17 Nov 2020
Combining real-time hyperlocalised monitoring with traditional reference quality monitoring and accurate predictive modelling is the most effective way to plan intervention strategies and effectively manage air quality.
If 2020 has taught us anything, it is to expect the unexpected. For many reasons, now is the perfect time to be questioning how we monitor air quality, where we monitor it and how we can do more to ensure the data we are interpreting is as relevant, accurate and reliable as possible. Only then can we make informed decisions about how, when and where to intervene to improve the air we breathe.
Compliance and reference quality monitoring
Since the inception of air quality monitoring regulations and environmental protection agency protocols — some 50 years ago, depending on the country — the traditional method of measuring the ratios of gases and pollution causing particulates in the air has been via the installation of large, fixed multi-analyser Air Quality Monitoring Stations (AQMS).
These stations, generally housed in oversized containers, require considerable investment in terms of initial purchase price, infrastructure to the site (electricity, communications, etc.) and ongoing maintenance. A high level of technical training and expertise is also needed to ensure that they operate to their highest potential. AQMS networks are predominantly commissioned to provide the requisite reference quality measurement data mandated by governments and environmental management authorities and to assist in research studies.
Even as gas analyser and sensor technology has evolved, the AQMS’ real-time, reference quality, traceable data remains the most accurate and proven way to measure pollutants, and continues to be the most reliable method of regulatory compliance to local and international standards. Unfortunately, due to their size and fixed location, they can only provide information pertaining to a particular site and cannot pinpoint the exact origin of certain pollutants or rule out contributing factors that may influence readings. The cost of purchasing and maintaining a station may also make it prohibitive for many countries to install a sufficient number to cover their entire region.
The question we need to ask ourselves is are we doing enough? Is monitoring air quality purely for the sake of ticking compliance boxes the perfect approach to tackling such a global issue? Especially when we consider that most regulations allow for rather large margins of error in measurement levels. For example, in the European Union an uncertainty level of 25% is acceptable when measuring particulate levels — meaning that if you introduced an intervention strategy, such as totally banning petrol or diesel cars, would that change have a significant impact on overall air quality (greater than 25%), or would it just conform to existing compliance standards?
Why we need to rethink the way we monitor and model
In a perfect world, all monitoring sensors would be reference quality with the highest level of accuracy at all times. But the way we monitor air quality and what is important to us as a population is changing. Just adhering to regulations is no longer a viable, long-term option in isolation, especially if we truly want to make positive changes to the air we breathe, mitigate future risk and keep communities safe. Strategic and well-informed intervention is the logical way forward if we are serious about affecting change and reducing our environmental impact.
Although some major cities and federal governments are already embracing newer technology that allows them to gather more localised, detailed data using networks of small sensors, others are only now beginning to understand the importance of being able to assess air quality conditions in specific areas, for example, in residential neighbourhoods, known pollution hotspots, high traffic zones and around schools.
The introduction of smaller, lower-cost sensors that deliver hyperlocalised environmental data has transformed our understanding of air quality monitoring. If the information that these small sensor networks gather is analysed and interpreted correctly as a complementary source of data to reference quality AQMS networks, it is the first step towards removing uncertainty and having a clearer understanding of what strategies would be successful if implemented and how meaningful intervention and holistic management would have the most positive impact.
What makes the ACOEM approach different
ACOEM has been researching, developing and redefining air quality monitoring technology for more than 40 years through its Ecotech, Air Monitors, Dynoptic Systems and Tunnel Sensors brands. The introduction of its AQMesh small sensors in 2015 was a game changer and while there are hundreds of small and low-cost sensors currently available on the market, AQMesh has been acknowledged as the most accurate, having been tested and used in commercial applications in more than 30 countries around the world.
The design, implementation, management and utilisation of data from a small sensor air quality management system is a highly specialised task and requires input from an experienced team of scientists, engineers and data specialists. In this respect, ACOEM and the Consortium it created with its partners in 2019, is currently the only provider that can pinpoint hyperlocal environmental trends in real time and highlight the source and apportionment of each pollutant with a high degree of accuracy.
ACOEM’s SMART AQM Consortium consists of ACOEM air pollution, noise, vibration and meteorological technology specialists, air quality modelling experts from Cambridge Environmental Research Consultants (CERC Ltd), and renowned atmospheric scientists and data professionals from FHCO (Cambridge).
To achieve optimal outcomes and best exploit the data generated, ACOEM’s air quality smart solution involves combining the following key components:
- A small sensor network providing fast response data (1-minute resolution).
- Assimilation of monitoring data into an advanced model such as ADMS along with up-to-date traffic flow, congestion and emissions profiles.
- Comparison and co-location with at least one reference grade monitoring site (AQMS) within the area of study for a period of time (to provide a level of traceability to a recognised reference or equivalent measurement standard).
Hardware alone can’t provide solutions
The ability to make informed decisions based on quality monitoring data, accurate predictive modelling and specialised management is invaluable to any intervention model. Governments need to understand which intervention strategies are working and which aren’t. They also need to rely on accurate data that considers the effect of external factors and anomalies.
The ACOEM solution gives users access to a calculated emissions inventory that factors in the ratio of CO2 pollution to other emissions, using modelling and mapping undertaken in a spatial context. This not only improves pollution predictions but also provides longer-term accurate data and monitoring that will determine if predictions were correct.
By using a smart data analysis methodology developed by the Consortium, vital information can be extracted from the initial monitoring data. An Emissions Index for combustion sources can be calculated by analysing the ratio of CO2 to other pollutants such as NO2, NO, PM, SO2, BC, etc. This provides information on source apportionment, to accurately identify the exact origin of the pollutant, and can assist a city or region to develop a more precise emissions inventory — an essential part of any air quality management strategy. As monitoring information is assimilated into the model in real time, it continually corrects and updates the model, making forecasting more accurate
Even small variations in air quality can be identified. This increased level of certainty not only assists authorities to make better informed decisions for invention, but also helps to create a more thorough predictive modelling framework for long-term planning.
Factors to consider
Perhaps the greatest impact on pollution levels actually comes from conditions we cannot directly control, like climatic and weather variations. Changes in wind direction and strength and temperature fluctuations have a significant effect on pollution levels. So too do environmental phenomena like volcanic eruptions and bushfires. The ability to isolate influencing factors removes a level of uncertainty, and is what sets ACOEM’s solution apart from monitoring and modelling alone.
Calculating the exact proportion of pollution caused by climatic extremes makes data interpretation significantly more accurate for local conditions. Attributing specific gas emission levels or particulate concentrations to their point of origin and getter a better understanding of the exact makeup of the pollutants can only be achieved by assimilating the data.
This same scale separation technology is also utilised to calibrate networks of sensors or monitors, without the need for expensive, labour intensive calibrations. Scale separation will significantly lower running costs compared to traditional or transfer standard methods. Once the complex algorithms are established, they can automate the calibration of an entire network and provide a dynamic quality control mechanism which alerts users of anomalies and outliers.
Case Study – Breathe London
ACOEM’s SMART AQM Consortium was responsible for delivering one of the most advanced air quality networks in the world through the Breathe London project — widely regarded as a global benchmark for urban air quality studies.
The largest project of its type, this multi-million-dollar study was established in 2018 in conjunction with the Greater London Authority, C40 Cities, the Environmental Defence Fund (Europe), the Clean Air Fund and Google Outreach. It has been successfully providing London’s local government with accurate data to indicate the effectiveness of its low and ultra-low emission zones designed to sustainably improve London’s air quality. November 2020 marks the completion of Stage 1 of the project.
Although there are approximately 100 AQM (Air Quality Monitoring) reference stations around London, the aim of the project was to create a mechanism for analysing more localised information. Breathe London has been leveraging data from its network of hyperlocal sensors to give the community an added level of control over their environment. People have been able to view data in real time and make informed decisions about their travel behaviour and movement based on localised accurate information.
Breathe London demonstrates how ACOEM’s SMART AQM Consortium powered by its small sensor AQMesh technology can be used to provide real time, hyperlocal air quality data which is accurate, powerful and engaging. When combined with advanced modelling techniques which assimilate the monitoring data, it provides insightful outputs that government authorities can use to explore “what if” scenarios. The information will drive the development of meaningful policies and interventions, and measure their efficacy in delivering positive change in sustainable air quality.
The network also provided important air quality insights during the recent COVID-19 lockdown period when air quality underwent a step change as traffic flow reduced dramatically for an extended period of time.
Prevention is better than cure
The saying may be old, but its message is anything but outdated. Given the right data interpretation and insight, local and regional governments are perfectly placed to make decisions that will help prevent or reduce pollution buildup in targeted locations.
Whether it is creating car-free exclusion zones around schools, street closures, incentives for phasing out of petrol or diesel engines, congestion taxes, hydrogen buses or widening footpaths to encourage greater bicycle usage, intervening in the propagation of pollution in highly-populated areas may be the best way to exert some control over air quality in the future.
Air quality doesn’t respect geographical boundaries
Unfortunately, mitigation strategies and intervention, no matter how well meaning, cannot work in isolation. Using the City of London and its Breathe London project as an example, it is well established that on any given day, despite the best intentions of government authorities to reduce emissions, up to 60% of measured pollution levels may be the result of pollutants coming across from continental Europe, especially during periods of strong easterly winds. So, any intervention that the government and environmental protection agencies in the UK may enact, can only have a very limited impact if other countries don’t introduce their own or a wider joint strategy to reduce emissions across the board.
What is required is a broader, global view of air quality management that goes beyond mere compliance with standards which in themselves can vary between countries, climate and other meteorological conditions. The long-term benefit of combining ongoing hyperlocal monitoring with AQMS measurements and continuously updated modelling is that it will make predictive forecasting more accurate and allow governments to collaborate and measure whether their intervention is in fact making a difference and producing the desired reductions in pollution levels.
While the focus on air quality monitoring networks has almost exclusively been for outdoor applications, there is a growing demand for more consistent and accurate indoor air quality management. The global spread of contagions like COVID-19, which is known to favour enclosed, high density indoor spaces, is further justification for the need to be able to monitor, measure, control and intervene where necessary to keep building occupants safer, or at least lessen the likelihood of creating an environment that encourages the virus to survive and transmit.
Ventilation engineers are increasingly becoming concerned not only with the amount of polluted air that may enter a building through its windows and ventilation systems, but also how to ensure that the air circulating within the building is ambient in terms of temperature, humidity and level of CO2 and aerosols.
Using the established principles of its small sensor networks, ACOEM is currently working with a key technology partner to introduce a Virus Transmission Index that can be easily integrated into its systems. Measuring a larger range of particles, and the ratio of aerosols to CO2, data collected and analysed from sensors located within building interiors will have the ability to identify when conditions are more or less likely to be conducive to the transmission of airborne diseases and viruses like COVID-19. It will add an extra layer of security when it comes to keeping residents, workers and visitors safe in indoor spaces by identifying any risks and minimising the impact.
The current global pandemic provides an opportunity for communities to look into the future and see how intervention can positively affect pollution levels. It also allows us to better understand how to use air quality monitoring to help minimise contagions.
Recent studies have concluded that where there are high levels of PM2.5, there is a proportionately higher incidence of COVID-19 transmission and mortality. The virus attaches itself to particulate matter, which is microscopic to the eye but is actually a combination of aerosol and solid. This helps make the virus viable for long periods of time. The virus is able to self-sustain, especially in enclosed areas and ventilation systems that facilitate air recirculation, like in aircrafts and public buildings.
Although scientists were expecting to see emissions and pollution levels reduce by 50% or more in cities and regions with enforced lockdowns during the pandemic, this was not always the case based on the movement of pollutants across geographic boundaries.
Depending on wind direction, there were days during the lockdown when air quality in London and Paris actually breached European air quality regulations without any road traffic.
It lends further support to the theory that intervention strategies can only produce significant reductions in pollution levels if these strategies are implemented by multiple authorities, or ideally globally.
The future begins today
The future of environmental monitoring will likely look significantly different to today. The number of large infrastructure reliant AQM Stations and networks that may have been in place for several decades will likely be reduced in favour of smaller networks of more localised smart sensors.
Reference quality accuracy and traceability will always be important but the ability of technology to hyperlocalise hotspots and identify anomalies quickly will be invaluable, especially in developing countries with a lack of resources or funding to support large-scale investments.
The idea behind the ACOEM SMART AQM Consortium is to give governments and authorities an added level of certainty when it comes to the data they are interpreting and using as a basis for decision making. Fundamentally, it is about taking the guesswork out of air quality management and using an accurate monitoring-modelling approach to make insightful changes.
Want to know more?
Air quality monitoring instruments and sensors can only provide basic information. It’s what you do with that data that separates the technology manufacturers from a company like ACOEM that has a vision and is committed to sustainable solutions and minimising environmental impact.
If you would like more information about the ACOEM Smart AQM Consortium and how ACOEM solutions can be tailored to your specific monitoring and modelling needs, please contact Keith Webster, ACOEM Business Development Manager, Europe & Southern/Eastern Africa, firstname.lastname@example.org or +44 7701 365732.
 Environmental Instruments Ltd – Looking for a ‘low cost’ air quality monitoring solution? 10 reasons why you should choose AQMesh, July 2106
 Silvia Comunian, Dario Dongo, Chiara Milani & Paola Palestini – Air Pollution and COVID-19: The Role of Particulate Matter in the Spread and Increase of COVID-19’s Morbidity and Mortality, International Journal of Environmental Research & Public Health, June 2020
 Matt McGrath – Coronavirus: Air pollution and CO2 fall rapidly as virus spreads, BBC News Science & Environment, March 2020