Abstract:
The research focuses on black carbon (BC), one of the major environmental and health
impairments that are emitted as a result of incomplete combustion. Additionally, the small size
of (BC) black carbon and its light-absorbing ability is one of the main components leading to
global warming, and adverse health effects. Though its influence is huge, the current
monitoring methods lack the cost effectiveness and efficiency to a greater extent. To overcome
this gap, a cheap and efficient device is developed for finding the BC measurements by using
Geospatial Integrated Technologies (GIT’s). The invention of the black carbon detecting
device, is used for detecting and finding black carbon (BC) concentration in the air, and is
based on a unique method and technology. IR and RGB LEDs, and LDR sensors are connected
in the both ends of the parallel section of a U-shaped tube to analyse BC
particles. Subsequently, they are connected through Arduino Nano, which communicates to
DHT sensors delivering data of temperature and humidity, and GPS unit which displays the
location data and PMS5003 sensor for finding particulate matter concentrations. Results are
shown on LCD screen for user convenience. The data from each sensor is stored in excel and
displayed in GUI and is visualized through heat maps made in ArcGIS Pro and statistical
dashboard made in tableau that displays the black carbon per minute concentrations and each
LED data recorded by LDR. The LDR data obtained from both chambers, one before filtration
and other after filtration of black carbon is monitored to quantify black carbon particles in the
air. By comparing the results of the black carbon detecting device with aethalometer and
measuring the R Square and Mean Absolute Percent Error (MAPE) through three different
machine learning algorithms shows an accuracy of 94%.
