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7/28/2019 An immision data based system of particulate matter levels modeling dedicated to implementation by local authorities
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An immision data based system of
particulate matter levels modelingdedicated to implementation by
local authorities
Jacek Bzdak, Mieczysaw Swioski,
Brnisaw Swioski, Marek Lasiewicz,Magdalena Kla, Jacek Szlachciak
7/28/2019 An immision data based system of particulate matter levels modeling dedicated to implementation by local authorities
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Topic of this
presentation
Development of modeling tools that can be
implemented at local scale.
Brief introduction to air pollution problem.
Characteristics of local authorities and
problems that can occur when implementing
models that work on local scale.
Developed models and their usage.
System for models.
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AIR POLLUTION
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Why air pollution needs
to be managed
Its a real health threat.
It is pssible t minimalize its impact n
people.
It costs!Average live time shortening
(in months) due to exposition
to anthropogenic Particle
Matter. [WHO]
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Particulate Matter
Particulate Matter (PM) is one kind of airpollutant.
It is composed from fine particles of dust (solid orfluid) that are suspended in the air for longperiods of time.
PM is separated into to fractions. Most notablyPM10 and PM2,5 (particles with aerodynamicdiameter smaller than 10 and 2,5).
PM is measured by determining mass of
suspended matter in given volume of air
3.
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Modelling PM
PM limit values are commonly exceeded even inlocalites that have no big industry. Often level forno other pollutant is exceeded in these localities.
Because of high uncertainty of measured levelsmodeling is harder (and if DMM model works forPM, it will almost certainly work for otherpollutant better).
There are some unique possible uses for PMmodels (mostly in measurement support).
There are still few monitoring points for PM2,5.
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Air pollution
management
Air pollution monitoring.
Informing the public.
Long term measures decreasing emission replacing home stoves, building better roads,
closing town for traffic.
Short term measures.
Informing the public.
Temporarily closing down roads for traffic.
Cleaning streets from dust
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Air pollution
monitoring
Air pollution monitoring is regulated by
directive 2008/50/EC on ambient air quality
and cleaner air for Europe.
Air pollution is being monitored through
Europe by network of manual and automatic
stations.
In Poland VIEPs are responsible for
monitoring.
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PM MODELING FOR LOCALAUTHORITIES
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CTM versus DMM
Chemical Transport Models
Work by calculatingpropagation and chemicaltransformations of emitedpllutants and itsprecursors.
Work using estimatedemission data.
Needs to be deployed onlarge area (pollutants cantravel from distant sources).
Much more applications.
Data Mining Models
Work by finding patterns inarchival data, and tries toapply these patterns to
current data (this process iscalled training)
Work using imision (datacollected by monitoringstations).
Can be deployed for singlemonitoring station.
Are more accurate
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Model applications
Short term prediction of pollutant levels
Serve as a trigger to preemptively use short term
measures, and as a warning for local people.
Predicting effects long term measures
Measurement assistance
Filling gaps in measurements; increasing
measurement accuracy; decreasing
measurements costs;
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SYSTEM FOR DMMS
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System for DMMs
DMMs need aggregated data from many
different sources (weather parametrs,
weather forecasts, pollutant levels)
This data must also be transformed.
This data needs to be aggregated and stored
in in house database.
Database must be very flexible.
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Database schema
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ANN models
To perform actual modelling we use Artifical
Neural Networks (ANN).
To enchance ANN performance we use such
techniques as wavelet transform (please see
positions in the bibliography for details).
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DMM model usage
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DEVELOPED MODELS
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Short term prediction
We use neural networks to create short term
(one day, two days) forecasts od PM levels.
These models need numerical weather
forecast.
It is discussed in depth on our poster (for
details visit it at incoming poster session).
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Spatial exrapolation
We can extrapolate PM levels on station using
PM levels on nearby stations and weather
parameters on all stations.
Distance between these stations can be large
(up to about 100km).
We need some PM level data on extrapolated
station (this data is used to create DMM
model).
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Example time series of both
extrapolated and measured data for
Zyrardow station.
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Amount of data needed
to train model
We worked on two year datasets.
Fill factor is amount of data used in training to
whole dataset:
=
.
To train model we need from 60 to 120 data
points.
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Fill Factor
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Conclusions
To enable local governments to managepollutant levels we need to provide them withproper tools (ie. models).
DMMs are suitable for small scale localdeployment.
Since each DMM has a very narrow
application --- single local government woulduse many DMMs, hence the need of a systemthat would hold them.
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Bibliography
[WHO]
[FINLAND]
[ZABRZE] [PREDICTOR]
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Thank you for your attention