鹿林山大氣汞分布變化: 氣象因子影響機制分析

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林華毅(Hua-Yi Lin) 查詢紙本館藏

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大氣科學學系

論文名稱

鹿林山大氣汞分布變化: 氣象因子影響機制分析
(Relationships between atmospheric mercury and meteorological parameters at mountain background site in Taiwan)

相關論文

★ 鹿林山大氣汞分布與乾濕沉降特徵及來源推估	★ 北台灣雨水汞濃度及濕沉降量之時空分布
★ 2009-2018年台灣市區與郊區之長期大氣汞濕沉降測量	★ Characterizations of atmospheric mercury concentration and deposition at a tropical mountain background site in East Asia: insight into potential driving mechanisms
★ 桃園大氣汞分布與沈降暨顆粒汞粒徑分布特徵

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摘要(中)

汞(Hg)是少數具有生物累積性的重金屬污染物，主要藉由大氣傳輸影響全球，不同的地理環境下，氣象因子對大氣汞濃度的影響不盡相同。以往鹿林山大氣汞研究多著重於多項氣象因子整體對大氣汞濃度的定性趨勢探討，缺乏個別氣象因子貢獻的定量分析，因此本研究分析台灣鹿林山大氣背景測站2016至2019年間之大氣汞與其他污染物及氣象觀測資料，並運用廣義相加模式(Generalized additive model, GAM)量化各污染物與氣象因子對大氣汞濃度變化的貢獻，進而推論影響高山測站大氣汞濃度變化的主要機制。
2016-2019年鹿林山測站氣態元素汞(Gaseous element mercury, GEM)、氣態氧化汞(Gaseous oxidized mercury, GOM) 與顆粒汞(Particulate-bound mercury, PBM)的平均濃度(±標準差)分別為1.54(±0.35) ng m-3、18.4(±31.3) pg m-3 與18.2(±41.9) pg m-3。大氣汞濃度日夜變化受山谷風帶動的氣團垂直運動影響，造成GEM濃度呈現日高夜低，而GOM與PBM濃度則是日低夜高。就季節變化而言，GEM平均濃度最高值發生於春季，此時鹿林山測站經常受到中南半島生質燃燒長程傳輸的影響，而GOM及PBM濃度則於冬、春兩季較高，與較多強沈降事件發生及較少降雨有關。GEM與溫度於冬、夏兩季呈現不同的相關性，冬季呈現負相關主要受季風氣候造成氣團來源不同影響；而夏季呈現正相關則是受到山谷風引起的氣團垂直運動機制影響。GOM及PBM濃度與溫度呈現負相關，主要受到氣團垂直運動影響，但也會受到濕沈降移除作用及二價汞於氣相與顆粒相間的分配作用影響而有所變化。相對濕度可呈現出氣團的垂直運動狀態，GEM與相對溼度於四季皆呈現正相關，GOM及PBM則與相對溼度呈現負相關；高PBM濃度出現在相對濕度低於20%，而高GOM濃度則出現在相對濕度介於20%至40%間，推測與不同高度大氣層的溫、溼度條件差異或是氣團來源區域差異有關。強太陽輻射可提供能量使GEM經由光氧化反應轉化成二價汞，導致GEM與日間太陽輻射量呈現負相關，而GOM及PBM濃度則與日間太陽輻射量呈現正相關，強太陽輻射伴隨的高溫，會使二價汞傾向以GOM形式存在於大氣中，導致PBM/RM (GOM + PBM，二價汞) 與太陽輻射量呈現負相關，尤其在溫度較高的夏、秋兩季負相關性特別顯著。
運用廣義相加模式擬合2018年GEM濃度，模式整體能解釋80%的GEM濃度變異量，模式於春季的擬合結果較差，主要受到月份參數的時間尺度過長，以及不同排放源污染物組成差異所影響。CO為所有參數中相對貢獻量最高者，對整體GEM濃度變異解釋量達47.8%，代表人為排放是影響GEM濃度最重要的因素。相對濕度為氣象因子中相對貢獻量最高的參數，主要呈現山谷風機制所引起的氣團垂直運動。月份參數可用來呈現季風氣候對污染物傳輸的影響。GEM與太陽輻射量整體呈現正相關主要受日夜變化所主導，但強太陽輻射會使GEM被氧化成二價汞，造成正相關性減弱。GEM濃度與氣壓呈現負相關性與季風氣候影響有關。CO2及溫度兩項參數皆同時會受到季節與日夜變化兩種不同時間尺度的影響，造成兩參數對GEM濃度變異的解釋性不高，但藉由太陽輻射量與CO2兩項參數的交互作用以呈現出不同大氣環境下GEM濃度的變化，可推論出植物行光合作用並非為影響白天GEM濃度變化的主要機制。

摘要(英)

Mercury (Hg) is a toxic metal of global concern due to its long-term persistence in the atmosphere and bioaccumulation in organisms. Various factors could contribute to the difference in ambient Hg concentration among sampling sites, in which meteorological factors could play a predominant role. Previous studies of Lulin Atmospheric Background Station (LABS) mainly focused on the qualitative analysis between atmospheric mercury and meteorological parameters, but lacked quantitative analysis of individual contribution of each meteorological parameter. This study analyzes the ambient Hg concentration and meteorological data at LABS in 2016-2019, and applied it to a generalized additive model (GAM) to evaluate and quantify the perspective contribution of certain meteorological parameters and air pollutants to the variation in ambient Hg concentration.
From 2016 to 2019, mean GEM, GOM, and PBM concentration at LABS were 1.54 ng/m3, 18.4 and 18.2 pg/m3, respectively. Hg diurnal cycle was mainly driven by the mountain-valley breeze which facilitated air mass vertical movement. For seasonal variation, high GEM values in spring could be attributed to the regional transport of biomass burning pollutants from Southeast Asia while high GOM and PBM concentrations in winter were related to strong subsidence events and low rainfall. Air mass origin and transport path difference were the major factors determining the relationship between GEM and temperature, while gas-particle partitioning plays an important role in determining the distribution of GOM and PBM concentrations. Relative humidity (RH) was chosen as an index to represent air mass vertical movement, in which RH showed a correlation with GEM and anti-correlations with GOM/PBM. When RH < 20%, high PBM subsidence events were observed while high GOM subsidence events occurred at 20% < RH < 40%, suggesting the distinct distribution of different Hg species over different atmospheric layers. Besides, strong solar radiation could enhance the photochemical reaction, leading to the positive trend with daytime GOM and PBM concentration.
With the input of aforementioned meteorological parameters, the GAM model can explain 80.2% of the variance of GEM concentration in 2018. However, the fitting result in spring is worse than other seasons due to a rougher time scale of the month parameter and the difference in the composition of pollutants from different emission sources. Among all considered parameters, carbon monoxide has the highest relative contribution, accounting for 47.8% of the total variance of GEM concentration, indicated anthropogenic emission as the main factor in determining/that primarily determined GEM concentration at LABS. Relative humidity has the highest relative contribution among all meteorological factors, representing the dominant contribution of vertical movement of air masses. The Month parameter was used to classify the air mass origin difference caused by seasonal monsoon shifts. Solar radiation exerts a strong positive correlation on GEM, but could weaken under extreme conditions when in-situ oxidation of GEM into GOM is favored. A Negative correlation between GEM and pressure was mainly affected by regional monsoon shifts. Different time-scale mechanisms could weaken the relative contribution of carbon dioxide and temperature on GEM variation. However, by using the interaction between carbon dioxide and solar radiation to investigate GEM concentration difference under different atmospheric conditions, it can be inferred that the absorption process of GEM by vegetation was not the main factor in determining GEM concentration in daytime.

關鍵字(中)

★ 大氣汞
★ 鹿林山大氣背景測站
★ 廣義相加模式

關鍵字(英)

★ Atmospheric mercury
★ Lulin Atmospheric Background Station
★ Generalized Additive Model

論文目次

中文摘要 i
英文摘要 iii
誌謝 v
目錄 vii
圖目錄 ix
表目錄 xi
第一章、緒論 1
1-1 研究動機 1
1-2 研究目的 2
第二章、文獻回顧 4
2-1 汞的基本性質與來源 4
2-2 全球大氣汞研究 5
2-3 高層大氣汞特性 7
2-4 大氣汞與氣象因子相關性 8
2-5 廣義相加模式於大氣汞研究應用 10
第三章、實驗方法 12
3-1 鹿林山空氣品質背景測站 12
3-2 大氣汞監測分析 13
3-3 污染物與氣象資料觀測分析 13
3-4 廣義相加模式介紹 14
3-5 後推軌跡分析方法 16
3-6 數據處理方法 16
第四章、結果與討論18
4-1 大氣汞分布說明18
4-1-1 日夜變化 19
4-1-2 季節變化 20
4-2 大氣汞與氣象因子相關性分析 25
4-2-1 溫度 25
4-2-2 相對濕度 29
4-2-3 太陽輻射量 32
4-3 廣義相加模型分析結果 55
4-3-1 模式整體結果 55
4-3-2 個別貢獻量解釋與機制探討 57
第五章、結論與建議 72
5-1 結論 72
5-2 建議 74
參考文獻 75
附錄 83

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指導教授

許桂榮(Guey-Rong Sheu)

審核日期

2021-8-9

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