Assessing urban population vulnerability and environmental risks across an urban area during heatwaves – Implications for health protection

2018·2.5 MB·English

by  H. L. Macintyre

ScienceoftheTotalEnvironment610–611(2018)678–690
ContentslistsavailableatScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Assessing urban populationvulnerability andenvironmental risks across
an urban area during heatwaves – Implications for health protection
H.L.Macintyrea,⁎,C.Heavisidea,b,c,J.Taylord,R.Picettib,P.Symondsd,X.-M.Caic,S.Vardoulakisb,c,e
aChemicalandEnvironmentalEffectsDepartment,CentreforRadiationChemicalandEnvironmentalHazards,PublicHealthEngland,Chilton,OxonOX110RQ,UK
bDepartmentofSocialandEnvironmentalHealthResearch,LondonSchoolofHygieneandTropicalMedicine,KeppelStreet,LondonWC1E7HT,UK
cSchoolofGeography,EarthandEnvironmentalSciences,UniversityofBirmingham,Edgbaston,BirminghamB152TT,UK
dUniversityCollegeLondon,InstituteforEnvironmentalDesignandEngineering,CentralHouse,14UpperWoburnPlace,LondonWC1H0NN,UK
eInstituteofOccupationalMedicine,ResearchAvenueNorth,Riccarton,Edinburgh,MidlothianEH144AP,UK
H I G H L I G H T S G R A P H I C A L A B S T R A C T
• Multiple factors are associated with
healtheffectsofheatexposure.
• CitiesintheWestMidlandshaveapro-
nouncedUHI.
• Carehomesandhospitalsareexposed
tohigherambienttemperaturesthan
average.
• Housingtypesmorelikelytooverheat
arelocatedinthewarmestpartsofthe
city.
Spatialdistributionoffactorsthatmayrelatetorisksassociatedwithheat-healtheffectsacrosstheWestMidlands.
a r t i c l e i n f o a b s t r a c t
Articlehistory: Heatwavescanleadtoarangeofadverseimpactsincludingincreasedriskofillnessandmortality;theheatwave
Received23May2017 inAugust2003hasbeenassociatedwith~70,000deathsacrossEurope.Duetoclimatechange,heatwavesare
Receivedinrevisedform7August2017 likelytobecomemoreintense,morefrequentandlastlongerinthefuture.Anumberoffactorsmayinfluence
Accepted7August2017
risksassociatedwithheatexposure,suchaspopulationage,housingtype,andlocationwithintheUrbanHeatIs-
Availableonline17August2017
land,andsuchfactorsmaynotbeevenlydistributedspatiallyacrossaregion.Wesimulatedandanalysedtwo
majorheatwavesintheUK,inAugust2003andJuly2006,toassessspatialvulnerabilitytoheatexposureacross
Editor:D.Barcelo
theWestMidlands,anareacontaining~5millionpeople,andhowambienttemperaturevariesinrelationtofac-
Keywords: torsthatinfluenceheat-relatedhealtheffects,throughweightingofambienttemperaturesaccordingtodistribu-
UrbanHeatIsland tionsofthesefactorsacrossanurbanarea.Additionallywepresentquantificationofhowparticularcentressuch
Spatialvulnerability ashospitalsareexposedtotheUHI,bycomparingtemperaturesattheselocationswithaveragetemperatures
Heatwaves acrosstheregion,andpresentingtheseresultsforbothdayandnighttimes.WefindthatUHIintensitywassub-
Healtheffects stantialduringbothheatwaves,reachingamaximumof+9.6°CinBirminghaminJuly2006.Previousworkhas
shownsomehousingtypes,suchasflatsandterracedhouses,areassociatedwithincreasedriskofoverheating,
andourresultsshowthatthesehousingtypesaregenerallylocatedwithinthewarmestpartsofthecity.Older
agegroupsaremoresusceptibletotheeffectsofheat.Ouranalysisofdistributionofpopulationbasedonage
groupshowedthereisonlysmallspatialvariationinambienttemperaturethatdifferentagegroupsareexposed
⁎ Correspondingauthor.
E-mailaddress:[email protected](H.L.Macintyre).
http://dx.doi.org/10.1016/j.scitotenv.2017.08.062
0048-9697/©2017ElsevierB.V.Allrightsreserved.
H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690 679
to.Analysisofrelativedeprivationacrosstheregionindicatesmoredeprivedpopulationsarelocatedinthe
warmestpartsofthecity.
©2017ElsevierB.V.Allrightsreserved.
1.Introduction providemultiplesurfacestoreflectandabsorbsunlight,increasing
urbanheating,andimpedingaircirculation.Heatfromhumanactivities
1.1.Heatexposureandhealth (suchasairconditioning,vehicles,andindustrialprocesses)canalso
addtotheUHIeffect.TheUHIeffectisoftenmostextremeduringanti-
Heatwaves,orextendedperiodsofhotweather,areassociatedwith cyclonic summer weather conditions, which are associated with
variousriskstohealthincludingheatexhaustion,heatstroke,emergen- heatwaves. In England and Wales, 82% of the population reside in
cyhospitalisations,anddeath.AsevereheatwaveinAugust2003has urbanareas(ONS,2011),leavingthemvulnerabletotheimpactsof
been associated with up to 70,000 excess deaths across Europe heatexposureduetotheUHIeffect.TheWestMidlandsisahighly
(Robineetal.,2008),withover2000excessdeathsestimatedinEngland urbanisedareaoftheUK,whichincludesthecityofBirminghamwith
(Johnsonetal.,2005),andarecordbreakingmaximumtemperatureof apopulationof1.1million,andhasanotableUHI(Bassettetal.,2016;
38.5°CreachedinsoutheastEngland.Hightemperatureswerealsore- Heavisideetal.,2015;Tomlinsonetal.,2012).Ahealthimpactassess-
cordedthroughoutmuchoftheUKduringthesummerof2006.Inthe mentfortheheatwaveof2003basedonhighresolutionmeteorological
WestMidlands,thetwoheatwaveeventsof2003and2006werecom- modellingsuggestedthatintheWestMidlands,aroundhalfoftheheat-
parableintermsofestimatedexcessmortality,beingaround10%higher relatedmortalityduringtheheatwavecouldbeattributedtotheUHI
thanbaselineratesatthistimeofyearinbothcases(HealthStatistics (Heavisideetal.,2016).TheUHIintensityisoftendefinedasthediffer-
Quarterly,2006;Johnsonetal.,2005).Inthefuture,heatwavesare enceintemperaturebetweenurbanandruralareas,andcanbequanti-
projectedtobecomemorefrequent,moreintense,andlastlonger,due fiedbycomparingambientairtemperatureobservationsatalocationin
toclimatechange(Kirtmanetal.,2013),whichwilllikelyleadtoin- thecentreofanurbanarea,andatalocationinsurroundingruralareas
creasesinheat-relatedmortality(Hajatetal.,2014;Mitchelletal., (Bassettetal.,2016;Hatchettetal.,2016;KettererandMatzarakis,
2016;Vardoulakisetal.,2014).Someevidencesuggeststhereisan 2015;KimandBaik,2002;Oke,1973,1982),orfromsatellitemeasure-
upperlimittowhichhumanscanadapttotemperature(Arbuthnott ments(Azevedoetal.,2016;Benzetal.,2017;Duetal.,2016).However,
etal.,2016;SherwoodandHuber,2010). observationstationsarelimitedinnumber,areoftennotsitedwithin
IntheUK,theriskoftemperature-relatedmortalityisprojectedto urbancentres,andmayonlycovercertaintimeperiods,whilesatellite
increasesteeplyintheUKoverthe21stcenturyunderclimatechange measurementsrecordlandsurfacetemperature(ratherthanairtem-
anddemographicchangescenarios,reaching260%by2050,and540% perature), and are often temporally limited and may have missing
by2080(comparedwiththe2000sheat-relatedmortalitybaselineof dataifitiscloudy.Theuseofmeteorologicalcomputersimulations
around2000prematuredeaths),withtheelderlybeingmostatrisk makesitpossibletoinvestigatespatialvariationsintemperatureacross
(Hajatetal.,2014;HPA,2012). urbanareas,andtoquantifytheUHIintensity,bycomparingtempera-
Futureclimateprojectionsareoftenproducedatrelativelycoarse turessimulatedbothwithandwithouturbansurfacessuchasbuildings
spatialresolution,duetothecostofcomputingpowerrequired.Factors androads.
relevantforthestudyofheat-exposureandhumanhealth,including
populationage,socioeconomicfactors,andthebuiltenvironmentsuch
1.3.Mappingspatialvariationinheat-exposureandrisk
asdwellingtypeandtheUrbanHeatIsland,areatamuchfinerspatial
scale. Our study aims to simulate ambient temperature across an
Heatexposureforurbanpopulationswillvaryacrossurbanisedareas,
urbanareaduringaheatwaveperiod,andsubsequentlyquantifythe
duetospatialvariationsinphysicalinfrastructurethatinfluencestheUHI.
variationinambienttemperaturewithotherfactorsthatrelatetoand
Susceptibilitytohealthrisksassociatedwithheatexposureisalsoinflu-
influence heat-related health effects. This includes population-
encedbyotherfactors,includingpopulationage,housingtype,socio-
weightingofambienttemperatures,aswellascalculatingtheambient
economicfactors,pre-existinghealthconditions,andlocationwithin
temperatureweightedaccordingtodistributionsofdifferenthousing
theUHI(Tayloretal.,2015;WolfandMcGregor,2013).Itisthereforeim-
types,populationage,anddeprivationscore,allfactorsthatinfluence
portantintermsofhealthprotectiontounderstandthepatternofrisk
heat-healthrelationships.Usingenvironmentalmodellingtechniques
acrossaregion,whichmayhelptargetresourcestoreduceheatriskin
inthiswaytolookathumanhealthinrelationtoheatexposureduring
themostvulnerableareas,sinceitispossiblethatenvironmentalrisks
heatwaves(whichwillbecomeincreasinglyimportantinthefuture
andvulnerablepopulationgroupsareco-locatedwithinurbanareas.
withclimatechange)isanovelwayofanalysingspatialdistributionof
AnumberofstudieshaveinvestigatedheatriskacrosstheGreater
risksacrossalargeurbanisedregion.Whilewehaveusedadetailed
Londonarea.WolfandMcGregor(2013)developedaHeatVulnerability
casestudyhere,thetechniqueandmetricsareapplicabletoanyscenar-
Index(HVI)forLondonbasedonprincipalcomponentanalysisofsocio-
io,whichwefeelisusefulforawiderscientificcommunity.
demographicfactorsrelatingtoheatvulnerability,combinedwithland
surfacetemperatures(derivedfromasatellitemeasurement),finding
1.2.TheUrbanHeatIsland(UHI) clusteringofhighvulnerabilityintheeastandcentralareasofthecity.
HeatriskhasalsobeenmappedacrossLondonusingabuildingphysics
PopulationsmaybeparticularlyatriskfromheatduetotheUrban model and monitored weather data, together with information on
HeatIsland(UHI)effect(Heavisideetal.,2017),wherebyambienttem- modelledUHI,housingtypeandpopulationage,findingthatbuilding
peraturesareoftenobservedtobehigherthanthoseinsurrounding typeandUHIhaveasignificantinfluenceonthedistributionofriskacross
less-urbanisedareas,particularlyatnight.ThemaincauseoftheUHI thecityforsummer2006(Tayloretal.,2015).Buildingfabrictypesand
isthemodificationoflandsurfaces,forexample,replacingnaturalsur- characteristics,andthusthermalproperties,willdependontheageof
faces(e.g.vegetationwhichprovidenaturalshadingandcoolingvia thebuilding,andmaybeanimportantmodifierforthermalcomfort
evaporation)bypavingorconstructionofbuildings.Urbanconstruction andenergyefficiency,inadditiontobehaviouralaspectsofbuildingoccu-
materials(suchasconcrete,tarmacandasphalt)generallyabsorb,re- pancythatcanalsomodifyheatexposurerisk(Vardoulakisetal.,2015).
tain and re-radiate heat more than natural surfaces. Buildings also An existing spatial heat risk assessment of Birmingham combined
680 H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690
informationderivedfromcreditreferenceagenciesongroupssuch shownthatwarmerclassrooms(25°Ccomparedto20°C)aredetri-
astheelderly,thosewithillhealth,highpopulationdensity,and mentaltoeducationalattainment(WargockiandWyon,2007),and
high-riseliving,findingthatpopulationsub-groupswithillhealth, schoolchildrenfeellethargicwhentemperatureandrelativehumidity
and who residein flats were located in the overall warmest and arehigh(Salthammeretal.,2016).Urbanisationandclimatechangead-
thereforehighestheatriskareasofBirmingham(Tomlinsonetal., verselyaffectthermalcomfortandairqualityinschools,andtheimpact
2011).Thisstudyusedremotesatellitesensingofthelandsurface ofsuchexposuremaygobeyonddiscomfortorillhealth,leadingtoa
temperatureathighspatialresolution(1km),althoughonlyasingle lifelongburdenofdisease(Salthammeretal.,2016).
snapshotoveronenightduringaheatwaveisused,andassuch,does Relationshipsbetweenheat-relatedmortalityandsocio-economic
notfullyrepresenttherangeoftemperaturesthatpopulationisex- anddemographicfactorsvaryregionallyacrosstheglobe.Relationships
posedtoduringtheheatwaveperiod.Similarstudiesinlargeurban havebeenfoundinsomeregionsbetweenheat-relatedmortalityand
areasworldwidehavefoundspatialoverlapofsensitivepopulations factorssuchasage,gender,thoseclassedasurban-poor,andareas
andinequityinexposuretotheUHI,accordingtoage,occupation,in- with a lack of high-income earners (with income believed to be a
come,andothersocio-demographicindicators,andthatmapping goodproxyforaccesstoairconditioning),however,theseareoften
canhelptargeteffortstoreduceheatrisk(e.g.viaplanningtheloca- notconsistentbetweencountries,insomecasesshowingnoclearrela-
tionofparks,coolorgreenroofs,andsocialcareefforts)(Mitchell tionshipforothersocio-economicindicators(HondulaandBarnett,
andChakraborty,2015;Weberetal.,2015;Wongetal.,2016). 2014;JohnsonandWilson,2009;Yuetal.,2010).IntheUK,regional
Whilemappingofriskscanbeausefultoolforraisingpublicaware- longtime-seriesanalysisshowedthestrongestheat-mortalityeffects
nessofrisks,itisnotclearifproducingmapsinthiswayleadstopolicy wereinLondon,andthestrongestcoldeffectsintheEastofEngland.
ormitigationactions,possiblyduetouncertaintyinrelationtopolicy Astrongerheateffectwasfoundforthoselivinginurbanareas,with
anddecisionmakingthataredifficulttoaddressinsuchstudies(Wolf nodifferenceforcoldeffects.Indicesofdeprivationwerenotfoundto
etal.,2015).Thereisanincreasingamountoftemperaturedataavail- beamodifierforheateffects,andonlyslightlyforcoldeffectsinsome
ablefromsatellitemeasurements,butthisdatasetonlygivesthetem- ruralareas,thoughthisisinfluencedbyhowwellindividualcircum-
peratureofthesurfacethesatellitedetects(e.g.theground,treetops stancesarerepresentedbyarea-levelindicators(Hajatetal.,2007).
or building roofs and walls), rather than air temperature which is TheWorldHealthOrganization(WHO)identifiedcommonheatrisk
morerelevantforhealth,whiletherelationshipbetweengroundand factorsincludingbeingelderly,havingpre-existingcardiovascularorre-
air temperatureis difficulttoquantify.Weaddresstheseissuesby spiratorydisease,livingalone,workingoutdoorsorbeinginvolvedin
usingsimulatedtemperaturesandquantifyingheatexposuredueto heavylabourindoorsclosetoindustrialheatsources(WorldHealth
theUHIforpopulationswhichcouldbeseentobeatrisk. Organization,2015).Insomeplaces,gender,natureofdwelling(e.g.
hospitalorcarehome),beingurbanandpoor,andhavingcertainmed-
1.4.Centresforhealthandsocialcare ical conditions such as diabetes may also be linked with greater
temperature-healtheffects,thoughcausalityiscomplextodetermine
Carehomesandhospitalsareplaceswheresensitivepopulationsmay ascertaintypesoffactorsmayinteractwithotherdeterminantsof
reside.Duringthe2003heatwaveacrossEurope,oftherecordedthere- health, as well as access to health-care systems (World Health
cordedmortalityamongtheunder75sintheUK,moreoftheexcess Organization,2015).Someindicatorsofsocioeconomicstatusmaybe
deathsweredistributedinresidentialandnursinghomesthanwould associated with heat-related health effects, though inconsistencies
havebeenexpected,andamongtheover75s,moreoftheexcessdeaths existbetweenstudies,withdifferentassociationsreportedbetween
weredistributedinhospitalsandnursinghomes(Kovatsetal.,2006).In countries,regionsandcitieswithin.Thereisalsosomeevidencethat
thesesettingstheremaybeissuessurroundingdependencyoncare- temperaturemodifiesthehealtheffectsofsomeairpollutants,suchas
giversinnursingandresidentialhomes,andtheimpactthismayhave ozone(Jerrettetal.,2009;WorldHealthOrganization,2015).
onpatients'andresidents'behaviourinrespondingtoenvironmental
changes(e.g.disempowermentorlossofautonomy),aswellasavailabil-
ityofassistancefordailylivingtasksininstitutions(Belminetal.,2007; 1.6.Summaryandaims
BrownandWalker,2008).Reportssuggestcareschemeshavea‘culture
ofwarmth’,aperceptionthattheelderlyarevulnerableonlytocold Wehaveidentifiedawiderangeoffactorsthatarelikelytoinfluence
(notexcessiveheat),andthatdesignforoverheatingisrare,withlow theriskofheat-relatedhealtheffects,includinghousingtype,popula-
prioritisationforfutureclimatechangeandoverheating(Guptaetal., tionage,deprivationindices,andthoseresidingincareandhealthfacil-
2016). ities. People who experience multiple risk factors may be more
Thoseinprisonsorcustodialfacilitiesarenotabletodirectlyaccess vulnerableduringperiodsofhotweather.Ananalysisofthespatialdis-
healthcareservices,andeffectivewaystoreduceexposuretoheatmay tributionofmultipleriskfactorsacrossaregionmayrevealco-location
belimited.Whiletherearefewstudiesexaminingheatriskinprisons, ofsuchfactors,andidentifyplaceswhenstrategiestomitigateheatrisk
lawsuitsweresuccessfullybroughtagainstprisonsinTexasandLouisi- mightbemosteffective.
anaintheUS,whereinmateshaddiedorsufferedasaresultofheatex- Weaddresstheseissuesaspartofanurbancasestudyfortwore-
posure(HumanRightsClinic,2015;ThePromiseofJusticeInitiative, centsignificantheatwaveevents.Weinvestigatefirstlyvariationsin
2013).ThepopulationintheUKisageing,asistheprisonpopulation. environmentalriskfactorsacrossthecity(temperature,UHIintensi-
NotonlyhastheprisonpopulationintheUKincreasedfrom56,000to ty,andthermalcomfortindices),andsecondly,distributionofother
over85,000inthelasttenyears,thenumberofthoseaged60and factorswhichmayrelatetovulnerabilityfromenvironmentalrisks
over in custody are the fastest growing group, increasing by 120% (age,housingtype,anddeprivationindex).Aspatialanalysisallows
from2002to2013(HouseofCommons,2013;Offendermanagement ustodeterminewhetherthesesetsofrisksareco-locatedwithinour
statisticsquarterly,2016).Otherfactorssuchasdruguse,aswellas studycityofBirminghamandtheWestMidlandsregionoftheUK.
prevalenceofmentalhealthconditionsamongthoseincustodymay Thirdly,weinvestigatethepositionofspecificlocationswheresensi-
alsocontributetohealthriskfromexposuretoheat. tivepopulationsarelikelytobebased,suchashospitals,care-homes,
schools,andprisons,withrespecttotheintensityandpositionofthe
1.5.Socialanddemographicfactors UHI,withthehypothesisthatsincethesetypesofinstitutionsare
oftensituatedinurbancentres,populationsmaybeatriskfromex-
Aswellastheelderly,babiesandyoungchildrenarealsovulnerable posuretohightemperaturesasaconsequenceoftheeffectsofthe
toeffectsassociatedwithheatexposure(PHE,2015).Studieshave UHI.
H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690 681
Fig.1.(a)ThedomainscoveredbytheWRFmodelsimulation.Thecentraldomain(redbox)isexpandedinFig.1(b);(b)UrbanclassificationsusedintheurbancanopyschemeintheWRF
modelinthecentraldomain.Theareashowncoversapproximately80×80km.
2.Methodsanddata temperaturebecomesmoresensitivetosurfacemoistureproperties,
whichcanbedifficultforthemodeltocapture.Soilmoisturewasadjust-
Inthisstudywefirstmodelledtheambientairtemperatureata edinthemodeltobetteraccountfortheunusuallydryconditions,which
heightof2macrosstheWestMidlands,andquantifiedtheUHIintensi- ledtoimprovementsinthemodelperformance(Fig.2andTable2).
tyacrosstheregionfortheheatwaveperiods(August2003andJuly Weranthemodelfortwoheatwaveperiods(2nd–10thAugust2003,
2006)usingamesoscalemeteorologicalmodel.UsingGIStechniques and16th–27thJuly2006),andinordertoquantifytheUHIintensity,two
wethenextracted(fromthemodelleddata)andanalysedtheambient simulationswererunforeachheatwaveperiod.An‘urban’simulation
temperaturesthatcorrespondtodifferentriskfactorssuchaspopula- was run as described above, using the urban categories shown in
tionage,dwellingtype,anddeprivationindices. Fig.1b,andisintendedtorepresenttheurbanmorphologyofBirming-
hamandtheWestMidlands.Thesimulationisthenrunagain,butwith
2.1.Modellingtemperature urbanlandcoverreplacedbyruralcroplandandpastureintheinnermost
domain,asatheoretical‘rural’case.Bycomparingresultsbetweenthe
TheWeatherResearchandForecasting(WRF)modelisaregional ‘urban’and‘rural’simulations,theUHIintensitycanbequantified.
weathermodelthatcanberunathighresolution(1km)tosimulate Thetemperaturethatpeopleexperienceor‘feel’canbeinfluenced
themeteorologyacrossaregion(Chenetal.,2011).WeusedtheWRF byanumberoffactorssuchashumidity,windspeed,exposuretodirect
modelwithfournesteddomains(Fig.1a)eachwithgridresolutionsof solarradiation,andtheamountofclothingtheyarewearing(Höppe,
36km,12km,3km,and1kminthesmallest(central)domain,with 1999;Steadman,1984).Nationalweatherservicessometimesprovide
feedbackofvariables fromsmallergridstoparentgrids. Themodel a‘heatindex’or‘apparenttemperature’aspartofweatherforecasts
time-stepwas180,60,20and5sforthefourdomains,respectively, duringperiodsofwarmerweather,toaccountfortheeffectofhumidity,
withoutputeveryhour.Initialandlateralboundarymeteorologicalcon- withassumptionsabouthumanbodymass,height,clothing,amountof
ditionswereprovidedbytheEuropeanCentreforMedium-rangeWeath- physicalactivity,sunlightandultravioletradiationexposure,andthe
erForecasts(ECMWF)ERA-interimreanalysis(Deeetal.,2011)ata windspeed.Theheatindexiscalculatedfromtemperatureandrelative
spatialresolutionof0.5°every6h.Therewere39pressurelevelsabove humidityfollowingthemethodoftheNOAAWeatherPredictionCenter
thesurfaceto1hPa.Weusedanurbancanopyscheme,BuildingEnergy (www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml).Thiscalcu-
Parameterisation(BEP),whichmodelstheeffectofbuildingsonenergy lationisarefinementofaresultobtainedbymultipleregressionde-
andmomentumfluxesinsideandimmediatelyabovetheurbanstreet tailedinNationalWeatherService(NWS)TechnicalAttachment(SR
canyons(Heavisideetal.,2015;Martillietal.,2002).Informationon 90–23).Accountingforthehumidityonlysignificantlyinfluencesthe
buildingandroadproperties(e.g.buildingheight,streetcanyonwidth, heatindexaboveabout26°C,andthiscalculationisnotvalidforex-
materialpropertiessuchasalbedo,thermalconductivityandheatcapac- tremesoftemperatureorhumidity(Steadman,1984).
ity)isincludedforthreeurbancategories:Industrial/commercial,high-
intensityresidential,andlow-intensityresidentialacrosstheWestMid- 2.2.Populationandbuiltenvironmentcharacteristics
landsregion(Fig.1b).Detailsofparametersusedforeachurbancategory
aregiveninTable1a.Land-surfacedatausedasaninputtoWRFforall PopulationdataaretakenfromtheNationalPopulationDatabase
domainswerebasedontheUSGeologicalSurvey(USGS)24-category (2015)griddedat100mresolution,whichgivestotalpopulationat
land-usedata,andfortheinnerdomainweusedtwolocaldatasetsto eachgridpoint.InformationonagegroupsisavailableatOutputArea1
generatetheurbancategories.WeusedtheNoahLandSurfaceModel,a (OA)levelfromthemostrecentcensus(2011),andattachedtoeach
relativelycomplexcommunitymodel,whichisoftencoupledwithan 100mpopulationpointbasedonwhichOAthepointfallswithin.The
urbancanopyscheme,andhasfourlayersofsoilmoistureandtempera- informationisthensummedacrosseachWRF1kmgridbox.
ture(Tewarietal.,2004). DataonhousingtypewasobtainedatLowerSuperOutputArea2
ThemodelhasbeenpreviouslyrunforthisregionfortheAugust (LSOA)levelfromthemostrecentcensus(2011,availablefrominfuse.
2003heatwave;detailsofthemodelsimulationandvalidationarede-
tailedinHeavisideetal.(2015).Wehaveextendedtheanalysisbyre- 1 OutputAreasarethelowestgeographicallevelatwhichcensusestimatesareprovid-
runningthe2003heatwaveusinganupdatedversionofWRF(v3.6.1), ed.Thereare181,408OutputAreasinEnglandandWales.TheaveragepopulationperOA
andincludingafurthersimulationrunduringaheatwaveperiodin is309.
2 LowerSuperOutputAreashaveanaverageof~1500residentsand650households.
July2006.Inordertotestperformance,themodeloutputiscompared
Measuresofproximity(togiveareasonablycompactshape)andsocialhomogeneity(to
withobservationsfromMIDASobservationalweatherstationsacross
encourageareasofsimilarsocialbackground)arealsoincluded.Thereare34,753LSOAs
theWestMidlands(MetOffice,2012).Duringheatwaveperiods,air inEnglandandWales.http://neighbourhood.statistics.gov.uk/.
682 H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690
Table1
(a)DetailsofurbancategoriesusedinBEP.(b)GeneralWRFmodelsetupdetails.
(a)
Category 1:Industrial/commercial 2:High-intensityresidential 3:Low-intensityresidential
Albedo Roof 0.1989 0.1997 0.2027
Wall 0.1989 0.1997 0.2027
Ground 0.1989 0.1997 0.2027
Surfaceemissivity Roof 0.9239 0.9274 0.9292
Wall 0.9239 0.9274 0.9292
Ground 0.9239 0.9274 0.9292
Averagebuildingheight 25m 15m 10m
(b)
Modelsetting Option Reference
Longwaveradiation RapidRadiativeTransferModel(RRTM) Mlaweretal.(1997)
Short-waveradiation Dudhiascheme Dudhia(1989)
Boundarylayerphysics Bougeault–Lacarrere(designedforusewithBPEurbanscheme) BougeaultandLacarrere(1989)
Urbanphysics BEPurbanscheme Martillietal.(2002)
ukdataservice.ac.uk).Dataisavailableonthenumberofdwellingsof disability,education,skillsandtraining,barrierstohousingandservices,
eachtypewithineachLSOA.Thenumberofdwellingsofeachhousing livingenvironment,andcrime,whicharethenweightedandcombined
typeineachWRFmodelgridboxiscalculatedbysummingthefractions tocreatetheoverallIMDscoresforeachLSOA(ONS,2007).Forthis
ofeachLSOAthatintersectwitheachgridbox,withhousingassumedto work,IMDsfor2007wereused,asthisistheclosestavailableyearto
bedistributedevenlyacrosseachLSOA.Thisiscombinedwiththe2m thosemodelledusingWRF.TheIMDscoreatLSOAlevelisattachedto
airtemperaturesimulatedinthemodelduringtheheatwavesof2003 each100mpopulationgridpoint,andthentheseareusedtocalculate
and2006,toestimatetheaverageambienttemperaturethatdifferent population-weightedIMDscoresforeachWRF1kmgrid-box.Once
housingtypesacrosstheWestMidlandsareco-locatedwith.Informa- theIMDscoresforeachWRFgridboxarecalculated,thesearethenas-
tiononhousingtypewasavailable(e.g.detached,terrace,flat,etc.) sociatedwiththetemperaturesfromtheWRFmodel,andrankedrela-
butinformationonhousingagewasnotrecordedspatially. tivetoeachother,fromleast-tomost-deprived.Thedataisthensplit
Thelocationsofhospitals,carehomes,childcarecentres,schools, intopopulationdeciles(inorderofleasttomostdeprived),andtheas-
andprisonswereconsideredwhere‘sensitivepopulations’aremore sociatedtemperaturesplotted.
likelytoreside.TheNationalPopulationDatabaseprovidesinformation
ontheselocationsastheymayhavehighdensitiesofresidents,manyof
whommaynotbeabletoprotectthemselves(othershaveadutyofcare 3.Results
towardsthem).Peopleattheselocationsmaybemorevulnerableto
harmandpotentiallyhardertoevacuateinanemergencysituation. 3.1.Environmentalrisks
Thetemperatureatthelocationofeachofthecentresofinterestisex-
tractedfromthe(hourly)modeloutputusingbilinearinterpolationto 3.1.1.Temperature/heatindex
thenearestmodelledgridpoints.Themeantemperatureanomalyfor Fig.3ashowsthemodelledmean2mairtemperaturefrom2nd–
eachlocationiscalculatedbytakingthemeantemperatureattheselect- 10thAugust2003,andFig.3bshowsthesamefor16th–27thJuly
edlocationovertheheatwaveperiod,andsubtractingthemeantem- 2006.Theobservedaverageandtheoverallminimumtemperatures
peratureacrosstheentiredomainforthesameperiod. arehigherby1.3°Cin2006comparedwith2003.Averagedailymax-
TheEnglishIndicesofMultipleDeprivation(IMD)scoresandranks imum ambient temperatures are similar (difference of 0.3 °C),
areproducedperiodicallybytheDepartmentforCommunitiesand thoughtemperaturesreachedatleast30°Conmoredaysin2006
LocalGovernment,andaretheofficialmeasureofrelativedeprivation than2003(seeFig.S1inSupplementarymaterial).Comparisonof
forsmallareas(LSOAbasedonthe2011census)inEngland.TheIMD Fig.3withthelanduseinFig.1bshowsthaturbanareasaregener-
bringstogether37differentindicatorswhichcoverspecificaspectsor allywarmerthanthesurroundingruralareasintheWestMidlands
dimensionsofdeprivation,suchasincome,employment,healthand modelleddomain.
Fig.2.(a)ModelledandobservedambienttemperaturesatMIDASsitesacrosstheWestMidlandsinJuly2006.(b)Taylordiagramshowingstatisticalcomparisonbetweenadjustedand
unadjustedmodel.Unadjustedsimulationisshowninred,andsimulationwithreducedinitialmoistureisshowninblue.Abettercomparisonwithobservationsisindicatedbyproximity
to‘REF’onthehorizontalaxis.
H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690 683
Table2
Statisticalcomparisonbetweenobservedairtemperature,andmodelledtemperatures(withsoilmoistureadjustment).
Edgbaston Coventry Coleshill ChurchLawford
Observed Modelled Observed Modelled Observed Modelled Observed Modelled
Mean(°C) 22.54 22.59 22.31 22.64 22.12 22.44 22.32 22.24
Standarddeviation(°C) 4.54 4.95 4.78 5.11 5.19 5.15 5.21 5.40
RMSD(°C) – 1.62 – 1.81 – 1.79 – 1.79
Correlation – 0.94 – 0.94 – 0.94 – 0.94
Theaveragecalculatedheatindexforeachoftheheatwaveperiods surroundingair.Overalltheaverageimpactofurbansurfaceson2mtem-
isshowninSupplementaryFig.S2.Thepatternandmagnitudeare peratureandtheaverageimpactontheheatindexarealmostidentical
verysimilartothe2mtemperaturesshowninFig.3.Additionaldiscus- (Fig.5andFig.S3).
sionoftheheatindexmaybefoundintheSupplementarymaterial.
3.2.Socialfactors
3.1.2.UrbanHeatIsland
TheaverageUHIintensityacrossthewholeperiod(definedasthe 3.2.1.Housing
differencein2mtemperaturebetweenasimulationwithurbanland Buildingsareanimportantmodifiertopopulationexposuretoheat,
coverandasimulationwithrurallandcover)forbothheatwavepe- with different types of housing being more or less susceptible to
riodsisshowninFig.4.Statisticsforthetwoheatwavesareshownin overheating,forexampletop-floorflatsaremorelikelytooverheat
Table3. than detached houses (Beizaee et al., 2013; Symonds et al., 2017;
Whilehigheraveragetemperatureswerereachedacrossthedo- Tayloretal.,2015).Fig.6showstheaverage2mairtemperaturethat
mainduringthe2006heatwavethanthe2003heatwave(Fig.3), differenthousingtypesacrosstheWestMidlandsareco-locatedwith
themagnitudeandspatialdistributionoftheUHIintensityremains duringheatwaveperiods,comparedtotheaveragetemperatureacross
remarkablysimilar(Fig.4).Therearebroadsimilaritiesinthespatial thewholeregion.Theanomalyrepresentsthedifferencefromtheaver-
andtemporaldiurnalpatternsoftheUHIintensitybetweenthetwo agetemperatureacrosstheentireregionforthetimeperiodconsidered
heatwaves(Fig.4,Fig.S2),althoughsomelocaliseddifferencesexist. (thistemperature,representingzeroanomaly,islabelledatthebaseof
TheaverageUHIintensityinthecitycentrewasslightlyhigherin theverticalaxisontheright).Theaveragetemperatureacrossallhous-
2003(+2.2°Cin2003comparedwith+2.0°Cin2006),butthe ingtypesintheregionisshownbythehorizontalbar(againwithabso-
UHIintensityatnighttimewaslargerin2006(+3.1°Cin2006com- lutevaluelabelledattherightaxis).Detachedhouses,whichtendtobe
paredwith+2.5°Cin2003). locatedtowardsmoresuburbanareas,areco-locatedwithambient
Bycombiningthedatafromthetwoheatwaveperiods,wehavea temperatures almost 0.2 °C lower than the average for all housing
largerdatasetavailabletoanalysethedistributionoftemperatures, types.Bycontrast,terracedhousesandflatsareco-locatedwithambient
andtheUHIeffectstypicalduringheatwavesacrosstheregion.Fig.5 temperaturesover0.1°Chigherthantheaverageforallhousingtypesin
showsthespatialdistributionoftheUHIintensityacrosstheWestMid- theregion,and0.6°Cwarmerthanthewholedomainaveragetemper-
lands,determinedfromboththeAugust2003andJuly2006heatwaves ature.Ouranalysisshowsthathousingtypeswhicharemorelikelyto
combined,andillustratesthemoreintenseUHIeffectatnighttimecom- overheat(e.g.flats)areoftenlocatedinthewarmestpartsofthecity,
paredwithduringtheday. andtheeffectsaremuchlargeratnight(Fig.6b).Thedifferencesbe-
Theheatindexandthedifferenceinheatindexbetweenurbanand tweenambienttemperaturesforeachhousingcategoryareallstatisti-
ruralsimulationsareshowninSupplementarymaterial(Figs.S2and callysignificant(pb0.05;seeSupplementarymaterialfordetails).
S3),andverysimilarpatternstotheUHIareshown.Thespecifichumidity Whilethetemperaturevariationbetweendifferenthousingtypesis
(i.e.watervapourmixingratio)islowerintheurbansimulation(Supple- b1°C,itshouldbenotedthattheaverageUHIintensityfortheregion
mentaryFig.S4)duetothepresenceofurbansurfacesreplacingvegetat- wasabout0.6to0.7°C(andjustover2°Cinthecentreoftheurban
ed ones that would otherwise provide a supply of moisture to the area)duringheatwaves.
Fig.3.Mean2mairtemperaturessimulatedacrosstheWestMidlandsduring(a)heatwaveperiodfrom2nd–10thAugust2003,and(b)fortheheatwaveperiodduring16th–27thJuly
2006.Letteredpointsshowmonitoringstationsusedformodelvalidation.EB=Edgbaston;CH=Coleshill;CV=Coventry;CL=ChurchLawford.
684 H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690
Fig.4.MeanUHIintensityforthewholetime-periodacrosstheWestMidlands(a)2nd–10thAugust2003,and(b)16th–27thJuly2006.SnapshotsofhighUHIintensityforeachperiodare
shownin(c)11pm5thAugust2003,and(d)11pm17thJuly2006.Letteredpointsshowmonitoringstationsusedformodelvalidation.EB=Edgbaston;CH=Coleshill;CV=Coventry;
CL=ChurchLawford.Notethatthecolourscalesarenotallequal.
3.2.2.Populationage 75yearoldsresideinareas+0.5°C).Duringthedaytime,thereis
Fig.7showstheaverageoutdoortemperatureanomaliesdiffer- onlyamodestdifferenceinambienttemperaturesexposureincom-
entagegroupsareexposedto,basedonresidentialaddress.The parisontotheregionalaverage;howeveratnighttime,population-
anomalyrepresentsthedifferencefromtheaveragetemperature weightedtemperaturesareover1°Chigherthantheregionalaver-
acrosstheentireregionforthetimeperiodconsidered(thistemper- age(Fig.7b,c).Itshouldbenotedthatthesefiguresarebasedonres-
ature,representingzeroanomaly,islabelledatthebaseoftheverti- identialpopulation,sothosepresentinhealthandcarecentresare
calaxisontheright).Theaverageacrossallagegroupsisshownby notconsideredhere.Overall,thedifferencesinexposuretoambient
thehorizontalbar(againwithabsolutevaluelabelledattheright temperaturesfordifferentpopulationgroupsaremodest,likelya
axis).ThewholeWestMidlandspopulationisexposedtotempera- reflectionthatpopulationagegroupsarelessheterogeneouslydis-
turesabout+0.6°Chigherthantheregionalaverage(aspopulations tributedacrossthisregion.Thedifferencesbetweenambienttem-
resideinmoreurbanisedareas)(Fig.9a).Youngadultsandthevery peratures for each age group are all statistically significant (p b
youngareexposedtoslightlyhighertemperaturesthanthepopula- 0.05;seeSupplementarymaterialfordetails),withtheexception
tionaverage(e.g.20–24yearoldsresideinareas+0.7°Cwarmer ofthe15andthe16–17yearoldagegroups(p=0.25),the60–64
thantheregionalaverageof+0.6°C),whiletheelderlyareexposed and 65–69 year old age groups (p = 0.069), and the 85–89 and
toslightlylowertemperaturesthanthepopulationaverage(e.g.65– 90+yearoldagegroups(p=0.055).
Table3
StatisticsformodelledtemperatureandtheUHIintensityforthetwoheatwaveperiodsinAugust2003andJuly2006.
Averagetemp(°C) AverageUHI(°C) DaytimeaaverageUHI(°C) NighttimeaaverageUHI(°C) MaxUHI(°C)
Aug2003 Regionalb 20.5 +0.7 +0.5 +1.0 +6.1(5pm10th;11pm5th;9pm9th)
Citycentrec 21.7 +2.2 +2.0 +2.7
July2006 Regionalb 21.7 +0.6 +0.1 +1.2 +9.6(11pm17th)
Citycentrec 23.0 +2.0 +1.0 +3.1
a Daytimeisdefinedas08:00–19:59;Nighttimeis20:00–07:59.
b RegionalisthewholeareashowninFig.1a.
c CitycentreisapointinthecentreofBirminghamcity.
H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690 685
Fig.5.(a)AverageUHIintensityduringheatwaveperiods(2–10Aug2003and16–27July2006)acrosstheWestMidlands;Averagesover(b)daytimeand(c)nighttime.AverageUHI
intensitywas+2.1°C(+1.4°Cindaytime,+2.9°Catnighttime).
3.2.3.Deprivation representsthedifferencefromtheaveragetemperatureacrosstheen-
Fig.8showstheaverageambienttemperatureexposureforincreas- tireregionforthetimeperiodconsidered(i.e.day,night,oralltimes).
inglydeprivedpopulationdeciles,calculatedfrompopulationweighted Thehorizontallineshowsthepopulationweightedtemperaturefor
scoresacrossthedomainrankedrelativetoeachother.Theanomaly thetimeperiod.Themostdepriveddecileofpopulationacrossthe
Fig.6.(a)Averageoutdoortemperatureanomaly(comparedwiththeaverageacrossthedomainduringtheheatwaveperiod)withstandarddeviationsplottedoverthebars.Theanalysis
isalsobrokendowninto(b)nighttimeand(c)daytime.
686 H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690
Fig.7.(a)Averageoutdoortemperatureanomaly(comparedwiththeaverageacrossthedomainduringtheheatwaveperiod)fordifferentagegroupsacrosstheWestMidlands,with
standarddeviationsplotted.Theanalysisisalsobrokendowninto(b)nighttimeand(c)daytime.
regionisexposedtotemperatures+1.0°Chigherthantheaveragefor deprivationscoresforallLSOAsinEnglandrangefrom0.37to85.46.
theregion(+0.5°Cduringtheday,and+1.5°Catnight).Themostde- Forcomparison,thedeprivationscorescalculatedforeachdecilein
priveddecileofpopulationfortheregionexperiencestemperatures Fig.8rangefrom6.08to56.17,placingthemaroundthe12%and96%
consistentlyhigherthantheaveragefortheregion,evenduringthe mostdeprivedrelativetoEngland.Thedifferencesbetweenambient
daytime (each decile represents ~0.5 million people). The overall temperaturesforeachdeprivationdecileareallstatisticallysignificant
Fig.8.(a)Averageoutdoortemperatureanomaly(comparedwiththeaverageacrossthedomainduringtheheatwaveperiod)forpopulationdecilesrankedbydeprivationacrossthe
WestMidlands,withstandarddeviationsplotted.Theanalysisisalsobrokendowninto(b)nighttimeand(c)daytime.
H.L.Macintyreetal./ScienceoftheTotalEnvironment610–611(2018)678–690 687
(pb0.05;seeSupplementarymaterialfordetails),withtheexceptionof alsoincludecentreswithspecialisedfacilitiesforthosewithmental
deciles4and5(p=0.989). healthissues,sensoryimpairment,learningdifficulties,andthosewho
misusedrugsandalcohol.Childcarecentreshaveadutyofcaretopro-
3.3.Locationbasedassessmentofvulnerabilityfromheat tectpeoplewhocannotprotectthemselves,withpotentiallyhighpop-
ulations, including nurseries, crèches, toddler groups, afterschool
Sensitivereceptorssuchashospitals,carehomes,childcarefacilities, clubs,holidayschemes,andyouthclubs.Schoollocationsareunlikely
schools,andprisonstendtobelocatedinthemosthighlyurbanised tobeoccupiedatnight,whentemperatureanomaliesarehigher,and
areasoftheregion,withtheexceptionofprisonswhichareoftenlocat- thoseofschoolageareexposedtoambienttemperaturesclosetothe
edontheoutskirtsofthecity.Thedistributionoftemperatureanoma- average for the population (though still above regional average).
liesforeachlocationtypeisshowninFig.9.Theanomaliesarebased Whilecollegeshaveveryhighpopulations,theyagainareunlikelyto
onthenumberofreceptors(i.e.buildings),andnotthepopulationat beoccupiedatnight.
each location, as this will fluctuate. The majority of hospitals, care Theanomaliespresentedherearebasedonambienttemperature,
homes,schools and childcare centresare locatedin areas warmer althoughbuildingsplayanimportantroleindeterminingactualexpo-
thanthedomainaverage(94%ofhospitals,90%ofcarehomes,87%of sure,particularlyatnighttime.Studiesusingbuildingsimulationstoes-
schools,and88%ofchildcarecentres).Therearesevenprisonslocated timate indoor temperatures find a significant influence of building
withinthestudyarea,withonlytwoofthesebeingwarmeronaverage characteristicsonindoortemperatures(Tayloretal.,2015).However
(Fig.9e). thesestudiesoftenuseasingleambienttemperatureacrossalldwelling
types,whereasthisstudyshowsthatdifferentbuildingtypesarelikely
4.Discussion tobeexposedtodifferentambienttemperaturesacrossaregion.Our
studycouldhelpinformdevelopmentofinputstobuildingmodelsby
Inthisstudyweusedhourlyambienttemperaturedataathighspa- providingamodifiertoambienttemperaturebasedonthelikelyout-
tialresolution(1km)acrossadenselypopulatedregionoftheUK,cap- doortemperaturesforaparticularbuildingtype.
turing temperature variation across the period of two different Whilewehaveincludedthehighestspatiallyresolveddatathatwas
heatwaveepisodes.Weemployenvironmentalmodellingtechniques availableatthetime,theanalysismakessomeassumptionsaboutdistri-
to examine human health in relation to heat exposure during butionofcertainfactors.DataforhousingtypesatLSOAlevel,andpop-
heatwaves,whichwillbecomeincreasinglyimportantinthefutureas ulationagesatOAlevelwasthefinestlevelofdatathatwasavailable.
climatechangeisprojectedtoincreaseheat-healthimpacts.Previous AnyvariationsthatmayexistwithinanindividualLSOAorOArespec-
studieshaveexaminedthespatialdistributionofvulnerabilitytoheat tively could not be captured by this analysis. The BEP multi-layer
riskacrossaregion,oftenusingambienttemperaturesthataretime- urbancanopyschemeusedwithWRFcancapturetheeffectsofthe
varyingbuthavenospatialvariation,orthatarewellrepresentedspa- builtenvironmentonenergyandmomentumfluxes(accountingfor
tiallybutonlyforasingleorlimitedtimeframe(Tayloretal.,2015; shadingandreflectionsbybuildings)atsub-gridscale,providingasuit-
Tomlinson et al., 2011; Wolf and McGregor, 2013). By using well- ablerepresentationofhowurbanareasinfluencemeteorologyina
validatedmeteorologicalmodellingtechniques,wehavegeneratedan weathermodelsuchasWRF.However,microclimaticeffectsatindivid-
ambienttemperaturedataset,basedonmodelledoutput,athightem- ual building level are not explicitly captured, as regional weather
poralandspatialresolutionfromwhichtocalculateambienttempera- modelscanonlyberundownto1kmhorizontalgridscaleconstrained
turesexposureco-locatedwithvariousriskfactorsthatrelatetoand bythevalidsub-gridscaleturbulenceschemes,andthereforeresultsfor
influence heat-related health effects. These include population- individuallocationsinterpolatedfromthe1kmgridmayhaveaddition-
weightedtemperatureexposureandtherelationshipbetweenUHIin- alvariation.TheIMDsareameasureofrelativedeprivation,calculated
tensityanddemographicfactorssuchasagegroup.Previousstudies byweightingdataondifferentindicatorsthatcanbeusedasmeasures
havemappedsuchrisks,butourworkextendsthisanalysisbycalculat- ofdeprivation.TheIMDsarecalculatedperiodically,andalthoughitis
ingtheambienttemperatureweightedaccordingtodistributionsofdif- agoodmeasureforcomparingareaswitheachother,thismeasurecan-
ferenthousingtypes,population(includingage),anddeprivationscore, notbeeasilycomparedwithotheryearsorusedtoidentifytrends,as
allbeingfactorsthatinfluenceheat-healtheffects.Wehavealsobroken therankingisperformedseparatelyeachtimeanewdatasetisreleased.
downtheanalysisbydayandnight;whilethewholeregioniswarmest Thisstudyfocusesprimarilyontheeffectsofheat,althoughother
duringthedaytime(andurbanareasarestillwarmerthanruralonesat environmentalorsocioeconomicfactorsincitiesthatmayimpacthealth
thistime),thespatialvariationintemperatureacrosstheregionismuch couldbeconsideredinfuturestudies.Suchfactorsmayincludeairpol-
greateratnight,duetotheinfluenceoftheUHI(Fig.5).Whilethereis lution,floodrisk,socialisolationorgreenspace.Coldeffectscouldalso
noclearindicatorofwhichtemperaturemetricismoststronglycorre- beinvestigated.Theseandotherfactorsidentifiedasinfluencinghealth
latedwithmortality,andthereisvariationbetweencountriesandcities maybeincludedinfuturestudiestodevelopandoverallenvironmental
(Davisetal.,2016),highmortalityratioshavebeenassociatedwithhigh riskmapforcities.Finally,thisstudyexaminespastheatwaveeventsin
nighttimelandsurfacetemperaturesduringtheAugust2003heatwave twoyearsacrosstheUK,allowingmodelvalidationagainstobserva-
inParis(Doussetetal.,2011),andthereisevidencethathighernight tions.Futurescenariosunderclimatechangecouldbeconsideredinfur-
timetemperatureshaveadetrimentalimpactonsleepquality,which therwork.
islinkedwithothernegativehealthoutcomes(Lacketal.,2008).
Wefoundthatalmostalllocationswheresensitivepopulationsare 5.Conclusions
likely to be located (hospitals, care homes, schools, colleges) were
warmerthantheregionalaveragebyupto2°C,andthenumberthat PreviousresearchsuggeststhattheUHImaycontributearoundhalf
arewarmerisgreateratnighttime(Fig.9).Ofthesevenprisonlocations oftheheatrelatedmortalityexperiencedduringheatwaves(Heaviside
withinthestudyarea,mostwerelocatedinmoreruralorsuburban etal.,2016).Increasingurbanisationandclimatechangewillincrease
areas,andthereforewerenotexposedtohigheraverageambienttem- heatrelatedhealthrisksinurbanareas.Wehavedevelopedanovel
peraturesforthisregion. risk mapping methodology that combines high spatial resolution
Locationsclassifiedashospitalshereincludeafewsmalltreatment modellingoftemperature,populationage,andbuildingtypestoidentify
centressuchasfamilyplanning,dialysiscentres,etc.,andthereforedo locations and population sub-groups at higher health risk during
notnecessarilyhaveresidentpopulationswhomightbeexposedto heatwaves.Wehavesimulatedandquantifiedthemagnitudeandchar-
thelargertemperatureanomaliesatnight.Locationsclassedascare acteristicsoftheUHIintheWestMidlandsduringtwoheatwavepe-
homeshaveahighdensityofresidents,presentdayandnight,and riods(August2003andJuly2006),usingtheWRF-BEPmodel.The