diff --git a/datasets/grdi-v1.data.mdx b/datasets/grdi-v1.data.mdx index 4471310c6..3833d5b7b 100644 --- a/datasets/grdi-v1.data.mdx +++ b/datasets/grdi-v1.data.mdx @@ -1,26 +1,29 @@ --- id: grdi-v1 -name: "The Global Gridded Relative Deprivation Index, Version 1" +name: 'The Global Gridded Relative Deprivation Index, Version 1' description: This dataset characterizes the relative levels of multidimensional deprivation and poverty based on sociodemographic and satellite data media: src: ::file ./grdi--dataset-cover.jpg alt: Shacks along a river almost collapsing author: name: Jordan Opel - url: https://unsplash.com/photos/3VLHF9b9Plg + url: https://unsplash.com/photos/photo-of-houses-near-body-of-water-3VLHF9b9Plg taxonomy: - - name: Topics + - name: Theme values: - - Environmental Justice + - Disasters - name: Source values: - - NASA CIESIN + - NASA + - name: Product Type + values: + - Model Output layers: - - id: grdi-cdr-raster - stacCol: grdi-cdr-raster - name: GRDI Child Dependency Ratio + - id: grdi-v1-raster + stacCol: grdi-v1-raster + name: GRDI type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) Child Dependency Ratio (CDR) Constituent raster" + description: 'Global Gridded Relative Deprivation Index (GRDI), where higher values indicate higher deprivation' zoomExtent: - 0 - 16 @@ -43,7 +46,7 @@ layers: stacCol: grdi-filled-missing-values-count name: GRDI Constituent Inputs type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) raster showing count of constituent inputs that were filled in per cell using the Fill Missing Values tool." + description: 'Global Gridded Relative Deprivation Index (GRDI) showing count of constituent inputs that were filled in per cell using the Fill Missing Values tool' zoomExtent: - 0 - 16 @@ -62,11 +65,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - - id: grdi-imr-raster - stacCol: grdi-imr-raster - name: GRDI Infant Mortality Rate + - id: grdi-v1-built + stacCol: grdi-v1-built + name: GRDI Built-Up Area (BUILT) type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) Infant Mortality Rate (IMR) Constituent raster" + description: 'Ratio of built-up area (more urban areas) to non-built up area (more rural areas), where low values imply higher deprivation' zoomExtent: - 0 - 16 @@ -85,11 +88,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - - id: grdi-shdi-raster - stacCol: grdi-shdi-raster - name: GRDI Subnational Human Development Index + - id: grdi-cdr-raster + stacCol: grdi-cdr-raster + name: GRDI Child Dependency Ratio (CDR) type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) Subnational Human Development Index (SHDI) Constituent raster" + description: 'Ratio between the population of children (ages 0 to 14) to the working-age population (age 15 to 64). A higher ratio is generally associated with younger age structures, which implies higher relative deprivation' zoomExtent: - 0 - 16 @@ -108,11 +111,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - - id: grdi-v1-built - stacCol: grdi-v1-built - name: GRDI v1 built-up area + - id: grdi-imr-raster + stacCol: grdi-imr-raster + name: GRDI Infant Mortality Rate (IMR) type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) built-up area (BUILT) Constituent raster, indexed 0 to 100" + description: 'Number of deaths in children under 1 year of age per 1,000 live births in the same year' zoomExtent: - 0 - 16 @@ -131,11 +134,11 @@ layers: - '#21918c' - '#5ec962' - '#fde725' - - id: grdi-v1-raster - stacCol: grdi-v1-raster - name: GRDI v1 raster + - id: grdi-shdi-raster + stacCol: grdi-shdi-raster + name: GRDI Subnational Human Development Index (SHDI) type: raster - description: "Global Gridded Relative Deprivation Index (GRDI), V1 raster" + description: 'Index of human well-being derived from a combination of “three dimensions: education, health, and standard of living (Smits & Permanyer, 2019)”. Lower SHDIs imply higher deprivation' zoomExtent: - 0 - 16 @@ -156,9 +159,9 @@ layers: - '#fde725' - id: grdi-vnl-raster stacCol: grdi-vnl-raster - name: GRDI VNL Constituent raster + name: GRDI VIIRS Night Lights (VNL) type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) VIIRS Night Lights (VNL) Constituent raster" + description: 'Intensity of nighttime lights measured by the VIIRS satellite instrument for the year 2020 as a dimension where lower values imply higher deprivation' zoomExtent: - 0 - 16 @@ -179,9 +182,9 @@ layers: - '#fde725' - id: grdi-vnl-slope-raster stacCol: grdi-vnl-slope-raster - name: GRDI VNL Slope Constituent raster + name: GRDI VIIRS Night Lights (VNL) Slope type: raster - description: "Global Gridded Relative Deprivation Index (GRDI) VIIRS Night Lights (VNL) Slope Constituent raster" + description: 'Difference in intensity of nighttime lights measured by the VIIRS satellite instrument between the years 2012 and 2020 (VNL Slope). Higher values (increasing brightness) imply decreasing deprivation and lower values imply increasing deprivation' zoomExtent: - 0 - 16 @@ -202,104 +205,40 @@ layers: - '#fde725' --- - + - ## About - - The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) dataset characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage. - - GRDIv1 has six input components, or dimensions, that are combined to determine the degree of relative deprivation: - - **The child dependency ratio (CDR)** defined as the ratio between the population of children (ages 0 to 14) to the working-age population (age 15 to 64), where a higher ratio implies a higher dependency on the working population (UN DESA 2006). We interpret the CDR as a dimension where higher dependency ratios, generally associated with younger age structures, imply higher relative deprivation. - - **Infant mortality rates (IMRs)** defined as the number of deaths in children under 1 year of age per 1,000 live births in the same year, are a common indicator of population health (Reidpath & Allotey, 2003; Schell et al., 2007). Higher IMRs imply higher deprivation. + The Global Gridded Relative Deprivation Index (GRDI), Version 1 dataset characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDI is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final GRDI layer. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage. + GRDI has six input components, or dimensions, that are combined to determine the degree of relative deprivation: + - **Ratio of Built-up Area to Non-built up Area (BUILT)** Evidence suggests that global rural populations are more likely to experience a higher degree of multidimensional poverty when compared to urban populations, other things being equal. Therefore, **the ratio of built-up area to non-built up area (BUILT)** is a dimension where low values imply higher deprivation. + - **The Child Dependency Ratio (CDR)** defined as the ratio between the population of children (ages 0 to 14) to the working-age population (age 15 to 64), where a higher ratio implies a higher dependency on the working population. We interpret the CDR as a dimension where higher dependency ratios, generally associated with younger age structures, imply higher relative deprivation. + - **Infant Mortality Rate (IMR)** defined as the number of deaths in children under 1 year of age per 1,000 live births in the same year. Higher IMRs imply higher deprivation. - **The Subnational Human Development Index (SHDI)** attempts to assess human well-being through a combination of “three dimensions: education, health, and standard of living (Smits & Permanyer, 2019)”. Lower SHDIs imply higher deprivation. - - Global rural populations are more likely to experience a higher degree of multidimensional poverty when compared to urban populations, other things being equal (Castañeda et al., 2018; Laborde Debucquet & Martin, 2018; Lee & Kind, 2021; UN DESA, 2021; UNDP & OPHI, 2020). Therefore, we consider **the ratio of built-up area to non-built up area (BUILT)** as a dimension where low values imply higher deprivation. - - **Intensity of nighttime lights**, closely associated with anthropogenic activities, economic output, and infrastructure development (Elvidge et al., 2007; Ghosh et al., 2013; Lu et al., 2021; Small et al., 2013). We interpret the average intensity of nighttime lights for the year 2020 (VIIRS Night Lights (VNL) 2020) as a dimension where lower values imply higher deprivation. - - For the sixth component we calculated a **linear regression from annual VNL data between 2012 and 2020 (VNL slope) and considered the slope as a dimension where higher values (increasing brightness)** imply decreasing deprivation and lower values (decreasing brightness) imply increasing deprivation. - - ### Cite this dataset: - - > Center for International Earth Science Information Network - CIESIN - Columbia University. 2022. Global Gridded Relative Deprivation Index (GRDI), v1. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/3xxe-ap97. Accessed 29 June 2022. - - ### Data Access - - The data are available as GEOTiff raster format for the Global Gridded Relative Deprivation Index (GRDI), v1 from the [web page](https://alpha.sedac.ciesin.columbia.edu/data/set/povmap-grdi-v1) - - Dataset Description and documentation accessible at: Center for International Earth Science Information Network - CIESIN - Columbia University. 2022. Documentation for the Global Gridded Relative Deprivation Index (GRDI), v1. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/xwf1-k532. Accessed 29 June 2022. - - ### References: - - - Cardona, O.-D., van Aalst, M. K., Birkmann, J., Fordham, M., McGregor, G., Perez, R., Pulwarty, R. S., Schipper, E. L. F., Sinh, B. T., Décamps, H., Keim, M., Davis, I., Ebi, K. L., Lavell, A., Mechler, R., Murray, V., Pelling, M., Pohl, J., Smith, A.-O., & Thomalla, F. (2012). Determinants of Risk: Exposure and Vulnerability. In C. B. Field, V. Barros, T. F. Stocker, & Q. Dahe (Eds.), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (pp. 65-108). Cambridge University Press. https://doi.org/10.1017/CBO9781139177245.005 + - Intensity of nighttime lights is closely associated with anthropogenic activities, economic output, and infrastructure development. The average intensity of nighttime lights for the year 2020 as measured from the VIIRS satellite instrument provides the **VIIRS Night Lights (VNL)** as a dimension where lower values imply higher deprivation. + - For the sixth component, a linear regression from annual VNL data between 2012 and 2020 **(VNL Slope)** was calculated and considered as a dimension where higher values (increasing brightness) imply decreasing deprivation and lower values (decreasing brightness) imply increasing deprivation. - - Castañeda, A., Doan, D., Newhouse, D., Nguyen, M. C., Uematsu, H., & Azevedo, J. P. (2018). A New Profile of the Global Poor. World Development, 101(C), 250-267. + ## Data Summary + - **Temporal Extent:** Input dimensions ranging from 2010 - 2020 + - **Temporal Resolution:** + - **Spatial Extent:** Global + - **Spatial Resolution:** 30 arc-second (~1 km x 1 km) + - **Data Units:** relative level of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel represented as an index from 0 to 100 + - **Data Type:** Research - - Center For International Earth Science Information Network-CIESIN-Columbia University. (2015). A Step-by-Step Guide to Vulnerability Hotspots Mapping: Implementing the Spatial Index Approach. http://ciesin.columbia.edu/documents/vmapping_guide_final.pdf - - - Center For International Earth Science Information Network-CIESIN-Columbia University. (2018a). Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. https://doi.org/10.7927/H45Q4T5F - - - Center For International Earth Science Information Network-CIESIN-Columbia University. (2018b). Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H46M34XX - - - Center For International Earth Science Information Network-CIESIN-Columbia University. (2018c). Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4TD9VDP - - - Center For International Earth Science Information Network-CIESIN-Columbia University. (2021). Global Subnational Infant Mortality Rates, Version 2.01 (2015) [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/0GDN-6Y33 - - - Chen, X., & Nordhaus, W. (2015). A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa. Remote Sensing, 7(4), 4937-4947. https://doi.org/10.3390/rs70404937 - - - Dooley, C., Leasure, D., Boo, G., & Tatem, A. (2021). Gridded maps of building patterns throughout sub-Saharan Africa, version 2.0 [Data set]. University of Southampton. Southampton, UK. Source of building footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020/2021. https://doi.org/10.5258/SOTON/WP00712 - - - Elvidge, C. D., Safran, J., Tuttle, B., Sutton, P., Cinzano, P., Pettit, D., Arvesen, J., & Small, C. (2007). Potential for global mapping of development via a nightsat mission. GeoJournal, 69(1), 45-53. https://doi.org/10.1007/s10708-007-9104-x - - - Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C., & Taneja, J. (2021). Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922 - - - Facebook Connectivity Lab, & Center for International Earth Science Information Network - CIESIN - Columbia University. (2016). High Resolution Settlement Layer (HRSL). https://www.ciesin.columbia.edu/data/hrsl/ - - - Geofabrik. (2018). OpenStreetMap Data Extracts. http://download.geofabrik.de/ - - - Ghosh, T., Anderson, S. J., Elvidge, C. D., & Sutton, P. C. (2013). Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being. Sustainability, 5(12), 4988-5019. https://doi.org/10.3390/su5124988 - - - Global Data Lab. (2020, March). GDL Code & Shapefiles. Human Development Indices. https://globaldatalab.org/shdi/shapefiles/ - - - Kummu, M., Taka, M., & Guillaume, J. H. A. (2018). Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Scientific Data, 5(1), 180004. https://doi.org/10.1038/sdata.2018.4 - - - Kummu, M., Taka, M., & Guillaume, J. H. A. (2020). Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015 (Version 2, p. 481877286 bytes) [Data set]. Dryad. https://doi.org/10.5061/DRYAD.DK1J0 - - - Laborde Debucquet, D., & Martin, W. (2018). Implications of the global growth slowdown for rural poverty. Agricultural Economics, 49(3), 325-338. https://doi.org/10.1111/agec.12419 - - - Lee, Y. F., & Kind, M. (2021). Reducing poverty and inequality in rural areas: Key to inclusive development. https://www.un.org/development/desa/dspd/2021/05/reducing-poverty/ - - - Lu, D., Wang, Y., Yang, Q., Su, K., Zhang, H., & Li, Y. (2021). Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China. Remote Sensing, 13(2), 284. https://doi.org/10.3390/rs13020284 - - - Microsoft. (2019). CanadianBuildingFootprints (1.1) [Computer software]. Microsoft. https://github.com/microsoft/CanadianBuildingFootprints - - - OPHI. (2015a). Global Multidimensional Poverty Index. https://ophi.org.uk/multidimensional-poverty-index/ - - - OPHI. (2015b). Policy - A Multidimensional Approach. https://ophi.org.uk/policy/multidimensional-poverty-index/ - - - Ravallion, M., Chen, S., & Sangraula, P. (2007). New Evidence on the Urbanization of Global Poverty. Population and Development Review, 33(4), 667-701. - - - Reidpath, D., & Allotey, P. (2003). Infant mortality rate as an indicator of population health. Journal of Epidemiology and Community Health, 57(5), 344-346. https://doi.org/10.1136/jech.57.5.344 - - - Rignall, K., & Atia, M. (2017). The global rural: Relational geographies of poverty and uneven development. Geography Compass, 11(7), e12322. https://doi.org/10.1111/gec3.12322 - - - Schell, C. O., Reilly, M., Rosling, H., Peterson, S., & Ekstrӧm, A. M. (2007). Socioeconomic determinants of infant mortality: A worldwide study of 152 low-, middle-, and high-income countries. Scandinavian Journal of Public Health, 35(3), 288-297. - - - Small, C., Elvidge, C. D., & Baugh, K. (2013). Mapping urban structure and spatial connectivity with VIIRS and OLS night light imagery. Joint Urban Remote Sensing Event 2013, 230-233. https://doi.org/10.1109/JURSE.2013.6550707 - - - Smits, J., & Permanyer, I. (2019). The Subnational Human Development Database. Scientific Data, 6(1), 190038. https://doi.org/10.1038/sdata.2019.38 - - - Smits, J., & Steendijk, R. (2015). The International Wealth Index (IWI). Social Indicators Research, 122(1), 65-85. https://doi.org/10.1007/s11205-014-0683-x - - - UN Department of Economic and Social Affairs (DESA). (2006). Dependency Ratio. https://www.un.org/esa/sustdev/natlinfo/indicators/methodology_sheets/demographics/dependency_ratio.pdf + + - - UN Department of Economic and Social Affairs (DESA). (2021). World Social Report 2021: Reconsidering Rural Development. + + +## Source Data Access +Center for International Earth Science Information Network - CIESIN - Columbia University. 2022. Global Gridded Relative Deprivation Index (GRDI), v1. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). [https://doi.org/10.7927/3xxe-ap97](https://doi.org/10.7927/3xxe-ap97) - - UN Development Programme (UNDP). (2018). What Does it Mean to Leave No One Behind: A UNDP Discussion Paper and Framework for Implementation (p. 29). Bureau for Policy and Programme Support. https://www.undp.org/publications/what-does-it-mean-leave-no-one-behind +## Acknowledgment - - UN Development Programme (UNDP). (2020). Human Development Report 2020. Human Development Report 2020 | UNDP HDR. http://report.hdr.undp.org +Funding for development and dissemination of this dataset was provided under the U.S. National Aeronautics and Space Administration (NASA) contract 80GSFC18C0111 for the continued operation of the Socioeconomic Data and Applications Center (SEDAC), which is operated by the Center for International Earth Science Information Network (CIESIN) of Columbia University. - - UN Development Programme (UNDP) & OPHI. (2020). Global Multidimensional Poverty Index 2020—Charting pathways out of multidimensional poverty: Achieving the SDGs. +## Dataset Preparation & Disclaimer - - UN General Assembly (UNGA). (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E +All data displayed in Earth.gov has been transformed from the original format (GeoTIFF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. - - World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune - Frequently Asked Questions. World Bank. https://www.worldbank.org/en/research/brief/poverty-and-shared-prosperity-2020-reversals-of-fortune-frequently-asked-questions - \ No newline at end of file + diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx index 71a5a4d84..63d6346e2 100644 --- a/datasets/no2.data.mdx +++ b/datasets/no2.data.mdx @@ -1,32 +1,29 @@ --- id: no2 -name: 'Nitrogen Dioxide' -featured: true -description: "Since the outbreak of the novel coronavirus, atmospheric concentrations of nitrogen dioxide have changed by as much as 60% in some regions." -usage: - - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/main/notebooks/quickstarts/no2-map-plot.ipynb' - label: View example notebook - title: 'Static view in VEDA documentation' - - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https://github.com/NASA-IMPACT/veda-docs&branch=main&urlpath=lab/tree/veda-docs/notebooks/quickstarts/no2-map-plot.ipynb" - label: Run example notebook - title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)' +name: 'Recent Changes in Atmospheric Nitrogen Dioxide' +description: 'Since the outbreak of the novel coronavirus, atmospheric concentrations of nitrogen dioxide have changed by as much as 60% in some regions.' media: src: ::file ./no2--dataset-cover.jpg alt: Power plant shooting steam at the sky. author: name: Mick Truyts - url: https://unsplash.com/photos/x6WQeNYJC1w + url: https://unsplash.com/photos/sea-waves-crashing-on-shore-under-white-clouds-x6WQeNYJC1w taxonomy: - - name: Topics + - name: Theme values: - Air Quality - - Covid 19 + - name: Source + values: + - NASA + - name: Product Type + values: + - Satellite Observations layers: - id: no2-monthly stacCol: no2-monthly - name: No2 + name: Nitrogen Dioxide (NO₂) type: raster - description: 'Global nitrogen dioxide data organized into monthly metrics' + description: 'Global monthly concentration of NO2 in the troposphere' zoomExtent: - 0 - 20 @@ -47,19 +44,21 @@ layers: } legend: type: gradient - min: "Less" - max: "More" + min: 'Less' + max: 'More' + unit: + label: Molecules NO₂/cm² stops: - - "#3A88BD" - - "#C9E0ED" - - "#E4EEF3" - - "#FDDCC9" - - "#DD7059" + - '#3A88BD' + - '#C9E0ED' + - '#E4EEF3' + - '#FDDCC9' + - '#DD7059' - id: no2-monthly-diff stacCol: no2-monthly-diff - name: No2 (Diff) + name: Nitrogen Dioxide (NO₂) Difference type: raster - description: 'Global nitrogen dioxide data which displays the difference from the same time 1 year ago' + description: 'Change in global, monthly concentration of NO2 in the troposphere compared to the same month’s average from from 2015 - 2019' zoomExtent: - 0 - 20 @@ -79,116 +78,52 @@ layers: } legend: type: gradient - min: "< -3" - max: "> 3" - stops: - - "#3A88BD" - - "#C9E0ED" - - "#E4EEF3" - - "#FDDCC9" - - "#DD7059" - - id: OMI_trno2-COG - stacCol: OMI_trno2-COG - name: OMI_trno2 Annual - type: raster - description: "NASA OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column" - zoomExtent: - - 0 - - 16 - sourceParams: - colormap_name: reds - rescale: - - 0 - - 30E14 - legend: + min: '< -3' + max: '> 3' unit: - label: mol/cm2 - type: gradient - min: 0 - max: 30e14 + label: Molecules NO₂/cm² stops: - - '#ffffff' - - '#fdd1bf' - - '#e02d26' - - '#67000c' + - '#3A88BD' + - '#C9E0ED' + - '#E4EEF3' + - '#FDDCC9' + - '#DD7059' --- - -
- -
- -Nitrogen dioxide (NO2) is a common air pollutant primarily emitted from the burning of fossil fuels in cars and power plants. Lower to the ground, nitrogen dioxide can directly irritate the lungs and contributes to the production of particulate pollution and smog when it reacts with sunlight. - -During the COVID-19 pandemic, scientists have observed considerable decreases in nitrogen dioxide levels around the world. These decreases are predominantly associated with changing human behavior in response to the spread of COVID-19. As communities worldwide have implemented lockdown restrictions in an attempt to stem the spread of the virus, the reduction in human transportation activity has resulted in less NO2 being emitted into the atmosphere. - -These changes are particularly apparent over large urban areas and economic corridors, which typically have high levels of automobile traffic, airline flights, and other related activity. - -NASA has observed subsequent rebounds in nitrogen dioxide levels as the lockdown restrictions ease. - - - -## Scientific research -[Ongoing research](https://airquality.gsfc.nasa.gov/) by scientists in the Atmospheric Chemistry and Dynamics Laboratory at NASA’s Goddard Space Flight Center and [new research](https://science.nasa.gov/earth-science/rrnes-awards) funded by NASA's Rapid Response and Novel research in the Earth Sciences (RRNES) program element seek to better understand the atmospheric effects of the COVID-19 shutdowns. + + Nitrogen dioxide (NO2) is a common air pollutant primarily emitted from the burning of fossil fuels in cars and power plants. Lower to the ground, nitrogen dioxide can directly irritate the lungs and contributes to the production of particulate pollution and smog when it reacts with sunlight. + During the COVID-19 pandemic, scientists have observed considerable decreases in nitrogen dioxide levels around the world. These decreases are predominantly associated with changing human behavior in response to the spread of COVID-19. As communities worldwide have implemented lockdown restrictions in an attempt to stem the spread of the virus, the reduction in human transportation activity has resulted in less NO2 being emitted into the atmosphere. These changes are particularly apparent over large urban areas and economic corridors, which typically have high levels of automobile traffic, airline flights, and other related activity. NASA has observed subsequent rebounds in nitrogen dioxide levels as the lockdown restrictions ease. + Presented here is a monthly record of NO2 in the troposphere, which is the lowest layer of the atmosphere most directly affected by human activity, as measured by the [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html) on NASA’s Aura satellite from January 2016 through September 2023. Also included is a monthly record showing the change in tropospheric NO2 from the selected month compared to an average of tropospheric NO2 for the same month from 2015 - 2019 (for example, difference in NO2 between October 2023 vs October 2015 - 2019 average). This difference highlights changes in NO2 from a baseline time frame of 2015 - 2019, just before the COVID-19 pandemic. -For nitrogen dioxide levels related to COVID-19, NASA uses data collected by the joint NASA-Royal Netherlands Meteorological Institute (KNMI) [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html) aboard the Aura satellite, as well as data collected by the Tropospheric Monitoring Instrument (TROPOMI) aboard the European Commission’s Copernicus Sentinel-5P satellite, built by the European Space Agency. + ## Data Summary -OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations. + - **Temporal Extent:** January 2016 - Ongoing + - **Temporal Resolution:** Monthly + - **Spatial Extent:** Global + - **Spatial Resolution:** 0.1 degrees x 0.1 degrees + - **Data Units:** Molecules of nitrogen dioxide per square centimeter (Molecules NO2/cm2) + - **Data Type:** Research -Scientists will use these data to investigate how travel bans and lockdown orders related to the novel coronavirus are impacting regional air quality and chemistry, as well as why these restrictions may be having inconsistent effects on air quality around the world. - - + + -
- NO2 levels over South America from the Ozone Monitoring Instrument - - NO2 levels over South America from the Ozone Monitoring Instrument. The dark green areas in the northwest indicate areas of no data, most likely associated with cloud cover or snow. - -
- -## Interpreting the data -Nitrogen dioxide has a relatively short lifespan in the atmosphere. Once it is emitted, it lasts only a few hours before it dissipates, so it does not travel far from its source. + + ## Source Data Access + The monthly tropospheric NO2 data can be accessed here: + [https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI/V03/L3/OMNO2d_HR/OMNO2d_HRM/](https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI/V03/L3/OMNO2d_HR/OMNO2d_HRM/) -Because nitrogen dioxide is primarily emitted from burning fossil fuels, changes in its atmospheric concentration can be related to changes in human activity, if the data are properly processed and interpreted. + {/* ## Acknowledgment + Acknowledgement of the teams/organizations that contributed to the product. This can often be found in dataset documentation from source */} -Interpreting satellite NO2 data must be done carefully, as the quantity observed by satellite is not exactly the same as the abundance at ground level, and natural variations in weather (e.g., temperature, wind speed, solar intensity) influence the amount of NO2 in the atmosphere. In addition, the OMI and TROPOMI instruments cannot observe the NO2 abundance underneath clouds. For more information on processing and cautions related to interpreting this data, please click [here](https://airquality.gsfc.nasa.gov/caution-interpretation). - - + ## Dataset Preparation & Disclaimer + The monthly tropospheric NO2 and NO2 difference were created using this product: - - -## Additional resources -### NASA Features -* [Airborne Nitrogen Dioxide Plummets Over China](https://earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china) -* [Airborne Nitrogen Dioxide Decreases Over Italy](https://earthobservatory.nasa.gov/blogs/earthmatters/2020/03/13/airborne-nitrogen-dioxide-decreases-over-italy/) -* [NASA Satellite Data Show 30 Percent Drop In Air Pollution Over Northeast U.S.](https://www.nasa.gov/feature/goddard/2020/drop-in-air-pollution-over-northeast) -* [Airborne Particle Levels Plummet in Northern India](https://earthobservatory.nasa.gov/images/146596/airborne-particle-levels-plummet-in-northern-india) -* [NASA Satellite Data Show Air Pollution Decreases over Southwest U.S. Cities](https://www.nasa.gov/feature/goddard/2020/nasa-satellite-data-show-air-pollution-decreases-over-southwest-us-cities) -* [Nitrogen Dioxide Levels Rebound in China](https://earthobservatory.nasa.gov/images/146741/nitrogen-dioxide-levels-rebound-in-china?utm_source=card_2&utm_campaign=home) + Nickolay A. Krotkov, Lok N. Lamsal, Sergey V. Marchenko, Eric J.Bucsela, William H. Swartz, Joanna Joiner and the OMI core team (2019), OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13x24 km V003, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC). [10.5067/Aura/OMI/DATA2017](https://doi.org/10.5067/Aura/OMI/DATA2017) -### Explore the Data -* [How to Find and Visualize Nitrogen Dioxide Satellite Data](https://earthdata.nasa.gov/learn/articles/feature-articles/health-and-air-quality-articles/find-no2-data) -* [COVID-19 Data Pathfinder](https://earthdata.nasa.gov/learn/pathfinders/covid-19) -* [Reductions in Nitrogen Dioxide Associated with Decreased Fossil Fuel Use Resulting from COVID-19 Mitigation](https://svs.gsfc.nasa.gov/4810) + All data displayed in Earth.gov has been transformed from the original format (NetCDF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. -### Explore the Missions -* [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html) -* [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/outreach.html) -* [Pandora Project](https://pandora.gsfc.nasa.gov/) - - + + +