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• W3149392816 abstract "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." @default.
• W3149392816 created "2021-04-13" @default.
• W3149392816 creator A5043267366 @default.
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• W3149392816 creator A5090437515 @default.
• W3149392816 creator A5090902995 @default.
• W3149392816 date "2021-05-29" @default.
• W3149392816 modified "2023-10-09" @default.
• W3149392816 title "Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration" @default.
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