This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.
Keywords: air monitoring; carbon; drone; kernel; pollutant; quadcopter.