MoreAir: A Low-Cost Urban Air Pollution Monitoring System

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.

Indoor air quality prediction systems for smart environments: A systematic review

“…After applying the above-mentioned eligibility criteria, the authors obtained 40 papers for the third stage, which were studied in detail. In this list, two papers only focus on outdoor air quality [64,97], eight papers do not include any AI-specific prediction algorithms [22,31,47,50,84,91,116,122] or were based on some mathematical approaches. Three papers [12,96,137] only focused on thermal comfort (temperature and/or humidity data) or other smart building aspects instead of air quality.…”

“…Repeated exposure to these pollutants can hamper the health quality of an individual. The impact of indoor air pollution (IAP) is equally high in the urban buildings as well due to excessive use of chemicalrich cleaning agents, oil-based pains, fragrant decorations, and other toxic consumer products and building elements [47,72]. Unfortunately, household air pollution caused more than 4.3 million premature deaths in 2012, mostly in middle and low-income countries [18,44,54,58,65,74].…”

Remote Tracking Gas Molecular via the Standalone-Like Nanosensor-Based Tele-Monitoring System

“…A typical example is that China has deployed air quality WSNs in major cities (e.g., Shanghai and Beijing) [2,3]. With the help of wireless communicating technology, such as ZigBee, Wibree, and Sigfox, air quality in the WSNs deployed cities can be remotely tracked [1,[12][13][14]. Generally, high-performance tracking the level of air pollutants and operating at power-saving mode are two basic criteria for designing the WSNs so that each sensor node is able to effectively monitor the variation of air pollutants and to keep working for a long period without charging or replacing the battery [14].…”

“…In comparison with those expensive stationary monitoring stations, wireless sensor node demonstrates the advantage of cost-effective and low-energy consumption as well as simple configuration [ 10 , 11 ]. Furthermore, wireless sensor networks (WSNs) that consist of a number of air quality sensor nodes hold the potential to increase the achievable spatial density of measurements [ 11 , 12 ]. In light of these merits, there has been a growing interest in the development and deployment of WSNs that employ smart air quality sensors.…”

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis

“…In this sense, the cities are increasingly aware of the potential for low-cost ‘citizen science’ sensors to help support the results of their air quality modeling [ 15 , 16 ]. These sensors offer air pollution monitoring at a lower cost and smaller size than conventional methods, making it possible for them to be installed in many more locations [ 17 , 18 , 19 ]. However, the accuracy of input data in air quality modeling is as important as the quantity of measures.…”