FMI-ENFUSER Modelling System

The speciality of the FMI-ENFUSER (The Finnish Meteorological Institute's ENvironmental information FUsion SERvice) is the high utilization of measurement data. By using large quantities of historical measurement data and multivariable regression, the model can be set up without knowledge of local emission. Secondly, the key meteorological parameters and emission output rates can be fine-tuned in real-time so that the modelling output aligns with the measurement evidence as well as possible.

The model has been coupled in real time with the regional chemical transport model (SILAM), the local AQ monitoring network, and several sources providing meteorological data (HIRLAM, ECMWF). In addition, the model uses high resolution Geographic Information System data (GIS) to describe the local environment with a 5m resolution, including a detailed description of the urban landscape, vegetation and ground elevation. Existing available emission information can also be utilized in the modelling; as an example in Helsinki area, residential heating inventory from local authorities is being used and the local shipping emissions can be taken into account by accessing FMI-STEAM shipping emission data (Figure 1).

Figure 1: The core components of FMI-ENFUSER modelling system, which utilizes latest sensor observations, meteorological data and regional air quality forecast in the production of high resolution output for the current and future air quality in urban locations. GIS-datasets and emission inventories together with archived concentration time series are used for the calibration of the model.

The modelling system aims to provide near real-time information on urban air quality with a resolution of 10 to 15m for the hourly concentrations of PM2.5, PM10, NO2 and O3. Additionally, based on these modelled concentrations the Air Quality Index (AQI) is also provided. Since the timeliness of AQ information provided by the system is crucial, the system has been highly optimized and can provide new datasets each hour. An example visualization of the hourly AQI (in a scale of 1 to 5) from the model is shown in Figure 2. In Helsinki area the model data will be made public in the early spring of 2018.

Figure 2: Modelled Air Quality Index (AQI) in Helsinki area for a selected time given by the model.

FMI-ENFUSER has been designed to be portable and can be setup in other regions outside of Finland. This due to the fact that the model relies mostly on globally available, open access information apart from the existing measurement network. Outside of Finland the model has been previously setup in China, Langfang and in India, Delhi. However, in these two test sites the model was demonstrated with historical data without real-time capabilities. In the Nanjing Air Quality Testbed project (NAQT, 2017-2020) the ENFUSER will be setup with full operational capabilities, however.

The main product of the modelling system are the high resolution air quality representations that are provided for the selected urban areas. In addition, the modelling system and its output could be utilized further as follows:

• Air quality sensor benchmarking

  • FMI-ENFUSER can detect and notify when the measurements of certain air quality sensor frequently deviates from the assimilated "big picture" and thereby help in the network's management and maintenance.

    • During operational use the model can also detect strong, erratic peaks or sudden dips for each available sensor measurement time series and handle such suspicious input data accordingly.

• Measurement site selection

  • In the near future FMI-ENFUSER can assist in the intelligent selection of additional measurement sites when the established sensor network is being expanded. The methodology for this currently being developed.

• Tool for decision making

  • Automatic and personalized alerts can be provided based on the current and forecasted air quality output. Informed decisions based on the data can be made and pre-emptive administrative actions can be carried out accordingly.

• Personal exposure estimation

  • The high resolution modelling output can be used to predict personal exposure of citizens, e.g. by assessing the pollutant exposure based on a GPS-based tracking of activities.

• Averaged long term pollutant concentrations

  • The modelling system can be used to assess high resolution annual average concentrations. This information can in turn be used to provide citizens the tools to assess their personal long term exposure in their location of interest.

The development history of FMI-ENFUSER dates back to the year of 2011. In EU/PESCaDO project a module for environmental information fusion was introduced. Since then, the FMI-ENFUSER has continuously evolved and has been utilized in several projects including CLEEN-MMEA, TEKES-INKA, TEKES-CITYZER, TEKES-NAQT, Smart&Clean-HAQT and the CLIMOB project.

Additional information of the FMI-ENFUSER:

Johansson, L., Epitropou, V., Karatzas, K., Karppinen, A., Wanner, L., Vrochidis, S., Bassoukos, A., Kukkonen, J. and Kompatsiaris I. Fusion of meteorological and air quality data extracted from the web for personalized environmental information services. Environmental Modelling & Software, Elsevier, 64, 143-155. 2015.

Johansson, L., Karppinen, A. and Loven, K., Evaluation of Air Quality Using Dynamic Land-use Regression and Fusion of Environmental Information, Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval, Pages 33-38, ACM New York, NY, USA, ISBN: 978-1-4503-3558-4, 2015.