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The rise of low-cost sensing for managing air pollution in cities

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TLDR
In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract
Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

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Citations
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References
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Journal ArticleDOI

Long-term evaluation and calibration of three types of low-cost PM2.5 sensors at different air quality monitoring stations

TL;DR: In this article, the performance of low-cost PM2.5 sensors and developed calibration models for correcting for the PM 2.5 sensor data (PM2,S) were evaluated based on Met One BAM-1020 data at various locations using long-term (≥one year) data of Plantower PMS5003, Sensirion SPS30, and Honeywell HPMA115S0 PM25 sensors at all monitoring locations.
Journal ArticleDOI

Development of a low-cost sensing platform for air quality monitoring: application in the city of Rome

TL;DR: LILI-1, a prototype of a low-cost air quality platform, allows measuring meteoclimatic parameters and concentrations of principal air quality pollutants by means of electrochemical and optical sensors and shows its ability to increment spatial resolution and obtain distributed data throughout the city.
Journal ArticleDOI

Deep-MAPS: Machine-Learning-Based Mobile Air Pollution Sensing

TL;DR: A machine-learning-based mobile air pollution sensing framework, coined Deep-MAPS, is proposed that can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.
Journal ArticleDOI

The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project.

TL;DR: The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures and ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.
Journal ArticleDOI

Automatic sensor arrangement system for building energy and environmental management

TL;DR: This paper proposes two types of automatic sensor arrangement system for building energy and environmental management which are automatic Sensor arrangement system based on optimal disposition and automatic sensor arrangements based on installation expenses, and explains each simulator algorithm and shows arrangement positions of sensors in a designated room as a form of two dimensions.
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