A good quality small sensor air quality monitoring system should last 10 years or more, and of course certified monitoring stations (reference, FRM, FEM) should last much longer.

Looking at the many small sensor systems used for outdoor air pollution monitoring, the question may be ‘how long will my air quality monitor work between services’? The best systems operate for two years between sensor changes, and sensors should be low cost and locally replaceable, with no need to return equipment to the manufacturer for service or calibration.

Whether considering the operational life of the product or time between each service, it is important not to assume that all products are the same. Some systems are not designed to work for more than a year or so, which might meet the needs of a shorter project and budget, but it may cause a series of unscheduled equipment failures in the middle of an important monitoring period. So, it’s worth asking for examples of long-running projects where long-term operation of the system can be proven.

Any intervention during the operation of an air quality system costs money, even if it is ‘only’ the time and effort of local teams, so this must be minimised. Scheduled service work is undesirable, but unscheduled maintenance demands are worse. AQMesh sensor replacements are based on carefully calculated duty cycles – balancing initial costs against expected life and product performance – and the pods have been proven over years of operation around the world.

Of course, any equipment will be affected by operating conditions but small microsensor users should not expect their system to fail prematurely in harsh conditions. ‘Fit for purpose’ systems will be proven in intense heat, heavy rain, strong winds, freezing temperatures and snowfall: an expected 10-year lifetime should not just apply to easy conditions.

Having said all that, product lifetime will always be extended by careful use and following the manufacturer’s advice. The AQMesh user manual is now supplemented by a range of remote diagnostic tools, which detect signs of product ‘distress’, such as lower voltage from a dirty solar panel or potentially blocked air sample path.

Even if few air quality monitoring projects extend to 10 years, a product which is designed to last that long is fit to be used in a series of projects and can be upgraded or traded in, as requirements change.

Here at AQMesh we have a number of long-standing users who re-use their pods for new projects, and we are happy to discuss how we can help you with your own air quality monitoring requirements.

“How many air quality monitors do I need?” is a question we regularly hear, and the easy responses – “it depends”, “how big is your budget?” – are not very helpful.

To give a better idea, it will depend on all these factors:-

Which pollutants you want to measure

Some pollutants are mixed better / are more homogeneous / more background in ambient air, such as PM2.5 and O3. Others are less so, and can be affected by a local source, like NO. Background pollutants can be measured with fewer measurement points than those which will vary greatly over short distances. For example, O3 may range between 50ppb and 60ppb across a city, but NO could vary from 0ppb to 1,000ppb within just 100m.

Your analysis capability

If you have the resources or skills to carry out detailed analysis, you will get more information out of fewer measurement points. For example, if using wind speed and direction data alongside air quality readings, you can look at an area in terms of pollution sources and areas potentially affected by air quality. The resulting plots and mapping allow reading levels to be visualised across space. Taking it further, measurement points can be linked to emissions inventories and modelling can fill the gaps to give an estimated reading for every geographical point. The more measurement points, the more accurate the estimates are likely to be. Additionally, some analysis techniques – such as long distance scaling or network calibration – require a minimum number of measurement points in order to work, which will therefore determine how many pods you might need. As an example, the long distance scaling method offered by AQMesh requires a minimum of 6 different locations.

The area you’re monitoring in

Multiple pollution sources (think busy city vs. a factory in open countryside) create a more complex air quality situation, as do canyons (naturally confined air corridors or streets between high buildings). A single source within an open environment could achieve a lot with just one pod upwind and one downwind, but a city environment means that NO or NO2 readings could be massively different just other sides of a road junction.

Local conditions

If your air quality monitoring location is generally windy you will have to work harder (install more measurement points to pick up plumes) to capture pollutant bursts before they are swept away.

Environmental justice

We have seen customers distribute pods based on one per ZIP code, to achieve fairness to local communities. This is a good idea in itself, but a ZIP code can include a wide range of pollution levels so all the factors about choosing a precise monitoring point still apply.

And, of course, budget!

Seriously, small sensor systems are described as monitoring ‘hyperlocal’ air quality for a reason and even the densest networks will be leaving some gaps where air quality variation is not recorded. So, measurement points can be added infinitely – air quality mapping of an area will improve in accuracy, but there are obviously diminishing returns.

Because, even after all this, “it depends”, just talk to us about your air quality monitoring requirements and we will be more than happy to share our recommendations and give you a more helpful answer to “how many air quality monitors will I need?”

We have a little competition going on between our customers, even if they don’t know it: who can present us with the challenge of the most remote operation for continuously monitoring air quality?

We are used to dealing with people for whom visiting their site is a full day’s driving or even means chartering a plane. So, we get it: air quality monitoring equipment set up on these remote sites needs to work out there – and stay working.

Having had longer to review, test and reject communication technology than other available air quality monitoring systems, we long ago settled on the cellular phone network as the most effective way to achieve reliable data transfer globally in the widest possible range of environments. Whilst we do have one other trick up our sleeve (we’ll come onto that) mobile networks continue to change around us, and we’ve taken advantage of that and invested in this approach over others.

2G is the little star of machine-to-machine communications – we started with that and in many parts of the world it’s very much available, underpinning numerous M2M systems. We’ve already digested withdrawal of 3G provision in most locations, and AQMesh is future-proofed with 5G+, LTE and NB-IoT capability. However, it’s not as simple as that, with band availability varying hugely in different regions. Our global SIM will roam to available networks and bands, but sometimes there’s only one band available in a particular area and it requires a specific local SIM. As well as having the tools to identify this situation, we support users through installation of a local SIM and securing connections.

Even if the network is weak and connection is intermittent, AQMesh is designed to store sensor output on the hardware until a connection is achieved. What this means is that even with an unreliable network signal, the pod will connect when it can and catch-up readings if there is a period where it can’t make a connection. Whilst it’s great to have regular updates and set up near real-time exceedance alerts – which is the norm – it’s reassuring to know that no data will be lost even in areas of marginal network access.

So, having installed an AQMesh pod (very quick process) and ensured connectivity (simply connect power in 99% of cases), how do you keep it running for years? We have developed a range of remote diagnostics tools, allowing us to proactively detect faults and data quality queries, update firmware over the wire and even power cycle if necessary. This is an important tool for the remote support for life we provide as standard with every pod we sell or rent.

A combination of our global SIM and supporting local SIMs has so far achieved communications with our air quality monitors used in mining, oil and gas, construction and various other industries – even on ships – but we are often asked whether we can handle the situation where there really is no cellular network at all. As a result, we offer a PoE to satellite modem option, if you really do need to send air quality data from the furthest corners of the planet. Just get in touch if you think you can present us with our remotest communications challenge yet 😉

Can’t decide which air quality monitoring approach to use? Go for all of them!

We often come across customers who are agonising over which sensor system – or even which technology – to use and we suggest a hybrid network. Whilst the best air quality monitoring networks will always include a reference station, which can provide data traceability back to an approved standard, there are good reasons to broaden the range of measurement approaches.

We are yet to come across a project where budget was not a consideration. We are also very familiar with the questions, “how many measurement points do I need” or “how many can I have with my budget?”

By using each sort of technology to its strengths, hybrid networks simply help your budget go further. The common denominator required is that all measurement devices of a given type read the same; that readings are repeatable and precise. From that point it is possible to cross-relate time- and location-specific measurements, applying correction factors as necessary. This approach can work when choosing between small sensor systems, as long as you are satisfied that similar instruments will produce similar readings: using different brands in your project may take a little longer to manage, but reduces risks and leaves options open.

So, measurements from air samples or passive samplers, analysed in a laboratory, can be compared to reference or equivalence readings, as well as output from small sensor systems, ranging from more expensive (but still much cheaper than reference) near-reference small sensor systems, mid-range and even the cheapest or ‘home-made’ microsensor platforms. Examples we have come across include networks managed by Cheltenham Borough Council, UK, which uses a combination of diffusion tubes, reference equipment and AQMesh pods, and Kitchener, Canada which combines AQMesh with reference. SAMHE also integrates indoor and outdoor air quality measurements, and there is a shale oil producer in the Baltic region which uses reference equipment for H2S and SO2 alongside data from AQMesh. The Breathe London pilot even incorporated data from mobile Google cars.

Increasingly, the final output of such networks drives the crossing of such boundaries. For example, all local air quality measurements across Iceland are published on a single platform, with the network operator ensuring that data accuracy is managed through appropriate data quality assurance measures.

We are happy to discuss all types of hybrid air quality monitoring networks and how AQMesh can play its part in your objectives.

So how do you do it? Many presentations at the recent ASIC conference revolved around calibration of small sensor air quality systems, including that given by AQMesh Technical Business Development Manager, John Downie. Offered the opportunity to poll the ASIC audience, we chose to ask delegates ‘Please indicate which of these calibration methods for small sensors you would consider for your small sensor network’. Whilst numbers responding were not huge, ‘periodic co-location of each instrument with FRM/FEM creating seasonal correction factors’ came out top, indicating a thorough – if labour-intensive – approach is generally most common and taken as the ‘norm’.

So what if you don’t have the ‘boots on the ground’ to carry out the many instrument movements that would be necessary with a network of any size? Is there a short-cut? Can AI help? The poll did show that about a quarter of respondents were considering ‘desk-based calibration, using machine learning or AI’, about twice the proportion thinking about ‘desk-based calibration using network calibration method without machine learning or AI’.

We tried using AI on electrochemical sensor output a few years ago but found that – at the time – the machine learning was very good at latching onto interfering factors, such as temperature, and less good at finding the weak (valid) signal in amongst the noise. We were greatly impressed by the later (non-AI) network calibration work done by University of Cambridge, our partners in the Breathe London pilot, and have developed our own approach. This series of repeatable calculations can be applied to a network of five instruments upwards and we feel there are some clear differences (advantages, of course) comparing the AQMesh approach to local calibration with AI-driven approaches:

The fundamental issue with machine learning and AI being used either for compensation of sensor interferences or for calibration adjustments is that their whole premise is to “evolve”: they spot differences and change to account for those changes. This means that over time the results may be closer to reference, but they will never follow the same process for adjustment as they did previously. This raises significant traceability questions about when processing changes and makes it impossible for these methods to become certified to a standard which require a fixed processing of inputs, managed by a version number.

It’s worth noting that PAS 4023 (Annex D) distinguishes between sensor systems that are ‘self-contained’ – with readings derived by the system alone, using a series of repeatable calculations – and those that require training against a reference station. The PAS also emphasises how important it is that individual sensor systems should perform very similarly to one another, so that remote comparisons across a network can be made with confidence. Another reason why a proven, global correction algorithm used by every instrument has the edge over individual site-based AI. Strong inter-instrument comparability also means that a network can be meaningfully compared against itself, in the absence of a reference station, as offered by AQMesh’s network normalisation option.

Each time we think we have found a spectacularly remote air quality monitoring location, an even more inaccessible spot is reported by one of our users.

Full-day trips to visit a location have now been beaten by customers who need to charter a plane to reach them. So, remote diagnostics and support are very important.

Luckily, IoT communications, cloud data management and over 10 years of experience supporting AQMesh have allowed us to continually improve our ability to supply and support AQMesh in remote locations. Pods have been used from the edges of the arctic to undeveloped deserts – as well as on ships – with the help of a few features.

Robust design, low maintenance intervals

AQMesh was designed to be rugged, for use all over the world and with an expected maintenance interval of two years. We have always understood that field maintenance requirements must be kept to a minimum, and pods operating for year after year, in the harshest environments – from deserts to extreme cold – demonstrate design effectiveness. This includes protecting electronics from the elements and mitigating electromagnetic interferences, as well as taking measures to keep insects, wildlife and birds out/off.  The unobtrusive pod design has also ensured a very low rate of vandalism and theft.

QA flags and notifications

The AQMesh data stream includes vital pieces of information which allow users and the AQMesh support team to check that pods are functioning correctly and provide an early warning system. Users can register for email notifications for their pods – it is always better to find out that power is running low or data is no longer being transmitted at the time, rather than when the project ends and it’s time to review data.

Remote scaling / calibration

Whilst AQMesh was a leader in co-location comparison and the ‘gold pod’ technique for in-field calibration, these approaches do require regular site visits to move pods around. We have now developed a method that can provide remote calibration of a sensor network, with or without an available reference station, that does not rely on artificial intelligence.

Diagnostic information

The AQMesh team can access additional diagnostic information remotely, such as performance indicators from the optical particle counter, solar pack battery voltage or sensor failures. Some of these indicators are available to users via their secure online or API access, and some can be used by our global technical support team. The team uses the full range of diagnostic information available, including SIM connection attempts, to provide free support for the life of the equipment. Their over-riding goal is to fix any problem without asking users to visit the site.

Over the wire intervention and updates

AQMesh firmware developments now allow power cycles to be triggered remotely, firmware to be updated over the wire or remote sampling and transmissions interval changed.

Power

We have learned from the many challenges that power supplies can present to remote operation. Whilst the original lithium thionyl chloride battery offered unbeaten long-term autonomous operation of gas sensors, increasing shipping limitations have turned our focus to direct power supply and solar. We invested in a full technical investigation to identify a mains to 12V DC transformer that could cope with ‘dirty’ power supplies, as well as in-pod measures to manage spiky or intermittent power.

Having seen so many problems from simple solar-panel-plus-battery arrangements, we designed our own smart solar pack, which squeezes the most power out of any location, manages power delivery and provides online voltage measurements. We are mindful that sampling and reading rates are defined by the project – and potentially certification – and the power supply must deliver the same sampling throughout the year. Readings should not be compromised by the difficulty of providing autonomous power.

Communications

The global SIM supplied with a standard AQMesh pod will roam across networks to find the best connection at each transmission, and has proven to be a very reliable way of transferring sensor output from hardware to our cloud server for over 10 years in more than 70 countries. Occasionally, we find that only a single, specific network is available – or a customer would prefer to use their own SIM – in which case we can programme the pod to work with a locally-sourced SIM contract. To achieve autonomous communication, the AQMesh LTE CAT M1 modem uses the latest LTE communications standard, including support for NB-IoT where available. In the most extreme cases, satellite communication is the only viable option and AQMesh can connect via an ethernet port to a suitable modem to connect this way. Reliable communications are key to remote data access and support.

The growing need for remote, long-term monitoring, in all conditions, drives our continuous development from data QA to comms, and we welcome challenges.

We have some thoughts here at AQMesh about the common features of successful, well-run small sensor air quality monitoring projects. This is our list, but we’d love to hear your ideas.

• One main contact who remains constant throughout the project
• Clear project objective(s) and timescale, even if the client doesn’t share them with us
• The main contact requests and distributes relevant information to all the right people – guides, videos, manuals
• Planned installation locations are reviewed – by the client and by our team – on Google maps and using photos
• Direct communication between installation staff is actively encouraged
• Regular data reviews, so any issues are addressed quickly
• All available resources are used, such as our site technicians’ ‘app’

These points seem to be critical, wherever the project is, and whether it is totally confidential, ‘typical’ or novel.

What would you add to the list for successfully running a small sensor air quality monitoring network?

A UK local authority installed nine AQMesh air quality monitoring systems at different points across a busy town, measuring nitrogen dioxide (NO2) at 15 minute intervals, monitoring 24/7.

These locations were established monitoring points, where measurements had been taken previously using diffusion tubes, limited to one average reading every few weeks.

AQMesh – in common with all lower cost air quality systems – can provide near real-time air quality information, with high frequency measurements that allow daily and weekly patterns to be seen. However such systems are not certified, as are reference stations or diffusion tubes. As a result, AQMesh readings need to be ‘calibrated’ against certified readings, at some point in the network, to provide confidence in data accuracy and traceability to an approved standard.

Typically such ‘calibration’ is carried out by mounting at least one AQMesh ‘pod’ very close to a reference station, so pod and reference are sampling the same air and readings can be compared. However this approach does require staff to move pods from position to position, which can be time-consuming and therefore costly. An alternative approach was used for this network, similar to the one developed by the University of Cambridge and used in a major project in London (Breathe London pilot). One of the authority’s reference stations (location in red on map) was used to ‘calibrate’ the network of pods and the other (location in green on map) was used to cross-check network accuracy.

AQMesh network deployment (BELOW): AQMesh locations marked in blue, reference station used for calibration in red, reference station used for control co-location in green

The four-month project demonstrated that the AQMesh network showed that stakeholders could have the same high confidence in readings when the network was calibrated remotely as when pods were co-located for calibration (the gold standard for this technology), but with significant savings in field support and reduced data loss.

Everyone loves holidays, whether Christmas or anything else, right? So what’s special about ‘air quality’ people? What we get so excited about are ‘free’ experiments, where distinct changes in activity help to peel away the layers of air pollution measured. Over the years, various studies have been published, showing residual air pollution levels when other sources drop – or increase – significantly.

Around this time of year there are changes in emissions activity around schools, businesses and industry, roads (both increases and decreases), burning of solid fuel in households, domestic heating, and so on. As well as looking at changes in measurement over time (hourly or shorter intervals) and space (hyperlocal monitoring means you can literally measure at any point you wish, from a specific point on a specific road junction to a school playground), measurement of multiple parameters is an eye-opener.

Studies by the University of Cambridge have shown how small sensor air quality measurements can be used in conjunction with their scale separation technique to distinguish between local and regional or background sources. Comparing the proportion of different pollutants in this way can give a ‘fingerprint’. CO2 measurements provide a baseline combustion level against which generally traffic-related NO / NO2 / NOx can be compared. Looking at PM fractions against CO2 and other gases can also provide more insights than individual measurements alone can provide. And, of course, dramatic shifts over time – like holidays – sharpen that focus.

A network of sensor systems has the additional benefit of showing whether pollution is being displaced from one location to another, with this forming part of the analysis around other behavioural change triggers, such as the introduction of a traffic Low Emission Zone (LEZ). It can also help identify hyperlocal sources of pollution, where high levels of pollutants are only seen by one of the monitoring points.

One memorable headline from several years ago was that a higher amount of PM2.5 in one London borough over the Christmas period could be attributed to domestic solid fuel combustion (cosy wood-burners) than road traffic. So, whether it is reduced traffic around schools, increased traffic at shopping centres or chestnuts roasting on all those open fires, the holidays can provide a curious insight to local air quality data and pollution patterns.

Happy holidays from the team at AQMesh.

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.