基于MERRA-2数据的2000-2009年伊拉克地区二氧化硫人为排放分布

Noor M. Abbas, J. Rajab
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引用次数: 2

摘要

二氧化硫(SO2)是一种无色的空气污染物,肉眼无法看到。燃烧的化石燃料,包括煤、石油和天然气,是二氧化硫的最大来源。通常,在燃煤电厂、炼油厂和工业化地区附近,二氧化硫污染达到危险水平。本研究利用MERRA-2数据分析了2000年1月至2009年12月伊拉克人为SO2排放的趋势、时空分布,并对巴格达、摩苏尔、巴士拉、穆萨纳、巴比伦和基尔库克6个站点的序列和趋势进行了分析。分析了研究期间的月度SO2。根据每个SO2源的背景,检查了SO2的波动。结果表明:2002 - 2006年各站SO2值明显下降,2006 - 2009年各站SO2值呈上升趋势。年度趋势分析显示,巴格达、穆萨纳和巴比伦的结果是积极的,而巴士拉、摩苏尔和基尔库克的结果是消极的。SO2值在巴士拉、基尔库克和巴比伦地区差异较大,在巴格达、摩苏尔和Al-muthana地区差异较小。在研究期间,大部分站点的月度SO2人为排放值相对稳定,只有巴比伦和基尔库克有波动。冬季和春季SO2值高于夏季SO2值。本研究旨在通过卫星观测有效地反映研究区SO2的时空变化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sulfur Dioxide (SO2) anthropogenic emissions distributions over Iraq (2000-2009) using MERRA-2 data
The Sulfur dioxide (SO2) is a colorless air pollutant cannot been seen with unaided eye. The fossil fuels burning, including coal, oil and gas, are the largest source of SO2. Often the SO2 Pollution reaches hazardous levels near the coal-fired plants, oil refineries, and in industrialized areas. This study analyzed the trend, spatial and temporal distributions of anthropogenic SO2 emissions in Iraq from January 2000 to December 2009, and series and trend analyses over six stations (Baghdad, Mosul, Basra, Muthanna, Babylon , and Kirkuk) using MERRA-2 data. The monthly SO2 are analyzed for the study period. The SO2 fluctuations were checked, depending on the background of each SO2 sources. The results shows clear reductions of SO2 values from 2002 till 2006, and the SO2 values increases during 2006 to 2009 over all stations. The annual trend analyses shows positive results over Baghdad, Al-Muthanna, and Babylon, and negative results over Basra, Mosul and Kirkuk. A large differences of SO2 values were over Basra, Kirkuk and Babylon, and slight difference over Baghdad, Mosul and Al-muthana. The monthly SO2 anthropogenic emissions values shows relatively stable over most stations, and the only fluctuation over Babylon and Kirkuk during study period. Observed higher SO2 values in the winter and spring than its values in the summer. This research pretends the satellites observation efficiently shows the spatial and temporal variations of SO2 for the considered study area
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