Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors.

Traditional air monitoring approaches using regulatory monitors have historically been used to assess regional-scale trends in air pollutants across large geographical areas. Recent advances in air pollution sensor technologies could provide additional information about nearby sources, support the siting of regulatory monitoring stations, and improve our knowledge of finer-scale spatiotemporal variation of ambient air pollutants and their associated health effects. Sensors are now being developed that are much smaller and lower cost than traditional ambient air monitoring systems and are capable of being deployed as a network to provide greater coverage of a given area. The CitySpace project conducted by the US EPA and the Shelby County Health Department included the deployment of a network of 17 sensor pods using Alphasense OPC-N2 particulate matter (PM) sensors integrated with meteorological sensors in Memphis, TN for six months. Sensor pods were collocated with a federal equivalent method (FEM) tapered element oscillating microbalance (TEOM) monitor both before and after the primary study period. Six of the sensor pods were found to meet the data quality objective (DQO) of coefficient of determination (R2) greater than 0.5 when collocated with the TEOM. Seven pods were decommissioned before the end of the study due to mechanical failure. The six pods meeting the DQO were used to examine the spatiotemporal variability of fine PM (PM2.5) across the Memphis area. One site was found to have higher relative PM2.5 concentrations when compared to the other sites in the network. The 1-min data from this sensor pod were evaluated to quantify the regional urban background and local-scale contributions to PM2.5 at that monitoring location. This method found that approximately 20% of the PM2.5 was attributed to local sources at this location, compared to 9% at a local regulatory monitoring site. Additionally, the 1-min data were combined with 1-min wind speed and wind direction data to examine potential sources in the area using the nonparametric trajectory analysis (NTA) technique. This method geographically identified local source areas that contributed to the measured concentrations at the high reading sensor location throughout the course of the study.

Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado.

Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015-2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.

Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States.

Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, 0.25 to 0.76, and 0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results - some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2 adj-orig = 0.57, R2 adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.