由于人为和生物质燃烧排放以及不利的天气条件,东非的空气污染水平正在恶化。本研究调查了2001-2021年东非地区空气污染的变化及影响因素。研究发现,该地区的空气污染是异质的,在污染热点(PHS)中观察到增加的趋势,而在污染冷点(PCS)中观察到减少的趋势。分析确定了四个主要污染期:高污染期1,低污染期1,高污染期2和低污染期2,发生在2月3月,4月-5月,6月-8月和10月-11月,分别。该研究还表明,污染物向研究区域的远距离迁移主要受到东部遥远来源的影响,西方,南方,和大陆的北部。季节性气象条件,例如高纬度地区的高海平面压力,来自北半球的冷空气团,干旱植被,北方冬天的干燥和不太潮湿的气氛,进一步影响污染物的运输。发现污染物的浓度受气候因素的影响,如温度,降水,和风的模式。该研究确定了不同季节的不同污染模式,由于植被活力高,降水适中,一些地区人为污染最小。使用普通最小二乘(OLS)回归和去趋势波动分析(DFA),这项研究量化了空气污染的空间变化幅度。OLS趋势表明,66%的像素呈现下降趋势,而34%的像素呈现上升趋势,和DFA结果表明,36%,15%,49%的像素表现出抗持久性,随机,以及空气污染的持续存在,分别。该地区空气污染呈上升或下降趋势的地区,可用于优先考虑改善空气质量的干预措施和资源,也突出了。它还确定了空气污染趋势背后的驱动力,例如人为或生物质燃烧,这可以为旨在减少这些来源的空气污染排放的政策决定提供信息。关于持久性的发现,可逆性,空气污染的可变性可以为改善空气质量和保护公众健康的长期政策的制定提供信息。
East Africa\'s air pollution levels are deteriorating due to anthropogenic and biomass burning emissions and unfavorable weather conditions. This study investigates the changes and influencing factors of air pollution in East Africa from 2001 to 2021. The study found that air pollution in the region is heterogeneous, with increasing trends observed in pollution hot spots (PHS) while it decreased in pollution cold spots (PCS). The analysis identified four major pollution periods: High Pollution period 1, Low Pollution period 1, High Pollution period 2, and Low Pollution period 2, which occur during Feb-Mar, Apr-May, Jun-Aug and Oct-Nov, respectively. The study also revealed that long range transport of pollutants to the study area is primarily influenced by distant sources from the eastern, western, southern, and northern part of the continent. The seasonal meteorological conditions, such as high sea level pressure in the upper latitudes, cold air masses from the northern hemisphere, dry vegetation, and a dry and less humid atmosphere from boreal winter, further impact the transport of pollutants. The concentrations of pollutants were found to be influenced by climate factors, such as temperature, precipitation, and wind patterns. The study identified different pollution patterns in different seasons, with some areas having minimal anthropogenic pollution due to high vegetation vigor and moderate precipitation. Using Ordinary Least Square (OLS) regression and Detrended Fluctuation Analysis (DFA), the study quantified the magnitude of spatial variation in air pollution. The OLS trends indicated that 66 % of pixels exhibited decreasing trends while 34 % showed increasing trends, and DFA results indicating that 36 %, 15 %, and 49 % of pixels exhibited anti-persistence, random, and persistence in air pollution, respectively. Areas in the region experiencing increasing or decreasing trends in air pollution, which can be used to prioritize interventions and resources for improving air quality, were also highlighted. It also identifies the driving forces behind air pollution trends, such as anthropogenic or biomass burning, which can inform policy decisions aimed at reducing air pollution emissions from these sources. The findings on the persistence, reversibility, and variability of air pollution can inform the development of long-term policies for improving air quality and protecting public health.