Global Clean Air

Dr. Sanjeev Goyal Presents “Local Air Management Plan” (LAMP), a New Methodology for Hyperlocal Air Pollution Monitoring at India Clean Air Summit

By EDF Web Team / Published: August 27, 2023

Dr. Sanjeev Goyal, Head and Chief Scientist at the National Environmental Engineering Research Institute (NEERI) presented a promising new methodology to support Indian cities combating air pollution through ward-level action at the prestigious 2023 India Clean Air Summit (ICAS) in Bengaluru.  ICAS is the preeminent annual air quality event in India. It brings together governments, scientists, policy makers, NGOs, academia and students working on clean air in India and is hosted by the Centre for Study of Science Technology and Policy (CSTEP).

Dr. Sanjay Goyal presents at the India Clean Air Summit

Dr. Sanjay Goyal presents at the India Clean Air Summit

Dr. Goyal presented key results on the pilot study conducted by NEERI around Sirifort Auditorium in New Delhi in winter of 2023 during the session “Technology for Change”. Using a combination of stationary and mobile air quality sensors, the study team mapped the hyperlocal variations in the concentration of PM2.5 and NO2 within a small area covering less than 4 square kilometers.

The pilot study underscored the need for a “Local Air Management Plan” (LAMP) for each hotspot within the city using data from regulatory grade monitors, sensors, micro emission inventory and dispersion modelling. It showed how different emission sources like waste burning, domestic activities, roadside eateries and vehicles lead to diurnal variations in pollutant concentrations, which are further altered by weather patterns.

In his talk, Dr. Goyal emphasized the need to support authorities use hyperlocal monitoring to help non-attainment cities prioritize actions in hotspots support achievement of the targets of India’s National Clean Air Program. The pilot was supported by Environmental Defense Fund, which has pioneered hyperlocal monitoring cities across the world, including London, UK, Houston and Oakland in the US, and Cangzhou, China. EDF’s Air Quality team in India was actively involved in the pilot project, headlined by

ICAS 2023 is centered around the theme of “Mission LiFE” – India’s most ambitious policy yet to address climate change and the need to prioritise clean air for sustainable development. Hosted in collaboration with Indian Aerosol Science and Technology Association (IASTA)— the foremost institution in aerosol research in India, ICAS 2023’s focus is to understand how both national and global policies related to sustainable development and net zero can reflect on and address air pollution.

 

Posted in Academic / Comments are closed

Stronger national fine particle air pollution standards will provide significant health benefits and reduce disparities

By Ananya Roy / Published: June 22, 2022

This blog is co-authored by Taylor Bacon, Analyst, US Clean Air and Climate; Maria Harris, Senior Scientist; and Mindi DePaola, Program Manager, Office of the Chief Scientist.

A new EDF report finds that strengthening federal protections for fine particle air pollution (PM2.5) to 8 µg/m3 will have large health benefits and reduce air pollution-related health disparities in Black, Hispanic and low-income communities across the United States. That’s because these communities bear the brunt of harm from the nation’s most pervasive and deadly air pollutant.

The report comes as the U.S. Environmental Protection Agency, under President Biden, is reviewing the National Ambient Air Quality Standards for fine particle pollution (PM2.5). The agency is expected to propose a new standard this summer.

Wide disparities in exposure and health effects of air pollution

The analysis by Industrial Economics, Inc. finds that in 2015, PM2.5 resulted in 120,000 premature deaths and 75,000 respiratory emergency room visits. Children and older adults are particularly vulnerable.

Disparities in exposure and resulting health outcomes were substantial across the U.S.:

  • Black, Asian and Hispanic Americans had greater likelihood (84%, 58%, and 113% higher, respectively) than others of living in neighborhoods where air pollution levels were above 10 µg/m3. 
  • Black Americans over age 65 were three times more likely to die from exposure to particulate matter than others.
  • People of color were six times more likely to visit the emergency room for air pollution-triggered childhood asthma than white people.

For decades, communities of color and low wealth have been targeted for environmental hazards that others did not want: power plants, landfills, shipping ports, freeways and factories. The resulting inequities in pollution exposure are further aggravated by longstanding discriminatory disinvestment, poor housing, limited health care, educational and economic opportunities perpetuating health disparities, intergenerational poverty and higher vulnerability to health impacts of air pollution.

The report shines a light on what communities exposed to particle pollution everyday already know: they’re surrounded by pollution sources that are harming their health and shortening lives. 

EPA can set protective standards which will provide health benefits and reduce disparities

In 2020, the Trump administration retained the existing standard for PM2.5 of 12 µg/m3, ignoring a large and growing body of scientific evidence indicating that this standard was not adequate to protect public health. Environmental and health groups petitioned EPA to reconsider this decision, and in the fall of 2021, EPA launched a review of the PM2.5 standards. As part of this review, EPA took stock of the new science since the last review and considered the policy implications of this new research. In their policy assessment, EPA found strong evidence that the current annual standard of 12 µg/m3 does not adequately protect human health and considered alternate standards between 8 and 11 ug/m3. The Clean Air Scientific Advisory Committee (CASAC), a panel of independent scientists convened to advise EPA, recommended a range of 8-10 µg/m3 for the annual standard.

EDF’s report builds on EPA’s analysis of racial and ethnic disparities in pollution exposure and health impacts under the current and alternative standards, and it supplements EPA’s policy assessment by addressing some of the suggestions made by CASAC for future reviews, including greater attention to risk disparities, expanding the geographic scope of the analysis and considering current PM2.5 levels in estimating the benefit of alternative standards.

The report supports both EPA’s and CASAC’s conclusions that the current standard is not adequate to protect health and finds significantly larger benefits of an 8 μg/m3 annual standard over 10 μg/m3. 

  • Nationally, a standard of 8 µg/m3 would have 3.5 times greater health benefits than a standard of 10 µg/m3 (16,000 premature deaths and 10,000 respiratory emergency room visits avoided at 8 µg/m3 vs. 4,600 premature deaths and 3,000 respiratory emergency room visits avoided at 10 µg/m3).
  • A standard of 8 µg/m3 would go further to reduce inequities in the health burden of air pollution than a standard of 10 µg/m3, particularly between Black and white populations. People experiencing poverty would see 30% higher benefits in terms of reduced mortality compared to higher income communities.

As seen in the figure above, even with strengthened standards, substantial disparities in the health impact of particulate pollution would persist. It is essential that EPA also takes complementary actions that directly tackle environmental injustice.

Fine scale data offers insights on disparities

In their policy analysis of alternative standards, EPA utilized regulatory monitor data and modeling at a scale of 12 km2 to determine exposures to air pollution and benefits of alternate standards in 47 major metropolitan areas. However, outside of cities, there are few regulatory monitors and limited modeling to provide air quality information.

To better understand current PM2.5 exposures and potential health benefits of a stronger pollution limit for every community, we utilized fine scale satellite, land use and emissions-based data that offer a clearer picture of air pollution. We found significant health impacts of PM2.5 not reflected in EPA’s analysis of 47 metro areas: PM2.5 causes an additional 83,000 premature deaths and 49,000 emergency room visits for respiratory diseases. Black people and people experiencing poverty bear a higher burden of air pollution health impacts with similar disparities in both urban and rural areas.

Nearly 40 percent of the lives saved from a stronger standard of 8µg/m3 are outside of the areas evaluated by EPA. Critically, our report finds that communities outside of EPA’s analysis would see limited annual benefits of an alternative standard of 10 µg/m3–420 lives saved–but significant benefits of a standard of 8µg/m3–5,800 lives saved.

The pollution data forming the basis of this analysis have been evaluated using monitoring data, and thus in areas where there is limited monitoring there is lower certainty in the levels estimated (like large areas outside of those evaluated by the EPA). This makes clear the implications of blind spots in air pollution monitoring. Our report indicates a substantial health burden of air pollution in these areas and large benefits from a strong standard of 8µg/m3. This can, however, only be validated and enforced by expansion of regulatory monitoring in these areas.

We have an opportunity to act now

EPA is expected to propose a new standard this summer and will take comments from the public at that time. It is imperative that the proposed standard reflects both EPA’s and the Biden administration’s commitment to environmental justice in that it adequately protects the people at greatest risk. This report shows that strengthening the National Annual Ambient Air Quality Standard for PM2.5 from 12µg/m3 to 8µg/m3 would go the furthest towards reducing this disproportionate burden of air pollution and is a critical immediate step. 

Editor’s note: This blog was updated on March 23, 2023 to reflect findings from an updated version of the original analysis.

Also posted in Environmental Justice, Government Official/Policymaker, Health, Public Health/Environmental Official, USA / Comments are closed

Introducing Air Tracker: A backward take on air quality to pinpoint sources

By Tammy Thompson / Published: June 13, 2022

EDF’s new Air Tracker tool allows us to better understand how local air pollution behaves, illuminating the path it takes from a likely source area. Because this tool allows us to look backwards at where pollution likely originated, it shifts the focus, putting communities and people first. Developing it required a shift in thinking. 

Most atmospheric scientists focus on particle and air movement to help us predict what’s going to happen in the future. As a scientist working in air pollution, I wanted to use those same principles to look backwards so I could better understand how the emissions upwind of us mix and travel through the air, providing a better picture of what we’re breathing at any given time. This way, we don’t have to model every single source to know what’s important to who and when.

When I joined EDF in 2019, our scientists had already successfully shown how mobile air monitoring programs could highlight dramatic differences in pollution levels within individual city blocks. We wanted to go beyond showing the presence of pollution–and illustrate how it traveled to get there. 

 

EDF and academia joined forces to leverage cutting edge insights.

To do this, we enlisted the help of John C. Lin, an atmospheric scientist from the University of Utah who developed the STILT model (which has since been incorporated into NOAA’s HYSPLIT model). He and his team were already working with our partners at Google Earth Outreach on a source apportionment project. We also tapped Paul Dille (who pulled in Randy Sargent and Amy Gottsegen) from CREATE Lab at Carnegie Mellon University, whose Smell PGH application allows users to better understand the pollution landscape in Allegheny County, PA. EDF colleagues Alex Franco, Mindi DePaola and Grace Tee Lewis provided invaluable insight and help as well.

Air Tracker runs on  real-time, trusted, scientific models coupled with air pollution and weather data to help residents, scientists and cities learn more about the air they’re breathing. While Air Tracker is currently mapping fine particle pollution trajectories in Houston, Salt Lake City and Pittsburgh, we designed it to work with other primary pollutants anywhere in the world. 

Air Tracker allows users to trace the path of likely sources of air pollution in Houston, Pittsburgh and Salt Lake City.

Filling current air monitoring gaps

Despite advances in low-cost mobile and stationary monitoring networks, existing air pollution tracking is still lacking. Currently designed to provide us with a solid understanding of background air pollution, the federal and state government  system of monitors essentially smudges out the rough edges to create averages, which underemphasizes the very real, very harmful pollution burden many urban–often historically vulnerable–communities face. 

Air Tracker can help counter that averaging effect. It allows users to click anywhere within their city map to see the most likely source area of the air they’re breathing at any given time. 

Beyond the mapping application, it can improve air quality efforts in the following ways:

 

  • Placement of new monitors and networks

For communities that have long suspected they’ve been subjected to dirty air, Air Tracker can help them show that their air is influenced by nearby facilities. This can help them place monitors in specific locations to show just how much pollution they face and when it’s at its worst. 

Cities wanting to get serious about air quality can also use the tool to design either stationary or mobile monitoring efforts. It can also help them answer questions about specific facilities that are known emitters, while spotting ones that may not have been on their radar.  

  • Hold polluters accountable 

Even in cities like Houston–where a lack of zoning has allowed industry to flourish unchecked, putting homes, schools and entire communities in the path of harmful pollution–it can be hard to pinpoint which facilities are most likely responsible for localized emissions. The models behind AIr Tracker’s source area development use wind and weather data to illuminate which pollution sources are the most likely culprits, giving regulators a powerful enforcement tool.   

  • Putting communities and people first

Because Air Tracker can look backwards at pollution’s path, we can start with communities and people first when seeking to map exposure and its impacts. This can help correct for the current distortion of our current air pollution monitoring system, which wrongly assumes all people are exposed equally.  

We know communities face an unequal burden from air pollution. Our hope is that Air Tracker will allow us to better capture and highlight those discrepancies so the people living there can get the relief they need and deserve. Read more about the methodology here.

 

Also posted in Community Organizer, Concerned Citizen, Homepage, Houston, Salt Lake City, Science / Comments are closed

Making the most of sensor data: How tracking performance of lower-cost sensors allows cities to reveal actionable insights about local air pollution

By Dan Peters / Published: February 23, 2022

Lower-cost air quality sensors can be a game changer for cities looking to understand and improve air quality at the neighborhood level. However, issues with accuracy have been a key barrier to their adoption. Our new paper shows how users can make the most of their data by evaluating sensor performance on a continuous basis.

Collocating sensors to track performance

As part of the Breathe London consortium, we installed 100 sensor devices across the city  to measure key pollutants including nitrogen dioxide (NO2) and particulate matter for more than two years. Lower-cost sensors like the ones we installed are more sensitive than reference-grade instruments to environmental factors like temperature, relative humidity, or even levels of other pollutants. That can make their measurements less reliable in some environments, or even in certain seasons of the year.

To make sure our data was both accurate and useful, the Breathe London consortium developed rigorous quality assurance procedures. For our NO2 dataset, the procedures included multiple methods to calibrate the sensors, as well as applying an algorithm to correct for sensitivity to ozone, which the sensor can mistake for NO2.

While most of our sensors were collecting measurements at new locations across Greater London, we also installed two “test” sensors alongside London reference-grade monitors for most of the project. By tracking when data from these “test” sensors deviated from the more expensive reference instruments, we had an indication of how sensors across our network were performing at different times.

In the left panel, the “test” sensor measurements show a large deviation from the collocated reference monitor (right), indicating a period when the sensor was not performing well.

This approach provided a reality check for our pollution data. If the sensor network reported high NO2 values but the “test” sensors were completely off track from the reference at that time, we could infer that the network result may have been affected by poor sensor performance and adjust accordingly. This kind of ongoing sensor evaluation is important. Without it, users could mistake erroneous sensor data as evidence of major pollution events or local hotspots.

Why performance matters

Our NO2 sensors performed well most of the time, producing data that revealed a variety of actionable insights, including:

  • Times of day and days of week with the highest pollution levels
  • Regional pollution episodes (for example, a multi-day period with high pollution caused by weather conditions)
  • Hotspot detection
  • Impacts of sources on pollution patterns at different locations
  • Long-term trends (for example, seasonal changes or year-over-year improvements)

Improving our understanding of air pollution in cities around the world

While the uncertainties associated with lower-cost sensors may make them unsuitable for some applications, our project demonstrates a way to generate actionable insights from sensors. The Breathe London network’s NO2 data shows that with rigorous quality assurance and ongoing evaluation of sensor performance, cities can utilize lower-cost sensors to better understand local air pollution. That can allow more communities to take advantage of this relatively new technology, even if they do not have the resources to purchase a network of more costly  reference-grade monitors.

Also posted in London, Monitoring, Science / Comments are closed

Catalogue of Indian Emission Inventory Reports (Jan 2022)

By Kevin Tuerff / Published: January 11, 2022

 

Indian Emission Inventory Report_DIGITAL FILE

(By PAARTHA BOSU, NEW DELHI, INDIA)  A detailed air emission inventory (EI) is a comprehensive list of pollutants within a pre-defined geographical area and is beneficial for developing clean air action plans. It can also test the effectiveness of pilot interventions towards air quality abatement. Emission inventories have been prepared for several Indian cities and states. However, several of these EI reports have not been given due attention. This report presents a database of all publicly available EI reports and several previously un-referred studies for India to help policymakers and scientists prepare reckoner of all the work done in the area.

EI studies have been tabulated as per the source contribution (total emissions, transport, residential, industrial, power plants, agriculture, waste and others) along with details such as geography, grid size, emission factors used, and type of data collected (primary surveys vs secondary literature). Each sector list also consists of the pollutants studied and highlights those reports that have closely followed the existing CPCB guidelines.

As per various operating sections of the Air Act 1981, air pollution monitoring, calculation of pollution load, preparation of emission inventory, preparation of action plan for air pollution control should be done as per the SOPs issued by CPCB from time to time. Therefore, emission inventory prepared by agencies and experts using other methodology may not be tenable per Air Act 1981. In its order for Critically Polluted Areas and Non-Attainment Cities, the National Green Tribunal mentioned that methodologies recommended by CPCB should be followed for such studies.

Robust EI reports form the mainstay of a city’s source apportionment and mitigation strategies. Therefore, scrutiny of the EI reports is required, especially now that all 132 non-attainment cities have been mandated to carry out source apportionment studies. Furthermore, periodically revised emission inventories could help check each sector’s efficacy of control actions. Finally, regional emission inventories now need to be prioritised as the airshed approach has gained prominence in air pollution management in India. About 200 EI reports have been collated and made available with hyperlinks for researchers and policymakers to use. They have also been sectorally classified for ease.

Key Findings

  1. An easy to use ready reckoner of air pollution emission inventory studies for India was created. These reports were catalogued as per sectors; Total emissions, Transport emissions, Industrial and Power Plant emissions, Residential emissions and Emissions from Agriculture, Waste and other miscellaneous sectors.
  2. It was found that only some of the studies followed the CPCB guidelines closely of using indigenous emission factors and primary data for creating emission inventories
  3. Geographically, most of the studies were concentrated in the Indo-Gangetic Plain, focusing on Delhi and the National Capital Region. Multiple emission inventories for the same city and region leads to uncertainties. Instead, a common framework for EI development should be followed. EIs should be periodically updated every few years to test the efficacy of interventions. For instance, in the transport sector, EI for the current year could help gain insights on the effects of introduction on BS VI mass emission standards on road transport emissions. In the residential sector, the introduction of LPG in rural households would have led to a reduction in emissions, and this should reflect in the latest EI report
  4. Emission factors will determine the accuracy of estimations. However, our Indian conditions are distinct from our western counterparts. Therefore, relying on the emission factors developed by USEPA might lead to inaccuracies. Thus, the transport sector emission factors developed by the Automotive Research Association of India (ARAI) were used.
  5. Inventories need to be developed for toxics like VOCs and heavy metals like mercury. Doing so will enable the development of standards for these pollutants

Download the report

For further details on the report:

Parthaa Bosu (pbosu@edf.org)

Swagata Dey (sdey@edf.org)

Also posted in Government Official/Policymaker, India, Science / Comments are closed

Air Pollution Research Reveals Exposure Disparities in Bay Area

By Maria Harris / Published: November 19, 2021

After working with EDF and partners to map hyperlocal pollution in Oakland, CA using Google Street View vehicles, researchers Dr. Joshua Apte (University of California, Berkeley) and Dr. Sarah Chambliss (University of Texas at Austin) collected additional mobile data across the San Francisco Bay Area to expand understanding of street-level air quality and disparities in pollution exposure. Their new paper, “Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring” was published in September in the Proceedings of the National Academies of Sciences. It builds on previous work in Oakland published by Dr. Apte in 2017. I recently spoke with Dr. Chambliss about the latest findings.

What were the key findings of this new research?

Dr. Chambliss: In this study, we broadened the geographic scope of our mobile pollution measurements beyond Oakland to neighborhoods across the Bay Area. Throughout the other areas we drove across the SF Bay Area, we saw some of the same types of patterns that we originally described in the original Oakland study: steep increases in concentrations near major roads (especially for nitric oxide, or NO) and some additional localized peaks that could be attributable to other localized sources that we are still working to identify.

We also saw evidence that the types of sources contributing to local pollution differ among study areas: some areas have more prominent peaks for black carbon, others for NO. The mix of pollution is different in different areas around the Bay. We saw that some neighborhoods were much cleaner than others, and some neighborhoods had higher levels of some pollutants but were not higher for every pollutant. Because we had looked at so many different types of neighborhoods, we saw an opportunity to extend the Oakland analysis by also asking: Who lives in the neighborhoods that are more polluted, and how do pollution patterns compare to or interact with patterns of racial/ethnic segregation that persist in the Bay Area?

After connecting the street-level air pollution data with census data, we found that there were systematic differences in pollution exposure across racial/ethnic groups. Specifically, Black and Hispanic/Latino people had 10-30% higher average exposure to NO, nitrogen dioxide (NO2) and ultrafine particles (UFP) than the population as a whole, while white non-Hispanic residents had 20-30% lower average exposure. The neighborhoods where we measured the cleanest air tended to have higher proportions of white residents, as well. In contrast, neighborhoods where more people of color lived tended to have higher concentrations not just near roadways but in areas of the neighborhood we would consider “background” locations: residential areas where we expect conditions to be cleaner.

Why do these disparities in air pollution exposure matter?

Dr. Chambliss: Air pollution can have major short-term and long-term health impacts. Studies have shown linkages among the group of pollutants we looked at–NO and nitrogen dioxide (NO2), black carbon, and ultrafine particles- with hospital visits, chronic lung and heart disease, with particular risks for the health of newborns and the elderly.

Because air pollution causes systemic inflammation, its impacts spread far beyond the lungs: there is evidence of air pollution affecting cognitive development and diabetes prevalence, for example. Those exposed to higher air pollution are at higher risk of a wide range of health problems. When disparities fall along lines of socioeconomic status or other social vulnerabilities, the health risks caused by air pollution can compound with issues like lower access to medical care or less capacity to handle the financial burden of health issues.

How did you collect such detailed street-level pollution data?

Dr. Chambliss: We had several partnerships that allowed us to achieve this level of coverage. A partnership with Google Earth Outreach allowed us to use Google Street View vehicles to drive “blackout” patterns, where we drove down every road in a study area each time we visited. We also partnered with Aclima, Inc., who installed laboratory-grade instrumentation in these cars and kept the equipment maintained and calibrated for near-daily driving.

We drove two of these “mobile laboratories” nearly every weekday over a 32-month period, visiting different neighborhoods each day and revisiting each neighborhood every 6 weeks or so to collect measurements representing different seasonal conditions.

What kind of policy implications do you see for this work?

Dr. Chambliss: That there are higher pollution levels in neighborhoods with more people of color isn’t a new finding in and of itself, but the level of spatial detail that we could bring to this analysis provided some additional insights. Often, within one neighborhood or several adjoining neighborhoods, there is a wide range in the outdoor pollution levels at different addresses. And these differences do not typically lie along racial/ethnic lines. It’s only when you zoom out to look at city-wide patterns of segregation that you see racial/ethnic disparity in exposures. This is strongly influenced by neighborhoods where the lowest levels of pollutants like NO2 and UFP are higher than even peak levels in cleaner neighborhoods.

This gives us an indication of how policies could be improved to geographically target pollution mitigations to better address disparity and promote environmental justice. Look specifically at communities where the baseline pollution levels are higher and where residents are predominantly people of color. This segregation is often connected with historically racist policies such as discriminatory lending policies or racial covenants built into housing deeds. While those policies may have ended, they leave a persistent legacy placing communities of people of color in areas with higher pollution and greater environmental health risks. To help reverse these patterns of environmental injustice, it’s critical to work to clean up the air pollution sources within those neighborhoods.

What does work like this mean for the future of hyperlocal air pollution monitoring?

Dr. Chambliss: An implication of how localized some pollutant peaks are – a phenomenon that mobile monitoring is particularly suited to measure – is that when you cut emissions from a particular source or type of source, you will see major benefits very close to that source but more moderate reductions everywhere else. If you want to evaluate the full benefits of such a policy, making measurements with fuller spatial coverage may show a magnitude of improvement that wouldn’t be reflected at a single fixed monitoring site. For example, anti-idling policies would help specifically at locations with a lot of truck activity, like ports or warehouses, but it may not be obvious from the outset where the most idling occurs. Mobile monitoring is a way to find those areas that really benefit.

Another thing this research shows is how important it is to spread out measurements over a broader geography as much as possible, given time and resource constraints. It would be great to do a similar study in another US city, because each one has a unique history of growth, industrialization and zoning, and segregation or discriminatory housing policies. It would also be interesting to look at cities outside of the US where urban development patterns, both demographic and land-use related, are much different.

What’s next for you in this field?

Dr. Chambliss: We are continuing to work with these mobile monitoring data to gather further insight into what features of the urban environment lead to pollution hot spots.

 

 

Also posted in Health, Homepage, Oakland / Comments are closed

Global Clean Air Blog: Houston students thriving during lockdown by learning about air quality

By Kevin Tuerff / Published: January 26, 2021

 

By Shannon Thomas, Project Manager, EDF Environmental Youth Council Program

2019 photo: Houston Environmental Youth Council

Ask parents and educators about the spring 2020 semester, and they’ll likely tell you similar versions of the same story: students were just hitting their strides with various projects and the end of the academic year was in sight. Then it all came to a screeching halt.

The same was true for the students I work with in EDF’s Environmental Youth Council, an educational program for high-school-age youth from communities in Houston that are most affected by high levels of air pollution.

Pasadena Memorial High School teens had learned about the harmful impacts of engine idling as a part of the program. So when a few of our students noticed their classmates leaving their cars running while watching videos on their phones or doing their makeup in the mornings, they were rightfully concerned. They prepared a proposal, went to their principal, and convinced him to adopt a no idling policy in the student parking lot. The students purchased signage and began developing a marketing campaign to support the new policy just as everything shut down.

The signs went into a closet, and I wondered what would become of our program and its 30 students. One of the hallmarks of this program, which started in 2019, has been the creative ways we engage our students. What would happen without the trips to Washington, DC, the hands-on experiments, bus rides to the top of a 200-foot pile of garbage and engaging guest speakers?

Light has a funny way of pushing through darkness, and teenagers can still surprise me.

Growing while meeting virtually

Despite going fully virtual this academic year, we didn’t just keep our Council going; we nearly doubled its size, to 55 students. Teens from Pasadena Memorial, Pasadena High School, and Raul Yzaguirre School for Success in the East End meet online to learn not only about environmental health and science, but also civic leadership, thanks to grant funding from the Gulf Research Program of the National Academy of Sciences, Engineering and Medicine.

By going fully virtual, we’ve actually been able to engage more students. And while the teaching strategies have changed, we’re still able to educate them about the science of air quality and the physical impacts of pollution on the body. 

We’re also hopefully inspiring them to become environmental leaders in their communities, which, due to their proximity to oil and gas refineries, chemical facilities and other industrial sites, are disproportionately impacted by pollution. 

Developing new environmental leaders

One graduate of the program who recently moved away told me she didn’t realize that the odor she smelled every day wasn’t normal. By teaching these students about what’s going on around them and the levers of power that can change it, I hope they’ll develop into leaders who will fight for cleaner air in their communities. 

Houston ship channel

Houston ship channel

So while we haven’t been able to do our boat tour down the Houston Ship Channel this year and won’t be able to visit local Congressional representatives at the U.S. Capitol, I’m excited that such an engaged group of students will emerge from this pandemic with a deeper understanding of the air they breathe and the change they can make. 

 

Read other Global Clean Air blogs here. 

 

Also posted in Health, Houston / Comments are closed

Pollution data sharing norms are shifting

By Millie Chu Baird / Published: October 22, 2020

Sharing code in the tech community hasn’t always been considered a virtue. But GitHub, with its easy interface and mammoth user base, has shown how allowing developers to build on one another’s software code can accelerate innovation of new projects and solve bugs with existing applications, all in a transparent, open-source code hosting platform. The air quality data space is ripe for this kind of move.

Opening access to air quality data

Today, in an effort to address this critical need, Environmental Defense Fund (EDF) is unlocking our new Air Quality Data Commons (AQDC), an open-access data platform where people can share and use data from low-and medium-cost air quality sensors. With the introduction of the AQDC, researchers now have access to more than the 60 million plus data points from EDF and our partners’ air pollution studies in Oakland, Houston and London.

Until recently, few outside of government could afford the expensive, specialized equipment needed to measure air pollution other than well-funded scientists, whose data was typically private until after the publication of a peer-reviewed paper. Even then, when they wanted to share their data with others in the field, they could do so only on an ad-hoc basis with limited infrastructure in place to support such collaboration.

Now, as scientists, cities and residents are taking advantage of new low-cost, high-quality sensors, and the amount of air quality data is growing rapidly, as is the need to store and share it. To unlock the benefits of the data for both scientists and society, it must be open and easily accessible.

The Fourth Wave of Environmental Innovation

Transparency drives innovation

Many of our academic partners have long expressed the desire to share their data — once they’ve had the opportunity to analyze it. However, they’ve lacked a platform that would allow them to do so. Similarly, donors are increasingly demanding that the data gleaned from the projects they’ve funded be available for others to use and explore. By building this community, we hope people will see a benefit to not only accessing available data but sharing their own — they can ask questions of fellow air quality scientists about trends they are seeing and learn from others who may have new was of analyzing existing data.

Our partner Karin Tuxen-Bettman, Program Manager for Google Earth Outreach sees value for cities and Google as well. “By adding to the Air Quality Data Commons, cities can feel confident their investments in air monitoring — whether through a fixed stationary network or city-owned vehicle fleets equipped with sensors — are creating enormous value,” she says. “Validated data shared on the AQDC will contribute to the larger database that Google’s Environmental Insights Explorer will pull from, enabling us to build hyperlocal air quality maps for more cities. By making this data available through a transparent process, the AQDC can accelerate action required to improve air quality.”

We look forward to growing this group of data scientists, companies and cities sharing and analyzing data into a robust community who will contribute to the scientific knowledge base, so we can better understand air pollution problems around the world.

The revolution of smaller, cheaper air pollution sensors has brought us here, but the full potential of this revolution will only be realized when a larger community of scientists, cities, residents and activists use the data we collect to take action and improve local air quality. Join us by downloading our data from the AQDC, or upload your own. We look forward to sharing and learning with you.

We are entering a new era of environmental innovation that is driving better alignment between technology and environmental goals — and results. #FourthWave

This was originally posted on Medium.

Also posted in Monitoring, Partners, Science / Authors: / Comments are closed

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.

Also posted in Health, London, Science, UK / Authors: , / Comments are closed

Pandemic exposes need for cities to improve air pollution data collection to protect public health

By Harold Rickenbacker / Published: July 15, 2020

Harold Rickenbacker, Ph.D., Manager, EDF+Business.

This is the fourth in a series of Global Clean Air blogs on COVID-19 and air pollution. EDF scientists and program experts share data about pollution levels during quarantine from a local and global perspective, and provide recommendations for governments and companies to Rebuild Better.

Los Angeles, California.

Los Angeles, California.

We’ve long known that air pollution is linked to health problems like heart disease and asthma, and that these risks are highest for the elderly and people with existing heart and lung diseases. Now, new evidence shows the same people who have lived with polluted air for decades are also at increased risk for severe illness from Coronavirus.

These findings are generating unprecedented urgency to clean the air we breathe and underscoring the importance for cities across the globe to make air pollution monitoring a priority in a post-pandemic world.

But as local leaders grapple with how to tackle air pollution and protect vulnerable communities, they’re faced with a big challenge: they lack the localized data needed to properly protect public health and reduce harmful emissions.

New, lower-cost sensor technology is allowing scientists, advocates and government officials to map air pollution at the hyperlocal level, which can reveal pollution patterns within neighborhoods and even individual city blocks.

Policymakers tasked with rebuilding healthier and more resilient communities in a post-pandemic world can use localized data to work more effectively with residents and stakeholders to implement powerful interventions that reduce air pollution in overburdened communities.

What better data can tell us

Everyone deserves to breathe clean air, but where you live determines how likely pollution is to worsen or shorten your life. And while most conventional monitoring systems can provide a general sense of a city’s air quality, they can’t account for air pollution at the neighborhood level, where people live, work, and play.

There are two ways local leaders can leverage hyperlocal air quality data to inform solutions for improving community health.

Finding the pollution culprit: Source apportionment

It’s common to see local variations in air pollution concentrations – a spike at one end of the block, but not the other. It’s more difficult, though, to determine the reasons behind that spike.

Through a process called source apportionment, cities can pinpoint the origin of air pollution emissions – using the most sophisticated methods, this information can be obtained at the level of down to a single idling truck, a specific power plant, or even a smoke stack from that plant. This level of specificity can show how individual sources are responsible for fluctuations in air pollution levels, as well as how much impact one sector of sources is having compared to others. For example, is a city’s biggest air pollution problem its diesel trucks or its power generation facilities?

Knowing exactly where pollution is coming from empowers city officials to see how much of their air pollution originates within their city boundaries, versus how much might be coming from a source in the neighboring region. Local governments, such as Salt Lake City are using these insights to identify where they have the authority to implement tailored interventions, or where they can collaborate with neighboring municipalities to clean the air for all.

Understanding the true impact air pollution has on health

person with asthma using an inhaler

The science is clear that air pollution is harmful to human health. Yet, we know little about where and how people are most affected.

Are the higher asthma rates in one neighborhood the result of pollution coming from local truck traffic, and not an upwind power plant? Is your community more or less affected than others in your city and why? Unfortunately, too often we’re seeing it’s low-income and minority populations that are hit the hardest, and tools like Health Impact Assessments (HIAs) are being used to prioritize environmental justice and policy actions.

HIAs were introduced as an independent tool to help practitioners, and decision makers incorporate and weigh public health considerations in decision making. Cities, including New York, London and Barcelona are using HIAs to evaluate what the potential health gains are from adopting various policies. In the Bay Area and in Houston, satellite and sensor data are being used to make the invisible visible.

As local leaders devise solutions for improving air quality, it’s critical that human health is made a top consideration. Making health a factor in the cost benefit analysis will enable cities to show that not only did their policies drive down pollution levels, they too improved public health.

Cleaner skies for a healthier tomorrow

Even in cities that might meet health-based standards, air pollution can burden or even shorten residents’ lives. And it’s disproportionately impacting some communities more than others.

The global pandemic reinforces the need for policies guided by sound science that safeguard our health and climate. At the same time, it’s bringing longtime inequities into sharper focus.

As we look into the future, it’s critical that local leaders harness hyperlocal data to forge evidence-based policies that drastically reduce pollution while building more resilient, inclusive communities.

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How cities around the world are responding during the pandemic

Air pollution at some commuter hotspots in London, UK halved in the first four weeks of lockdown, according to new research by our European colleagues. A separate survey showed one in six with lung conditions noticed their conditions were improved during the lockdown. London also temporarily expanded its Congestion Charge to new hours during the evenings and weekends to help reduce traffic pollution. Ultra-low emission vehicles and electric vehicles may drive in Central London without paying the hefty fees.

In Houston, One Breath Partnership is educating residents about environmental racism and the disparities of health impacts of pollution and COVID-19 on Black and Brown communities.

In Beijing, China, local authorities used the biggest political gathering of the year, China’s “Two Sessions,” to set an example and promote cleaner transportation options to the public. During the National People’s Congress (NPC) and the Chinese People’s Political Consultative Conference (CPPCC) in May, NPC and CPPCC representatives from Beijing all used electric vehicles for conference-related transportation. Beijing also has been promoting green transportation through programs such as waiving bike-sharing fees during rush hour and providing additional discounts to frequent bike-sharing users. EDF’s bike-sharing air quality monitoring pilot can help provide supporting data and in turn evaluate the social impact of the city’s approaches to reducing air pollution.

Paris, France is subsidizing purchases of electric bicycles, up to half the cost, or 500 Euros.

Bogotá, Colombia has responded to the pandemic by accelerating existing efforts to encourage low-carbon and cleaner forms of urban transport, such as adding 80 km of ciclovia (bicycle lanes) to the existing 560 km network and making greater provision for pedestrians.

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EDF surveyed sustainability leaders across the globe to better understand cities’ unique challenges and opportunities for air pollution management. According to preliminary results, 82% of respondents recognize Health Impact Assessment (HIA) and source apportionment as necessary tools to take action on air pollution. To this point, EDF is interested in learning the current status of air quality in your region, and where you are in your air pollution management journey, including any obstacles and/or successes you’ve faced along the way. Please take 10 minutes to complete this Air Pollution Management Needs Assessment.

This was originally posted to the EDF Health blog.

Also posted in Community Organizer, Concerned Citizen, Government Official/Policymaker, Health, Public Health/Environmental Official / Authors: / Comments are closed