請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89997
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊振義 | zh_TW |
dc.contributor.advisor | Jehn-Yih Juang | en |
dc.contributor.author | 王姿雅 | zh_TW |
dc.contributor.author | Tzu-Ya Wang | en |
dc.date.accessioned | 2023-09-22T16:59:32Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-08 | - |
dc.identifier.citation | Aalto, J., Pirinen, P., Heikkinen, J.,Venäläinen, A. (2013). Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models. Theoretical and Applied Climatology, 112(1), 99-111. doi:10.1007/s00704-012-0716-9
Amani, M., Brisco, B., Afshar, M., Mirmazloumi, S. M., Mahdavi, S., Mirzadeh, S. M. J., . . . Granger, J. (2019). A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing. Big Earth Data, 3(4), 378-394. doi:10.1080/20964471.2019.1690404 ASHRAE. (2010). Standard 55, Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers. In (Vol. 145): American Society of Heating, Refrigerating and Air conditioning Engineers. Bernhard, M. C., Kent, S. T., Sloan, M. E., Evans, M. B., McClure, L. A.,Gohlke, J. M. (2015). Measuring personal heat exposure in an urban and rural environment. Environ Res, 137, 410-418. doi:10.1016/j.envres.2014.11.002 Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H.,Tinz, B. (2012). Comparison of UTCI to selected thermal indices. International Journal of Biometeorology, 56(3), 515-535. doi:10.1007/s00484-011-0453-2 Chiu, H.-C., Chang, H.-Y., Mau, L.-W., Lee, T.-K.,Liu, H.-W. (2000). Height, weight, and body mass index of elderly persons in Taiwan. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 55(11), M684-M690. Coccolo, S., Kämpf, J., Scartezzini, J.-L.,Pearlmutter, D. (2016). Outdoor human comfort and thermal stress: A comprehensive review on models and standards. Urban Climate, 18, 33-57. doi:10.1016/j.uclim.2016.08.004 Cohen, P., Potchter, O.,Matzarakis, A. (2013). Human thermal perception of Coastal Mediterranean outdoor urban environments. Applied Geography, 37, 1-10. doi:10.1016/j.apgeog.2012.11.001 Council of Agriculture, E. Y. (2020). The Survey of Agriculture and Farmland Resources. Retrieved from https://map.coa.gov.tw/farmland/survey.html CWB. (2023). Heat Information. Retrieved from https://www.cwb.gov.tw/V8/E/P/Warning/W29.html Deilami, K., Kamruzzaman, M.,Liu, Y. (2018). Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. International Journal of Applied Earth Observation and Geoinformation, 67, 30-42. doi:10.1016/j.jag.2017.12.009 Fang, Z., Lin, Z., Mak, C. M., Niu, J.,Tse, K.-T. (2018). Investigation into sensitivities of factors in outdoor thermal comfort indices. Building and Environment, 128, 129-142. Fanger, P. O. (1972). Thermal comfort, analysis and application in environmental engineering. In: McGraw Hill, New York. Fiala, D., Havenith, G., Bröde, P., Kampmann, B.,Jendritzky, G. (2012). UTCI-Fiala multi-node model of human heat transfer and temperature regulation. International Journal of Biometeorology, 56, 429-441. Gagge, A., Stolwijk, J. A.,Nishi, Y. (1972). An effective temperature scale based on a simple model of human physiological regulatiry response. Memoirs of the Faculty of Engineering, Hokkaido University, 13(Suppl), 21-36. Gagge, A. P., Fobelets, A.,Berglund, L. (1986). A standard predictive Index of human reponse to thermal enviroment. Transactions/American Society of Heating, Refrigerating and Air-Conditioning Engineers, 92(2B), 709-731. Givoni, B., Khedari, J., Wong, N., Feriadi, H.,Noguchi, M. (2006). Thermal sensation responses in hot, humid climates: effects of humidity. Building research and information, 34(5), 496-506. Gonzalez, R., Nishi, Y.,Gagge, A. (1974). Experimental evaluation of standard effective temperature a new biometeorological index of man's thermal discomfort. International Journal of Biometeorology, 18(1), 1-15. Gulyás, Á., Unger, J.,Matzarakis, A. (2006). Assessment of the microclimatic and human comfort conditions in a complex urban environment: Modelling and measurements. Building and Environment, 41(12), 1713-1722. doi:https://doi.org/10.1016/j.buildenv.2005.07.001 Höppe, P. (1999). The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology, 43(2), 71-75. doi:10.1007/s004840050118 Han, J., Yang, W., Zhou, J., Zhang, G., Zhang, Q.,Moschandreas, D. J. (2009). A comparative analysis of urban and rural residential thermal comfort under natural ventilation environment. Energy and Buildings, 41(2), 139-145. doi:10.1016/j.enbuild.2008.08.005 Heaviside, C., Macintyre, H.,Vardoulakis, S. (2017). The Urban Heat Island: Implications for Health in a Changing Environment. Curr Environ Health Rep, 4(3), 296-305. doi:10.1007/s40572-017-0150-3 Hu, K., Guo, Y., Yang, X., Zhong, J., Fei, F., Chen, F., . . . Qi, J. (2019). Temperature variability and mortality in rural and urban areas in Zhejiang province, China: An application of a spatiotemporal index. Sci Total Environ, 647, 1044-1051. doi:10.1016/j.scitotenv.2018.08.095 Hu, L., Wilhelmi, O. V.,Uejio, C. (2019). Assessment of heat exposure in cities: Combining the dynamics of temperature and population. Sci Total Environ, 655, 1-12. doi:10.1016/j.scitotenv.2018.11.028 Huang, G., Zhou, W.,Cadenasso, M. L. (2011). Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD. J Environ Manage, 92(7), 1753-1759. doi:10.1016/j.jenvman.2011.02.006 Hwang, R.-L.,Lin, T.-P. (2007). Thermal comfort requirements for occupants of semi-outdoor and outdoor environments in hot-humid regions. Architectural Science Review, 50(4), 357-364. Jacobs, C., Singh, T., Gorti, G., Iftikhar, U., Saeed, S., Syed, A., . . . Siderius, C. (2019). Patterns of outdoor exposure to heat in three South Asian cities. Sci Total Environ, 674, 264-278. doi:10.1016/j.scitotenv.2019.04.087 Jiang, J.,Tian, G. (2010). Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia environmental sciences, 2, 571-575. Johansson, E., Thorsson, S., Emmanuel, R.,Krüger, E. (2014). Instruments and methods in outdoor thermal comfort studies – The need for standardization. Urban Climate, 10, 346-366. doi:https://doi.org/10.1016/j.uclim.2013.12.002 Kang, C., Park, C., Lee, W., Pehlivan, N., Choi, M., Jang, J.,Kim, H. (2020). Heatwave-Related Mortality Risk and the Risk-Based Definition of Heat Wave in South Korea: A Nationwide Time-Series Study for 2011-2017. Int J Environ Res Public Health, 17(16). doi:10.3390/ijerph17165720 Kenny, G. P., Poirier, M. P., Metsios, G. S., Boulay, P., Dervis, S., Friesen, B. J., . . . Flouris, A. D. (2017). Hyperthermia and cardiovascular strain during an extreme heat exposure in young versus older adults. Temperature, 4(1), 79-88. Koppe, C., Kovats, S., Jendritzky, G.,Menne, B. (2004). Heat-waves: risks and responses: World Health Organization. Regional Office for Europe. Kovach, M. M., Konrad, C. E.,Fuhrmann, C. M. (2015). Area-level risk factors for heat-related illness in rural and urban locations across North Carolina, USA. Applied Geography, 60, 175-183. doi:10.1016/j.apgeog.2015.03.012 Kovats, R. S.,Hajat, S. (2008). Heat stress and public health: a critical review. Annu Rev Public Health, 29, 41-55. doi:10.1146/annurev.publhealth.29.020907.090843 Li, D., Sun, T., Liu, M., Yang, L., Wang, L.,Gao, Z. (2015). Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environmental Research Letters, 10(5). doi:10.1088/1748-9326/10/5/054009 Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., . . . Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14-37. Lin, T.-P., Matzarakis, A.,Hwang, R.-L. (2010). Shading effect on long-term outdoor thermal comfort. Building and Environment, 45(1), 213-221. doi:10.1016/j.buildenv.2009.06.002 Lin, T. P.,Matzarakis, A. (2008). Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. Int J Biometeorol, 52(4), 281-290. doi:10.1007/s00484-007-0122-7 Lin, T. P., Yang, S. R., Chen, Y. C.,Matzarakis, A. (2019). The potential of a modified physiologically equivalent temperature (mPET) based on local thermal comfort perception in hot and humid regions. Theoretical and Applied Climatology, 135, 873-876. Lugo-Amador, N. M., Rothenhaus, T.,Moyer, P. (2004). Heat-related illness. Emergency Medicine Clinics, 22(2), 315-327. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., . . . Gomis, M. (2021). Climate change 2021: the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, 2. Matzarakis, A., Rutz, F.,Mayer, H. (2007). Modelling radiation fluxes in simple and complex environments--application of the RayMan model. Int J Biometeorol, 51(4), 323-334. doi:10.1007/s00484-006-0061-8 Matzarakis, A., Rutz, F.,Mayer, H. (2010). Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. Int J Biometeorol, 54(2), 131-139. doi:10.1007/s00484-009-0261-0 Mayer, H.,Höppe, P. (1987). Thermal comfort of man in different urban environments. Theoretical and Applied Climatology, 38(1), 43-49. doi:10.1007/BF00866252 Mutiibwa, D., Strachan, S.,Albright, T. (2015). Land surface temperature and surface air temperature in complex terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4762-4774. NCDR. (2016). 應用 RCP8.5 氣候情境評估氣候變遷下之災害風險圖 Retrieved from NCDR: https://dra.ncdr.nat.gov.tw/public/upload/Publication/20120702522350318473GKM2VLPB.pdf NCDR. (2022). The latest climate scenario analysis and disaster application evaluation report in IPCC AR6. Retrieved from https://tccip.ncdr.nat.gov.tw/km_publish_technical_report.aspx Nicholls, R. J., Hanson, S., Herweijer, C., Patmore, N., Hallegatte, S., Corfee-Morlot, J., . . . Muir-Wood, R. (2008). Ranking port cities with high exposure and vulnerability to climate extremes: exposure estimates. NOAA. (2023). Climate at a Glance: Global Time Series. Retrieved from https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series Osczevski, R. J. (1995). The basis of wind chill. Arctic, 48(4), 372-382. Ouyang, W., Liu, Z., Lau, K., Shi, Y.,Ng, E. (2022). Comparing different recalibrated methods for estimating mean radiant temperature in outdoor environment. Building and Environment, 216, 109004. doi:https://doi.org/10.1016/j.buildenv.2022.109004 Pantavou, K., Theoharatos, G., Mavrakis, A.,Santamouris, M. (2011). Evaluating thermal comfort conditions and health responses during an extremely hot summer in Athens. Building and Environment, 46(2), 339-344. doi:10.1016/j.buildenv.2010.07.026 Park, C. Y., Thorne, J. H., Hashimoto, S., Lee, D. K.,Takahashi, K. (2021). Differing spatial patterns of the urban heat exposure of elderly populations in two megacities identifies alternate adaptation strategies. Science of The Total Environment, 781. doi:10.1016/j.scitotenv.2021.146455 Perini, K.,Magliocco, A. (2014). Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban Forestry & Urban Greening, 13(3), 495-506. Potchter, O., Cohen, P., Lin, T. P.,Matzarakis, A. (2018). Outdoor human thermal perception in various climates: A comprehensive review of approaches, methods and quantification. Sci Total Environ, 631-632, 390-406. doi:10.1016/j.scitotenv.2018.02.276 Rothfusz, L. P.,Headquarters, N. S. R. (1990). The heat index equation (or, more than you ever wanted to know about heat index). Fort Worth, Texas: National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology, 9023, 640. Sherwood, S. C.,Huber, M. (2010). An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences, 107(21), 9552-9555. Silva, T. G. F. d., Santos, G. C. L., Duarte, A. M. C., Turco, S. H. N., Cruz Neto, J. F. d., Jardim, A. M. d. R. F.,dos Santos, T. S. (2019). Black globe temperature from meteorological data and a bioclimatic analysis of the Brazilian Northeast for Saanen goats. Journal of Thermal Biology, 85, 102408. doi:https://doi.org/10.1016/j.jtherbio.2019.102408 Staiger, H., Laschewski, G.,Gratz, A. (2012). The perceived temperature - a versatile index for the assessment of the human thermal environment. Part A: scientific basics. International Journal of Biometeorology, 56(1), 165-176. doi:10.1007/s00484-011-0409-6 Steadman, R. G. (1984). A universal scale of apparent temperature. Journal of Applied Meteorology and Climatology, 23(12), 1674-1687. Sun, Y. Y. (2013). Rural land consisting of outdoor thermal environment of comfort and study the relationship between heat island intensity. Suter, M. K., Miller, K. A., Anggraeni, I., Ebi, K. L., Game, E. T., Krenz, J., . . . Spector, J. T. (2019). Association between work in deforested, compared to forested, areas and human heat strain: An experimental study in a rural tropical environment. Environ Res Lett, 14(8). doi:10.1088/1748-9326/ab2b53 Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S.,Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152-170. doi:https://doi.org/10.1016/j.isprsjprs.2020.04.001 Tartarini, F.,Schiavon, S. (2020). pythermalcomfort: A Python package for thermal comfort research. SoftwareX, 12, 100578. doi:https://doi.org/10.1016/j.softx.2020.100578 Tartarini, F., Schiavon, S., Cheung, T.,Hoyt, T. (2020). CBE Thermal Comfort Tool: Online tool for thermal comfort calculations and visualizations. SoftwareX, 12, 100563. doi:https://doi.org/10.1016/j.softx.2020.100563 Tartarini, F., Schiavon, S., Hoyt, T.,Mackey, C. (2020). Pythermalcomfort Documentation. Retrieved from https://pythermalcomfort.readthedocs.io/en/latest/index.html Tartarini, F., Schiavon, S., Jay, O., Arens, E.,Huizenga, C. (2022). Application of Gagge’s energy balance model to determine humidity-dependent temperature thresholds for healthy adults using electric fans during heatwaves. Building and Environment, 207, 108437. doi:https://doi.org/10.1016/j.buildenv.2021.108437 TCCIP. (2023a). AR6統計降尺度日資料資料說明文件. Retrieved from TCCIP: https://tccip.ncdr.nat.gov.tw/upload/data_document/20220708151649.pdf TCCIP. (2023b). AR6統計降尺度雨量資料資料生產履歷. Retrieved from TCCIP: https://tccip.ncdr.nat.gov.tw/upload/data_profile/20220718101540.pdf Thom, E. C. (1959). The discomfort index. Weatherwise, 12(2), 57-61. Thorsson, S., Lindberg, F., Eliasson, I.,Holmer, B. (2007). Different methods for estimating the mean radiant temperature in an outdoor urban setting. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(14), 1983-1993. Tong, S., Wang, X. Y., Yu, W., Chen, D.,Wang, X. (2014). The impact of heatwaves on mortality in Australia: a multicity study. BMJ open, 4(2), e003579. Walther, E.,Goestchel, Q. (2018). The P.E.T. comfort index: Questioning the model. Building and Environment, 137, 1-10. doi:https://doi.org/10.1016/j.buildenv.2018.03.054 Wang, Y. C., Hsu, H. H., Chen, C. A., Tseng, W. L., Hsu, P. C., Lin, C. W., . . . Liang, H. C. (2021). Performance of the Taiwan earth system model in simulating climate variability compared with observations and CMIP6 model simulations. Journal of Advances in Modeling Earth Systems, 13(7), e2020MS002353. Wenjing, C., JinXing, H.,Xiaomin, Y. (2009, 12-14 Aug. 2009). A study on temperature interpolation methods based on GIS. Paper presented at the 2009 17th International Conference on Geoinformatics. WHO. (2018). Heat and Health. Retrieved from https://www.who.int/news-room/fact-sheets/detail/climate-change-heat-and-health Xiao, J., Spicer, T., Jian, L., Yun, G. Y., Shao, C., Nairn, J., . . . Weeramanthri, T. S. (2017). Variation in population vulnerability to heat wave in Western Australia. Frontiers in public health, 5, 64. Zhao, Z.-Q., He, B.-J., Li, L.-G., Wang, H.-B.,Darko, A. (2017). Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energy and Buildings, 155, 282-295. doi:https://doi.org/10.1016/j.enbuild.2017.09.046 Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A.,Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69, 175-185. doi:https://doi.org/10.1016/j.jag.2017.12.006 內政部. (2020). 109年第10週內政統計通報_老化與扶養. 台北市: 內政部 行政院主計總處. (2021). 國情統計通報(第 037 號). Retrieved from https://www.stat.gov.tw/public/Data/132162358VPAVQ8D.pdf 林子平. (2014). 台灣傳統聚落微氣候變化對熱舒適性影響之研究-傳統聚落戶外空間熱環境實測及長期熱舒適性模擬. 陳振菶. (2023). 氣候變遷對職場安全衛生之挑戰─談戶外高氣溫危害預防. 台灣勞工季刊, 73, 35-45. Retrieved from https://www.mol.gov.tw/2578/21000/58320/ 勞動部職業安全衛生署. (2019). 高氣溫戶外作業勞工熱危害預防指引. Retrieved from https://www.osha.gov.tw/48110/48461/48517/48523/56529/ 雲林縣衛生局. (2021). 109年度雲林縣衛生性別圖像統計分析. 雲林縣: 雲林縣政府 衛生福利部國民健康署. 預防熱傷害分眾宣導資料. Retrieved from https://www.ilshb.gov.tw/uploads/files/subject/0theme/Health/107/4.%E9%A0%90%E9%98%B2%E7%86%B1%E5%82%B7%E5%AE%B3%E5%88%86%E7%9C%BE%E5%AE%A3%E5%B0%8E%E8%B3%87%E6%96%99.pdf | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89997 | - |
dc.description.abstract | 近年來,在氣候變遷的影響下,極端高溫事件越來越頻繁地發生,使得人們面臨比以往更嚴峻的熱壓力。當人們長期暴露於高溫中,可能導致健康問題,特別像是經濟地位較低、老年人以及其他弱勢群體等更需要注意。除此之外,土地利用和覆蓋的變化(LULC)也會影響熱環境。雖然農村地區的土地利用變化相對都市較小,但非植被區域的擴張仍會影響該地區溫度的變化。為了瞭解農村地區年長者的熱壓力,並且降低暴露於高溫的風險,本研究的目標為分析在過去研究中較少被關注的農村地區熱環境和熱舒適。
本研究的研究樣區是雲林縣,雲林除了是一個重要的農業大縣之外,老年人口比例也相對較高。因此,為了找出雲林地區熱環境的時空特徵,首先分析了氣象參數(溫度、相對濕度、風速和平均輻射溫度)的空間分佈。接著,使用了生理等效溫度(physiological equivalent temperature, PET)作為熱舒適指標,用來分析農村地區老年人的熱舒適。並且,使用Python套件Pythermalcomfort進行PET和相關參數的計算。而土地利用與覆蓋的資料則是從衛星Landsat9的遙測資料進行監督式分類所獲取。 本研究首先分析了雲林地區年長者的熱舒適度和環境,並且揭示了幾個PET較高的熱點和時間點。另外,研究也顯示在農村地區土地利用對熱環境的影響為人造物和裸露地兩種土地利用與覆蓋的地表溫度較高。此外,在未來情境的熱舒適分析中,研究顯示年長者的不舒適(PET超過42°C)發生頻率有增加的趨勢。除此之外,本研究還試圖利用暴露度識別在不同情境下可能受到熱傷害的人群,以利相關政策擬定與資源分配。最後,研究總結相關單位所提出的一些熱傷害防治與相關建議,例如增加綠地、提供適當的遮蔭,以及在較涼爽的時間(4:00至7:00之間)進行體力活動等,提供相關單位參考。總之,本研究利用PET找出高齡農村地區在夏季時風險較高的時段和地區,期望可以提供熱風險預防參考的相關資訊。 | zh_TW |
dc.description.abstract | In recent years, under the influence of climate changes, more and more extreme heat events have occurred and make people face more frequent heat stress than before. Moreover, long-term exposure to high temperatures may cause health issues, especially the people of low economic status, elderly and other vulnerable groups. In addition, changes in land use and land cover (LULC) also affect the thermal environment. Although land-use change in rural areas is relatively milder than that in urban areas, the expansion of non-vegetation areas still influences the variation of temperature in those areas. To quantify the heat stress and reduce the risk of long-term exposure to high temperatures of elderly in rural areas, the goal of this study is to analyze the spatial-temporal characteristics of the thermal environment and heat-related comfortability of aging rural areas, which have been rarely mentioned in the past research.
The study area of this research is Yunlin County, an important agricultural county with a relatively high degree of aging. To find the spatial-temporal characteristics of the thermal environment in Yunlin, the spatial distribution of meteorological parameters (temperature, relative humidity, wind velocity, and mean radiant temperature) were analyzed. Furthermore, to evaluate the effects of spatial characteristics on the human comfortability in this aging rural area, a thermal comfort index physiological equivalent temperature (PET) was utilized. In addition, the data of LULC is acquired from the remote sensing images with the supervised classification. The PET and the relevant parameters in the Yunlin area are calculated using the meteorological data of Yunlin and surrounding weather stations with the Python package, Pythermalcomfort. This study first analyzed the thermal comfort and environment for the elderly in Yunlin, revealing several hotspots and time with high PET. The impact of land use on thermal conditions was also showed, with built-up and bare soil areas displaying higher temperatures. In addition, for the projection of future scenarios showed a trend of discomfort for the elderly is increasing, with a high frequency of PET exceeding 42°C. This study also tried to identify the individual who may be affected by the heat injuries under different scenarios by exposure analysis. Finally, some heat preventions and suggestions were summarized, increasing greenery, ensuring suitable shading, seeking cooler spaces on hot days, and scheduling physically demanding activities during cooler hours (between 4:00 and 7:00), etc.. In conclusion, this study utilized PET to identify high-risk periods and locations for aging rural areas in Yunlin during the summer, with the goal of providing valuable information of heat risks. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:59:32Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-09-22T16:59:32Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii Contents v List of Figures vii List of Tables x Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 6 Chapter 2 Literature Review 7 2.1 Thermal Comfort 7 2.2 The Physiological Equivalent Temperature (PET) 9 2.3 Thermal Environment in Rural Areas 13 2.4 Heat Risks for the Elderly 15 Chapter 3 Data and Methodology 17 3.1 Study Area 17 3.2 Meteorological Data 20 3.3 Land Use and Land Cover Data 23 3.4 Land Surface Temperature Data 24 3.5 PET Calculation 25 3.5.1 Mean Radiant Temperature 26 3.5.2 Metabolic Rate 27 3.5.3 Clothing Level 30 3.6 The Setting of Thermal Comfort Scenarios of Elderly 32 Chapter 4 Results 35 4.1 Thermal Environment in Yunlin Area 35 4.1.1 Thermal Environment in Summer (JJAS) in 2022 35 4.1.2 Thermal Comfort in Summer (JJAS) in 2022 38 4.1.3 Variation of Daily Thermal Environment and Thermal Comfort 44 4.2 Thermal Environment and Thermal Comfort under Different LULC in Yunlin Area 50 4.2.1 Land Use and Land Cover in Yunlin Area 51 4.2.2 Comparison of Thermal Environment between Different LULC 52 4.2.3 Comparison of Thermal Comfort between Different LULC 57 4.3 Thermal Comfort in Yunlin Area 62 Chapter 5 Discussions 70 5.1 Heat Exposure in Yunlin 70 5.2 Prevention and Improvement Suggestions for Heat-risk 76 Chapter 6 Conclusions and Suggestions 83 6.1 Conclusions 83 6.2 Suggestions 85 Reference 87 | - |
dc.language.iso | en | - |
dc.title | 探討環境與地理因子對高齡化農村熱環境的影響: 以雲林縣為例 | zh_TW |
dc.title | Investigation Effects of Environmental and Geographical Factors on Thermal Environment in Aging Rural Areas in Yunlin County | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 謝宜桓;林博雄 | zh_TW |
dc.contributor.oralexamcommittee | Yi-Huan Hsieh;Po-Hsiung Lin | en |
dc.subject.keyword | 熱環境,熱舒適,年長者,生理等校溫度(PET),農地, | zh_TW |
dc.subject.keyword | Thermal environment,thermal comfort,elderly,physiologically equivalent temperature (PET),agricultural fields, | en |
dc.relation.page | 94 | - |
dc.identifier.doi | 10.6342/NTU202303431 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-09 | - |
dc.contributor.author-college | 理學院 | - |
dc.contributor.author-dept | 地理環境資源學系 | - |
dc.date.embargo-lift | 2026-08-08 | - |
顯示於系所單位: | 地理環境資源學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 11.17 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。