Person: Diez, Sebastian
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Publication Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study(2024) Diez, Sebastian; Lacy, Stuart; Coe, Hugh; Urquiza, Josefina; Priestman, Max; Flynn, Michael; Marsden, Nicholas; Martin, Nicholas A.; Gillott, Stefan; Bannan, Thomas; Edwards, Pete M.In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often “black boxes”, which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution.Publication QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation(2024) Diez, Sebastian; Lacy, Stuart; Urquiza, Josefina; Edwards, Petethe QUaNt study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. the resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors’ data and calibrated products from sensor manufacturers across the three sites. this publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset’s utility and reliabilityPublication Study of the Suitability of a Personal Exposure Monitor to Assess Air Quality(2024) Aljofi, Halah E.; Bannan, Thomas J.; Flynn, Michael; Evans, James; Topping, David; Matthews, Emily; Diez, Sebastian; Edwards, Pete; Coe, Hugh; Brison, Daniel R.; Tongeren, Martie van; Johnstone, Edward D.; Povey, AndrewLow-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2s (PLFs) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r < 0.4). Moderate correlation was observed for one PLF unit at the indoor location and two units at the roadside location when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.