IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.

The Importance of Direct Progeny Measurements for Correct Estimation of Effective Dose Due to Radon and Thoron.

Radon (Rn), thoron (Tn), and thoron progeny (TnP) were measured in seven inhabited areas of the uranium and thorium bearing region of Lolodorf, located in southwestern Cameroon. Then the equilibrium factor (F) between thoron and its progeny was determined in order to show the importance of direct progeny measurements for correct estimation of effective dose due to radon, thoron and their progenies. A total of 220 RADUET detectors were used to measure indoor radon and thoron and 130 TnP monitors for thoron progeny indoors.

NATURAL RADIATION EXPOSURE TO THE PUBLIC IN MINING AND ORE BEARING REGIONS OF CAMEROON.

Within the framework of a joint research project on natural radiation exposure and its health effects in Cameroon from 2014 to 2017, the Institute of Geological and Mining Research and the Hirosaki University worked together to carry out natural radiation survey in mining and ore bearing regions of Cameroon. Air kerma rates were measured using car-borne survey method. In-situ gamma spectrometry was used to determine activity concentrations of 238U, 232Th and 40K in soil.