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High-Resolution Air Quality Prediction Using Low-Cost Sensors

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Document pages: 7 pages

Abstract: The use of low-cost sensors in air quality monitoring networks is still amuch-debated topic among practitioners: they are much cheaper than traditionalair quality monitoring stations set up by public authorities (a few hundreddollars compared to a few dozens of thousand dollars) at the cost of a loweraccuracy and robustness. This paper presents a case study of using low-costsensors measurements in an air quality prediction engine. The engine predictsjointly PM2.5 and PM10 concentrations in the United States at a very highresolution in the range of a few dozens of meters.It is fed with the measurements provided by official air quality monitoringstations, the measurements provided by a network of more than 4000 low-costsensors across the country, and traffic estimates. We show that the use oflow-cost sensors measurements improves the engine s accuracy verysignificantly. In particular, we derive a strong link between the density oflow-cost sensors and the predictions accuracy: the more low-cost sensors arein an area, the more accurate are the predictions. As an illustration, in areaswith the highest density of low-cost sensors, the low-cost sensors measurements bring a 25 and 15 improvement in PM2.5 and PM10 predictions accuracy respectively.An other strong conclusion is that in some areas with a high density oflow-cost sensors, the engine performs better when fed with low-cost sensors measurements only than when fed with official monitoring stations measurementsonly: this suggests that an air quality monitoring network composed of low-costsensors is effective in monitoring air quality. This is a very importantresult, as such a monitoring network is much cheaper to set up.

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