Ellen and I'm a researcher at the George Washington University and today I'm
going to be talking about estimating the health impacts of air pollution using
satellite derived concentrations so satellite data allows us to estimate
ground level concentrations at increasingly finer resolutions, ah, these
high-resolution pollution estimates can then be used to estimate the health
impacts and what neighborhoods within cities might be at a higher risk of
disease and mortality that's associated with exposure to air pollution, so in
this study we wanted to estimate the health impacts of air pollution at the
local level and to do this we look at Oakland California within the Bay Area
as a case study, so until recently these type of health impact assessments have
been reported at the global, country or county level, but this type of reporting
can mask potential spatial heterogeneity at the local level. So in this study we
varied the pollution sources and rates of disease in order to see how our
health impacts vary when we use these varying concentration sources. We hope
that this type of information can inform actions that cities can take, to take
action to reduce disparities in health risks attributable to air pollution
Next,
so in order to do this we use what's called a health impact function
that's derived from epidemiology studies and these look at linking the health
outcomes of association to air pollution
so the attributable fraction of disease
is the portion of disease that can be thought of or can be described as
resulting from a particular risk factor or exposure in this case air pollution
so here I'm going to be talking mostly about mortality related to air pollution
but we also looked at cardiovascular mortality as an emergency room visits
and new cases of asthma
sorry guys it's a lot of pressure
okay so from these epidemiological studies
we're able to derive a risk estimate that quantitatively assesses the
relationship between a health outcome and what we would expect for each health
outcome increase in concentration
so in order to apply this spatially we
actually need a couple more concentration input or data inputs first
we need the population of our population of interest and the spatial distribution
of that population we also need the baseline disease rates or baseline
mortality of that population and the spatial distribution of that baseline
disease rate and then we also need to know the pollution concentrations and
the spatial distribution of our paths pollution concentrations so in this
study we use concentrations from three sources a fine particulate matter from a
satellite drive model, land..., nitrogen dioxide from two data sources first a
land-use regression model that incorporated satellite troposphere at
column estimates of nitrogen dioxides and nitrogen dioxide from a innovative
mobile monitoring data set where in Google Street View cars took
measurements of nitrogen dioxide
the result of this calculation is the number
of cases that we can expect within a population that could be attributed to
air pollution
so long-term exposure to fine particulate matter is associated
with increased risk of mortality and it contributes a large burden of death and
disability in the US and globally so for our fine particulate matter data set we
used a satellite derived model that was originally developed at Harvard
University this dataset provided daily estimates of fine particulate matter at
the one kilometer resolution in the United States so this was a satellite
derived model, it was a neural network hybrid model that incorporated aerosol
optical depth from the moderate resolution imaging spectroradiometer on
the Earth observing satellite it also incorporated a 3d chemical transport
model central, site monitoring data and numerous other meteorological and
land-use variables the result is that we can see the estimates show that within
the Bay Area the daily maximum concentrations for fine particulate
matter regularly exceeded the US Environmental Protection Agency's daily
threshold of 35 micrograms per meter cubed
so our second concentration of interest is nitrogen dioxide and nitrogen dioxide
is a traffic-related air pollutant exposures which has been associated with
increased asthma incidents and increased mortality so we wanted to compare how to
varying NO2 concentration datasets influenced our health impact estimates
so for our first model for NO2 we used a land-use regression model and land-use
regression basically just takes land-use characteristics and other model inputs
to estimate data where we don't have measured concentrations so this
particular model developed to Oregon State University incorporated satellite
troposphere at column estimates of nitrogen dioxides from the DAICHI
instrument and the Gomi 2 instrument the researchers found that the satellite
column concentrations and measures of road density were the largest
contributors to NO2 ground level concentrations
so in order to compare
our land-use regression estimates we wanted to use measure data of NO2 and
for this we used a innovative mobile monitoring technique wherein Google
Street View cars took repeated 30 meter Road segment measurements of NO2 in
neighborhoods throughout the Bay Area and for Oakland this is Oakland
California this resulted in three million, over three million data points
which was then aggregated to an annual average and then we aggregated to 100
meters here
the Google Street data sets concentrations found peak concentrations
near roadways and what we can also see is that when we compare the land-use
regression to the Google Streetview the land-use regression had a higher median
concentration of values but the Google Street View concentrations had a larger
range of values owing to those peak concentrations near roadways so we
hypothesized that this highly resolved spatial data near roadways in
combination with highly resolved baseline disease rates would allow us to
identify disparities in health burden attributable to air pollution within cities
so the other portion of our project was to look at how baseline
disease rates influenced the risks attributable to air pollution within
cities so County baseline disease rates are pretty easy to obtain but they can
mask potential spatial heterogeneity for neighborhoods so for example if you
apply a County based on disease rate to entire neighborhood you might or the
entire county you might overestimate the baseline disease in some areas and
underestimate it in others so for our study we obtained census block group or
neighborhood level based on disease rates and what we found is when we
compared county and neighborhood level based on disease rates in our health
impact function was the census block group yielded a higher median burden of
disease as seen by the box plots and it also yield the different spatial
patterns as compared to the county rates we can see in the maps, so from this we
concluded that without this type of highly resolved neighborhood baseline
disease rates we might not adequately identify disparities in the health
burden attributable to air pollution within cities
so the other portion of our study was of course to compare how the satellite
drive Land-use regression model compared to our Google Streetview
data set and what we found is that the land-use regression model yielded higher
estimates of mortality attributable to air pollution and that was owing to the
higher concentrations of the land-use regression model we did also find that the
land-use regression and the Google Streetview yielded fairly similar
overall spatial patterns but the Google Street View data set was able to
identify a larger magnitude of spatial heterogeneity within cities particularly
with hotspots near high-density roadways such as this is I 880 in Oakland California
so in this presentation we've seen how highly resolved spatial data
can help Public Health's researchers identify areas within cities that are
experiencing higher rates of disease attributable to air pollution the data
sources we explored were satellite derived concentration estimates
innovative mobile monitoring and highly resolved based on disease rates
particularly we demonstrated that the county and that the more highly resolved
baseline disease rates did a good job of letting us see where these disparities
within cities exist so if we don't have that highly resolved baseline to deta
data we might not adequately identify these
so just to kind of bring this all
together at a spring 2019 meeting at the George Washington University we got
together local and state representatives of different governments and they really
emphasized the need for this type of highly localized data in order for them
to inform their decision-making on the local level so it's our hope that this
type of local health impact assessment can be used in cities worldwide to help
inform local decision-making
so that I'd just like to thank our in collaborator
collaborators at the Environmental Defense Fund, University of Texas Austin
Harvard University, the Alameda County Public Health Department and Oregon
State University and of course generous funding from the Environmental
Defense Fund and NASA for funding our group at the George Washington University