在東南亞（Southeast Asia，SEA）的幾個主要城市(如：曼谷、吉隆坡、新加坡、雅加達等)中，有許多的空氣品質問題，如粉塵、光煙霧、霾害等，其發生之主要原因為露天生質燃燒排放的煙物、氣膠進入大氣中。隨著大氣氣膠監測技術進步，觀測露天燃燒已有一系列的衛星產品。本研究蒐集多種衛星資料如：氣膠光學厚度(Aerosol Optical Depth，AOD)、降雨、城市夜光、Burned area (BA)、Active fire (AF)以評估2002年至2011年大氣氣膠光學厚度與氣候、人類活動及生質燃燒之時空分布關聯性。本研究應用衛星資料如下：1. 利用中分辨率成像光譜儀(Moderate Resolution Imaging Spectroradiometer，MODIS)取得AOD；2. 三種BA產品包括透過植被改變與土地覆蓋分類取得的MCD45A1、從AF取得的GFED4.0以及包含了小規模火源的GFED4.0s；3. AF（MCD14ML）；4. 美國國家海洋和大氣管理局(National Oceanic and Atmospheric Administration，NOAA)取得地表風；5. 利用IGBP分類所取得的土地覆蓋數據（MCD12Q1）；6. 全球降水氣候計畫（Global Precipitation Climatology Project，GPCP）取得雨量資料；7. 代表人類活動情況的DMSP-OLS夜間燈光，以上所有衛星數據的轉換、顯示和分析皆利用ESRI ArcGIS®10.2中的空間分析工具進行分析。
為了更容易了解各種影響因子和大氣氣膠之間的關係，結果分為五個部分討論。首先，分析了2002年至2011年氣膠光學厚度之時空變化。我們從每月AOD分布圖定義了位於北部和南部熱帶區域的高氣膠區域（High Aerosol Areas，HAAs）。北部的HAA包括：緬甸、越南、遼國、泰國和柬埔寨，此區AOD高峰月份為十一月至三月。南部的HAA包括：馬來西亞、蘇門答臘、爪哇和卡里曼丹，其中AOD高峰月份為五月到十月。一般來說，AOD在每區的高峰月份發生在旱季，同時也提供證據表明了東南亞的AOD分布在時間上有一定程度與生質燃燒的關聯是一致的。
第二，是最近發布的BA產品（GFED4.0s）顯示，在北部熱帶區的緬甸有最大年燃燒面積，其次是柬埔寨和泰國。在南部熱帶區發生火災主要分布在印尼，其燃燒高峰月在每一地區也與旱季時間一致。可以發現到，在北部熱帶區域中的燃燒面積比在南部熱帶區多了十倍；但是平均每年的AOD在南部HAA與北部HAA非常相似。這證明了泥炭地發生生質燃燒會排放更多的懸浮微粒。
第三，評估AOD和氣候之間的相關性，發現AOD與降雨呈現反比，可見雨季時會導致AOD有一定程度上的減少。而每月平均風速可解釋部分AOD在北部的HAA大規模移動（十一月至次年四月），但對於南部的HAA，風和AOD的空間分布之間沒有顯著的相關性。第四，AOD普遍在城市和大都市地區較高，但AOD與人類活動的強度之間沒有顯著時間分布相關性。
最後，我們量化AOD與生質燃燒間的關聯性並將研究區域聚焦於兩個HAA地區，並利用不同的BA產品代表生質燃燒情況。我們應用這三個BA產品（包括：MCD45A1，GFED4.0和GFED4.0s），GFED4.0s兼顧GFED4.0和燃燒造成小規模火災，並能更好地解釋AOD在HAA區域的時空分布（北部和南部HAA分別R=0.5和0.85）。常用的MCD45A1 產品和AOD之間的相關性不顯著（北部和南部HAA分別R =0.25和0.58）。相比其他燃燒產品（MCD14ML），我們發現MCD45A1燃燒面積對於AOD的相關性最低，並懷疑此產品是透過植被變化去計算燃燒面積，因此可能嚴重低估了燃燒的區域。為了更好的量化AOD和生物質燃燒之間的關係，這項研究提出兩個簡單的迴歸模型，其使用遙測取得燃燒產品資訊，並分別估計在HAA區域(北部、南部) AOD每月的分布情況。北的HAA的迴歸模型採用MCD14ML資料作為自變量，獲得R2＝0.76；南部的HAA迴歸模型使用GFED4.0s 資料作為自變量，獲得R2＝0.85。由上述可知此經驗模型可解釋在HAA區AOD的時間趨勢。

Major cities in Southeast Asia (SEA) are faced with severe air quality problems including dust, smog and haze pollution, which are mainly caused by atmospheric aerosols (smoke) from biomass burning. Technological advances in monitoring atmospheric aerosol and biomass burning have been fostered by a series of new space based satellite instruments and data products. In this study, a variety of satellite product maps of aerosol optical depth (AOD), precipitation, wind, city light, burned area (BA) and active fire were collected and processed to evaluate the spatial and temporal variations among atmospheric aerosol, climate factors, human activities and biomass burning in SEA during 2002-2011. Satellite data applied in this study includes: 1) the Moderate Resolution Imaging Spectroradiometer (MODIS) derived AOD; 2) three MODIS BA products, including the BA derived from vegetation change and land-cover classification (MCD45A1), the BA derived from active-fire (GFED4.0), and the combination of GFED4.0 and BA caused by small-scale fires (GFED4.0s); 3) the MODIS active fire data (MCD14ML); 4) the National Oceanic and Atmospheric Administration (NOAA) surface wind data; 5) the MODIS International Geosphere-Biosphere Programme (IGBP) classes land cover dataset (MCD12Q1); 6) the Global Precipitation Climatology Project (GPCP) monthly precipitation dataset; and 7) the DMSP-OLS nighttime light representing the strength of human activities. All satellite data was converted, visualized, summarized and analyzed using the spatial analyst tool within ESRI ArcGIS® 10.2.
To better understand the cause and effect relationships between various causative factors and atmospheric aerosols, the results were organized into five sections. First, the spatial and temporal variations of aerosol optical depth in SEA during 2002 to 2011 were examined. High aerosol areas (HAA) located in the northern and southern intertropical zone are identified, respectively, from the monthly AOD distribution maps. The northern HAA consists of Myanmar, Vietnam, Laos, Thailand, and Cambodia, with the peak AOD months are from November to March. The southern HAA includes Malaysia, Sumatra, Java, and Kalimantan, with the peak AOD months are from May to October. Generally, the peak AOD months are consistent with the dry season in each region, which provides evidence that the temporal AOD distribution in SEA is partly related to biomass burning.
Second, the recently released BA product (GFED4.0s) shows that Myanmar has the largest annual BA in north intertropical zone, followed by Cambodia, and Thailand. Burned areas in south intertropical zone are mainly distributed in Indonesia. The peak burning months are also consistent with the dry months in each region. Noted that the burning area in the northern intertropical zone is ten times higher than that found in southern intertropical zone. However, the level of annual average AOD in the southern HAA is very similar with that in the northern HAA. It is evidence that biomass burning in peatlands results in a higher emission factor of particulate matter.
Third, the correlations between AOD and climate factors were assessed. The level of AOD is generally inversely proportional to precipitation, which is partly related to less biomass burning occurring during the wet seasons. The monthly average wind climatology can partly explain the large scale movement of aerosol plumes in the northern HAA during the burning months (November to next April). For the southern HAA, there is no significant correlation between wind and the spatial distribution of AOD. Fourth, the level of AOD is generally high in urban and metropolitan areas, however, there is no significant temporal correlation between AOD and the strength of human activity.
Finally, to seek a quantifiable linkage between AOD and biomass burning, the study area focuses on HAAs only, and different products representing biomass burning are applied. Among the three BA products applied (MCD45A1, GFED4.0, and GFED4.0s), GFED4.0s considers both the BA identified by GFED4.0 and BA caused by small-scale fires, and can better explain the temporal and spatial distributions of AOD in HAAs (R=0.5 and 0.85 for northern and southern HAA, respectively). The correlation between commonly used MCD45A1 BA and AOD is not significant (R=0.25 and 0.58 for north and south HAA, respectively). Compared to other BA or active fire products, it was found that the MCD45A1 BA has the lowest correlation to AOD, and it is suspected that the BA derived from vegetation-change may seriously underestimate the area of burning in SEA. To better quantify the relationship between AOD and biomass burning, this study develops two simple regression models for the estimation of monthly AOD from remotely sensed burning products in HAAs. The regression model developed for northern HAA uses MCD14ML active fire data as the independent variable and obtained a R2 value of 0.57. The model developed for southern HAA uses GFED4.0s BA data as the independent variable and obtained a R2 value of 0.76. Generally, the empirical models can explain well the temporal trends of AOD in HAAs.

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