跳到主要內容

臺灣博碩士論文加值系統

(44.211.117.197) 您好!臺灣時間:2024/05/22 01:16
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:張琳
研究生(外文):Ling Chang
論文名稱:應用神經網絡建立全臺灣近地面臭氧濃度之推估模型
論文名稱(外文):Applying Neural Network to Establish the Prediction Model of Ground Level of Ozone in Taiwan
指導教授:黃彬芳黃彬芳引用關係
學位類別:碩士
校院名稱:中國醫藥大學
系所名稱:職業安全與衛生學系碩士班
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:61
中文關鍵詞:空氣污染物神經網絡臭氧
外文關鍵詞:Air pollutionsNeural NetworkOzone(O3)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:51
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
中文摘要 III
英文摘要 V
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
第二章 文獻回顧 3
2.1 空氣污染物對健康之影響 3
2.2 空氣污染物濃度推估方法 5
2.2.1 插值模式 5
2.2.2 迴歸模式 6
2.2.3 土地利用模式 6
2.2.4 機器學習法 7
2.3 神經網絡模式 8
2.4 影響臭氧濃度之因素 10
第三章 研究材料與方法 15
3.1 研究設計 15
3.2 數據資料 16
3.2.1 研究區域 16
3.2.2 空氣污染物地面監測數值 16
3.2.3 OMI衛星數值 17
3.2.4 氣象因子 18
3.2.5 土地利用資料 19
3.2.6 數據整合 20
3.3 神經網絡 21
3.4 模式驗證 22
第四章 結果 26
4.1 地面O3實測值 26
4.2 衛星OMI-O3數值 26
4.3 氣象因子 27
4.4 神經網絡推估模式 29
第五章 討論 50
5.1 與先前文獻之比較 50
5.2 本研究之優勢 52
5.3 本研究之限制 52
第六章 結論 53
第七章 參考文獻 54
BerrisfordP, DeeD, FieldingK, FuentesM, KallbergP, KobayashiS, et al. 2011. The ERA-Interim Archive.

BoersmaKF, EskesHJ, DirksenRJ, van derARJ, VeefkindJP, StammesP, et al. 2011. An improved tropospheric NO 2 column retrieval algorithm for the Ozone Monitoring Instrument. Atmos Meas Tech 4:1905–1928.

BretonCV., WangX, MacKWJ, BerhaneK, LopezM, IslamTS, et al. 2012. Childhood air pollutant exposure and carotid artery intima-media thickness in young adults. Circulation 126:1614–1620.

ChenTF, TsaiCY, ChangKH. 2013. Performance evaluation of atmospheric particulate matter modeling for East Asia. Atmos Environ 77:365–375.

DabassA, TalbottEO, BilonickRA, RagerJR, VenkatA, MarshGM, et al. 2016. Using spatio-temporal modeling for exposure assessment in an investigation of fine particulate air pollution and cardiovascular mortality. Environ Res 151:564–572.

DiQ, RowlandS, KoutrakisP, SchwartzJ. 2017. A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. J Air Waste Manag Assoc 67:39–52

HanL-H, ZhangH-L, XiangX, ZhangP, ChengS-Y, WeiW, et al. 2017. [Precipitation and Its Effects on Atmospheric Pollutants in a Representative Region of Beijing in Summer]. Huan Jing ke Xue= Huanjing Kexue 38:2211–2217.

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. 2018. ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 10-08-2021)

HoHC, WongMS, YangL, ShiW, YangJ, BilalM, et al. 2018. Spatiotemporal influence of temperature, air quality, and urban environment on cause-specific mortality during hazy days. Environ Int 112:10–22.

Hong Shengmao, Jiao Li, He Xi, Zhou Chunyu. 2009. The Variation of Ozone Concentrations in Urban Districts of Hangzhou and Their Relationship with Meteorological Factors.

HsuCY, WuJY, ChenYC, ChenNT, ChenMJ, PanWC, et al. 2019. Asian culturally specific predictors in a large-scale land use regression model to predict spatial-temporal variability of ozone concentration. Int J Environ Res Public Health 16.

HwangSL, GuoSE, ChiMC, ChouCT, LinYC, LinCM, et al. 2016. Association between atmospheric fine particulate matter and hospital admissions for chronic obstructive pulmonary disease in Southwestern Taiwan: A population-based study. Int J Environ Res Public Health 13:5–13.
JungCR, LinYT, HwangBF. 2015. Ozone, particulate matter, and newly diagnosed Alzheimer’s disease: A population-based cohort study in Taiwan. J Alzheimer’s Dis 44:573–584.

JungCR, ChenWT, LinYT, HwangBF. 2017. Ambient air pollutant exposures and hospitalization for Kawasaki disease in Taiwan: A case-crossover study (2000–2010). Environ Health Perspect 125:670–676.

KamińskaJA. 2019. A random forest partition model for predicting NO 2 concentrations from traffic flow and meteorological conditions. Sci Total Environ 651:475–483.

KampaM, CastanasE. 2008. Human health effects of air pollution. Environ Pollut 151:362–367.

KinneyP. 1999. The Pulmonary Effects of Outdoor Ozone and Particle Air Pollution. Semin Respir Crit Care Med 20:601–607.

KokenPJM, PiverWT, YeF, ElixhauserA, OlsenLM, PortierCJ. 2003. Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly people in Denver. Environ Health Perspect 111:1312–1317.

LeeYL, ChenJH, WangCM, ChenML, HwangBF. 2018. Association of Air Pollution Exposure and Interleukin-13 Haplotype with the Risk of Aggregate Bronchitic Symptoms in Children. EBioMedicine 29:70–77.

LiR, CuiL, HongboF, LiJ, ZhaoY, ChenJ. 2020. Satellite-based estimation of full-coverage ozone (O3) concentration and health effect assessment across Hainan Island. J Clean Prod 244:118773.

LiR, CuiL, LiJ, ZhaoA, FuH, WuY, et al. 2017. Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014–2016. Atmos Environ 161:235–246.

LiR, WangZ, CuiL, FuH, ZhangL, KongL, et al. 2019. Air pollution characteristics in China during 2015–2016: Spatiotemporal variations and key meteorological factors. Sci Total Environ 648:902–915.

LimH, KwonHJ, LimJA, ChoiJH, HaM, HwangSS, et al. 2016. Short-term effect of fine particulate matter on children’s hospital admissions and emergency department visits for asthma: A systematic review and meta-analysis. J Prev Med Public Heal 49:205–219.

Prüss-ÜstünA, WolfJ, CorvalánC, BosR, NeiraM. 2016. Preventing disease through healthy environments: a global assessment of the burden of disease from environmental risks.

SchalkoffR. 1997. Artificial neural networks.

ShierV, NicosiaN, ShihR, DatarA. 2019. Ambient air pollution and children’s cognitive outcomes. Popul Environ 40:347–367.

SilveiraC, RoebelingP, LopesM, FerreiraJ, CostaS, TeixeiraJP, et al. 2016. Assessment of health benefits related to air quality improvement strategies in urban areas: An Impact Pathway Approach. J Environ Manage 183:694–702.

SonY, Osornio-VargasÁR, O’NeillMS, HystadP, Texcalac-SangradorJL, Ohman-StricklandP, et al. 2018. Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters. Sci Total Environ 639:40–48.

StafoggiaM, JohanssonC, GlantzP, RenziM, ShteinA, HooghKde, et al. 2020. A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden. Atmosphere (Basel) 11.

TranchantBJS, VincentAP. 2000. Statistical interpolation of ozone measurements from satellite data (TOMS, SBUV and SAGE II) using the kriging method. Ann Geophys 18:666–678.

ZhangZ, ZhangX, GongD, QuanW, ZhaoX, MaZ, et al. 2015. Evolution of surface O3 and PM2.5 concentrations and their relationships with meteorological conditions over the last decade in Beijing. Atmos Environ 108:67–75.

ZhaoS, YuY, YinD, HeJ, LiuN, QuJ, et al. 2016. Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. Environ Int 86:92–106.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top