Explore AQMesh

Community monitoring vs. industrial monitoring

06-Jun-2024Community | Community monitoring | Industrial | Industrial monitoring | Networks

Community monitoring vs. industrial monitoring

Communities and industry are monitoring air quality around the same areas, so they both want the same thing, right? Er, no, not really ..

Even though both types of AQMesh user may measure the same pollutants using the same instrument, their objectives and needs are often different. The community users we deal with – mostly in the UK and USA, but plenty of other places too – tend to be more interested in identifying pollution events and relating that to what they are experiencing. The first step is a sort of validation of what they believe to be happening all around them. This is not to say that the review of data is selective or unscientific, it’s just experience-focused. For example, school monitoring projects are generally focused on identifying periods of elevated air pollution outside and around the school at different times in the school day and finding the cause / source.

On the other hand, air quality monitoring around communities by the industries that may be the source of the pollution takes a different approach. Our industrial customers – from oil and gas, construction, mining, landfill and other sectors – want accurate air pollution measurements to demonstrate that they are within compliance of local environmental regulations. Another aspect is that there is often more than one potential source of pollution in an area so an industrial AQMesh user may be keen to understand more about what pollution is coming from where (and hopefully proving that a neighbouring facility is causing the issue, not them). Accurate wind data is required to carry out such source apportionment analysis. AQMesh offer a wind speed and direction sensor option and normally only one pod in an area needs to be gathering this information.

Whilst communities and industries may have slightly different air pollution monitoring objectives, they recognise the benefit of using the same instruments, so the data is comparable. A version of this desire to be able to make meaningful comparisons is where government monitoring uses a particular type of equipment and the potential industrial polluters being monitored choose to use the same technology. For example, a community in Texas, USA, installed AQMesh pods outside suspected polluters, so the industrial facility (or rather a consultancy they hired) bought AQMesh systems to monitor themselves. This helps to build trust that data collection and analysis will be done correctly and in an unbiased manner. Another factor bringing all parties together is when there is a natural cause for the pollution affecting people, such as volcanoes (see our news items about airport and community in Iceland) and wildfires.

In the USA, industrial companies are aware that use of uncertified equipment – other than FRM / FEM – means they cannot be obliged to report on data. This creates a ‘safe space’ for potential polluters to understand the air quality around their operations and their impact on it, ahead of compliance demands.

And then there are data centres, where the focus is not on the potential for pollutants to harm people but infrastructure. Hydrogen sulphide monitoring can warn of potential damage to sensitive copper circuits and HVAC maintenance intervals can be managed by monitoring of PM levels, helping to prevent machinery failures.

So, whilst different customers are all using the same air quality monitoring systems and measuring the same pollutants, the reasons driving the project may be entirely different. Either way, our experienced team can support a range of objectives and help interpret your data with meaningful context.

ASIC discussions look at AI vs non-AI calibration

22-May-2024ASIC | Calibration | Hybrid networks | Network calibration | Networks

ASIC discussions look at AI vs non-AI calibration

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:

 

AQMesh LDS

AI / Machine Learning

Confidence in repeatability of measurements from individual instruments

Yes

?

Confidence in repeatability across seasons & locations

Yes

?

Traceability back to only self-contained* measurements

Yes

No

Calibration method repeatable with the same output

Yes

?

Drift correction / interpolation between calibration intervals

Yes

No

Method approved by DEFRA / Environment Agency

Yes

No

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.

You really want to measure that H2S range??

29-Apr-2024Emissions monitoring | Fenceline | H2S | H2S monitoring | Industrial | Oil & Gas

You really want to measure that H2S range??

Requests to measure hydrogen sulphide (H2S) in ambient air at unthinkably high levels seem to be at odds with our efforts to detect and report single-figure parts per billion H2S emissions. So, why are we asked for such high ranges?

We think this may be explained by operators who are used to measuring high concentration in a gas stream – typically biogas or industrial – and then simply transferring the range across when looking at fenceline monitoring. It’s great to see the growing interest in monitoring fugitive emissions at site boundaries – including H2S – but we need to dial back the gas range expected when looking at ambient pollution.

H2S sensors for gas stream measurement are offered at parts per million ranges from 0-50ppm to 0-10,000ppm. Bear in mind that US Department of Labor guidelines say that H2S odour becomes offensive at only 3-5ppm, with prolonged exposure causing headaches, nausea and insomnia, and causes “nearly instant death” at 1,000-2,000pm. Dilution of any emission in swirling ambient air means that parts per million measurements are inappropriate, and even significant H2S leaks usually register peaks of just a few parts per billion by the time gas has reached the fence line.

That’s why the AQMesh sensor measures from 0-10,000ppb (0-10ppm), with a limit of detection of less than 1ppb. Measuring at such low levels means operators can pick up emissions much earlier and much further away from the source than would be the case with the higher range sensor typically used for measuring the gas stream. Picking up a low level at a suitable point on the industrial boundary should avoid dangerous levels of H2S building up near the source.

Monitoring in ambient air is gentler on sensors, too, so if you are used to sensor poisoning and condensate problems, that benefit does offset the ‘needle in a haystack’ challenge of picking up fugitive H2S emissions. With the potential to move an AQMesh pod from location to location, and add a wind speed and direction sensor to help with source apportionment, it is very satisfying to support our users doing just that.

Who’s behind AQMesh?

18-Apr-2024Company news | Emissions monitoring | Gas detection | Methane

Who’s behind AQMesh?

Did you know that we have always been part of wider group of companies, offering a portfolio of emissions, leak detection and gas stream monitoring products, software and services, specialising in methane?

For decades, the Ecotec group has been designing, selling and supporting equipment for monitoring landfill gas and biogas. Gazomat in France developed laser-based portable and mobile equipment, used extensively to check very low level methane emissions from natural gas pipelines and networks. California-based Oxigraf specialises in oxygen sensor technology.

What does this mean for AQMesh? Our range of ambient air monitoring sensors – including TVOC, H2S and CO2 – matches and complements the group portfolio of industrial emissions monitoring solutions. We now also offer a methane option, delivering readings alongside all the other channels AQMesh provides, including wind speed and direction. This means we have continuous, stationary methane monitoring options at sub-ppm limit of detection with the laser sensor, or a lower sensitivity small sensor CH4 option.

Let us know if you’d like to know more about our industrial fenceline monitoring options or an introduction to the rest of the Ecotec range of equipment.

“Maintenance-free air quality monitoring” – is it a myth?

12-Apr-2024Maintenance | Networks | Performance | Product

“Maintenance-free air quality monitoring” – is it a myth?

There’s no doubt that small sensor systems can have an advantage over their cumbersome reference station cousins – in terms of maintenance requirements. We are often asked about ‘service’ requirements for our pods and the honest answer is that the default position, in normal working conditions, is ‘none’. However ..

There are consumables, conditions can have an impact and sometimes ‘stuff happens’. We have seen small sensor systems claiming they require no maintenance at all, so would like to offer some thoughts on these three areas:

Consumables

Some sensors and sensor components need to be replaced at a defined interval (two years for AQMesh). This may be because the product is designed to optimise accuracy and component duty cycle, such as the AQMesh pump sampling particulate matter, which is expected to last two years on standard settings. If the pump is not replaced at two years, in-field failure is the likely outcome at some point. AQMesh uses a pump to provide reliable sampling over a set period. An alternative, such as a fan, may not be offered with a finite duty cycle, or with one that is theoretically longer, but failure modes can be undetectable remotely and readings affected. Is that better?

Many small sensor systems use Alphasense’s electrochemical sensors, which come with a recommended two-year life. Sure, they last longer than two years and keep producing numbers, but who has tested the sensors beyond two years and will be answerable for accuracy after two years? Users of these systems need to look ‘under the hood’ here – is the system claiming to be maintenance-free ignoring sensor manufacture advice? Are they reducing operation or sampling to extend the duty cycle?

Monitoring conditions

In a nice, clean monitoring location it is possible to predict performance of a monitoring system over several years. But, when the system is faced with tough conditions – such as sandstorms or an acidic environment – some intervention may be necessary to keep the system running. Good design and remote diagnostics can help to minimise this, but it is often the pods installed in the most remote locations (hardest to reach if necessary) that are hit by the toughest conditions.

‘Stuff happens’

Most of the stuff that happens to AQMesh pods is hard to predict. One academic study, monitoring air quality around treated road surfaces, mounted pods a few centimetres above the road and they were swamped in a rainstorm. One contractor installed a pod on its side. Solar-powered pods are sometimes installed with great difficulty, only to find the system is constantly in shade. We encourage as much pre-installation planning as possible – and provide a wealth of manuals, guides and videos to avoid these scenarios – but there are sometimes situations which require on-site intervention. We have seen ‘maintenance free’ fulfilled by offering replacement equipment under warranty, instead of maintenance, but customers still have to make a site visit anyway, so we don’t really buy that one.

AQMesh started out offering uninterrupted 2-year gas monitoring with autonomous power but we quickly found that much monitoring was project-based and that users preferred to have the comfort of planned maintenance arrangements that would maximise the chances of achieving high data capture and high data quality throughout the project.

Why do air quality consultants love AQMesh?

25-Mar-2024Consultants | Networks

Why do air quality consultants love AQMesh?

Our pods continue to be pretty popular among environmental and engineering consultancies, both here in the UK and further afield, so we asked some of them what appeals to them most about AQMesh. We were pleasantly surprised by the range of responses we received although there were a number of recurring themes.

  • AQMesh is a brand synonymous with quality in precision of data, robustness and support
  • Pods are compact and easy to install with a range of reliable power options, including a smart solar pack for year-round monitoring
  • Particle (PM) readings are almost instant – available as soon as the first transmission interval, for confidence the unit is working when leaving site
  • LTE-backed communications are reliable in almost any monitoring location, from urban areas to remote industrial sites
  • Sensors and power supplies are changeable in the field, without the need to return to factory
  • Downloading data from the platform is easy and intuitive
  • Data ownership and security is never an issue – client data is always 100% confidential

Are you an AQMesh user? What’s your favourite feature?

Some projects run more smoothly than others – what makes the difference?

13-Mar-2024Hybrid networks | Networks

Some projects run more smoothly than others – what makes the difference?

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?

Why we love PAS4023

13-Feb-2024Data validity | Networks | Performance

Why we love PAS4023

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.

A rural air quality saga

10-Jan-2024Rural | TrafficUK

A rural air quality saga

We installed an AQMesh pod on a rural road to see the impact of local road closures and increased traffic but we left it in place and have been able to monitor how the air quality changes from autumn to winter. This time covered changing traffic patterns, wood-burning, bonfire night, the Christmas break and then no traffic, with the road closed by floods. So, what did we see?

Of course these various factors do not neatly arrange themselves separately, so elevated PM2.5 and NO2 coincided with both heavy traffic through the village and the cold snap, which would have seen all the local kerosene boilers and wood-burners turned up. NO2 did fall from the weekend before Christmas, coinciding with local road closures ending and commuting pausing, but we are not measuring wind direction and speed on this pod, which may have also had an impact, limiting the conclusions we can draw.

One feature that is most puzzling is how there is still a distinct uplift in NO2 during the day from 2nd January, despite the road being closed. Ah, but looking at NO over the same period suggests that the NOx source is not here. The same is almost certainly true of PM, where off-peak PM never falls below 7µg/m3, showing a distinct baseline, with peaks rarely seeming to relate to local activity.

Other highlights include peaks of most pollutants late on the night of bonfire Saturday (4th November) and the aligned spikes – including noise – on the evening of Friday 1stDecember when traffic was backed up through the hamlet. The endless hours of holiday entertainment perusing air quality readings ..

Installing additional pods would have helped to separate roadside from more background pollution and wind speed and direction monitoring and analysis would have helped to clarify some of the issues here, particularly regarding sources. But a simple project with a single pod gives a huge amount of information – bearing in mind what this information is NOT showing – and is a great basis for further monitoring or identifying potential actions.

Do air quality people love holidays more than everyone else?

12-Dec-2023Environmental | Hybrid networks | Industrial | Networks | Traffic

Do air quality people love holidays more than everyone else?

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.