Air quality mapping and visualisation: An affordable solution based on a vehicle-mounted sensor network

Abstract This paper describes a prototype of the ExpoLIS system, which aims at: (1) informing citizens regarding the air quality of their surroundings and how to cope with it (e.g., choosing commuting routes according to a health model); and (2) gathering dense spatiotemporal air quality data to support the empirical work of environmental experts. The system is composed of: (1) an affordable and custom vehicle-mounted sensor network for air quality monitoring; (2) a server to store, process, and map all gathered geo-referenced sensory data; and (3) a set of user-centred visualisation and prediction services tailored for citizens and environmental experts. Experimental validation of each component of the proposed system shows that the current prototype is capable of tracking spatiotemporal air quality changes and of providing users with access to these events via a set of interfaces. The results show evidence of a strong correlation in static situations (R2 of 0.96 for PM2.5) between the proposed low-cost all-weather system and a high-cost equipment with no weather protection. The results also show a weaker correlation (R2 of 0.57 for PM2.5), but still satisfactory, in dynamic settings. In short, this paper presents experimental evidence that supports the claim that the ExpoLIS system is feasible and valuable to both citizens and environmental scientists.

[1]  Wei Dong,et al.  Mosaic: A low-cost mobile sensing system for urban air quality monitoring , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[2]  Liviu Iftode,et al.  Real-time air quality monitoring through mobile sensing in metropolitan areas , 2013, UrbComp '13.

[3]  Maksymilian Włodarski,et al.  Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown , 2020, Sensors.

[4]  A. Terenchenko,et al.  The performance assessment of low-cost air pollution sensor in city and the prospect of the autonomous vehicle for air pollution reduction , 2020, IOP Conference Series: Materials Science and Engineering.

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

[6]  S M Almeida,et al.  Effects of Exposure to Particles and Ozone on Hospital Admissions for Cardiorespiratory Diseases in SetúBal, Portugal , 2014, Journal of toxicology and environmental health. Part A.

[7]  Sabrina Grassini,et al.  A Remotely Controlled Calibrator for Chemical Pollutant Measuring-Units , 2007, IEEE Transactions on Instrumentation and Measurement.

[8]  Kwong-Sak Leung,et al.  A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems , 2015, Sensors.

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

[10]  M. Viana,et al.  Testing the performance of sensors for ozone pollution monitoring in a citizen science approach. , 2019, The Science of the total environment.

[11]  S. Almeida,et al.  Particle exposure and inhaled dose while commuting in Lisbon. , 2019, Environmental pollution.

[12]  Pedro Mariano,et al.  Game-Like 3D Visualisation of Air Quality Data , 2020 .

[13]  Hrvoje Jasak,et al.  A tensorial approach to computational continuum mechanics using object-oriented techniques , 1998 .

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

[15]  S. Almeida,et al.  Children's exposure and dose assessment to particulate matter in Lisbon , 2020 .

[16]  James D. Freihaut,et al.  Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources , 2019, Aerosol Science and Technology.

[17]  Pedro Mariano,et al.  Pollution Prediction Model Using Data Collected by a Mobile Sensor Network , 2020, 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech).

[18]  Roger D Peng,et al.  Spatial misalignment in time series studies of air pollution and health data. , 2010, Biostatistics.

[19]  Pedro Mariano,et al.  An Affordable Vehicle-Mounted Sensing Solution for Mobile Air Quality Monitoring , 2020, 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech).

[20]  Lothar Thiele,et al.  Deriving high-resolution urban air pollution maps using mobile sensor nodes , 2015 .

[21]  Roderic L. Jones,et al.  Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments , 2019, Atmospheric Measurement Techniques.

[22]  Dennis Luxen,et al.  Real-time routing with OpenStreetMap data , 2011, GIS.

[23]  Richard T. Burnett,et al.  How is cardiovascular disease mortality risk affected by duration and intensity of fine particulate matter exposure? An integration of the epidemiologic evidence , 2011 .