UC Riverside

Recent advances in air quality sensor technology have allowed for governments, academia, and communities to use sensors to measure air pollution at unprecedented spatial and temporal scales. While use of low-cost sensors in ambient air monitoring applications has dramatically increased in the past several years, there remain unanswered questions and challenges with designing, implementing, and deploying low-cost sensors. These challenges include quantifying the performance of sensors, developing defensible methods for deploying sensor networks, and communicating sensor data to the public in an understandable and meaningful way. This body of work addresses portions of these challenges by systematically evaluating the performance of sensors, deploying sensors with scientifically defensible methodology for a specific application, and developing methodologies and tools for disseminating and communicating community air monitoring data as information to the public. The main objective of this research is to provide clarity and vision on the appropriate use and applications of low-cost air quality sensors for ambient air monitoring. Systematically evaluating the performance of commercially available low-cost sensors with respect to regulatory grade instrumentation and understanding the measurement error associated with sensors is critically important when choosing a sensor for a specific monitoring application (i.e. fence-line monitoring, community monitoring, hot spot identification, mobile monitoring) or drawing conclusions from data collected by low-cost sensors. The first part of this research investigates the performance of 12 commercially available low-cost particulate matter (PM) sensors against regulatory-grade instrumentation. The next phase of this research includes the design of an ozone sensor node for a specific ambient air monitoring application - parallel monitoring to select a relocation site for a regulatory ambient air monitoring station. The sensor selection, sensor node development, and network deployment methodologies were designed to collect defensible data for determining a relocation site. In the last phase of this research, data tools are developed to store, process, analyze, and visualize large data sets generated by low-cost sensors to provide communities with information from their community monitoring networks.