MoreAir: A Low-Cost Urban Air Pollution Monitoring System

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.

[1]  F. D. Pooley,et al.  Applying Definitions of “Asbestos” to Environmental and “Low-Dose” Exposure Levels and Health Effects, Particularly Malignant Mesothelioma , 2011, Journal of toxicology and environmental health. Part B, Critical reviews.

[2]  S. Osowski,et al.  Data mining methods for prediction of air pollution , 2016, Int. J. Appl. Math. Comput. Sci..

[3]  Shiva Nagendra Sm,et al.  Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots , 2019, Sustainable Cities and Society.

[4]  Grant R. McKercher,et al.  Characteristics and applications of small, portable gaseous air pollution monitors. , 2017, Environmental pollution.

[5]  Laura Hallett,et al.  Evaluation of the Alphasense optical particle counter (OPC-N2) and the Grimm portable aerosol spectrometer (PAS-1.108) , 2016, Aerosol science and technology : the journal of the American Association for Aerosol Research.

[6]  Luca Shindler,et al.  Development of a low-cost sensing platform for air quality monitoring: application in the city of Rome , 2019, Environmental technology.

[7]  Magda Sibley,et al.  ‘Hybrid Transitions: Combining Biomass and Solar Energy for Water Heating in Public Bathhouses’ , 2015 .

[8]  Mounir Ghogho,et al.  Analyzing the Accuracy of Historical Average for Urban Traffic Forecasting Using Google Maps , 2018, IntelliSys.

[9]  F. Pope,et al.  of Birmingham Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring , 2018 .

[10]  Mauricio Camargo,et al.  Air quality monitoring using stationary versus mobile sensing units: a case study from Lorraine, France , 2018 .

[11]  Roman Neruda,et al.  Sensor Data Air Pollution Prediction by Kernel Models , 2016, 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid).

[12]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[13]  Kai Zhang,et al.  Air pollution and health risks due to vehicle traffic. , 2013, The Science of the total environment.

[14]  J. Mcdonald,et al.  Health implications of environmental exposure to asbestos. , 1985, Environmental health perspectives.

[15]  Yves Rybarczyk,et al.  Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review , 2018, Applied Sciences.

[16]  P. Schneider,et al.  Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway , 2019, Atmosphere.

[17]  Mohammad Aurangojeb Relationship between PM10, NO2 and particle number concentration: validity of air quality controls , 2011 .

[18]  R. Srivastava,et al.  Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar. , 2011, Environmental pollution.

[19]  Michele Penza,et al.  Urban Air Quality Monitoring with Networked Low-Cost Sensor-Systems , 2017 .

[20]  Rafael Borge,et al.  Using statistical methods to carry out in field calibrations of low cost air quality sensors , 2018, Sensors and Actuators B: Chemical.

[21]  Kracht Oliver,et al.  Spatial representativeness of air quality monitoring sites: Outcomes of the FAIRMODE/AQUILA intercomparison exercise , 2017 .

[22]  Jennifer K Vanos,et al.  Low-cost mobile air pollution monitoring in urban environments: a pilot study in Lubbock, Texas , 2018, Environmental technology.

[23]  Ron Williams Evaluation of Elm and Speck Sensors , 2015 .

[24]  Pavlos S. Kanaroglou,et al.  Modeling NOx and NO2 emissions from mobile sources: A case study for Hamilton, Ontario, Canada , 2008 .

[25]  Kai Meng Mok,et al.  KALMAN FILTER BASED PREDICTION SYSTEM FOR WINTERTIME PM10 CONCENTRATIONS IN MACAU , 2008 .

[26]  Barbiere Maurizio,et al.  Review of sensors for air quality monitoring , 2019 .

[27]  Gb Stewart,et al.  The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks , 2013 .

[28]  Marek Tobiszewski,et al.  Current air quality analytics and monitoring: a review. , 2015, Analytica chimica acta.

[29]  Ulrich Pöschl,et al.  Atmospheric aerosols: composition, transformation, climate and health effects. , 2005, Angewandte Chemie.

[30]  Piero Toscano,et al.  Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors , 2018, Sensors.

[31]  J. C. Wagner,et al.  Asbestos and Disease , 1979, British Journal of Cancer.

[32]  R. Srivastava,et al.  Evaluation of environmental impacts of Integrated Industrial Estate—Pantnagar through application of air and water quality indices , 2011, Environmental monitoring and assessment.

[33]  Ujjwal Kumar,et al.  ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) , 2010 .

[34]  J. Salmond,et al.  Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network , 2013 .

[35]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[36]  Harri Niska,et al.  Methods for imputation of missing values in air quality data sets , 2004 .

[37]  Piotr Batog,et al.  Optical particulate matter sensors in PM2.5 measurements in atmospheric air , 2018 .

[38]  Jennifer A Ailshire,et al.  Fine particulate matter air pollution and cognitive function among older US adults. , 2014, American journal of epidemiology.

[39]  W. Geoffrey Cobourn,et al.  Accuracy and reliability of an automated air quality forecast system for ozone in seven Kentucky metropolitan areas , 2007 .