Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0 – Part 1: The country contributions

A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline value. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants emitted in different European nations. In this study, we evaluate a country source contribution forecasting system aimed at assessing the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0), which allows the consideration of differences in the source attribution. We also compared the PM10 concentrations, and both models present satisfactory agreement in the 4 d forecasts of the surface concentrations, since the hourly concentrations can be highly correlated with in situ observations. The correlation coefficients reach values of up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; the values are 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models underpredict the highest hourly concentrations measured by the urban stations (mean underestimation of 36 %), which is to be expected given the relatively coarse model resolution used (0.25∘ longitude × 0.125∘ latitude). For the source attribution calculations, LOTOS-EUROS uses a labelling technique, while the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions, and then it is compared to a reference run where no changes are applied. Different percentages (5 %, 15 %, and 50 %) for the reduced emissions in the EMEP/MSC-W model were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. one model grid cell, nine grid cells, and grid cells covering the definition given by the Global Administrative Areas – GADM). We found that the combination of a 15 % emission reduction and a larger domain (nine grid cells or GADM) helps to preserve the linearity between emission and concentrations changes. The nonlinearity, related to the emission reduction scenario used, is suggested by the nature of the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this nonlinearity is observed in the NO-3, NH+4, and H2O concentrations, which is related to gas–aerosol partitioning of the species. The use of a 15 % emission reduction and of a larger city domain also causes better agreement on the determination of the main country contributors between both country source calculations. Over the 34 European cities investigated, PM10 was dominated by domestic emissions for the studied episode (1–9 December 2016). The two models generally agree on the dominant external country contributor (68 % on an hourly basis) to PM10 concentrations. Overall, 75 % of the hourly predicted PM10 concentrations of both models have the same top five main country contributors. Better agreement on the dominant country contributor for primary (emitted) species (70 % is found for primary organic matter (POM) and 80 % for elemental carbon – EC) than for the inorganic secondary component of the aerosol (50 %), which is predictable due to the conceptual differences in the source attribution used by both models. The country contribution calculated by the scenario approach depends on the chemical regime, which largely impacts the secondary components, unlike the calculation using the labelling approach

Source attribution of particulate matter in Berlin

The exposure to ambient particulate matter in metropolitan areas is a major health problem. A prerequisite for formulating effective mitigation strategies it to understand the origin of particulate matter in terms of source regions and sectors. We performed a source attribution of particulate matter (PM) for the Berlin agglomeration area covering the period from 2016 to 2018 using the LOTOS-EUROS chemistry transport model. The (3 year-) mean modelled urban background PM2.5 concentration (10.4 μg/m³) is largely explained by households (3.2 μg/m³) and industry & energy (2.0 μg/m³), while the remaining source sectors contribute the other half. The modelled annual mean urban increment for PM2.5 is mainly attributed to households (1.6 μg/m³) and traffic (0.5 μg/m³). With respect to its relative shares the PM10 source attribution looks similar to that of PM2.5 throughout the year, but with enhanced natural contributions. From a geographical perspective the main source area for the PM2.5 in Berlin is Germany (5.1 μg/m³) itself, followed by the contributions from transboundary transport (3.4 μg/m³). The German sources could be further split into Berlin (2.6 μg/m³), Brandenburg (0.7 μg/m³) and remaining states of Germany (1.8 μg/m³). About one third of the foreign shares can be attributed to Germany's neighbouring countries Poland and Czech Republic. During episodes these contributions can significantly differ, e.g. in February 2017 the Polish contribution is about 1/3rd. The sectoral contributions agree with previous findings except that our study indicates lower contributions for traffic. The model's underestimation of total PM is largely caused by an underestimation of the coarse mode PM. Both the coarse mode urban increment as well as the regional background concentrations are underestimated by the model, especially during summer. We suggest that the enhanced coarse material (in the city) during warm seasons is predominated by (road) resuspension processes which need more of our attention to further improve our models

Source attribution of nitrogen oxides across Germany: Comparing the labelling approach and brute force technique with LOTOS-EUROS

Millions of people are exposed to enhanced levels of nitrogen dioxide in urbanized areas, leading to severe health effects. Moreover, nitrogen oxides contribute to the formation of ozone and particulate matter, and as such have wider health related impacts. A substantial reduction of nitrogen oxides may offer considerable health benefits for the human society. As a first step, this requires a detailed understanding of source sector contributions to nitrogen oxide levels. Whereas many regions have information on the local (traffic) contributions, the source contributions to the rural and urban background levels are commonly not available. In this study we compared and evaluated the results of two source attribution techniques to quantify the contribution of 5 source sectors to background nitrogen oxide levels across Germany. The results of a labelling technique were compared to brute force simulations with variable emission reduction percentages. The labelled NO2 source contributions of the main sectors averaged for all urban background stations are road transport (45 ± 5%), non-road transport (24 ± 6%), energy & industry (20 ± 3%), households (10 ± 6%), and the remaining source sectors (1 ± 1%). For the brute force technique, the explained mass differs from the unperturbed baseline concentration after scaling the impact of each sensitivity simulation to 100%. The attributed concentration of NO2 is lower in urban background areas (−3 ± 5%) and larger in the rural background (4 ± 6%) than that of the labelling. Largest deviations up to −15% are calculated for the major cities along the Rhine and Main. The annual average overestimation for NO is about 53 ± 24% for urban and 40 ± 26% for rural background sites based on a 20% reduction of emissions. On shorter time scales the differences are larger. These deviations are caused by (the lack of) regime changes in the titration of ozone, most notably present at ozone-limiting conditions during nocturnal winter periods. As a consequence, the differences between the methodologies are larger for smaller emission reduction percentages applied in the brute force technique. Similarly, for small-sized emission source sectors larger deviations were found compared to large-sized sector categories. Hence, applying the brute force technique for the source attribution for a single sector should be avoided as there is no way to verify for consistency and quantify the error for the sector and total explained contribution. We recommend applying the labelling approach to estimate sector contributions in forthcoming studies for nitrogen oxides

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area

Forest–atmosphere exchange of reactive nitrogen in a remote region – Part I: Measuring temporal dynamics

Long-term dry deposition flux measurements of reactive nitrogen based on the eddy covariance or the aerodynamic gradient method are scarce. Due to the large diversity of reactive nitrogen compounds and high technical requirements for the measuring devices, simultaneous measurements of individual reactive nitrogen compounds are not affordable. Hence, we examined the exchange patterns of total reactive nitrogen (Sigma N-r) and determined annual dry deposition budgets based on measured data at a mixed forest exposed to low air pollution levels located in the Bavarian Forest National Park (NPBW), Germany. Flux measurements of Sigma N-r were carried out with the Total Reactive Atmospheric Nitrogen Converter (TRANC) coupled to a chemiluminescence detector (CLD) for 2.5 years. The average Sigma N-r concentration was 3.1 mu g N m(-3). Denuder measurements with DELTA samplers and chemiluminescence measurements of nitrogen oxides (NOx) have shown that NOx has the highest contribution to Sigma N-r (similar to 51.4 %), followed by ammonia (NH3) (similar to 20.0 %), ammonium (NH4+) (similar to 15.3 %), nitrate NO3- (similar to 7.0 %), and nitric acid (HNO3) (similar to 6.3 %). Only slight seasonal changes were found in the Sigma N-r concentration level, whereas a seasonal pattern was observed for the contribution of NH3 and NOx center dot NH3 showed highest contributions to Sigma N-r in spring and summer, NOx in autumn and winter. We observed deposition fluxes at the measurement site with median fluxes ranging from -15 to -5 ng Nm(-2) S-1 (negative fluxes indicate deposition). Median deposition velocities ranged from 0.2 to 0.5 cm s(-1). In general, highest deposition velocities were recorded during high solar radiation, in particular from May to September. Our results suggest that seasonal changes in composition of Sigma N-r global radiation (R-g), and other drivers correlated with R-g were most likely influencing the deposition velocity (v(d)). We found that from May to September higher temperatures, lower relative humidity, and dry leaf surfaces increase v(d) of Sigma N-r. At the measurement site, Sigma N-r concentration did not emerge as a driver for the Sigma N(r)v(d). No significant influence of temperature, humidity, friction velocity, or wind speed on Sigma N-r fluxes when using the meandiurnal-variation (MDV) approach for filling gaps of up to 5 days was found. Remaining gaps were replaced by a monthly average of the specific half-hourly value. From June 2016 to May 2017 and June 2017 to May 2018, we estimated dry deposition sums of 3.8 and 4.0 kg N ha(-1) a(-1), respectively. Adding results from the wet deposition measurements, we determined 12.2 and 10.9 kg N ha(-1) a(-1) as total nitrogen deposition in the 2 years of observation. This work encompasses (one of) the first long-term flux measurements of Sigma N-r using novel measurements techniques for estimating annual nitrogen dry deposition to a remote forest ecosystem

Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets

To monitor the effect of current nitrogen emissions and mitigation strategies, total (wet + dry) atmospheric nitrogen deposition to forests is commonly estimated using chemical transport models or canopy budget models in combination with throughfall measurements. Since flux measurements of reactive nitrogen (Nr) compounds are scarce, dry deposition process descriptions as well as the calculated flux estimates and annual budgets are subject to considerable uncertainties. In this study, we compared four different approaches to quantify annual dry deposition budgets of total reactive nitrogen (ΣNr) at a mixed forest site situated in the Bavarian Forest National Park, Germany. Dry deposition budgets were quantified based on (I) 2.5 years of eddy covariance flux measurements with the Total Reactive Atmospheric Nitrogen Converter (TRANC); (II) an in situ application of the bidirectional inferential flux model DEPAC (Deposition of Acidifying Compounds), here called DEPAC-1D; (III) a simulation with the chemical transport model LOTOS-EUROS (Long-Term Ozone Simulation – European Operational Smog) v2.0, using DEPAC as dry deposition module; and (IV) a canopy budget technique (CBT). Averaged annual ΣNr dry deposition estimates determined from TRANC measurements were 4.7 ± 0.2 and 4.3 ± 0.4 kg N ha−1 a−1, depending on the gap-filling approach. DEPAC-1D-modeled dry deposition, using concentrations and meteorological drivers measured at the site, was 5.8 ± 0.1 kg N ha−1 a−1. In comparison to TRANC fluxes, DEPAC-1D estimates were systematically higher during summer and in close agreement in winter. Modeled ΣNr deposition velocities (vd) of DEPAC-1D were found to increase with lower temperatures and higher relative humidity and in the presence of wet leaf surfaces, particularly from May to September. This observation was contrary to TRANC-observed fluxes. LOTOS-EUROS-modeled annual dry deposition was 6.5 ± 0.3 kg N ha−1 a−1 for the site-specific weighting of land-use classes within the site's grid cell. LOTOS-EUROS showed substantial discrepancies to measured ΣNr deposition during spring and autumn, which was related to an overestimation of ammonia (NH3) concentrations by a factor of 2 to 3 compared to measured values as a consequence of a mismatch between gridded input NH3 emissions and the site's actual (rather low) pollution climate. According to LOTOS-EUROS predictions, ammonia contributed most to modeled input ΣNr concentrations, whereas measurements showed NOx as the prevailing compound in ΣNr concentrations. Annual deposition estimates from measurements and modeling were in the range of minimum and maximum estimates determined from CBT being at 3.8 ± 0.5 and 6.7 ± 0.3 kg N ha−1 a−1, respectively. By adding locally measured wet-only deposition, we estimated an annual total nitrogen deposition input between 11.5 and 14.8 kg N ha−1 a−1, which is within the critical load ranges proposed for deciduous and coniferous forests

Nitrogen deposition shows no consistent negative nor positive effect on the response of forest productivity to drought across European FLUXNET forest sites

Atmospheric reactive nitrogen (N) deposition is an important driver of carbon (C) sequestration in forest ecosystems. Previous studies have focused on N-C interactions in various ecosystems; however, relatively little is known about the impact of N deposition on ecosystem C cycling during climate extremes such as droughts. With the occurrence and severity of droughts likely to be exacerbated by climate change, N deposition—drought interactions remain one of the key uncertainties in process-based models to date. This study aims to contribute to the understanding of N deposition-drought dynamics on gross primary production (GPP) in European forest ecosystems. To do so, different soil water availability indicators (Standardized Precipitation Evapotranspiration Index (SPEI), soil volumetric water) and GPP measurements from European FLUXNET forest sites were used to quantify the response of forest GPP to drought. The computed drought responses of the forest GPP to drought were linked to modelled N deposition estimates for varying edaphic, physiological, and climatic conditions. Our result showed a differential response of forest ecosystems to the drought indicators. Although all FLUXNET forest sites showed a coherent dependence of GPP on N deposition, no consistent or significant N deposition effect on the response of forest GPP to drought could be isolated. The mean response of forest GPP to drought could be predicted for forests with Pinus trees as dominant species (R2 = 0.85, RMSE = 8.1). After extracting the influence of the most prominent parameters (mean annual temperature and precipitation, forest age), however, the variability remained too large to significantly substantiate hypothesized N deposition effects. These results suggest that, while N deposition clearly affects forest productivity, N deposition is not a major nor consistent driver of forest productivity responses to drought in European forest ecosystems

Parameterization of oceanic whitecap fraction based on satellite observations

In this study, the utility of satellite-based white-cap fraction (W) data for the prediction of sea spray aerosol (SSA) emission rates is explored. More specifically, the study aims at evaluating how an account for natural variability of whitecaps in the W parameterization would affect SSA mass flux predictions when using a sea spray source function (SSSF) based on the discrete whitecap method. The starting point is a data set containing W data for 2006 together with matching wind speed U-10 and sea surface temperature (SST) T. Whitecap fraction W was estimated from observations of the ocean surface brightness temperature T-B by satellite-borne radiometers at two frequencies (10 and 37 GHz). A global-scale assessment of the data set yielded approximately quadratic correlation between W and U-10. A new global W(U-10) parameterization was developed and used to evaluate an intrinsic correlation between W and U-10 that could have been introduced while estimating W from T B. A regional-scale analysis over different seasons indicated significant differences of the coefficients of regional W(U-10) relationships. The effect of SST on W is explicitly accounted for in a new W(U-10, T) parameterization. The analysis of W values obtained with the new W(U-10) and W(U-10, T) parameterizations indicates that the influence of secondary factors on W is for the largest part embedded in the exponent of the wind speed dependence. In addition, the W(U-10, T) parameterization is able to partially model the spread (or variability) of the satellite-based W data. The satellite-based parameterization W(U-10, T) was applied in an SSSF to estimate the global SSA emission rate. The thus obtained SSA production rate for 2006 of 4.4 x 10(12) kg year(-1) is within previously reported estimates, however with distinctly different spatial distribution.Peer reviewe

Microbial co-cultivation induces a metabolic shift, promoting syngas conversion to chain-elongated acids

Introduction: Syngas, a mixture of H2, CO and CO2, can be generated from a wide range of (low-biodegradable wastes) and is a suitable feedstock for biotechnological processes. Several microorganisms are able to use syngas for growth, but main natural products from this fermentation are acetate and ethanol. In order to extend the range of products from syngas fermentation, we constructed a synthetic co-culture of Clostridium autoethanogenum, a carboxydotrophic acetogen, with Clostridium kluyveri, a bacterium employing the reverse -oxidation pathwaya. C. autoethanogenum converted syngas to acetate and ethanol, and C. kluyveri elongated these products to butyrate and caproate. Methods: Experiments in batch bottles and chemostats were conducted to study the differences in physiological behavior between monocultures of C. autoethanogenum and co-cultures of C. autoethanogenum and C. kluyveri. In addition to physiological characterization a transcriptomics approach was used to unravel the molecular functioning of this co-cultureb. Results: Expression of the central carbon- and energy-metabolism of C. autoethanogenum in pure or in co-culture with C. kluyveri remained unaltered. However, the electron flux from CO to intermediate products (acetate/ethanol) was substantially shifted towards the production of ethanol. In co-culture conditions fed with additional acetate, the metabolism of C. autoethanogenum could be pushed to produce only ethanol from CO, resulting in high yields of chain elongated acids by the co-culture. Conclusions: The results suggest that thermodynamics and metabolic dependence between the two strains, rather than gene expression, plays a key role in the ratio of products formed during CO fermentation by C. autoethanogenum. Overall this suggests that microbial interactions can be exploited to steer the syngas fermentation process towards products of interest, enhancing both the efficiency and the products spectrum of syngas fermentation technology.info:eu-repo/semantics/publishedVersio

Data assimilation of CrIS NH3 satellite observations for improving spatiotemporal NH3 distributions in LOTOS-EUROS

Atmospheric levels of ammonia (NH3) have substantially increased during the last century, posing a hazard to both human health and environmental quality. The atmospheric budget of NH3, however, is still highly uncertain due to an overall lack of observations. Satellite observations of atmospheric NH3 may help us in the current observational and knowledge gaps. Recent observations of the Cross-track Infrared Sounder (CrIS) provide us with daily, global distributions of NH3. In this study, the CrIS NH3 product is assimilated into the LOTOS-EUROS chemistry transport model using two different methods aimed at improving the modeled spatiotemporal NH3 distributions. In the first method NH3 surface concentrations from CrIS are used to fit spatially varying NH3 emission time factors to redistribute model input NH3 emissions over the year. The second method uses the CrIS NH3 profile to adjust the NH3 emissions using a local ensemble transform Kalman filter (LETKF) in a top-down approach. The two methods are tested separately and combined, focusing on a region in western Europe (Germany, Belgium and the Netherlands). In this region, the mean CrIS NH3 total columns were up to a factor 2 higher than the simulated NH3 columns between 2014 and 2018, which, after assimilating the CrIS NH3 columns using the LETKF algorithm, led to an increase in the total NH3 emissions of up to approximately 30 %. Our results illustrate that CrIS NH3 observations can be used successfully to estimate spatially variable NH3 time factors and improve NH3 emission distributions temporally, especially in spring (March to May). Moreover, the use of the CrIS-based NH3 time factors resulted in an improved comparison with the onset and duration of the NH3 spring peak observed at observation sites at hourly resolution in the Netherlands. Assimilation of the CrIS NH3 columns with the LETKF algorithm is mainly advantageous for improving the spatial concentration distribution of the modeled NH3 fields. Compared to in situ observations, a combination of both methods led to the most significant improvements in modeled monthly NH3 surface concentration and NH4+ wet deposition fields, illustrating the usefulness of the CrIS NH3 products to improve the temporal representativity of the model and better constrain the budget in agricultural areas