Μηχανική ποιότητας αέρα · the problem of air pollution. ... early 20...

EnvironmentalEngineeringLaboratory

16. Μηχανική ποιότητας αέρα Enve-Lab – Aristotle University of Thessaloniki Enve-Lab, 2015

Department of Chemical EngineeringSchool of EngineeringAristotle University of Thessaloniki

Μηχανική ποιότητας αέρα




Atmospheric structure and composition




Air Pollutant

• Contaminant that affects human life, plant life, animal life and property could be termed as an air pollutant.

– Introduction of a non-physical chemical– Change of the composition of the natural atmosphere (e.g.CO2 increase)

• Air pollutants are classified into two categories:

Primary pollutants: These pollutants are emitted from a source directly into the atmosphere. e.g. Sulfur dioxide and Hydrocarbons

Secondary pollutants: These are formed due to the chemical reaction among two or more pollutants.

e.g. Peroxyacetyl nitrate (PAN )




The problem of air pollution

Κλίμακες του προβλήματος• Παγκόσμια (παγκόσμια θέρμανση, μείωση

στρατοσφαιρικού όζοντος)• Περιφερειακή (όξινη βροχή)• Τοπική• Αστική• Κλίμακα δρόμου• Ρύπανση εσωτερικών χώρων

Τί είναι ατμοσφαιρική ρύπανση?Ατμοσφαιρική ρύπανση ορίζεται η μεταβολή της σύστασης ατμόσφαιρας, είτε από την είσοδο ενώσεων που δεν υπάρχουν στη φυσική της σύσταση, είτε από μεταβολή της συγκέντρωσης υπαρχόντων ενώσεων

Οι κυριότεροι ατμοσφαιρικοί ρύποι είναι:• Το Διοξείδιο του Θείου (SO2)• Το Μονοξείδιο του Άνθρακα (CO)• Τo Διοξείδιo του Αζώτου (NO2)• Το Όζον (O3)• Τα Αιωρούμενα Σωματίδια (κυρίως PM2.5, PM10)• Το Βενζόλιο (C6H6)• Ο Μόλυβδος (Pb)• Το διοξείδιο του Άνθρακα (CO2) σε συγκέντρωση

ανώτερη της φυσιολογικής

Πηγές ατμοσφαιρικών ρύπων:Ανθρωπογενείς πηγές• Βιομηχανική δραστηριότητα• Παραγωγή ενέργειας• Μεταφορές• Οικιακή θέρμανση• Χρήση καταναλωτικών προίόντων

Φυσικές πηγές• Βιογενείς εκπομπές• Ηφαίστεια

Επιπτώσεις ατμοσφαιρικής ρύπανσης• Ανθρώπινη υγεία• Οικοσύστημα - Βιόσφαιρα• Τεχνητό περιβάλλον




Air pollution scales

Global & regional scale

Urban scale

Street canyon Indoor pollution

Acid rain

Global warming




Major air pollutants

• Particulate matter (UFPs, PM1, PM2.5, PM10)

• Sulfur dioxide (SO2)

• Carbon monoxide (CO)

• Nitrogen dioxide (NO2)

• Ozon (O3)

• Benzene (C6H6)

• Lead (Pb)

• (CO2) at levels beyond normal




Source Classification

Transportation sources: Includes emissions from transportation sources during the combustion process

Stationary combustion sources: These sources produce only energy and the emission is a result of fuel combustion

Industrial sources: These sources emit pollutants during the manufacturing of products

Solid waste Disposal: Includes facilities that dispose off unwanted trash

Miscellaneous: sources that do no fit in any of the above categories like, coal mining etc.

Natural sources: Volcanos




Air pollution effects




Pre-Industrial Era Early-Industrial Era

Early 20th Century Late 20th Century

Eras of Air Pollution

Early 21st Century




Units for measurement of Air Pollution

There are two units of measurement. They are as follows: • µg/m3 and ppm (parts per million)

At 25°C and 1 atm

• At 00 C and at a pressure of 76 cm of Hg, volume of the air is 22.41 l/mol. • To obtain volume at any temperature, use gas law

P1V1/T1 = P2V2/T2




Atmospheric processes




Emissions inventory

Emissions control

Meteorological data

Meteorological model

Emissions model

Emissions process

Meteorology process

Air quality model

Air quality assessment

( ) ( )ii i i i i i i

C VC K C P L C S Rt

∂= −∇⋅ +∇ ⋅ ∇ + − + −

∂

Transportation

Dispersion

Chemical transformation

Emissions

Removal

Navier-Stokes equation

Atmospheric processes –Air pollution modelling




Air pollution models

( ) ( ) ( ) ( )2 2 2

2 2 2, , exp exp exp2 2 2 2y z z z z

z h z h yQC x y zuπ σ σ σ σ σ

− − − + − + +

2c V c D c St∂

= − ∇ + ∇ +∂

( ) ( ) t

div grad Sρ ρυ Φ Φ

∂Φ + Φ −Γ Φ = ∂

( ) ( )11 1 1 1 1 2 2 1a

dCV Q C C E kC V Q C Cdt −= − + − + −

( ) ( )22 2 2 2 1 2 1 2a

dCV Q C C E kC V Q C Cdt −= − + − + −

Indoor Outdoor

Air pollution models CALINE 4 OSPM STREET CALTOX SILAM CMAQ REMSAD UAM-V CAMx PHOΕNIX FEM3MP ANSYS-CFX INTERA

Box models

Computational Fluid Dynamics (CFD) models

Choosing a model:• Nature and complexity of the

problem• Input data limitations• Computational cost

Gauss models

Lagrange Euler

FEM3MP CFD model

Empirical modelsANN models




Air pollution study fields

Nested gridding

Development of algorithms for adapting the problem on the proper scale

Air Toxics

PM

Acid Rain

Visibility

Ozone

Mobile Sources

Industrial Sources

Area Sources

(Cars, trucks, airplanes,boats, etc.)

(Power plants, factories, refineries/chemical plants, etc.)

(Homes, small business, farming equipment, etc.)

NOx, VOC,Toxics

NOx, VOC, SOx, Toxics

NOx, VOC,Toxics

Chemistry

Meteorology

Atmospheric Deposition

“One model approach” (US EPA)

All sources and processes are taken into account simultaneously




Air quality assessment

Analytical methods

Remote sensing

• Satellites• LIDAR• Beta attenuation

High importance of spatial and temporal resolution• Passive sampling• Active sampling




Integrated system for air pollution monitoring

Data Fusion

EO data

Image processing

Emission inventory

AOT

AQ and Met data

Z2=CPM(bscat,RH) σz2=cost

Atmospheric dispersion model

Z1=CPM(Mod)σz1=f(w)

K=σ2z1/(σ2

z1+σ2z2)

CKPM=Z1+K(Z2-Z1)

Model Fusion




Human exposure assessment

• Direct measurements

• Calculation based on the respective concentration levels of the locations encountered and the residence time

T n nn

C f C= ⋅∑

High importance of spatial and temporal resolution• Passive sampling• Active sampling




Air quality models validation

Indoor airOutdoor air

INDOORTRON

INDOORTRON modelled in ANSYS-CFX

Measured PM concentrations

Modelled PM concentrations

Models fusion




Particulate matter

Chemical speciation Emissions inventory

Source apportionment

Positive matrix factorizationPrincipal Component Analysis

Regression analysis Chemical exctractionPM sampler

Beta-Attenuation

Lidar




Biomass in Thessaloniki – Practical example




Biomass in Thessaloniki – Practical example

Chemical analysis• Source apportionment (e.g. Levoglucosan, BC, metals)• Refined risk assessment (e.g. PAHs analysis, redox activity)

1

2

n

Meteorological parameters prediction

Ambient air PM monitoring

network

HRT deposition+

INTERA comp. platform

Training loop

Ambient air PM

TemperatureHumidityWind speedWind directionPrecipitation

PMPM day before

Indoor concentrations

Population exposure

Dynamics in time

Database

Anthropometric data Time activity data Housing data

ANNForecasted

Measured

0

30

60

90

120

150Urban background Other sources

Biomass

0

5

10

15

20

25

Nasopharyng eal Trach eo bro nchial Pulmonary bronchioles Nasopharyng eal Trach eo bro nchial Pulmonary bronchioles

Traffic Urban backgroun d

Inde

x

Region specific oxidative potential index

0

5

10

15

20

25

Nasopharyng eal Trach eo bro nchial Pulmonary bronchioles Nasopharyng eal Trach eo bro nchial Pulmonary bronchioles

Traffic Urban backgroun d

Inde

x

Lung cancer risk

Health impact

Monetary impact




Sampling sites(10/2012 – 4/2013)

Urban background station

Traffic station

Sampling filters

Before After




Measurements(10/12 – 4/13)

0

20

40

60

80

100

120

140

160

5/1/20

137/1

/2013

9/1/20

1311

/1/20

1313

/1/20

1315

/1/20

1317

/1/20

1319

/1/20

1321

/1/20

1323

/1/20

1325

/1/20

1327

/1/20

1329

/1/20

1331

/1/20

132/2

/2013

4/2/20

136/2

/2013

8/2/20

1310

/2/20

1312

/2/20

1314

/2/20

1316

/2/20

1318

/2/20

1320

/2/20

1322

/2/20

1324

/2/20

1326

/2/20

1328

/2/20

132/3

/2013

4/3/20

136/3

/2013

8/3/20

1310

/3/20

1312

/3/20

1314

/3/20

1316

/3/20

1318

/3/20

1320

/3/20

1322

/3/20

1324

/3/20

1326

/3/20

1328

/3/20

1330

/3/20

131/4

/2013

3/4/20

135/4

/2013

7/4/20

139/4

/2013

11/4/

2013

13/4/

2013

15/4/

2013

PM co

ncen

tratio

n (μ

g/m

3 )

PM1PM2.5PM10

0

20

40

60

80

100

120

140

160

5/1/20

137/1

/2013

9/1/20

1311

/1/20

1313

/1/20

1315

/1/20

1317

/1/20

1319

/1/20

1321

/1/20

1323

/1/20

1325

/1/20

1327

/1/20

1329

/1/20

1331

/1/20

132/2

/2013

4/2/20

136/2

/2013

8/2/20

1310

/2/20

1312

/2/20

1314

/2/20

1316

/2/20

1318

/2/20

1320

/2/20

1322

/2/20

1324

/2/20

1326

/2/20

1328

/2/20

132/3

/2013

4/3/20

136/3

/2013

8/3/20

1310

/3/20

1312

/3/20

1314

/3/20

1316

/3/20

1318

/3/20

1320

/3/20

1322

/3/20

1324

/3/20

1326

/3/20

1328

/3/20

1330

/3/20

131/4

/2013

3/4/20

135/4

/2013

7/4/20

139/4

/2013

11/4/

2013

13/4/

2013

15/4/

2013

PM1PM2.5PM10

0

20

40

60

80

100

120

140

160

180

200PM2.5

PM10

0

20

40

60

80

100

120

140

160

180

200

PM co

ncen

tratio

n (μ

g/m

3 )

PM2.5PM10

Traffic site – October-December 2012

Traffic site – January-April 2013

Urban background site – October-December 2012

Urban background site – January-April 2013




0

1

2

3

4

5

6

7

8

PM1 PM2.5 PM10 PM1 PM2.5 PM10

Urban background Traffic

Levo

gluc

osan

(μg/

m3 )

Levoglucosan and black carbon

0

1

2

3

4

5

6

7

8

PM1 PM2.5 PM10 PM1 PM2.5 PM10

Urban background Traffic

Blac

k car

bon

(μg/

m3 )

Levoglucosan -Biomass burning

tracer

Black Carbon-Fossil fuels combustion

tracer




0

30

60

90

120

150

PM co

ncen

tratio

n (μ

g/m

3 )

Traffic site Other sourcesBiomass

0

30

60

90

120

150 Urban background Other sourcesBiomass

Biomass contribution to PM10




R² = 0.7559R² = 0.7776

0

30

60

90

120

0 30 60 90 120

Pred

icted

(μg/

m3 )

Observed (μg/m3)

Urban backgroundPM10 Urban BackgroundPM2.5 Urban Background

ANN model performance

R² = 0.7559

R² = 0.702

0

30

60

90

120

0 30 60 90 120

Pred

icted

(μg/

m3 )

Observed (μg/m3)

Traffic PM10 Traffic

PM2.5 Traffic




0

50

100

150

200

250

PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5

Measured Modeled 2012 Modeled 2011 Measured Modeled 2012 Modeled 2011 Measured Modeled 2012 Modeled 2011

Without fireplace With fireplace

Warm period Cold period

Indo

or P

M (μ

g/m

3 )

5%-95%

Median

Mean

Indoor PM concentrations



Department of Chemical EngineeringSchool of EngineeringAristotle University of ThessalonikiPM exposure

0

50

100

150

200

250

PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5

2012 2011 2012 2011 2012 2011

Fireplace

Warm period Cold period

Expo

sure

to P

M (μ

g/m

3 )

5%-95%

Median

Mean




0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1 hour 2 hour 3 hour

PM co

ncen

tratio

n (μ

g/m

3 )

PM 0.5-0.7μm no fireplace

with fireplace

0.00.10.20.30.40.50.60.70.8


PM co

ncen

tratio

n (μ

g/m

3 )

PM 0.7-1μm no fireplace

with fireplace

0

2

4

6

8

10

12

14


PM co

ncen

tratio

n (μ

g/m

3 )

PM 1-5μm no fireplace

with fireplace

0123456789


PM co

ncen

tratio

n (μ

g/m

3 )

PM 5-10μm no fireplace

with fireplace

Indoor PM concentrations during open fireplaces operation




0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM co

ncen

tratio

n(μ

g/m

3 )

Time of the day

OutdoorIndoor (home)ExposureExposure (Inh. Coef)

Intra-day PM exposure variability




ΤΕQ/[PM] (10-2 ng/μg)

Urban Traffic

PM1.0 PM2.5 PM10 PM1.0 PM2.5 PM10

11.3 9.49 7.53 13.5 10.4 7.51

PAHs and cancer risk




0

5

10

15

20

25

Nasopharyngeal Tracheobronchial Pulmonarybronchioles


Traffic Urban background

Inde

x

Region specific oxidative potential index

25%-75%

Median

Mean

0.00

0.07

0.14

0.21

0.28

0.35

UFPs PM1 PM2.5 PM10 UFPs PM1 PM2.5 PM10


ROS

(pm

ol/m

in/μ

g)

ROS (DDT activity)

25%-75%

Median

Mean

0

50

100

150

200

250

300

350




PM m

ass (μg

)

HRT deposition

25%-75%

Median

Mean

0

30

60

90

120

UFPs PM1 PM2.5 PM10 UFPs PM1 PM2.5 PM10


PM co

ncen

tratio

n (μ

g/m

3 )

PM concentrations 25%-75%MedianMean

Region specific oxidativepotential index




Health impact assessment Mortality and morbidity

0

500

1000

1500

2000

2500

3000

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4

Warm period (8months)

Cold period (4 months)

Case

s (#)

Mortality (annual)

0200400600800

10001200140016001800

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Case

s(#)

Chronic bronchitis new cases

0

100

200

300

400

500

600

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Case

s(#)

Cardiovascular hospital admissions

0100200300400500600700800

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Case

s(#)

Respiratory hospital admissions (annual)




020406080

100120140160180

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Mone

tary

cost

(106 €

)

Chronic bronchitis new cases cost

0

1

2

3

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Mone

tary

cost

(106 €

)

Cardiovascular hospital admissions cost

0

100

200

300

400

500

600

2011 2012 2011 2012 Scenario1

Scenario2

Scenario3

Scenario4



Case

s(#)

Cardiovascular hospital admissions

0

500

1000

1500

2000

2500

3000

3500

2011 2012 2011 2012 Scenario 1 Scenario 2 Scenario 3 Scenario 4



Mone

tary

cost

(106

€)

Mortality cost

Monetary impact assessment Mortality and morbidity

Μηχανική ποιότητας αέρα · the problem of air pollution. ... early 20...

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