Abstract: The Air Quality Sensor Performance Evaluation Center (AQ-SPEC) at the South Coast AQMD has been evaluating and deploying sensors since 2014, which has led to the creation of resources and expertise to share, some of which will be showcased in this tutorial. AQ-SPEC has evaluated the performance of 170+ sensors from over 100 different sensor vendors, with results posted on AQ-SPEC’s website: http://www.aqmd.gov/aq-spec. This tutorial will provide perspectives gained from eight years of performance testing in both field and laboratory environments, as well as insights into the development of protocols for evaluating sensors in the field, laboratory, on mobile platforms, and those deployed in indoor environments.

As air quality sensors are integrated into existing regulatory-grade air monitoring networks, data management techniques are required to obtain the most value from these networks. This tutorial will provide an in-depth discussion on the data management techniques for establishing Quality Control rules and processes, developing sensor health dashboards, and applying remote calibration techniques. Insights will be shared on the development of AQPortal, a cloud-based data management platform designed to ingest, store, process, and visualize air monitoring data.

Educational outreach has become essential to inform and educate communities. This tutorial will highlight the lessons learned in developing a Sensor Educational Toolkit. The toolkit includes engaging training videos and an air sensor guidebook on project planning, operating sensors, and understanding the data. Data analysis and visualization tools such as an open-source R package entitled, AirSensor, will be highlighted. The Toolkit was shaped through the collaborative effort with 14 different California communities over the past five years and the experience of deploying nearly 400 sensors. While sensors do have the potential to enhance our capacity to monitor, the deployment and integration into existing networks needs to be implemented appropriately.