1 00:00:00,000 --> 00:00:04,350 Ellen and I'm a researcher at the George Washington University and today I'm 2 00:00:04,350 --> 00:00:07,980 going to be talking about estimating the health impacts of air pollution using 3 00:00:07,980 --> 00:00:12,599 satellite derived concentrations so satellite data allows us to estimate 4 00:00:12,600 --> 00:00:19,170 ground level concentrations at increasingly finer resolutions, ah, these 5 00:00:19,170 --> 00:00:23,160 high-resolution pollution estimates can then be used to estimate the health 6 00:00:23,160 --> 00:00:27,019 impacts and what neighborhoods within cities might be at a higher risk of 7 00:00:27,020 --> 00:00:32,520 disease and mortality that's associated with exposure to air pollution, so in 8 00:00:32,520 --> 00:00:36,210 this study we wanted to estimate the health impacts of air pollution at the 9 00:00:36,210 --> 00:00:40,860 local level and to do this we look at Oakland California within the Bay Area 10 00:00:40,860 --> 00:00:45,980 as a case study, so until recently these type of health impact assessments have 11 00:00:45,989 --> 00:00:53,180 been reported at the global, country or county level, but this type of reporting 12 00:00:53,190 --> 00:00:58,710 can mask potential spatial heterogeneity at the local level. So in this study we 13 00:00:58,710 --> 00:01:04,049 varied the pollution sources and rates of disease in order to see how our 14 00:01:04,049 --> 00:01:10,080 health impacts vary when we use these varying concentration sources. We hope 15 00:01:10,080 --> 00:01:14,580 that this type of information can inform actions that cities can take, to take 16 00:01:14,580 --> 00:01:19,670 action to reduce disparities in health risks attributable to air pollution 17 00:01:19,920 --> 00:01:20,740 Next, 18 00:01:23,520 --> 00:01:26,939 so in order to do this we use what's called a health impact function 19 00:01:26,939 --> 00:01:32,090 that's derived from epidemiology studies and these look at linking the health 20 00:01:32,090 --> 00:01:35,240 outcomes of association to air pollution 21 00:01:35,900 --> 00:01:37,860 so the attributable fraction of disease 22 00:01:37,860 --> 00:01:42,060 is the portion of disease that can be thought of or can be described as 23 00:01:42,060 --> 00:01:48,259 resulting from a particular risk factor or exposure in this case air pollution 24 00:01:48,259 --> 00:01:52,500 so here I'm going to be talking mostly about mortality related to air pollution 25 00:01:52,500 --> 00:01:57,540 but we also looked at cardiovascular mortality as an emergency room visits 26 00:01:57,540 --> 00:02:00,140 and new cases of asthma 27 00:02:06,040 --> 00:02:09,660 sorry guys it's a lot of pressure 28 00:02:12,300 --> 00:02:14,760 okay so from these epidemiological studies 29 00:02:14,760 --> 00:02:20,580 we're able to derive a risk estimate that quantitatively assesses the 30 00:02:20,580 --> 00:02:24,630 relationship between a health outcome and what we would expect for each health 31 00:02:24,630 --> 00:02:27,560 outcome increase in concentration 32 00:02:30,200 --> 00:02:33,080 so in order to apply this spatially we 33 00:02:33,090 --> 00:02:38,010 actually need a couple more concentration input or data inputs first 34 00:02:38,010 --> 00:02:42,870 we need the population of our population of interest and the spatial distribution 35 00:02:42,870 --> 00:02:48,030 of that population we also need the baseline disease rates or baseline 36 00:02:48,030 --> 00:02:52,410 mortality of that population and the spatial distribution of that baseline 37 00:02:52,410 --> 00:02:57,150 disease rate and then we also need to know the pollution concentrations and 38 00:02:57,150 --> 00:03:02,340 the spatial distribution of our paths pollution concentrations so in this 39 00:03:02,340 --> 00:03:07,320 study we use concentrations from three sources a fine particulate matter from a 40 00:03:07,320 --> 00:03:14,370 satellite drive model, land..., nitrogen dioxide from two data sources first a 41 00:03:14,370 --> 00:03:17,960 land-use regression model that incorporated satellite troposphere at 42 00:03:17,960 --> 00:03:24,020 column estimates of nitrogen dioxides and nitrogen dioxide from a innovative 43 00:03:24,030 --> 00:03:27,560 mobile monitoring data set where in Google Street View cars took 44 00:03:27,560 --> 00:03:30,500 measurements of nitrogen dioxide 45 00:03:33,220 --> 00:03:36,000 the result of this calculation is the number 46 00:03:36,000 --> 00:03:40,200 of cases that we can expect within a population that could be attributed to 47 00:03:40,200 --> 00:03:41,360 air pollution 48 00:03:45,240 --> 00:03:49,320 so long-term exposure to fine particulate matter is associated 49 00:03:49,320 --> 00:03:53,790 with increased risk of mortality and it contributes a large burden of death and 50 00:03:53,790 --> 00:03:58,980 disability in the US and globally so for our fine particulate matter data set we 51 00:03:58,980 --> 00:04:02,280 used a satellite derived model that was originally developed at Harvard 52 00:04:02,280 --> 00:04:07,769 University this dataset provided daily estimates of fine particulate matter at 53 00:04:07,769 --> 00:04:13,760 the one kilometer resolution in the United States so this was a satellite 54 00:04:13,760 --> 00:04:19,360 derived model, it was a neural network hybrid model that incorporated aerosol 55 00:04:19,380 --> 00:04:23,819 optical depth from the moderate resolution imaging spectroradiometer on 56 00:04:23,819 --> 00:04:29,759 the Earth observing satellite it also incorporated a 3d chemical transport 57 00:04:29,759 --> 00:04:35,219 model central, site monitoring data and numerous other meteorological and 58 00:04:35,219 --> 00:04:41,520 land-use variables the result is that we can see the estimates show that within 59 00:04:41,520 --> 00:04:44,999 the Bay Area the daily maximum concentrations for fine particulate 60 00:04:44,999 --> 00:04:50,129 matter regularly exceeded the US Environmental Protection Agency's daily 61 00:04:50,129 --> 00:04:54,500 threshold of 35 micrograms per meter cubed 62 00:04:55,460 --> 00:05:00,360 so our second concentration of interest is nitrogen dioxide and nitrogen dioxide 63 00:05:00,360 --> 00:05:04,860 is a traffic-related air pollutant exposures which has been associated with 64 00:05:04,860 --> 00:05:09,979 increased asthma incidents and increased mortality so we wanted to compare how to 65 00:05:09,979 --> 00:05:15,319 varying NO2 concentration datasets influenced our health impact estimates 66 00:05:15,319 --> 00:05:22,439 so for our first model for NO2 we used a land-use regression model and land-use 67 00:05:22,439 --> 00:05:27,060 regression basically just takes land-use characteristics and other model inputs 68 00:05:27,060 --> 00:05:32,279 to estimate data where we don't have measured concentrations so this 69 00:05:32,279 --> 00:05:35,939 particular model developed to Oregon State University incorporated satellite 70 00:05:35,939 --> 00:05:40,860 troposphere at column estimates of nitrogen dioxides from the DAICHI 71 00:05:40,860 --> 00:05:47,520 instrument and the Gomi 2 instrument the researchers found that the satellite 72 00:05:47,520 --> 00:05:51,180 column concentrations and measures of road density were the largest 73 00:05:51,180 --> 00:05:55,040 contributors to NO2 ground level concentrations 74 00:05:58,460 --> 00:05:59,939 so in order to compare 75 00:05:59,939 --> 00:06:04,649 our land-use regression estimates we wanted to use measure data of NO2 and 76 00:06:04,649 --> 00:06:09,689 for this we used a innovative mobile monitoring technique wherein Google 77 00:06:09,689 --> 00:06:14,159 Street View cars took repeated 30 meter Road segment measurements of NO2 in 78 00:06:14,159 --> 00:06:18,360 neighborhoods throughout the Bay Area and for Oakland this is Oakland 79 00:06:18,360 --> 00:06:23,089 California this resulted in three million, over three million data points 80 00:06:23,089 --> 00:06:28,319 which was then aggregated to an annual average and then we aggregated to 100 81 00:06:28,319 --> 00:06:30,830 meters here 82 00:06:31,960 --> 00:06:36,500 the Google Street data sets concentrations found peak concentrations 83 00:06:36,500 --> 00:06:41,810 near roadways and what we can also see is that when we compare the land-use 84 00:06:41,810 --> 00:06:45,560 regression to the Google Streetview the land-use regression had a higher median 85 00:06:45,560 --> 00:06:49,819 concentration of values but the Google Street View concentrations had a larger 86 00:06:49,819 --> 00:06:55,610 range of values owing to those peak concentrations near roadways so we 87 00:06:55,610 --> 00:07:00,259 hypothesized that this highly resolved spatial data near roadways in 88 00:07:00,259 --> 00:07:04,520 combination with highly resolved baseline disease rates would allow us to 89 00:07:04,520 --> 00:07:09,880 identify disparities in health burden attributable to air pollution within cities 90 00:07:15,520 --> 00:07:19,660 so the other portion of our project was to look at how baseline 91 00:07:19,669 --> 00:07:23,840 disease rates influenced the risks attributable to air pollution within 92 00:07:23,840 --> 00:07:30,439 cities so County baseline disease rates are pretty easy to obtain but they can 93 00:07:30,439 --> 00:07:34,909 mask potential spatial heterogeneity for neighborhoods so for example if you 94 00:07:34,909 --> 00:07:39,110 apply a County based on disease rate to entire neighborhood you might or the 95 00:07:39,110 --> 00:07:42,889 entire county you might overestimate the baseline disease in some areas and 96 00:07:42,889 --> 00:07:47,599 underestimate it in others so for our study we obtained census block group or 97 00:07:47,599 --> 00:07:51,529 neighborhood level based on disease rates and what we found is when we 98 00:07:51,529 --> 00:07:54,770 compared county and neighborhood level based on disease rates in our health 99 00:07:54,770 --> 00:08:00,919 impact function was the census block group yielded a higher median burden of 100 00:08:00,919 --> 00:08:05,659 disease as seen by the box plots and it also yield the different spatial 101 00:08:05,659 --> 00:08:11,210 patterns as compared to the county rates we can see in the maps, so from this we 102 00:08:11,210 --> 00:08:14,930 concluded that without this type of highly resolved neighborhood baseline 103 00:08:14,930 --> 00:08:20,870 disease rates we might not adequately identify disparities in the health 104 00:08:20,870 --> 00:08:24,560 burden attributable to air pollution within cities 105 00:08:30,580 --> 00:08:35,080 so the other portion of our study was of course to compare how the satellite 106 00:08:35,089 --> 00:08:38,510 drive Land-use regression model compared to our Google Streetview 107 00:08:38,510 --> 00:08:42,680 data set and what we found is that the land-use regression model yielded higher 108 00:08:42,680 --> 00:08:47,450 estimates of mortality attributable to air pollution and that was owing to the 109 00:08:47,450 --> 00:08:51,950 higher concentrations of the land-use regression model we did also find that the 110 00:08:51,950 --> 00:08:55,370 land-use regression and the Google Streetview yielded fairly similar 111 00:08:55,370 --> 00:08:59,750 overall spatial patterns but the Google Street View data set was able to 112 00:08:59,750 --> 00:09:05,660 identify a larger magnitude of spatial heterogeneity within cities particularly 113 00:09:05,660 --> 00:09:11,660 with hotspots near high-density roadways such as this is I 880 in Oakland California 114 00:09:16,860 --> 00:09:21,440 so in this presentation we've seen how highly resolved spatial data 115 00:09:21,440 --> 00:09:26,540 can help Public Health's researchers identify areas within cities that are 116 00:09:26,540 --> 00:09:32,900 experiencing higher rates of disease attributable to air pollution the data 117 00:09:32,900 --> 00:09:35,770 sources we explored were satellite derived concentration estimates 118 00:09:35,770 --> 00:09:40,720 innovative mobile monitoring and highly resolved based on disease rates 119 00:09:40,720 --> 00:09:45,589 particularly we demonstrated that the county and that the more highly resolved 120 00:09:45,589 --> 00:09:50,959 baseline disease rates did a good job of letting us see where these disparities 121 00:09:50,959 --> 00:09:54,680 within cities exist so if we don't have that highly resolved baseline to deta 122 00:09:54,680 --> 00:09:57,680 data we might not adequately identify these 123 00:09:59,180 --> 00:10:00,640 so just to kind of bring this all 124 00:10:00,650 --> 00:10:05,390 together at a spring 2019 meeting at the George Washington University we got 125 00:10:05,390 --> 00:10:09,800 together local and state representatives of different governments and they really 126 00:10:09,800 --> 00:10:14,450 emphasized the need for this type of highly localized data in order for them 127 00:10:14,450 --> 00:10:18,950 to inform their decision-making on the local level so it's our hope that this 128 00:10:18,950 --> 00:10:24,140 type of local health impact assessment can be used in cities worldwide to help 129 00:10:24,140 --> 00:10:26,540 inform local decision-making 130 00:10:29,720 --> 00:10:32,920 so that I'd just like to thank our in collaborator 131 00:10:32,930 --> 00:10:36,920 collaborators at the Environmental Defense Fund, University of Texas Austin 132 00:10:36,920 --> 00:10:41,390 Harvard University, the Alameda County Public Health Department and Oregon 133 00:10:41,390 --> 00:10:44,600 State University and of course generous funding from the Environmental 134 00:10:44,600 --> 00:10:47,630 Defense Fund and NASA for funding our group at the George Washington University