Is it normal to get excited about a Publicly Available Standard? For us it feels like a long time coming, and this first step on the long road towards an ISO standard for small sensor air quality monitoring is very welcome.

Whether you are using small sensor systems (with or without MCERTS) or reference grade equipment, you need a clear and agreed process for data, to show that data is trustworthy and reliable. PAS4023 is now freely available to the public to guide users on how to get accurate and valid data from small sensor systems. The standard essentially aims to provide a method for using small sensor systems which ensures any data produced is consistent and comparable to a known standard.

This takes a lot of the risk out of use of this type of monitoring equipment, especially where it has held back adoption in applications where it would add real value.

A QA plan makes all the difference. Without one, data is always open to challenge, however ‘good’ it is. If it can be demonstrated that a project is using the same equipment in the same place, at the same time, and following the PAS4023 document, data is more meaningful and can be used by government bodies.

In the 10+ years that AQMesh has been commercially available, it is the lack of this sort of clear guidance – put together by a team of experts – that has undermined confidence. So, a big thank you from us to the team behind the Standard.

Learn more about PAS4023 here and chat to us about how we can help you manage an air quality monitoring network you can rely on.

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.

This a great report, full of common sense and helpful advice, applicable globally, particularly where air quality monitoring is currently limited. Limited by budgets, logistics, and many factors discussed so eloquently at the ASIC Ghana conference last month.

Central to the report is the position that “air quality monitoring is the first step for understanding a population’s exposure and taking action” and the report lays out clearly the monitoring methodologies available, from passive sampling to modelling.

Low cost sensors are put in their place – but very much given a place – offering the cheapest and simplest way to understand spatial and temporal air pollution variability. To the report’s plea that “ideally, every nation should have access to at least one reference-grade monitor” we would add “well-maintained”. It can be a huge challenge maintaining a reference station in many parts of the world – accessing calibration gases, skills, parts and budgets – but, as the report says, they do indeed open the door to many other air quality methods. We have been involved in many projects where our pods came out of the box and performed well, but that the project ‘failed’ because the objective was comparison with reference standard and that could not be achieved because the reference station could not be brought up to spec within the project timescale. Valuable real-time information, showing minute-by-minute, hourly, daily and weekly trends in air quality across a target area, can be discarded for this reason, which is a shame.

Multiple monitoring methods, or hybrid networks as we refer to them, used effectively, can most effectively achieve the baselining and measurement of progress discussed in the report. It was difficult to fully recognise the generic Low Cost Sensor described in the report, but that is not too surprising, given that the sector is so dynamic. Sources referred to, dated 2017, would be based on fieldwork from preceding years, and an awful lot has changed in Low Cost Sensor world in the last six years or so. Are they really low capital and medium operating costs? That may be true of some, but there are also slightly more expensive options with lower operating costs. We genuinely do not see drifting baselines, but that’s maybe because we have never rated metal oxide sensors. Also, since a step change in the electrochemical sensors making their way into the market in 2016, there has been continuous improvement in temperature correction of electrochemical output, and temperature effects did look like drift over certain time periods.

We would certainly agree with the comment that low cost sensors are “not yet suitable for replacing reference-grade monitors” but the remote calibration techniques and diagnostics offered by the latest generation of systems goes a long way to address the highlighted need for data quality assurance and quality control.

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.

Data collected as part of the UKRI SPF Clean Air Program has proven that AQMesh out-of-the-box performance for PM2.5 exceeds new US EPA targets*, with excellent results for PM1 and PM10 as well.

AQMesh prides itself on its accuracy and has always backed up its claims by publishing performance data online, making it easy for users to see real results, from real trials, in real world conditions. Since the product was first commercially launched in 2012, after being developed in collaboration with the University of Cambridge, it has been out in the field continually in numerous trials. Data from these extensive global co-location comparison trials against certified reference equipment / field equivalent methods have continued to be made publicly available, as well as being used to drive product development and performance improvement.

The most recent advancement has been the launch of the latest gas processing algorithm, V5.3. Now the current production standard, and available as a free remote upgrade for existing users, the new algorithm has been developed from over 140 co-location datasets across four continents using a wide range of extreme environments and conditions over extensive time periods. Its benefits include improved out-of-the-box accuracy, particularly at the extremes of the temperature range, reducing the requirements for seasonal scaling.

Combined with improvements in small sensor technology, AQMesh processing algorithms are carefully developed from real world datasets and understanding how sensors respond in varying conditions. All AQMesh algorithms are fully traceable, fixed by version number and have been achieved with no use of machine learning or artificial intelligence. This means no training period is required and co-location with reference is not a necessity unless validation of data is required. These features, along with unique out-of-the-box accuracy, allows quick access to reliable data after installation, enables flexibility in monitoring locations without access to reference and ensures the pods can be moved between locations efficiently to make the most of the time available.

In addition to continual improvement of sensor performance, the team at AQMesh is always looking to add new hardware options to the pods to offer even more flexibility and users can now benefit from TVOC monitoring. The new electrochemical TVOC sensor is one of eight available sensors in the suite of gas options on offer. It can be specified within a single AQMesh pod alongside five other gases out of NO, NO2, O3, CO, SO2 or H2S, as well as an additional NDIR CO2 sensor, and PM1, PM2.5, PM4, & PM10 via an optical particle counter. Wind speed & direction and noise monitoring are also available, with humidity, pressure and pod temperature included as standard.

These new results for PM1, PM2.5 and PM10 are the latest example of the product’s high performance, with AQMesh previously been recognised as the most accurate multi-parameter small sensor system for outdoor air quality monitoring in the 2019 AIRLAB Microsensors Challenge. It was also the system used by the Environmental Defense Fund to develop a blueprint for hyperlocal air quality monitoring following the successful 3 year Breathe London pilot.

All AQMesh co-location comparison results can be viewed on the AQMesh website, alongside numerous independent and academic studies which verify AQMesh as a high-performing small sensor air quality monitoring system.

*As published in the Air Sensor Toolbox on the EPA website.

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”

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.

Refinement and development of the AQMesh small sensor air quality monitoring system over many years, and through numerous global co-location comparisons, brings a wealth of unique benefits.

As well as a range of comparison datasets across climates from the Middle East to Scandinavia, each AQMesh pod has always been subject to a comprehensive factory set-up, following best practise of co-location comparison with reference equipment.

The AQMesh onsite reference station means each sensor has been tested through a meaningful and rigorous quality control process before it leaves the UK factory. The sensitivity of each sensor to the target pollutant is measured against the reference equipment and sensors which show a response outside the defined range are rejected. Adjustments can also be applied to optimise consistency, precision and accuracy through AQMesh’s proprietary data processing algorithms.

At the AQMesh factory, a custom-built mobile enclosure houses climate-controlled reference/equivalence analysers sampling air from an ambient ‘cage’ which can hold up to 100 AQMesh pods at a time. As well as sensor characterisation, this allows pod-to-pod precision to also be evaluated at the same time. The reference equipment includes Thermo Scientific 42i for NO, NO2 and NOx, Thermo Scientific 43i for SO2, Ecotech Serinus 10 for O3, Ecotech Serinus 30 for CO as well as the industry standard FIDAS 200 for PM1, PM2.5, PM4 and PM10. The enclosure is maintained at a constant, consistent temperature of 18 +/-2 degrees centigrade all year round, and all analysers are serviced regularly and calibrated themselves before each sensor characterisation batch.

Since 2015, the electrochemical sensors used in AQMesh have gone through this ambient characterisation during manufacture as part of the stringent AQMesh quality assurance process, which also includes strict criteria for particulate matter (PM). This means every AQMesh pod out in the field to date has been through specific quality control measures to ensure the efficiency and reliability of each sensor in real-world conditions.

Access to their own reference station, and the ongoing development of AQMesh since it first commercially launched in 2012, has also enabled the team to continually review the product’s long-term performance. Baseline stability of AQMesh’s electrochemical sensors has been proven over many years of independent global co-location trials against certified reference equipment.

Another benefit that AQMesh brings to the table is its low cost of ownership, as pods require little to no ongoing maintenance – it is simply recommended that the electrochemical sensors are replaced as standard every two years. And those sensors will have, of course, undergone the same thorough characterisation and quality control as the ones each pod first came with.

This combination of long-standing factory “calibration”, extensive global co-location comparison field testing now spanning over eight years in real, ambient conditions across a range of environments and climates, and a number of other unique benefits means AQMesh is arguably the most reliable and accurate small sensor air quality monitoring system available on the market today.

The results of the 2019 AIRLAB Microsensors Challenge* were revealed in Paris on 21^st January and AQMesh was awarded the highest score for accuracy of all multi-parameter products presented for monitoring of outdoor air quality.

In this independent international comparison of small sensor air quality monitoring products, a range of manufacturers submitted three examples of their product to be evaluated in a trial carried out by AirParif, the body responsible for monitoring air quality across Paris, and a jury of French and international experts. The objective of the project is to offer potential users an independent assessment regarding the adequacy and performance of products with respect to intended use, such as indoor, outdoor or mobile air quality monitoring.

34 commercially available small sensor air quality monitoring systems, or ‘microsensors’, were assessed by the Airparif teams over 4 months, with half of the systems manufactured outside France. These evaluations covered around 44 performance criteria (depending on category) and 15 pollutants were studied during the four-month project. Trials were carried out in the Paris region – in a metrology laboratory, in motion on vehicles and people, and on Airparif reference stations, depending on the category of use.

AQMesh was evaluated for the highest number of pollutant measurements offered within a single system for outdoor air quality monitoring: NO, NO2, O3, PM1, PM2.5 and PM10. The scoring applied for cost relates to the product specification submitted for the Challenge, and AQMesh compared well with the top performers in the category based on cost per pollutant measured. In addition to the 6 pollutants measured with AQMesh in this study, other options available in a single AQMesh pod include CO, H2S, SO2, CO2, noise, wind speed and wind direction. All AQMesh units also measure temperature, pressure and relative humidity.

Full results of the Challenge are available on the AIRLAB website:

http://www.airlab.solutions/en/news/results-international-challenge-2019

The AIRLAB summary suggests that a product life for the small sensor systems in general of 12-18 months but an AQMesh unit is expected to operate optimally in the field for at least five years. The report also mentions that the results were obtained with Paris pollution levels and the weather conditions and that users selecting a product for use in different outdoor conditions should verify operation in those conditions.

The UK-based AQMesh team submitted the product for the Challenge with their distribution partner in France, Addair, but the product is available either directly or via a network of global distributors.

* This Challenge is part of the activities of AIRLAB, accelerator of technological or behavioural solutions to improve air quality, and was created by Airparif and its founding partners in September 2017, with funding from the Ile-de-France Region. www.airlab.solutions/en

Recent co-location comparison trials against certified reference equipment continue to prove AQMesh performance and reliability for localised air quality monitoring.

Trials in the USA, UK and Western Europe this year have delivered high correlation coefficients (R2 values) for key pollutants such as nitrogen dioxide (NO2), ozone (O3) and fine particulate matter (PM2.5). An R2 value of 0.92 against reference for O3 was achieved in Southern USA over the Summer, as well as an R2 value of 0.94 against reference for NO2 in Northern USA during the cold season.

Co-location trials for AQMesh and field equivalent methods have been taking place globally for several years, with the results published on the AQMesh website, demonstrating how performance and accuracy continues to improve with each new version of the product. A number of independent studies have also been carried out, verifying the AQMesh system’s capability.

AQMesh is a small sensor air quality monitoring system for measuring pollutant gases and particles in ambient air. It is a flexible, quick to install and easy to use air quality monitor that can deliver localised, real-time readings, aiming to improve the spatial resolution, scope and accuracy of gathering air quality data.

Its range of wireless power options includes a recently improved smart solar panel, which is now larger than the previous and has a more efficient charge, allowing for year-round operation for standard gas and particulate AQMesh pods across Western Europe and regions on a similar latitude.

AQMesh pods can now monitor up to 6 gases out of NO, NO2, NOx, O3, CO, SO2, CO2 and H2S using the latest generation of sensors, as well as PM1, PM2.5, PM10 and total particle count (TPC) with a light-scattering optical particle counter. In addition to pollutants, AQMesh can measure noise, relative humidity, pod temperature and atmospheric pressure, all within a single compact unit. Data is completely secure on the AQMesh cloud server, only accessible by a secure login, which allows the user to manage their pods, view customisable graphical data, and download the data for further analysis.

AQMesh is currently in use throughout the world in a variety of air quality monitoring applications and projects, including smart city networks, indoor-outdoor air quality management, employee health and safety, traffic pollution mitigation studies and air quality modelling. Recent case studies show it forming part of a major ‘hyperlocal’ street-by-street monitoring system throughout London (UK), as well as being used in a similar project across 50 zip code areas in Minnesota (USA).