论文标题
建模非洲空气污染的时空趋势
Modelling spatio-temporal trends of air pollution in Africa
论文作者
论文摘要
大气污染仍然是全球主要的公共卫生威胁之一，估计每年7万人死亡。在非洲，快速的城市化和运输基础设施不佳正在加剧问题。在本文中，我们分析了非洲不同地理区域的PM2.5的时空变化。在某些城市，如拉各斯，阿布贾和巴马科，西非地区仍然受到高水平污染的影响最大，每天平均为40.856 $μg/m^3 $。在东非，乌干达报告的污染水平最高，每日平均浓度为56.14 $μg/m^3 $和38.65 $μg/m^3 $，kigali的平均浓度为38.65美元。在非洲中部地区的国家中，每日最高的平均浓度为90.075 $μg/m^3 $，记录在N'djamena中。我们比较了三个数据驱动模型，以预测污染水平的未来趋势。神经网络的表现优于高斯过程和Arima模型。
Atmospheric pollution remains one of the major public health threat worldwide with an estimated 7 millions deaths annually. In Africa, rapid urbanization and poor transport infrastructure are worsening the problem. In this paper, we have analysed spatio-temporal variations of PM2.5 across different geographical regions in Africa. The West African region remains the most affected by the high levels of pollution with a daily average of 40.856 $μg/m^3$ in some cities like Lagos, Abuja and Bamako. In East Africa, Uganda is reporting the highest pollution level with a daily average concentration of 56.14 $μg/m^3$ and 38.65 $μg/m^3$ for Kigali. In countries located in the central region of Africa, the highest daily average concentration of PM2.5 of 90.075 $μg/m^3$ was recorded in N'Djamena. We compare three data driven models in predicting future trends of pollution levels. Neural network is outperforming Gaussian processes and ARIMA models.
