# Trend analysis and forecasting air pollution in Rwanda

arXiv: https://arxiv.org/abs/2205.10024 · HTML: https://mlsanity.com/paper/2205.10024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 20 May 2022.

## Built on by (most cited first, 2 of 2)

- [Modelling spatio-temporal trends of air pollution in Africa](https://mlsanity.com/paper/2208.12719/) (2022)
- [Modelling and characterization of fine Particulate Matter dynamics in Bujumbura using low cost sensors](https://mlsanity.com/paper/2312.12003/) (2023)

## Abstract (arXiv)

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.