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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. The air quality assessment for Lørenskog covers mapping of the air quality through traffic, emission and dispersion calculations of PM10, PM2,5 and NO2 for the present situation (2019) and future scenarios (2030) with existing and possible future measures. Based on the calculations and in coordination with Lørenskog municipality and the reference group, a plan for improved local air quality and a management plan for periods with high concentration levels is proposed for political processing.

Trend calculations of air pollutants for the periods 2005-2019 have been applied. Sulphur dioxide shows the largest decrease of all pollutants with a reduction of the order of 60-70 %. The agreement between reported emission data and measured concentrations are quite good. For NO2, a mismatch between the trend in air concentrations and NOx emissions is found. While the overall NOx emissions are reported to be reduced by 45 %, the measured NO2 data indicate a decline of the order of 30 %. For PM data (PM10 and PM2.5) we find an opposite mismatch, meaning that the PM concentrations show stronger downward trends than the reported emissions. This can be explained by the additional effect of reducing gaseous precursors of secondary PM. For O3, our findings are in line with earlier published studies noting that the annual mean ozone concentration has increased while the high peaks have been reduced. But the reduction of the peaks is now within only a few percent and non-significant., OCP/EEA/ACC/18/001-ETC/ATNI

We present observations of three protoplanetary discs in visible scattered light around M-type stars in the Upper Scorpius OB association using the Space Telescope Imaging Spectrograph (STIS) instrument on the Hubble Space Telescope (HST). The discs around stars 2MASS J16090075–1908526, 2MASS J16142029–1906481, and 2MASS J16123916–1859284 have all been previously detected with the Atacama Large Millimeter/submillimeter Array (ALMA), and 2MASS J16123916–1859284 has never previously been imaged at scattered light wavelengths. We process our images using reference star differential imaging, comparing and contrasting three reduction techniques – classical subtraction, Karhunen–Loève Image Projection, and non-negative matrix factorization, selecting the classical method as the most reliable of the three for our observations. Of the three discs, two are tentatively detected (2MASS J16142029–1906481 and 2MASS J16123916–1859284), with the third going undetected. Our two detections are shown to be consistent when varying the reference star or reduction method used, and both detections exhibit structure out to projected distances of ≳200 au. Structures at these distances from the host star have never been previously detected at any wavelength for either disc, illustrating the utility of visible-wavelength observations in probing the distribution of small dust grains at large angular separations.

This report examines air quality trends in Europe based on a combination of in-situ observations available in the AQ e-reporting database, air quality chemical transport models (CTM) and statistical modelling/machine learning using Generalized Additive Models (GAM). We focus on providing more insight on how meteorological variability, natural factors, and emission from the main activity sectors might have influence the development in PM10, NO2 and ozone concentrations between 2000 and 2017. Documenting the long term evolution of air quality in Europe allows us also to understand the extent of improvement of detrimental impacts on human health or ecosystems. 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., OCP/EEA/ACC/18/001-ETC/ATNI

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. Rapporten presenterer en oversikt over dataassimilasjonsmetoder som kan anvendes for luftkvalitetsmodellering. Innledningsvis beskrives kort historisk bakgrunn for bruk av dataassimilasjon i numerisk værvarsling, der vi legger vekt på forskjellene mellom anvendelse av assimilasjon i meteorologiske varslingsmodeller og i spredningsmodeller for luftkvalitet. Basert på disse forskjellene beskrives så ønskede egenskaper til assimilasjonsmetoder for luftkvalitetsmodellering. Deretter gis en oversikt over tilgjengelige assimilasjonsmetoder, der vi søker å identifisere de mest aktuelle for vårt bruk som grunnlag for videre anvendelser i prosjektet.

Previous studies under the European Topic Centre on Air pollution and Climate change Mitigation (ETC/ACM) on the link between trends and meteorology have shown that these links could be estimated by CTMs (chemical transport models). CTMs are useful tools for explaining pollutant trends in terms of the separate impact of individual physio-chemical drivers, such as emissions, and meteorology. It requires, however, multi-year calculations with CTMs designed in specific ways to allow the subtraction of various model scenarios. The method could also be sensitive to the years selected for calculating the perturbations in boundary conditions and meteorology and to uncertainties in emission data. The statistical GAM model that has been developed under ETC/ACM and European Topic Centre on Air pollution, noise, transport and industrial pollution (ETC/ATNI) provides a complementary method for separating the influence of meteorological variability from other processes. This model represents a completely different approach that is based only on observed links between local meteorological parameters (like temperature, wind, etc) and observed pollutant concentration levels. Thus, the model does not contain any representation of the real processes in the atmosphere. 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 the peak levels) with gradually lower performance for NO2, PM10 and PM2.5 in that order. With respect to area, the model generally produced the best predictions for Central Europe (Germany, Netherlands, Belgium, France, Austria, Czech Republic) and the poorest for (southern Europe). For wintertime NO2, particularly poor agreement between the GAM model and the measurements was found for the North Italian region. The model agreement for southern Europe and the Iberian Peninsula was also fairly low, although it is variable from site to site. The number of stations with measurements of PM10 and PM2.5 with sufficient length was substantially lower than for O3 and NO2, and thus, a region-by-region comparison of the model performance was not really possible. In general, the PM10 data indicated a better agreement between the model and the measurements for summer than for winter. Furthermore, the GAM model seemed to perform better for the background urban than for rural sites. Poorest performance for PM10 was found for background rural sites in winter. Withdrawing the meteorological factor by the GAM model can help in identifying significant trends. The main reason for this is that meteorology introduces a year-to-year variability which could mask the underlying trend. Meteorology could also induce a trend in the concentrations but this is a matter of length of the timeseries. For short time periods (typically less than 10 years) the variations in meteorology could lead to spurious effects reflecting the weather conditions at the start and end year. On a long timescale the effects of climate change (trends in temperature, precipitation etc) will certainly lead to trends in the concentration of pollutants, but this is beyond the scope of this report. For rural ozone, a statistically significant decline is calculated in the meteorologically adjusted trends in all regions except for the inflow region at the north-western boundary of Europe that shows only minor variations during the 2000-2017 period. Such a decline is also found in the non-adjusted record, but then it is non-significant because of the meteorological variability. For the 2000-2017 period we could, however, not conclude that meteorology alone has caused significant trends in the ozone levels. Some regions show a steady decline in ozone while others show a curve peaking in the early 2000s. Many of the regions indicate a flattening of the ozone trend in the last part of the period. The meteorology adjusted trends for NO2 show a similar pattern as for O3 with decreasing levels in all regions. As for O3 the NO2 trends are seen as a steady decline in some regions and a curve peaking in the early 2000s in other regions. Marked downward meteorology adjusted trends are found for PM10 as well a substantial variability from year to year caused by meteorology. The GAM model estimates a significant meteorologically induced increase in PM10 in the North Italian region, but the number of stations is too few to make strict conclusions on this trend. For PM2.5 the amount of data is insufficient and the monitoring history is considerably shorter and thus we focus on the period 2008-2017 only. A small downward meteorology adjusted trend is estimated for PM2.5, except for the Iberian Peninsula region. To sum up, the GAM model is now in a phase that makes it ready for implementation and use on a regular (annual basis) for areas where its performance is satisfactory., OCP/EEA/ACC/18/001-ETC/ATNI © European Topic Centre on Air pollution, transport, noise and industrial pollution, 2020 Reproduction is authorized, provided the source is acknowledged. Information about the European Union is available on the Internet. It can be accessed through the Europa server (www.europa.eu). The withdrawal of the United Kingdom from the European Union did not affect the production of the report. Data reported by the United Kingdom are included in all analyses and assessments contained herein, unless otherwise indicated.