Session A4: Healthy-Polis Workshop on Climate Change and Urban Health II Experimental Campaign in a Heavily Trafficked Roundabout in Madrid for the Assessment of Air Quality Monitoring Station Representativeness in Terms of Population Exposure to NO 2 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) rborge@etsii.upm.es
OUTLINE 1. Introduction 2. Methodology 2.1. NO 2 concentration 2.2. Pedestrian simulation 2.3. Exposure assessment 3. Results and discussion 4. Conclusions
1. INTRODUCTION 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 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)
Many urban areas in Europe are struggling to meet these air quality standards, particularly for NO 2 Exceedances imply excessive population exposure 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) Annual mean NO 2 (2012). Red and dark dots correspond to exceedances of the annual limit value (40 µg/m 3 ). Source: EEA (2014)
However, exceedances are determined from routinely measurements of urban air quality monitoring stations Industrial It is unclear what is the temporal and spatial representativeness of such monitoring stations It is difficult to assess the actual population exposure Urban background Traffic To what extent information from air quality monitoring stations is representative for population exposure and therefore the assessment of AQ limit values compliance?
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
19th highest hourly NO 2 concentration (µg/m 3 ) 19th highest hourly NO 2 concentration (µg/m 3 ) 19th highest hourly NO 2 concentration (µg/m 3 ) 2 nd Healthy Polis Workshop (during Kunshan Forum) Measures and policies are being implemented in the city and a positive trend of AQ levels is observed Observed NO 2 values (corresponding to the annual and hourly NO 2 limit values defined in the European AQ Directive) in the Madrid air quality monitoring network for the years 2010-2013 NO 2 annual mean (Annual LV) 400 350 300 250 200 400 350 300 250 200 150 19th highest hourly NO 2 concentration (µg/m 3 ) 19th highest hourly NO 2 concentration (µg/m 3 ) 400 350 300 250 200 150 100 50 0 400 0 10 20 30 40 50 60 70 80 350 300 250 200 150 100 50 2010 400 Annual mean NO 2 concentration (µg/m 3 ) 350 2012 300 250 200 150 0 0 10 20100 30 40 50 60 70 80 Annual mean NO 2 concentration (µg/m Traffic 3 ) stations 19th highest hourly NO 2 concentration (µg/m 3 ) 19th highest hourly NO 2 concentration (µg/m 3 ) 400 350 300 250 200 150 100 50 0 400 0 10 20 30 40 50 60 70 80 350 300 250 200 150 100 50 Annual mean NO 2 concentration (µg/m 3 ) 0 Traffic stations 0 10 20 30 40 50 60 70 80 Urban Annual background mean NO 2 concentration stations (µg/m 3 ) 150 100 50 Traffic stations Urban background stations Suburban background stations 100 50 0 Urban background stations Suburban background stations 0 10 20 30 40 50 60 70 80 50 0 0 10 20 30 Suburban 40 background Annual 50 stations mean NO 60 70 2 concentration (µg/m 3 ) 80 0 Annual mean NO 0 10 20 30 40 50 2 concentration (µg/m 3 ) 60 70 80 Annual mean NO 2 concentration (µg/m 3 ) 2011 2013 Expected (modelled) NO 2 ambient concentration in the Madrid metropolitan area as a result of Madrid s Air Quality Plan NO 2 annual 1h 99.8th percentile (1-h LV)
NO 2 Annual mean (2014) 2 nd Healthy Polis Workshop (during Kunshan Forum) However issues in specific traffic-related hot-spots such as Fernandez Ladreda (FL) square, remain FL air quality monitoring station Traffic stations in the Madrid City Council Air Quality Network
2. METHODOLOGY NO 2 concentration 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. NO 2 ) are diffused into the tube and captured by an absorbent (e.g. triethanolamine TEA-)
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 knowconcentration patterns to derive concentration results
A concentration map was created by interpolating individual tube results through a spline with barriers algorithm (minimum curvature method) NO 2 (µg/m 3 )
Observed NO 2 (µg/m 3 ) average- Observed NO 2 (µg/m 3 ) 2 nd Healthy Polis Workshop (during Kunshan Forum) 180 160 140 120 100 80 60 40 20 0 M T W T F S S M T W T F S S M T W T F S S 1 14 27 40 53 66 79 92 105 118 131 144 157 170 183 196 209 222 235 248 261 274 287 300 313 326 339 352 365 378 391 404 417 430 443 456 469 482 495 508 521 Day 90 Hourly NO 2 concentration values were taken from the FL air quality monitoring station An average daily variation pattern was computed 80 70 60 50 40 30 20 10 0 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 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 NO 2 (µg/m 3 ) NO 2 (µg/m 3 ) 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)
Average traffic intensity Average traffic intensity 2 nd Healthy Polis Workshop (during Kunshan Forum) Pedestrian simulation 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 Week days Weekends
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) PT Stops PT Route PT Frequency Also metro stations were included in the simulation as input/output areas
Pedestrians (person s) 2 nd Healthy Polis Workshop (during Kunshan Forum) 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 700000 600000 500000 400000 300000 200000 100000 0 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour
Exposure assessment NO 2 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 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/m 3 )
Total exposure (person s µg/m 3 ) 2 nd Healthy Polis Workshop (during Kunshan Forum) 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 60000000 50000000 40000000 30000000 20000000 10000000 0 H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 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/m 3 ) 4-5 AM Total exposure (person s µg/m 3 ) 8-9 AM
TTE/TE index 2 nd Healthy Polis Workshop (during Kunshan Forum) 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 NO 2 levels in FL monitoring station would underestimate population exposure in that particular 300x300 m domain 1.30 1.20 1.10 1.00 0.90 0.80 0.70 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 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 exposurerepresentative 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 micrositing 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
Acknowledgments: The TECNAIRE-CM research project was funded by the Madrid Greater Region (S2013/MAE-2972) www.tecnaire-cm.org 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
Session A4: Healthy-Polis Workshop on Climate Change and Urban Health II Thank you for your attention! rborge@etsii.upm.es