A study was conducted to investigate the effectiveness of three air purification systems in reducing the exposure of children to air contaminants inside nine classrooms of three Southern California schools. Continuous and integrated measurements were conducted to monitor the indoor and outdoor concentrations of ultrafine particles (UFPs), fine and coarse particulate matter (PM2.5 and PM10 , respectively), black carbon (BC), and volatile organic compounds. An heating, ventilating, and air conditioning (HVAC)-based high-performance panel filter (HP-PF), a register-based air purifier (RS), and a stand-alone air cleaning system (SA) were tested alone and in different combinations for their ability to remove the monitored pollutants. The combination of a RS and a HP-PF was the most effective solution for lowering the indoor concentrations of BC, UFPs, and PM2.5 , with study average reductions between 87% and 96%. When using the HP-PF alone, reductions close to 90% were also achieved. In all cases, air quality conditions were improved substantially with respect to the corresponding baseline (preexisting) conditions. Data on the performance of the gas-absorbing media included in the RS and SA unit were inconclusive, and their effectiveness, lifetime, costs, and benefits must be further assessed before conclusions and recommendations can be made. The installation of effective air filtration devices in classrooms may be an important mitigation measure to help reduce the exposure of school children to indoor pollutants of outdoor origin including ultrafine particles and diesel particulate matter, especially at schools located near highly trafficked freeways, refineries, and other important sources of air toxics.

As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users’ experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled “Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors”, approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled “Community in Action: A Comprehensive Toolkit on Air Quality Sensors”. This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health.

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O₃) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O₃ analyzer at the station along with a network of three O₃ sensing nodes was used to evaluate the spatial and temporal variability of O₃ across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O₃ site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O₃ concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R² values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O₃ concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O₃ trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site.