Dataset Information

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

ABSTRACT: Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM_2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM_2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m^-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.

SUBMITTER: Hassani A

PROVIDER: S-EPMC10569052 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

Hassani Amirhossein A Schneider Philipp P Vogt Matthias M Castell Núria N

Environmental science & technology 20230927 40

Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess perf ...[more]

PMID: 37756014

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Project description:Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM2.5. However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) Each PurpleAir sensor is comprised of two identical sensors and the divergence between their measurements can be observed, and (2) There are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of 'flagged' measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of operation. Approximately 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by -0.12 μg/m3(95% CI: -0.13 μg/m3, -0.10 μg/m3) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Dataset Information

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

Publications

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

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