Hvordan kan vi bedre forudsige, hvilke problemer. påvirkninger beboerne i en given bolig vil blive udsat for. Geo Clausen

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1 Hvordan kan vi bedre forudsige, hvilke problemer beboerne i en given bolig vil blive udsat for? Geo Clausen Center for Indeklima og Energi Danmarks Tekniske Universitet Centre for indoor air and health in dwellings Realdania research WP1: Eksponering i danske boliger Formål At få bedre mulighed for at forudsige hvilke indeklimamæssige påvirkninger beboerne i en given bolig vil blive udsat for. Anvendelse En eksponeringsmodel, som kan forudsige påvirkningerne fra indeklimaet i boliger, har store anvendelsesmuligheder i forbindelse med projektering, særligt ved nybyggeri, men også ved renovering af eksisterende boliger. Eksponeringsmodellen tænkes på langt sigt implementeret i de it-systemer, som rådgiverne bruger til beregning af termiske og indeklimamæssige forhold 2 DTU Civil Engineering, Technical University of Denmark 1

2 Six parameters influencing human heat balance Person Clothing Activity level Environment: Air temperature Air humidity Mean radient temperature Air velocity Fangers comfort equation PMV= (0.303 e -0.36M ){( M-W) * 10-3 * [ (M-W)- p a ] * [( M-W) ] * 10-5 M ( p a ) M ( 34-t a ) -3.96* 10-8 f cl * [(t cl + 273) 4 - (t r + 273) 4 ] - f cl h c ( t cl - t a )} where: t cl = (M-W) - I cl {3.96 * 10-8 f cl * [(t cl + 273) 4 ( t r + 273) 4 ] + f cl h c ( t cl - t a )} h c = 2.38 ( t cl - t a ) 0,25 for 2.38 ( t cl - t a ) 0,25 > 12,1 var h c = 12,1 v ar for 2.38 ( t cl - t a ) 0,25 < 12,1 var 2

3 Predicted Mean Vote PMV -PPD 3

4 Example Reasons for local thermal discomfort Radiant asymmetry Local air velocity (draught) Warm or cold floors Vertical air temperature differences 4

5 Træk WP1: Eksponering i danske boliger Formål At få bedre mulighed for at forudsige hvilke indeklimamæssige påvirkninger beboerne i en given bolig vil blive udsat for. Delprojekter Luftkvalitet i boliger Detaljerede målinger af årstidsvariationer i luftskifte og luftforurening i fem eksisterende boliger. Konkrete påvirkninger fra indeklimaet Undersøgelse af hvordan beboerne i 58 eksisterende boliger helt konkret bliver påvirket af indeklimaet. Eksponeringsmodel Udvikling af modeller, der kan forudsige de indeklimamæssige påvirkninger, som beboerne i en given bolig vil blive udsat for. 10 DTU Civil Engineering, Technical University of Denmark 5

6 Model eller tommelfingerregel? 11 Modellering af luftskifte Målt luftskifte 5,0 4,5 4,0 3,5 3,0 2,5 R² = 0,45 2,0 1,5 10 1,0 0,5 0,0 0,0 0,5 1,0 1,5 2,0 2,5 Beregnet luftskifte 12 6

7 Main-Model Model-Building Model-Behavior Building-related parameters: Volume of the measured room X X Size of dwelling X X Location of measured room X X Type of ventilation in the home X X Vicinity to trafficked road X X Constr. year of the dwelling X X Behavior-related parameters: No. of people sleeping in the bedroom X X Sharing the bedroom X X Average door opening during night X X Average window opening during night X X No. of people living in the dwelling X Other parameters: Condensation in winter on the window X Outdoor temperature X stepwise forward and backward linear regression 13 Modellering af luftskifte Målt luftskifte 5,0 4,5 4,0 3,5 3,0 2,5 2,0 1,5 10 1,0 0,5 Bygningsparametre: 9% af variationen Brugeradfærd: 35% of variation R² = 0,45 0,0 0,0 0,5 1,0 1,5 2,0 2,5 Beregnet luftskifte 14 7

8 Sæsonvariation, luftskifte te h 1 Air change ra h 1 Air change rate h A B C D E Spring Ta = 11 C Time [Hour] Autumn Ta = 11 C 5 A B 4 C D 3 E 2 1 te h 1 Air change ra 1 Air change rate h A B C D E Summer Ta = 21 C Time [Hour] A B C D E winter Ta = 3 C Time [Hour] Time [Hour] Measurements in 58 homes 58 homes visited between October 2011 February 2012 Inspection, Questionnaires CO 2, T, RH in bedroom and living room 48 hour measurement of UFPs (10 300nm) in living room Dust sampling: EDC (1month), vacuumed settled dust Biomarkers (blood, urine x 2, saliva) 16 8

9 40% 35% 30% Odense: Median=0.43 København: Median= % % af boliger 20% 15% 10% 5% 0% >1.75 Luftskifte (h 1 ) CO 2 concentration Average CO2 bedroom 48 hour 876 ppm Average CO2 bedroom bd ppm Highest CO2 bedroom 48 hour 1562 ppm Average CO2 living r. 48 hour 787 ppm Average CO2 living r ppm Highest CO2 living r. 48 hour 1313 ppm In the bedroom, 27% of the measured time the CO2 concentration was above 1000ppm. During night time (23-07), 47% of time the CO2 was above 1000ppm. 18 9

10 Particle measurements Cooking Candle Unknown Candle 19 Particle concentration and diameter All datapoints Average part ticle number conc. (#/cm3) Unoccupied Occupied asleep Occupied awake 20 10

11 Daily Integrated Particle Exposure Daily Int Exp Awake (#/cm3.h/d) Daily Int Exp Asleep (#/cm3.h/d) Contribution of background and indoor sources Indoor sources explain about 75% of the total daily exposure Background Indoor Sources Daily Integrated Exposure (#/cm3.h/d) 22 11

12 Contribution of peaks/events Contribution of each type of event to the total exposure Analyses of events explained on average 92% of the total exposure from indoor sources Background Cooking Toaster Candle Window Unknown Daily Integrated Exposure (#/cm3.h/d) Contribution of peaks/events Average contribution (%) of each type of event to the sum of exposure from all peaks/events in the home Cooking Toaster Candle Window Unknown Aver Aver Aver Aver Aver N N N N N

13 Linear Regression Model Variables explaining 71% of the variation in the natural logarithm of the daily integrated particle exposure: - air exchange - total time of occupancy per day - home volume - background particle level - daily total source duration - daily number of events cooking, toasting, candle burning, window opening, unknown events Only four variables are significant in the model (background PN level, number of window and toasting events and source duration of candle burning). Several variables have unexpected coefficients (e.g. negative for number of cooking events or source duration of toasting) Lys er ikke bare lys Particles / cm Candle 1 2 Candle 2 Candle 3 Candle 4 Candle Time (s) 26 13

14 Ultrafine partikler afgivet fra lys Scented soy wax in a glass. Organic, GMO free Soy wax in a glass. Organic, GMO free Candle wax Paraffin Candle wax stick light Liquid paraffin Paraffin tealight candle Pure candle wax Palm wax. organic Soy wax. Organic, GMO free Average particle concentration (#/cm 3 ) 27 Dataindsamlingen fortsætter.. 60 boliger (45 besøgt indtil nu) Personer bærer rygsæk med måleudstyr 48 timer (personlig eksponering) Hvor stor en del af den samlede eksponering til ultrafine partikler foregår i hjemmet? Korrelation til sundhedsudfald? 28 14

15 Spørgsmål? 29 Relation to WP2 WP 1 WP 2 Task 1.1 Task 1.2 Pilotstudy in 5 Cross sectional study in selected homes 500 homes Population based study of health effects of indoor air Task 1.3 Development of exposure model 30 DTU Civil Engineering, Technical University of Denmark 15