Abstract
Poor air quality influences the quality of life in the urban environment. The regulatory observation stations provide the backbone for the city administration to monitor urban air quality. Recently a suite of cost-effective air quality sensors has emerged to provide novel insights into the spatio-temporal variability of aerosol particles and trace gases. Particularly in low concentrations these sensors might suffer from issues related e.g., to high detection limits, concentration drifts and interdependency between the observed trace gases and environmental parameters. In this study we characterize the optical particle detector used in AQT530 (Vaisala Ltd.) air quality sensor in the laboratory. We perform a measurement campaign with a network of AQT530 sensors in Helsinki, Finland in 2020–2021 and present a long-term performance evaluation of five sensors for particulate (PM10, PM2.5) and gaseous (NO2, NO, CO, O3) components during a half-year co-location study with reference instruments at an urban traffic site. Furthermore, short-term (3–5 weeks) co-location tests were performed for 25 sensors to provide sensor-specific correction equations for the fine-tuning of selected pollutants in the sensor network. We showcase the added value of the verified network of 25 sensor units to address the spatial variability of trace gases and aerosol mass concentrations in an urban environment. The analysis assesses road and harbor traffic monitoring, local construction dust monitoring, aerosol concentrations from fireworks, impact of sub-urban small scale wood combustion and detection of long-range transport episodes on a city scale. Our analysis illustrates that the calibrated network of Vaisala AQT530 air quality sensors provide new insights into the spatio-temporal variability of air pollution within the city. This information is beneficial to, for example, optimization of road dust and construction dust emission control as well as provides data to tackle air quality problems arising from traffic exhaust and localized wood combustion emissions in the residential areas.
Highlights
Air quality is one of the grand challenges that the society faces at the moment (Gimeno, 2013; Lappalainen et al, 2014; Arnold et al, 2016; Kulmala et al, 2016)
The aim of this work is to 1) characterize the Vaisala AQT530 air quality sensors’ laser particle counters in the laboratory for their detection efficiency as a function of particle size and number concentration using monodisperse particles, 2) conduct a colocation study with a suite of sensors to explore the long-term performance of the sensors against verified instrumentation of gaseous and particulate pollutants at an air quality monitoring station, and 3) deploy the sensor network consisting of 25 sensor units in different regions in the Helsinki metropolitan area to explore the benefits of such a network of sensors for specific air quality challenges, such as long-range transport, local hot-spot of trace gas or aerosol emissions and road dust episodes
The key parameter is the detection efficiency (Deff), which is defined as: Deff where Cs is the particle concentration measured by the sensor and Cref the particle number concentration measured by the reference instrument
Summary
Air quality is one of the grand challenges that the society faces at the moment (Gimeno, 2013; Lappalainen et al, 2014; Arnold et al, 2016; Kulmala et al, 2016). The regulatory observations typically represent different urban environments (e.g., traffic sites, urban background, and rural) and the air pollution at these sites are reported and taken as representatives for similar urban environments (e.g., Duyzer et al, 2015; Rohde and Mu;̈ ller, 2015). Wireless data transfer is a characteristic feature of sensors, and low power consumption coupled with a possibility for battery operation enables them to be placed more freely within the urban infrastructure These features are the foundation of cost-efficient and convenient air quality monitoring, which facilitates the highdensity deployment and the consequent higher spatiotemporal air quality data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
