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 !