Abstract View
Development and Evaluation of a Low-Cost Black and Brown Carbon Filter Analyzer
EMILY FLOESS, Andrew Grieshop, North Carolina State University
Abstract Number: 425
Working Group: Instrumentation and Methods
Abstract
Carbonaceous aerosols from combustion are major contributors to climate forcing. Black (BC) and elemental carbon (EC) are both pure carbon produced by incomplete combustion which are quantified in two different ways, by using light absorption across all wavelengths and thermo-optically, respectively. Organic carbon (OC) is measured thermo-optically. Light absorbing OC, brown carbon, (BrC), absorbs light mainly in ultraviolet (UV) and blue wavelengths. Current methods to measure these components are costly but more measurements are needed globally, especially in developing countries, to understand their sources and climate forcing role. To increase the accessibility of these measurements, we are developing an open-source, easy to build, low-cost instrument for measuring these particles. The system uses a Raspberry Pi, UV/Infrared (IR) Pi-camera, UV (370 nm wavelength), IR (850 nm) and red (620 nm) LEDs to quantify light absorption from quartz filter samples. A camera image shows light absorbed through a filter under varying illumination and is compared with a calibrated reference. The instrument is calibrated and evaluated using filter samples from field based biomass burning emissions tests from multiple sources. The BC, EC, BrC, and OC of these samples is measured using the Magee Scientific Soot Scan Optical Transmissometer, Sunset Lab OC-EC Aerosol Analyzer, Aethlabs microAeth, and BrC extraction and compared with the low-cost analyzer results to evaluate the accuracy, repeatability, sensitivity, and limits of detection of the low-cost analyzer. The analyzer can detect particle loading from the range of collected field samples (0 to 38 μg/cm2 EC), has accurately estimated filter EC loading, and preliminary results show promise in quantifying UV and IR absorption, which can be used to estimate black and brown carbon. The ability for the sensor to estimate OC loadings on filters will also be explored.