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datasets/co2.data.mdx

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+---
+id: co2
+name: "Carbon Dioxide"
+description: "The Impact of the COVID-19 Pandemic on Atmospheric CO2"
+media:
+  src: ::file ./co2--dataset-cover.jpg
+  alt: Power plant shooting steam at the sky.
+  author:
+    name: Marek Piwnicki
+    url: https://unsplash.com/photos/WiZOyYqzUss
+thematics:
+  - air-quality
+  - eis
+layers:
+  - id: co2-mean
+    stacCol: co2-mean
+    name: Mean CO2
+    type: raster
+    description: "The average background concentration of carbon dioxide (CO₂) in our atmosphere."
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rdylbu_r
+      rescale:
+        - 0.000408
+        - 0.000419
+    legend:
+      type: gradient
+      min: "< 408 ppm"
+      max: "> 419 ppm"
+      stops:
+        - "#4575b4"
+        - "#91bfdb"
+        - "#e0f3f8"
+        - "#ffffbf"
+        - "#fee090"
+        - "#fc8d59"
+        - "#d73027"
+  - id: co2-diff
+    stacCol: co2-diff
+    name: Difference CO2
+    type: raster
+    description: "The changes in carbon dioxide (CO₂) levels in our atmosphere versus previous years."
+    initialDatetime: newest
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: coolwarm
+      rescale:
+        - -0.0000015
+        - 0.0000015
+    legend:
+      type: gradient
+      min: "-1.5 ppm"
+      max: "1.5 ppm"
+      stops:
+        - "#384cc2"
+        - "#6384eb"
+        - "#8fb3fd"
+        - "#bad1f8"
+        - "#dedbda"
+        - "#f4c3ac"
+        - "#f39779"
+        - "#db5c48"
+        - "#b50021"
+---
+
+<Block type='wide'>
+<Prose>
+  ## Tracking CO2
+
+  Lockdowns and other social distancing measures implemented in response to the COVID-19 pandemic have led to temporary reductions in carbon dioxide (CO2) emissions from fossil fuel combustion and other human activities.
+
+Scientists largely agree that the build-up of excess CO2 and other greenhouse gases within Earth's atmosphere has contributed to the rapid increase of global climate change. Determining whether these temporary reductions in CO2 emission are significant enough to contribute to the overall lowering of the world's carbon footprint will require more time and rigorous scientific study.
+
+However, initial studies suggest that although COVID-19-related CO2 emission reductions are expected to slow the speed at which CO2 accumulates in the atmosphere, they will not reduce the overall atmospheric concentration of CO2.
+
+CO2 emission reductions have been accompanied by comparable, or even greater, reductions in emissions of short-lived air pollutants, such as nitrogen dioxide (NO2). While fossil fuel combustion emits far more CO2 than NO2, much smaller relative changes are expected for atmospheric CO2 because it has a much longer atmospheric lifetime and there is much more CO2 in the atmosphere than NO2. Therefore, time-dependent, regional-scale changes in CO2 concentrations are expected to be no larger than 1 part per million (ppm), out of the normal 415 ppm CO2 background - a change of only 0.25%.
+
+To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan's Greenhouse gases Observing SATellite (GOSAT) during the first few months of 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing, Tokyo, Mumbai, and New York. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 pandemic.
+
+</Prose>
+</Block>
+
+<Block>
+<Prose>
+  ### Regional Scale Changes in CO2 across the Globe
+
+  To determine whether short-term reductions in CO2 emissions from coronavirus shutdowns are even detectable on a regional scale, scientists must create new methods of data analysis with enough sensitivity and precision to distinguish between normal seasonal changes in background CO2 levels and small perturbations caused by coronavirus shutdowns.
+
+  To do this, scientists compare the timing of model-derived global atmospheric CO2 concentration variations constrained by OCO-2 measurements with CO2 emission changes estimated from fossil fuel use statistics from the Global Carbon Project. These comparisons focus on months coinciding with peak COVID-19 isolation periods to see if the emission reductions were accompanied by detectable, regional-scale CO2 changes.
+
+  The maps below show these comparisons for the peak periods of the lockdowns in China (early February), southern Europe (early April) and the eastern U.S. (late April). The results show small (about 0.5 parts per million, or 0.125%) reductions in CO2 over each region at times that are well aligned with the largest CO2 emissions reductions in those regions reported by the Global Carbon Project. The CO2 map for late April (panel c) also appears to show a rebound in CO2 levels over East Asia and northern Pacific Ocean in late April, as China began to emerge from its coronavirus lockdowns. Many features are not likely to be associated with the lockdowns. The enhanced CO2 values in the southern hemisphere are probably due in part to the large wildfires over Australia in late December 2019, while the enhanced values in central Asia in April include contributions from wildfires in Siberia.
+
+</Prose>
+</Block>
+
+<Block>
+<Figure>
+  <Image
+  src={new URL('./co2_figure1.png', import.meta.url).href}
+  alt='Atmospheric CO2 differences in ppm'
+  />
+  <Caption 
+    attrAuthor='(Top) Global Carbon Project (Bottom) NASA' 
+  >
+    **Top row**: Reported country-by-country reductions in fossil fuel use during the most intense periods of the COVID-19 lockdowns in a.) China (early February), b.) Europe (early April) and c.) Northeast U.S. (late April). Brighter blue colors indicate greater reductions.  
+    **Bottom row**: observed changes in atmospheric CO2 concentration differences derived from OCO-2 measurements. Blue shades indicate reductions in CO2, while red shades indicate increases relative to the baseline CO2 climatology.
+  </Caption> 
+</Figure>
+</Block>
+
+<Block>
+<Figure>
+  <Image
+  src={new URL('./co2_figure2.png', import.meta.url).href}
+  alt='Bar chart of CO2 concentration in Beijing for years 2017 through 2020'
+  />
+  <Caption 
+    attrAuthor='ESA/JAXA' 
+  >
+  Monthly time series of lower atmospheric CO2 enhancements over Beijing, China for January 2017 through April 2020 derived from GOSAT data. The results for January through April of prior years are shown in blue, while those for 2020 are shown in green.
+  </Caption> 
+</Figure>
+<Prose>
+  ### CO2 Changes over Large Urban Areas
+
+  Scientists use GOSAT data to determine changes in atmospheric CO2 over large urban areas, which experienced the largest changes in economic activity associated with the onset of the COVID-19 pandemic. While OCO-2 is optimized for detecting the subtle, regional-scale changes in CO2, GOSAT has advantages for tracking changes in CO2 emissions over large cities.
+
+  GOSAT observations were analyzed to reveal CO2 concentration enhancements, such as fossil fuel emissions that contribute to higher levels of CO2 lower down in atmosphere over cities, relative to the CO2 concentrations at higher altitudes, which are less affected by city emissions. The figure below shows the CO2 concentration enhancements over Beijing, China, derived from GOSAT observations collected in January through April of each year from 2017 to 2020. The results from earlier years illustrate the amount of month-to-month variability in the observed CO2 enhancements that is typical during this season. However, while the CO2 concentration enhancements vary substantially from month-to-month, they are generally much lower in 2020 than in earlier years.
+</Prose>
+</Block>
+
+<Block>
+<Prose>
+  Further inspection of the Beijing results reveals that all months in 2020 have smaller CO2 enhancements relative to prior years. While this behavior is consistent with reported COVID-19-related reductions in fossil fuel emissions from Beijing, it is important to remember that these results include variations in CO2 concentrations not only from COVID-19 shutdowns, but also from other processes such as photosynthesis and respiration by plants and transport by passing weather systems.
+
+  Similar results were derived for the other cities. Shanghai shows reduced CO2 enhancements from February through April 2020. For New York, CO2 values were higher in January 2020, close to normal for February, and lower in March, as lockdowns were imposed. There is no data for New York in April due to cloud cover. In New Delhi, Mumbai and Dhaka, the story is somewhat more mixed. The CO2 enhancements are smaller or almost the same in February, reflecting the large role of natural processes, such as year-to-year differences in CO2 uptake and release by forests and crops. In March 2020, CO2 enhancements are higher than in earlier years in New Delhi, and lower in Mumbai and Dhaka. The CO2 enhancements decrease across all three cities in April, as lockdowns are implemented. However, these changes are very difficult to attribute to the pandemic because of the large-scale natural CO2 changes seen across India during this season.
+</Prose>
+</Block>

datasets/epa-agriculture.data.mdx

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+---
+id: epa-agriculture
+name: EPA - Agriculture
+description: Emissions from agriculture include enteric fermentation, manure management, rice cultivation, and field burning of agricultural residues
+media:
+  src: ::file ./epa-agriculture--cover.jpg
+  alt: Tractors tending a corn field
+  author:
+    name: James Baltz
+    url: https://unsplash.com/photos/jAt6cN6zl8M
+thematics:
+  - eis
+layers:
+  - id: epa-annual-emissions_4b_manure_management
+    stacCol: EPA-annual-emissions_4B_Manure_Management
+    name: Manure Management
+    type: raster
+    description: Emissions from sector 4B from manure management.
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 46428019752
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 46428019752
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-monthly-emissions_4b_manure_management
+    stacCol: EPA-monthly-emissions_4B_Manure_Management
+    name: Manure Management (monthly)
+    type: raster
+    description: Emissions from sector 4B from manure management (monthly).
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 46428019752
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 46428019752
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-annual-emissions_4c_rice_cultivation
+    stacCol: EPA-annual-emissions_4C_Rice_Cultivation
+    name: Rice Cultivation
+    type: raster
+    description: Emissions from sector 4C from rice cultivation.
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 38327875010
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 38327875010
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-monthly-emissions_4c_rice_cultivation
+    stacCol: EPA-monthly-emissions_4C_Rice_Cultivation
+    name: Rice Cultivation (monthly)
+    type: raster
+    description: Emissions from sector 4C from rice cultivation (monthly).
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 54815427133
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 54815427133
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-annual-emissions_4a_enteric_fermentation
+    stacCol: EPA-annual-emissions_4A_Enteric_Fermentation
+    name: Enteric Fermentation
+    type: raster
+    description: >-
+      Emissions from sector 4A from enteric fermentation (fermentation that
+      takes place in the digestive systems of animals).
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 43784598420
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 43784598420
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-annual-emissions_4f_field_burning
+    stacCol: EPA-annual-emissions_4F_Field_Burning
+    name: Field Burning
+    type: raster
+    description: Emissions from sector 4F from agricultural field burning.
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 800788398
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 800788398
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+  - id: epa-monthly-emissions_4f_field_burning
+    stacCol: EPA-monthly-emissions_4F_Field_Burning
+    name: Field Burning (monthly)
+    type: raster
+    description: Emissions from sector 4F from agricultural field burning (monthly).
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      colormap_name: rainbow
+      rescale:
+        - 0
+        - 4510689853
+      nodata: 0
+    legend:
+      type: gradient
+      min: 0
+      max: 4510689853
+      stops:
+        - "#60007d"
+        - "#30137d"
+        - "#1960ae"
+        - "#7ac300"
+        - "#f2ce00"
+        - "#ef6a01"
+        - "#cc0019"
+---
+
+<Block>
+  <Prose>
+    ## Gridded 2012 Methane Emissions
+
+    A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types.
+
+    This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference.
+
+    Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper)
+
+  </Prose>
+</Block>