22 Air Monitoring
THE SUITABILITY OF A MOBILE COMMUNICATIONS NETWORK TO DELIVER HIGH-RESOLUTION AIR QUALITY MEASUREMENTS
In most countries, mobile network operators (MNOs) deploy tens of thousands of base stations to deliver mobile communications services to their customers. It is estimated that there are approximately 7 million base stations worldwide [1]. These deployments positively correlate with population density and base stations come in many diff erent types from large rural masts, approx. 20-25 metres high, to street works locations which are typically around 10-15 metres high, to rooftop sites, whose height depend on the height of the building that hosts the equipment.
In addition to providing wireless connectivity to subscribers, a mobile communications network (MCN) can also become a powerful environmental sensor thanks to the following key characteristics: 1) its pervasive infrastructure can be used as real estate for hosting a wide range of sensors in a cost-effective manner (weather, air quality, noise, etc.) 2) its ability to capture subscriber mobility patterns in aggregated and anonymised form in compliance with General Data Protection Regulation (GDPR) 3) its intrinsic ability to detect humidity and rain at certain frequencies, given the physical properties of the wireless channel.
By strategically leveraging these benefi ts, an MNO can deliver a high-resolution and real-time environmental data set that can enrich and add resolution to existing sources of data and be used for a wide variety of use cases, for example: to increase accuracy and resolution of weather forecasting and nowcasting models, to calculate accurate risk factors for wildfi re and fl oods and understand their impact on communities as they occur, to build high-resolution air quality (AQ) maps that can inform policy makers on the most effective pollution mitigation strategies.
We refer to this concept as “Network as a Sensor”.
Vodafone is already unlocking the power of its mobility patterns through its Vodafone Analytics practice [2] and, together with Vantage Towers and its ecosystem of partners, is running several pilots across Europe to prove how telecommunications infrastructure can deliver high-resolution and real-time data assets on weather [3].
This paper focusses on a pilot on AQ in the UK, where Vodafone is working in partnership with Vantage Towers, Cornerstone, Wireless DNA, Scotswolds, Cambridge University, and Cambridge Environmental Research Consultants (CERC). The pilot is live in the city of Glasgow, where a network of AQ sensors is being deployed on a selection of base stations in the city centre to monitor AQ at high resolution in space and time. The data derived from the sensors can be used in harmony with mathematical
modelling to build high-resolution AQ maps and accurate forecasts and identify local sources of air pollution versus the regional background.
Initial fi ndings suggest that:
• Existing base stations are suitable for hosting AQ sensors, and there is no detectable detrimental effect of electromagnetic radiation on the sensor behaviour
• The accuracy of the chosen sensors agrees well with Reference methods
• Most base stations are in ideal locations for the assessment of AQ exposure
While this article is being written, the project is entering its second phase of execution, with the key objective of assimilating sensor data into CERC’s Atmospheric Dispersion Modelling System (ADMS-Urban) [4, 5, 6] in order to constrain emission inventories which can then provide key information for policy decisions on the local and regional scale, and which can produce high spatial resolution maps for public dissemination, including AQ forecasts.
Network as a Sensor for Air Quality
Mitigation and Management As conventional monitoring networks often lack spatial granularity and are typically sparse and fragmented across countries, an MCN can overcome these limitations by enabling measurements of AQ at high granularity in space and time and provide broad spatial coverage with a consistent performance across country borders. Moreover, the presence of an MCN correlates with population density, which makes its spatial distribution ideal for better understanding the impacts of pollution hot spots on communities.
The data generated by such a network has the potential to be transformative as it can truly drive strategic change and mitigation actions.
This high spatial and temporal resolution data can in fact empower stakeholders to achieve the following, at scale:
• Measure air pollution for action and mitigation, in addition to compliance with standards and guidelines, by truly understanding pollution spikes at different times of the day in different locations of a given area
• Understand those emissions which are generated locally, and those that are imported from elsewhere due to long-range transport
• Accurately calculate local emission indices, revealing the amount of pollutant produced per unit of carbon-based fuels combusted
Figure 1: Illustration of a compact AQ monitor attached to a base station
In the future, it will also be possible to use CO2 their own right as part of carbon footprint studies.
measurements in
The intended outcome of this study is to demonstrate how this pilot can potentially be scaled to provide a “data as a service” platform across Vodafone’s entire global operation and beyond, underpinned by the following:
• Cost-effective deployment of compact AQ sensors attached to mobile communications infrastructure in key strategic locations, as illustrated in Figure 1, measuring a range of key airborne pollutants at 1-minute resolution. This deployment strategy not only enables easy access to available real estate, power, and data connectivity, but also ensures a consistent and long-term approach in maintaining these sensors calibrated and fi t for purpose by leveraging the standard site maintenance procedures adopted by MNOs.
• Standardised cloud-based methodology to deploy, maintain, calibrate sensors against reference measurements, and automatically perform quality control and quality assurance (QA/QC).
• AQ model that ingests measurement data, amongst a variety of other data sources, and produces high resolution pollution maps and prediction of pollution patterns with improved accuracy
• Additional source apportionment outputs obtained by separating local vs regional emissions to allow better targeting of intervention policies
• A dashboard and visualisation system to allow users to access AQ data or AQ maps in near real time
IET NOVEMBER / DECEMBER 2023
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