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      City Scale Particulate Matter Monitoring Using LoRaWAN Based Air Quality IoT Devices

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          Abstract

          Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment, six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that (i) the physical device developed can operate at a city scale; (ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends; (iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project, a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.

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          A comparative study of LPWAN technologies for large-scale IoT deployment

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            High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data.

            Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km(2) area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.
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              End-user perspective of low-cost sensors for outdoor air pollution monitoring.

              Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                08 January 2019
                January 2019
                : 19
                : 1
                : 209
                Affiliations
                [1 ]Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO16 7QF, UK; P.J.Basford@ 123456soton.ac.uk (P.J.B.); F.Bulot@ 123456soton.ac.uk (F.M.J.B.); mac1g12@ 123456soton.ac.uk (M.A.-C.); cd3g16@ 123456soton.ac.uk (C.D.); S.J.Cox@ 123456soton.ac.uk (S.J.C.)
                [2 ]Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, UK; nhcs1g13@ 123456soton.ac.uk (N.H.C.E.); Gavin.Foster@ 123456noc.soton.ac.uk (G.L.F.)
                [3 ]Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK; M.Loxham@ 123456soton.ac.uk
                [4 ]National Oceanography Centre, Southampton, Southampton SO14 3ZH, UK; andmor@ 123456noc.ac.uk
                Author notes
                [* ]Correspondence: sjj698@ 123456zepler.org
                Author information
                https://orcid.org/0000-0003-3864-7072
                https://orcid.org/0000-0001-6058-8270
                https://orcid.org/0000-0003-3337-4650
                https://orcid.org/0000-0003-1562-5137
                https://orcid.org/0000-0002-9168-3294
                https://orcid.org/0000-0002-1089-5683
                https://orcid.org/0000-0003-3688-9668
                https://orcid.org/0000-0001-6459-538X
                https://orcid.org/0000-0002-5465-411X
                Article
                sensors-19-00209
                10.3390/s19010209
                6339063
                30626131
                86541ad5-391d-4d11-8bdb-9fd15bc0b9b6
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 November 2018
                : 20 December 2018
                Categories
                Article

                Biomedical engineering
                internet of things,wireless sensor networks,air quality,lorawan,raspberry pi,urban pollution

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