Environmental Engineering Reference
In-Depth Information
order to assess the problem and in order to be capable of taking effective measures
against the pollution, it is necessary to get a better understanding of the local
pollution distribution. The National People's Congress in China asked to work on
developing clean energy sources and changing energy use patterns, in order to try
to bring the air quality in most Chinese cities up to the international standards.
The AMFIC project (www.amfic.eu) aims to combine satellite observations,
ground-based measurements and modelling techniques in order to assess air
quality. Within AMFIC the AURORA regional-scale air-quality model is used in
order to assess air-quality levels at the city level. Besides retrospective analyses,
air-quality models can also be used to forecast air-pollution levels and to investigate
the effect of mitigation strategies on air quality. In this contribution we present
AURORA results for the city of Shenyang as well as a tool to derive high-resolution
emission maps for air pollutants.
2. Methodology
Crucial input to air-quality models are the emission data for both gaseous and
particulate pollutants. When applying models at the city level at high resolution, it
is of paramount importance that the geographical information of the emissions has
the same scale as the level on which the concentration levels need to be calculated.
For this reason, E-MAP, which is a tool developed by VITO, is used to spatially
distribute emissions (Maes et al., 2009). It is based on a top-down methodology
where total emissions, known for large regions (for instance, a country), are
spread by making use of additional geo-information such as population density,
the road network, land use, … Also time-variable data like the day-night rhythm
can be used to disaggregate the date.
The disaggregation of emission data uses so-called substitute or surrogate
variables, in order to weight statistical emission data. The most simple system to
calculate the local emission out of a more global dataset is to use the following
equation:
E
L
= E
T
* V
L
/ V
T
in which E
L
is the local emission, E
T
the total emission, V
L
the local value of the
substitute variable and V
T
the total value of the substitute value. Examples of sub-
stitute variables are population density, fuel use, traffic volumes. In short, variables
that represent the real local emissions fairly well but are easier to measure than the
real emissions. The E-MAP tool is optimized by picking the most suitable
substitute variables, which of course is a crucial step in the process.
In order to derive high-resolution maps for Chinese cities we start with the
ACESS (Ace-Asia and Trace-P Modelling and Emission Support System) emission
database. ACESS estimates anthropogenic emissions of the gaseous pollutants SO2,
NOx, CO, NMVOC, as well as for PM10 and PM2.5 for Asia, broken down over
four economic sectors (industry, transport, power plants and residential heating
