diff --git a/.delta/delta b/.delta/delta index ff030eea6..26d95b569 100755 --- a/.delta/delta +++ b/.delta/delta @@ -1,5 +1,31 @@ #!/usr/bin/env node -require('dotenv').config(); +const dotenv = require('dotenv'); + +const NODE_ENV = process.env.NODE_ENV; + +const dotenvFiles = [ + '.env', + // Don't include `.env.local` for `test` environment + // since normally you expect tests to produce the same + // results for everyone + NODE_ENV === 'test' ? null : '.env.local', + `.env.${NODE_ENV}`, + `.env.${NODE_ENV}.local` +].filter(Boolean); + +const env = {}; + +for (let dotenvFile of dotenvFiles) { + const config = dotenv.config({ path: dotenvFile }); + if (config.parsed) { + Object.assign(env, config.parsed); + } +} + +for (let key in env) { + process.env[key] = env[key]; +} + const { execSync } = require('child_process'); const path = require('path'); diff --git a/.delta/ui b/.delta/ui index 03d7b0bfc..45d25dd1b 160000 --- a/.delta/ui +++ b/.delta/ui @@ -1 +1 @@ -Subproject commit 03d7b0bfc06450d59aadef0ab0169b232d9b8604 +Subproject commit 45d25dd1b814edd58f45aec976c463164d0ff9c7 diff --git a/.env b/.env index ddd64de6e..1e619c335 100644 --- a/.env +++ b/.env @@ -13,9 +13,6 @@ API_RASTER_ENDPOINT='https://staging-raster.delta-backend.com' # Endpoint for the STAC server. No trailing slash. API_STAC_ENDPOINT='https://staging-stac.delta-backend.com' -# The mapbox public token obtained from your account -MAPBOX_TOKEN='pk.eyJ1IjoiY292aWQtbmFzYSIsImEiOiJja2F6eHBobTUwMzVzMzFueGJuczF6ZzdhIn0.8va1fkyaWgM57_gZ2rBMMg' - MAPBOX_STYLE_URL='mapbox://styles/covid-nasa/ckb01h6f10bn81iqg98ne0i2y' # If the app is being served in from a subfolder, the domain url must be set. diff --git a/.env.local-sample b/.env.local-sample new file mode 100644 index 000000000..0e89aa9e9 --- /dev/null +++ b/.env.local-sample @@ -0,0 +1,3 @@ +# Get your public token from Mapbox dashboard +# Then copy this file, name it as .env.local +MAPBOX_TOKEN='YOUR_MAPBOX_TOKEN' \ No newline at end of file diff --git a/.github/workflows/deploy-prod.yml b/.github/workflows/deploy-prod.yml index 3929e743a..1a9d76b0e 100644 --- a/.github/workflows/deploy-prod.yml +++ b/.github/workflows/deploy-prod.yml @@ -59,6 +59,8 @@ jobs: run: ./.delta/setup - name: Build production + env: + MAPBOX_TOKEN: ${{secrets.MAPBOX_TOKEN}} run: PUBLIC_URL="${{ env.DOMAIN_PROD }}" yarn build deploy: diff --git a/.github/workflows/deploy-staging.yml b/.github/workflows/deploy-staging.yml index 72605a138..184e658f4 100644 --- a/.github/workflows/deploy-staging.yml +++ b/.github/workflows/deploy-staging.yml @@ -58,6 +58,8 @@ jobs: run: ./.delta/setup - name: Build Staging + env: + MAPBOX_TOKEN: ${{secrets.MAPBOX_TOKEN}} run: PUBLIC_URL="${{ env.DOMAIN_STAGING }}" yarn stage deploy: diff --git a/.github/workflows/deploy-surge.yml b/.github/workflows/deploy-surge.yml index ee4d1d8e8..b39ae8637 100644 --- a/.github/workflows/deploy-surge.yml +++ b/.github/workflows/deploy-surge.yml @@ -55,8 +55,11 @@ jobs: run: ./.delta/setup - name: Build staging + env: + MAPBOX_TOKEN: ${{secrets.MAPBOX_TOKEN}} run: yarn stage + deploy: runs-on: ubuntu-latest needs: build diff --git a/.gitignore b/.gitignore index aa2fb4ae8..78ab71936 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ node_modules bower_components .sass-cache test/bower_components +.env.local ################################################ diff --git a/datasets/fb-population--dataset-cover.png b/datasets/fb-population--dataset-cover.png new file mode 100644 index 000000000..235369f5c Binary files /dev/null and b/datasets/fb-population--dataset-cover.png differ diff --git a/datasets/fb_population.ej.data.mdx b/datasets/fb_population.ej.data.mdx new file mode 100644 index 000000000..169cdd7dd --- /dev/null +++ b/datasets/fb_population.ej.data.mdx @@ -0,0 +1,85 @@ +--- +id: facebook_population_density +name: 'Facebook Population Density' +description: 'Facebook high-resolution population density with a 30m resolution' +media: + src: ::file ./fb-population--dataset-cover.png + alt: 2015 high resolution population density for Paris. + author: + name: NASA + url: +thematics: + - environmental-justice +layers: + - id: facebook_population_density + name: 'Facebook Population Density' + type: raster + description: 'Facebook high-resolution population density with a 30m² resolution' + zoomExtent: + - 0 + - 20 + sourceParams: + resampling_method: bilinear + bidx: 1 + colormap_name: ylorrd + rescale: + - 0 + - 69 + legend: + type: gradient + min: "0" + max: "69" + stops: + - "#ffffcc" + - "#fee187" + - "#feab49" + - "#fc5b2e" + - "#d41020" + - "#800026" +--- + + + +In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Meta [formerly known as Facebook] used census data and computer vision techniques (Convolutional Neural Networks) to identify buildings from publicly accessible mapping services to create population density datasets. These high-resolution maps estimate the number of individuals living within 30-meter grid tiles on a global scale. The population estimates are based on data from the Gridded Population of the World (GPWv4) data collection. + + + + + +## Scientific research +The 30-meter resolution population density dataset uses machine learning techniques to produce global population maps using building footprints and census population data from CIESIN. The high resolution population data can be used to support the applications community when assessing the impacts of natural disasters or assessing risk at near global coverage. + + + + +
+ + + Population density in units of people per square meter for New York, NY, USA for 2015 + +
+ +## Interpreting the data +Population density is provided in units of people per square meter, at the near global scale and is available for the year 2015. The data is gridded at 30m resolution and is only available in locations where buildings are present (i.e. there will be no data over large parks or industrial areas). Higher population density regions (shaded in red) typically correspond to urban centers and lower population (in yellow) to rural and suburban locations. + + + + + +## Credits +1. https://dataforgood.facebook.com/dfg/docs/methodology-high-resolution-population-density-maps + +2. https://dataforgood.facebook.com/dfg/tools/high-resolution-population-density-maps + +3. T. G. Tiecke et al., ‘Mapping the world population one building at a time’, CoRR, https://arxiv.org/abs/1712.05839, 2017. + + diff --git a/datasets/hls-events-ej--dataset-cover.png b/datasets/hls-events-ej--dataset-cover.png new file mode 100644 index 000000000..dc91b8cf3 Binary files /dev/null and b/datasets/hls-events-ej--dataset-cover.png differ diff --git a/datasets/hls-events.ej.data.mdx b/datasets/hls-events.ej.data.mdx new file mode 100644 index 000000000..5ed6fceed --- /dev/null +++ b/datasets/hls-events.ej.data.mdx @@ -0,0 +1,107 @@ +--- +id: hls_events +name: 'Harmonized Landsat Sentinel-2 (Selected Events)' +description: '30-meter resolution harmonized Landsat 8/9 and Sentinel-2A/B data products' +media: + src: ::file ./hls-events-ej--dataset-cover.png + alt: 2017 harmonized Landsat 8 shortwave infrared (SWIR) false color composite image that provides enhanced contrast to detect flood extent. + author: + name: NASA + url: https://nasa.gov/ +thematics: + - environmental-justice +layers: + - id: hls-l30-002-ej + name: HLS Landsat SWIR + type: raster + description: 'Harmonized Landsat SWIR: small subset near Puerto Rico' + zoomExtent: + - 4 + - 20 + sourceParams: + post_process: swir + assets: + - B07 + - B05 + - B04 + compare: + datasetId: hls_events + layerId: hls-l30-002-ej + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } + - id: hls-s30-002-ej + name: HLS Sentinel-2 SWIR + type: raster + description: 'Harmonized Sentinel-2 SWIR: small subset near Puerto Rico' + zoomExtent: + - 4 + - 20 + sourceParams: + post_process: swir + assets: + - B12 + - B8A + - B04 +--- + + + +Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location. + +The data currently in this dashboard represents a subset of the available data. Data will continue to be added to this dashboard over time. + + + + + +## Scientific research +The production of atmospherically corrected HLS products is a collaborative effort between NASA, the U.S. Geological Survey (USGS), and the European Space Agency (ESA). Bandpass adjustments applied to Sentinel-2 data spectral bands match surface reflectance values in corresponding spectral bands in Landsat 8 and 9. This adjustment allows for 30m observation of the land surface every 2-4 days. HLSL30 and HLSS30 products are typically used for land use land cover applications including land use change, land use classification, fire monitoring, agricultural monitoring, and flooding, among others. + + + + +
+ + + Harmonized Landsat 8 SWIR image provides enhanced contrast to detect flood extent in Puerto Rico before and after Hurricane Maria in 2017. + +
+ +## Interpreting the data +HLS imagery in support of Environmental Justice shows the impact of flooding for Hurricanes Maria and Ida that made landfall in Puerto Rico (2017) and New Orleans, LA (2021) respectively. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue. + + + + + +## Credits +1. Citation for HLS data set: Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. + +2. Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSS30.002 + +3. Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSL30.002 + + + + + +## Additional Resources +1. [HLSL30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlsl30v002/) + +2. [HLSS30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlss30v002/) + +3. [HLS Webinar with LPDAAC](https://www.youtube.com/watch?v=N2S4KGNo_XY) + + diff --git a/datasets/nighttime-lights-ej--dataset-cover-neworleans.jpeg b/datasets/nighttime-lights-ej--dataset-cover-neworleans.jpeg new file mode 100644 index 000000000..c942add36 Binary files /dev/null and b/datasets/nighttime-lights-ej--dataset-cover-neworleans.jpeg differ diff --git a/datasets/nighttime-lights.ej.data.mdx b/datasets/nighttime-lights.ej.data.mdx new file mode 100644 index 000000000..3dfaaf00b --- /dev/null +++ b/datasets/nighttime-lights.ej.data.mdx @@ -0,0 +1,107 @@ +--- +id: nighttime-lights-ej +name: 'Nighttime Lights supporting Environmental Justice' +description: 'High definition nighttime lights can be used to identify regions impacted by natural disaster and/or power outages to better inform disaster response efforts.' +media: + src: ::file ./nighttime-lights-ej--dataset-cover-neworleans.jpeg + alt: Nighttime lights for New Orleans, LA on August 31, 2021. + author: + name: NASA Earth Observatory + url: https://appliedsciences.nasa.gov/our-impact/news/satellite-observes-power-outages-new-orleans +thematics: + - environmental-justice +layers: + - id: nightlights-hd-1band + name: Nightlights EJ + type: raster + description: 'High definition nighttime lights can be used to identify regions impacted by natural disaster and/or power outages to better inform disaster response efforts.' + zoomExtent: + - 7 + - 20 + sourceParams: + bidx: 1 + colormap_name: inferno + rescale: + - 0 + - 255 + compare: + datasetId: nighttime-lights-ej + layerId: nightlights-hd-1band +--- + + + +Images of Earth at night give us an extraordinary view of human activity over time. The nighttime environment illuminates Earth features, including city infrastructure, lightning flashes, fishing boats navigating open water, gas flares, aurora, and natural hazards, such as lava flowing from an active volcano. Paired with the moonlight, researchers can also spot snow and ice, as well as other reflective surfaces that allow nighttime land and ocean analysis. + +Researchers are using night light observations to support disaster response and recovery to identify regions most impacted by natural disasters or similar power outages. + + + + + +## Scientific research +Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. + +Black Marble HD downscales radiances from the 500-meter product to street level using optical imagery from Landsat 8, a NASA and U.S. Geological Survey (USGS) satellite, along with OpenStreetMap ancillary layers. This helps visualize impacted areas at the neighborhood scale to identify differences in recovery time across the regions. + +
+ + + Nighttime lights from the Black Marble HD product over New Orleans, LA on August 9, 2021. + +
+ + + +
+ + + Comparison of nightlights data for Puerto Rico pre-landfall (17 July 2017) and post-landfall (20 September 2017) for Hurricane Maria. + +
+ +## Interpreting the data +30-meter resolution Black Marble High Definition (HD) nighttime lights are visualized for New Orleans, LA and Puerto Rico to highlight the impacts and recovery from the landfalls of Hurricane Ida and Hurricane Maria respectively. For each natural disaster, there is a pre-, during-, and post-event image for baseline comparisons. Within the nightlights data layer, brighter areas indicate locations with higher light emissions. One limitation of nightlights data is the presence of cloud cover as clouds can reduce the surface brightness as observed by the satellite when conditions on the ground remain unchanged. + + + + + +## Credits +1. Black Marble data courtesy of [Universities Space Research Association (USRA) Earth from Space Institute (EfSI)](https://www.usra.edu/efsi-our-mission) and NASA Goddard Space Flight Center’s [Terrestrial Information Systems Laboratory](https://science.gsfc.nasa.gov/earth/terrestrialinfo/) using VIIRS day-night band data from the Suomi National Polar-orbiting Partnership and Landsat-8 Operational Land Imager (OLI) data from the U.S. Geological Survey. + + + + + +## Additional resources +### NASA Features + +* [Night Lights Show Slow Recovery from Hurricane Maria](https://www.earthobservatory.nasa.gov/images/144371/night-lights-show-slow-recovery-from-maria) + +* [Satellite Observes Power Outages in New Orleans](https://earthobservatory.nasa.gov/images/148777/satellite-observes-power-outages-in-new-orleans) + +### Explore the Missions + +* [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) + + diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx index 874dd102a..b76c7276e 100644 --- a/datasets/no2.data.mdx +++ b/datasets/no2.data.mdx @@ -18,7 +18,7 @@ layers: - id: no2-monthly name: No2 type: raster - description: 'Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc sodales semper risus, suscipit varius diam facilisis non.' + description: 'Global nitrogen dioxide data organized into monthly metrics' zoomExtent: - 0 - 20 @@ -26,7 +26,7 @@ layers: resampling_method: bilinear bidx: 1 color_formula: gamma r 1.05 - colormap_name: coolwarm + colormap_name: rdbu_r rescale: - 0 - 1.5e16 @@ -42,16 +42,15 @@ layers: min: "Less" max: "More" stops: - - "#99c5e0" - - "#f9eaa9" - - "#f7765d" - - "#c13b72" - - "#461070" - - "#050308" + - "#3A88BD" + - "#C9E0ED" + - "#E4EEF3" + - "#FDDCC9" + - "#DD7059" - id: no2-monthly-diff name: No2 (Diff) type: raster - description: 'Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc sodales semper risus, suscipit varius diam facilisis non.' + description: 'Global nitrogen dioxide data which displays the difference from the same time 1 year ago' zoomExtent: - 0 - 20 @@ -159,4 +158,4 @@ Interpreting satellite NO2 data must be done carefully, as the quantity observed * [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/outreach.html) * [Pandora Project](https://pandora.gsfc.nasa.gov/) - \ No newline at end of file + diff --git a/datasets/ps-bluetarp--dataset-cover.png b/datasets/ps-bluetarp--dataset-cover.png new file mode 100644 index 000000000..fbeba8e04 Binary files /dev/null and b/datasets/ps-bluetarp--dataset-cover.png differ diff --git a/datasets/ps_blue_tarp_detections.ej.data.mdx b/datasets/ps_blue_tarp_detections.ej.data.mdx new file mode 100644 index 000000000..56e965376 --- /dev/null +++ b/datasets/ps_blue_tarp_detections.ej.data.mdx @@ -0,0 +1,93 @@ +--- +id: ps_blue_tarp_detections +name: 'Blue tarp detections' +description: 'Machine learning generated blue tarp detections using Planetscope 3-band RGB imagery' +media: + src: ::file ./ps-bluetarp--dataset-cover.png + alt: Blue tarp detections for Jefferson Parish, LA on February 12, 2022 + author: + name: NASA + url: +thematics: + - environmental-justice +layers: + - id: blue-tarp-detection + name: 'Blue tarp detections' + type: raster + description: 'machine learning generated blue tarp detections. Includes copyrighted material of Planet. All rights reserved.' + zoomExtent: + - 14 + - 20 + sourceParams: + resampling: cubic_spline + bidx: 1 + colormap_name: reds + rescale: + - 0 + - 400 + - id: blue-tarp-planetscope + name: Planetscope input RGB imagery used for blue tarp detection + type: raster + description: 'Planetscope input RGB imagery used for blue tarp detection. Includes copyrighted material of Planet. All rights reserved.' + zoomExtent: + - 14 + compare: + datasetId: ps_blue_tarp_detections + layerId: blue-tarp-detection + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } +--- + + + +Planetscope provides 3-band RGB imagery at 3-meter ground resolution which can support building-scale analysis of the land surface. In the aftermath of natural disasters associated with high wind speeds, homes with damaged roofs typically are covered with blue tarps to protect the interior of the home from further damage. Using machine learning, blue tarps can be detected from the Planetscope imagery using pre-event cloud free images to detect blue pixels and potential impacts after a natural disaster. + + + + + +## Scientific research +Detection of blue tarps from high resolution imagery can inform disaster response of the most impacted locations. Additionally, given the frequency with which Planetscope scenes are retrieved from the satellite, the rate of recovery for a given location can also be tracked over time. This can also support disaster response to provide aid to specific locations where recovery efforts are lacking. + + + + +
+ + + Blue tarp detections in Jefferson Parish, LA on February 12, 2022. + +
+ +## Interpreting the data +Machine learning-based detections of blue tarps are displayed as red pixels on the basemap and are available for both Hurricane Maria (2017) and Hurricane Ida (2021). The input true color imagery used to produce the detections are also added as an additional layer and can be used for qualitative validation of the detections. + + + + + +## Credits + +1. Source data made available through the NASA Commercial Smallsat Data Acquisition (CSDA) Program + + + + + +## Additional resources +1. [Machine Learning activities at NASA IMPACT](https://impact.earthdata.nasa.gov/project/ml.html) +2. [Commercial Smallsat Data Acquisition Program](https://www.earthdata.nasa.gov/esds/csda) + + diff --git a/datasets/so2--dataset-cover.png b/datasets/so2--dataset-cover.png new file mode 100644 index 000000000..07c11eb8a Binary files /dev/null and b/datasets/so2--dataset-cover.png differ diff --git a/datasets/so2.data.mdx b/datasets/so2.data.mdx new file mode 100644 index 000000000..21a2d07f1 --- /dev/null +++ b/datasets/so2.data.mdx @@ -0,0 +1,117 @@ +--- +id: so2 +name: 'Sulfur Dioxide' +description: "NASA OMI/Aura Sulfur Dioxide (SO2) Total Column" +media: + src: ::file ./so2--dataset-cover.png + alt: 2018 Social Vulnerability Index (SVI) based on minority status and language score. + author: + name: NASA + url: +thematics: + - air-quality +layers: + - id: OMSO2PCA-COG + name: 'Sulfur Dioxide' + type: raster + description: 'NASA OMI/Aura Sulfur Dioxide (SO2) Total Column' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling_method: bilinear + bidx: 1 + colormap_name: rdylbu_r + rescale: + - 0 + - 1 + compare: + datasetId: so2 + layerId: OMSO2PCA-COG + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + legend: + type: gradient + label: SO2 Total column (DU) + min: "0" + max: "1" + stops: + - "#a50026" + - "#f46d43" + - "#fee090" + - "#e0f3f8" + - "#74add1" + - "#313695" +--- + +
+ + + Comparison of total column SO2 for India for 2005 and 2021 + +
+ +The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decision + + + + + +## Scientific research +Sulfur/Sulphur Dioxide (SO2) is one of the US Environmental Protection Agency's (EPA) six major regulated criteria pollutants (Tropospheric Ozone, Nitrogen Dioxide, Sulfur Dioxide, Lead, PM2.5 and PM 10 particulates). It irritates the eyes, nose, and lungs. High concentrations of SO2 can result in temporary breathing impairment. It is produced by combustion of coal, fuel oil, and gasoline, since these fuels contain sulfur in the combustion, and in the oxidation of naturally occurring sulfur gases. It is a precursor to sulfuric acid, which is a major constituent of acid rain. SO2 is injected into the stratosphere by volcanic eruptions. SO2 also is a major precursor to PM2.5, which is a significant health concern, and a main contributor to poor visibility. + + + + +
+ + + Comparison of total column SO2 for China for 2006 and 2021 + +
+ +## Interpreting the data +Global total column SO2 observations are generated using a principal component analysis algorithm using a single observation in the planetary boundary layer. The single observation is obtained from the "best pixel" of the level-2 OMI/Aura SO2 total column observations for the observation day. SO2 observations in support of air quality are available from 2005-2021 at 0.25 x 0.25 degree spatial resolution. Higher concentrations of SO2 are shaded in yellow and lower concentrations in purple. + + + + + +## Credits +1. Can Li, Nickolay A. Krotkov, and Peter Leonard (2020), OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10.5067/Aura/OMI/DATA3008 + + + + + +## Additional resources +* [OMI Aura Sulfur Dioxide (SO2) Total Column Dataset Access](https://disc.gsfc.nasa.gov/datasets/OMSO2e_003/summary) + +### Explore the Missions +* [SO2 Monitoring Home Page](https://so2.gsfc.nasa.gov/) + +* [Aura Project Home Page](https://aura.gsfc.nasa.gov/) + + diff --git a/datasets/svi_household.ej.data.mdx b/datasets/svi_household.ej.data.mdx index 69a2b9fcb..1124e5eac 100644 --- a/datasets/svi_household.ej.data.mdx +++ b/datasets/svi_household.ej.data.mdx @@ -30,7 +30,7 @@ layers: layerId: social-vulnerability-index-household mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -62,7 +62,7 @@ layers: layerId: social-vulnerability-index-household-nopop mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -97,16 +97,16 @@ The Household Composition & Disability Score (HCDS) is one of the four themes us - Comparison of the household composition and disability score for New Orleans, LA between 2014 and 2016. + Comparison of the household composition and disability score for New Orleans, LA between 2014 and 2016. For this color scale, 0 corresponds to least vulnerable and 1 is most vulnerable. diff --git a/datasets/svi_housing.ej.data.mdx b/datasets/svi_housing.ej.data.mdx index 021fd0300..da4087aaf 100644 --- a/datasets/svi_housing.ej.data.mdx +++ b/datasets/svi_housing.ej.data.mdx @@ -34,7 +34,7 @@ layers: layerId: social-vulnerability-index-housing mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -66,7 +66,7 @@ layers: layerId: social-vulnerability-index-housing-nopop mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -101,16 +101,16 @@ The Housing Type & Transportation Score (HTTS) is one of the four themes used in - Comparison of the housing and transportation score for Philadelphia, PA between 2016 and 2018. + Comparison of the housing and transportation score for Philadelphia, PA between 2016 and 2018. For this color scale, 0 corresponds to least vulnerable and 1 is most vulnerable. diff --git a/datasets/svi_minority.ej.data.mdx b/datasets/svi_minority.ej.data.mdx index 4047f4af2..80ea9141a 100644 --- a/datasets/svi_minority.ej.data.mdx +++ b/datasets/svi_minority.ej.data.mdx @@ -30,7 +30,7 @@ layers: layerId: social-vulnerability-index-minority mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -62,7 +62,7 @@ layers: layerId: social-vulnerability-index-minority-nopop mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -97,16 +97,16 @@ The Minority Status & Language Score (MSLS), as with the other SVI thematic area - Comparison of the minority and language score for Blackfeet Indian Reservation between 2016 and 2018. + Comparison of the minority and language score for Blackfeet Indian Reservation between 2016 and 2018. For this color scale, 0 corresponds to less vulnerable and 1 is most vulnerable. diff --git a/datasets/svi_overall.ej.data.mdx b/datasets/svi_overall.ej.data.mdx index efeb2b40b..521115637 100644 --- a/datasets/svi_overall.ej.data.mdx +++ b/datasets/svi_overall.ej.data.mdx @@ -30,7 +30,7 @@ layers: layerId: social-vulnerability-index-overall mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -62,7 +62,7 @@ layers: layerId: social-vulnerability-index-overall-nopop mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -97,16 +97,16 @@ The SVI Overall Score provides the overall, summed social vulnerability score fo - Comparison of the overall social vulnerability index for Houston, TX between 2016 and 2018. + Comparison of the overall social vulnerability index for Houston, TX between 2016 and 2018. For this color scale, 0 corresponds to less vulnerable and 1 is most vulnerable. diff --git a/datasets/svi_socioeconomic.ej.data.mdx b/datasets/svi_socioeconomic.ej.data.mdx index 7712ae0a0..3cdb7f8ab 100644 --- a/datasets/svi_socioeconomic.ej.data.mdx +++ b/datasets/svi_socioeconomic.ej.data.mdx @@ -30,7 +30,7 @@ layers: layerId: social-vulnerability-index-socioeconomic mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -62,7 +62,7 @@ layers: layerId: social-vulnerability-index-socioeconomic-nopop mapLabel: | ::js ({ dateFns, datetime, compareDatetime }) => { - return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; } legend: type: gradient @@ -102,7 +102,7 @@ The Economic Status Score, like the three other themes, is used in observing a c attrAuthor='NASA' attrUrl='https://nasa.gov/' > - Socioeconomic vulnerability score for Huntsville, AL for 2018. + Socioeconomic vulnerability score for Huntsville, AL for 2018. For this color scale, 0 corresponds to less vulnerable and 1 is most vulnerable. @@ -112,10 +112,10 @@ The Economic Status Score, like the three other themes, is used in observing a c - - - -## Additional resources -### NASA Features - -### Explore the data - -### Explore the Missions - - - diff --git a/discoveries/air-quality-and-covid-19.discoveries.mdx b/discoveries/air-quality-and-covid-19.discoveries.mdx index 4d7fac393..e373e5726 100644 --- a/discoveries/air-quality-and-covid-19.discoveries.mdx +++ b/discoveries/air-quality-and-covid-19.discoveries.mdx @@ -12,7 +12,6 @@ media: url: https://unsplash.com/photos/U-Kty6HxcQc pubDate: 2020-12-01 thematics: - - air-quality - covid-19 --- diff --git a/discoveries/ej--discovery-cover.jpeg b/discoveries/ej--discovery-cover.jpeg new file mode 100644 index 000000000..c942add36 Binary files /dev/null and b/discoveries/ej--discovery-cover.jpeg differ diff --git a/discoveries/hurricane-maria-and-ida.discoveries.mdx b/discoveries/hurricane-maria-and-ida.discoveries.mdx index c7aa8e79b..4d66c2f8d 100644 --- a/discoveries/hurricane-maria-and-ida.discoveries.mdx +++ b/discoveries/hurricane-maria-and-ida.discoveries.mdx @@ -1,44 +1,522 @@ --- featuredOn: - covid-19 -id: 'hurricane-maria-and-ida' +id: "hurricane-maria-and-ida" name: Connecting Disaster Recovery with Environmental Justice description: "Featuring Hurricane María and Hurricane Ida" media: - src: ::file ./air-quality-and-covid-19--discovery-cover.jpg - alt: Clear nightsky with crescent moon above the mountains - author: - name: Benjamin Voros - url: https://unsplash.com/photos/U-Kty6HxcQc -pubDate: 2020-12-01 + src: ::file ./ej--discovery-cover.jpeg + alt: Nighttime view of New Orleans thematics: - environmental-justice --- + + + + + + ## Connecting Disaster Recovery with Environmental Justice: Hurricane María + + Hurricane María made landfall in Puerto Rico as a Category 4 or 5 hurricane on September 20, 2017, leaving a path of destruction in its wake. [Over 1.5 million people on the island lost power, leading to the longest blackout in US history](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). Although efforts to repair the damage on the island were extensive, the [areas with the most severe and prolonged impacts were areas of lower socioeconomic status](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). These communities lacked the resources and the representation to repair damage quickly, leading to long-term lack of access to electricity, water, and other critical supplies. + + NASA hosts a wide variety of continuous Earth observation data useful in environmental justice research. This dashboard features a selection of NASA datasets from across the Agency, including socioeconomic data, Earth observation analysis, and other combined datasets. These tools allow users to visualize and download data to understand the environmental issues brought on by Hurricane María. Merging Earth data and socioeconomic data can help communities like those in Puerto Rico to better prepare for and respond to future natural disasters. + + ## Connecting Disaster Recovery with Environmental Justice: Hurricane Ida + + Known as the city that can barely catch its breath between storms, New Orleans experienced another devastating event on August 29, 2021 as Hurricane Ida made landfall as a Category 4 hurricane. The effects of the storm were widespread, causing millions of dollars worth of damage and affecting the lives and homes of millions of people. + + [Disadvantaged communities](https://www.nature.com/articles/d41586-021-02520-8) in Louisiana and across the country already struggle with higher rates of asthma, cancer, and COVID-19 infections. These communities are often hardest-hit by storms like Ida. Research has shown that disadvantaged communities often receive less federal aid than other communities, only prolonging their hardships. NASA is prioritizing open access to environmental justice data such as the datasets in this dashboard in an effort to help communities better prepare for and respond to natural disasters and to help shed light on cases of environmental injustice. + + + + + + + ### Night Light Detection + + **Shining Light on the Aftermath of Hurricanes** + + 30-meter resolution Black Marble HD nighttime lights were visualized for New Orleans, LA and Puerto Rico to highlight the impacts and recovery from the landfalls of Hurricane Ida and Hurricane Maria respectively. For each natural disaster, there is a pre-, during-, and post-event image for baseline comparisons. Within the night lights data layer, brighter areas indicate locations with higher light emissions. The observation data captures how the intensity and distribution of night lights changed during the recovery efforts. + + **Additional Resources** + + * [Explore the Missions](https://blackmarble.gsfc.nasa.gov/) + * [Nighlights Show Slow Recovery from Maria](https://www.earthobservatory.nasa.gov/images/144371/night-lights-show-slow-recovery-from-maria) + * [Satellite Observes Power Outages in New Orleans](https://earthobservatory.nasa.gov/images/148777/satellite-observes-power-outages-in-new-orleans) + + +
+ + + Comparison of nightlights data for Puerto Rico pre-landfall (17 July 2017) and post-landfall (20 September 2017) for Hurricane Maria. + +
+ + + +
+ + + Harmonized Landsat 8 SWIR image provides enhanced contrast to detect flood extent in Puerto Rico before and after Hurricane Maria in 2017. + +
+ + ### Flood Detection + + **Watching the Waters Recede** + Harmonized Landsat Sentinel-2 (HLS) imagery shows the impact of flooding for Hurricanes Maria and Ida. This data supports disaster recovery and associated environmental justice issues as the degree and extent of flooding can be monitored in support of recovery efforts. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue. + + **Additional Resources** + [HLSL30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlsl30v002/) + [HLSS30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlss30v002/) + [HLS Webinar with LPDAAC](https://www.youtube.com/watch?v=N2S4KGNo_XY) + + + + + + + ### Blue Tarp Detection + + **Overview** + + The Interagency Implementation and Advanced Concepts Team (IMPACT), based at NASA’s Marshall Space Flight Center in Huntsville, Alabama, undertook an experimental study to determine if the application of artificial intelligence to NASA satellite images could accurately detect the number of blue tarps deployed on rooftops in the aftermath of hurricanes such as María and Ida as a way of predicting the relative severity of damage experienced by the respective local communities. Correlating these detections over time by zip codes and cross-referencing socio-economic data (such as that contained in the Visualization, Exploration, and Data Analysis (VEDA) dashboard), relationships between socio-economic factors and disaster recovery can be identified. + + +
+ + + Blue tarp detections in Jefferson Parish, LA on February 12, 2022. + +
+ + - - ## Connecting Disaster Recovery with Environmental Justice: Hurricane María + + + ## Approach + + The IMPACT machine learning team identified the bounding boxes for the New Orleans and San Juan regions. They ordered PlanetScope 3 band data from Planet Labs for dates before and after both hurricanes made landfall. They then combined all scenes available for the respective regions for each date. The scenes were segmented using shapefiles for each zip code in the metro areas. In the next step, the team used [US Building Footprints](https://github.com/microsoft/USBuildingFootprints) by Microsoft to limit the detections to just buildings and reduce false positives. A threshold for the color blue was defined and used to segment out blue pixels detected within the building footprints segmentations. For each of the above dates, the model counted the detection of blue pixelated building segments (interpreted to be blue tarps) in each zip code. + + ## "The Ground Truth" + + In order to establish an external benchmark, the team gathered local news reports published in the immediate aftermath of both Hurricane Ida and Hurricane María that included reports of damage. The news reports were grouped by the zip codes of the locations mentioned. Each reported zip code was then assigned an estimated level of damage severity. The damage experienced in a zip code was considered as: + * low impact (L) if the reported damages were minor (e.g. dangling boards or tree debris); + * medium impact (M) if the damages were significant but did not lead to complete loss of structures; or + * high impact (H) if the damages were severe and led to complete loss of structures. + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Hurricane Ida
Estimated DamageZip
H70001
H70003
H70094
L70112
M70113
H70114
M70117
M70119
L70130
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Hurricane María
Estimated DamageZip
L00901
L00911
M00912
H00915
M00918
M00920
H00956
H00962
M00979
+
+ + Estimated damage by zip code + +
- Hurricane María made landfall in Puerto Rico as a Category 4 or 5 hurricane on September 20, 2017, leaving a path of destruction in its wake. [Over 1.5 million people on the island lost power, leading to the longest blackout in US history](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). Although efforts to repair the damage on the island were extensive, the [areas with the most severe and prolonged impacts were areas of lower socioeconomic status](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). These communities lacked the resources and the representation to repair damage quickly, leading to long-term lack of access to electricity, water, and other critical supplies. + A statistical analysis of the rankings demonstrates a high degree of correlation between the maximum number of detected blue tarp counts in each zip code and the level of estimated damage. The three instances where the order of rows should have been reversed, the difference in the blue tarp counts between each pair was not significant: - NASA hosts a wide variety of continuous Earth observation data useful in environmental justice research. This dashboard features a selection of NASA datasets from across the Agency, including socioeconomic data, Earth observation analysis, and other combined datasets. These tools allow users to visualize and download data to understand the environmental issues brought on by Hurricane María. Merging Earth data and socioeconomic data can help communities like those in Puerto Rico to better prepare for and respond to future natural disasters. + * 70114(H) with 70119(M) p=0.177; + * 70113(M) with 70130(L) p=0.452; + * 00912(M) with 00901(L) p=0.478. - **About the Data** + Overall the number of detected blue tarps corresponded with the estimated amount of damage experienced by each zip code based on ground-level reports. - *Rest of the text will be an explanation about the data used. No need to be reviewed by PB* - +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Hurricane Ida
Estimated DamageZipMax. Tarps
H700032112
H700942068
H700011066
M70119883
H70114632
M70117543
L70130213
M70113181
L70112102
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Hurricane María
Estimated DamageZipMax. Tarps
H00915308
H00962242
H00956240
M00979137
M00920110
M0091888
L0090142
M0091240
L0091123
+
+ + Estimated damage based on number of tarps detected + +
+ + - - ## Connecting Disaster Recovery with Environmental Justice: Hurricane Ida + + However, when analyzing detected blue tarps as a percentage of total building footprints in each zip code, a similar pattern emerges that suggests the on-the-ground observations and damage assessments do not necessarily capture the complete picture. - Known as the city that can barely catch its breath between storms, New Orleans experienced another devastating event on August 29, 2021 as Hurricane Ida made landfall as a Category 4 hurricane. The effects of the storm were widespread, causing millions of dollars worth of damage and affecting the lives and homes of millions of people. + The graphs show a zip code in each of the areas affected by the hurricanes that were estimated as having sustained a low degree of storm damage: 70112 in New Orleans and 00901 in San Juan. As noted previously, in line with the on-the-ground reports, both rank low on the graphs showing the number of blue tarps detected. - [Disadvantaged communities](https://www.nature.com/articles/d41586-021-02520-8) in Louisiana and across the country already struggle with higher rates of asthma, cancer, and COVID-19 infections. These communities are often hardest-hit by storms like Ida. Research has shown that disadvantaged communities often receive less federal aid than other communities, only prolonging their hardships. NASA is prioritizing open access to environmental justice data such as the datasets in this dashboard in an effort to help communities better prepare for and respond to natural disasters and to help shed light on cases of environmental injustice. + +
+ + + Number of tarps detected in the aftermath of Hurricane Ida + +
+ + +
+ + + Number of tarps detected in the aftermath of Hurricane Maria + +
+ + When the zip codes are analyzed in terms of the number of detected blue + tarps as a percentage of the total building footprints, a pattern emerges in + each of the affected areas. Both 70112 and 00901 jump high up in the damage + estimate, significantly higher than their low designation would have + predicted (p=0.054 and p = 0.009 respectively). + + - **About the Data** + +
+ + + Tarps as a percentage of the total buildings in the aftermath of Hurricane + Ida + +
+ + + +
+ + + Tarps as a percentage of the total buildings in the aftermath of Hurricane + Maria + +
+ + + + + ## Findings and Future Avenues of Research + + The ability to detect blue tarp counts for the entirety of a zip code from satellite imagery holds the possibility of surfacing areas of relatively severe damage that may be obscured in ground-level observations. In both these zip codes the small absolute number of damaged buildings and significantly lower building footprint densities (i.e., 761.49/sq. km. in 70112 (p=0.0119) and 713.16/ sq. km. in 00901 (p=0.008)), may have caused these communities to be overlooked by on-the-ground coverage of the storm damage. However, as a percentage of total buildings, the damage sustained in both these zip codes was significantly underestimated by on-the-ground reports when compared to the highest ranked zip codes in medium damage estimate groupings (p=0.012 for 70112 and p=0.004 for 00901). + + These findings have potential environmental justice implications as zip code 70112 and 00901 share similar demographic profiles. Both zip codes are minority majority. The median household income for zip code 70112 is $20,457, and for 00901 it is $14,720 (65.7% and 47.3% the national median respectively). Both zip codes contain a higher than average population density: + * 70112 contains 3,655 residents in a land area of 2.26 square kilometers (an effective density of 4,255/sq. mile). + * 00901 contains 7,080 residents in a land area of 2.54 square kilometers (7,217/sq. mile). + + Both zip codes have indicators that suggest a high degree of multifamily housing. In zip code 70112, 2,563 housing units exist within 1,720 building footprints for an average ratio of 1.49 units per building. There are 4,990 housing units among the 1,810 building footprints in zip code 00901 for an average ratio of 2.76 units per building. In terms of population density, zip code 70112 has a ratio of 2.13 residents per building footprint, and zip code 00901 has 3.91 residents per building footprint. It follows from these residential densities that the number of people directly affected by even an apparently low number of damaged buildings (in terms of raw counts) is not insignificant. + + The environmental justice implications of being able to more quickly identify overlooked storm-damaged communities is considerable. Further research in this area can establish how satellite-based detections can inform and enhance disaster recovery efforts, especially in vulnerable communities. + + + + + + + ## Credits and Sources + - Black Marble data courtesy of [Universities Space Research Association (USRA) Earth from Space Institute (EfSI)](https://www.usra.edu/efsi-our-mission) and NASA Goddard Space Flight Center’s [Terrestrial Information Systems + - Laboratory](https://science.gsfc.nasa.gov/earth/terrestrialinfo/) using VIIRS day-night band data from the Suomi National Polar-orbiting Partnership and Landsat-8 Operational Land Imager (OLI) data from the U.S. Geological Survey + - Citation for HLS data set: Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. + - Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSS30.002 + - Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSL30.002 + - [https://www.wdsu.com/article/parish-to-parish-the-latest-on-what-we-know-ten-days-post-ida/37511472#](https://www.wdsu.com/article/parish-to-parish-the-latest-on-what-we-know-ten-days-post-ida/37511472#) + - [https://www.nola.com/news/hurricane/article_bb0c2268-2144-11ec-8dce-672b7a624bed.html](https://www.nola.com/news/hurricane/article_bb0c2268-2144-11ec-8dce-672b7a624bed.html) + - [https://www.nola.com/multimedia/photos/collection_59f4a7f4-0973-11ec-b836-cfb3d4ff5589.html#20](https://www.nola.com/multimedia/photos/collection_59f4a7f4-0973-11ec-b836-cfb3d4ff5589.html#20) + - [https://www.nola.com/news/hurricane/article_ef41b052-0998-11ec-9c32-97779ca0847b.html](https://www.nola.com/news/hurricane/article_ef41b052-0998-11ec-9c32-97779ca0847b.html) + - [https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1084218&dswid=9465](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1084218&dswid=9465) + - [https://digitalscholarship.tsu.edu/jpmsp/vol22/iss2/5/](https://digitalscholarship.tsu.edu/jpmsp/vol22/iss2/5/) + - [https://www.unitedstateszipcodes.org/70112/](https://www.unitedstateszipcodes.org/70112/) + - [https://www.unitedstateszipcodes.org/00901/](https://www.unitedstateszipcodes.org/00901/) - *Rest of the text will be an explanation about the data used. No need to be reviewed by PB* - + diff --git a/discoveries/hurricane-maria-ida-chart1.csv b/discoveries/hurricane-maria-ida-chart1.csv new file mode 100644 index 000000000..0bb4ee17c --- /dev/null +++ b/discoveries/hurricane-maria-ida-chart1.csv @@ -0,0 +1,55 @@ +Zip,Month,Number of Tarps +70003,08/2021,3 +70094,08/2021,5 +70001,08/2021,5 +70114,08/2021,12 +70119,08/2021,5 +70117,08/2021,2 +70113,08/2021,0 +70130,08/2021,1 +70112,08/2021,0 +70003,09/2021,995 +70094,09/2021,771 +70001,09/2021,378 +70114,09/2021,72 +70119,09/2021,144 +70117,09/2021,47 +70113,09/2021,38 +70130,09/2021,38 +70112,09/2021,9 +70003,11/2021,2112 +70094,11/2021,2068 +70001,11/2021,1066 +70114,11/2021,350 +70119,11/2021,825 +70117,11/2021,425 +70113,11/2021,160 +70130,11/2021,171 +70112,11/2021,51 +70003,12/2021,1562 +70094,12/2021,1746 +70001,12/2021,1015 +70114,12/2021,555 +70119,12/2021,883 +70117,12/2021,543 +70113,12/2021,181 +70130,12/2021,213 +70112,12/2021,102 +70003,01/2022,1442 +70094,01/2022,1646 +70001,01/2022,870 +70114,01/2022,632 +70119,01/2022,645 +70117,01/2022,543 +70113,01/2022,166 +70130,01/2022,169 +70112,01/2022,38 +70003,02/2022,490 +70094,02/2022,791 +70001,02/2022,440 +70114,02/2022,414 +70119,02/2022,451 +70117,02/2022,377 +70113,02/2022,121 +70130,02/2022,137 +70112,02/2022,23 \ No newline at end of file diff --git a/discoveries/hurricane-maria-ida-chart2.csv b/discoveries/hurricane-maria-ida-chart2.csv new file mode 100644 index 000000000..41be2fe05 --- /dev/null +++ b/discoveries/hurricane-maria-ida-chart2.csv @@ -0,0 +1,55 @@ +Zip,Month,Number of Tarps +00915,02/2017,6 +00962,02/2017,13 +00956,02/2017,6 +00979,02/2017,9 +00920,02/2017,18 +00918,02/2017,3 +00912,02/2017,5 +00901,02/2017,2 +00911,02/2017,6 +00915,09/2017,308 +00962,09/2017,158 +00956,09/2017,155 +00979,09/2017,113 +00920,09/2017,11 +00918,09/2017,88 +00912,09/2017,11 +00901,09/2017,9 +00911,09/2017,5 +00915,10/2017,296 +00962,10/2017,158 +00956,10/2017,108 +00979,10/2017,137 +00920,10/2017,10 +00918,10/2017,81 +00912,10/2017,11 +00901,10/2017,9 +00911,10/2017,3 +00915,01/2018,306 +00962,01/2018,242 +00956,01/2018,240 +00979,01/2018,131 +00920,01/2018,110 +00918,01/2018,83 +00912,01/2018,40 +00901,01/2018,42 +00911,01/2018,23 +00915,11/2018,266 +00962,11/2018,67 +00956,11/2018,28 +00979,11/2018,107 +00920,11/2018,13 +00918,11/2018,72 +00912,11/2018,17 +00901,11/2018,0 +00911,11/2018,11 +00915,10/2019,18 +00962,10/2019,17 +00956,10/2019,14 +00979,10/2019,15 +00920,10/2019,8 +00918,10/2019,2 +00912,10/2019,3 +00901,10/2019,4 +00911,10/2019,5 \ No newline at end of file diff --git a/discoveries/hurricane-maria-ida-chart3.csv b/discoveries/hurricane-maria-ida-chart3.csv new file mode 100644 index 000000000..117e4b16d --- /dev/null +++ b/discoveries/hurricane-maria-ida-chart3.csv @@ -0,0 +1,55 @@ +Zip,Month,Percentage of Buildings +70003,08/2021,0.01 +70094,08/2021,0.02 +70001,08/2021,0.02 +70114,08/2021,0.10 +70119,08/2021,0.03 +70117,08/2021,0.02 +70113,08/2021,0.00 +70130,08/2021,0.02 +70112,08/2021,0.00 +70003,09/2021,3.59 +70094,09/2021,2.33 +70001,09/2021,1.52 +70114,09/2021,0.60 +70119,09/2021,0.72 +70117,09/2021,0.38 +70113,09/2021,1.01 +70130,09/2021,0.69 +70112,09/2021,0.52 +70003,11/2021,7.62 +70094,11/2021,6.26 +70001,11/2021,4.29 +70114,11/2021,2.93 +70119,11/2021,4.14 +70117,11/2021,3.41 +70113,11/2021,4.25 +70130,11/2021,3.11 +70112,11/2021,2.97 +70003,12/2021,5.63 +70094,12/2021,5.28 +70001,12/2021,4.09 +70114,12/2021,4.65 +70119,12/2021,4.44 +70117,12/2021,4.35 +70113,12/2021,4.81 +70130,12/2021,3.87 +70112,12/2021,5.93 +70003,01/2022,5.20 +70094,01/2022,4.98 +70001,01/2022,3.50 +70114,01/2022,5.30 +70119,01/2022,3.24 +70117,01/2022,4.35 +70113,01/2022,4.41 +70130,01/2022,3.07 +70112,01/2022,2.21 +70003,02/2022,1.77 +70094,02/2022,2.39 +70001,02/2022,1.77 +70114,02/2022,3.47 +70119,02/2022,2.27 +70117,02/2022,3.02 +70113,02/2022,3.22 +70130,02/2022,2.49 +70112,02/2022,1.34 \ No newline at end of file diff --git a/discoveries/hurricane-maria-ida-chart4.csv b/discoveries/hurricane-maria-ida-chart4.csv new file mode 100644 index 000000000..c8d79a782 --- /dev/null +++ b/discoveries/hurricane-maria-ida-chart4.csv @@ -0,0 +1,55 @@ +Zip,Month,Percentage of Buildings +00915,02/2017,0.05 +00962,02/2017,0.12 +00956,02/2017,0.05 +00979,02/2017,0.10 +00920,02/2017,0.13 +00918,02/2017,0.03 +00912,02/2017,0.15 +00901,02/2017,0.11 +00911,02/2017,0.22 +00915,09/2017,2.52 +00962,09/2017,1.51 +00956,09/2017,1.40 +00979,09/2017,1.23 +00920,09/2017,0.08 +00918,09/2017,0.94 +00912,09/2017,0.34 +00901,09/2017,0.50 +00911,09/2017,0.18 +00915,10/2017,2.42 +00962,10/2017,1.51 +00956,10/2017,0.98 +00979,10/2017,1.49 +00920,10/2017,0.07 +00918,10/2017,0.86 +00912,10/2017,0.34 +00901,10/2017,0.50 +00911,10/2017,0.11 +00915,01/2018,2.50 +00962,01/2018,2.31 +00956,01/2018,2.17 +00979,01/2018,1.42 +00920,01/2018,0.79 +00918,01/2018,0.88 +00912,01/2018,1.22 +00901,01/2018,2.32 +00911,01/2018,0.83 +00915,11/2018,2.17 +00962,11/2018,0.64 +00956,11/2018,0.25 +00979,11/2018,1.16 +00920,11/2018,0.09 +00918,11/2018,0.77 +00912,11/2018,0.52 +00901,11/2018,0.00 +00911,11/2018,0.40 +00915,10/2019,0.15 +00962,10/2019,0.16 +00956,10/2019,0.13 +00979,10/2019,0.16 +00920,10/2019,0.06 +00918,10/2019,0.02 +00912,10/2019,0.09 +00901,10/2019,0.22 +00911,10/2019,0.18 \ No newline at end of file diff --git a/discoveries/no2-and-so2-charts.csv b/discoveries/no2-and-so2-charts.csv new file mode 100644 index 000000000..b69204ce8 --- /dev/null +++ b/discoveries/no2-and-so2-charts.csv @@ -0,0 +1,35 @@ +Row,Year,Data Type,United States Change from 2005,China Change from 2005,India Change from 2005 +1,2005,Nitrogen Dioxide,0.00,0.00,0.00 +2,2006,Nitrogen Dioxide,-10.00,-0.27,0.92 +3,2007,Nitrogen Dioxide,-22.12,14.96,9.05 +4,2008,Nitrogen Dioxide,-28.00,5.99,14.01 +5,2009,Nitrogen Dioxide,-47.13,9.27,25.27 +6,2010,Nitrogen Dioxide,-43.28,21.59,27.18 +7,2011,Nitrogen Dioxide,-46.47,39.49,36.00 +8,2012,Nitrogen Dioxide,-41.73,32.43,32.17 +9,2013,Nitrogen Dioxide,-44.51,32.47,33.30 +10,2014,Nitrogen Dioxide,-46.73,7.80,44.27 +11,2015,Nitrogen Dioxide,-51.19,1.94,46.13 +12,2016,Nitrogen Dioxide,-52.29,-2.07,63.05 +13,2017,Nitrogen Dioxide,-50.70,-1.65,73.19 +14,2018,Nitrogen Dioxide,-56.35,-4.76,84.61 +15,2019,Nitrogen Dioxide,-55.60,-10.05,67.26 +16,2020,Nitrogen Dioxide,-60.73,-18.32,50.23 +17,2021,Nitrogen Dioxide,-55.06,-21.05,70.17 +18,2005,Sulfur Dioxide,0.00,0.00,0.00 +19,2006,Sulfur Dioxide,-30.45,-3.42,12.35 +20,2007,Sulfur Dioxide,-30.95,12.68,23.13 +21,2008,Sulfur Dioxide,-49.32,-11.73,39.04 +22,2009,Sulfur Dioxide,-51.11,-24.95,36.80 +23,2010,Sulfur Dioxide,-60.86,-31.05,40.92 +24,2011,Sulfur Dioxide,-73.99,-16.12,46.40 +25,2012,Sulfur Dioxide,-74.49,-14.02,72.84 +26,2013,Sulfur Dioxide,-74.36,-31.00,65.83 +27,2014,Sulfur Dioxide,-71.86,-46.42,91.42 +28,2015,Sulfur Dioxide,-84.62,-63.69,102.86 +29,2016,Sulfur Dioxide,-79.77,-70.13,114.40 +30,2017,Sulfur Dioxide,-86.37,-80.94,160.54 +31,2018,Sulfur Dioxide,-81.74,-84.49,169.84 +32,2019,Sulfur Dioxide,-71.87,-89.75,167.06 +33,2020,Sulfur Dioxide,-81.31,-89.48,113.51 +34,2021,Sulfur Dioxide,-90.84,-91.91,163.60 \ No newline at end of file diff --git a/discoveries/no2-and-so2.discoveries.mdx b/discoveries/no2-and-so2.discoveries.mdx new file mode 100644 index 000000000..5773438ca --- /dev/null +++ b/discoveries/no2-and-so2.discoveries.mdx @@ -0,0 +1,133 @@ +--- +featuredOn: + - air-quality +id: 'no2-and-so2' +name: Nitrogen Dioxide and Sulfur Dioxide +description: "A picture diary of air quality in the northeastern regions of India, China, and the United States." +media: + src: ::file ./air-quality-and-covid-19--discovery-cover.jpg + alt: Clear nightsky with crescent moon above the mountains + author: + name: Benjamin Voros + url: https://unsplash.com/photos/U-Kty6HxcQc +pubDate: 2020-12-01 +thematics: + - air-quality +--- + + + Air pollution is now considered the world's largest environmental health threat, accounting for an estimated seven million deaths every year, according to the Global Burden of Disease. Monitoring the gasses that produce air pollution provide data to help identify the sources of these pollutants, and potentially lessen their health impact. + + Air quality levels in the northeastern regions of India, China, and the U.S. are monitored and recorded daily—all from the watchful eye of one satellite and its instruments. Stationed 440 miles above Earth’s surface, the Ozone Monitoring Instrument (OMI), stationed on NASA's Aura satellite, provides a unique insight on gasses such as nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as aerosols, such as smoke, dust, and sulfates. Since its launch, OMI data has seen hazardous gas emissions drop significantly in some areas, while rising in others. Scientists are using the images and data collected to better understand the role of human activities in these changes, and which changes occur through natural processes. + + NASA scientists began monitoring and measuring NO2 and SO2 in 2004. As it is with many areas on the globe, the air pollution levels in northeastern India, China and the U.S. have changed significantly over the past two decades, specifically for air pollutants such as NO2 and SO2. Sulfur dioxide is an air pollutant that leads to acid rain, haze, and many health-related problems. This pollutant is primarily produced through the burning of coal to generate electricity. Nitrogen dioxide is a gas emitted into the air from the burning of fossil fuels. NO2 is injected into the atmosphere in the emissions from cars, trucks, buses, and power plants. + + To better understand how pollution caused by NO2 and SO2 emissions is changing around the world, scientists are using data from the northeastern regions of India, China, and the U.S. for several case studies. These studies monitor changes in these pollutants over time and can inform actions that attempt to clean the air. + + + + + + ## United States + For example, this image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States. + + The decreases in the U.S. can be attributed in large part to improved emissions from cars and trucks, as well as a reduction in the sulfur content in fuel. Some improvement is also the result of technology that does a better job of trapping nitrogen and sulfur oxides from power plant smokestacks. + + +
+ + +
+ + + + + ## China + Satellite observations for northeastern China show a different story. The data for SO2 (red) show a 90% reduction in this pollutant from 2005-2021. The atmospheric levels of NO2 initially increased after 2005, then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. + + This highlights the fact that it is a bit easier to remove SO2 emissions via the installation of SO2 scrubbers (or flue gas desulfurization systems) into the smokestacks of coal power plants. By comparison, NO2 is released by all combustion sources (coal, oil, natural gas, gasoline, diesel, etc.), and as a result it is a bit more difficult to control. + +
+ + +
+ + + +
+ + + Comparison of total column SO2 for China for 2006 and 2021 + +
+ + + + + ## India + The time series charts for northeastern India show a steady increase for both NO2 and SO2 from 2005-2018, after which both leveled off, perhaps due to the deployment of air pollution control technology. The significant drop in both SO2 and NO2 in 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion. + +
+ + +
+ + + +
+ + + Comparison of total column SO2 for India for 2005 and 2021 + +
+ + + + + ## Future Research + + These observations are informed by almost two decades of daily air pollution data that show an increasingly clear picture of where the gas comes from, how the amount entering the atmosphere has changed over time, and whether changes in air quality control are making a difference. + + + \ No newline at end of file diff --git a/docs/MDX_BLOCKS.md b/docs/MDX_BLOCKS.md index 3bdd0ff73..9120efa72 100644 --- a/docs/MDX_BLOCKS.md +++ b/docs/MDX_BLOCKS.md @@ -326,9 +326,11 @@ For example, if you put an image `image.jpg` inside of the folder where your mdx ## Chart +![Screenshot of chart component with sample data](./media/chart.png) + | Option | Type | Default | Description| |---|---|---|---| -| dataPath | string | `''` | Path for data. The data should be either in `csv` or `json` format. | +| dataPath | string | `''` | Path for data. The data should be either in `csv` or `json` format. Use parcel's URL builder to use local file. (Refer to the example below.) | | xKey | string | `''` | Attribute to be used for x axis. | | yKey | string | `''` | Attribute to be used for y axis | | idKey | string | `''` | Attribute for each data point | @@ -337,13 +339,13 @@ For example, if you put an image `image.jpg` inside of the folder where your mdx | highlightEnd | string | `''` | End point of x axis to draw highlighted area. | highlightLabel | string | `''` | Label for highlighted area. | -Syntax for Chart used in Wide Figure Block looks like this +Syntax for Chart used in Wide Figure Block looks like this. Check how the data is formatted in [example.csv](./media/example.csv). ```jsx
{ return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; } -``` \ No newline at end of file +``` diff --git a/docs/media/example.csv b/docs/media/example.csv new file mode 100644 index 000000000..394d9be61 --- /dev/null +++ b/docs/media/example.csv @@ -0,0 +1,10269 @@ +Test Date,County,New Positives,Cumulative Number of Positives,Total Number of Tests Performed,Cumulative Number of Tests Performed +03/01/2020,Albany,0,0,0,0 +03/02/2020,Albany,0,0,0,0 +03/03/2020,Albany,0,0,0,0 +03/04/2020,Albany,0,0,0,0 +03/05/2020,Albany,0,0,3,3 +03/06/2020,Albany,0,0,0,3 +03/07/2020,Albany,0,0,5,8 +03/08/2020,Albany,0,0,2,10 +03/09/2020,Albany,0,0,3,13 +03/10/2020,Albany,0,0,2,15 +03/11/2020,Albany,0,0,13,28 +03/12/2020,Albany,1,1,11,39 +03/13/2020,Albany,2,3,26,65 +03/14/2020,Albany,3,6,108,173 +03/15/2020,Albany,6,12,108,281 +03/16/2020,Albany,11,23,140,421 +03/17/2020,Albany,13,36,267,688 +03/18/2020,Albany,7,43,331,1019 +03/19/2020,Albany,18,61,450,1469 +03/20/2020,Albany,27,88,499,1968 +03/21/2020,Albany,35,123,577,2545 +03/22/2020,Albany,4,127,197,2742 +03/23/2020,Albany,19,146,144,2886 +03/24/2020,Albany,6,152,198,3084 +03/25/2020,Albany,19,171,93,3177 +03/26/2020,Albany,16,187,112,3289 +03/27/2020,Albany,8,195,101,3390 +03/28/2020,Albany,10,205,73,3463 +03/29/2020,Albany,12,217,61,3524 +03/30/2020,Albany,9,226,72,3596 +03/31/2020,Albany,14,240,108,3704 +04/01/2020,Albany,13,253,106,3810 +04/02/2020,Albany,14,267,105,3915 +04/03/2020,Albany,26,293,123,4038 +04/04/2020,Albany,12,305,117,4155 +04/05/2020,Albany,14,319,89,4244 +04/06/2020,Albany,14,333,86,4330 +04/07/2020,Albany,9,342,74,4404 +04/08/2020,Albany,37,379,229,4633 +04/09/2020,Albany,47,426,332,4965 +04/10/2020,Albany,20,446,159,5124 +04/11/2020,Albany,32,478,223,5347 +04/12/2020,Albany,21,499,193,5540 +04/13/2020,Albany,36,535,216,5756 +04/14/2020,Albany,13,548,106,5862 +04/15/2020,Albany,33,581,301,6163 +04/16/2020,Albany,38,619,273,6436 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+01/31/2021,Chautauqua,33,6514,395,164348 +02/01/2021,Chautauqua,46,6560,443,164791 +02/02/2021,Chautauqua,36,6596,766,165557 +02/03/2021,Chautauqua,71,6667,1267,166824 +02/04/2021,Chautauqua,69,6736,1273,168097 +02/05/2021,Chautauqua,46,6782,1239,169336 +02/06/2021,Chautauqua,37,6819,784,170120 +02/07/2021,Chautauqua,27,6846,646,170766 +02/08/2021,Chautauqua,14,6860,318,171084 +02/09/2021,Chautauqua,49,6909,1515,172599 +02/10/2021,Chautauqua,31,6940,2663,175262 +02/11/2021,Chautauqua,33,6973,788,176050 +02/12/2021,Chautauqua,31,7004,1632,177682 +02/13/2021,Chautauqua,40,7044,3020,180702 +02/14/2021,Chautauqua,14,7058,357,181059 +02/15/2021,Chautauqua,13,7071,344,181403 +02/16/2021,Chautauqua,16,7087,1123,182526 +02/17/2021,Chautauqua,35,7122,962,183488 +02/18/2021,Chautauqua,18,7140,2448,185936 +02/19/2021,Chautauqua,12,7152,2963,188899 +02/20/2021,Chautauqua,33,7185,2031,190930 +02/21/2021,Chautauqua,12,7197,786,191716 +02/22/2021,Chautauqua,4,7201,225,191941 +02/23/2021,Chautauqua,12,7213,1055,192996 +02/24/2021,Chautauqua,44,7257,2453,195449 +02/25/2021,Chautauqua,11,7268,2430,197879 +02/26/2021,Chautauqua,20,7288,2111,199990 +02/27/2021,Chautauqua,33,7321,2340,202330 +02/28/2021,Chautauqua,15,7336,574,202904 +03/01/2021,Chautauqua,5,7341,132,203036 +03/02/2021,Chautauqua,20,7361,1555,204591 +03/03/2021,Chautauqua,18,7379,1729,206320 +03/04/2021,Chautauqua,23,7402,2080,208400 +03/05/2021,Chautauqua,23,7425,1434,209834 +03/06/2021,Chautauqua,18,7443,2814,212648 +03/07/2021,Chautauqua,12,7455,281,212929 +03/08/2021,Chautauqua,21,7476,1001,213930 +03/09/2021,Chautauqua,10,7486,2272,216202 +03/10/2021,Chautauqua,8,7494,2168,218370 +03/11/2021,Chautauqua,23,7517,1997,220367 +03/12/2021,Chautauqua,10,7527,1943,222310 +03/13/2021,Chautauqua,11,7538,2428,224738 +03/14/2021,Chautauqua,24,7562,532,225270 +03/15/2021,Chautauqua,20,7582,219,225489 +03/16/2021,Chautauqua,16,7598,1755,227244 +03/17/2021,Chautauqua,37,7635,1915,229159 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+01/06/2022,Chemung,302,16251,1641,359562 +01/07/2022,Chemung,269,16520,1913,361475 +01/08/2022,Chemung,293,16813,1447,362922 +01/09/2022,Chemung,160,16973,674,363596 +01/10/2022,Chemung,138,17111,721,364317 +01/11/2022,Chemung,294,17405,1621,365938 +01/12/2022,Chemung,381,17786,1670,367608 +01/13/2022,Chemung,289,18075,1735,369343 +01/14/2022,Chemung,239,18314,1709,371052 +01/15/2022,Chemung,217,18531,1377,372429 +01/16/2022,Chemung,122,18653,666,373095 +01/17/2022,Chemung,85,18738,403,373498 +01/18/2022,Chemung,118,18856,824,374322 +01/19/2022,Chemung,127,18983,1040,375362 +01/20/2022,Chemung,154,19137,1325,376687 +01/21/2022,Chemung,183,19320,1350,378037 +01/22/2022,Chemung,139,19459,840,378877 +01/23/2022,Chemung,67,19526,495,379372 +01/24/2022,Chemung,87,19613,673,380045 +01/25/2022,Chemung,101,19714,795,380840 +01/26/2022,Chemung,108,19822,968,381808 +01/27/2022,Chemung,106,19928,1071,382879 +01/28/2022,Chemung,72,20000,942,383821 +01/29/2022,Chemung,81,20081,704,384525 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+07/15/2020,Chenango,3,178,129,13856 +07/16/2020,Chenango,3,181,197,14053 +07/17/2020,Chenango,1,182,173,14226 +07/18/2020,Chenango,0,182,112,14338 +07/19/2020,Chenango,2,184,134,14472 +07/20/2020,Chenango,4,188,175,14647 +07/21/2020,Chenango,3,191,306,14953 +07/22/2020,Chenango,3,194,204,15157 +07/23/2020,Chenango,2,196,212,15369 +07/24/2020,Chenango,7,203,220,15589 +07/25/2020,Chenango,0,203,102,15691 +07/26/2020,Chenango,0,203,249,15940 +07/27/2020,Chenango,0,203,92,16032 +07/28/2020,Chenango,2,205,147,16179 +07/29/2020,Chenango,1,206,108,16287 +07/30/2020,Chenango,1,207,189,16476 +07/31/2020,Chenango,2,209,193,16669 +08/01/2020,Chenango,2,211,289,16958 +08/02/2020,Chenango,0,211,206,17164 +08/03/2020,Chenango,1,212,111,17275 +08/04/2020,Chenango,0,212,179,17454 +08/05/2020,Chenango,0,212,95,17549 +08/06/2020,Chenango,3,215,213,17762 +08/07/2020,Chenango,0,215,155,17917 +08/08/2020,Chenango,1,216,165,18082 +08/09/2020,Chenango,0,216,122,18204 +08/10/2020,Chenango,2,218,520,18724 +08/11/2020,Chenango,0,218,95,18819 +08/12/2020,Chenango,0,218,158,18977 +08/13/2020,Chenango,0,218,185,19162 +08/14/2020,Chenango,0,218,227,19389 +08/15/2020,Chenango,0,218,278,19667 +08/16/2020,Chenango,0,218,249,19916 +08/17/2020,Chenango,0,218,173,20089 +08/18/2020,Chenango,0,218,183,20272 +08/19/2020,Chenango,1,219,144,20416 +08/20/2020,Chenango,3,222,419,20835 +08/21/2020,Chenango,1,223,280,21115 +08/22/2020,Chenango,0,223,131,21246 +08/23/2020,Chenango,0,223,119,21365 +08/24/2020,Chenango,0,223,144,21509 +08/25/2020,Chenango,0,223,144,21653 +08/26/2020,Chenango,1,224,173,21826 +08/27/2020,Chenango,0,224,361,22187 +08/28/2020,Chenango,1,225,290,22477 +08/29/2020,Chenango,2,227,210,22687 +08/30/2020,Chenango,1,228,153,22840 +08/31/2020,Chenango,1,229,349,23189 +09/01/2020,Chenango,0,229,158,23347 +09/02/2020,Chenango,1,230,134,23481 +09/03/2020,Chenango,1,231,164,23645 +09/04/2020,Chenango,2,233,330,23975 +09/05/2020,Chenango,2,235,176,24151 +09/06/2020,Chenango,0,235,294,24445 +09/07/2020,Chenango,4,239,187,24632 +09/08/2020,Chenango,1,240,80,24712 +09/09/2020,Chenango,2,242,120,24832 +09/10/2020,Chenango,2,244,232,25064 +09/11/2020,Chenango,1,245,446,25510 +09/12/2020,Chenango,0,245,314,25824 +09/13/2020,Chenango,0,245,255,26079 +09/14/2020,Chenango,0,245,93,26172 +09/15/2020,Chenango,0,245,92,26264 +09/16/2020,Chenango,0,245,218,26482 +09/17/2020,Chenango,3,248,223,26705 +09/18/2020,Chenango,0,248,338,27043 +09/19/2020,Chenango,0,248,283,27326 +09/20/2020,Chenango,0,248,173,27499 +09/21/2020,Chenango,0,248,87,27586 +09/22/2020,Chenango,0,248,107,27693 +09/23/2020,Chenango,0,248,161,27854 +09/24/2020,Chenango,0,248,166,28020 +09/25/2020,Chenango,1,249,457,28477 +09/26/2020,Chenango,0,249,186,28663 +09/27/2020,Chenango,2,251,103,28766 +09/28/2020,Chenango,0,251,111,28877 +09/29/2020,Chenango,2,253,174,29051 +09/30/2020,Chenango,0,253,157,29208 +10/01/2020,Chenango,4,257,262,29470 +10/02/2020,Chenango,2,259,457,29927 +10/03/2020,Chenango,2,261,298,30225 +10/04/2020,Chenango,1,262,148,30373 +10/05/2020,Chenango,1,263,78,30451 +10/06/2020,Chenango,2,265,98,30549 +10/07/2020,Chenango,2,267,247,30796 +10/08/2020,Chenango,1,268,295,31091 +10/09/2020,Chenango,3,271,312,31403 +10/10/2020,Chenango,4,275,247,31650 +10/11/2020,Chenango,2,277,400,32050 +10/12/2020,Chenango,3,280,189,32239 +10/13/2020,Chenango,10,290,93,32332 +10/14/2020,Chenango,1,291,267,32599 +10/15/2020,Chenango,3,294,297,32896 +10/16/2020,Chenango,10,304,389,33285 +10/17/2020,Chenango,9,313,281,33566 +10/18/2020,Chenango,17,330,337,33903 +10/19/2020,Chenango,1,331,233,34136 +10/20/2020,Chenango,6,337,114,34250 +10/21/2020,Chenango,10,347,198,34448 +10/22/2020,Chenango,11,358,205,34653 +10/23/2020,Chenango,7,365,356,35009 +10/24/2020,Chenango,16,381,447,35456 +10/25/2020,Chenango,5,386,198,35654 +10/26/2020,Chenango,2,388,120,35774 +10/27/2020,Chenango,9,397,404,36178 +10/28/2020,Chenango,11,408,438,36616 +10/29/2020,Chenango,5,413,174,36790 +10/30/2020,Chenango,6,419,324,37114 +10/31/2020,Chenango,6,425,317,37431 +11/01/2020,Chenango,3,428,253,37684 +11/02/2020,Chenango,2,430,207,37891 +11/03/2020,Chenango,2,432,151,38042 +11/04/2020,Chenango,11,443,333,38375 +11/05/2020,Chenango,6,449,383,38758 +11/06/2020,Chenango,6,455,401,39159 +11/07/2020,Chenango,5,460,205,39364 +11/08/2020,Chenango,8,468,159,39523 +11/09/2020,Chenango,4,472,245,39768 +11/10/2020,Chenango,5,477,316,40084 +11/11/2020,Chenango,3,480,287,40371 +11/12/2020,Chenango,9,489,327,40698 +11/13/2020,Chenango,6,495,302,41000 +11/14/2020,Chenango,6,501,145,41145 +11/15/2020,Chenango,5,506,167,41312 +11/16/2020,Chenango,6,512,181,41493 +11/17/2020,Chenango,6,518,232,41725 +11/18/2020,Chenango,13,531,344,42069 +11/19/2020,Chenango,11,542,187,42256 +11/20/2020,Chenango,9,551,344,42600 +11/21/2020,Chenango,12,563,268,42868 +11/22/2020,Chenango,15,578,360,43228 +11/23/2020,Chenango,5,583,205,43433 +11/24/2020,Chenango,19,602,353,43786 +11/25/2020,Chenango,3,605,273,44059 +11/26/2020,Chenango,18,623,406,44465 +11/27/2020,Chenango,6,629,212,44677 +11/28/2020,Chenango,10,639,314,44991 +11/29/2020,Chenango,4,643,276,45267 +11/30/2020,Chenango,6,649,203,45470 +12/01/2020,Chenango,3,652,306,45776 +12/02/2020,Chenango,13,665,409,46185 +12/03/2020,Chenango,17,682,335,46520 +12/04/2020,Chenango,28,710,577,47097 +12/05/2020,Chenango,14,724,487,47584 +12/06/2020,Chenango,15,739,287,47871 +12/07/2020,Chenango,15,754,188,48059 +12/08/2020,Chenango,13,767,307,48366 +12/09/2020,Chenango,14,781,471,48837 +12/10/2020,Chenango,34,815,339,49176 +12/11/2020,Chenango,20,835,588,49764 +12/12/2020,Chenango,33,868,410,50174 +12/13/2020,Chenango,12,880,356,50530 +12/14/2020,Chenango,12,892,150,50680 +12/15/2020,Chenango,25,917,253,50933 +12/16/2020,Chenango,11,928,352,51285 +12/17/2020,Chenango,17,945,448,51733 +12/18/2020,Chenango,17,962,206,51939 +12/19/2020,Chenango,10,972,298,52237 +12/20/2020,Chenango,16,988,212,52449 +12/21/2020,Chenango,8,996,176,52625 +12/22/2020,Chenango,24,1020,316,52941 +12/23/2020,Chenango,16,1036,473,53414 +12/24/2020,Chenango,38,1074,316,53730 +12/25/2020,Chenango,28,1102,450,54180 +12/26/2020,Chenango,6,1108,228,54408 +12/27/2020,Chenango,21,1129,202,54610 +12/28/2020,Chenango,31,1160,271,54881 +12/29/2020,Chenango,19,1179,170,55051 +12/30/2020,Chenango,35,1214,536,55587 +12/31/2020,Chenango,36,1250,411,55998 +01/01/2021,Chenango,25,1275,278,56276 +01/02/2021,Chenango,21,1296,370,56646 +01/03/2021,Chenango,30,1326,501,57147 +01/04/2021,Chenango,11,1337,109,57256 +01/05/2021,Chenango,20,1357,295,57551 +01/06/2021,Chenango,36,1393,493,58044 +01/07/2021,Chenango,37,1430,436,58480 +01/08/2021,Chenango,41,1471,592,59072 +01/09/2021,Chenango,31,1502,422,59494 +01/10/2021,Chenango,27,1529,326,59820 +01/11/2021,Chenango,29,1558,329,60149 +01/12/2021,Chenango,23,1581,424,60573 +01/13/2021,Chenango,31,1612,456,61029 +01/14/2021,Chenango,42,1654,760,61789 +01/15/2021,Chenango,26,1680,628,62417 +01/16/2021,Chenango,24,1704,473,62890 +01/17/2021,Chenango,46,1750,459,63349 +01/18/2021,Chenango,8,1758,202,63551 +01/19/2021,Chenango,37,1795,539,64090 +01/20/2021,Chenango,26,1821,461,64551 +01/21/2021,Chenango,29,1850,569,65120 +01/22/2021,Chenango,38,1888,816,65936 +01/23/2021,Chenango,38,1926,673,66609 +01/24/2021,Chenango,17,1943,496,67105 +01/25/2021,Chenango,16,1959,230,67335 +01/26/2021,Chenango,18,1977,303,67638 +01/27/2021,Chenango,24,2001,523,68161 +01/28/2021,Chenango,48,2049,735,68896 +01/29/2021,Chenango,29,2078,762,69658 +01/30/2021,Chenango,32,2110,529,70187 +01/31/2021,Chenango,9,2119,389,70576 +02/01/2021,Chenango,14,2133,171,70747 +02/02/2021,Chenango,13,2146,389,71136 +02/03/2021,Chenango,29,2175,513,71649 +02/04/2021,Chenango,20,2195,508,72157 +02/05/2021,Chenango,19,2214,521,72678 +02/06/2021,Chenango,23,2237,516,73194 +02/07/2021,Chenango,12,2249,303,73497 +02/08/2021,Chenango,7,2256,120,73617 +02/09/2021,Chenango,10,2266,253,73870 +02/10/2021,Chenango,19,2285,378,74248 +02/11/2021,Chenango,13,2298,669,74917 +02/12/2021,Chenango,9,2307,644,75561 +02/13/2021,Chenango,12,2319,478,76039 +02/14/2021,Chenango,8,2327,226,76265 +02/15/2021,Chenango,1,2328,125,76390 +02/16/2021,Chenango,1,2329,298,76688 +02/17/2021,Chenango,9,2338,527,77215 +02/18/2021,Chenango,6,2344,508,77723 +02/19/2021,Chenango,15,2359,553,78276 +02/20/2021,Chenango,17,2376,499,78775 +02/21/2021,Chenango,7,2383,282,79057 +02/22/2021,Chenango,2,2385,106,79163 +02/23/2021,Chenango,5,2390,255,79418 +02/24/2021,Chenango,7,2397,530,79948 +02/25/2021,Chenango,13,2410,638,80586 +02/26/2021,Chenango,17,2427,556,81142 +02/27/2021,Chenango,7,2434,608,81750 +02/28/2021,Chenango,5,2439,258,82008 +03/01/2021,Chenango,4,2443,109,82117 +03/02/2021,Chenango,9,2452,393,82510 +03/03/2021,Chenango,9,2461,527,83037 +03/04/2021,Chenango,13,2474,601,83638 +03/05/2021,Chenango,13,2487,570,84208 +03/06/2021,Chenango,15,2502,495,84703 +03/07/2021,Chenango,3,2505,237,84940 +03/08/2021,Chenango,5,2510,146,85086 +03/09/2021,Chenango,17,2527,300,85386 +03/10/2021,Chenango,4,2531,309,85695 +03/11/2021,Chenango,10,2541,752,86447 +03/12/2021,Chenango,13,2554,514,86961 +03/13/2021,Chenango,9,2563,511,87472 +03/14/2021,Chenango,7,2570,197,87669 +03/15/2021,Chenango,15,2585,153,87822 +03/16/2021,Chenango,13,2598,374,88196 +03/17/2021,Chenango,11,2609,309,88505 +03/18/2021,Chenango,16,2625,752,89257 +03/19/2021,Chenango,14,2639,521,89778 +03/20/2021,Chenango,12,2651,578,90356 +03/21/2021,Chenango,8,2659,175,90531 +03/22/2021,Chenango,12,2671,159,90690 +03/23/2021,Chenango,15,2686,335,91025 +03/24/2021,Chenango,14,2700,271,91296 +03/25/2021,Chenango,23,2723,776,92072 +03/26/2021,Chenango,27,2750,697,92769 +03/27/2021,Chenango,21,2771,534,93303 +03/28/2021,Chenango,13,2784,218,93521 +03/29/2021,Chenango,13,2797,192,93713 +03/30/2021,Chenango,21,2818,333,94046 +03/31/2021,Chenango,17,2835,471,94517 +04/01/2021,Chenango,17,2852,599,95116 +04/02/2021,Chenango,14,2866,611,95727 +04/03/2021,Chenango,13,2879,468,96195 +04/04/2021,Chenango,14,2893,161,96356 +04/05/2021,Chenango,7,2900,119,96475 +04/06/2021,Chenango,12,2912,279,96754 +04/07/2021,Chenango,22,2934,359,97113 +04/08/2021,Chenango,22,2956,714,97827 +04/09/2021,Chenango,17,2973,614,98441 +04/10/2021,Chenango,14,2987,397,98838 +04/11/2021,Chenango,20,3007,204,99042 +04/12/2021,Chenango,11,3018,189,99231 +04/13/2021,Chenango,13,3031,387,99618 +04/14/2021,Chenango,17,3048,436,100054 +04/15/2021,Chenango,10,3058,579,100633 +04/16/2021,Chenango,19,3077,505,101138 +04/17/2021,Chenango,14,3091,704,101842 +04/18/2021,Chenango,13,3104,166,102008 +04/19/2021,Chenango,21,3125,172,102180 +04/20/2021,Chenango,13,3138,297,102477 +04/21/2021,Chenango,18,3156,483,102960 +04/22/2021,Chenango,27,3183,756,103716 +04/23/2021,Chenango,19,3202,624,104340 +04/24/2021,Chenango,11,3213,452,104792 +04/25/2021,Chenango,6,3219,234,105026 +04/26/2021,Chenango,10,3229,139,105165 +04/27/2021,Chenango,9,3238,377,105542 +04/28/2021,Chenango,18,3256,645,106187 +04/29/2021,Chenango,12,3268,535,106722 +04/30/2021,Chenango,20,3288,566,107288 +05/01/2021,Chenango,10,3298,561,107849 +05/02/2021,Chenango,11,3309,189,108038 +05/03/2021,Chenango,13,3322,101,108139 +05/04/2021,Chenango,9,3331,294,108433 +05/05/2021,Chenango,11,3342,651,109084 +05/06/2021,Chenango,11,3353,603,109687 +05/07/2021,Chenango,3,3356,513,110200 +05/08/2021,Chenango,10,3366,471,110671 +05/09/2021,Chenango,3,3369,119,110790 +05/10/2021,Chenango,7,3376,135,110925 +05/11/2021,Chenango,12,3388,256,111181 +05/12/2021,Chenango,4,3392,598,111779 +05/13/2021,Chenango,5,3397,586,112365 +05/14/2021,Chenango,5,3402,571,112936 +05/15/2021,Chenango,7,3409,360,113296 +05/16/2021,Chenango,3,3412,167,113463 +05/17/2021,Chenango,8,3420,101,113564 +05/18/2021,Chenango,12,3432,341,113905 +05/19/2021,Chenango,5,3437,502,114407 +05/20/2021,Chenango,9,3446,513,114920 +05/21/2021,Chenango,5,3451,456,115376 +05/22/2021,Chenango,4,3455,376,115752 +05/23/2021,Chenango,0,3455,125,115877 +05/24/2021,Chenango,1,3456,101,115978 +05/25/2021,Chenango,2,3458,283,116261 +05/26/2021,Chenango,4,3462,394,116655 +05/27/2021,Chenango,0,3462,410,117065 +05/28/2021,Chenango,4,3466,421,117486 +05/29/2021,Chenango,2,3468,293,117779 +05/30/2021,Chenango,1,3469,99,117878 +05/31/2021,Chenango,2,3471,47,117925 +06/01/2021,Chenango,2,3473,99,118024 +06/02/2021,Chenango,2,3475,295,118319 +06/03/2021,Chenango,1,3476,585,118904 +06/04/2021,Chenango,2,3478,455,119359 +06/05/2021,Chenango,5,3483,444,119803 +06/06/2021,Chenango,5,3488,130,119933 +06/07/2021,Chenango,1,3489,77,120010 +06/08/2021,Chenango,3,3492,220,120230 +06/09/2021,Chenango,2,3494,314,120544 +06/10/2021,Chenango,1,3495,505,121049 +06/11/2021,Chenango,0,3495,340,121389 +06/12/2021,Chenango,3,3498,388,121777 +06/13/2021,Chenango,0,3498,73,121850 +06/14/2021,Chenango,1,3499,129,121979 +06/15/2021,Chenango,2,3501,170,122149 +06/16/2021,Chenango,2,3503,344,122493 +06/17/2021,Chenango,1,3504,413,122906 +06/18/2021,Chenango,0,3504,269,123175 +06/19/2021,Chenango,0,3504,116,123291 +06/20/2021,Chenango,0,3504,58,123349 +06/21/2021,Chenango,1,3505,73,123422 +06/22/2021,Chenango,2,3507,134,123556 +06/23/2021,Chenango,2,3509,190,123746 +06/24/2021,Chenango,1,3510,204,123950 +06/25/2021,Chenango,1,3511,132,124082 +06/26/2021,Chenango,0,3511,134,124216 +06/27/2021,Chenango,1,3512,38,124254 +06/28/2021,Chenango,1,3513,65,124319 +06/29/2021,Chenango,1,3514,162,124481 +06/30/2021,Chenango,2,3516,143,124624 +07/01/2021,Chenango,0,3516,137,124761 +07/02/2021,Chenango,0,3516,69,124830 +07/03/2021,Chenango,0,3516,71,124901 +07/04/2021,Chenango,0,3516,74,124975 +07/05/2021,Chenango,0,3516,23,124998 +07/06/2021,Chenango,0,3516,50,125048 +07/07/2021,Chenango,2,3518,147,125195 +07/08/2021,Chenango,0,3518,182,125377 +07/09/2021,Chenango,0,3518,95,125472 +07/10/2021,Chenango,1,3519,136,125608 +07/11/2021,Chenango,0,3519,61,125669 +07/12/2021,Chenango,0,3519,36,125705 +07/13/2021,Chenango,0,3519,101,125806 +07/14/2021,Chenango,0,3519,140,125946 +07/15/2021,Chenango,0,3519,102,126048 +07/16/2021,Chenango,0,3519,89,126137 +07/17/2021,Chenango,0,3519,132,126269 +07/18/2021,Chenango,1,3520,25,126294 +07/19/2021,Chenango,2,3522,67,126361 +07/20/2021,Chenango,2,3524,77,126438 +07/21/2021,Chenango,1,3525,82,126520 +07/22/2021,Chenango,1,3526,93,126613 +07/23/2021,Chenango,1,3527,79,126692 +07/24/2021,Chenango,4,3531,87,126779 +07/25/2021,Chenango,0,3531,75,126854 +07/26/2021,Chenango,5,3536,133,126987 +07/27/2021,Chenango,2,3538,106,127093 +07/28/2021,Chenango,8,3546,101,127194 +07/29/2021,Chenango,7,3553,175,127369 +07/30/2021,Chenango,9,3562,153,127522 +07/31/2021,Chenango,5,3567,163,127685 +08/01/2021,Chenango,3,3570,135,127820 +08/02/2021,Chenango,2,3572,126,127946 +08/03/2021,Chenango,6,3578,252,128198 +08/04/2021,Chenango,5,3583,172,128370 +08/05/2021,Chenango,7,3590,180,128550 +08/06/2021,Chenango,9,3599,159,128709 +08/07/2021,Chenango,8,3607,124,128833 +08/08/2021,Chenango,14,3621,86,128919 +08/09/2021,Chenango,9,3630,160,129079 +08/10/2021,Chenango,14,3644,216,129295 +08/11/2021,Chenango,4,3648,225,129520 +08/12/2021,Chenango,24,3672,239,129759 +08/13/2021,Chenango,12,3684,233,129992 +08/14/2021,Chenango,10,3694,212,130204 +08/15/2021,Chenango,11,3705,139,130343 +08/16/2021,Chenango,8,3713,139,130482 +08/17/2021,Chenango,13,3726,344,130826 +08/18/2021,Chenango,12,3738,398,131224 +08/19/2021,Chenango,13,3751,212,131436 +08/20/2021,Chenango,16,3767,212,131648 +08/21/2021,Chenango,10,3777,325,131973 +08/22/2021,Chenango,7,3784,138,132111 +08/23/2021,Chenango,10,3794,227,132338 +08/24/2021,Chenango,15,3809,250,132588 +08/25/2021,Chenango,14,3823,196,132784 +08/26/2021,Chenango,15,3838,305,133089 +08/27/2021,Chenango,11,3849,257,133346 +08/28/2021,Chenango,11,3860,193,133539 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+02/27/2021,Erie,252,65215,24568,1547780 +02/28/2021,Erie,204,65419,4778,1552558 +03/01/2021,Erie,152,65571,3734,1556292 +03/02/2021,Erie,218,65789,8224,1564516 +03/03/2021,Erie,270,66059,14788,1579304 +03/04/2021,Erie,285,66344,9038,1588342 +03/05/2021,Erie,260,66604,14284,1602626 \ No newline at end of file diff --git a/thematic/agriculture.thematic.mdx b/thematic/agriculture.thematic.mdx deleted file mode 100644 index bebf9ce74..000000000 --- a/thematic/agriculture.thematic.mdx +++ /dev/null @@ -1,32 +0,0 @@ ---- -id: agriculture -name: 'Agriculture' -description: "Sed sed lectus vitae odio vestibulum mattis. Integer iaculis nisl lectus, vel aliquet nulla imperdiet in." -media: - src: ::file ./agriculture--cover.jpg - alt: 'Maisernte' - author: - name: no one cares - url: https://unsplash.com/photos/l_5MJnbrmrs - -about: - title: Observing Agriculture - description: Lorem ipsum dolor sit amet, consectetur adipiscing elit vitae ornare lectus. ---- - - -## Lorem ipsum dolor sit - -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut vitae ornare lectus, ac accumsan erat. Cras eget eleifend ligula. Curabitur ac nisi tempor, molestie lorem vitae, consectetur turpis. Etiam hendrerit et sapien ac interdum. - -Phasellus nec magna tristique, imperdiet mauris hendrerit, posuere sem. Vestibulum in lobortis tortor. - -Vestibulum justo ante, bibendum at vehicula sit amet, dignissim sit amet augue. Nunc luctus orci mi, sed pretium nibh consequat sit amet. In congue metus a ullamcorper sollicitudin. - -### Maecenas non eros - -Cras eget eleifend ligula. Curabitur ac nisi tempor, molestie lorem vitae, consectetur turpis. Etiam hendrerit et sapien ac interdum. - -Vestibulum justo ante, bibendum at vehicula sit amet, dignissim sit amet augue. Nunc luctus orci mi, sed pretium nibh consequat sit amet. In congue metus a ullamcorper sollicitudin. - - diff --git a/thematic/air-quality.thematic.mdx b/thematic/air-quality.thematic.mdx index 6c9695c95..df004c520 100644 --- a/thematic/air-quality.thematic.mdx +++ b/thematic/air-quality.thematic.mdx @@ -1,7 +1,7 @@ --- id: air-quality name: 'Air Quality' -description: "Sed sed lectus vitae odio vestibulum mattis. Integer iaculis nisl lectus, vel aliquet nulla imperdiet in." +description: "NASA is Keeping a watchful eye on our atmosphere across the world" media: src: ::file ./air-quality--cover.jpg alt: 'White clouds.' @@ -11,22 +11,20 @@ media: about: title: Observing Air Quality - description: Lorem ipsum dolor sit amet, consectetur adipiscing elit vitae ornare lectus. + description: NASA is Keeping a watchful eye on our atmosphere across the world --- -## Lorem ipsum dolor sit -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut vitae ornare lectus, ac accumsan erat. Cras eget eleifend ligula. Curabitur ac nisi tempor, molestie lorem vitae, consectetur turpis. Etiam hendrerit et sapien ac interdum. +**Traffic, wildfires, and power plants** all emit air pollutants and greenhouse gasses as a product of combustion. Humans have known for more than a century that burning fossil fuels and other materials generates smoke that contains a mixture of potentially hazardous particles and trace gasses. Breathing air with a high concentration of gasses from combustion can irritate airways in the human respiratory system and contribute to respiratory diseases and other health problems. +Using long-term global observations from satellites and ground-based instruments, scientists have kept a watchful eye on several gasses emitted by combustion for more than a decade. These Earth observations help predict and track the pollutants that affect human health and contribute to global climate change. -Phasellus nec magna tristique, imperdiet mauris hendrerit, posuere sem. Vestibulum in lobortis tortor. +**NASA scientists are leading much of this research** and share stories here that show how data from satellites are used to enhance the scientific understanding of combustion-related trace gasses. Several of these combustion-related gasses negatively impact human health and some contribute to global climate change. The data also provide fundamental information for improving climate change prediction and investigating solutions to reduce air pollution in major cities around the world. -Vestibulum justo ante, bibendum at vehicula sit amet, dignissim sit amet augue. Nunc luctus orci mi, sed pretium nibh consequat sit amet. In congue metus a ullamcorper sollicitudin. +**Data from the Ozone Monitoring Instrument (OMI)** show atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels for various regions in the United States, India, and China. This instrument has been tracking the annual average abundance of these gasses since 2005. These charts highlight different trends in these combustion gasses; in some countries the levels of these gasses have decreased dramatically, while other countries show a long-term increase. +In the eastern US, for example, both NO2 and SO2 levels decreased dramatically from 2005 to 2015. SO2 concentrations over the Ohio River valley and western Pennsylvania fell by 80%, and NO2 levels in the region fell by more than 40%, in response to clean air legislation. -### Maecenas non eros +The annual OMI data can be explored to generate maps and trends in these two combustion-related gasses at both the regional and city scales. Click here to learn more about this research. -Cras eget eleifend ligula. Curabitur ac nisi tempor, molestie lorem vitae, consectetur turpis. Etiam hendrerit et sapien ac interdum. - -Vestibulum justo ante, bibendum at vehicula sit amet, dignissim sit amet augue. Nunc luctus orci mi, sed pretium nibh consequat sit amet. In congue metus a ullamcorper sollicitudin. diff --git a/thematic/eis--cover.png b/thematic/eis--cover.png new file mode 100644 index 000000000..e3933455a Binary files /dev/null and b/thematic/eis--cover.png differ diff --git a/thematic/eis-fire-about.png b/thematic/eis-fire-about.png new file mode 100644 index 000000000..887c9e859 Binary files /dev/null and b/thematic/eis-fire-about.png differ diff --git a/thematic/eis-freshwater-about.png b/thematic/eis-freshwater-about.png new file mode 100644 index 000000000..3b71b1e9a Binary files /dev/null and b/thematic/eis-freshwater-about.png differ diff --git a/thematic/eis-ghg-about.png b/thematic/eis-ghg-about.png new file mode 100644 index 000000000..27bf250a0 Binary files /dev/null and b/thematic/eis-ghg-about.png differ diff --git a/thematic/eis-sea-level-about.png b/thematic/eis-sea-level-about.png new file mode 100644 index 000000000..684396a6d Binary files /dev/null and b/thematic/eis-sea-level-about.png differ diff --git a/thematic/eis.thematic.mdx b/thematic/eis.thematic.mdx new file mode 100644 index 000000000..15f34e0ad --- /dev/null +++ b/thematic/eis.thematic.mdx @@ -0,0 +1,137 @@ +--- +id: eis +name: 'EIS' +description: "Earth Information Systems" +media: + src: ::file ./eis--cover.png + alt: 'EIS Logo' + +about: + title: About + description: Earth Information Systems (EIS) +--- + + + ## Earth Information System (EIS) + + Today’s most pressing Earth Science research questions increasingly require an integrated observing and analysis strategy that synthesizes many datasets with a variety of simulation models. This integration requires contributions from large groups of diverse experts at a wide range of institutions, and the results of this work need to be effectively disseminated to end users. The NASA Earth Information System (EIS) is an interdisciplinary collaborative research and applications activity that combines Earth Science observations and modeling capabilities to produce new science and support decision-making. Crucially, the emphasis of EIS is on producing and delivering actionable information by working closely with stakeholders to tackle their most pressing problems and inform their decisions with NASA data. For example, EIS seeks to help stakeholders provide answers to questions such as: + + * Will the air in my area be unsafe to breathe because of smoke from fires?​* + + * Will my area flood during the next big storm?​* + + * Will there be enough water for my crops this season?​* + + EIS currently has four topic areas: fires, freshwater, sea level change, and greenhouse gasses. + + ## Fires + + Fires are an integral part of the Earth system, primarily through land-atmosphere interactions that determine fire risk, fire spread, and fire emissions. Information on fires is used by a variety of stakeholders, including scientists, firefighters, land managers, public health officials, and the public. Fires also intersect many NASA measurement, research, and applications areas, including terrestrial ecology, meteorology, atmospheric chemistry, and land cover and land use change. NASA data products and models provide real-time information on active fires, smoke, air quality, and forecasts of changing fire weather and seasonal fire risk. The full potential of these independent efforts has not yet been realized, in part due to the need to build new connections among existing components. + + The overarching vision of the EIS-Fire project is to combine satellite, airborne, and ground observations with models to provide a platform for analysis and decision support for pre-fire risk, active fire situational awareness, and post-fire impacts. To that end, the current phase of EIS-Fire has 5 major objectives: + + * **Real-time global fire event monitoring**: Tracking every fire on Earth, every 12 hours, using an automated algorithm that converts satellite measurements of fire radiative power on a pixel-by-pixel basis to maps of the active fire perimeter and cumulative burned area. + * **Fire risk modeling and forecasting**: Leveraging our ability to map fire events globally to evaluate how good models of fire spread really are and how to make them better + * **Develop 1-5 day fire spread forecasts**: Combine our improved knowledge of individual fire events with fire weather forecasts using machine learning. + * **Improve representation of fires and fire emissions in atmospheric models**: Update model information related to emissions from different land cover types, improve the representation of the diurnal cycle of fires, and evaluate new model predictions against satellite, airborne, and ground-based measurements of atmospheric composition and air quality. + * **Post-fire debris flow monitoring, forecasting, and management**: Combine maps of individual fire events, satellite land cover observations, and model-based products of precipitation and runoff to provide near-real-time assessments and forecasts of post-fire debris flow risk. + + In working on these tasks, the EIS-Fire is partnering with a variety of stakeholders, including the US Forest Service (USFS), the US Federal Emergency Management Agency (FEMA), the US Department of Energy, and regional Air Quality Management Districts. + + + + + +
+ Diagram of full lifecycle of fire monitoring +
+ + + + + + ## Freshwater + + Freshwater is what makes Earth habitable, sustaining ecosystems and human civilization. The global water cycle enables the supply of water and regulation of weather patterns. The cycling of water links the changes on land with the ocean and atmosphere. The quantification of the variability of the components of the water cycle is key to understanding and managing water availability. However, thoroughly understanding freshwater is complex because multiple Earth Systems interact with each other including processes that govern precipitation, ground moisture retention, snow cover, depth & melt, evapotranspiration, and vegetation coverage and function. + + The EIS-Freshwater project provides an interface to describe NASA water cycle science in a simple manner that appeals to a broad set of users including scientists, applied science users, water managers, and the general public. The objectives of the current phase of EIS-Freshwater are as follows: + + 1. **Fire-hydrology interactions:** Quantify hydrological causes and impacts of fires + 2. **Inland and coastal flooding:** Examine influence of sea level change, water cycle acceleration, and water management on coastal flooding, and evaluate flooding impacts. + 3. **Water security assessment:** Evaluate how water availability and quality are affected by changes in water and nutrient fluxes + 4. **Shifts in water cycle fluxes and stocks:** Evaluate how water fluxes, stocks, and extremes are changing, including the role of climate change + 5. **Science translation with AI:** Develop a natural language interface to freshwater datasets and models powered by Natural Language Processing (NLP) + + EIS-Freshwater stakeholders include the Amazon Environmental Research Institute - Brazil (IPAM-Brazil), the US Federal Emergency Management Agency (FEMA), the US Global Change Research Program (USGCRP), the US Environmental Protection Agency (EPA), the Nature Conservancy, the Bangladesh Water Development Board, the US Department of Agriculture, National Geographic, and many more. + + + + + +
+ Freshwater and the Earth +
+ + + + + + ## Sea Level Change + + Understanding sea-level variability is challenging because sea-level science is highly multidisciplinary, with the contributing processes acting on a very broad range of spatial and temporal scales. Individual research groups typically need access to the same observational datasets, software, and model output; however, most work on a variety of on-premises platforms that are not directly linked to one another. The fractured nature of current data and computing systems makes it challenging to make rapid progress on the timescales of scientific research grants (3-5 years). As NASA’s Earth System Observatory comes to fruition, new tools and platforms are needed to allow multidisciplinary teams, spread across various institutions, to make faster scientific progress, especially given that future missions will collect exponentially more data than the current program of record. + + The overarching goal of the EIS-Sea Level Change activity is to produce a system that supports collaborative scientific research, streamlines the process of creating sea level change products from raw observations, and facilitates the development of impactful tools requested by stakeholders. The current phase of the EIS-Sea Level Change activity focuses on: + + 1. **Ice sheet contribution to sea-level change:** Improving estimates of ice sheet mass change by connecting model ensembles with satellite observations in the cloud. + 2. **Sea-level change attribution with ECCO:** Advancing our understanding of the role of ocean processes in sea-level change and increasing the ease of accessibility for ECCO model output. + 3. **Support for SWOT science team:** Developing a science collaboration environment in the cloud for SWOT Science Team research; a trailblazer for the Earth System Observatory. + 4. **N-SLCT portal tools:** Creating clear and easy-to-use tools for stakeholders and decision makers. + + EIS-Sea Level Change stakeholder engagement is primarily through the existing [NASA Sea Level Change Team (NSLCT)](https://sealevel.nasa.gov/science-team/overview), which has a practitioner advisory board with members from the US Geological Survey, the US National Snow and Ice Data Center, the US Naval Facilities Engineering Command, the Philadelphia Water Department, and other organizations. + + + + +
+ Diagram of water cycle +
+ + + + + + ## Greenhouse Gasses + + Understanding the changes in source and sinks of carbon dioxide (CO2) and methane (CH4) and how they are changing is a signature challenge in Earth System Science and critically important for nations working to implement effective climate change mitigation strategies. Though land and ocean carbon sinks currently absorb about half of anthropogenic CO2 emissions, changes in these exchanges of carbon in a warming world lead to large uncertainties in model projections of future climate. Changes in CH4 concentrations over recent decades are less well understood with recent studies debating a variety of different, plausible mechanisms for changes in the atmospheric growth rate. Satellites have provided a transformation in understanding of many components of the carbon cycle. When combined with high quality modeling and data assimilation systems, these data can monitor recent changes in greenhouse gas (GHG) concentrations, provide attribution of the most important emission and loss processes, and take stock of the carbon sequestered by land biomass across the world. Though several NASA programs, (Carbon Monitoring System, OCO-2 Science Team, Interdisciplinary Science) have funded development of groundbreaking prototype products, the lack of integration and regular cadence of product delivery updates has limited the ability of NASA to provide authoritative and timely information on GHGs thereby limiting rapid science synthesis and support for stakeholders needs at a variety of administrative levels. + + The overarching vision of Earth Information System – Greenhouse Gasses (EIS-GHG) is to provide a suite of coherent datasets and cyberinfrastructure needed to support research and applications on the recent changes on GHG concentrations and their drivers. To that end, EIS-GHG will provide comprehensive information needed to characterize the state of greenhouse gasses with reliability, transparency, and low latency. For the first year of this pilot project, the main goals are to expand access to and interoperability of existing datasets while building the transparency needed for open science. Specific tasks include: + + 1. Designing and prototyping a data system for NASA GHG monitoring products that supports carbon cycle research and applications + 2. Delivering high quality, low latency information about changes in GHGs + 3. Providing rigorous benchmarking for NASA GHG products in support of open source science + 4. Demonstrating cross-cutting analysis: Balance of natural and anthropogenic anomalies on the carbon balance during the COVID era (2020-2021) + 5. Promoting community engagement and report key findings including scoping needs for coherent, long term GHG monitoring and information system + + EIS-GHG stakeholders include the World Meteorological Organization (WMO), the US Environmental Protection Agency (EPA), the UN Intergovernmental Panel on Climate Change (IPCC), and the Committee on Earth Observation Satellites (CEOS), among others. + + + + + +
+ Process for monitoring greenhouse gasses +
+ + diff --git a/thematic/sandbox.thematic.mdx b/thematic/sandbox.thematic.mdx deleted file mode 100644 index 603950dcd..000000000 --- a/thematic/sandbox.thematic.mdx +++ /dev/null @@ -1,20 +0,0 @@ ---- -id: sandbox-area -name: Sandbox -description: A general purpose Sandbox thematic area to house datasets while their final home is not ready. -media: - src: ::file ./sandbox--cover.jpg - alt: Star trails in the night sky - -about: - title: Sandbox thematic area - description: A general purpose Sandbox thematic area to house datasets while their final home is not ready. ---- - - -## Unstable content -This Sandbox thematic area is used to add datasets to Delta before they are moved to the appropriate thematic area. - -**Datasets may be added/removed at any time.** - -