Climate Change and Health Seminar Series: "Climate change, air pollution, and public health: Bridging science to policy"

March 01, 2023

Information

Dr. Anenberg joined the Yale Center on Climate Change and Health to discuss her work on air pollution monitoring and ways to impact policy work.

Speaker:

Dr. Susan Anenberg, Associate Professor and Chair, Environmental and Occupational Health Department, Director of the GW Climate and Health Institute, George Washington University

Date: February 27, 2023

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00:00Today is my great pleasure and
00:04to introduce to this speaker,
00:06doctor Susan Annenberg.
00:08Susan is associate professor and chair
00:11of the Department of Environmental
00:14and Occupational Health in the George
00:18Washington University and she's also
00:20the current director of the DW PAN and
00:24Health Initiative Institute and Doctor.
00:27August Research focus on the
00:29health implications of air
00:30pollution and climate change,
00:32from both local to global skills.
00:35And we talk a lot about
00:37like policy implications.
00:38And Doctor Annenberg is really
00:40the true pioneer of making the
00:43science of this policy relevant.
00:46So she serves on the US EPA Science
00:49Advisory Board and the Clean Air
00:52Act Advisory Committee and The Who
00:55Global air pollution and Health
00:58Technical Advisory Group and the
01:00National Academy of Sciences Committee
01:02to advise the US Global change.
01:05Research programs.
01:06She also serves as currently the
01:09President of the Jail House section
01:11of the American Geophysical Union.
01:14So without first deal,
01:15let's welcome those energy.
01:19And for being here today,
01:21I really appreciate you taking the time
01:23out of your days to to to be here.
01:25So I'm Susan Annenberg from
01:27George Washington University,
01:28and I will be talking today
01:31about linking climate change,
01:32air pollution and human health and
01:34bridging science to the policy,
01:35which is really what I'm
01:37very passionate about doing.
01:44Shape that, people.
01:47OK, so before I start,
01:50let me just say that a lot of the work
01:52that I'm going to show today is really
01:53standing on the shoulders of giants.
01:55This is work that would not be
01:57possible without the people who have
01:59spent many years that, you know,
02:01detecting associations between air
02:03pollutants and health outcomes,
02:05developing air pollution exposure
02:06datasets that are open and publicly
02:09available for others to use.
02:10And I appreciate the efforts
02:12of many people in this room and
02:14contributing to that science.
02:15And this really makes the the bridging.
02:18From science to the policy possible
02:20by creating these datasets and
02:23associations that others can use.
02:25But based on the information that
02:27we have from Epidemia epidemiology
02:29and exposure science,
02:31we know that air pollution continues
02:33to be a leading health risk
02:35factor in nearly all countries.
02:37Is currently considered to be
02:39the 4th leading risk factor
02:40affecting global mortality.
02:42That's not the 4th leading
02:44environmental risk factor.
02:45That's the 4th leading overall risk factor.
02:48And and really indicates that air pollution
02:50needs to be central on the global health
02:53agenda for improving people's health.
02:55And if you look at the diseases
02:57that air pollution impacts,
02:58it is not a small fraction of these diseases
03:01that air pollution is responsible for.
03:03I mean this is a, you know,
03:0640% of chronic obstructive pulmonary disease,
03:0820% of diabetes,
03:0920% of ischemic heart disease and you can
03:11read the rest of the the percentages there.
03:14So this is a preventable risk
03:16factor that it is responsible for.
03:19Millions of premature deaths
03:20globally and a very large,
03:22very substantial fraction of the incidence
03:26of these diseases around the world.
03:29And we also know that climate
03:31change worsens air pollution.
03:33So climate change is contributing
03:35to worsening ozone,
03:36increased wildfire smoke,
03:38increased dust,
03:39worsened allergy conditions,
03:41and even potentially impacting
03:43airborne infectious diseases,
03:45both the spread and the severity
03:47of airborne infectious diseases.
03:49So air pollution and climate
03:51change are highly interlinked.
03:53This is just one of the ways
03:54that they're interlinked,
03:55and we're going to talk about
03:56some of the others.
03:57But climate change is now worsening.
03:59Air pollution making it harder
04:01for us to protect the air,
04:02making it healthy for people
04:05to breathe and and.
04:06One of the ways that we one of the most
04:09prominent effects of climate change on
04:11air pollution is now wildfire smoke.
04:14So I just want to look at some
04:16of the most recent work that I
04:17was a part of looking at PM 2.5,
04:20you know,
04:21very fine particle concentrations
04:22across the United States,
04:24the Eastern US and then the Western
04:26US and we see across the the last
04:29couple decades for the United States,
04:31PM 2.5 concentrations have
04:33been declining substantially.
04:34That's a huge public health win and.
04:37Is the result of many years of
04:40effective regulations under the
04:42Clean Air Act in the United States.
04:44So PM 2.5 concentrations have been
04:46declining substantially and even more
04:48substantially in the eastern US,
04:50where we have very strong anthropogenic
04:52emissions that have been controlled
04:53from our power plants and our vehicles
04:55over the last couple of decades.
04:57And we've seen this very dramatic decrease.
04:59Again, 2.5 crowded Eastern
05:01US in the western US,
05:03we have a different story here with a lot
05:05of interannual variability in those PM 2.
05:0725 concentrations in the last 5-10 years,
05:10and that's driven by wildfire smoke.
05:13If you draw a line through this
05:15very large interannual variability,
05:16you see that PM 2.5 concentrations are
05:18actually increasing in the Western US,
05:20despite the very effective
05:21regulations that we have on power
05:24plants and industry and vehicles.
05:25And that different disparate
05:27picture between the western US and
05:30the eastern US is driving what we
05:32see here for the US on average,
05:34that we actually see that the PM 2.5
05:37concentrations are beginning to flatten out.
05:39They're not declining to the same
05:41degree as they have been for
05:42the past couple of decades.
05:43We're actually seeing that they're starting
05:45to stagnate in the coming decades.
05:47We might actually start to see that
05:49they're starting to rise again.
05:50And this makes it more difficult for
05:52us to attain our national ambient
05:54air quality standards for PM 2.5
05:56because of this climate induced
05:59change and wildfire smoke keeping
06:02PM 2.5 concentrations high.
06:03I had the honor of working with the
06:06US Environmental Protection Agency on
06:08their climate change impacts and risk
06:11analysis project, their Sierra project.
06:13I used to actually work at the
06:15EPA from 2010 to 2014,
06:17and when I was there,
06:18we were starting this project to quantify
06:20the different damages of climate
06:22change on life in the United States,
06:24and that includes air pollution,
06:26but it also includes a lot of other
06:27things like labor and extreme temperature
06:29mortality and coastal property
06:31and roads and back at that time,
06:33the only.
06:34Estimate of how climate change impacted
06:37air pollution and therefore damages
06:39through human health in the US was via ozone.
06:43So temperature worsens ozone and that
06:47contributes to premature mortality.
06:50And you can see that that back in about 2014,
06:542015,
06:54air quality was the 4th largest
06:57damage of climate change in the
06:59United States once valued.
07:01But we really recognized, you know,
07:03we also think that climate change
07:05is influencing.
07:05Yeah,
07:062.5 and PM 2.5 has a very strong
07:08relationship with premature mortality.
07:10So if we were able to quantify the
07:12impacts of climate change on PM 2.5,
07:14in addition to ozone,
07:15we likely would get an A large,
07:18potentially a larger number.
07:19We would likely get a different number.
07:21Back in 2015,
07:23climate models were still very uncertain
07:25about where the precipitation happens,
07:28what's going to happen to PM
07:292.5 in different locations.
07:31And that still remains a a big uncertainty.
07:33But we do know that climate
07:35change is driving.
07:36Quote UN quote natural sources of PM 2.5,
07:39which are no longer,
07:40I think can no longer be considered
07:43fully natural anymore because
07:45climate change is impacting them.
07:47So dust exposure in the southwest US,
07:50Wildfire PM 2.5, which we just talked about.
07:53So I partnered with the EPA and a
07:55number of other scientists and we
07:57quantified the potential damages of
07:59climate change on dust exposure and
08:02therefore premature mortality in the US
08:04and same with wildfire smoke exposure.
08:07And we valued that.
08:08And we came up with about $47 billion a
08:11year from climate induced contributions
08:14to dust exposure and its effects on
08:17premature mortality and about $25
08:19billion a year for wildfire smoke.
08:21And if you add those together
08:23with the ozone impact that we had
08:25previously quantified,
08:26we see that air pollution is one
08:28of the largest damages of climate
08:31change in the United States.
08:33And this is an estimate that I
08:35think is likely to grow, I think.
08:37We underestimated this impact due to the
08:39methods that were available at the time,
08:41so I think this number is likely
08:43to get larger.
08:44Another reason it's underestimating
08:46the damages of climate change
08:48on air pollution is because.
08:51We can't just add together the impacts
08:53of heat on mortality and the impacts
08:56of air pollution on mortality.
08:58These actually have synergistic effects.
09:00So the total impact of increased
09:02heat and increased air pollution
09:04is more than the sum of its parts.
09:07In the previous slide I just showed you
09:09we were only capturing the impact of
09:10each of these risk factors individually,
09:12not considering the others.
09:14But because we know that there
09:16are these synergistic effects,
09:17we're likely missing some of these
09:19damages of both heat exposure.
09:22And air pollution.
09:23And as more research comes out looking
09:25at the pollen impacts as well,
09:26I think that could be potentially
09:28a factor to consider here too.
09:31So I talked about how there's
09:33different links between climate
09:35change and air pollution.
09:37We talked about this one,
09:38how climate change can impact air pollution.
09:40Air pollution can also impact climate change.
09:42We have short lived climate pollutants,
09:44for example black carbon and
09:46methane that contributes to poor
09:48air quality and warm the climate.
09:50This arrow here is.
09:52Sorry should go from climate change
09:54to public health.
09:55Not that the other association between
09:57climate change and air pollution
09:59that I want to talk about is how
10:01they share the same emission sources.
10:03Anytime we burn anything,
10:05primarily fossil fuels but also biofuels,
10:07we're releasing both airplanes
10:09and greenhouse gases.
10:10So if we want to address climate
10:12change and air pollution,
10:13we should be reducing the amount of
10:16fuel that is burned and therefore
10:19addressing those emission sources.
10:22What we've done so far in the
10:24United States by to.
10:25Bring down our PM 2.5 levels.
10:27We've tried to break this arrow
10:29between emission sources to air pollution.
10:31So we put catalytic
10:32converters on our vehicles.
10:33We put diesel particulate
10:34filters on our trucks,
10:35scrubbers on our power plants,
10:36and these have been very effective
10:38at reducing the amount of pollution
10:40from these emission sources.
10:42But they've done nothing to this era here.
10:44We're still continuing to make
10:46greenhouse gases largely unabated,
10:47and that climate change is contributing
10:49to the air pollution problem.
10:51So if we want to again mitigate both
10:54air pollution and climate change.
10:56We need to be burning less stuff,
10:57primarily fossil fuels,
10:59but also biofuels.
11:00I have focused a lot of my work,
11:02especially the most recent years,
11:03on the urban context,
11:04and the reason for that is because
11:06a lot of cities around the world
11:08are experiencing poor air quality.
11:09This is just a map of nitrogen
11:11dioxide concentrations in the US,
11:13but a lot of cities around the world are
11:16experiencing much greater levels of of
11:18pollution than we do in cities in the US,
11:21especially in cities in Africa and Asia,
11:24which are rapidly growing.
11:26These are experiencing
11:27rising air pollution levels.
11:30They're also, cities are also
11:31experiencing CO2 emissions growth.
11:33Right now,
11:33cities are responsible for about 3/4
11:35of global greenhouse gas emissions,
11:37and that's projected to rise as
11:40the world continues to urbanize.
11:42We also have very strong
11:44health inequality effects.
11:45So this is a map of Washington,
11:47DC, where I live.
11:48And the green colors here show the
11:50pediatric asthma emergency department
11:52visit rate for 10,000 people.
11:54And the red dots show life expectancy.
11:57We have about a 20 year life expectancy
11:59differential between neighborhoods
12:00in the southeast quadrant of the city
12:03right here versus neighborhoods in
12:05the northwest quadrant of the city.
12:0720 year life expectancy differential
12:09between people that live about
12:112 miles away from each other.
12:12We also have very dramatic differences in
12:16pediatric asthma Ed visit rate as well.
12:19So this is just, you know,
12:21DC is not unique.
12:22We have problems for sure,
12:23but we're not unique.
12:24Most of the cities across the country
12:26are experiencing problems like this and
12:28then we have growth growing populations.
12:30So right now about half the world's
12:32population lives in urban areas.
12:34That's expected to grow to about 2/3 by 2050.
12:37And nearly all of that increase is
12:40anticipated to happen in African
12:42and Asian cities, where, again,
12:44pollution levels are also continuing to rise.
12:47So there's a lot of problems happening
12:50simultaneously in the urban context,
12:52and if we were to address the
12:54way that our cities burn fuel,
12:57we likely would be able to get
12:59at multiple of these problems.
13:02What we what we can't see,
13:04we can't fix.
13:05We have to be able to see the
13:07pollution in order to fix it.
13:09Right now this is where the monitoring
13:11happens for air pollution around
13:12the world you can see most of the
13:15monitors are in the US and Europe,
13:17and increasingly in China and in India.
13:20But much of the world is left uncovered.
13:23And even in places that look like
13:25they're densely covered by monitors,
13:27like take Washington DC,
13:29we only have 5 monitors,
13:31looks like 4, but two.
13:33We only have 5 monitors for the
13:35entire city of Washington DC,
13:37so how are we supposed to
13:39capture the inequality
13:40and pollution levels if we if these are
13:44this is our only source of information?
13:47Luckily, we have a new source of information
13:50which is Earth observing satellites.
13:52So NASA, the European Space Agency
13:54and other space agencies around the
13:56world have been launching satellites
13:58and they are constantly taking
14:00pictures about miseric composition.
14:02And we can tease out that information
14:05and understand what are people exposed
14:07to in places that have no monitors.
14:09This is a map of what nitrogen dioxide
14:12look looks like from the Tropo ME
14:14sensor on the Sentinel 5P satellite
14:15from the European Space Agency.
14:17Um, this map was created by Dan
14:19Goldberg and you can see where N 2 is
14:22the highest and the fact that we have
14:24the full geospatial coverage here.
14:25So we can get beyond the monitors,
14:27we can get beyond the monitors
14:29and see what people are exposed
14:31to all around the world.
14:32So what does satellite data look like?
14:34Well, this is a daily snapshot of
14:36satellite and O2 Tropo mean No2 nitrogen
14:39dioxide that Dan Goldberg created.
14:41It's, this is now available on our website.
14:45You can download for every day.
14:46It's automatically putting up this image
14:48of No2 concentrations over the US and
14:51over different regions of the US and you
14:53can see there's a lot of white areas,
14:56right?
14:56These are where clouds are.
14:57So the satellites can't see through clouds.
14:59We're still limited in that way
15:01and there's also a lot of noise.
15:03This is just one snapshot per day.
15:05The TROPONE sensor is polar orbiting.
15:08That means it goes around the
15:10earth and it takes an image of the
15:13atmospheric composition at about 1:30
15:14PM everywhere on Earth local time.
15:16So just the one snapshot per day,
15:19and this is what it produces.
15:20Pretty noisy.
15:21But when we start to average
15:23over longer time periods,
15:24we have a lot more data and
15:26it starts to look more smooth.
15:28So this is a season of data of N2
15:32concentrations over the US and then
15:35the comparison for 2021 to 2019.
15:37And again you can get this on our website.
15:44So what we can do with the full
15:46geographical coverage of satellite
15:48data and increasingly high spatial
15:49resolution as well is that we can
15:51start to tease out what is happening
15:53in all urban areas globally.
15:56And there's about 13,000
15:57urban areas globally.
15:59So we can use that continuous spatial
16:02map that we get from satellite data
16:05and integrate and aggregate that
16:07up to the urban areas from Veronica
16:10Sutherlands and Ross Mohegan and.
16:12Danny Balashov,
16:13who have all worked with me,
16:15have done this for PM 2.5,
16:17for N2 and for ozone.
16:19So we now have available on a Nice
16:22website as well interactive website
16:24the the levels of these three
16:26pollutants as well as their trends
16:28overtime and their contributions to
16:29the burden of disease in those cities
16:32for all 13,000 cities globally.
16:33We've given the data to the health
16:35Effects Institute who runs the
16:37state of Global Air project and
16:38they have published this report,
16:40air quality and health in cities
16:42for the first time.
16:43Making the data more available for
16:45cities around the world to use.
16:47And, you know,
16:47I think it's important to note
16:49that in most of these,
16:50probably the vast majority
16:51of these 13,000 cities,
16:53there is no air quality monitoring.
16:55So this is the first time that
16:57there's really any estimate of the
16:59pollution levels in those cities.
17:01They're likely to be very uncertain,
17:03probably wrong in a lot of different ways,
17:05but at least it's a first,
17:06you know, first guess at what,
17:09an educated guess at what pollution levels
17:11are driven by the observations from these.
17:14The lights.
17:16The other thing we can do
17:17with the satellite data,
17:18with the continuous coverage,
17:19the continuous geospatial
17:20coverage from the satellite data,
17:22is that we can get at what is
17:24happening within individual cities.
17:26And again,
17:27we know that cities are experiencing
17:29health inequality issues.
17:30There's a long history of science
17:32telling us that air pollution
17:34levels are inequitably distributed
17:36within cities as well.
17:38But again,
17:38we can't get that just from
17:40the four or five monitors that
17:41we have in individual city.
17:42So we need to use the,
17:44you know,
17:45we need to use approaches for
17:47estimating pollution levels between
17:48those monitors to understand
17:50inequality and air pollution levels.
17:52So this is a study led by Maria Castillo,
17:55who's now an urban planning student at MIT.
17:58And we partnered with the
18:00DC local government,
18:01the DC Department of Energy and Environment
18:03and the Office of HealthEquity,
18:05who had they had.
18:07Settlement funds from the
18:09Volkswagen Diesel gate scandal.
18:11Anyone remember in 2015 there
18:14was a big revolution that.
18:17Volkswagen vehicles were equipped
18:18with these defeat devices,
18:20pieces of software that would turn
18:22the emission control equipment on
18:24when the vehicle was undergoing
18:26regulatory testing of emissions and
18:28then off when they were being driven
18:30around in real world driving conditions.
18:32And that was leading to substantially
18:34higher orders of magnitude higher
18:36NOx emissions in real world
18:38driving conditions than during
18:40certification testing.
18:41So there's a big lawsuit,
18:42people went to jail and now cities
18:45have access to settlement funds.
18:47They can use to direct resources
18:48to improve air quality.
18:49So the DC government had settlement
18:51funds and they came to us and they said,
18:53can you help us understand how air
18:55pollution is contributing to the health
18:57inequality problem in the city so
18:58that we might be able to direct these
19:00resources to places that are overburdened?
19:03So we estimated PM 2.5 attributable
19:06mortality using one of those continuous
19:08data sets of PM 2.5 in this case from
19:10the Washu group led by Randall Martin.
19:13And we estimated PM 2.5 mortality rates and.
19:17We saw that the highest PM 2.5
19:19mortality rates occurred in the
19:20eastern half of the city,
19:21and lower PM 2.5 mortality rates
19:23in the western half of the city.
19:25And this lined up almost exactly
19:26with the map of racial segregation,
19:28segregation in the city,
19:29so the eastern half of the cities
19:31primarily black and the western
19:33half of the cities primarily white.
19:35I know what that noise is.
19:39This research was received some
19:41interest from NASA and they created
19:43this really nice looking map and
19:46they made it the image of the day
19:49on the NASA Earth Observatory,
19:51which was really cool.
19:52And because so many people follow
19:54the NASA Earth Observatory, if you
19:56don't you should on Instagram or you know,
19:58whatever your social media choices.
20:00They posted it there and it got picked up
20:03by another influential Instagram accounts.
20:06Washingtonian probs.
20:07Which has hundreds of thousands of followers.
20:10So this was a way that our,
20:12you know study which was published in an
20:16esoteric journal Geo Health was picked
20:18up and brought to people who wouldn't
20:21normally read papers of Geo Health.
20:23And I know they tell you,
20:24you never read the social media
20:25comments about you know your work.
20:27But you know in a lapse of judgment
20:29one day I decided to read those
20:31comments and anyone want to take a
20:32guess at the most frequent comment
20:34of the Washingtonian probs account
20:36got when they when they posted?
20:39Yes. Thank you for your work.
20:43That would be nice.
20:46The most frequent comment was done.
20:50So you know, I think people know this,
20:52people know that air pollution
20:53is inequitably distributed.
20:54But again, if you don't
20:56show it with data and maps,
20:57then it's difficult to address.
21:00In this case, again, we work directly
21:01with the DC local government.
21:03So it was a way that they were able
21:05to help us design the study to answer
21:07the question that they had and then
21:09they can use the results to, you know,
21:11to determine how they're using those,
21:13those settlement funds.
21:14Fun Facts on Friday I just
21:17recorded a video at NASA studio,
21:19NASA Goddard Space Flight Center.
21:21They are going to now have it now and
21:24then that the lobby of NASA headquarters,
21:26a giant screen with me talking
21:28about this study.
21:29And they did not tell me my face was going
21:32to be up there at the size of my course.
21:34So I'm not excited about that.
21:35But I'm excited that they are,
21:38that they're highlighting this important
21:39work because I really think it does
21:41show the value of satellite data and
21:43what it can tell us in terms of.
21:45Real world's problems that
21:47we're experiencing in cities.
21:49So that was PM 2.5 and CO2 is a pollutant
21:52that a lot of us don't think that much about.
21:55We often think about PM 2.5.
21:57That's the largest contributor to the
21:59burden of disease from air pollution,
22:01followed by ozone.
22:03And O2 is a precursor to both
22:06PM 2.5 and ozone.
22:07So if we want to address those pollutants,
22:09we have to know where the
22:11No2 is and and control it.
22:13It's also a high resolution tracer for urban
22:16traffic in particular it's associated itself.
22:20With asthma development,
22:21that's just not that's not
22:22just asthma exacerbation,
22:24but new development of asthma among children.
22:27And very conveniently it is highly correlated
22:30satellite and O2 is highly correlated
22:33with ground level O2 from monitors.
22:35So this,
22:36for example,
22:37is a scatter plot created by my
22:39colleague Dan Goldberg and Gage Kerr
22:41who showed that trouble me No2 columns.
22:44That's the amount of N2 in the
22:45column of air between the satellite
22:47and the surface of the Earth.
22:49Is highly correlated to N2 at the
22:51ground level monitor monitored by our
22:53AQS monitor monitors our air quality
22:56system monitors and so this makes it
22:58a very convenient pollutant to study.
23:00Whereas for PM 2.5,
23:02the satellites are monitoring
23:03at different quantity,
23:04aerosol optical depth and then we need
23:05to do a bunch of science to convert
23:07that to ground level PM 2.5 here,
23:09even if we just took the Tropo
23:11Vienna 2 columns,
23:12we have a pretty good sense for
23:14where where the ground level 2 is.
23:17So around the time that the pandemic hit,
23:20we had just hired Dr.
23:22Gage Kerr as a postdoc and we
23:26were wondering whether or not we
23:28could use these troponin data.
23:31So troponin data started that
23:33the records started in 2018,
23:35so it was very new.
23:37And you know, when the
23:39pandemic hit in spring 2020,
23:41Dan Goldberg had been going through
23:43these energy readings and looking at
23:44different urban areas and seeing how the
23:46trends differed in different cities.
23:48And we wondered, you know,
23:49could we use this data set to explore
23:52how No2 changed during the pandemic?
23:55There are a lot of people working on air
23:57quality changes during the pandemic.
23:58Of course,
23:59there's a whole community of people.
24:01We actually got on the phone
24:03once a month talking about air
24:04quality changes during COVID.
24:05But we wanted to take this a step
24:07further and really leverage the
24:09value of the satellite data with
24:11that complete geospatial coverage.
24:12And one of the, you know,
24:14values of that satellite data is the
24:16fact that we can look within cities,
24:17different subpopulations.
24:18Living within cities.
24:20So we had no idea whether we could use
24:22this data set to explore disparities.
24:24And I know two concentrations,
24:26but we we thought,
24:27let's just give it a shot, see what happens.
24:30Probably we won't see anything.
24:32Well,
24:32it turned out we did see
24:33something and it was really,
24:35really striking to me.
24:36So prior to the pandemic in 2019,
24:39the least white census tracts
24:41across the United States had no
24:43two concentrations that were
24:45about double the concentrations
24:46and the most white census tract.
24:49Again, that's prior to the pandemic.
24:52During the lockdowns in 2020,
24:54both the orange dots and the
24:56blue dots shifted left,
24:57and that indicates that No2 dropped
24:59for both the least white census tracts
25:01and the most white census tracts.
25:03Just good thing,
25:04you know,
25:04we had about 50% fewer passenger
25:06vehicles on the road.
25:08It's a good thing that we can
25:10observe and O2 just by itself.
25:11That was useful to know that we
25:14could use this tromi data set
25:16to observe that drop in and O2
25:18during this natural experiment.
25:21But.
25:21One thing that we found that
25:23was really concerning was that
25:25during the 2020 lockdowns,
25:27then O2 concentrations in the
25:28least white Census tracts were
25:30still about 50% higher than the
25:32concentrations and the most white
25:34census tracts prior to the pandemic.
25:37And this indicates that the
25:39disparities in antipollution were
25:40so large prior to the pandemic that
25:43even about a 50% drop in passenger
25:45vehicle traffic was not enough
25:47to eliminate those disparities.
25:49And that held,
25:50that pattern held for nearly all
25:52major cities across the US and also
25:55held for educational attainment
25:56and for income.
25:57But really,
25:58that only tells us about exposure.
26:01We're really just concentrations,
26:02not even exposure.
26:03It doesn't tell us about the
26:05susceptibility of the population.
26:07That is breathing those concentrations.
26:09So Gage took this a step further
26:12and looked at both PNC .5 and N2,
26:15and not just the concentrations,
26:16but the health outcomes that are
26:19associated with those concentrations.
26:20So he's comparing PM 2.5 attributable
26:23mortality per 100,000 people and
26:26that NATO attributable pediatric
26:28asthma incidence rate as well.
26:30And let's just look at
26:32PM 2.5 first. We see that PM 2.5
26:34concentrations are dropping over time for
26:36both the most white and the least white
26:38census tracks across the United States.
26:40This is very similar to the graph I
26:42showed you at the beginning, showing
26:43that PM concentrations are going down,
26:45starting to stagnate a little bit
26:47due to those Western US fires.
26:49But the disparities persist,
26:50as many others have found
26:52in the literature as well,
26:54that PM 2.5 concentrations and
26:56associated disease burdens are higher
26:59for the least weight census tracts,
27:01and then they are for the
27:03most white census tracts.
27:04And the relative disparity,
27:06the relative ratio between blue
27:07dots and the orange dots here,
27:09is actually rising over time.
27:11So the relative disparity is getting
27:13worse even though the levels are coming
27:15down for both populations subgroups.
27:17For No2,
27:18on the right hand side here we see
27:20that no two and its associated impact
27:23on asthma incidents among children
27:25is also decreasing over time,
27:27again to very successful
27:28regulations under Clean Air Act.
27:30But the disparity is much,
27:32much larger than it is for PM 2.5.
27:34In fact,
27:35the relative disparity is about 7 1/2,
27:37meaning that the most the least
27:39white census tracts have values
27:41that are about 7 1/2 times larger
27:43than the most white census tracts,
27:46whereas that value is only 1.3.
27:48For PM 2.5 not to diminish 1.3,
27:51that's still 30% larger PM
27:53mortality impacts for the least
27:55white Census tracts compared to
27:56the most white census tracts,
27:58but no two exhibits far greater
28:01disparity than PM 2.5 does.
28:04Now,
28:04all all of this that I've just
28:07showed you is based on one expose 1
28:10concentration data set per analysis.
28:12And there's a lot of people working on a
28:14lot of different concentration data sets,
28:15both PMC .5 and No2,
28:17and we don't know which one is the best.
28:19People are using different methods,
28:21they're using different approaches,
28:22different data inputs.
28:23And so we wanted to know how much
28:25is of the result that we that I just
28:28showed is actually driven by features
28:29of the one data set that we used as
28:32opposed to other datasets where we
28:34find this across multiple datasets.
28:36So gauge is now comparing No2 disparities
28:41for four population subgroups using
28:43the EPA air quality system regulatory
28:46monitors on the left hand side here.
28:48For the 10 most populous cities in the US,
28:51the numbers on the right show the
28:53number of monitors in those cities.
28:55And we see a pattern that's
28:56kind of all over the place,
28:58in fact no pattern.
28:59So this these air quality system
29:01monitors are not able basically
29:04to capture the disparities that
29:06we think exist and that a lot of
29:08other studies have found to exist.
29:10When we use a land use regression
29:12model for nitrogen dioxide,
29:14which uses statistical approaches
29:16to approximate No2 concentrations
29:18at pretty high resolution across
29:21the entire continental US,
29:23we see a stronger pattern pop out here.
29:28So for every major city we
29:30have the the lowest
29:31No2 concentrations in the non
29:33Hispanic white population and higher
29:36concentrations among the Hispanic,
29:38Asian and black populations.
29:40The ordering. Differs by by city,
29:42but it's very similar to what we find
29:45using just the troponin No2 columns.
29:48So this is the land use regression model.
29:50Approximates surface level
29:52and O2 concentrations.
29:54The Tropo me data is No2 columns that
29:58are more directly from the satellite
30:01and we see a very similar pattern here.
30:03We see that for both the non Hispanic white
30:06population has the lowest No2 concentrations.
30:08For some cities we see that.
30:10Ordering of the population
30:12subgroups is very similar,
30:13so in Philadelphia the
30:15ordering is very similar.
30:16In other cities we see differences,
30:18but nevertheless there's much closer
30:21consistency between the land use
30:22regression data set and the troponin data
30:24set compared with the monitor data set.
30:26It's really not surprising.
30:28I mean the monitor data set was not
30:30intended to be used for this purpose
30:32and we're really was intended to
30:34monitor regional average pollution
30:36and not neighborhood scale pollution
30:38that differs within cities.
30:40So that was for No2.
30:42That would really LED us to wonder,
30:43OK,
30:44well the data set that you use for N2 has
30:47a big impact on the estimated disparities.
30:50What about for PM 2.5,
30:52which is a prudent that doesn't vary
30:55as much spatially as an O2 does,
30:56and the two has a very
30:58short atmospheric lifetime,
30:58it stays pretty close to the mission source.
31:01PM 2.5 has a lot more emission sources.
31:05A lot of it is secondarily formed
31:07in the atmosphere.
31:08It lives longer in the atmosphere,
31:09so it spreads out and sort
31:11of smooth spatially.
31:12So we but there's a lot
31:14of attention on PM 2.5,
31:15right,
31:16the Justice 40 initiative of
31:17this current administration.
31:19This is a new initiative that
31:20is aimed at 40% of the benefits.
31:23Of federal investments going
31:25to disadvantaged communities,
31:26the data set they're using to do that,
31:28to identify communities as
31:30disadvantaged as a 12 kilometer
31:33spatial resolution for PM 2.5.
31:35That's this CMAC model monitor
31:38fusion data set.
31:39That's the one that's used in EJ screen.
31:41It's used in a lot of EPA regulatory
31:43support documents and now it's used
31:45in the climate and economic justice
31:47screening tool suggest that is
31:48used for the Justice 40 initiative.
31:49So we wondered,
31:50if we used a different high resolution
31:53data set that's now available
31:55from the scientific community,
31:57would that lead to differences in
31:59which communities are flagged as
32:00disadvantaged in the Justice 40 initiative?
32:03So we're now comparing.
32:06The CMAC Model monitor fusion data
32:08set at 12 kilometer spatial resolution
32:10with the the data set I talked
32:13about earlier from the Washu team,
32:15the bins unclear at all data set that
32:19fuses satellites with a geophysical model.
32:22And then there's a new data set led
32:24by Haresh mini that's available at 50
32:27meter resolution within cities and
32:291 kilometer resolution outside of cities.
32:32And you can see just looking
32:33at the spatial resolution,
32:34the spatial distribution
32:35in Los Angeles at the top,
32:37Chicago in the middle and Phoenix
32:39on the bottom. These datasets,
32:40they look somewhat similar in terms
32:42of their being a BLOB over the city.
32:45But once you start to look a little bit
32:47closer, they really differ in terms of
32:50which neighborhoods are popping out
32:51at having the highest concentrations.
32:53So this is still a work in progress,
32:56but this is led by Doctor Tess Carter,
32:58who just recently finished her PhD at MIT.
33:02And I just want to point your
33:03attention to the top few rows here,
33:05which show all census tracts,
33:07urban tracts and rural tracks across the US.
33:11On the left hand side here is that
33:13comparing the most non Hispanic
33:16white populations to the least
33:18non Hispanic white populations and
33:20then on the right hand side is most
33:23Hispanic versus least Hispanic.
33:25And we see for each of these
33:27three datasets the CMAC Fusion,
33:29the vans angular .01 is that
33:32spatial resolution and then a mini,
33:35all three of these data sets are
33:37very consistent in what they show
33:40for at those geographies.
33:41And it's very similar to for each region.
33:44The absolute magnitude of the values
33:46of the PM 2.5 concentrations differ,
33:50but the disparities,
33:51the patterns and disparity are similar.
33:54This is on a regional average basis.
33:56So what this tells us,
33:58I think I'm still processing this,
34:00is that on a regional average basis,
34:03this EMAC data set not so bad for
34:05estimating those disparities.
34:07And you can imagine why that might be.
34:09For PM 2.5,
34:10we have two things happening simultaneously.
34:12We have.
34:14We have regional PM 2.5 concentrations.
34:18PM is sort of higher in California
34:20and the southwest US than it is in
34:22other parts of the US and we have
34:24that happening at the same time as
34:26regional sorting of populations.
34:28There's a very large Hispanic population,
34:30for example,
34:31in California and the southwest
34:33breathing those high PPM concentrations
34:35in that same region.
34:36So that's sort of regional nature of
34:39both population sorting as well as pollution.
34:41That's one effect.
34:42The second effect is what's
34:44happening in urban areas.
34:46PM 2.5 has some intra
34:49urban spatial variability,
34:51or so the literature tells us.
34:53And that,
34:54you know,
34:55driven by anthropogenic sources
34:57within cities could be contributing
34:59to differences in neighborhood scale
35:02pollution levels within cities.
35:05So this maybe is actually not that
35:07surprising that this lines up
35:09pretty well regardless of the data
35:11set because the spatial resolution
35:12of data set doesn't matter that
35:14much for that regional effect,
35:16that first effect I was describing.
35:18But for the intra urban effects,
35:21the 12 core meter data set is not
35:23going to be able to capture those
35:26that intra urban variability.
35:28So what do we see within cities?
35:30We see something different so.
35:32In the top 10 most populated
35:35cities across the US.
35:37One thing is consistent and that
35:39the non Hispanic white population
35:41has the lowest PM 2.5 concentration
35:43in all three of these datasets.
35:46So we see a lot of the
35:48dark blue color left of 1.
35:49One is the average the the the mean PM 2.5
35:55concentration for the entire population.
35:58The non Hispanic white population has
36:00lower than average concentrations for
36:03every one of these major cities in
36:05all of the datasets but the ordering.
36:07Of the other population subgroups
36:09really varies quite a bit depending on
36:12the data set and that again is driven
36:14by the spatial distribution of the
36:17concentrations in the input datasets.
36:18I want to point out a couple other things.
36:21The CMAC Fusion data set,
36:22that 12 kilometer data set that's
36:25being used by the Justice 40 initiative
36:27team right now that has the least
36:30variability between population subgroups.
36:32And again not surprising,
36:34this is 12 kilometer datasets not
36:36capturing that heterogeneity.
36:37But we definitely see that play out
36:39or we have the the narrowest range
36:42here for I just picked up Philadelphia
36:46for Chicago and for New York.
36:48But then there's really interesting
36:50things that happen.
36:50So New York, Chicago,
36:52and Phoenix all show pretty
36:53different effects here,
36:54where in New York we have the same
36:59ranking of population subgroups in
37:01terms of their PM 2.5 concentration for
37:03both of the two high resolution datasets,
37:06but not in the CMAC Fusion data set.
37:09In Chicago,
37:10we hardly get much variation at
37:12all in any of the three datasets.
37:14And then in Phoenix,
37:15all three of the data data sets,
37:17including CMAC, the CMAC Fusion data set,
37:20do have similar disparities
37:23across these population subgroups.
37:25So we're still trying to dig into
37:26each of these cities and understand
37:28why they're showing these different
37:30different patterns.
37:31I'm really excited about the future because.
37:34The satellite data we have available
37:35right now,
37:36these this polar orbiting satellite data,
37:37that's a major improvement over what we had,
37:40what we had before,
37:41which is no satellite data,
37:43but we are now launching geostationary
37:46satellites which are going to hover
37:48over the US as the earth spins.
37:49It'll always be taking measurements
37:51over the US so tempo is launching
37:54in April and that will be a
37:56geostationary satellite that's
37:58measuring atmospheric composition.
38:00Really excited about that.
38:02And then Noah is working.
38:04On Geo EXO,
38:06which is an operational satellite that is
38:09intended to launch in the early twenty 30s.
38:11And there's so many stages of
38:14explaining why this is important.
38:16So they asked us to help them explain
38:18why this is important for air quality
38:20management and for public health.
38:21So we've been really happy to be
38:24working with them and showing them
38:27the value of satellite data for for
38:30managing air quality and for public health.
38:33And this is work led by Doctor Kate Odell,
38:36who is quantifying the number of
38:37four air quality alert days across
38:39the US that you would get if you
38:42had a geostationary.
38:43Satellite which is taking measurements
38:44across all hours of the daylight versus
38:47if you only had that one snapshot
38:49from a polar orbiting satellite
38:51at 1:30 PM and she's showing that
38:53the number of air quality alert days
38:55is much much higher for the Geo case,
38:58that's the Geo stationary
38:59case versus the Leo case.
39:01Leo stands for low Earth orbit,
39:02which is the polar orbiting satellites.
39:05And we wanted to look at the disparities
39:08in the populations that are receiving
39:11receiving these air quality alerts
39:13if we had the geostationary data
39:16versus the polar orbiting data.
39:18And she finds that actually you know
39:21while the magnitude differs overall,
39:23the pattern of who, what you know the,
39:27the population sub categories
39:29experiencing these poor air quality
39:31alert days is actually pretty
39:33similar depending regardless of the.
39:36Geostationary or the polar
39:38orbiting satellite?
39:39Really quickly,
39:40I want to go back to the framing
39:42of climate change, because again,
39:44air pollution and climate change
39:45come from the same sources.
39:48Anytime we burn fossil fuels
39:49and we burn biofuels,
39:51or releasing both air
39:52pollutants and greenhouse gases,
39:54we want to solve a lot of the
39:55problems that I just talked about.
39:57We could be burning less fuel and also
39:59be gaining by reducing CO2 emissions.
40:02So I have been able to partner
40:03for the last few years with C-40
40:06cities as well as a variety of
40:08other partners who had been.
40:09Planning,
40:10the largest worldwide effort for cities to
40:13undertake urban climate action planning.
40:15And these are cities that have
40:18committed to very deep decarbonization
40:20and creating ambitious plans
40:22for reducing greenhouse gases.
40:24And we help them understand not
40:26just their greenhouse gas reduction,
40:28which they're already very good at,
40:30but now understand also the reduction
40:33of PM 2.5 that they would get from
40:36taking those ambitious actions
40:38to reduce greenhouse gases.
40:40This is the framework that we
40:41did this within,
40:42and we implemented this in six
40:44pilot cities around the world.
40:45And I just want to show two of the
40:47examples of these are actually
40:49graphs that are now in these cities
40:51climate action plans for the first
40:53time integrating air quality into
40:55their climate action planning.
40:57So Buenos Aires saw their PM 2.5
41:01concentrations go down from about
41:0312 micrograms per meter cubed
41:05in 2050 to around 8,
41:08which was under the World
41:09Health Organization.
41:10Headline at the time we did this analysis,
41:12but it's now over the W 1 because
41:14that would has been reduced.
41:16And then Johannesburg took a bit
41:18of a different approach here where
41:20they looked at each type of action
41:22they could implement and they they
41:24looked at the percent of total
41:27PPM concentration reduction from
41:28that action versus the percent
41:30of total CO2 emission reductions.
41:32And the one that achieved the
41:35greatest dual benefit was a mode
41:38shift from on road vehicles.
41:40We're now helping them understand
41:41CO2 emissions a little bit more.
41:43So right now,
41:45each city is developing its own
41:47urban inventory of CO2 emissions,
41:51and that has advantages,
41:54strengths and weaknesses.
41:56The scientific community is very
41:58hard at work developing gridded
42:00CO2 emission data sets as well
42:03based on satellite observations
42:05of light at night and
42:06other data sources.
42:07And so we're looking at whether or
42:09not the self reported inventories
42:11from the cities match what we think
42:13might be happening in the scientific
42:15community using these gridded datasets.
42:17And this is work led by Doctor Doyon
42:20on where we he's comparing the GPC
42:23inventory that's the self reported
42:25inventory versus a very widely used.
42:27Um, globally gridded emissions inventory
42:30called Edgar and he sees that the there,
42:33sorry, in this other one is,
42:34is ODC, as well as the different
42:37gridded CO2 emissions data set.
42:39They want it pretty well.
42:41This is actually better than I might
42:43have expected prior to this project,
42:44but he sees a lot more scatter outside
42:47of the US and Europe and a lot more
42:50consistency in US and European cities.
42:53So just to conclude that climate
42:55change is worsening air pollution,
42:58which is already a leading factor
43:01for global health around the world.
43:03We have now access to data that we
43:06that's completely unprecedented,
43:07these novel geospatial datasets they're
43:11increasingly capable of providing.
43:13Information about pollution
43:14levels everywhere in the world
43:16with full geospatial coverage,
43:18high temporal frequency and in some cases
43:21now building long temporal trends too.
43:24Some of these,
43:25some of these satellites have been
43:26flying for years and that's enabled
43:28us to do a lot of different things.
43:30I just talked today about air pollution
43:32levels globally and at 13,000 cities,
43:34as well as intra urban disparities.
43:36But people are using these satellite
43:38data sets and all kinds of unique and
43:40very useful ways like spotting wildfire,
43:42smoke and dust storms.
43:44Thanks.
43:45And you know,
43:46I really think that this
43:47improved information,
43:48if we integrate this into our
43:51environmental management techniques,
43:52including policy development,
43:54we can achieve multiple societal
43:59improvements simultaneously.
44:00I've been really,
44:01really fortunate to be in a position
44:04now where I can be training the next
44:06generation to be using data sets like this,
44:09and there's new ways of doing environmental
44:12health that are now possible.
44:14So bringing that in,
44:16bringing in systems approaches
44:18and an equity and justice lens in
44:22addition to engaging multidisciplinary
44:24teams and diverse partners,
44:26I talked about some of the partners
44:28I've worked with including C-40
44:30cities and the DC government.
44:31That's just sort of scratching the
44:34surface that if you work directly
44:37with these action oriented partners
44:39from the beginning of a project,
44:41you can actually design a project
44:43to achieve the needs that they have.
44:45To improve life for people.
44:47And, you know,
44:48leveraging novel geospatial datasets
44:49is not something that I was,
44:51you know,
44:52well,
44:52actually I was trained to use novel
44:54geospatial datasets that were novel
44:55at the time that I did my training,
44:57which is before satellites.
44:59But you know,
45:00a lot of people in the field
45:02didn't have that,
45:03don't yet have that training and
45:04something that we can bring into
45:06public health more frequently.
45:08There's a lot of communities of
45:10practice to plug into as well.
45:12We've developed the climate and
45:14Health Institute at GW.
45:15We just are now completing a NASA
45:17supported team called satellite data
45:19for environmental justice that brought
45:21together a lot of people that were
45:23using satellite data for this purpose
45:25and a plug to shameless plug to get
45:28involved in the AGU health community,
45:32which you know includes a lot of
45:34people who are using these big
45:36geospatial datasets to answer
45:38environmental health problems.
45:40Very excited that Doctor Chen is
45:42part of that community as well.
45:43So that's it for me.
45:45Just wanted to acknowledge a lot
45:47of support and again reiterate
45:49that without open data sets none
45:51of this would have been possible.
45:53So thank you to the data set developers.
45:55Thank you.
46:01I think it takes around
46:035 to 10 minutes for Q&amp;A,
46:05so if you do have a question please.
46:08Sure. Thank you so much for stopping.
46:12It's really interesting.
46:12I, I know that one of the major
46:15concerns amongst environmental justice
46:17communities with datasets such as EJ
46:20screen is that they're not specific
46:22enough that they don't get down
46:24to that really granular level of.
46:27Look, fenceline impacts umm.
46:29And I'm curious how,
46:32when working with large datasets
46:35from satellites such as troponin,
46:38which only takes about once
46:39a day measurement,
46:40you can also bring in those qualitative
46:43data points from environmental justice
46:46communities on the ground to our
46:49experiencing air pollution impacts.
46:51I love that question because it really
46:53shows the value not just in this kind
46:56of quantitative data work that I do,
46:58but in the lived experience as well.
47:00And we've we've run into this
47:02multiple projects and I just couldn't
47:04agree more with that because.
47:06As I showed the we still have disagreement
47:08between several of the high resolution
47:10datasets that we're looking at.
47:12I mean they are better I think than
47:14the course resolution data set.
47:16But if you're,
47:16let's say you're looking at a map of
47:19Houston and you've got our land use
47:21regression data set of N2 and then
47:23the tricomi data set of two and you
47:25live in an area which is high in
47:27one data set and not in the other,
47:29what then?
47:29And you know where that is the
47:31reality we are in right now,
47:33we're in this messy space of data sets.
47:36Not matching at that granular scale
47:38and I just think it shows the
47:41limitation of what we can do with a,
47:44you know,
47:45one-size-fits-all approach
47:45you consistent across the US.
47:47We need to bring in people's lived
47:50experience and understanding of the
47:53local sources affecting their community
47:55for this datasets to be improved.
47:58How we do that, I think let's like.
48:01Get creative, right?
48:02I mean, we could bring in story
48:04maps of people's life experiences,
48:06you know, there's a lot of ways it's,
48:08it's not even just about,
48:09you know, community monitoring,
48:11which can be quite helpful.
48:13And, you know,
48:14we're rapidly expanding that
48:15in the US right now,
48:16but, you know.
48:17You have people going out and
48:20writing about their experiences,
48:21taking videos of their experience.
48:23So I think that's sort of community
48:25contributed,
48:26qualitative approach has a lot of value.
48:32Yes, just make sure there's any
48:34students have a question for us.
48:36Is there a hand over there?
48:38Umm, so my question or this, first of all,
48:41thank you for the fabulous presentation.
48:42I greatly enjoyed it.
48:44My question, slash comment is
48:46about environmental disparity.
48:47So I you know, a lot of times we see
48:49more and more and more beautiful,
48:51beautiful, more and more detailed maps.
48:53However, if we could press a button today
48:56that made exposure equal across the world,
48:59first of all, we press it.
49:00Second of all,
49:01environmental disparities would
49:02still exist because people
49:04respond differently to health.
49:06So my comment to you my question.
49:08Is. What are your thoughts on this?
49:10Because I have had.
49:12Like when I talk with communities,
49:1599 to 100% of them talk about
49:17exposure without talking about the
49:19fact that and it is a fact that we
49:22know that people respond differently.
49:24And to what degree do you think
49:27environmental health disparities should be?
49:29Are there may be some environmental
49:32disparities not incorporated into the
49:34world's most perfect exposure map?
49:37The way they agreed to you that we
49:38have very focused on pollution levels,
49:40and the same pollution level
49:42can cause dramatic different,
49:43dramatically different impacts
49:45for different populations.
49:46I showed that map of Washington,
49:48DC and the high PM 2.5 mortality rate
49:50on the eastern half and the low PM 2.5
49:53mortality rate on the western half.
49:55That actually comes from a pretty consistent
49:59PM 2.5 concentration for the entire city,
50:02but vastly different mortality rates.
50:06The, you know,
50:07Southeast Quadrant has had no hospital.
50:09GW Building went out.
50:10I'm very happy that that that's happening.
50:12But no hospital,
50:13so no access to healthcare,
50:14no easy access to healthcare.
50:17This is the same,
50:17you know,
50:18in cities all around the country
50:20and around the world that there's,
50:22you know,
50:22social determinants of health are a major,
50:24major.
50:25Doctor Diamond exposure and I
50:27think in terms of addressing it,
50:30I mean we have like I said,
50:32the there's this time and economic
50:34justice screening tools being used
50:35now for the Justice 40 initiative.
50:37We have EJ screen to show where
50:40these disadvantaged communities
50:42are in a nationwide basis.
50:43Some are not accounting for those
50:47that increase susceptibility,
50:48increase mortality rates,
50:50higher mortality rates,
50:51higher health outcome rates.
50:52The Cbest tool right now is.
50:55Includes poverty and one
50:57additional indicator.
50:58So that could be PM 2.5 and EJ.
51:02Screen has an index.
51:03I think if we were to use more of like that
51:06index approach that brings in poverty,
51:08brings in health and some of these
51:11other social determinants of health in
51:13addition to the pollution exposure,
51:15we can start to identify not just
51:17who is experiencing bad pollution,
51:19but who is most impacted by that bad.
51:23Thank you.
51:25Any idea what's causing the differences
51:28in disparities between cities?
51:30I'm originally from Chicago.
51:31The expressways run through
51:32black and brown neighborhoods,
51:34which is true everywhere.
51:36But disparities there,
51:38both knocks and five were fairly
51:40modest compared to the other cities.
51:44That's it's such a great question.
51:46And we now have a big project with
51:48an environmental Defense fund to
51:49dig into Chicago specifically to
51:51understand that because Chicago
51:52does have a whole lot of trucking
51:54that is coming through the city.
51:56And as you say it is associated
52:00geographically with with with
52:02black and Hispanic populations.
52:03There is no some other major roads
52:05that are more in wealthier whiter
52:08neighborhoods like Lakeshore
52:09Drive going going north.
52:11So when you take like an urban average.
52:14It also very much depends on learning.
52:16It very much depends on how
52:17you define what the city is.
52:19Are you looking just at Chicago,
52:20the entire county, entire MSA?
52:23And actually we've seen that the
52:26disparities flipped depending
52:27on how you define their opinion.
52:30More details coming soon at Chicago,
52:32so that's an interesting one.
52:35Thank you so much for the fascinating part.
52:37I just have a question about the time trends.
52:40You showed that by racial,
52:43ethnic of their exposure and O 2:00 PM,
52:48but how, how does that change over time?
52:52Is there any like convergence
52:53across those groups?
52:56They have to share share the slides
52:58but the the project that I showed
53:00that had the PM on the left hand side
53:02and the No2 on the right hand side
53:03that showed PM mortality rates and
53:05then No2 attributable asthma rates.
53:07Those do show trends overtime
53:09and the concentrations for both
53:11PM and NS are going down for all
53:14population subgroups really great.
53:16But the relative disparities
53:18are increasing for both parents
53:20because of the like the the changes
53:22in that that overall magnitude.
53:24So the. That's this one.
53:26Thank you.
53:27So the overtime,
53:29the PM concentrations have come
53:31down approximately the same amount
53:33for all population subgroups
53:35and that leads to an increased.
53:38Ratio between the population subgroups and
53:39then for N2 this doesn't really look like it,
53:42but these orange dots are going down as well.
53:46Much greater energy reductions
53:47for the least white communities,
53:50but still we see rising ratios of
53:54disparity relative disparities.
53:57Thank
53:57you. The the reason I ask this is
54:00from the population migration and
54:03point of view is very mixed picture.
54:05The data shows that it's more
54:08segregation across cities unless so
54:11within cities in many parts of America.
54:14So that's interesting.
54:17We only looked at the the temporal
54:18trends and the pollution levels,
54:19not where where people are living,
54:21so that would be an interesting
54:23question to look into.
54:25Uh, we we do have a comment online,
54:27but I think it's more like
54:29suggestion we can look at and
54:31thank you all for coming because
54:32we have a class right office.
54:34So we have to end today.
54:35Thank you all and thanks.