integration of measured, modelled & remotely sensed air quality data & impacts on the south...
Post on 20-Dec-2015
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INTEGRATION OF MEASURED, INTEGRATION OF MEASURED,
MODELLED & REMOTELY SENSED AIR MODELLED & REMOTELY SENSED AIR
QUALITY DATA & IMPACTS ON THE QUALITY DATA & IMPACTS ON THE
SOUTH AFRICAN HIGHVELDSOUTH AFRICAN HIGHVELD
Kubeshnie Bhugwandin - October 2007Kubeshnie Bhugwandin - October 2007
Overview
• Introduction• Aim of Study• Hypothesis• Data and Methodology• Preliminary Results• Problems Encountered
Introduction
• S.A. is a major regional contributor to aerosols and trace gases
• Mpumalanga Highveld is one of the most highly industrialised areas in Southern Africa
• Emission densities ranked amongst the highest in the world
• Fifteen of S.A.’s coal fired power stations are located here
Introduction
Introduction
• Several datasets have been created• Never been an integration of
datasets derived from the different methods of air quality
• This study proposes to compare surface data using a GIS in order to determine the most accurate estimate of ground level SO2 and NO2 concentrations
Introduction
Aim of Study
• To integrate air quality data to determine most accurate ground level SO2 and NO2 concentrations
• Improvement of modelled concentration fields using measurements
• Determine how satellite retrievals compare to ground based measurements
• Limited studies conducted to assess impacts
Aim of Study
• To assist Eskom in quantifying impacts and assessing potential risk
• To produce one integrated set of: - ambient air quality data- maps of potentially sensitive
ecosystems- Maps of potential exposure to poor
air quality by population groupings• Over arching aim to improve model
accuracy to assist in better predicts and decion making in this region
Hypotheses
• That modelled air quality predictions can be improved using measured data
• That satellite retrievals of SO2 and NO2
concentrations are representative of surface concentrations
• That integration of available datasets with a GIS will improve the evaluation of impacts from power station emissions on ecosystems and human health
Data & Methodology
Data & information for the project:1. Eskom database of ground
measurements for 20032. Modelled data derived from
Calpuff modelling exercise conducted by Eskom
3. SAWS meterological data as an input for modelling
4. SCIAMACHY retrievals from TEMIS website
Data & Methodology
• Comparisons to be made a) SCIAMACHY & MODELLED {annual avg}b) SCIAMACHY & MEASURED {monthly and
annual avg}c) MODELLED & MEASURED {maximum
hourly,daily and annual avg}• Modelled fields adjusted by a correction
factor• New layer overlayed with population
density, towns, land cover, vegetation and ecosystem datasets to assess risk from power station emissions
Preliminary Results
Preliminary Results
Preliminary Results
Preliminary Results
Problems Encountered
• Unavailablity of 2003 SCIAMACHY data
Earliest files from September 2004.• Unable to geo-reference files in TNT
MIPS , GEO MEDIA & ERDAS Files in HDF version 4 formatt• TEMIS website gives global / daily
coverage. Suggestion is to allow user to retrieve data for a specific location.
Problems Encountered
Thank you !