Vehicle exhaust emissions inspection system for roadworthiness enforcement
Date
2017
Authors
Mwenda, Reuben Kiogora
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Air pollution has been a growing concern as Kenya tries to industrialize. Increase in the number of vehicles and factories as well as constructions in Nairobi make this all the more critical. This polluted air has far reaching consequences which include illnesses that lead to death. Measuring the concentration of air pollutants is necessary to establish the quality of air in the city. By extension, measuring the concentration of pollutants being emitted through vehicle exhaust fumes can help establish if the vehicle is worthy to be on the road.To best measure the degree of these pollutants, random on-the-road inspection of vehicle exhaust emissions is key. However, this has not been achieved by the Kenyan law enforcement agencies. The ability to inspect the emissions from cars on the road will help law enforcement remove unroadworthy vehicles from the roads and thus minimize air pollution caused by vehicles. Conventional inspection methods are done in controlled environments such as laboratories. Vehicles are driven in and are inspected while they remain stationary. These controlled tests fall short of revealing the true state of a vehicle’s exhaust emissions; the fumes emitted while a car is on open road are different in composition from those emitted in such a controlled environment. In addition, manufacturers can tweak their vehicles to emit gases that are within the prescribed thresholds. This research presents a model that utilizes a carbon monoxide sensor to assess the level of carbon monoxide gas produced from a vehicle exhaust to the air and register these to back-end server hosted on the cloud. The model has an LED which will display a color (red, amber or green) to display the severity of the gas level in the exhaust. This model was tested against conventional testing equipment and found to have an accuracy of 91%.
Description
Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore University
Keywords
Machine to Machine, Internet of Things, Air pollution, Sensor, Microcontroller, Arduino