3 datasets found
  1. N

    NYCCAS Air Pollution Rasters

    • data.cityofnewyork.us
    • s.cnmilf.com
    • +1more
    application/rdfxml +5
    Updated May 11, 2017
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    Department of Health and Mental Hygiene (DOHMH) (2017). NYCCAS Air Pollution Rasters [Dataset]. https://data.cityofnewyork.us/Environment/NYCCAS-Air-Pollution-Rasters/q68s-8qxv
    Explore at:
    json, csv, application/rdfxml, application/rssxml, xml, tsvAvailable download formats
    Dataset updated
    May 11, 2017
    Dataset authored and provided by
    Department of Health and Mental Hygiene (DOHMH)
    Description

    Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2).

    Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

  2. g

    NYCCAS Air Pollution Rasters | gimi9.com

    • gimi9.com
    Updated May 11, 2017
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    (2017). NYCCAS Air Pollution Rasters | gimi9.com [Dataset]. https://gimi9.com/dataset/ny_q68s-8qxv/
    Explore at:
    Dataset updated
    May 11, 2017
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

  3. A

    NYCCAS Air Pollution Rasters

    • data.amerigeoss.org
    • data.wu.ac.at
    zip
    Updated May 15, 2019
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    United States (2019). NYCCAS Air Pollution Rasters [Dataset]. https://data.amerigeoss.org/pt_PT/dataset/nyccas-air-pollution-rasters
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 15, 2019
    Dataset provided by
    United States
    Description

    Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2).

    Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2015 (years 1-7) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: http://www1.nyc.gov/assets/doh/downloads/pdf/environmental/comm-air-survey-08-14.pdf

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Department of Health and Mental Hygiene (DOHMH) (2017). NYCCAS Air Pollution Rasters [Dataset]. https://data.cityofnewyork.us/Environment/NYCCAS-Air-Pollution-Rasters/q68s-8qxv

NYCCAS Air Pollution Rasters

Explore at:
13 scholarly articles cite this dataset (View in Google Scholar)
json, csv, application/rdfxml, application/rssxml, xml, tsvAvailable download formats
Dataset updated
May 11, 2017
Dataset authored and provided by
Department of Health and Mental Hygiene (DOHMH)
Description

Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2).

Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

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