Testing the capability of the chemistry transport model LOTOS-EUROS to forecast PM10 levels in the Netherlands

Since particulate matter has a direct and adverse impact on public health, a good air quality forecast is important. Several European countries presently use statistical forecasting models, which have their limitations, especially for PM10. An alternative approach is to use a chemistry transport model. Here, the ability of the chemical transport model LOTOS-EUROS to forecast PM10 concentrations in the Netherlands was investigated. LOTOS-EUROS models several PM10 components individually. For sulphate, nitrate and ammonium aerosol the evaluation against observations shows that the modelled annual mean concentrations are within 20% of the measured concentration and that the temporal correlation is reasonably good (R > 0.6). For sea salt the model tended to overestimate the measured concentrations. For elemental carbon the correspondence with black smoke observations was reasonable. However, total PM10 is seriously underestimated, due to unmodelled components (secondary organic aerosols, mineral dust) and missing sources. Therefore, a simple bias correction for four seasons was derived based on the years 2004–2006. The model was compared with the Dutch operational statistical model PROPART and ground-level observations. With bias correction, LOTOS-EUROS performed better than PROPART regarding the timing of events. The major flaw of LOTOS-EUROS was that high values (>50 μg m^?3) were still underestimated. Another advantage of LOTOS-EUROS over the statistical model was the more detailed information in space and time, which facilitates communication of the forecast to the general public.     

Sea salt concentrations across the European continent

The oceans are a major source for particles that play an important role in many atmospheric processes. In Europe sea salt may contribute significantly to particulate matter concentrations. We have compiled sodium concentration data as a tracer for sea salt for 89 sites in Europe to provide more insight in the distribution of sea salt across Europe. The annual average sea salt concentrations above land were estimated to range between 0.3 and almost 13 μg m^?3. Maximum concentrations are found at the Irish coast. At coastal sites along the Atlantic and North Sea coast concentrations tend to be around 5 μg m^?3. More inland locations up to about 300 km away from the coast tend to show concentrations between 2 and 5 μg m^?3, whereas sites further away from the coast are characterized by lower concentrations. An analysis of the representativity of the data with respect to a long term average showed that the long average is associated with a standard deviation of around 15%. The compilation of observations provides an improved overview of sea salt concentrations in Europe as well as an improved basis for model validation. Verification of the results of the LOTOS-EUROS model learned that the model represents well the spatial variability of the observed sea salt concentrations very well. However, the absolute concentrations are significantly overestimated due to large uncertainties in the emission and dry deposition parameterizations. Using the high explained variability in the gradients across Europe, the bias-corrected modelled distribution serves as a best estimate of the sea salt distribution across Europe for 2005.     

Degassing of H/He, CFCs and SF6 by denitrification: Measurements and two-phase transport simulations

The production of N₂ gas by denitrification may lead to the appearance of a gas phase below the water table prohibiting the conservative transport of tracer gases required for groundwater dating. We used a two-phase flow and transport model (STOMP) to study the reliability of ³H/³He, CFCs and SF₆ as groundwater age tracers under agricultural land where denitrification causes degassing. We were able to reproduce the amount of degassing (R² = 69%), as well as the ³H (R² = 79%) and ³He (R² = 76%) concentrations observed in a ³H/³He data set using simple 2D models. We found that the TDG correction of the ³H/³He age overestimated the control ³He/³He age by 2.1 years, due to the accumulation of ³He in the gas phase. The total uncertainty of degassed ³H/³He ages of 6 years (± 2 σ) is due to the correction of degassed ³He using the TDG method, but also due to the travel time in the unsaturated zone and the diffusion of bomb peak ³He. CFCs appear to be subject to significant degradation in anoxic groundwater and SF₆ is highly susceptible to degassing. We conclude that ³H/³He is the most reliable method to date degassed groundwater and that two-phase flow models such as STOMP are useful tools to assist in the interpretation of degassed groundwater age tracer data.     

The need for ammonia abatement with respect to secondary PM reductions in Europe

In Europe, secondary particulate matter (PM) comprises 50% or more of PM 2.5. To reduce PM concentrations requires lowering precursor emissions. Since the 1980s, SO(2) emissions have decreased by more than 60%, while particle concentrations have decreased less. NO(x) and NH(3) emissions have decreased slightly. The role of ammonia in particle formation is addressed here. It is shown that secondary PM concentrations can only be effectively reduced if ammonia emissions are decreased in much the same way as those of SO(2) and NO(x).     

Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal

Five air quality models were applied over Portugal for July 2006 and used as ensemble members. Each model was used, with its original set up in terms of meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and the ensemble of ozone (O₃) and particulate matter (PM) is performed using monitoring data from 22 background sites. The ensemble approach, based on the mean and median of the five models, did not improve significantly the skill scores due to large deviations in each ensemble member. Different bias correction techniques, including a subtraction of the mean bias and a multiplicative ratio adjustment, were implemented and analysed. The obtained datasets were compared against the individual modelled outputs using the bias, the root mean square error (RMSE) and the correlation coefficient. The applied bias correction techniques also improved the skill of the individual models and work equally well over the entire range of observed O₃ and PM values. The obtained results revealed that the best bias correction technique was the ratio adjustment with a 4-day training period, demonstrating significant improvements for both analysed pollutants. The increase in the ensemble skill found comprehends a bias reduction of 88 % for O₃, and 92 % for PM10, and also a decrease in 23 % for O₃ and 43 % for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach was also examined with successful skills comparing to the uncorrected ensemble for both pollutants.     

Impact changes of climatic extremes on arable farming in the north of the Netherlands

Agriculture is vulnerable to climate change in multiple ways. Here, we use the northern region of the Netherlands as a case study to explore how risk assessments for climate change impacts on crop production can address multiple vulnerabilities. We present a methodology, which we call agro climate calendar (ACC) that (i) includes potential yield losses, as well as loss of product quality, and (ii) assesses the risks of a variety of climate factors including weather extremes and the emergence and abundance of pests and diseases. Climate factors are defined for two time slices: 1990 (1976–2005) and 2040 (2026–2055); the frequency of occurrence of the factors is compared for the two periods, and the resulting frequency shifts are presented in a crop calendar on a monthly basis. This yields an indication of the magnitude and direction of changes in climatic conditions that can lead to damage by extreme events and pests and diseases. We present results for the two most important crops in the region, seed potato, and winter wheat. The results provide a good overview of risks from climate factors, and the most important threats and opportunities are identified. This semi-quantitative approach is firmly rooted in farm management, which is the level where operational and strategic decisions are made. Thus, the approach is well suited to assist local stakeholders such as farmers and policy makers to explore farm-level adaptation. This work is complementary to previous modeling work that focused mainly on the relation between mean climate change factors (i.e., temperature) and crop yield.     

A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe

Fine particulate matter (PM) is relevant for human health and its components are associated with climate effects. The performance of chemistry transport models for PM, its components and precursor gases is relatively poor. The use of these models to assess the state of the atmosphere can be strengthened using data assimilation. This study focuses on simultaneous assimilation of sulphate and its precursor gas sulphur dioxide into the regional chemistry transport model LOTOS–EUROS using an ensemble Kalman filter. The process of going from a single component setup for SO₂ or SO₄ to an experiment in which both components are assimilated simultaneously is illustrated. In these experiments, solely emissions, or a combination of emissions and the conversion rates between SO₂ and SO₄ were considered uncertain. In general, the use of sequential data assimilation for the estimation of the sulphur dioxide and sulphate distribution over Europe is shown to be beneficial. However, the single component experiments gave contradicting results in direction in which the emissions are adjusted by the filter showing the limitations of such applications. The estimates of the pollutant concentrations in a multi-component assimilation have found to be more realistic. We discuss the behavior of the assimilation system for this application. The model uncertainty definition is shown to be a critical parameter. The increased complexity associated with the simultaneous assimilation of strongly related species requires a very careful specification of the experiment, which will be the main challenge in the future data assimilation applications.     

Skill and uncertainty of a regional air quality model ensemble

Recently several regional air quality projects were carried out to support the negotiation under the Clean Air For Europe (CAFE) programme by predicting the impact of emission control policies with an ensemble of models. Within these projects, CITYDELTA and EURODELTA, the fate of air quality at the scale of European cities or that of the European continent was studied using several models. In this article we focus on the results of EURODELTA. The predictive skill of the ensemble of models is described for ozone, nitrogen dioxide and secondary inorganic compounds, and the uncertainty in air quality modelling is examined through the model ensemble spread of concentrations.For ozone daily maxima the ensemble spread origin differs from one region to another. In the neighbourhood of cities or in mountainous areas the spread of predicted values does not span the range of observed data, due to poorly resolved emissions or complex-terrain meteorology. By contrast in Atlantic and North Sea coastal areas the spread of predicted values is found to be larger than the observations. This is attributed to large differences in the boundary conditions used in the different models. For NO₂ daily averages the ensemble spread is generally too small compared with observations. This is because models miss highest values occurring in stagnant meteorology in stable boundary layers near cities. For secondary particulate matter compounds the simulated concentration spread is more balanced, observations falling nearly equiprobably within the ensemble, and the spread originates both from meteorology and aerosol chemistry and thermodynamics.