Mitigating and adapting thermal stress with urban green - Quantification with measurements and micro scale models. Andreas Matzarakis
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1 Mitigating and adapting thermal stress with urban green - Quantification with measurements and micro scale models Andreas Matzarakis
2 Outline Assessment of climate and climate Change: air temperature, Thermal comfort for humans Thermal indices Free/available models - limitations Limitation/Challenges and existing/future solutions? Examples: Palma, Athens, Valencia, Italia
3 Effect of the thermal atmosphere on humans Assessment of effects of climate Not only air temperature Air humidity Wind Radiation Thermo-physiology (activity and clothing) Energy balance of humans Physiologically Equivalent Temperature Thermal index (Matzarakis, 2007)
4 Pattern: Ta, PET
5 Concept: equivalent temperatures 1.1 m 1.1 m Modern Thermal Indices (derived thermal indices: PMV, PET, SET*, PT, UTCI) Physiologically Equivalent Temperature (PET): Definition: thermal comfort: PET = 20 C M work = 80 W I cl = 0.9 clo T a T mrt VP T mrt = T a v = 0.1m/s VP = 12 hpa v T core T core T Skin T Skin
6 Thermal perception and stress Thermal indices (PMV, PET), Thermal perception, Physiological stresss Threshold values of thermal indices PMV and PET for different grades of thermal sensitivity of human beings and physiological stress on human beings (according to Matzarakis and Mayer, 1996) Adjustment of the assessment scale: Taiwan, (Nigeria), Israel, Greece, Hungary,
7 Thermal Sensation in different climate zones Thermal Western/ Tel Aviv Nigeria Taiwan sensation Middle Europe Differences in thermal perception Very cold Cold Cool Slightly cool Neutral Slightly warm Warm Hot Very Hot Sources: Matzarakis & Mayer, 1996; Lin and Matzarakis, 2008; Cohen et al. 2012; Omonijo et al
8 Rome: Perception and micro-climate San Silvestro square, Rome Martinelli, Matzarakis, 2015
9 Input Data Weather/Climate data Average human response Example: T core, Tmrt v vp Ta Multi-node Thermophysiogical Model + Clothing Model local T skin, skin wettedness, thermal sensation, etc. Thermal Index e.g. PET. UTCI,. Input: challenges (wind, Tmrt) Source: G. Jendritzky All thermal indices require same meteorological input
10 Mean radiant temperature T mrt - Six directions T a RH u, v, w T g T mrt (G o, G u, G N, G E, G S, G W, L o, L u, L N, L E, L S, L W ) (Foto: H. Mayer)
11 Micro scale models (free available) SkyHelios RayMan ENVI-met Solweig
12 Micro scale models Details (free available) RayMan - Radiation fluxes - Sunshine duration, sun paths - Sky View Factor - Thermal indices (PET, UTCI, ) - Point model, (Spatial) - Requires Ta, VP/RH, v, (bew/g) - many possib./flexible input/output SOLWEIG - Spatial - SVF, Tmrt, PET, - only digital formats - Black Box ENVI-met - Spatial - Ta, RH, wind, Tmrt, - SVF, (PET), PMV - Primäry air pollutants - Adaptationsfact. for Parameters - long running time - Outputs require huge knowledge SkyHelios - Spatial - flexible in Input/Output - Combination of data formats - Interface to RayMan, ENVI-met - SVF, G, Tmrt, (v), - moderate/short running time
13 RayMan Pro - A Tool for Applied Climatology (urban climatology, human-biometeorology, tourism climatology, ) Sunshine duration Sun paths Shadow Global radiation Mean radiant temperature Predicted Mean Vote (PMV) Phys. Equiv. Temp. (PET) Stand. Effec. Temp. (SET*) Universal Thermal Climate Index (UTCI) Perceived Temperature (pt) new: mpet Simple environments Complex environments Topography Fish-Eye Hemisph. input/svf Meteo data Climate data...
14 FIFA 2022 Los Angeles Time, 23. August 2014
15 Doha, air temperature Period: March 1999 to Jan 2014 (Matzarakis and Fröhlich, 2014) FIFA 2022
16 Doha, PET Period: March 1999 to Jan 2014 (Matzarakis and Fröhlich, 2014) FIFA 2022
17 Doha, air temperature Period: March 1999 to Jan 2014 (Matzarakis and Fröhlich, 2014) FIFA 2022
18 Doha, PET Period: March 1999 to Jan 2014 (Matzarakis and Fröhlich, 2014) FIFA 2022
19 Frequency Frequencies CLM, A1B Climate Scenario Palma de Mallorca Ta base Ta Air temperature ( C)
20 Frequencies Frequency CLM, A1B Climate Scenario Palma de Mallorca PET (61-90) PET (21-50) PET tmrt=ta PET ( C)
21 Frequency (%) Frequencies CLM, A1B Climate Scenario Palma de Mallorca PET (61-90) PET (21-50) PET wind-1 PET wind PET ( C)
22 Valencia Ta and PET in Summer and Winter Calle Sierpes (Seville). Device for the limitation of solar radiation reaching the road surface. After: Goméz, F., Pérez Cueva, A., Valcuende, M., Matzarakis, A., 2013, Ecol. Eng.
23 Long-term analysis - Cities selection Italian climate zones Martinelli, Matzarakis, 2015
24 Long-term analysis - Courtyard typologies Courtyards proportions Courtyards Sky View Factor Courtyards sunshine duration in summer (colors) and winter (lines) Martinelli, Matzarakis, 2015
25 Long-term analysis - Results Daily median of PET and median of PET at 15:00 LST over the year for four exemplar cities Martinelli, Matzarakis, 2015
26 SkyHelios Sun paths Sun duration/diagram Shade Sky view factor(s) Roughness Local climate zones (partially) Global radiation Mean radiant temperature Wind speed and direction PET and UTCI Vector and grid data Google Earth implementation Interfaces and outputs for RayMan Interface/Output for Climate Mapping Tool
27 New Generation: + Thermal indices Running time Add. input parameters (WD) Not only hot spots
28 Results Place of Old Synagogue SkyHelios: Area with high SVF will be only little larger, but moved westwards. Fröhlich and Matzarakis, 2013
29 SVF after - Place of Old Synagogue MP1 Green area MP2 KG I - North MP3 KG II - North MP PET35 PET35a Δ (h) MP4 UB - Northeast MP5 Theatre MP6 KG II - Middle MP7 Bus stop SVF Results: RayMan Pro/SkyHelios Effect: wind and Tmrt Fröhlich and Matzarakis, 2013
30 SVF before MP1 Green area MP2 KG I - North MP3 KG II - North MP4 UB - Northeast MP5 Theatre MP6 KG II - Middle MP7 Bus stop Results: SkyHelios Fröhlich and Matzarakis, 2013
31 SVF after - Place of Old Synagogue MP1 Green area MP2 KG I - North MP3 KG II - North MP ΔPET35 SVFo SVFn MP4 UB - Northeast MP5 Theatre MP6 KG II - Middle MP7 Bus stop Results: SkyHelios Fröhlich and Matzarakis, 2013
32 SkyHelios Output (Shade, GEOTIFF)
33 Wind SkyHelios and ENVI-met SkyHelios ENVI-met Diagnostic wind model Modified Parametrization Improved initial profile Roughness length 0 4 m possible Fröhlich, 2013 Prognostic wind model Roughness length up to 0.1 m Bruse und Fleer, 1998
34 CΤIS - Presentation of results for application Data: Rain: (Matzarakis, 2014, SER)
35 Statements/Summary Not only air temperature - Biometeorology Appropriate data and information Measurements and simulations Urban areas - modelling Combination of methods/data No clickable solutions Less case studies Models provide additional data: SD, Sunpaths, Focus Radiation and wind Recommendations to users of models Validation Consider possibilities and limitation aim of development PLEASE: read/consider manual
36 Thank you for your attention Ευχαριστώ πολύ
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