Abstract
Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.
Highlights
In the transport sector, fossil fuel-powered vehicles, such as motorcycles, cars, and buses, are major contributors to local air pollution [1]
As the relationship between NO2, carbon monoxide (CO), and meteorology is complex and nonlinear, we developed two artificial intelligence (AI) models to predict hourly NO2 and CO concentrations from readily observable local meteorological data
While the original dataset had five outputs, we focused of the problem, were recorded hourly by taking averages of the concentration values
Summary
Fossil fuel-powered vehicles, such as motorcycles, cars, and buses, are major contributors to local air pollution [1]. Two important compounds in air pollution are nitrogen oxides (NOx ) and carbon monoxide (CO). Primary NOx emissions are mostly in the form of nitric oxide (NO), which can react with ozone (O3 ) to form nitrogen dioxide (NO2 ). Emissions from transport vehicles are responsible for more than half of the NOx in the air and represent the largest anthropogenic source of CO [2,3]. In densely populated cities and industrialized areas, air quality has become an important measure of quality of life, as is the case in Vietnam. Controlling air quality (by controlling air pollution) is highly desirable to improve urban sustainability and quality of life [11], and it starts by measuring and forecasting air quality
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