|1||Please provide a short description of the state-of-the-art and/or current trends in the field? How does the result fit into it?|
|In the last decades, the mechanisms of air pollutant transport and transformation are the subject of intense scientific research. One of the principle aims of this research is to establish source/receptor relationships, i.e., to quantify to what extent various emission sources contribute to the occurrence of air pollution episodes. Nowadays, most of the physical and chemical processes leading to high air pollution levels seem to be understood. Nevertheless, air pollution still causes serious problems in many countries all over the world. Such problems may be classified depending on their characteristic length scale. Usually, global, continental, regional and local scale problems are distinguished. Observational evidence is only rarely sufficient to describe atmospheric transport and transformation of air pollutants. For this reason mathematical models have been developed to simulate wind flow and the dispersion of chemically reacting pollutants in the atmosphere. Over the past few years there has been considerable progress in the practical applications of mathematical models at all above mentioned scales. Growing interest in air quality management and forecasting has resulted in the development of tools for modelling of air pollutants’ transport and transformation. There are several state-off-the-art air quality models which can be applied for different air pollution problems.
The generally simplistic Gaussian and Box models are computational efficient and user friendly, but they are also subject to a number of limitations. More sophisticated modelling approaches are increasingly being developed for the study of urban air pollution. The continuous increase of hardware capabilities and the optimisation of numerical methods that occurred mainly during the last decade allowed mesoscale Eulerian models to emerge as an accurate tool for predicting urban airflow patterns and air pollutant dispersion on a local scale and in urban environments. Although the importance and usefulness of mesoscale meteorological models for air quality purposes cannot be questioned, they are very complicated and computationally expensive models and their ability to accurately represent flow patterns has focused on the mesoscale rather than on the urban scales. The urban scale Eulerian models, such as OFIS, offer a more computationally efficient alternative for realistically resolving urban scale dispersion and chemical phenomena.
Numerical air quality models are based on the mathematical description of transport and transformation of the trace species in the atmosphere. The transport equation calculates the changes in concentration fields for each modelled pollutant. These changes are due to advective transport, vertical diffusion and different sources and sinks, such as chemical transformation, anthropogenic and natural emissions, dry deposition and wet removal.
|2||What is the problem/need/knowledge gap that the research result is responding to? How was it addressed before?|
|Nowadays human activities in urban conurbations are undoubtedly concentrated in a relatively small area. More specifically, most economic activities involving the use and conversion of energy are accompanied by emissions of air pollutants, thereby degrading the environment, particularly the urban environment. Urban air pollution is the source of a range of problems, including most importantly health risks through the inhalation of gases and particles, accelerated corrosion and deterioration of materials, damage to historical monuments and buildings and damage to vegetation in and around the city. In order to tackle these problems, efficient, long-term air pollution abatement strategies need to be identified and implemented.
Thereupon, it is more than evident that exposure to air pollution represents a serious problem in many cities all over the world. Observations and computational tools are the most common ways to imprint air pollution levels in urban areas. Air quality modelling is used for determining and visualising the significance and impact of emissions to the atmosphere. Models are especially useful to policy-makers whose decisions are often based on emission measurements. Measurements are usually not representative for the whole of a big area, and their quality is sometimes questionable. A model can provide estimates of concentrations in areas with lack of measurements. Models are also necessary for forecasting and planning purposes. Besides, in order to perform health impact assessment, including exposure evaluations, the use of air quality models is essential.
In this direction, OFIS, an efficient modelling tool for assessing urban air quality, has been developed. The OFIS model belongs to the European Zooming Model (EZM) system, a comprehensive model system for simulations of wind flow and pollutant transport and transformation. It represents a robust and efficient tool for simulating air pollutant transport and transformation in an urban plume. The model was developed to serve a twofold aim; (i) allowing authorities to assess urban air quality by means of a fast, simple and still reliable model and (ii) refining a regional model simulation by estimating the urban subgrid effect on pollution levels. OFIS simulates concentration changes due to both advection and chemical transformation of species in its computational domain. The concentration values outside this domain are assumed to coincide with the regional background concentrations used for the calculation of the boundary conditions.
The computational domain consists of a two-layer gridded strip with a varying length and a width defined by the city size, with the city in the centre. The strip is oriented along the prevailing wind direction, altering every three hours. The first vertical layer extends up to 90m, while the second one is equal to the mixing height, thus varying with time. The restriction to this computational domain results in a high computational speed and a low output file size. For prescribing the time evolution of the mixing height as well as of the turbulent exchange coefficient between the two layers, a 1D version of the non-hydrostatic meteorological model MEMO is utilised. Dry deposition is accounted for by using the resistance model approach. Depending on the spatial information about emissions available for the area surrounding the city, either geographically distributed gridded emission data or emission data integrated to urban, suburban and rural totals can be used. Due to the modular structure of OFIS, chemical transformations can be treated by any suitable chemical reaction mechanism, the default being the EMEP MSC-W chemistry.
|3||What is the potential for further research?|
|Future improvements on OFIS’s structure will include an improved Secondary Organic Aerosols (SOA) module. Besides, the application of a refined OFIS’s computational procedure, which could support the use of more vertical layers in the model’s calculations would also contribute to the more detailed and accurate description of atmospheric chemistry and dynamics.|
|4||What is the proposed method of IPR-protection? (patent, license, trademark etc.)|
|LHTEE holds the copyright for the OFIS code. Its use can only take place under specific agreement of the prospective user and LHTEE.|
|5||What are the steps that need to be taken in order to secure the IPR-protection? What is the cost of IPR-protection?|
|6||What is you overall assessment of the scientific maturity of the research result?|
|OFIS is a state-of-the-art computational tool for simulating air pollutant transport and transformation in an urban plume. OFIS has been proved to be a valuable tool for air quality assessment and management in a wide variety of cases.|
KEYWORDS QUANTITATIVE ASSESSMENT (0-5).
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