Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

arXiv:1912.06384 [eess.SP]The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.Peer reviewe

Development of low-cost indoor air quality monitoring devices: Recent advancements

The use of low-cost sensor technology to monitor air pollution has made remarkable strides in the last decade. The development of low-cost devices to monitor air quality in indoor environments can be used to understand the behaviour of indoor air pollutants and potentially impact on the reduction of related health impacts. These user-friendly devices are portable, require low-maintenance, and can enable near real-time, continuous monitoring. They can also contribute to citizen science projects and community-driven science. However, low-cost sensors have often been associated with design compromises that hamper data reliability. Moreover, with the rapidly increasing number of studies, projects, and grey literature based on low-cost sensors, information got scattered. Intending to identify and review scientifically validated literature on this topic, this study critically summarizes the recent research pertinent to the development of indoor air quality monitoring devices using low-cost sensors. The method employed for this review was a thorough search of three scientific databases, namely: ScienceDirect, IEEE, and Scopus. A total of 891 titles published since 2012 were found and scanned for relevance. Finally, 41 research articles consisting of 35 unique device development projects were reviewed with a particular emphasis on device development: calibration and performance of sensors, the processor used, data storage and communication, and the availability of real-time remote access of sensor data. The most prominent finding of the study showed a lack of studies consisting of sensor performance as only 16 out of 35 projects performed calibration/validation of sensors. An even fewer number of studies conducted these tests with a reference instrument. Hence, a need for more studies with calibration, credible validation, and standardization of sensor performance and assessment is recommended for subsequent research

Near-Reference Air Quality Sensors Can Support Local Planning: A Performance Assessment in Milan, Italy

At present, 4.2 million deaths occur every year due to ambient air pollution, according to the World Health Organization. In view of reducing such a figure, air quality monitoring and reliable data are essential. Nevertheless, local authorities in urban environments, where pollution levels are highest, often face a dilemma. On the one hand, the high costs of reference monitors make their largescale adoption prohibitive, while the easily scalable low-cost sensors often feature significantly lower data quality and lack of calibration. Near reference monitors have been voiced as a promising solution, as they exhibit limited costs, though specific studies assessing their performance against reference monitors are still lacking. This article provides an in-depth assessment of three near reference sensors’ stations performance, through their collocation with regional reference monitors from December 2021 onwards. Two sensors were positioned at high-traffic locations, while the third recorded background pollution levels in Milan, Italy. The sensors’ performance was quantified not only via the coefficient of determination (R2) and the regression model, but also with the Mean Normalized Bias (MNB) and median values. After a first measurement period, sensors were re-calibrated to also appraise their behavioral change, generally exhibiting a performance increase. Results show high correlation for all hourly-recorded pollutants, with peaks for Ozone (O3) (R2 = 0.94) and BC (R2 = 0.93). Although location-specific, such results show an interesting potential for near reference sensors in support of urban air quality planning

Intelligent Air Pollution Sensors Calibration for Extreme Events and Drifts Monitoring

Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting low-cost sensors' measurements accurately and detecting the calibrators' drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators' strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of low-cost sensors suffered from sensing accuracy.Peer reviewe

Wearable Sensors For Personal Temperature Exposure Assessments: A Comparative Study

Heat exposure is the leading weather-related cause of death in the United States. The impacts of heat on human health has sparked research on different approaches to measure, map, and predict heat exposure at more accurate and precise spatiotemporal scales. Personal heat sensor studies rely on small sensors that can continuously measure ambient temperatures as individuals move through time and space. The comparison between different types of sensors and sensor placements have yet to be fully researched. The objective of this study is to assess the validity of personal ambient temperature sensors. To accomplish this objective, we evaluate the performance of multiple low-cost wearable sensors for measuring ambient temperature in a (1) field exposure study by varying the placement on human subjects and in a (2) field calibration study by co-locating sensors with fixed site weather station monitors. Bland-Altman analysis, correlation coefficients, and index of agreement statistics were used to quantify the difference between sensor and weather station ambient temperature measurements. Results demonstrated significant differences in measured temperatures for sensors based on sensor type and placement on participants. Future research should account for the differences in personal ambient temperature readings based on sensor type and placement