output from climate models on the regional modeling of ozone and PM' Statistical evaluationTable S1a.Annual average behavior of temperatures, 1989-2009.Standard deviations represent the day to day variability station RLE_ERA RLE_ECHAM RLE_MIROC # T>25 # T<5 T max std # T>25 C # T<5 C T max Std # T>25 # T<5 T max std Vredepeel 28 54 13.63 7.91 16 46 13.12 7.03 30 44 14.12 7.72
Abstract. Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2. 5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluation methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.
Abstract. Climate change may have an impact on air quality (ozone, particulate matter) due to the strong dependency of air quality on meteorology. The effect is often studied using a global climate model (GCM) to produce meteorological fields that are subsequently used by chemical transport models. However, climate models themselves are subject to large uncertainties and fail to adequately reproduce the present-day climate. The present study illustrates the impact of this uncertainty on air quality. To this end, output from the SRES-A1B constraint transient runs with two GCMs, i.e. ECHAM5 and MIROC-hires, has been dynamically downscaled with the regional climate model RACMO2 and used to force a constant emission run with the chemistry transport model LOTOS-EUROS in a one-way coupled run covering the period 1970–2060. Results from the two climate simulations have been compared with a RACMO2-LOTOS-EUROS (RLE) simulation forced by the ERA-Interim reanalysis for the period 1989–2009. Both RLE_ECHAM and RLE_MIROC showed considerable deviations from RLE_ERA in daily maximum temperature, precipitation and wind speed. Moreover, sign and magnitude of these deviations depended on the region. Differences in average concentrations for the present-day simulations were found of equal to (RLE_MIROC) or even larger than (RLE_ECHAM) the differences in concentration between present-day and future climate (2041–2060). The climate simulations agreed on a future increase in average summer ozone daily maximum concentrations (5–10 μg m−3) in parts of Southern Europe and a smaller increase in Western and Central Europe. Annual average PM10 concentrations increased (0.5–1.0 μg m−3) in North-West Europe and the Po Valley, but these numbers are rather uncertain. Overall, changes for PM10 were small, both positive and negative changes were found, and for many locations the two runs did not agree on the sign of the change. The approach taken here illustrates that results from individual climate runs can at best indicate tendencies and should therefore be interpreted with great care.
The aim of this paper is to evaluate the surface concentration of nitrogen dioxide (NO2) inferred from the Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI) NO2 tropospheric column densities over Central Europe for two time periods, summer 2019 and winter 2019–2020. Simulations of the NO2 tropospheric vertical column densities and surface concentrations from the Long-Term Ozone Simulation–European Operational Smog (LOTOS-EUROS) chemical transport model are also applied in the methodology. More than two hundred in situ air quality monitoring stations, reporting to the European Environment Agency (EEA) air quality database, are used to carry out comparisons with the model simulations and the spaceborne inferred surface concentrations. Stations are separated into seven types (urban traffic, suburban traffic, urban background, suburban background, rural background, suburban industrial and rural industrial) in order to examine the strengths and shortcomings of the different air quality markers, namely the NO2 vertical column densities and NO2 surface concentrations. S5P/TROPOMI NO2 surface concentrations are inferred by multiplying the fraction of the satellite and model NO2 vertical column densities with the model surface concentrations. The estimated inferred TROPOMI NO2 surface concentrations are examined further with the altering of three influencing factors: the model vertical leveling scheme, the versions of the TROPOMI NO2 data and the air mass factors applied to the satellite and model NO2 vertical column densities. Overall, the inferred TROPOMI NO2 surface concentrations show a better correlation with the in situ measurements for both time periods and all station types, especially for the industrial stations (R > 0.6) in winter. The calculated correlation for background stations is moderate for both periods (R~0.5 in summer and R > 0.5 in winter), whereas for traffic stations it improves in the winter (from 0.20 to 0.50). After the implementation of the air mass factors from the local model, the bias is significantly reduced for most of the station types, especially in winter for the background stations, ranging from +0.49% for the urban background to +10.37% for the rural background stations. The mean relative bias in winter between the inferred S5P/TROPOMI NO2 surface concentrations and the ground-based measurements for industrial stations is about −15%, whereas for traffic urban stations it is approximately −25%. In summer, biases are generally higher for all station types, especially for the traffic stations (~−75%), ranging from −54% to −30% for the background and industrial stations.
Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) delivers a range of full, free and open products in relation to atmospheric composition at global and regional scales. The CAMS Regional Service produces daily forecasts, analyses, and reanalyses of air quality in Europe. This Service relies on a distributed modelling production by eleven teams in ten European countries: CHIMERE (France), DEHM (Denmark), EMEP (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MINNI (Italy), MOCAGE (France), MONARCH (Spain), SILAM (Finland). The project management and coordination of the service is devoted to a Centralised Regional Production Unit. Each model produces every day 24 h analyses for the previous day and 97 h forecasts for 19 chemical species over a spatial domain at 0.1x01. degree resolution (approximately 10 km x 10 km) with 420 points in latitude and 700 in longitude and 10 vertical levels. Six pollen species are also delivered for the surface forecasts. The eleven individual models are then combined into an ENSEMBLE median. In total, more than 82 billion data points are made available for public use on a daily basis. The design of the system follows clear technical requirements in terms of consistency in the model setup and forcing fields (meteorology, surface anthropogenic emission fluxes, and chemical boundary conditions). But it also benefits from a diversity of in the description of atmospheric processes through the design of the eleven European Chemistry Transport Models (CTM) involved. The present article aims to provide a comprehensive technical documentation, both for the setup as well as for the diversity of CTM involved in the Service. We also include an overview of the main output products, their public dissemination and the related evaluation and quality control strategy.
In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).
Abstract. The unprecedented order, in modern peaceful times, for a near-total lockdown of the Greek population as a means of protection against severe acute respiratory syndrome coronavirus 2, commonly known as COVID-19, has generated unintentional positive side-effects with respect to the country's air quality levels. Sentinel-5 Precursor/Tropospheric Monitoring Instrument (S5P/TROPOMI) monthly mean tropospheric nitrogen dioxide (NO2) observations show an average change of −34 % to +20 % and −39 % to −5 % with an average decrease of −15 % and −11 % for March and April 2020 respectively, compared with the previous year, over the six larger Greek metropolitan areas; this is mostly attributable to vehicular emission reductions. For the capital city of Athens, weekly analysis was statistically possible for the S5P/TROPOMI observations and revealed a marked decline in the NO2 load of between −8 % and −43 % for 7 of the 8 weeks studied; this is in agreement with the equivalent Ozone Monitoring Instrument (OMI)/Aura observations as well as the ground-based estimates of a multi-axis differential optical absorption spectroscopy ground-based instrument. Chemical transport modelling of the NO2 columns, provided by the Long Term Ozone Simulation European Operational Smog (LOTOS-EUROS) chemical transport model, shows that the magnitude of these reductions cannot solely be attributed to the difference in meteorological factors affecting NO2 levels during March and April 2020 and the equivalent time periods of the previous year. Taking this factor into account, the resulting decline was estimated to range between ∼ −25 % and −65 % for 5 of the 8 weeks studied, with the remaining 3 weeks showing a positive average of ∼ 10 %; this positive average was postulated to be due to the uncertainty of the methodology, which is based on differences. As a result this analysis, we conclude that the effect of the COVID-19 lockdown and the restriction of transport emissions over Greece is ∼ −10 %. As transport is the second largest sector (after industry) affecting Greece's air quality, this occasion may well help policymakers to enforce more targeted measures to aid Greece in further reducing emissions according to international air quality standards.
