Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organization guidelines in Rwanda with a daily average of around 42.6 microgram per meter cube. Monitoring and mitigation strategies require an expensive investment in equipment to collect pollution data. Low-cost sensor technology and machine learning methods have appeared as an alternative solution to get reliable information for decision making. This paper analyzes the trend of air pollution in Rwanda and proposes forecasting models suitable to data collected by a network of low-cost sensors deployed in Rwanda.
翻译:虽然缺乏数据是大多数中低收入国家的一个全球性问题,但空气污染是全世界的一个主要公共卫生问题,以微粒物质(PM2.5)为形式的环境空气污染超过世界卫生组织在卢旺达的准则,每天平均约为每米42.6微克。监测和减轻战略要求对收集污染数据的设备进行昂贵的投资。低成本的传感技术和机器学习方法似乎是获取可靠信息以供决策的替代解决办法。本文分析卢旺达空气污染的趋势,并提出适合卢旺达部署的低成本传感器网络所收集数据的预测模型。