An immision data based system of particulate matter levels modeling dedicated to implementation by local authorities

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


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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

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An immision data based system of particulate matter levels modeling dedicated to implementation by local authorities