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2019
Trends in measured NO2 and PM. Discounting the effect of meteorology.
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.
ETC/ACM
2019
NILU - Norsk institutt for luftforskning har på oppdrag for Boliden Odda AS, utført sprednings- og avsetningsberegninger i forbindelse med utslipp fra sinkproduksjonsanlegget. Studien beregner luftkonsentrasjon og avsetning av svovel (forsuring), og konsentrasjon av metaller/svevestøv ved dagens sinkproduksjon og ved en planlagt utvidelse. Timesmiddel-, døgnmiddel- og årsmiddel-konsentrasjon av SO2 og PM10 er beregnet til å være innenfor grenseverdier og luftkvalitetskriterier ved dagens og utvidet produksjon. Beregningene viser mulig overskridelse av målsetningsverdien for kadmium ved en utvidelse av produksjonen. Utvidelse i produksjon gir et ytterligere bidrag til overskridelsen av tålegrensen (forsuring) i området rundt Odda. Økningen i avsetning forøvrig er beregnet å være i områder hvor tålegrensen er mer robust.
NILU
2019
2019
The carbonaceous aerosol accounts for an important part of total aerosol mass, affects human health and climate through its effects on physical and chemical properties of the aerosol, yet the understanding of its atmospheric sources and sinks is still incomplete. This study shows the state-of-the-art in modelling carbonaceous aerosol over Europe by comparing simulations performed with seven chemical transport models (CTMs) currently in air quality assessments in Europe: CAMx, CHIMERE, CMAQ, EMEP/MSC-W, LOTOS-EUROS, MINNI and RCGC. The simulations were carried out in the framework of the EURODELTA III modelling exercise and were evaluated against field measurements from intensive campaigns of European Monitoring and Evaluation Programme (EMEP) and the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI). Model simulations were performed over the same domain, using as much as possible the same input data and covering four seasons: summer (1–30 June 2006), winter (8 January – 4 February 2007), autumn (17 September- 15 October 2008) and spring (25 February - 26 March 2009). The analyses of models’ performances in prediction of elemental carbon (EC) for the four seasons and organic aerosol components (OA) for the last two seasons show that all models generally underestimate the measured concentrations. The maximum underestimation of EC is about 60% and up to about 80% for total organic matter (TOM). The underestimation of TOM outside of highly polluted area is a consequence of an underestimation of secondary organic aerosol (SOA), in particular of its main contributor: biogenic secondary aerosol (BSOA). This result is independent on the SOA modelling approach used and season. The concentrations and daily cycles of total primary organic matter (TPOM) are generally better reproduced by the models since they used the same anthropogenic emissions. However, the combination of emissions and model formulation leads to overestimate TPOM concentrations in 2009 for most of the models. All models capture relatively well the SOA daily cycles at rural stations mainly due to the spatial resolution used in the simulations. For the investigated carbonaceous aerosol compounds, the differences between the concentrations simulated by different models are lower than the differences between the concentrations simulated with a model for different seasons.
2019
Simulation of volcanic ash ingestion into a large aero engine: particle–fan interactions
Volcanic ash (VA) clouds in flight corridors present a significant threat to aircraft operations
as VA particles can cause damage to gas turbine engine components that lead to a
reduction of engine performance and compromise flight safety. In the last decade,
research has mainly focused on processes such as erosion of compressor blades and
static components caused by impinging ash particles as well as clogging and/or corrosion
effects of soft or molten ash particles on hot section turbine airfoils and components.
However, there is a lack of information on how the fan separates ingested VA particles
from the core stream flow into the bypass flow and therefore influences the mass concentration
inside the engine core section, which is most vulnerable and critical for safety. In
this numerical simulation study, we investigated the VA particle–fan interactions and
resulting reductions in particle mass concentrations entering the engine core section as a
function of particle size, fan rotation rate, and for two different flight altitudes. For this,
we used a high-bypass gas-turbine engine design, with representative intake, fan, spinner,
and splitter geometries for numerical computational fluid dynamics (CFD) simulations
including a Lagrangian particle-tracking algorithm. Our results reveal that
particle–fan interactions redirect particles from the core stream flow into the bypass
stream tube, which leads to a significant particle mass concentration reduction inside the
engine core section. The results also show that the particle–fan interactions increase
with increasing fan rotation rates and VA particle size. Depending on ingested VA size
distributions, the particle mass inside the engine core flow can be up to 30% reduced
compared to the incoming particle mass flow. The presented results enable future calculations
of effective core flow exposure or dosages based on simulated or observed atmospheric
VA particle size distribution, which is required to quantify engine failure
mechanisms after exposure to VA. As an example, we applied our methodology to a
recent aircraft encounter during the Mt. Kelud 2014 eruption. Based on ambient VA concentrations
simulated with an atmospheric particle dispersion model (FLEXPART), we
calculated the effective particle mass concentration inside the core stream flow along the
actual flight track and compared it with the whole engine exposure.
2019
In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanalysis. For this study, long-term observations of particle sulfate (SO2−4
), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2).
The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively.
Comparisons of observed and modeled trends in SO2−4
concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods, even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO2−4
concentrations compared with their gas-phase precursor (i.e., SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry.
An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO−3
) concentrations revealed that the relative reduction in HNO3 was larger than that for NO−3 during the P1 period, which was mainly attributed to an increased availability of “free ammonia”. By contrast, trends in modeled HNO3 and NO−3 concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data...
2019
2019
2019
Nanomaterial grouping: Existing approaches and future recommendations
The physico-chemical properties of manufactured nanomaterials (NMs) can be fine-tuned to obtain different functionalities addressing the needs of specific industrial applications. The physico-chemical properties of NMs also drive their biological interactions. Accordingly, each NM requires an adequate physico-chemical characterization and potentially an extensive and time-consuming (eco)toxicological assessment, depending on regulatory requirements. Grouping and read-across approaches, which have already been established for chemicals in general, are based on similarity between substances and can be used to fill data gaps without performing additional testing. Available data on “source” chemicals are thus used to predict the fate, toxicokinetics and/or (eco)toxicity of structurally similar “target” chemical(s). For NMs similar approaches are only beginning to emerge and several challenges remain, including the identification of the most relevant physico-chemical properties for supporting the claim of similarity. In general, NMs require additional parameters for a proper physico-chemical description. Furthermore, some parameters change during a NM's life cycle, suggesting that also the toxicological profile may change.
This paper compares existing concepts for NM grouping, considering their underlying basic principles and criteria as well as their applicability for regulatory and other purposes. Perspectives and recommendations based on experiences obtained during the EU Horizon 2020 project NanoReg2 are presented. These include, for instance, the importance of harmonized data storage systems, the application of harmonized scoring systems for comparing biological responses, and the use of high-throughput and other screening approaches. We also include references to other ongoing EU projects addressing some of these challenges.
2019
2019
2019
Two years of continuous in situ measurements of Arctic low‐level clouds have been made at the Mount Zeppelin Observatory (78°56′N, 11°53′E), in Ny‐Ålesund, Spitsbergen. The monthly median value of the cloud particle number concentration (Nc) showed a clear seasonal variation: Its maximum appeared in May–July (65 ± 8 cm−3), and it remained low between October and March (8 ± 7 cm−3). At temperatures warmer than 0 °C, a clear correlation was found between the hourly Nc values and the number concentrations of aerosols with dry diameters larger than 70 nm (N70), which are proxies for cloud condensation nuclei (CCN). When clouds were detected at temperatures colder than 0 °C, some of the data followed the summertime Nc to N70 relationship, while other data showed systematically lower Nc values. The lidar‐derived depolarization ratios suggested that the former (CCN‐controlled) and latter (CCN‐uncontrolled) data generally corresponded to clouds consisting of supercooled water droplets and those containing ice particles, respectively. The CCN‐controlled data persistently appeared throughout the year at Zeppelin. The aerosol‐cloud interaction index (ACI = dlnNc/(3dlnN70)) for the CCN‐controlled data showed high sensitivities to aerosols both in the summer (clean air) and winter–spring (Arctic haze) seasons (0.22 ± 0.03 and 0.25 ± 0.02, respectively). The air parcel model calculations generally reproduced these values. The threshold diameters of aerosol activation (Dact), which account for the Nc of the CCN‐controlled data, were as low as 30–50 nm when N70 was less than 30 cm−3, suggesting that new particle formation can affect Arctic cloud microphysics.
2019