2008). Residential outdoor air pollution and lung function in schoolchildren. Epidemiology. ISSN 1044-3983. 19(1), p. 129–137. doi: 10.1097/EDE.0b013e31815c0827.

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• Dang, Chetna; Dhaka, S K; Walker, Sam-Erik; Longiany, Gagandeep & Rawat, Wikas (2023). Long-term Meteorology-Adjusted and Unadjusted Trends of PM2.5 Using the AirGAM Model Over Delhi, 2007–2022 .
• Weydahl, Torleif; Markelj, Miha; Walker, Sam-Erik & Høyem, Harald (2023). Revidert tiltaksutredning for lokal luftkvalitet i Drammen kommune.
• Weydahl, Torleif; Johnsrud, Mona; Vo, Dam Thanh; Walker, Sam-Erik; Høiskar, Britt Ann Kåstad & Ranheim, Patrick (2020). Revidert tiltaksutredning for lokal luftkvalitet i Stavanger kommune.
• Guerreiro, Cristina; Solberg, Sverre; Walker, Sam-Erik & Schneider, Philipp (2020). Hvordan har luftkvalitet i Europa endret seg under lockdown og hvorfor?
• Schneider, Philipp; Castell, Nuria; Hamer, Paul David; Walker, Sam-Erik & Bartonova, Alena (2020). Networks of air quality sensors and their use for high-resolution mapping of urban air quality.
• Walker, Sam-Erik & Hjort, Nils Lid (2018). Focused model selection and inference using robust estimators.
• Walker, Sam-Erik & Hjort, Nils Lid (2017). Estimation and model selection by data-driven weighted likelihoods.
• Walker, Sam-Erik & Hjort, Nils Lid (2017). Estimation and model selection by data-driven weighted likelihoods.
• Walker, Sam-Erik & Hjort, Nils Lid (2017). Estimation and model selection via maximum weighted likelihoods.
• Walker, Sam-Erik & Hjort, Nils Lid (2017). Focused model selection and inference using robust estimators.
• Walker, Sam-Erik & Hjort, Nils Lid (2016). Model selection and focused inference using robust estimators.
• Walker, Sam-Erik & Hjort, Nils Lid (2016). Model selection and focused inference using robust estimators.
• Walker, Sam-Erik; Hjort, Nils Lid & Hermansen, Gudmund Horn (2015). Model selection and verification for ensemble based probabilistic forecasting of air pollution in Oslo, Norway .
• Walker, Sam-Erik (2015). Confidence distributions based on M-estimators.
• Madsen, Christian; Gehring, U; Walker, SE; Brunekreef, B; Stigum, Hein & Næss, Øyvind Erik [Show all 7 contributors for this article] (2009). Ambient Air Pollution Exposure, Residential Mobility and Term Birth Weight in Oslo, Nonway. Epidemiology. ISSN 1044-3983. 20(6), p. S151–S151.
• Gundersen, Cathrine Brecke; Reid, Malcolm; Rostkowski, Pawel; Bæk, Kine; Rundberget, Thomas & Vogelsang, Christian [Show all 15 contributors for this article] (2023). Screening Programme 2022. New environmental pollutants. Norsk institutt for vannforskning. ISSN 978-82-577-7642-8. Full text in Research Archive Show summary

  The 2022 Screening Programme followed up on some findings from the 2020 Screening Programme. This included further investigation of plastic-related substances, tyre-related substances, and disinfection agents. The 2022 Programme also included additional new PFAS, bisphenols, and new siloxanes.

• Solberg, Sverre; Claude, Anja; Reimann, Stefan & Walker, Sam-Erik (2023). VOC measurements 2021. NILU. ISSN 978-82-425-3132-2. Full text in Research Archive
• Høiskar, Britt Ann Kåstad; Walker, Sam-Erik; Weydahl, Torleif; Markelj, Miha; Andersen, André & Lopez-Aparicio, Susana [Show all 7 contributors for this article] (2023). Tiltaksutredning for lokal luftkvalitet i Lørenskog kommune. NILU. ISSN 978-82-425-3107-0. Full text in Research Archive Show summary

  NILU – Norsk institutt for luftforskning har, i samarbeid med Transportanalyse AS, utarbeidet en tiltaksutredning for bedre luftkvalitet i Lørenskog. Utredningen er gjennomført på oppdrag av Lørenskog kommune. Tiltaksutredningen omfatter en kartlegging av luftkvaliteten i Lørenskog kommune ved trafikkberegninger og utslipps- og spredningsberegninger for PM10, PM2,5 og NO2 for Dagens situasjon 2019 og Referansesituasjonen 2030 med eksisterende og eventuelle nye tiltak. Basert på resultatene fra beregningene og i samarbeid med oppdragsgiver og referansegruppen, er det foreslått en revidert handlings- og beredskapsplan som skal behandles politisk.

• Solberg, Sverre; Claude, Anja; Reimann, Stefan; Sauvage, Stéphane & Walker, Sam-Erik (2022). VOC measurements 2020. NILU. ISSN 978-82-425-3099-8. Full text in Research Archive
• Colette, Augustin; Solberg, Sverre; Aas, Wenche & Walker, Sam-Erik (2021). Understanding Air Quality Trends in Europe. Focus on the relative contribution of changes in emission of activity sectors, natural fraction and meteorological variability. ETC/ATNI. ISSN 978-82-93752-25-7. Show summary

  Emission changes are the main driver of all air pollutant trends. For NO2 and PM10, both the GAM and the CTM results indicate that emission changes contribute to at least 90% of the 2000-2017 trend. For ozone peaks (as 4MDA8), meteorology can be important. The GAM model estimates that it contributes to an increase counteracting mitigation effort up to a magnitude of 20 to 80% (compared to the effect of emission and background changes) in Austria, Belgium, Czech Republic, France, and Italy. Given the good skill of the GAM model to capture meteorological effect, this estimate can be considered quite robust. The relative contribution of agriculture and industry to the total PM10 mass has been reduced by around 30% for both sectors, but the similarity of evolution is not directly linked to the emission trends in the respective sectors. The relationship between emissions and concentrations is nonlinear and depends on availability of precursor gases to form ammonium sulphate and ammonium nitrate. The relative contribution of traffic sources to PM10 has been reduced with around 20%, while the trend attributed to residential heating is marginal. The heating sector has become a relatively more important contributor to the aerosol pollution and needs more attention. The model also indicates that the natural contributions (such as sea salt and dust) has had little impact on the long-term changes in PM10. The analysis includes observational data only from stations with data available for at least 14 years in the period 2000-2017. This drastically reduces the number of monitoring sites included in the analysis and the spatial representativity of the assessment, with bias towards countries benefiting from a long-term monitoring network. Further improvements of models as well as observational basis are needed to reduce the uncertainties. Understanding organic aerosols from the residential heating sector should be a priority.

• Hamer, Paul David; Walker, Sam-Erik & Schneider, Philipp (2021). Appropriate Assimilation Methods for Air Quality Prediction and Pollutant Emission Inversion. An Urban Data Assimilation Systems Report. NILU. ISSN 978-82-425-3027-1. Full text in Research Archive Show summary

  This report presents a review of data assimilation methods applicable to air quality. In the introduction, we first describe a brief history of data assimilation method development in the context of numerical weather prediction (NWP), and then we highlight key differences when applying data assimilation methods to air quality prediction from NWP applications. Based on these differences, we outline a set of key requirements for data assimilation when applied to air quality. Following this, we review the available data assimilation algorithms and attempt to identify suitable data assimilation methods that could be applied with air quality models. This review and its findings form the basis of the developments to be carried out in the Urban Data Assimilation Systems project.

• Ortiz, Alberto González; Guerreiro, Cristina; Soares, Joana; Antognazza, Frederico; Gsella, Artur & Houssiau, Michel [Show all 16 contributors for this article] (2020). Air quality in Europe - 2020 report. European Environment Agency. ISSN 978-92-9480-292-7. Show summary

  The Air quality in Europe report provides an annual assessment of the status and impacts of air quality and recent air quality trends. The report supports policy development and implementation in the field of air quality at both European and national levels.

• Solberg, Sverre; Walker, Sam-Erik; Guerreiro, Cristina & Colette, Augustin (2020). Statistical modelling for long-term trends of pollutants - Use of a GAM model for the assessment of measurements of O3, NO2 and PM. ETC/ATNI. Show summary

  The current report provides a short overview of previous years’ studies on long-term trends in O3, NO2 and PM and the role of meteorological variability for the concentration of these pollutants. The previous studies on the link between trends and meteorology has shown that these links could be estimated by a careful design of model setups using CTMs (chemical transport models). The conclusions from this work is that CTMs are certainly useful tools for explaining pollutant trends in terms of the separate impact of individual physio-chemical drivers such as emissions and meteorology although computationally demanding. The statistical GAM model that have been developed as part of the recent ETC/ACM and ETC/ATNI tasks could be considered as complementary to the use of CTMs for separating the influence of meteorological variability from other processes. The main limitation of the statistical model is that it contains no parameterisation of the real physio-chemical processes and secondly, that it relies on a local assumption, i.e. that the observed daily concentrations could be estimated based on the local meteorological data. We found clear differences in model performance both with respect to geographical area and atmospheric species. In general, the best performance was found for O3 (although not for peak levels) with gradually lower performance for NO2, PM10 and PM2.5 in that order. With respect to area, the model produced the best predictions for Central Europe (Germany, Netherlands, Belgium, France, Austria, Czech Republic) and poorer agreement with observations in southern Europe. Although the GAM model did not detect many meteorology induced long-term trends in the data, the model is well suited for separating the influence of meteorology from the other driving forces, such as emissions and boundary conditions. The GAM model thus provides robust and smooth long-term trend functions corrected for meteorology as well as the perturbations from year to year, reflecting the variability in weather conditions. One could consider to define a set of performance criteria to decide if the GAM model is applicable for a specific station and parameter.

• Walker, Sam-Erik & Schneider, Philipp (2020). A study of the relative expanded uncertainty formula for comparing low-cost sensor and reference measurements. NILU. ISSN 978-82-425-2997-8. Full text in Research Archive Show summary

  In this report, we investigate the relative expanded uncertainty (REU) formula for comparing low-cost sensors (microsensors) and reference measurements. The purpose of the REU formula is to check if microsensor measurements follow the data quality objective (DQO) of the European Air Quality Directive 2008/50/EC to be considered equivalent to a reference instrument. The project aimed to obtain a good understanding of the REU formula for its proper use in current and future projects involving microsensors.

• Weydahl, Torleif; Walker, Sam-Erik; Johnsrud, Mona; Vo, Dam Thanh & Ranheim, Patrick (2019). Air quality assessment for improved local air quality in Tromsø. NILU. ISSN 978-82-425-2996-1. Full text in Research Archive Show summary

  The air quality assessment covers mapping of the air quality in Tromsø through traffic, emission and dispersion calculations of PM10, PM2,5 and NO2 for the present situation (2016) and future scenarios (2023) with and without measures on particulate matter. Based on the calculations and in coordination with Tromsø municipality and the workgroup, a plan for improved local air quality and a management plan for periods with high concentration levels is proposed for political processing.

• Solberg, Sverre; Walker, Sam-Erik & Schneider, Philipp (2019). Trends in measured NO2 and PM. Discounting the effect of meteorology. ETC/ACM. Show summary

  This report documents a study on long-term trends in observed atmospheric levels of NO2, PM10 and PM2.5 based on data from the European Environmental Agency (EEA) Airbase v8 (EEA, 2018). The main aim is to evaluate to what extent the observed time series could be simulated as a function of various local meteorological data plus a time-trend by a Generalized Additive Model (GAM). The GAM could be regarded an advanced multiple regression model. If successful, such a model could be used for several purposes; to estimate the long-term trends in NO2 and PM when the effect of the inter-annual variations in meteorology is removed, and secondly, to “explain” the concentration levels in one specific year in terms of meteorological anomalies and long-term trends. The GAM method was based on a methodology developed during a similar project in 2017 looking at the links between surface ozone and meteorology. The input to the study consisted of gridded model meteorological data provided through the EURODELTA Trends project (Colette et al., 2017) for the 1990-2010 period as well as measured data on NO2, PM10 and PM2.5 extracted from Airbase v8. The measurement data was given for urban, suburban and rural stations, respectively. The analysis was split into two time periods, 1990-2000 and 2000-2010 since the number of stations differ substantially for these periods and since there is reason to believe that the trends differ considerably between these two periods. The study was focused on the 4-months winter period (Nov-Feb) since it was important to assure a period of the year with consistent and homogeneous relationships between the input explanatory data (local meteorology) and the levels of NO2 and PM. For NO2, this period will likely cover the season with the highest concentration levels whereas for PM high levels could be expected outside this period due to processes such as secondary formation, transport of Saharan dust and sea spray. When measured by the R2 statistic, the GAM method performed best for NO2 in Belgium, the Netherlands, NW Germany and the UK. Significantly poorer performance was found for Austria and areas in the south. For PM10 there were less clear geographical patterns in the GAM performance. Based on a comparison between the meteorologically adjusted trends and plain linear regression, our results indicate that for the 1990-2000 period meteorology caused an increase in NO2 concentrations that counteracted the effect of reduced emissions. For the period 2000-2010 we find that meteorology lead to reduced NO2 levels in the northwest and a slight increase in the south. The amount of observational data is much less for PM than for NO2. For the 1990-2000 period the number of sites with sufficient length of time series is too small to apply the GAM method on a European scale. For the 2000-2010 period, we find that the general performance of the GAM method is poorer for PM10 than for NO2. With respect to the link between PM10 and temperature, the results indicate a marked geographical pattern with a negative relationship in central Europe and a positive relationship in Spain, southern France and northern Italy. For PM10 during 2000-2010, the vast majority of the estimated trends are found to be negative. The difference between the GAM trend and the plain linear regression, indicates that meteorology lead to increased PM10 levels in the southern and central parts and decreased levels in the north. For PM2.5 it turned out that the amount of data in the entire period 1990-2010 was too small to use the GAM method in a meaningful way on a European scale. Only a few sites had sufficient time series and thus more recent data are required.

• Myhre, Cathrine Lund; Svendby, Tove Marit; Hermansen, Ove; Lunder, Chris Rene; Platt, Stephen Matthew & Fiebig, Markus [Show all 11 contributors for this article] (2019). Monitoring of greenhouse gases and aerosols at Svalbard and Birkenes in 2018. Annual report. NILU. ISSN 978-82-425-2985-5. Full text in Research Archive Show summary

  The report summaries the activities and results of the greenhouse gas monitoring at the Zeppelin Observatory situated on Svalbard in Arctic Norway during the period 2001-2018, and the greenhouse gas monitoring and aerosol observations from Birkenes for 2009-2018.

• Myhre, Cathrine Lund; Svendby, Tove Marit; Hermansen, Ove; Lunder, Chris Rene; Platt, Stephen Matthew & Fiebig, Markus [Show all 11 contributors for this article] (2018). Monitoring of greenhouse gases and aerosols at Svalbard and Birkenes in 2017 - Annual report. NILU. ISSN 978-82-425-2952-7. Full text in Research Archive Show summary

  The report summaries the activities and results of the greenhouse gas monitoring at the Zeppelin Observatory situated on Svalbard in Arctic Norway during the period 2001-2017, and the greenhouse gas monitoring and aerosol observations from Birkenes for 2009-2017.

• Solberg, Sverre; Walker, Sam-Erik; Schneider, Philipp; Guerreiro, Cristina & Colette, Augustin (2018). Discounting the effect of meteorology on trends in surface ozone: Development of statistical tools. ETC/ACM. Show summary

  This report presents the results using a statistical method to single out the influence of interannual meteorological variability on surface ozone. The reason for using such a tool is two-fold: Firstly, to explain the ozone levels in one specific year in terms of weather anomalies and secondly, to estimate the part of long-term ozone trends that is due to the meteorology alone. The method is a so-called GAM (generalized additive model), which could be regarded an advanced multiple regression method relating daily ozone levels to certain meteorological variables. The performance of the method was evaluated by comparing observed ozone data with those predicted by the GAM. This revealed a good to very good agreement in central Europe and Germany in particular. For southern Europe the performance was poorer. The method indicated that meteorology contributed to the downward trend in ozone seen at most sites for both 1990-2000 and 2000-2010.

• Karl, Matthias & Walker, Sam-Erik (2014). Method and tool selection within the pilot study of air quality in Poland: Data fusion methodologies. NILU. ISSN 978-82-425-2700-4.
• Steinbakk, Gunnhildur Högnadóttir; Thorarinsdottir, Thordis Linda; Lahoz, William A. & Walker, Sam-Erik (2014). Data assimilation and statistical post-processing for numerical air quality predictions. Norsk Regnesentral. Show summary

  This is a joint report based on work by NILU and NR on the use of data assimilation and statistical post-processing tools to improve the air quality prediction in the context of the Bedre Byluft programme. The objective is to improve Bedre Byluft air quality prediction at both the short and long-term (hours and days to weeks), in particular for meteorological conditions associated with high pressures, which historically have been difficult to predict. We present the tools; present first results and quantify the performance of the tools; and outline further work needed to make these tools operational.

• Clench-Aas, Jocelyne; Aldrin, Magne; Bartonova, A.; Follestad, Turid; Walker, Sam-Erik & Skjønsberg, Ole Henning [Show all 8 contributors for this article] (2000). The short- and long-term effects of air pollution on symptoms of reduced health in a panel of chool-age children. NILU. ISSN 82-425-1131-4.

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