Global Clean Air

Mobile monitoring reveals congestion effect for vehicle emissions in London

By lpadilla / Published: March 11, 2024
University of York mobile laboratory for measuring ambient air pollution.

University of York mobile laboratory for measuring ambient air pollution (Photo taken by Shona Wilde).

New study shows just how much congestion intensifies emissions from diesel vehicles  

In a recently published study, researchers from the University of York and Environmental Defense Fund show how traffic exacerbates nitrogen oxide (NOx) pollution from road vehicles, in particular from intense emitters like diesel trucks and buses, pointing to solutions that can bring an outsize benefit to air quality.  

Comparing pollution with targeted monitoring  

EDF and York designed a study that measured air pollution along two distinct routes in the London metropolitan area—one that was extremely congested in Central London and the other farther from the city center where drivers experience more free-flowing conditions including some highway driving. The Central London route was identified as a pollution hotspot in the Breathe London Pilot project, which provided motivation for the new targeted study.  

Using York’s mobile monitoring laboratory equipped with fast-response instruments, we collected ambient measurements of both NOx and Carbon Dioxide (CO2) for two weeks along the two different routes to quantify the emission intensity of the London fleet, which included a wide range of vehicle types driving in both heavy congestion and light-traffic conditions. We also used dashboard video recordings to identify which specific vehicle types were the likely cause of hotspots.  

Using the latest analysis methods, we mapped the spatial patterns of persistent emission sources, while simultaneously revealing the attributes of the most significant emitters within the vehicle fleet. We found that NOx emissions were a factor of two times higher for fleets with a high proportion of diesel vehicles operating in congested driving conditions, and a factor of five times higher for intense emitters like SCR-retrofit diesel buses and heavy goods vehicles in stop-and-go traffic. 

We then compared our data to an existing database of measurements from remote sensors, which measure vehicle emissions more directly at the tailpipe, to verify our ambient data against an established reference. The comparison further demonstrated the congestion effect whereby fleets predominately composed of vehicles rated with the highest emission standards (Euro 6/VI) generated NOx pollution that would be expected from a fleet of lower standard vehicles (Euro 2/II-5/V), when impacted by high traffic. 

Our new approach allowed us to focus on the emission intensity of both the overall fleet and specific high-emitting vehicles and make comparisons to established measurements of real-world emissions. The results provided greater insight than standard measurements solely focused on total ambient concentrations.  

Real-time, high-frequency air pollution measurements.

Real-time, high-frequency air pollution measurements. (Photo taken by Shona Wilde)

New methods increase understanding with fewer resources 

Beyond illustrating the impact congestion has on emission intensity, the study shows the feasibility of extracting valuable insights from reasonably short mobile monitoring campaigns. Compared to previous large-scale studies, such as the ~1-year long Breathe London mobile study, this campaign was less resource intensive, requiring just two weeks of driving and reducing vehicle and instrument maintenance. This campaign simplified field logistics and increased the efficiency of the data analysis. The new method could also prove useful to scientists and policy makers who want to learn more about local fleet emissions under a variety of traffic conditions in places that aren’t currently well-monitored. Mobile monitoring provided a continuous picture of emissions along a route, not limited to individual locations like stationary monitors.  

This approach offers policy makers a new way to spot specific vehicle types and conditions that produce greater emission intensity, so they can develop targeted interventions and monitor progress over time in a cost-effective manner.  

While there is no substitute for knowing the exact vehicle emitting pollution, as one might find using remote sensing, this new technique provides useful information in places where remote sensing is either impossible or impractical. It’s also especially useful when exploring the impacts of congestion, as remote sensing is not well suited for stop-and-go traffic.  

While the project focused on London, the methods and insights developed can be useful for other cities, particularly those where there are diesel vehicles operating in heavy traffic. 

Practical policy applications 

Because this method identifies the effects of the highest-polluting vehicles in the most congested areas, it gives local transportation officials a clear roadmap to develop the most impactful solutions.  

The adverse effect of congestion on tailpipe emissions can be eliminated with the transition to electric vehicles, which has added benefits for the climate, especially for heavy-duty diesel trucks and buses. Officials could also consider introducing solutions like restricting operating hours for these vehicles in high-traffic areas or creating bus lanes, both of which could ease congestion. The approach could help determine which city-owned vehicles need to come in for maintenance to restore performance of aging exhaust aftertreatment technology. 

In cities that already implement clean air zones and technology retrofit programs, air quality can improve even further if high congestion can be better addressed in places where diesel vehicles operate. In London, where the ULEZ has already helped reduce ambient Nitrogen Dioxide (NO2) concentrations, accelerating replacement of the most potent polluters with electric vehicles, starting with the most congested routes, would provide additional climate, health and mobility benefits improving the driving experience for everyone. 

To learn more about the study, read the article in Atmospheric Environment: X.  

Also posted in Academic, Community Organizer, Concerned Citizen, Environmental Justice, Government Official/Policymaker, Homepage, Monitoring, Public Health/Environmental Official, Science / Comments are closed

Discover what’s causing air pollution in London with this interactive map

By Elizabeth Fonseca / Published: September 7, 2021

Ever wonder where air pollution in your neighbourhood is coming from?

We’ve been working on a new Greater London map that displays detailed information on the sources of health-harming air pollution. Search for or click anywhere on the map to get a breakdown of pollution sources – for both nitrogen oxides (NOx) and fine particulate matter (PM2.5) pollution – at that particular spot.

What does the map display?

The map uses data produced by Cambridge Environmental Research Consultants (CERC) using the ADMS-Urban model as part of the Breathe London pilot project.

Based on modelled data for 2019, the map:

  • Displays an estimate of annual average NOx and PM5 pollution levels in London for major different sources of pollution.
  • Allows users to see a calculation of the pollution that people breathe, depending on where they are in the city and separated out by source category.
  • Provides distinct visual ‘layers’ for more than 20 individual sources (e.g., taxis, Transport for London buses, commercial gas), as well as grouped sources (e.g., all diesel vehicles).

The modelled data, which takes into account factors like wind and weather, is available on a 10 metre grid across London and provides the annual pollution concentrations experienced at 1m above ground level.

Which sources are included?

  1. Road transport: Cars, buses, lorries, etc. and particularly those that run on diesel fuel.
  2. Other transport: Other means of transportation that don’t involve the road, such as planes, trains and ships.
  3. Commercial and domestic fuel: Heating and powering of indoor spaces like our homes, offices and shops by combustion of fuels such as gas, oil and wood.
  4. Industrial and construction: Waste management activities like energy from waste plants and ‘Non-Road Mobile Machinery,’ i.e., construction sites and machines like diggers, excavators and diesel generators.
  5. Miscellaneous: Other smaller sources like sewage treatment and smaller household sources
  6. Background: Pollution produced outside of London that has been blown in by the wind.

Pollution health impacts

The map displays two pollutants: NOx and PM2.5. NOx are a sum of nitric oxide (NO) and nitrogen dioxide (NO2) which, along with PM2.5, are the main air pollutants of concern in London. They are harmful to human health and are associated with adverse health outcomes like asthma, strokes and cancer.

London also has emissions inventories for NOx and PM2.5, meaning there is a detailed list of all the activities contributing to these pollutants across the city. The model that is behind the dataset requires these emission inventories.

This is the first time that modelled pollution sources data has been displayed in this detail across Greater London on an interactive public map. With a better understanding of which activities are causing pollution and where, leaders and communities can develop targeted solutions that clean the air and protect people’s health.

Please see here for a recorded demo on how to use the map, explain how the data was calculated and answer your questions.

Also posted in Government Official/Policymaker, London / Comments are closed

Breathe London Data Reveals Big Drops In NO2 Pollution During Commuting Hours

By Catherine Ittner / Published: October 19, 2020

London businesses are starting to reopen and some nonessential workers, who have been working from home, are considering going back into their offices. But what impact might this have on air quality?

During the lockdown, air quality data from Breathe London shows that harmful nitrogen dioxide (NO2) pollution went down significantly during commute times – 25% in the morning and 34% in the evening.

To help maintain these lower levels of pollution as shops and offices begin to reopen, businesses should allow more flexible ways of working. A new survey confirms it’s what people want.

BL weekday covid

Less pollution during commute times

Before the lockdown, many people across the city followed similar schedules on weekdays. As a result, the Breathe London network of air pollution sensors often saw daily dips and peaks of NO2 – a gas produced by fossil-fuel combustion that is associated with heart and lung-related health impacts.

In the pre-lockdown patterns, the lowest levels of this pollution measured was in the wee hours of the morning (around 3-4 am), when most people are sleeping. After they wake up and start moving to school and work, many in their fossil-fuel powered vehicles, the monitors saw a pronounced pollution increase. This falls midday, but pollution rises again in the evening to a second spike as folks return to their homes.

After confinement measures went into place, Breathe London data shows that air pollution significantly decreased across the city, including in residential areas, indicating there have been benefits to Londoners’ health even away from busy roads.

To get a better sense of how lockdown and many people working from home was impacting air quality, we then zoomed in on weekday commuting hours. Across Greater London, NO2 pollution decreased around 25% during the morning commute (8-11am) and 34% in the evening (5-8pm). These pollution reductions were even greater in the city centre, where many businesses are located – 31% and 37% respectively in the Ultra Low Emission Zone.

More work flexibility and clean air action

As lockdown eases, people across the UK want more flexible working options and action to lower air pollution.

That’s the gist of a new survey, commissioned by charity Global Action Plan on behalf of Business Clean Air Taskforce, which finds that:

  • 87% of those currently working from home would like to continue to do so to some degree.
  • 72% of the public believe clean air is more important now because coronavirus can affect people’s lungs.
  • 74% want businesses to do more to improve air quality in the recovery.

Not everyone can work from home, so it’s important businesses provide the option for those who can – leaving the roads and public transport available for essential workers to travel safely.

Build back better

Data helps us understand how pollution changes across the city, and Breathe London data shows the confinement measures have helped lessen the pollution peaks typically associated with commuting.

To protect public health and prevent the return of higher pre-lockdown pollution levels, UK employers should build back better and give people what they want by offering more flexible work options.

For more information on how pollution levels changed since confinement measures went into place, please see the full Breathe London analysis.

This was originally posted to EDF Europe.

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How we used machine learning to get better estimate of London’s NO2 pollution reduction

By Greg Slater and Dan Peters / Published: October 5, 2020

A new analysis for UK Clean Air Day from Environmental Defense Fund Europe (EDF Europe) finds nitrogen dioxide (NO2) pollution was 40% lower than expected across London during the initial COVID-19 lockdown.

But how do we know about pollution that didn’t happen? We used a machine learning model to predict what the concentration of NO2 would have been if lockdown restrictions had not come into effect. Here’s how it works.

Removing the weather impact

Meteorology and seasonal patterns have a big impact on air quality, which needs to be taken into account when measuring changes in pollution. For example, a windy day could improve air quality by dispersing pollutants that might have otherwise accumulated locally. Meteorological and seasonal variations like this make it difficult to directly compare one period to another – are changes in pollution due to a policy intervention or behaviour change, or is it just the weather?

We wanted to isolate the impact of lockdown measures on London’s NO2 pollution, which is produced from fossil fuels and is associated with heart and lung-related health impacts.

Using open-source tools developed by researchers at the University of York (Grange, 2020), and data from over 100 regulatory air quality monitors, we built a machine learning model to help us do this. London’s long-running monitoring network provides years’ worth of historic pollution data, which is used to train and test the model, alongside a series of meteorological and temporal variables.

We can then use this model – with time and weather information from lockdown dates – to predict the pollution levels we would have expected to see had lockdown measures not occurred. These predictions mirror seasonal and meteorological changes in observed pollution levels much more closely than an historical average, for example, which may vary due to different weather during that period.

As a result, with this method the difference between expected and observed levels can be more directly attributed to the impact of lockdown restrictions rather than random weather variations.

London lockdown expected vs observed chart

40% less pollution

The figure above shows a comparison between average expected and observed NO2 concentrations. The gap between what we expected to see and what we actually saw increases dramatically after 16th March, when social distancing was strongly advised. The figure shows the close alignment of trends between expected and observed levels, illustrating how both are similarly influenced by meteorological effects during the period.

Overall, we found a 40% difference from mid-March to mid-June 2020 – i.e. NO2 pollution levels were 40% less than what the model predicted during lockdown. This is the average change across London’s different monitoring site types, including those close to roads (kerbside and roadside) and farther away from busy streets (urban background and suburban).

Changes in meteorology over time typically complicate air quality intervention analysis, but a machine learning method like this allows us to better isolate changes associated with interventions, like lockdown measures. This method has been used successfully in other recent air quality research – for example, Grange and Carslaw (2019) – and we will continue to use cutting-edge methods like this to better understand how London’s pollution levels are changing.

This analysis complements our previous lockdown assessment using data from the Breathe London monitoring network. We used data from the regulatory monitors here rather than Breathe London because training the model requires a longer historical record.

References:

This was originally posted to EDF Europe.

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Digging deeper into London air pollution reduction with Waze

By Anne Marie Borrego / Published: June 23, 2020

Editor’s Note: Today’s post is by Meg Dupuy-Todd, a Manager at Environmental Defense Fund. Meg has led projects as a part of EDF’s Global Clean Air initiative, including collaborations with Google’s Project Air View in London and Salt Lake City.

Covid-19 has disrupted everyone’s lives, including providing an unfortunate and unwelcome experiment to examine London’s changing commuting and pollution patterns.

At Environmental Defense Fund, we have been collecting air quality data as part of Breathe London, a collaborative project to map and measure pollution in London. We could see that the social distancing measures had changed nitrogen dioxide (NO2) pollution levels, but we wanted to provide a fuller picture and determine what factors might be at play, including traffic.

Here’s how we used Waze for Cities data to dig deeper into the cause of observed NO2 pollution reductions.

NO2 pollution changes

The Breathe London consortium, with partners including Google Earth Outreach, has collected hyperlocal data to help Londoners better understand pollution in the city. Displayed in real-time on an interactive map, the Breathe London project measures and displays data from air quality sensors across the city.

After social distancing was strongly encouraged in the city in mid-March, the Breathe London monitors saw substantial NO2 pollution reductions — across the full network and especially in the city’s Ultra Low Emission Zone (ULEZ) in Central London. The greatest reductions occurred during daytime hours, between 6:00–22:00, when we also expected traffic to be the highest.

FIGURE 1: Breathe London network hourly mean NO2 measurements during 17 March to 13 April 2020 compared to pre-confinement levels.

FIGURE 1: Breathe London network hourly mean NO2 measurements during 17 March to 13 April 2020 compared to pre-confinement levels.

Exploring Waze data

It was a given that restrictions to help reduce the spread of Covid-19 would change traffic patterns, as workplaces and businesses closed and many people began to work from home. As road transport is a major source of NO2 pollution, naturally, our assumption was there was a link with fewer vehicles on the road.

Through partnership with Waze for Cities program, we had access to Waze-generated anonymous incident and slow-down information in Greater London. Could the data help us understand where and how traffic had reduced?

From previous reviews of Waze data, we knew it would not provide us direct information on traffic volume — the number of vehicles on the road. Instead, we relied on the reported congestion information comparing speed on roads as compared to free-flow traffic. Using Google BigQuery, we were able to analyze the large volume of spatio-temporally resolved data to begin to get a picture of daily traffic congestion in London. Using two distinct geographical boundaries (Greater London and the ULEZ), we decided to calculate, for each hour of the day, the total length of roads where traffic was less than 60% of free flow speed as our proxy for traffic.

With our analysis, we saw that traffic congestion reduced to such an extent that it was approaching free-flow in the vast majority of Greater London roads after the stay-at-home order, even during what used to be peak commuting hours. This is most pronounced in the mostly commercial ULEZ.

FIGURE 2: Waze data — mean total length of congested roads by hour during 17 March to 13 April 2020 compared to pre-confinement levels. (Note difference in scales for Greater London and the ULEZ).

FIGURE 2: Waze data — mean total length of congested roads by hour during 17 March to 13 April 2020 compared to pre-confinement levels. (Note difference in scales for Greater London and the ULEZ).

Putting it all together

With both datasets in hand, our team was able to look at the differences over time of NO2 pollution and traffic congestion. We knew that traffic varied by day of week, so we compared the data to a pre-confinement median by day of week and hour of the day.

We found an apparent association between the reduced pollution levels and lower traffic congestion in London. Examining the daily pattern of traffic congestion also suggests a tie between the biggest drops in pollution and the biggest drops in congestion — which both occur in the late afternoon from around 3 to 7 pm. You can see this depicted in the chart below. Note the difference in “variability in road congestion due to traffic during confinement” (shown in red) and the “weekly congestion average” (shown in green).

FIGURE 3: Breathe London network NO2 measurements during 13 March to 13 April 2020 in comparison to the typical hourly pre-confinement levels.

FIGURE 3: Breathe London network NO2 measurements during 13 March to 13 April 2020 in comparison to the typical hourly pre-confinement levels.

There is still a lot more to learn about the changes in road transport emissions and the relationship to measured pollution, but by incorporating the Waze data into our Covid-19 analysis we were able to shed new light on why NO2 pollution has recently gone down in London.

To learn more about our assessment please visit BreatheLondon.org/covid19.

 

This was originally posted on Medium.

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