Article citationsMore>>

Bi, J., Wildani, A., Chang, A.H.H. and Liu, Y. (2020) Incorporating Low-Cost Sensor Measurements into High-Resolution PM_2.5 Modeling at a Large Spatial Scale. Environment Scientific Technology, 54, 2152-2162.
https://doi.org/10.1002/essoar.10501744.1

has been cited by the following article:

• TITLE: Modeling and Characterization of Fine Particulate Matter Dynamics in Bujumbura Using Low-Cost Sensors

  AUTHORS: Egide Ndamuzi, Rachel Akimana, Paterne Gahungu, Elie Bimenyimana

  KEYWORDS: Particulate Matter, Recurrent Neural Networks, Calibration

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.1, January 31, 2024

  ABSTRACT: Air pollution is a result of multiple sources including both natural and anthropogenic activities. The rapid urbanization of the cities such as Bujumbura, economic capital of Burundi, is one of these factors. The very first characterization of the spatio-temporal variability of PM2.5 in Bujumbura and the forecasting of PM2.5 concentration have been conducted in this paper using data collected during a year, from August 2022 to August 2023, by low-cost sensors installed in Bujumbura city. For each commune, an hourly, daily and seasonal analysis was carried out and the results showed that the mass concentrations of PM2.5 in the three municipalities differ from one commune to another. The average hourly and annual PM2.5 concentrations exceed the World Health Organization standards. The range is between 28.3 and 35.0 μg/m3. In order to make a prediction of PM2.5 concentration, an investigation of Recurrent Neural Networks with Long Short-Term Memory has been undertaken.