Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Concas, Francesco; Mineraud, Julien; Lagerspetz, Eemil; Varjonen, Samu; Liu, Xiaoli; Puolamäki, Kai; Nurmi, Petteri; Tarkoma, Sasu

doi:10.1145/3446005

Cited by 99 publications

(57 citation statements)

References 140 publications

(322 reference statements)

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“…A summary of the sensors used, reference stations, and techniques with feature list by the above works is depicted in Table 2. Apart from the works mentioned above, a survey was conducted by the authors in [16], which focuses on the operations required to implement the calibration pipeline and to analyze the effect of different sensing technologies on this pipeline. The works mentioned above deal with machine learning-based calibration.…”

Section: Aqmd Calibrationmentioning

confidence: 99%

AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer

Pramanik¹,

Karmakar²,

Kumar³

et al. 2022

Preprint

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Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this paper propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Information systems → Location based services.

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Section: Aqmd Calibrationmentioning

confidence: 99%

AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer

Pramanik¹,

Karmakar²,

Kumar³

et al. 2022

Preprint

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“…. However, the accuracy of these measurements is still debated (Duvall et al, 2020, Concas et al, 2021, although the development of more accurate, but still lower cost devices is ongoing for denser measurement networks, 3-D-measurements, and new modelling. Measurements are not only required for compliance and for monitoring long-term trends.…”

Section: Measuring Air Pollutionmentioning

confidence: 99%

“…Many manufacturers (more than 50 worldwide, with their numbers growing fast) are working in the market for air quality monitoring with different business models (Alfano et al, 2020). There are companies which produce and / or sell mediumcost sensors (MCS) with a cost per compound in the order of 100 and 1000 Euros and LCS in the order of 10 and 100 Euros for all key air pollutants (Concas et al, 2021). Furthermore, manufacturers and integrators often provide installation of LCS and MCS for networks and on mobile monitoring platforms.…”

Section: Low-cost Sensors and Citizen Science For Atmospheric Researchmentioning

confidence: 99%

Advances in Air Quality Research – Current and Emerging Challenges

Sokhi

Moussiopoulos

Baklanov

et al. 2021

Preprint

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Abstract. This review provides a community’s perspective on air quality research focussing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterising sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research and (iii) and to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground based and remote sensing instruments, including especially those on satellites. The research should also capitalize on the growing area of lower cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry and meteorology. Assessment of exposure to air pollution should consider both the impacts of indoor and outdoor emissions, as well as apply more sophisticated, dynamic modelling approaches. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high resolution distributions of these metrics over cities. The review also examines, how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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“…Finally, a few papers show how using a network of mobile lowcost sensors can improve air quality predictions' accuracy. [5] surveys the landscape of low-cost sensors for air quality monitoring and concludes that there is not enough large-scale studies and large datasets to show the value-added of low-cost sensors: that is the problem we are tackling in this paper. [2] shows the results of experiments in Mountain View and San Francisco, [13] presents modeling approaches on an experimental setup in Lausanne, Switzerland, and [24] presents a case-study in Beijing using mobile sensors.…”

Section: Related Workmentioning

confidence: 99%

High-Resolution Air Quality Prediction Using Low-Cost Sensors

Cassard¹,

Jauvion²,

Lissmyr³

2020

Preprint

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The use of low-cost sensors in air quality monitoring networks is still a much-debated topic among practitioners: they are much cheaper than traditional air quality monitoring stations set up by public authorities (a few hundred dollars compared to a few dozens of thousand dollars) at the cost of a lower accuracy and robustness. This paper presents a case study of using low-cost sensors measurements in an air quality prediction engine. The engine predicts jointly PM 2.5 and PM 10 (the particles whose diameters are below 2.5 µm and 10 µm respectively) concentrations in the United States at a very high resolution in the range of a few dozens of meters.It is fed with the measurements provided by official air quality monitoring stations, the measurements provided by a network of low-cost sensors across the country, and traffic estimates. We show that the use of low-cost sensors' measurements improves the engine's accuracy very significantly. In particular, we derive a strong link between the density of low-cost sensors and the predictions' accuracy: the more low-cost sensors are in an area, the more accurate are the predictions. As an illustration, in areas with the highest density of low-cost sensors, the low-cost sensors' measurements bring a 25% and 15% improvement in PM 2.5 and PM 10 predictions' accuracy respectively. In cities with the most low-cost sensors like Los Angeles and San Francisco, this improvement in the predictions' accuracy is very clearly reflected in air quality maps.An other strong conclusion is that in some areas with a high density of low-cost sensors, the engine performs better when fed with low-cost sensors' measurements only than when fed with official monitoring stations' measurements only: this suggests that an air quality monitoring network composed of low-cost sensors is effective in monitoring air quality. This is a very important result, as such a monitoring network is much cheaper to set up. CCS CONCEPTS• Applied computing → Environmental sciences; • Computing methodologies → Neural networks.

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Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Cited by 99 publications

References 140 publications

AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer

AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer

Advances in Air Quality Research – Current and Emerging Challenges

High-Resolution Air Quality Prediction Using Low-Cost Sensors

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