利用Sentinel-5 Precursor 衛星與地面觀測資料分析臺灣二氧化氮的時空分佈特性

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：35

、訪客IP：52.15.153.23

姓名

蔡怡貞(Yi-Jhen Cai) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

利用Sentinel-5 Precursor 衛星與地面觀測資料分析臺灣二氧化氮的時空分佈特性
(Spatiotemporal Characteristics of Tropospheric and Surface NO2 from Sentinel-5 Precursor (S5P) and in-situ Observations in Taiwan)

相關論文

★ 地球同步衛星觀測資料之雲區像素辨識	★ 結合掩星折射率與高光譜紅外線觀測之大氣溫溼度垂直剖面反演
★ 結合衛星反演資料與WRF模式探討梅雨鋒面水氣傳送關聯性之個案研究	★ Optimal Use of Satellite Sounding Products for Numerical Weather Prediction
★ The spatial correlation of satellite-estimated PM2.5 and epidemiological diseases in Taiwan	★ Assessment of the NWP Model Physical Fields from Radiative Quantity
★ 海表面風場與通量於熱帶氣旋發展影響之探討	★ 使用衛星資料評析全球預報模式之雲參數特性
★ 衛星輻射強度與反演產品之資料同化研究－－尼伯特颱風(2016)個案分析	★ 日本氣象同步衛星 Himawari-8 向日葵八號之雲微物理參數反演驗證與評估
★ 掩星資料於颱風快速增強機制之模擬研究－梅姬颱風(2010)	★ 利用多頻道衛星觀測評估WRF數值模式於不同微物理方案之雲特性:以梅雨鋒面降水系統個案為例
★ 應用多時期向日葵8號衛星影像進行雲像素的偵測與追蹤	★ 使用CloudSat及ECMWF再分析資料探討南海及海洋大陸地區深對流之環境因子
★ 使用 CloudSat 分析南海與海洋大陸地區之深對流與動力環境特徵	★ 印尼地區地表性質與雲特徵之探討

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

Sentinel-5 Precursor (S5P)衛星發射時間為2017年，屬於新一代環境衛星。其觀測目標在於空氣品質與環境監測，並提供較高解析度的氣膠、微量氣體等反演資料以利未來進行研究分析使用。但近幾年來較少研究人員使用S5P衛星上的酬載儀器TROPOMI所反演出來的產品進行臺灣污染物的相關分析。因此本篇研究使用TROPOMI所反演出的對流層二氧化氮(Nitrogen Dioxide, NO2) 與地面觀測近地表二氧化氮進行驗證及比較。二氧化氮屬於主要的都市地區污染物並且為二次性氣膠及臭氧的前驅物。其中二次性氣膠是引發極端空氣污染事件的成因之一，因此為了監測空氣品質，分析前驅氣體的時空變化趨勢顯得相當重要。此篇研究採用2018年6月至2019年5月的每日及每月平均資料進行相關性統計。使用日資料分析時，北部 (新北市/臺北市/基隆市)、雲林、嘉義、臺南及高雄屬於高度相關(r ≥ 0.5);低度相關(r < 0.3) 則出現在宜蘭、新竹、桃園。因污染物濃度日變化包含了許多複雜因素，如大氣環流不同、排放源強度不一、化學物質轉變及觀測時間上的誤差等，因此使用月平均資料分析，結果顯示各地區相關係數值較日資料增加許多，代表由兩儀器所觀測出來的資料相關性提升到中度、甚至高度相關，同時意味著對流層二氧化氮及近地表二氧化氮濃度變化趨勢相似，因此可證明TROPOMI所反演出的二氧化氮產品可使用在臺灣相關污染物研究中。
從時空分布角度來看，臺灣東側通常污染物平均濃度較低；都市地區則因排放量較多，濃度偏高。根據不同季節，臺灣冬季擁有最高平均濃度值;夏季則是最低。此結果也能瞭解到濃度變化與氣象場有一定的關聯性，因此結合再分析場並特別針對高濃度污染事件進行探討。極端污染事件一般出現在各縣市盛行北或東風、低風速及大氣較為穩定的情況下，因污染物擴散不易所造成。而當冬季邊界層高度相較於月平均低、溫度及相對濕度則相較於月平均時高，則極端越值地區 (Extreme Threshold Area, 如:桃園、臺中、彰化、高雄) 易有高濃度污染產生。除此之外，地面測站提供了近地表的污染物濃度觀測，藉由濃度變化可分析區域排放的影響; S5P衛星上的TROPOMI則可提供較大範圍 (包含都市、郊區與山區)、高解析度 (3.5×7.0 km2)、垂直層濃度 (近地表與高層) 的觀測，去理解大環境污染物的變化並增加地面測站所無法提供出的高層二氧化氮與季節之間的相關特性說明，同時驗證高層污染物對於空氣污染事件是不可忽略的重要因子。

摘要(英)

Sentinel-5 Precursor (S5P) is a new generation environmental satellite and provides products regarding trace gases concentrations along with cloud and aerosol information in global scale. This study focuses on nitrogen oxide (NOx), the main air pollutant in urban areas and the precursor of secondary aerosols and ozone, under the regional scale. As NOx is primary in the form of Nitrogen dioxide (NO2), this study aims to understand the spatiotemporal feature of NO2 in Taiwan. Tropospheric NO2 data retrieved from the TROPOMI onboard S5P and surface NO2 data collected by surface instruments from the Environmental Protection Administration (EPA) of Taiwan are used for analysis. The analyzed temporal period spans from June 2018 to May 2019, and surface observation is collocated with a daily revisit of S5P spatiotemporally. Those data are analyzed in 15 sub-regions in Taiwan to understand the relationship between tropospheric vertical column and surface concentration of NO2.
The preliminary results reveal high correlations between surface and tropospheric NO2 concentration in North (Taipei/ New Taipei City/ Keelung), Yunlin, Chiayi, Tainan, and Kaohsiung in S5P’s daily revisit rate, while Yilan, Taoyuan, and Hsinchu shows relatively low correlations. The mean concentration of NO2 on the monthly scale is analyzed to avoid possible impacts due to high-frequency variation in environmental factors. Results indicate there are high correlations in most counties between ground-based and space-borne observations. In other words, the tendency of surface NO2 is similar to the tropospheric NO2 in most countries in Taiwan, particularly on the monthly scale. Therefore, to monitor and analyze NO2 concentration, spaceborne TROPOMI on S5P might be an alternative option for conducting related research in Taiwan.
From the aspect of spatiotemporal characteristic analysis, Eastern Taiwan often has the lowest concentration; there is a higher concentration in the urban area because of the abundant emission sources. Moreover, during winter the concentration tends to be higher than summer, indicating certain connections between the environment and air pollutants. The NCEP reanalysis data, which represents the meteorological factors, is also applied in this study for investigating the uncertainty sources, revealing a strong correlation between wind direction and pollution concentration. The result shows that higher NO2 concentrations are usually under lower planetary boundary height (PBL), lower wind speed, lower tropospheric stability (LTS), lower 975 hPa temperature, and higher 975 hPa Relative humidity (RH), and this feature is enhanced in summer and spring.
An analysis of the collected data demonstrates that ground-based observation represents the local effect; satellite observation can represent the state of the environmental pollution, offering certain characteristics that cannot be identified by surface observation because of its ability to provide coverage of large area (urban, suburban and ocean) and observe total column density (near-surface and higher-level NO2). This study also proves that pollutants at higher altitudes play an important role in extreme air pollution events.

關鍵字(中)

★ 二氧化氮
★ Sentinel-5 Precursor (S5P)衛星
★ 時空分布特徵
★ 極端情況

關鍵字(英)

★ Nitrogen dioxide (NO2)
★ Sentinel-5 Precursor (S5P)
★ Spatiotemporal Characteristics
★ Extreme Scenario

論文目次

摘要 i
Abstract iii
誌謝 v
List of Tables viii
List of Figures ix
Chapter 1 Introduction and Objectives 1
1.1 Introduction 1
1.1.1 Geography location 5
1.1.2 The Observation of NO2 9
1.2 Research Objectives 13
Chapter 2 Data and Methodology 14
2.1 TROPOMI on Sentinel-5 Precursor (S5P) satellite 14
2.2 In-situ observations 17
2.3 Reanalysis Data 21
2.4 Study region 22
2.5 Methodology 25
Chapter 3 Results and Discussion 29
3.1 Comparison between Tropospheric Column and Surface NO2 29
3.1.1 Tendency of Daily NO2 29
3.1.2 Tendency of Month Average NO2 30
3.2 Spatiotemporal characteristics of NO2 in Taiwan 35
3.2.1 Spatiotemporal Distribution 35
3.2.2 Difference between winter and summer 36
3.3 Meteorological condition and NO2 42
3.3.1 Correlation Coefficient 42
3.3.2 Wind Field 45
3.4 Extreme air pollution events 55
3.5 Relationship among surface mixing ratio, atmospheric column density, and PBLH 69
3.5.1 Atmospheric column density estimation 69
3.5.2 Surface mixing ratio estimation 70
3.5.3 PBLH estimation 71
Chapter 4 Conclusions and Future Work 78
4.1 Conclusions 78
4.2 Future work 82
REFERENCES 84

參考文獻

底宗鴻,2008。高雄地區陸域及鄰近海域懸浮微粒物化特性分析及時空分佈探討。國立中山大學環境工程研究所碩士論文。
陳泰然、廖佩娟, 2011。臺灣地區冬季鋒面系統之天氣特徵研究,大氣科學,第三十九期第二號。
Adame, J.A., I. Gutierrez-Alvarez, J.P. Bolivar, M. Yela, 2020. Ground-based and OMI-TROPOMI NO2 measurements at El Arenosillo observatory: Unexpected upward trends, Environ. Poll., 264 (2020), doi: 10.1016/j.envpol.2020.114771.
Baldasano, J.M., 2020. COVID-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain), Sci. Total Environ., 741 (2020), doi: 10.1016/j.scitotenv.2020.140353.
Bauwens, M., S. Compernolle, T. Stavrakou, J.-F. Müller, J. van Gent, H. Eskes, P. F. Levelt, R. van der A, J. P. Veefkind, J. Vlietinck, Huan Yu, C. Zehner, 2020. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations, Geophysical Research Letter, 47 (11), doi: 10.1029/2020GL087978.
Benedict, B. K., Y. Zhou, B. C. Sive, A. J. Prenni, Kristi A. Gebhart, E. V. Fischer, a. Evanoski-Cole, A. P. Sullivan, Sara Callahan1, B.A. Schichtel, H. Mao, Y. Zhou, and J L. Collett Jr., 2019. Volatile organic compounds and ozone in Rocky Mountain National Park during FRAPPÉ, Atmos. Chem. Phys, 19, 499–521, doi: 10.5194/acp-19-499-2019.
Biswal, A., V. Singh, S. Singh, A. P. Kesarkar, K.Ravindra, R. S. Sokhi, M. P. Chipperfiel, S. S. Dhomse, R. J. Pope, T.Singh, and S. Mor, 2021. COVID-19 lockdown-induced changes in NO2 levels across India observed by multi-satellite and surface observations, Atmos. Chem. Phys., 21, 5235–5251, doi: 10.5194/acp-21-5235-2021.
Bösch, T., V. Rozanov, A. Richter, E. Peters, A. Rozanov, F. Wittrock, A. Merlaud, J. Lampel, S. Schmitt, M. de Haij, S. Berkhout, B. Henzing, A. Apituley, M. den Hoed, J. Vonk, M. Tiefengraber, M. Müller, and J. P. Burrows, 2018. BOREAS – a new MAX-DOAS profile retrieval algorithm for aerosols and trace gases, Atmos. Meas. Tech., 11, 6833–6859, doi: 10.5194/amt-11-6833-2018.
Cheng F.-Y. and C.-H. Hsu, 2019. Long-term variations in PM2.5 concentrations under changing meteorological conditions in Taiwan, Sci Rep, 9, 6636, doi: 10.1038/s41598-019-43104-x.
Choi, Y., Y. Kanaya, H. Takashima, H. Irie, K. Park and J. Chong, 2021. Long-Term Variation in the Tropospheric Nitrogen Dioxide Vertical Column Density over Korea and Japan from the MAX-DOAS Network, 2007–2017, Remote Sens., 13, 1937, doi:10.3390/rs13101937.
de Foy, B., Z. Lu, D. G. Streets, 2016. atellite NO2 retrievals suggest China has exceeded its NOx reduction goals from the twelfth Five-Year Plan, nature, 6, 35912, doi: 10.1038/srep35912.
Eskes, H., van Geffen, J., Boersma, F., Eichmann, K., Apituley, A., Pedergnana, M., Sneep, M., Veefkind Veekind, J., Loyola, D., 2019. Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual Nitrogen Dioxide.
Frieß, U., St. Beirle, L. A. Bonilla, T. Bösch, M. M. Friedrich, F. Hendrick, A. Piters, A. Richter, M. van Roozendael, V. V. Rozanov, E. Spinei, J.-L. Tirpitz, T. Vlemmix, T. Wagner, and Y. Wang, 2019. Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies using synthetic data, Atmos. Meas. Tech., 12, 2155–2181, doi: 10.5194/amt-12-2155-2019.
Ghude, S. D., S. Fadnavis, G. Beig, S. D. Polade, and R. J. van der A, 2008. Detection of surface emission hot spots, trends, and seasonal cycle from satellite-retrieved NO2 over India, J. Geophys. Res, 113, D20305, doi:10.1029/2007JD009615.
Goldberg, D. L., S. C. Anenberg, G. H. Kerr, A. Mohegh, Z. Lu, D. G. Streets, 2021. TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation with Surface NO2 Concentrations, Remote Sensing, 13, 10, doi: 10.3390/rs13112095.
Han, K. M., 2019. Temporal Analysis of OMI-Observed Tropospheric NO2 Columns over East Asia during 2006–2015, Atmos., 10, 658, doi:10.3390/atmos10110658.
Honninger, G., C. von Friedeburg and U. Platt, 2004. Multi axis differential optical absorption spectroscopy (MAX-DOAS), Atmos. Chem. Phys., 4, 231–254, doi: 1680-7324/acp/2004-4-231
Kang, H., B. Zhu, R. J. van der, C. Zhu, G. de Leeuw, X. Hou, J. Gao, 2019. Natural and anthropogenic contributions to long-term variations of SO2, NO2, CO, and AOD over East China, Atmos, 215, 284-293, doi:10.1016/j.atmosres.2018.09.012.
Knepp, T., M. Pippin, J. Crawford, G. Chen, J. Szykman, R. Long, L. Cowen, A. Cede, N. Abuhassan, J. Herman, R. Delgado, J. Compton, T. Berkoff, J. Fishman, D. Martins, R. Stauffer, A. M. Thompson, A. Weinheimer, D. Knapp, D. Montzka, D. Lenschow, D. Neil, 2013. Estimating surface NO2 and SO2 mixing ratios from fast-response total column observations and potential application to geostationary missions, J. Atmos. Chem., 72, 261–286, doi: 10.1007/s10874-013-9257-6.
Kollonige, D. E, A. M. Thompson, M. Josipovic, M. Tzortziou, J. P. Beukes, R. Burger, D. K. Martins, P. G. van Zyl, V. Vakkari, L. Laakso, 2017. OMI Satellite and Ground-Based Pandora Observations and Their Application to Surface NO2 Estimations at Terrestrial and Marine Sites, J. Geophys. Res., 123(2), 1441–1459, doi:10.1002/2017JD026518.
Kramer, J. Louisa, Roland J. Leigh, John J. Remedios, and Paul S. Monks, 2008. Comparison of OMI and ground-based in situ and MAX-DOASmeasurements of tropospheric nitrogen dioxide in an urban area, J. Geophys. Res., 113, D16S39, doi: 10.1029/2007JD009168.
Lamsal, L. N., R. V. Martin, A. van Donkelaar, M. Steinbacher, E. A. Celarier, E. Bucsela, E. J. Dunlea, and J. P. Pinto, 2008. Ground-level nitrogen dioxide concentrations inferred fromthe satellite-borne Ozone Monitoring Instrument, J. Geophys. Res., 113, D16308, doi:10.1029/2007JD009235.
Larkin A., J. A. Geddes, R. V Martin, Q. Xiao, Y. Liu, J. D Marshall, M. Brauer, and P. Hystad, 2017. A Global Land Use Regression Model for Nitrogen Dioxide Air Pollution, Environ. Sci. Technol., 51 (12), 6957−6964, doi: 10.1021/acs.est.7b01148.
Lee, C.-S., K.-H. Chang, H. Kim, 2019. Long-term (2005–2015) trends analysis of OMI retrieved NO2 columns in Taiwan. Atmos. Poll. Res., 10(3) 960–970, doi:10.1016/j.apr.2019.01.004.
Lee, Y.Y., Y.Y. Hsieh, G.P. Chang-Chien, W. Wang, 2019. Characterization of the Air Quality Index in Southwestern Taiwan, Aerosol Air Qual. Res., 19, 749–785, doi: 10.4209/aaqr.2019.02.0080.
Lin, C. A., Y.-C. Chen, C.-Y. Liu, W.-T. Chen, J. H. Seinfeld and Charles C.-K. Chou, 2019. Satellite-Derived Correlation of SO2, NO2, and Aerosol Optical Depth with Meteorological Conditions over East Asia from 2005 to 2015, Remote Sens., 11(15), 1738, doi: 10.3390/rs11151738.
Ma, T., F. Duan, K. He, Y. Qin, D. Tong, G. Geng, X. Liu, H. Li, S. Yang, S. Ye, B. Xu, Q. Zhang, Y. Ma, 2019. Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014–2016, J. Elsevier, 83, 8-20, doi: 10.1016/j.jes.2019.02.031.
Meng, Z.Y., G.A. Ding, X.B. Xu, X.D. Xu, H.Q. Yu, S.F. Wang, 2008. Vertical distributions of SO2 and NO2 in the lower atmosphere in Beijing urban areas, China, J. Elsevier, 390 (2008), 456-465, doi:10.1016/j.scitotenv.2007.10.012.
Ogen, Y., 2020. Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality, Sci. Total Environ., 726(2020). doi: 10.1016/j.scitotenv.2020.138605.
Ordo´n˜ez, C., Richter, Steinbacher, Zellweger, Nu¨ß, P. Burrows, and A. S. H. Pre´voˆt, 2006. Comparison of 7 years of satellite-borne and ground-basedtropospheric NO2measurements around Milan, Italy, J. Geophys. Res., 111, D05310, doi:10.1029/2005JD006305.
Paraschiv, S., D.-E. Constantin, S.-L. Paraschiv and M. Voiculescu, 2017. OMI and Ground-Based In-Situ Tropospheric Nitrogen Dioxide Observations over Several Important European Cities during 2005–2014, Int. J. Environ. Res. Public Health, 14, 1415, doi:10.3390/ijerph14111415.
Platt, U., 1994. Differential Optical Absorption Spectroscopy (DOAS), Air Monitoring by Spectroscopic Techniques, M. Siegri, Ed., Chemical Analysis Series, Vol. 127, John Wiley and Sons, 27–84.
Platt, U., J., Stutz, 2008. Differential Optical Absorption Spectroscopy: Principles And Applications, Springer-Verlag, Berlin, Heidelberg, doi:10.1007/978-3-540-75776-4.
Richter, A., J. P. Burrows, H. Nu¨ß, C. Granier, and Ulrike Niemeier, 2005. Increase in tropospheric nitrogen dioxide over China observed from space, Nature, doi:10.1038/nature04092.
Ronald J. van de, B. Mijling, J. Ding, M. E. Koukouli, F. Liu, Q. Li, H. Mao, and N. Theys, 2017. Cleaning up the air: effectiveness of air quality policy for SO2 and NOx emissions in China, Atmos. Chem. Phys., 17, 1775–1789, doi:10.5194/acp-17-1775-2017.
Tian, X., P. Xie, J. Xu, A. Li, Y. Wang, M. Qin, Z. Hu, 2018. Long-term observations of tropospheric NO2, SO2 and HCHO by MAX-DOAS in Yangtze River Delta area, China, J. Elsevier, 10, 1001-0742, doi:10.1016/j.jes.2018.03.006.
Tzortziou, M., O. Parker, B. Lamb, J. R. Herman, L. Lamsal, R. Stauffer, and N. Abuhassan, 2018. Atmospheric Trace Gas (NO2 and O3) Variability in South Korean Coastal Waters, and Implications for Remote Sensing of Coastal Ocean Color Dynamics, Remote Sens., 10, 1587; doi:10.3390/rs10101587.
USEPA., 2012. Our Nation’s Air Status and Trends Through 2010; Technical Report EPA-454/R-12-001; USEPA:Washington, DC, USA.
van Geffen, J. H. G. M., Eskes, H. J., Boersma, K. F., Maasakkers, J. D., and Veefkind, J. P., 2020. TROPOMI ATBD of the total and tropospheric NO2 data products, Report S5P-KNMI-L2-0005-RP, version 2.1.0, KNMI, De Bilt, the Netherlands, available at: http://www.tropomi.eu/documents/atbd/
van Geffen, J., K. F. Boersmal, H. Eskes, M. Sneep, M. ter Linden, M. Zara, and J. P. Veefkind, 2019. S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI, Atmos. Meas. Tech., 13, 1315–1335, doi.org/10.5194/amt-13-1315-2020.
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F., 2012. TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, doi:10.1016/j.rse.2011.09.027, 2012.
Veefkind, J. P., K. F. Boersma, J. Wang, T. P. Kurosu, N. Krotkov, K. Chance, and P. F. Levelt, 2011. Global satellite analysis of the relation between aerosols and short-lived trace gases, Atmos. Chem. Phys., 11, 1255–1267, doi:10.5194/acp-11-1255-2011.
Venter, Z. S., K. Aunan, S. Chowdhury, and J. Lelieveld, 2020. COVID-19 lockdowns cause global air pollution declines, medRxiv, 117(32), 18984-18990, doi: 10.1073/pnas.2006853117.
Verhoelst, T., S. Compernolle, G. Pinardi, J.-C. Lambert, H. J. Eskes, K.-U. Eichmann,A. M. Fjæraa, J. Granville, S. Niemeijer, A. Cede, M. Tiefengraber, F. Hendrick, A. Pazmiño, A. Bais, A. Bazureau, K. F. Boersma, K. Bognar, A. Dehn, S. Donner, A. Elokhov, M. Gebetsberger, F. Goutail, M. G. de la Mora, A. Gruzdev, M. Gratsea, G. H. Hansen, H. Irie, N. Jepsen, Y. Kanaya, D. Karagkiozidis, R. Kivi, K. Kreher, P. F. Levelt, C. Liu, M. Müller, M. N. Comas, A. J. M. Piters, J.-P. Pommereau, T. Portafaix, C. Prados-Roman, O. Puentedura, R. Querel, J. Remmers, A. Richter, J. Rimmer, C. R. Cárdenas, L. S. de Miguel, V. P. Sinyakov, W. Stremme, K. Strong, M. Van Roozendael, J. P. Veefkind, T. Wagner, F. Wittrock, M. Y. González, C. Zehner, 2021. Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks, Atmos. Meas. Tech., 14, 481–510, doi: 10.5194/amt-14-481-2021.
Wang, S.W., Q. Zhang, D. G. Streets, K. B. He, R. V. Martin, L. N. Lamsal, D. Chen, Y. Lei, and Z. Lu, 2012. Growth in NOx emissions from power plants in China: bottom-up stimates and satellite observations, Atmos. Chem. Phys., 12, 4429–4447, doi:10.5194/acp-12-4429-2012.
Xu, J.-W., R. V. Martin, A. van Donkelaar, J. Kim, M. Choi, Q. Zhang, G. Geng, Y. Liu, Z. Ma, L. Huang, Y. Wang, H. Chen, H. Che, P. Lin, and N. Lin, 2015. Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument, Atmos. Chem. Phys., 15, 13133–13144, doi:10.5194/acp-15-13133-2015.
Xue,Y. f., S. Zhang, Z. Zhou, K. Wang, K. Liu, X. Wang, A. Shi, K. Xu and H. Tian, 2019. Spatio-Temporal Variations of Multiple Primary Air Pollutants Emissions in Beijing of China, 2006–2015, Atmos., 10, 494, doi:10.3390/atmos10090494.
Yu C., T. Zhao, Y. Bai, L. Zhang, S. Kong, X. Yu, J. He, C. Cui, J. Yang, Y. You, G. Ma, M. Wu, and J. Chang, 2020. Heavy air pollution with a unique “non-stagnant” atmospheric boundary layer in the Yangtze River middle basin aggravated by regional transport of PM2.5 over China, Atmos. Chem. Phys., 20, 7217–7230, doi: 10.5194/acp-20-7217-2020.
Zhang, R., G. Wang, S. Guo, M. L. Zamora, Q. Ying, Y. Lin, W. Wang, M. Hu and Y. Wang, 2015. Formation of urban fine particulate matter. Chem. Rev., 115(10), 3803-3855, doi: 10.1021/acs.chemrev.5b00067.
Zhang, Yanda, Y.-J. Cai, F. Yu, G. Luo, C. C.K. Chou, 2020. Seasonal Variations and Long-term Trend of Mineral Dust Aerosols over the Taiwan Region, Aerosol Air Qual. Res., 21, 200433, doi:10.4209/aaqr.2020.07.0433.
Zheng, C., C. Zhao, Y. Lib, X. Wu, K. Zhang, J. Gao, Q. Qiao, Y. Ren, X. Zhang, F. Chai, 2018. Spatial and temporal distribution of NO2 and SO2 in Inner Mongolia urban agglomeration obtained from satellite remote sensing and ground observations, J. Elsevier, 188, 50-59, doi: 10.1016/j.atmosenv.2018.06.029.

指導教授

劉千義(Chian-Yi Liu)

審核日期

2021-8-2

推文