tecnaire-cm - experimental campaign in a heavily trafficked...

2nd Healthy Polis Workshop (during Kunshan Forum)

30 May 2015; Kunshan (China)

Experimental Campaign in a Heavily Trafficked

Roundabout in Madrid for the Assessment of Air Quality

Monitoring Station Representativeness in Terms of

Population Exposure to NO2

R. Borge, C. Quaassdorff, D. de la Paz, A. Narros, J. Pérez, J.M. de

Andrés, R. Viteri, M. Paredes

Laboratory of Environmental Modelling

Technical University of Madrid (UPM)

[email protected]

Session A4: Healthy-Polis Workshop on Climate Change and Urban Health – II



1. Introduction

2. Methodology

2.1. NO2 concentration

2.2. Pedestrian simulation

2.3. Exposure assessment

3. Results and discussion

4. Conclusions

OUTLINE



1. INTRODUCTION

Summary of the AQ Directive’s limit values, target values, long-term objectives, information

and alert threshold for the protection of human health. Source: EEA (2014)

•Poor urban air quality is one of the main environmental concerns worldwide

• According to WHO (WHO, 2014) outdoor air pollution caused 3.7 million premature

deaths in 2012, most of them in urban areas where both, emission sources and

population concentrate

•As a consequence,

increasingly stringent air

quality standards for the

protection of human health

are being put into force: e.g.

Directive 2008/50/EC on

ambient air quality and

cleaner air for Europe



Annual mean NO2 (2012). Red and dark dots correspond to

exceedances of the annual limit value (40 µg/m3). Source: EEA (2014)

•Many urban areas in Europe are struggling to meet these air quality standards, particularly

for NO2

Percentage of the urban population in the EU-28 exposed to air

pollutant concentrations above EU and WHO reference levels (2010-

2012). Source: EEA (2014)

•Exceedances imply excessive

population exposure



•However, exceedances are determined from routinely measurements of urban air quality

monitoring stations

•It is unclear what is the

temporal and spatial

representativeness of such

monitoring stations

•It is difficult to assess the actual

population exposure

To what extent information from air quality

monitoring stations is representative for

population exposure and therefore the

assessment of AQ limit values compliance?

Urban

backgroundTraffic

Industrial



•This study presents a methodology to assess the representativeness of air quality

monitoring stations in terms of population exposure to ambient air pollution

•It uses a particular location in Madrid (Spain) as a case study:

• Madrid city (Spain): 3.2 million

inhabitants in the city, more

than 5 million people in the

metropolitan area



Observed NO2 values (corresponding to the annual

and hourly NO2 limit values defined in the European

AQ Directive) in the Madrid air quality monitoring

network for the years 2010-2013

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80

Annual mean NO2 concentration (µg/m 3)

19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

Traffic stations

Urban background stations

Suburban background stations0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

Traffic stations


Suburban background stations0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80


19th

hig

hes

t h

ou

rly

NO

2 c

on

cen

trat

ion

(µg

/m3)

Traffic stations


Suburban background stations

2010

2012

2011

2013

•Measures and policies are being implemented in the city and a positive trend of AQ levels

is observed

NO2 annual mean (Annual LV)

NO2 annual 1h 99.8th percentile (1-h LV)

Expected (modelled) NO2 ambient

concentration in the Madrid metropolitan area

as a result of Madrid’s Air Quality Plan



•However issues in specific traffic-related hot-spots such as Fernandez Ladreda (FL)

square, remain

FL air quality

monitoring station

NO

2A

nnualm

ean (

2014)

Traffic stations in the Madrid City Council Air Quality Network



2. METHODOLOGY

•206 passive samplers (diffusion

tubes) were deployed in the area

•21 days (9 Feb. – 2 Mar. 2015)

•Unexpensive method useful to

understand spatial (not temporal)

gradients in urban areas with high

resolution

•Airbore pollutants (e.g. NO2) are

diffused into the tube and captured

by an absorbent (e.g.

triethanolamine –TEA-)

NO2 concentration



10

•Three tubes in the FL air quality

monitoring station to compute

relative error (10.1%) and correction

factor (0.91)

•The resultant specie is assess in a

spectrophotometer and related to know-

concentration patterns to derive concentration

results



•A concentration map

was created by

interpolating individual

tube results through a

spline with barriers

algorithm (minimum

curvature method)

NO2

(µg/m3)



•Hourly NO2 concentration values

were taken from the FL air quality

monitoring station

•An average daily variation pattern

was computed

0

20

40

60

80

100

120

140

160

180

1

14

27

40

53

66

79

92

10

5

11

8

13

1

14

4

15

7

17

0

18

3

19

6

20

9

22

2

23

5

24

8

26

1

27

4

28

7

30

0

31

3

32

6

33

9

35

2

36

5

37

8

39

1

40

4

41

7

43

0

44

3

45

6

46

9

48

2

49

5

50

8

52

1

Ob

serv

ed N

O2

(µg

/m3)

Day

M T W T F S S M T W T F S S M T W T F S S

0

10

20

30

40

50

60

70

80

90

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ob

serv

ed N

O2

(µg

/m3) –a

ve

rag

e-

Hour



•Spatial information from the passive tubes was combined with the temporal information

from the air quality monitoring station to produce 24 hourly concentration maps

•Results were interpolated to a 5 meter resolution grid to cross the results with those from

the pedestrian simulation

This assumes that temporal concentration variations recorded by the air quality monitoring station are representative all over the

domain (this hypothesis is deemed reasonable since concentration values in the area are directly related to road traffic that

follows a similar temporal pattern everywhere in the domain, but needs to be tested)

NO2

(µg/m3)

NO2

(µg/m3)



•Both traffic and pedestrian fluxes were simulated with a micro-scale modelling system

• The PTV VISSIM 6.00-19 microscale traffic flow model was selected to generate realistic

traffic data while the VISWALK module was used for pedestrian simulation

•15 representative scenarios (1 hour length) were simulated

Pedestrian simulation

Avera

ge tra

ffic

in

tensity

Week days Weekends

Avera

ge tra

ffic

in

tensity



•An intensive field campaign was made to compile all the information needed to feed the

traffic model:

Detailed network (lanes):

19 links, 22 connectors Movements

EMT buses Traffic lights

location and

phases



•The additional information provided for the pedestrian simulation includes:

- Definition pedestrian areas (sidewalks), conflict areas (crosswalks) and obstacles

- Routes and number of pedestrians in each of them

- Bus lines, stops and frequency. Pedestrians boarding and exiting the bus.

Sub-domain definition 300x300m Pedestrian areas Roads and crosswalks



•Every possible route along the square was defined as a Pedestrian Static Route

Decission using the collected data as input or output of the areas from the simulation.

•The social force approach generates the shortest path for the pedestrians to connect the

defined beginning and end of each route trough the pedestrian areas.

•Pedestrian speed distribution

in the range [2.6 – 5.8] km/h



•Pedestrian counts were made in all the 8 sampling areas defined to provide

representative inputs for all the 15 scenarios defined

•Data (counts) from each scenario were post

processed to assure consistency and flux

continuity



•Pedestrian counts included quantification of bus passengers getting on and off the

buses

•This observations were combined with the information regarding bus routes, stops and

frequencies provided by the Municipal Transport Company (EMT)

•Also metro stations were included in the simulation as input/output areas

PT Stops

PT Route

PT Frequency



•The location of each individual

pedestrian was computed with 2

second resolution

•Pedestrian locations were

interpolated to a 5 meter resolution

grid and integrated throughout 1

hour time for each scenario

•From the result of the 15 representative scenarios an average day of pedestrian fluxes

was derived

0

100000

200000

300000

400000

500000

600000

700000

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Pe

de

str

ians (

pers

on·s

)

Hour



•NO2 concentration values and pedestrians figures were multiplied for every grid cell (5x5

meters) within the pedestrian simulation sub-domain (300 x 300 m) to compute total

exposure every hour of the day

Exposure assessment

•Total exposure in the area can be computed by

aggregating individual grid cell results for every

hour or for the whole day (total exposure –TE–)

•In addition, total theoretical exposure –TTE –

was computed as the product of the total number

of pedestrians (persons·s) and the concentration

recorded in the air quality monitoring station

The TTE/TE ratio is propose as an index to assess the representativeness of the

air quality monitoring station to assess population exposure



3. RESULTS AND DISCUSSION

•Total exposure

Daily total

exposure

(person·s·µg/m3)



•Maximum exposure in the roundabout area, especially in the crosswalks of the main

streets

•High exposure figures also in bus stops and gathering areas

•Strong variation throughout the day: traffic emissions (and therefore concentration

values) and pedestrian temporal patterns rather similar maximum exposure during

the morning peak hour

0

10000000

20000000

30000000

40000000

50000000

60000000

H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24

To

tal e

xp

osu

re (

pers

on·s

·µg

/m3)

Hour



•Although general spatial patterns are similar throughout the day, changes on traffic

fluxes, pedestrian routes and public transportation schedules also have an effect on

exposure distribution

Total exposure

(person·s·µg/m3)

4-5 AM

Total exposure

(person·s·µg/m3)

8-9 AM



•This implies that it is difficult to provide exposure-representative information for a single

air quality monitoring station

•In this particular case, the TTE/TE ratio was below 1 (0.78 as an average), indicating that

recorded NO2 levels in FL monitoring station would underestimate population exposure

in that particular 300x300 m domain

0.70

0.80

0.90

1.00

1.10

1.20

1.30

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

TT

E/T

E in

de

x

Hour



•The TTE/TE ratio could be used to compute the concentration that the monitoring station

should measure for a value of 1

•If the spatial concentration pattern is known this allows the definition of a exposure-

representative location for the air quality monitoring station (area in blue in the examples)

4-5 AM 8-9 AM



4. CONCLUSIONS

•The methodology presented constitutes a preliminary case study for the assessment of

the representativeness of air quality monitoring stations from the population exposure

point of view

•The methods used to simulate pedestrians and to obtain concentration maps can only be

applied at microscale and may be used to support studies of monitoring station micro-

siting

•However, if pedestrians location and concentration values are provided by other means,

the methodology based on the TTE/TE index assessment may be applied to larger areas

and contribute to the definition monitoring points that may be representative of

population exposure and thus, assessment of the compliance of health-related ambient

air quality legal standards

•Meaningful criteria to define the extend of these areas, a critical factor for this kind of

analysis, should also be defined in the future



• The TECNAIRE-CM research project was funded by the Madrid Greater Region (S2013/MAE-2972)

• Acknowledgments:

• The Madrid City Council provided the traffic

cameras and partially funded this study

• The micro-scale traffic modelling was possible

thanks to the collaboration of the national traffic

authority (DGT), the Municipal Transport Company

(EMT, S.A.), Madrid Calle 30, S.A.

• VISSIM and VISWALK ware licensed by PTV Group

www.tecnaire-cm.org

http://www.ciemat.es/

http://www.ciemat.es/

http://www.emtmadrid.es/Home.aspx

http://www.emtmadrid.es/Home.aspx



Thank you for your [email protected]

Session A4: Healthy-Polis Workshop on Climate Change and Urban Health – II

tecnaire-cm - experimental campaign in a heavily trafficked...

Documents