Abstract
MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.
Highlights
The global air pollution crisis is a major issue that threatens our planet
We propose a novel approach to designing low-cost air pollution monitoring systems, which consists of a combination of (i) a novel sensor deployment strategy, based on mobile and nomadic sensors as well as on a prior medical survey, (ii) machine learning to perform model-based interpolation, and (iii) the Internet of Things to provide the users with real-time air quality data
We show that Random Forest (RF) and Support Vector Regression (SVR) better describe the non-linear impact of traffic flows and meteorology on particulate matters (PM) concentrations
Summary
The global air pollution crisis is a major issue that threatens our planet. It has several adverse effects on human health and the living ecosystem in general. [15] presents LILI-1, a low-cost solution for the monitoring of O3 , NO2 , PM10 , PM2.5 , temperature, relative humidity, and atmospheric pressure; the issues related to the choice of the hardware, calibration, deployment strategies and data evaluation are addressed Another challenge facing air quality forecasting is the large number of factors that influence air pollutants’ concentrations. We propose a novel approach to designing low-cost air pollution monitoring systems, which consists of a combination of (i) a novel sensor deployment strategy, based on mobile and nomadic sensors as well as on a prior medical survey, (ii) machine learning to perform model-based interpolation, and (iii) the Internet of Things to provide the users with real-time air quality data.
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