Hyperlocal air quality monitoring promises to fill in the gaps between sparse reference stations – great, lots more measurements. And each of your lovely small sensor monitoring stations can measure a dozen or more pollutants and environmental conditions – even better. Or is it? Even at a typical 15-minute reading average – let’s not think about 1-minute readings – that’s a lot of data to process and understand. The meta data attached to each reading is valuable, including data confidence flags, wind speed and direction, geographical location or processing algorithm version, but it adds to the pile of information to be managed.

Looking back to the beginning of small sensor systems – maybe 5-10 years ago – readings were typically downloaded as CSV/Excel files and analysed offline. Maybe you had access to some clever online trickery for analysis and some visualisation tools to get some sort of overview. More commonly we see customers calling data by API from our cloud server and building up their own air quality database. Which is great, but getting dangerously close to the situation in UK a decade or so ago when there was plenty of high quality data from AURN stations, but still no closer to actually moving the needle on air pollution levels. Plus these AQ database owners want to publish air quality information in real-time to the public or use it to generate meaningful alerts.

But is the mountain of data from small sensor systems even high quality, like the AURN network output? By definition it will not be and the truth is that it varies considerably between system types. And even the best systems have bad days and need output to be automatically scrutinised. Focus has moved emphatically onto quality assurance and there are some emerging and developing techniques which – used in conjunction with data confidence flags – can deliver the sort of real-time insights and publishable data that was the original dream.

Gas, PM and other sensors all have limitations and weaknesses that sensors system manufacturers should be well aware of, particularly if they are engaging with users and evaluating performance of their product in the field. This knowledge can be used to develop a sophisticated and transparent system of data confidence flags and/or data redaction. This is an evolving field, as what can look like a malfunction in one application is quite normal in another. For example, the levels of nitrogen oxides typically found in tunnels would look like a sensor failure in a roadside monitoring project. Clearly communicated data confidence indicators are overlaid on systematically processed readings, offering repeatability and traceability, and are not the same as AI-based data treatments.

Blessed with a large number of small sensor readings, which are ‘good enough’ (actually a compliment from one of our favourite leading academics in the field), which have been filtered using data confidence criteria, the next big win comes from network analysis. We call it Long Distance Scaling and it involves analysis of data from usually five or more measurement points (and ideally a reference station). Using this sort of approach, baselines can be separated from local sources, potential pollution sources identified by distance and direction, and the big picture starts to emerge from the mass. The best analysis we have seen of data from our pods is by the team at the University of Cambridge.

So, without the resources to manage quality assurance, there is a real danger that a project can sink under the weight of its own data. Large amounts of data can certainly help to build a better air quality picture – and even help with QA itself – but a clear data management plan, and the resources to carry it out, are essential.

AQMesh is being used in an innovative intelligent pedestrian crossing system being developed by three collaborators across Italy, Israel and Spain.

The Behavioural Enhancement for Air Improvement and Resilience (BEAIR) concept integrates smart lighting, intelligent cameras and AQMesh air quality monitors at pedestrian crossings, as well as an app that offers real-time information on air quality and local traffic. The system is designed to improve safety for both pedestrians and motorists.

The AQMesh pods used in this project measure 6  gases  as well as particles and use the bespoke AQMesh smart solar pack for autonomous power. Data is provided in near-real time via the AQMesh API.

The first pod and smart pedestrian crossing has been installed on a busy street in Genoa, close to the highway, port and subway station and aims to provide a safe and sustainable living environment for Genoa’s citizens.

More information about the BEAIR solution can be found here, and contact the team at AQMesh today to find out how we can support your air quality initiatives.

AQMesh Product Manager, Tom Townend, will be presenting at the EPA 2023 Air Sensors Quality Assurance Workshop on Wednesday 26^th July at 1pm ET (6pm BST). He will be speaking about our project in Minneapolis and quality control of large sensor networks measuring total volatile organic compounds (TVOC).

This cutting-edge small sensor air quality project takes hyperlocal monitoring to the next level by validating data – in this case the focus is TVOC – and identifying where pollution is coming from. In this case it will be possible to match the type of pollution seen with potential local sources. Whilst small sensor air quality devices generally give measurements at specific locations (to varying degrees of accuracy), the real value comes from confirming the accuracy of those readings and harnessing the power of a sensor network, particularly when combined with high quality comparison technology.

The City of Minneapolis installed 30 AQMesh pods in targeted locations, chosen with reference to the locations of potential VOC sources – such as foundries – and vulnerable communities. The pods measure NO, NO2, O3, CO and TVOC. The aim is to look for ways to reduce the exposure of local residents to harmful VOCs, such as benzene/BTEX. The two-year project will test proposed mitigation approaches.

Initially all 30 pods were co-located to show that they all read the same as each other, with any bias able to be easily corrected. Comparison against gas chromatograph (PAMS) allowed a breakdown of 60 VOCs present to be used to derive VOC-specific correction factors. Linearity was shown by an R2 of 0.68 with the pods operating uncorrected, and 0.92 with correction factors applied. Slope was shown to be 1.3 and 1 respectively, and offset around 2ppb.

The pods are taking measurements across 30 locations: one pod remaining at the reference site throughout the project with the rest positioned relative to air permitted facilities (likely pollution sources). Bag samples are being taken monthly as supplementary measurements, as part of the network, to identify which VOCs are present. These samples can then be used to determine the most appropriate correction factors to use for each individual AQMesh reading.

At the next stage of the project, Long Distance Scaling is used – an AQMesh technique for ‘network calibration’ which was inspired by an approach taken by Professor Rod Jones of the University of Cambridge with the 100+ AQMesh Breathe London pilot network. Hyperlocal events – the local ‘spikes’ – can be separated from more regional or background pollution events, by making comparisons across the whole network. Application of the right correction factors to TVOC spikes, and not background level, reduces the risk of overestimating pollution attributed to a particular source at a later stage.

Local spikes are identified with confidence and this is the starting point for pollution mitigation strategies. The next step is to understand where the spikes are coming from: local wind speed and direction information is used to create pollution rose plots. With an indication of the source and the VOC bag samples (identifying VOC variants) it is possible to use targeted correction factors to provide meaningful, quantified estimates of the fugitive VOC levels being measured by each pod.

Throughout the two-year project, network readings will be compared against the pod remaining at the gas chromatograph station. The near real-time, continuous flow of data from the 30 pods is assessed automatically, looking for failures and data points of lower confidence, and the network is scaled periodically. The same data QC approach is applied to each of the five sensors in all of the 30 pods, to provide the full circle of data confidence necessary to support mitigation investment and public sharing of pollution information for environmental justice.

The EPA EPA 2023 Air Sensors Quality Assurance Workshop is free to attend online and AQMesh distributor for North America, Ambilabs LLC, will be exhibiting at the event in person, so be sure to ask them any questions you may have about how AQMesh can support your air quality monitoring networks.

AQMesh is being used as part of the newly revived Breathe Easy Dallas initiative – a project designed to measure and understand air pollution at neighbourhood level.

As published by The Dallas Express in March 2023, eight AQMesh pods have so far been used by the Office of Environmental Quality & Sustainability (OEQ) with a view to extending the network to 40 pods across areas that are suspected to have higher-than-average levels of air pollution.

Carlos Evans, director of the OEQ, confirms the Breathe Easy Dallas initiative will aid the development of policies that ensure safe and clean air within communities, and was reported to state “We have a pretty good understanding of regional air quality, but we don’t have a good understanding of neighbourhood level air quality”.

AQMesh has been used in a number of similar projects across North America – and elsewhere around the world – where networks of small sensor systems are used to supplement information available from larger reference sites to build up a picture of localised air pollution levels. In Minneapolis, USA, 50 AQMesh pods are used to determine air quality levels at different zip codes. In Kitchener, Canada, several AQMesh pods have been used to measure air quality levels at schools across the region.

Developed, manufactured, supplied and supported from the UK, AQMesh is available across North America via its authorised and trained distributor, Ambilabs. The product is a small sensor air quality monitoring system that can measure up to 6 gases as well as PM, noise and wind speed and direction.

Researchers at Newcastle & Northumbria Universities have published the report on their study which used AQMesh to measure air quality around schools in Newcastle-upon-Tyne.

A network of 22 AQMesh pods was originally deployed in collaboration with Newcastle University and Newcastle City council to monitor air pollution outside schools near major traffic routes. The main objectives of the study were to determine levels of NO2, PM1, PM2.5 and PM10 – and whether these exceeded the WHO guidelines – as well as estimate children’s exposure to harmful particles.

Data from the pods showed that several of the 12 schools in the study exceeded both WHO and UK air quality regulations for short-term NO2 and PM10 concentrations and all 12 schools exceeded the guidelines short-term PM2.5 exposure.

Other findings showed higher levels of all pollutants during the winter months compared to summer, and increases in NO2 concentrations during the typical morning and evening commuter traffic.

For full details of the findings please read the published report.

A recent study using a network of five AQMesh pods has found that small sensor systems with a properly managed QA/QC process offer valuable air quality measurements, complementing data from expensive reference equipment. Networks of small monitoring nodes, such as AQMesh, can be easily installed in local areas to identify pollution sources and expand the scope and understanding of air pollution across a city.

Following on from the initial deployment of the five pods across Kitchener, Canada, in 2020, the team leading the project have now published their paper outlining the initial results of this study.

The five AQMesh pods were installed near elementary schools, supplementing the city’s only reference station, in order to demonstrate how pollution varies over short distances. Data from the pods highlighted the need for city-wide networks of small sensor air quality monitoring systems to build a more accurate picture of local pollution levels.

As part of the AQMesh team’s development into improving QA/QC methods for air quality monitoring networks, a network comparison method was used to put the pods through a rigorous QA/QC process. Data from the pods was validated through pod-to-pod comparison, while also providing traceability back to the reference instrument. The process works on the principle of identifying and separating local pollution events, leaving just the regional pollution which all pods and reference equipment respond to. This then provides a comparable data set for scaling via linear regression analysis.

Based on the confidence built by the QA/QC process, data from the pods indicated that levels of nitric oxide (NO), nitrogen dioxide (NO2), ground level ozone (O3) and fine particle matter (PM2.5) were mostly traffic related. Project leader, Dr. Hind Al-Abadleh, from Wilfrid Laurier University, hopes knowledge of how this source affects pollution levels in Kitchener will help speed up the electrification of the city’s transit system as well as encourage parents and carers to walk or cycle to school instead of drive.

The project was loosely based on the ground-breaking Breathe London pilot in the UK, in which 100 AQMesh pods were used to create a hyperlocal air quality monitoring network and publish a real-time pollution map online. Similarly in the USA, 50 AQMesh pods were deployed across Minneapolis – St. Paul. The team in Kitchener hope to expand their network in the same way with more AQMesh pods.

Tom Townend, AQMesh Product Manager, who worked closely on the project, says “The team from Wilfrid Laurier University and Hemmera Environmental Consultants have shown a great understanding of how to maximise the use of a small network to provide high quality data. The network analysis method used, alongside the latest AQMesh processing algorithms, allowed for frequent and detailed QA/QC of the network. This project shows how much can be achieved with even small networks of AQMesh pods across a city, and how they can provide the level of confidence and verified detail required by a wide range of air quality professionals.”

Please contact the AQMesh team to get more information on the QA/QC processes followed and how this might be used if your own network of AQMesh pods.

Two AQMesh pods measuring airborne particulate matter have been loaned to the University of Cambridge, ahead of the COP26 meeting in Glasgow, to support research into measurement of particulate matter. The pods have been monitoring PM1, PM2.5, PM4, PM10 mass estimates and Total Particle Count, since April 2021. The team from the University of Cambridge, led by Professor Rod Jones, are using data from these units, which have the heated inlet option to minimise the effect of air moisture on readings, to support their work on understanding differences in chemical composition between particles.

The compact small sensor air quality monitoring system, designed to measure levels of pollutant gases in ambient air, also offers a non-dispersive infrared (NDIR) carbon dioxide (CO2) sensor, providing accurate outdoor CO2 measurements. As well as monitoring deviations in background levels of CO2, analysis of data from the system can also identify combustion plumes and provide an indication of whether the gases are being produced by a local or distant source, and from which direction.

Professor Jones has previously used CO2 data from AQMesh pods during the Breathe London pilot project, in conjunction with other AQMesh gas measurements. AQMesh offers a range of ambient air measurements relevant to climate change studies, including NOx, SO2, CO, CO2, Black Carbon, TVOC and methane. The Ecotec group – which owns AQMesh – specialises in methane leak detection and gas stream methane monitoring. Applications include pipeline methane measurement for energy-from-waste on landfill, biogas, waste water treatment and agricultural sites. Methane leaks are also detected using a range of laser-based sensors, providing a critical resource for methane-generating operations, including the oil and gas industry.

COP26 is the upcoming 26^th United Nations Climate Change Conference, taking place in Glasgow, Scotland between 31^st October and 12^th November 2021. The aim of the conference is to progress global efforts towards the goals the UN Framework on Climate Change and the Paris Agreement – the legally binding international commitment to reduce carbon emissions, agreed at the COP21 conference in 2015.

CO2 emissions are a key factor in climate change and are largely caused through the burning of fossil fuels such as coal, gas and oil, which are burned to generate heat and electricity for the world’s power plants, cars, planes and industrial facilities, to name a few. Monitoring CO2 emissions is therefore vital in understanding, managing, mitigating and reducing sources of CO2 and its impact on the atmosphere and environment. Methane is an even more potent greenhouse gas and prompt identification of methane leaks is a critical part of the action plan to reduce greenhouse gas emissions.

AQMesh is an air quality monitoring ‘pod’ which can be mounted on a post, wall, fence or other position to measure ambient air pollution. Each pod measures about 20cm / 8 inches in each direction and weighs about 2Kg / 4lb. Sensor data is securely transmitted using the global mobile phone network to a cloud server, where carefully developed corrections for environmental conditions are made and data accessed by secure web login or API. Sensor options are offered on the basis that the level of sensitivity and selectivity for the target pollutant are fit for purpose, whether directed towards local air pollution or climate change pollutant monitoring.

The Breathe London pilot, which used 100 AQMesh pods as part of a ground breaking city-wide network of air quality monitoring stations, proved that small sensor monitoring technology can be deployed successfully to give results comparable with those of reference equipment.

An independent audit of the quality assurances and control procedures for the Breathe London network, conducted by National Physics Laboratory (NPL), has been published and highlighted encouraging results for AQMesh performance.*

Overall, a mix of comparison methods was used to scale and quality control the whole network, including AQMesh co-location with reference – the gold pod method – and Professor Rod Jones’ network calibration scaling method. The gold pod method, as developed by AQMesh, featured extensively and provided R2 values over 0.9 for both NO2 and PM2.5. NPL found that “the activities conducted for the audit purpose and the associated findings revealed a strong adherence of the data processing and management to the Breathe London QA/QC requirements.”*

The initial phase of the Breathe London project aimed to map air pollution across the city at an unprecedented level of local resolution, in order to develop a revolutionary air quality monitoring template that could then be replicated in cities across the globe.

As part of the project the AQMesh pods were installed at different locations throughout London, monitoring key pollutants in near real-time, including nitrogen dioxide (NO2) and fine particulate matter (PM2.5). The data collected by AQMesh was then published on a live interactive map showing current pollution levels across the city.

Data gathered during this pilot phase was successfully used to evaluate and inform public policies to mitigate sources of pollution, including monitoring the impact of the world’s first Ultra Low Emissions Zone (ULEZ). The network of AQMesh pods first established a baseline of pollution levels ahead of the ULEZ’s introduction, which began in central London on 8^th April 2019. A combination of the data from AQMesh and two Google Street Cars, fitted with mobile monitoring equipment, was then used to assess the impact the ULEZ was having on ambient pollution levels. Initial analysis of the network data found that concentrations of NO2 had reduced by approximately 13% when compared with the same period in the previous year.

The network was also able to identify new pollution hotspots, such as a bus garage which had not been picked up by previous monitoring and modelling efforts, despite being a significant source of NO2.

Covid-19 confinement regulations in March 2020 gave new insight into London’s pollution, with the network’s AQMesh pods reporting significant NO2 reduction immediately after restrictions were introduced.

This successful pilot phase of Breathe London has also led to the Environmental Defense Fund (EDF) publishing a ‘blueprint’ for other cities planning a hyperlocal air quality monitoring network. The guide for aspiring smart cities highlights a range of considerations for choosing small sensor systems and deploying them as a city-wide network.

Using the blueprint, global cities could deploy networks of such systems to identify and quantify air pollution in order to develop, implement and assess mitigation strategies.

*Section 4.1: NPL REPORT EAS (RES) 001 “AUDIT REPORT ON BREATHE LONDON FIXED NETWORK QUALITY ASSURANCE AND QUALITY CONTROL PROCEDURES”

Whilst there may be a growth in city-wide communications integration, “big data” and public interest in local air quality, the challenges of monitoring complex atmospheric chemistry have not changed. Integrating “sensors” can sound straightforward but information about air quality around a city must be handled carefully: the risks – to stakeholders, including the public, businesses and “city hall” – of providing misleading air quality information are significant. AQMesh has been used in a number of smart city projects and we would like to share our experiences of what can be achieved and where the difficulties lie.

Probably the most innovative smart city project we have been involved in is the Breathe London pilot. This project used 100 AQMesh pods to supplement the regulatory network and was led by the Environmental Defense Fund, which has issued a very helpful guide for other cities wanting to carry out local air quality monitoring. They highlight various considerations for choosing the right monitor, including defining your monitoring objectives, which pollutants you need to monitor (and the typical levels expected), installation sites, the quality of data required and your budget. Other factors listed in the guide focus on size, flexibility, longevity and vulnerability of the chosen system – ultimately, will it be fit for purpose and allow you to meet your objectives?

AQMesh has been used in a number of other smart city projects including UK (Newcastle) and USA (Minnesota), with 55 AQMesh pods installed throughout Newcastle and Gateshead as part of the Urban Observatory project, and 50 AQMesh pods deployed across Minneapolis and St. Paul, mapping live pollution levels across the cities. The objectives and approach taken by each of these projects was different, varying in terms of how and where measurement equipment was deployed, how data was analysed and how they chose to engage with the public.

These projects have all demonstrated just how much air quality varies across a city and over time. This critical information can be used to advise the public about how to protect their health and for local authorities to understand how they can mitigate, manage and measure air quality in their area of responsibility. But in order to get to this stage there are many challenges to overcome.

Choosing a small sensor system

There are many small sensor systems available and this article assumes that the smart city project managers have looked into – and asked for proof of – their chosen system’s capabilities. Products are offered for this application which are not sensitive or accurate enough to monitor meaningfully around the relevant thresholds. For example, an instrument that can only offer an NO2 measurement with an accuracy of ±30ppb is of little use when the annual average limit is 40ppb. If the sensor system can be shown to be sensitive enough and demonstrate good accuracy in environmental conditions similar to the new project, is it robust enough? Can it be installed easily? And how will it be powered?

Another consideration is how the network will communicate with the central server. A range of options are available, including LoRa, WiFi and direct ethernet connection, however these can sometimes be problematic, with limited coverage of LoRa and interrupted WiFi signals or router issues. The mobile phone network, as used with systems such as AQMesh, provides a universal and reliable approach. AQMesh is designed for each pod to send sensor output to the server using the global cellular network, where each unit is independent and costs can be managed. The latest AQMesh pods are fitted with LTE CAT M1 modems to take advantage of the growth in 5G / NB-IoT – an exciting area of development for smart cities. Some monitoring systems store and process data on the hardware itself which clearly has implications in terms of data security and back-up.

Getting sensor nodes installed

How many sensor nodes do you need? Where should they be? How will the hardware be installed? The first two questions are often answered in a non-scientific but equally important way: what is the budget and where is most important to measure? Many cities choose to monitor around schools and vulnerable communities, some want to understand the dynamics of a particular traffic corridor, some already know where pollution is concentrated and choose to focus there.

Installation of the systems is harder than it sounds. There are plenty of posts around a city and most small sensor systems are designed to attach to them. But who owns the posts and will they let you install equipment on them? Even trickier is getting a power supply from a lamp post, where the administration can be burdensome, access can be restricted (only certain contractors may be able to carry out installations) and the power supply may not suit sensitive equipment. Solar power is a convenient option but high buildings in a city can restrict the direct sunlight essential for generating power and this adds an extra dimension to the planning of unit siting. The bottom line is that planning for installation must start early and installing a network is likely to take longer than expected.

Quality assurance

Even the best small sensor air quality systems need to have their output managed through an appropriate QA/QC process. At a fundamental level it is important to confirm that the readings to be published can be relied upon. AQMesh users are supported by a range of measures which automate handling of the most common issues. For example, electrochemical sensors can fail but this can be detected remotely and the sensor output stopped (until the warranty replacement automatically issued can be installed). Measurement of particulate matter can be affected by high levels of moisture in the air and its effect on particles so AQMesh flags measurements that are likely to have been affected in this way and they can be automatically redacted (if the heated inlet option is not being used to address the issue at source). Even after these and other measures have been taken, it is necessary to confirm that readings are in line with those of a maintained, regulatory air quality management – or reference – station.

Many cities have a ready choice of reference stations to use in their smart city project. This can be by co-locating all units – or pods, in the case of AQMesh – with the reference station(s) or by co-locating a small number, which can then be adjusted against the reference station, before being co-located with other units in the network, in turn – this is known as the ‘gold pod’ method. The gold pod approach is proven to be effective but it does require regular moving of equipment and the people on the ground to do this. Emerging methodology – such as that used in the Breathe London pilot – has shown that similar accuracy can be achieved by remote comparison of nodes in the network and reference station(s). This “network calibration”, as pioneered by Professor Rod Jones at the University of Cambridge, is not the same as using AI to “train” a small sensor system. Network calibration must offer repeatability and traceability. As various air quality experts have pointed out, while modelling and remote calibration may have their place alongside monitoring, there is a danger of generating numbers that are more the product of modelling than measurement.

In any case, such discussions are redundant in cities which do not currently have any managed reference stations offering validated measurements. It is much easier to bring small sensor systems into a city, which is just starting to measure air quality, than it is to install and maintain a reference station. A smart city network can still be set up without a reference system, with nodes calibrated within a mesh, but QA/QC systems must be in place. An air quality index may be helpful for communicating pollution levels but it will still be possible to publish misleading information about local pollution if all the steps described are not considered.

In conclusion

As always, setting expectations is key. Whilst air quality may be just one part of a complex, integrated smart city project, air quality monitoring itself is inherently challenging. Small sensor air quality monitors are deceptively small and versatile but they are pieces of scientific equipment which have been carefully designed to achieve the most accurate possible air quality information. And use of data from even the best small sensor air quality systems, when used optimally, requires care. Having said all that, the investment in effort can pay back in vital and meaningful information which can protect the health of large numbers of people in a new and dynamic way.

Air quality is rarely out of the news and there are many initiatives in this field, from developments around conventional monitoring to IoT and smart city initiatives. Following the phrase “you can’t manage what you can’t measure”, there is huge growth in local air pollution monitoring in order to improve air quality, given the known health issues.

Small sensor air quality monitoring devices can be mounted flexibly, offering localised air quality information and data analysis, but they vary in what they can measure, how accurately they measure it, and how readings are accessed by users. Critically they also vary in terms of the reliability of data delivery.

The AQMesh system sends sensor output to the cloud server, using 2G/4G/5G as available across the world, where it is processed into meaningful readings. AQMeshData.net is a secure cloud-based server designed, developed, managed and supported by the AQMesh team, based on nearly 10 years of experience supporting AQMesh in use around the world. Data access is available through a range of secure packages, from secure login to view and download data, to the API option for seamless air quality data feeds into other systems and media. Settings for AQMesh pods in the field can be managed using this interface, as well as applying scaling. A basic data delivery option is also available completely free of charge.

Accessing data on the server as opposed to downloading readings from the instrument brings a number of benefits to the user, including power consumption kept to a bare minimum so that a range of power supply options including batteries and autonomous solar can be used. Sensor output is stored on the pod until safe receipt by the server is confirmed.

There is no need for any site visits to retrieve data and, should anything untoward happen to the pod, data is still perfectly safe and accessible. Boots on the ground are also not required should there be a signal loss or power interruption, as the pods can easily reconnect themselves and continue to capture data. Faults and failures are detected, diagnosed and resolved remotely – and swiftly – through AQMesh’s proactive systems and technical support team, ensuring that data delivery is maximised. In addition to diagnostics, users are able to set up their own customisable real-time pollution alerts.

AQMesh’s proprietary data processing algorithms have been carefully developed through extensive global co-location comparison trials – now spanning over eight years – in real-world conditions across all seasons and continents. This allows AQMesh to offer meaningful correction of cross-gas effects and interference from environmental conditions. All AQMesh algorithms are fully traceable and fixed by version number, and, unlike other systems, have been achieved with no use of machine learning or artificial intelligence.

Data security is taken very seriously at AQMesh. Encrypted disks, complex authentication, audit logs and proactive systems for continuous detection of vulnerabilities and intrusions are routinely examined, making the AQMeshData server – and users’ data – as secure as possible.

Users of AQMesh and AQMeshData – whether receiving free of charge data or accessing the premium real-time service – will benefit from lifetime ‘upgrades’ to the latest processing algorithms, as pods can be easily switched remotely to the most up to date version.