The Development of an Air Quality Modelling and Forecasting System at SAWS

Transcription

1 The Development of an Air Quality Modelling and Forecasting System at SAWS Bheki Sibiya

2 Outline Of Presentation Introduction and background info. SAWS goal Air Quality Modelling and Forecasting unit - current activities AQMFS Tools -NAME III -Meteorological data -Emission information Preliminary Results Conclusion Future plans

3 SAWS goals Develop and implement a numerical air quality modelling and forecasting system in SA To provide forecast products To support national air quality management (and CC) policy development. To Support Air quality research (locally and globally) Other relevant products that may be of importance to different stakeholders

4 Current Activities Using global (NWP & Emissions) data for Experimental tests with NAME III Working on a higher degree (Masters in Env. Sc.) project aiming at evaluation and improvement of the model Configuring SAWS UMSA12 model to produce NAME input

5 AQMS Tools The AQMS is composed of three separate modules: - The dispersion model - Meteorological (NWP) data + topography &land use (all from UM) - Emission information

6 AQMS FLOW Emissions (g/s) NO, NO2, SO2, PM10, O3 & VOC NAME Dispersion Model Modelled (g/m 3 )SO2, CO, O3, etc 3-D Met data Topography &Land Use (NWP) Chemical Scheme (STOCHEM)

7 Dispersion model-name III Developed and used by the UK Met Office for operational air quality forecasting and research. Offline Lagrangian particle model - pollutant modelled by large numbers of parcels released into the model atmosphere Model driven by meteorological fields from both the point station and NWP model(um, ECMWF, etc)

8 NAME III model Cont. Particles are transported by local mean wind in 3-dimensions Diffusion by turbulence is represented by random walk techniques, displacing particles in both the horizontal and vertical Sets of reactions are represented by STOCHEM a global chemistry model (more than 30 predominant species) Single model integrating short and long range capabilities

9 NAME III-main process diagram Grid location Concentration Calculated Transportation by 3D wind Chemical transformation and deposition Air Parcel Released Recalculate air parcel mass

10 Meteorological data The UM NWP data is used to drive NAME (currently 40 km resolution data from UM global model is used for NAME runs) Produces 3D NWP data for the NAME input Can be configured from a spatial resolution of a 1 km to globe -SAWS UM configured to 12 km and is planned to be used for NAME input

11 Emissions information Sourced from global emission inventories (IPCC). Original data have a temporal resolution of a month and a by spatial resolution (2000 projections). Classified into Anthropogenic sources: - Traffic - Industrial - Energy - Domestic - Agric waste - Municipal waste and Natural sources: - Forest and grass fires Interpolated to finer resolutions (e.g 12 & 15 km)

12 Example of NAME Output

13 Preliminary Results: Model predictions vs Observed 1-30 June 2007

14 Preliminary Results: Model predictions vs Observed 1-30 June 2007

15 Preliminary Results :SO2 concentrations at four VTAPA stations October 2007 Station Bias RMSE NMSE Fractional Bias Diepkloof Sebokeng Kliprivier Zamdela

16 Preliminary Results O3 concentrations at four VTAPA stations October 2007 Station Bias RMSE NMSE FB Diepkloof Sebokeng Kliprivier Zamdela

17 Preliminary Results: Diepkloof Wind roses Figure 1: Predicted wind Roses Figure 2 : Measured wind roses

18 Preliminary Results: Sebokeng wind roses Figure 3 Predicted wind Rose Figure 4 Measured wind roses

19 Preliminary Results Kliprivier wind roses Figure 5 Predicted wind Rose Figure 6 Measured wind roses

20 Conclusion The modelling system is currently unable to simulate the concentrations of pollutants and the uncertainty is thought to be mainly from two different sources : - Low resolution NWP data is used to drive the model- work for producing 12 km NWP is in progress. -The global emissions are very low in temporal and spatial resolution- This is expected to be addressed from the implementation of SAAQIS phase 2

21 Development of a more realistic national industrial emission profile- SAAQIS phase II still under construction (transport, biogenic, agriculture?) Air Quality modelling and forecasting - Model evaluation Future plans - Research in atmospheric dispersion modelling. -Online public forecast of air quality Collaboration with other institutions

22 Acknowledgements I would like to thank the SAAQIS team for providing me with air quality data The UK Met Office for assisting with the NWP data and technical support. The GEIA community

23 THANK YOU