The modifying effect of the building envelope on population

exposure to PM2.5 from outdoor sources

Abstract A number of studies have estimated population exposure to PM2.5 byexamining modeled or measured outdoor PM2.5 levels. However, few havetaken into account the mediating effects of building characteristics on theingress of PM2.5 from outdoor sources and its impact on population exposure inthe indoor domestic environment. This study describes how building simulationcan be used to determine the indoor concentration of outdoor-sourced pollutionfor different housing typologies and how the results can be mapped usingbuilding stock models and Geographical Information Systems software todemonstrate the modifying effect of dwellings on occupant exposure to PM2.5

across London. Building archetypes broadly representative of those in theGreater London Authority were simulated for pollution infiltration usingEnergyPlus. In addition, the influence of occupant behavior on indoor levels ofPM2.5 from outdoor sources was examined using a temperature-dependentwindow-opening scenario. Results demonstrate a range of I/O ratios of PM2.5,with detached and semi-detached dwellings most vulnerable to high levels ofinfiltration. When the results are mapped, central London shows lower I/Oratios of PM2.5 compared with outer London, an apparent inversion ofexposure most likely caused by the prevalence of flats rather than detached orsemi-detached properties.

J. Taylor1, C. Shrubsole1,M. Davies1, P. Biddulph2, P. Das1,I. Hamilton2, S. Vardoulakis3,A. Mavrogianni1, B. Jones4,E. Oikonomou2

1Bartlett School of Graduate Studies, UCL, London, UK,2UCL Energy Institute, The Bartlett, UCL, London, UK,3Centre for Radiation, Chemical and EnvironmentalHazards, Public Health England, Oxfordshire, UK,4Department of Architecture and Built Environment,University of Nottingham, Nottingham, UK

Key words: PM2.5; Indoor air quality; Building stock model;EnergyPlus; Geographical information systems.

J. TaylorBartlett School of Graduate Studies, UCL Central House14 Upper Woburn Place, London WC1H 0NN, UKTel.: +44 (0)20 7679 2000Fax: +44 (0)20 7679 7453e-mail: j.g.taylor@ucl.ac.uk

Received for review 17 December 2013. Accepted forpublication 3 April 2014.

This is an open access article under the terms of theCreative Commons Attribution License, which permitsuse, distribution and reproduction in any medium, pro-vided the original work is properly cited.

Practical ImplicationsPopulation exposure to air pollution is typically evaluated using the outdoor concentration of pollutants and does notaccount for the fact that people in London spend over 80% of their time indoors. In this article, building simulation isused to model the infiltration of outdoor PM2.5 into the domestic indoor environment for dwellings in a Londonbuilding stock model, and the results mapped. The results show the variation in relative vulnerability of dwellings topollution infiltration, as well as an estimated absolute indoor concentration across the Greater London Authority(GLA) scaled by local outdoor levels. The practical application of this work is a better understanding of the modifyingeffect of the building geometry and envelope design on pollution exposure, and how the London building stock mayalter exposure. The results will be used to inform population exposure to PM2.5 in future environmental epidemiologi-cal studies.

Introduction

Due to high volumes of traffic, a dense road network,and proximity to major traffic hubs such as Heathrow,London experiences a high level of outdoor PM2.5 air

pollution relative to the rest of the UK. Populationexposure to PM2.5 has been associated with negativehealth effects. In England, the fraction of mortalityattributable to anthropogenic particulate air pollutionin 2011 is estimated to be 5.36%, while in Greater

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Indoor Air 2014 © 2014 The Authors Indoor Air Published by John Wiley & Sons Ltdwileyonlinelibrary.com/journal/inaPrinted in Singapore. All rights reserved INDOOR AIR

doi:10.1111/ina.12116

London it is 7.17% (PHE, 2013). Earlier studies haveestimated that in London in 2008, PM2.5 causedmortality equivalent to around 4000 deaths and that apermanent 1 lg/m3 reduction in PM2.5 would add400 000 years of life for the current population(Miller, 2010). Internationally, PM2.5 is estimated tocause about 3% of all mortality from cardiopulmo-nary disease, about 5% of mortality from cancer ofthe trachea, bronchus, and lung, and around 1% ofmortality from acute respiratory infections in childrenunder 5 years old (Cohen et al., 2005). While the totalPM2.5 emissions in the UK are predicted to decreaseby 25% by 2020 relative to 2009 levels, there is noknown ‘safe’ level of PM2.5 and there will continue tobe health risks associated with exposure (DEFRA,2013).

A number of studies have examined the epidemio-logical relationship between exposure to pollution andnegative health effects [for example, Atkinson et al.(2013) and Tonne and Wilkinson (2013)]. However,these studies focus on outdoor pollution concentra-tions and population health and do not account forpollution in the indoor environment. Individuals indeveloped countries spend the majority of their timeindoors; a study of pollution exposure in different mic-roenvironments in London found participants werespending 80% of their time indoors, with 48–53% oftheir time spent in their homes during summer andwinter, respectively (Kornartit et al., 2010). Therefore,the indoor pollution levels have a significant influenceon an individual’s exposure to pollution, and a build-ing’s airtightness and the manner in which it is oper-ated can have a major impact on pollution ingressfrom the outdoor environment. Epidemiological stud-ies typically use pollution measurements from urbanbackground monitoring stations or modeled outdoorpollutant concentrations to estimate exposure; how-ever, this may not offer a true representation of theexposure to a population spending time largelyindoors. Indeed, a study of population exposure toPM2.5 in different microenvironments found a goodcorrelation between residential indoor levels and per-sonal exposures (Lai et al., 2004).

PM2.5 infiltration into buildings from externalsources will depend on a number of factors, includingthe location, height, orientation, sheltering, and perme-ability of the building envelope, building geometry, theventilation systems of the building, weather and urbanmeteorology conditions such as urban street canyons,and building occupant practices such as window open-ing and heating. In addition to infiltration, concentra-tions of PM2.5 in dwellings will be affected byemissions from indoor sources such as cooking, smok-ing, as well as general domestic activities such as clean-ing, dusting, and showering (Shrubsole et al., 2012).Removal of PM2.5 from indoor and outdoor sourcesfrom the indoor air can occur through exfiltration,

deposition onto surfaces, and filtration using mechani-cal ventilation systems.

Examining the relationship between indoor and out-door pollution levels can be performed using field mea-surements or through modeling approaches. A numberof studies have monitored the indoor concentration ofPM2.5 in different countries [see Chen and Zhao (2011),for a comprehensive review]. In the UK, there havebeen field studies measuring indoor PM2.5 in dwellingswith roadside, urban, and rural measurements (Joneset al., 2000) and distance to major roads (Kinghamet al., 2000); studies comparing indoor UK levels toother European cities (Hoek et al., 2008; Lai et al.,2006); seasonal variations in indoor PM2.5 exposureconcentrations (Mohammadyan, 2005; Wheeler et al.,2013); short-term temporal variations associated withindoor activities (Gee et al., 2002; Wigzell et al., 2013);and in the homes of individuals with respiratoryillnesses (Osman et al., 2007). A comparison betweendifferent building types by Nasir and Colbeck (2013)monitored indoor PM2.5 levels in three different types ofdwelling and found differences between them; however,differing occupant practices make it difficult to isolatethe influence of the building on indoor PM2.5 levels.

Modeling methods have also been used to character-ize the indoor concentration of PM2.5. Multizone masstransport models can be used to calculate concentra-tion levels in buildings for exposure assessments (Mil-ner et al., 2011). Studies examining the influence ofventilation and filtration interventions (Emmerichet al., 2005) and energy efficiency interventions (Daset al., 2013) on indoor PM2.5 concentrations have beenperformed using the CONTAM modeling tool. Theindoor PM2.5 concentration across a building stock hasbeen modeled using CONTAM for dwellings based onBoston public housing developments (Fabian et al.,2012) and the impact of energy efficient refurbishmentsin London’s domestic stock (Shrubsole et al., 2012).

While a number of studies have examined the rela-tionship between indoor and outdoor air pollution lev-els in dwellings, there has been little research on howthe relative infiltration of a geographically distributedbuilding stock can modify pollution exposure across anurban area. Chen et al. (2012) used typical infiltrationrates of dwellings in US cities to estimate indoor expo-sure to particulate matter in a study examining short-term mortality rates; however, this study did not exam-ine more local variations in building types and pollu-tion levels. Furthermore, existing infiltration modelingapproaches have estimated ventilation according to aschedule of activities without coupling behavior toindoor conditions such as temperature. The researchpresented here examines how the characteristics andgeographical distribution of residential building typesin the London building stock may affect the exposurelevels of dwelling occupants to PM2.5 from exter-nal sources. The whole-building simulation tool

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Taylor et al.

EnergyPlus 8.0 (US-DOE, 2013) was used to model theinfiltration of PM2.5 into the indoor environment fordwellings broadly representative of the Greater Lon-don area (GLA) building stock. Two different scenar-ios were considered to demonstrate the influences ofthe building envelope and occupant practices: (i) pollu-tion infiltration through cracks in the building fabriconly and (ii) infiltration through cracks and tempera-ture-dependent window opening. In both cases, tricklevents were included where appropriate, while extractfans were excluded due to their intermittent use andtheir assumed small contribution to time-averagedindoor concentrations of outdoor PM2.5. Othermechanical ventilation systems, such as mechanicalventilation heat recovery (MVHR) or air conditioning(AC), were ignored due to their rarity in the UKdomestic stock. The simulation results were used todevelop indoor/outdoor (I/O) ratios describing therelationship between outdoor and indoor concentra-tion of PM2.5. These functions were then applied to cal-culate the indoor concentrations of outdoor-sourcedPM2.5 based on mapped external concentrations, tem-poral variations, and geographical location of dwellingtypes. The results were combined with existing PM2.5

pollution maps to understand how dwellings may affectpopulation exposure to particulate air pollution.

Method

The research area selected was the GLA, an areaencompassing the 32 boroughs of London (Figure 1).

The area has good mapped coverage of building dataand has been the focus of both measured and modeledstudies of PM2.5 in outdoor air, with data on observedor estimated outdoor PM2.5 levels available from arange of sources. The different inputs required for themodel and how they relate to the project workflow canbe seen in Figure 2. While the London populationspends a significant amount of their time inside officesor buildings that are not their homes, commercialbuildings can have significantly different indoor pollu-tion levels due to HVAC system operation, filters, andcomplex building geometries. Spatial and archetypeinformation on the commercial building stock is notwidely available, and thus, this study focuses only ondwellings.

Building archetypes

A total of 15 dwelling archetypes developed for studiesinto overheating risk in London were used as a basisfor the EnergyPlus modeling of PM2.5 penetrationthrough the building envelope (Oikonomou et al.,2012). This English Housing Survey (EHS) (DCLG,2008), derived archetypes, with unique built form/ageclassifications, represents 76% of the known dwellingstock in the GLA according to the GeoinformationGroup (GG) Building Class Geodatabase (GG, 2013).

Building fabrics were modeled with U-values derivedfor the building archetypes using the Standard Assess-ment Procedure for Energy Rating of Dwellings (SAP)(BRE, 2009), with the assumption that buildings have

Fig. 1 Research area: Greater London. Areas without dwelling information are shown in gray

3

The modifying effect of the building envelope

the most frequently occurring building fabric typesaccording to the EHS. The building fabric can influ-ence the indoor temperature in dwellings (Mavrogianniet al., 2012), which may lead to changes in the windowopening behavior of the building occupants. Internaltemperatures can also have an influence on airflowdynamics at low wind speeds due to stack effects. How-ever, the overall effect of variations in insulation levelson yearly average indoor PM2.5 levels is expected to besmall, and so potential retrofits were ignored. FabricU-values for the building archetypes can be seen inTable S1.

The permeability of the building archetypes wasdetermined using the methodology in the SAP docu-mentation (BRE, 2009), which accounts for infiltrationthrough chimneys and vents, walls, floors, and win-dows and increased infiltration in multistorey build-ings. Infiltration rates were calculated for each buildingin the EHS database (with and without any reductionto the rate caused by party walls), and the mean foreach built form/age classification of the archetypes inthe study determined. The infiltration rates were then

converted to a permeability using the ‘rule of 20’ speci-fied in the SAP methodology. The distribution of theestimated permeabilities in the EHS was comparedwith that of a field measurement study (Stephen, 2000),with good results.

Simulations were run with and without the presenceof trickle vents, and the results weighted according tothe estimated prevalence of the vents across the UKbuilding stock [all dwellings post-1990, and 5% of pre-1990 dwellings (DECC, 2011)], assumed to be the sameas in London. A description of the building archetypesand estimated permeability can be seen in Table 1.

Building simulation

Models of the building archetypes were developed inEnergyPlus, a dynamic thermal simulation tool. Ener-gyPlus version 8 can model airflow through buildingsusing the validated AirflowNetwork tool and air pollu-tion transport using the Generic Contaminant trans-port algorithm. The advantage of using a coupleddynamic thermal and contaminant model is that theeffect of occupant window-opening behavior inresponse to internal temperatures can be addressedrather than using fixed schedules. The EnergyPlus

Scenario 1

No Occupation

Scenario 2

Window

LONDON BUILDING ARCHETYPES

EnergyPlus v8.0

Building Geometries (Oikonomou et al, 2012)

Building Fabrics (Mavrogianni et al, 2012)

Permeability (SAP, 2009)

U-Values (SAP, 2009)

OS Address Point

Postcode Boundaries

DEFRA Background PM

2.5 Levels

Model Inputs

MAPPED PM2.5

IO RATIOS FOR LONDONDWELLINGS

MAPPED ABSOLUTE INDOOR PM2.5

FROM OUTDOOR SOURCES IN LONDON DWELLINGS

Engl

ish H

ousin

g Su

rvey

GG Build Class Dataset

London Air Hour-Month PM

2.5 Levels

Fig. 2 Research workflow and data inputs

Table 1 Dwelling archetype descriptions and permeability estimated from the EHS andSAP

Archetypecode Dwelling archetype

Agebracket

Frequency instock, %

Estimatedpermeability(m3/h/m2 at 50 Pa)

H01 Late Victorian/EdwardianTerrace (Large T)

1902–1913 15.4 17.2

H02 WW1 & WW2 SimpleTerrace

1914–1945 14.5 14.9

H03 WW1 & WW2 LargeSemidetached

1914–1945 8.8 16.1

H04 ‘60s & ‘70s Tall Purpose-built Flats

1960–1979 5.7 16.2

H05 Late Victorian/EdwardianSimple Terrace

1902–1913 5.5 17.2

H06 Post-War Tall Purpose-built Flats

1946–1959 4.7 13.2

H07 Recent Tall Purpose-built Flats

1980–2008 3.6 9.2

H08 Late Victorian/EdwardianSimple Terrace (attic)

1902–1913 2.9 17.2

H09 WW1 & WW2 Bungalow 1914–1945 2.4 17.9H10 ‘60s & ‘70s Simple

Terrace1960–1979 2.4 12.3

H11 ‘60s & ‘70s Line-builtWalk-up Flats

1960–1979 2.3 9.8

H12 WW1 & WW2 Line-builtWalk-up Flats

1914–1945 2.1 10.1

H13 Recent Terrace withShop Below

1980–2008 2.1 12.2

H14 Post-War Step-LinkedTerrace

1946–1959 1.9 13.4

H15 Post-War Line-builtWalk-up Flats

1946–1959 1.8 11.6

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Taylor et al.

Generic Contaminant model has undergone intermodelcomparison against the CONTAM model, with goodresults (Taylor et al., 2013).

Indoor air simulations were run for the wholeyear using a Prometheus Test Reference Year (TRY)hourly weather file for Islington, Central London(Eames et al., 2011), with an outdoor PM2.5 concen-tration of 14.7 lg/m3 based on the 2010 averagebackground concentration for the GLA (LondonAir, 2014). Simulations were run with four differentorientations of the building (North, West, South,and East). Flats were modeled as being on a middlefloor, with adjoining flats to the sides, above, andbelow. Dwellings with adjoining dwellings to thesides (terraced dwellings, semi-detached, and flats)were assumed to have a net air and contaminantflow of zero between the dwellings, and party wallswere not modeled exposed to wind, sun, or pollutedexternal air. Dwellings with adjoining dwellings tothe top and bottom (flats) were modeled with identi-cal dwellings above and below and shafts betweenlevels to account for stack effects. Terraced houseswere modeled as being mid-terrace with end terracesconsidered to be semi-detached. Indoor PM2.5 levelswere output only for mid-floor flats as these repre-sent the majority of dwellings in purpose-built build-ings. Local wind speeds were modeled according toan urban terrain, while the solar and wind exposureeffects of neighboring but unattached properties werealso taken into account.

The infiltration of air was modeled through cracksin the externally exposed facades (walls, roofs, andground floors of the buildings) and, when open, win-dows. An even distribution of permeability wasassumed across all surfaces, although the net airflowacross party walls was assumed to be insignificant atnormal operating pressures. Cracks were modeled atthe top and bottom of external walls to account fordifferences in wind pressure according to the heightof the building. Vented cellars and lofts were placedabove and below the buildings, allowing free move-ment of outdoor air into these spaces. Cracks in thecellar ceilings and loft floors allowed air from thecellar and loft spaces, respectively, to enter thebuilding based on the defined permeability of theenvelope. In the case of flats, air from the cellar andloft entered the ground floor and top floor flats,respectively, and did not directly enter the studiedmid-floor flat through these pathways. Internal walls,floors, and ceilings were also given cracks, allowingfor the completion of the airflow network and themodeling of stack effects. Cracks were assigned ref-erence air mass flow coefficients based on the build-ing permeability and the surface area, and air massflow exponents were set to 0.66, as per Jones et al.(2013). Windows and doors were modeled assumingtwo-way flow.

There are a number of studies that estimate indoorPM2.5 deposition and penetration into the buildingenvelope. PM2.5 deposition was modeled using a depo-sition rate of 0.19/h (Long et al., 2001), with a penetra-tion factor of 0.8 when windows were closed and 1.0when windows were open. These values were used toperform an initial estimation of I/O ratios using a sin-gle-compartment box model (Long et al., 2001), typi-cal air change rates of UK dwellings (BRE, 2009), andexisting empirical studies of infiltration rates in the UK(Hoek et al., 2008), giving confidence that the valueswere suitable for modeling UK dwellings. Penetrationfactor and deposition rate of PM2.5 are also highlydependent on particle size (Long et al., 2001); howeverto simplify analysis, it was modeled as a single contam-inant. Indoor pollutant levels and infiltration airchange rates (ACH) were calculated every minute andoutput hourly.

Simulation of typical London dwellings. Two differentscenarios were simulated to examine building perfor-mance and the influence of occupant behavior:Scenario 1: No occupant interaction with ventilation

components was modeled, and infiltrationwas only due to the permeability of theexternally exposed fac�ades of the dwell-ings. The dwellings were heated to a set-point of 20°C, and internal gains due tooccupant metabolism, hot water, and elec-trical equipment modeled as per Mavrogi-anni et al. (2012). Internal doors wereassumed to be open at all times, with theexception of bedroom doors, which wereclosed at night. This represents the base-case performance of the building in termsof pollutant ingress.

Scenario 2: Temperature-driven window opening bybuilding occupants. There are a numberof both static and adaptive standards thatcan be used to estimate the temperature-related comfort of building occupants (CI-BSE, 2013). The CIBSE Guide A summer-time thermal comfort standards define anupper temperature threshold for comfortof 25°C for living rooms and 23°C forbedrooms (CIBSE, 2006). Internal tem-peratures were calculated inside the dwell-ing throughout the year. When internaloperative temperatures exceeded the CI-BSE summertime thermal comfort stan-dards for living rooms during the day(07:00–22:00) or bedrooms during thenight (22:00–07:00), windows were openedin the room. When internal temperaturesdropped below the thresholds, they wereclosed. In both cases, the windowsremained closed if the external temperature

5

The modifying effect of the building envelope

was greater than the internal temperature.Indoor heating and door-opening behav-ior was modeled as per Scenario 1. Whilethere is a great deal of uncertainty whenmodeling building occupant window-opening behavior, the window openingassumptions used are broadly in line withexisting field studies of occupant behavior(Dubrul, 1988; Fabi et al., 2012).

Data collation and analysis. Analysis of the hourlyindoor PM2.5 pollutant predictions of the EnergyPlusmodels was carried out in SAS 9.3 (SAS Institute,2013). Occupant exposure to indoor PM2.5 was con-sidered to be dependent on the hourly room occupa-tion schedule described in Shrubsole et al. (2012). Ascript was written to import the EnergyPlus outputfiles and retrieve hourly PM2.5 levels from the roomoccupied at that point in time. The script then calcu-lated the hourly I/O ratio, and then the monthly,hourly–monthly (the ratio for each time of the day,averaged across the month), seasonal, and yearlymean I/O ratio for the simulation period. The resultsfor the typical dwellings were summarized accordingto the archetype and the occupation scenario. Inaddition, yearly average ACH values were calculatedfor the occupied rooms.

London experiences a significant diurnal and sea-sonal variation in outdoor PM2.5 levels. To account forthis, the mean hourly–monthly outdoor PM2.5 levelwas obtained (London Air, 2014), and the percentdeviation of the temporal values from the backgroundmean calculated. These values were matched againstthe calculated mean hourly–monthly I/O ratios andused to calculate a temporally scaled I/O ratio for eachmonth and season of the simulation period.

Sensitivity analysis. To explore the sensitivity of themodel to variations in input parameters, a differentialsensitivity analysis (DSA) was performed for penetra-tion factor, deposition rate, building permeability, ret-rofit level, wind exposure, London climate, andoccupant window and door-opening behavior. Themethodology and results of the DSA are discussed fur-ther in the Appendix S1.

GIS analysis

Geographical information systems (GIS) data wereused to map the spatial variation in the I/O ratio ofPM2.5 pollution based on the EnergyPlus results and tocalculate the absolute indoor concentrations due tooutdoor sources only based on predicted outdoor pol-lution levels. GIS analysis was performed in ArcGIS10.1 (ESRI, 2013). Data obtained for the research areaincluded the following:

• The GG Building Class topographic map, showingbuilding footprints and building data, such as ageand structure type (GG, 2013).

• Ordnance Survey (OS) Address Point data (OS,2013), showing the number of domestic addresseswithin each building footprint.

• Department for Environment Food and RuralAffairs (DEFRA) map of estimated outdoor annualmean PM2.5 levels across London for 2010 (DE-FRA, 2011) (Figure 3).

• Postcode and borough boundary information fromthe UK Census (UK Data Service, 2013).

The GG Building Class database contains buildingfootprint, built form, and age data for the GreaterLondon Authority. Dwellings were filtered from theBuilding Class data to remove all non-domestic prop-erties from the analysis. The Building Class database

Fig. 3 Estimated outdoor PM2.5 concentrations in Greater London for 2010 (DEFRA, 2011)

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Taylor et al.

was filtered further to remove all dwellings that did nothave built form or age information, or that did notmatch the archetypes used in this study. The remainingdwellings accounted for 76% of the known Londondomestic building stock (and 46% of the total domesticstock), or around 1.5 million dwellings.

The OS Address Point layer was used to determinethe number of dwellings within a GG Building Classbuilding footprint, identifying buildings with multipleoccupancy. The Address Point layer was filtered toshow only domestic addresses within the filtered Build-ing Class footprints. The building classification datafrom the Building Class database were joined to thedomestic address points through a spatial join.

The modeled I/O ratios for the different buildingarchetypes for each scenario were joined to the AddressPoint database based on the building archetype classifi-cations. The mean I/O ratios of the address points werecalculated for each postcode area using the SpatialOverlay tool, and the results mapped to show differencesin I/O ratio for dwellings in postcodes across London.

The I/O ratios of dwellings were then used to scaleestimated outdoor concentrations of PM2.5 to predictabsolute indoor concentrations. The DEFRA map fortotal annual mean outdoor PM2.5 concentrations fromall sources in the GLA was joined spatially to theaddress point data. The local outdoor PM2.5 concen-trations were then used to estimate average monthlyindoor concentrations due to outdoor sources only forscenarios 1 and 2, based on the temporally scaled I/Oconcentration ratios for each month and season, withthe assumption that the monthly variation in back-ground PM2.5 levels was spatially consistent. The abso-lute indoor concentrations were summarized bycalculating the mean monthly, seasonal, and yearlyindoor concentration in each postcode.

Results

Building simulation

An example of the monthly average I/O ratio for abungalow with trickle vents can be seen in Figure 4.The simulation results showed a slight decline in thePM2.5 I/O ratio in Scenario 1 during the summer per-iod (May 1st–August 30th) relative to the winter per-iod (Dec 1st–March 30th), largely attributable to adrop in infiltration caused by a 18% decrease in aver-age wind speeds over this period. Compared with Sce-nario 1, Scenario 2 predicted an increase in averagemonthly I/O ratio during the summer period whenwindows were operable, exceeding winter levels. Sharpshort-term increases in the indoor pollutant concen-trations could be seen under Scenario 2, with windowopening allowing the indoor PM2.5 levels to approachthe simulated ambient outdoor levels when internaltemperatures exceeded the 25°C threshold. Trickle

vents were observed to increase the I/O ratios in allbuildings.

The EnergyPlus results show a range of annual aver-age I/O ratios of PM2.5 concentrations resulting fromexternal sources in dwellings across London(Figure 5). Detached and semi-detached propertieswith larger permeabilities showed higher amounts ofpollution infiltration into the indoor air, while flatsshowed a much lower I/O ratio of pollution. Openingwindows when temperatures exceed a comfort thresh-old led to an increase in the I/O ratio in all buildingarchetypes, particularly in archetypes prone to over-heating during the summer such as purpose-built flats(Mavrogianni et al., 2012). The yearly average ACHfor the archetypes can be seen in Table S2. The resultsof the sensitivity analysis (Appendix S1) indicate thatScenario 1 I/O ratios are highly sensitive to permeabil-ity, penetration factor, and deposition rate, and lesssensitive to weather file, retrofit level, and occupantwindow and door-opening behavior. Relative to Sce-nario 1, Scenario 2 results were more sensitive to retro-fit level and less sensitive to permeability andpenetration factor, reflecting the influence of tempera-ture-coupled window opening. The degree of parame-ter sensitivity also varied between archetypes accordingto the number of exposed external walls, the tendencyof buildings to overheat, and the cross-ventilationpotential of the dwellings.

GIS analysis

The results of the GIS analysis indicate that many ofthe dwellings with a higher I/O PM2.5 ratio exist outsideof Central London (Figure 6). This is likely due to flatsbeing the dominant dwelling type in the more denselypopulated center, while detached and semi-detached

0.0

5.0

10.0

15.0

20.0

25.0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PM2.

5 co

ncen

tratio

n (u

g/m

3 )

Aver

age

PMI 2.

5 I/O

Rat

io

MonthScenario 2 Scenario 1 London

Outdoor PM2.5Average

Fig. 4 Average monthly PM2.5 I/O ratios for the bungalow liv-ing room under scenarios 1 and 2 (modeled with trickle vents),and monthly variation in London background PM2.5 levels(London Air, 2014)

7

The modifying effect of the building envelope

properties are more commonly found in the outskirtsof the city. Interestingly, Figure 6 contrasts with manyoutdoor pollution maps (for example, Figure 3), whichshow elevated PM2.5 concentrations in Central Lon-don. There is insufficient building stock data to calcu-late average I/O ratios for 9.8% of postcodes in theresearch area. The majority of postcodes with insuffi-cient data are located in Central London, where thereare low numbers of residential properties. For Scenario2, window opening during summer reduced much ofthe spatial variation seen in other seasons.

The results of the estimated indoor PM2.5 concentra-tions scaled for the DEFRA estimated levels of out-door pollution can be seen in Figure 7 (Scenario 1) andFigure 8 (Scenario 2). Accounting for the modifyingeffect of buildings leads to an apparent inversion of therisk of PM2.5 exposure when compared to estimates ofexposure based on outdoor concentration estimates.Locations with detached and semi-detached dwellingsclose to pollution sources, such as motorways, majorroads, and mainline train tracks, become apparent ashaving high indoor PM2.5 levels from outdoor sources.Maps showing the seasonal variation estimated abso-lute levels can be seen in the Supplementary Materials(Figure S1). These results indicate that despite anincrease in the infiltration due to window opening inScenario 2 during the summer, the lower outdoorPM2.5 levels mean that the absolute indoor concentra-tions are still higher during the winter.

Discussion

This work has shown how building simulation can beused to determine the indoor PM2.5 concentration fromoutdoor sources in a set of building archetypes, andthe results mapped to estimate population exposure inindoor domestic environments. The differences in thePM2.5 I/O ratios predicted by EnergyPlus show, insome cases, a two-fold difference between dwelling

types, indicating the importance of considering thepotentially modifying effect of the building envelopewhen examining population exposure to air pollution.Occupant behavior can also have a major influence onexposure to outdoor pollution, with simulation resultsindicating that window opening during hot weathercan cause spikes in indoor levels due to outdoorsources of PM2.5. Higher infiltration during the sum-mer due to window opening is consistent with existingempirical studies (Hanninen et al., 2011). The resultsreflect the fact that dwellings with a higher exposedexternal surface area to internal volume ratio may bemore susceptible to higher indoor concentration levelsfrom outdoor sources.

The mapped results of the I/O ratios across Londonindicate that areas in outer London have higher num-bers of detached and semi-detached dwellings that aremore susceptible to outdoor pollution infiltration dueto their greater externally exposed surface-area-to-vol-ume ratio. This is in contrast to outdoor pollutiondata, which suggest that higher pollution levels can begenerally found in Central London and near majorroads and motorways. When PM2.5 I/O ratios arescaled against outdoor levels, there is an apparentinversion of exposure risk. The densely populated areasof Central London have the lowest estimated levels ofindoor PM2.5 from outdoor sources despite the highoutdoor concentrations due to attenuation by the pre-dominant built form (flats and terraced dwellings) andtheir lower fabric permeability. The worst-affectedareas were those around a busy circular road (NorthCircular) and along a major railway routes and high-ways heading East and West. This study has focusedon London; however, the results may provide insightinto other urban areas with dense modern flats in thecity center and older detached properties in the suburbsor besides major traffic routes.

While there is a lack of empirical data for PM2.5 I/Oratios or infiltration rate in UK dwellings, the results

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15

PM2.

5 In

door

/Out

door

Rat

io

Building Archetype

Winter Scenario1 Spring Scenario 1 Summer Scenario 1 Fall Scenario 1

Winter Scenario 2 Spring Scenario 2 Summer Scenario 2 Fall Scenario 2

Fig. 5 The seasonal average I/O ratio of PM2.5 pollution from outdoor sources, weighted according to room occupancy schedule andestimated frequency of trickle vents

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Taylor et al.

are consistent with previous research. The measure-ments of PM2.5 in dwellings in Birmingham estimatedan infiltration factor of 0.37 (Hoek et al., 2008), withinthe range of values obtained in the modeling workdetailed. Indoor PM2.5 measurements obtained by Na-sir and Colbeck (2013) are similar in magnitude, butare difficult to compare directly with modeled resultswithout a schedule of indoor activities, an understand-ing of the outdoor levels during the measuring period,and building geometry and construction information.

Other UK studies have found I/O ratios close to orgreater than one due to the presence of indoor sources(Jones et al., 2000; Lai et al., 2006). International stud-ies have found infiltration factors ranging from 0.30 to0.70 in European studies (Hanninen et al., 2011), and0.30 to 0.82 internationally (Chen and Zhao, 2011);these values are similar to the 0.33 to 0.60 (Scenario 1)and 0.45 to 0.62 (Scenario 2) ranges predicted by ourmodel. The ACH of the archetypes (Tables S2) are sim-ilar to those in empirical studies of UK dwellings

Fig. 6 Seasonal average I/O PM2.5 ratios for dwellings across London for Scenario 1 and Scenario 2

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(AIVC, 1994; BRE, 2009; Dimitroulopoulou et al.,2005; Warren and Webb, 1980), while the lower ACHcalculated for flats and attached dwellings relative todetached properties have been found in a number ofprevious studies (e.g. Persily et al., 2006).

There are a number of limitations that need to beconsidered in this work. While extensive, the coverageof the Building Class database lacked built form and/or age information for 32% of the dwellings in Lon-don, and not all of the known dwellings had arelevant archetype, which was modeled. Developingbuilding archetypes for each combination of builtform and age is unrealistic and would take a signifi-cant amount of time to simulate using currently avail-able building simulation tools. The archetypes areintended to represent average buildings in Londonrather than a specific property, and deviations of

individual buildings from the nominal archetypes areminimized when the results are considered over awider geographical scale – in this case, postcode. Mid-floor flats are assumed to represent the majoritydwelling type in multidwelling buildings and representan ‘average’ of potential stack effects. It was not pos-sible to model flats at different levels, as there is noinformation on the vertical distribution of addressesin the London building stock. The PM2.5 levels out-side dwellings toward the top of the building are likelyto be lower than levels at the bottom due to the gen-erally larger distance from outdoor sources and theinfluence of local meteorology effects, specificallyincreases in wind speeds (Vardoulakis et al., 2008);however, higher wind pressures may increase infiltra-tion rates. All simulations were run with wind speedsmodified to reflect an urban terrain; however, terrain

Fig. 7 Estimated absolute indoor PM2.5 concentrations from outdoor sources, based on I/O ratio (Scenario 1) and estimated temporaland spatial variations in outdoor concentrations. The inset shows outdoor concentrations from Figure 3

Fig. 8 Estimated absolute indoor PM2.5 concentrations from outdoor sources, based on I/O ratio (Scenario 2) and estimated temporaland spatial variations in outdoor concentrations. The inset shows outdoor concentrations from Figure 3

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Taylor et al.

type in London varies from densely built central cityareas to less dense outer suburbs. The increased expo-sure to wind forces in suburban areas is expected tolead to higher I/O ratios and potentially an increasein the apparent inversion of risk.

Internal sources are an important contributor toindoor PM2.5 levels, but have not been included in thisstudy. While some building types allow higher levels ofoutdoor PM2.5 infiltration due to a high ACH, suchbuildings may also have a greater ability to allowindoor-produced PM2.5 to exfiltrate. This may meanthat occupants of different building types may beexposed to different ratios of indoor-sourced tooutdoor-sourced PM2.5. The chemical and toxicologi-cal profile of indoor sources of PM2.5 may differ fromthat of outdoor sources, meaning that they may lead todifferent health effects (Wilson et al., 2000).

Assumptions were also required in modeling theoccupant behavior in Scenario 2. Window-openingbehavior is complex, and indoor temperature is not thesole driver. Furthermore, top-level flats are more sus-ceptible to overheating (Mavrogianni et al., 2012), afact which suggests that occupants may open windowsmore frequently to reduce the internal temperaturesand therefore temporarily drive up indoor levels of out-door pollutants.

Only domestic properties were modeled and mappedin this study. While research suggests that people in theLondon spend over 80% of their time indoors (Kor-nartit et al., 2010), this includes time spent at work in,for example, offices, or engaging in leisure activities inshopping malls and theaters. Nonetheless, epidemio-logical analyses typically use the home postcode as anindicator of exposure, and this research is able to offerinsight into how their dwellings may influence thisexposure. This study has examined the indoor pollu-tion levels throughout the day as an indicator of build-ing performance and as such does not consider the factthat certain socio-demographic groups may spend alonger time than others in their home.

The modeling methodology used also carries with ita number of uncertainties. The EnergyPlus airflow net-work model is based on a validated airflow model, andinitial comparisons between it and the indoor air qual-ity model CONTAM give confidence in the results forcontaminant transport (Taylor et al., 2013). Air leak-age paths were assumed to be distributed across allbounding surfaces in the dwellings including partywalls, which were assumed to be fully permeable. Inreality, party walls may contribute up to 30% of airleakage at 50 Pa pressure differential in UK dwellings(Stephen, 2000). The calculated distribution of perme-abilities for the EHS dwellings matched the measureddistribution from the research of Stephen (2000) whenthe sheltering factor was included in calculations andwas slightly higher when sheltering was excluded.Using the slightly higher values for buildings with

party surfaces (equivalent to 22.5% higher for flatswith three bounding surfaces) and applying them onlyto external walls, an attempt was made to compensatefor the differences in permeability between externaland party walls. However, further research is requiredto understand the permeability of different buildingsand surface types in the UK housing stock. ModelingPM2.5 as a single contaminant is an important simplifi-cation and must be acknowledged.

Retrofit and airtightness measures, such as draughtproofing, replacement windows, loft insulation, andthe sealing of suspended floors, can reduce the perme-ability of a dwelling (Hong et al., 2004). There hasbeen a significant focus on decarbonizing dwellings inthe UK by limiting the heat loss through the buildingenvelope. Building regulations specifying the air tight-ness requirements for new dwellings, as well as retrofitsto reduce the permeability of existing structures, areone of the means to achieve energy use reductions.These measures will have the additional benefit ofreducing pollutant infiltration into dwellings andreducing the I/O ratios of outdoor pollutants.

While a number of assumptions were necessary forthis research, the results provide an insight into thepotential modifying effects of the built form and build-ing envelope on pollution infiltration in the Londondwelling stock. Further field work is required to con-firm the influence of built form and building perme-ability on the infiltration of outdoor pollution indoors.This research has implications for assessing the popu-lation exposure to pollutants from outdoor sourcesand can be used to supplement existing research intoindoor air quality in London. Future research willincrease the number of building archetypes to be rep-resentative of the entire UK, while additional pollu-tants will also be modeled from both outdoor andindoor sources.

Conclusions

This analysis has mapped the potential indoor expo-sure of the London population to different PM2.5 levelsfrom outdoor sources based on domestic buildingstock characteristics. The relative vulnerability of dif-ferent dwellings to PM2.5 ingress has been demon-strated, and dwelling stock databases used to indicateareas where the stock is most vulnerable to high out-door pollutant levels. This research indicates that flatshave a reduced I/O ratio for PM2.5 from outdoorsources when compared to detached and semi-detacheddwellings. The higher concentration of flats in CentralLondon leads to an apparent inversion of exposure toindoor PM2.5 from outdoor sources when compared toestimates of exposure based on outdoor concentrationestimates. The results can provide insight into otherurban areas with spatial variations in building stockand outdoor pollution levels.

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The modifying effect of the building envelope

Acknowledgements

This research was carried out with funding from theNatural Environment Research Council as part of theAWESOME Project (Air pollution and WEather-related health impacts: methodological study based onspatio-temporally disaggregated multipollutant modelsfor present day and future) (NE/I007938/1).

Supporting Information

Additional Supporting Information may be found inthe online version of this article:

Appendix S1. Supporting information.Table S1. Thermal conductivity properties of the build-ing envelope.Table S2. Modeled annual average Air Change Rates(h-1) or (ACH) for the dwelling archetypes under thetwo scenarios.Table S3. Variations in parameter inputs for DSA.Table S4. Variations in yearly average I/O ratio forScenario 1 for different parameters.Table S5. Variations in summer average I/O ratio forScenario 2 for different parameters.Figure S1. Seasonal changes in estimates absoluteindoor and outdoor PM2.5 levels across London.

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