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MODELING AND DEVELOPMENT OF THE DYNAMIC ENVIRONMENTAL SIMULATION CHAMBER (DESC) FOR CALIBRATION OF AIR QUALITY MONITORING SENSORS Filiz KAZAN Ambient air quality has a significant impact on human health and the environment we live in with evidence showing that pollution increases the number of cardiovascular diseases, lung diseases, cancer, asthma cases and proven influences on the mortality rates. The United States Environmental Protection Agency (U.S. EPA) established the National Ambient Air Quality Standard (NAAQS) to limit exposure of the public to critical pollutants and monitors ambient air near highways and selected locations with elaborate monitoring stations. Instruments and analyzers utilized in current monitoring stations comprise laboratory-grade technology of high accuracy, which on the flipside, however, are characterized by high cost, complexity, and need for specialized operators. This effectively limits the number of monitoring stations and thus, the density of the monitoring network. Therefore, the development of low-cost sensors and gaining a better understanding of their responses and limits will benefit denser monitoring coverage of large areas with a growing number of monitoring stations. With recent sensor technology developments, increased numbers of miniaturized and low-cost sensors are commercialized and become available on the market. These sensors can be defined as low-cost (<$500) and are easy to operate due to the simplicity of the sensors. However, the response, accuracy, and possible interference of low-cost sensors must be characterized in detail. Numerous studies have been completed, focusing on the evaluation of low-cost sensor technologies and compare the sensor responses between laboratory testing versus field monitoring. Sensor readings were found to be affected by environmental conditions such as meteorology and from multi-constituent combinations in field experiments resulting in sensor cross-sensitivities. The primary objective of this study is to evaluate the ambient air gas sensor responses due to the activity and meteorological events such as temperature, wind speed, humidity, as well as possible cross-interfering constituents. The secondary objective of this work is to tune the calibration function and understand possible interfering factors of the commercialized gas sensors for indoor/outdoor air quality monitoring. For the purpose of evaluating and calibrating the low-cost sensors, a custom-designed Dynamic Environmental Simulation Chamber (DESC) was developed and commissioned. In a first step, analysis of the simulation chamber was performed as a function of different geometries, wind speeds, and air properties using a Computational Fluid Dynamics (CFD) analyzing software (Fluent, ANSYS, USA) for the determination of flow profiles, gas mixing homogeneity, and sensor location positioning in the loop where wind velocity is vital for sensor responses. Based on the modeling results, the DESC was built and connected to a computer-controlled system that allows changing the composition of pollutant concentrations via injection of synthetic gases, temperature (25-110 C), relative humidity (0-95%), and wind speed (0-5 m/s) instantly by a custom-made control software. In the second step, CO2 gas sensors were evaluated in the DESC, including, i) a high and low-speed Senseair K30 (range 10000ppm), ii) a FIGARO FG-030 (range 5000ppm), and iii) a COZIR (range 2000ppm). The evaluated CO2 sensors were all based on nondispersive infrared detection (NDIR) technology which has interference with CO, humidity, and temperature. The experiments were performed with variable gas compositions and meteorological conditions using a design of experiment approach. The gas mixture components were CO, CO2 and zero air. Fourier-Transform infrared spectroscopy (FTIR, MKS 2030-HS) was used in order to measure the gas concentrations and to cross-reference with the sensor readings. The calibration functions were generated as a function of pressure, temperature, humidity (i.e. water content), and CO as interference gas. The calibration models improved the sensor responses when compared to raw measurements. For the K30 sensors, the overall relative error was decreased from ±10 % to ±3 %. For the Figaro, the overall relative error was decreased from ±15 % to ±4 %. Moreover, for the COZIR, the overall relative error was decreased from ±22 % to ±14 %. The COZIR correction model was found to not significantly decrease the relative error, and thus, will need further investigation.