air_quality_Maharashtra.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "z8jOifJtoHlx",
        "outputId": "013dca52-cd78-4ca4-daff-3ecd156f037b"
      },
      "source": [
        "!pip install geemap"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting geemap\n",
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            "  Downloading jupyter_server-1.11.2-py3-none-any.whl (394 kB)\n",
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            "\u001b[?25hCollecting jupyterlab-server~=2.3\n",
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            "  Downloading websocket_client-1.2.1-py2.py3-none-any.whl (52 kB)\n",
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            "  Downloading anyio-3.3.4-py3-none-any.whl (78 kB)\n",
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            "\u001b[?25hCollecting sniffio>=1.1\n",
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            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.7/dist-packages (from anyio<4,>=3.1.0->jupyter-server~=1.4->jupyterlab>=3->geemap) (2.10)\n",
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            "  Downloading json5-0.9.6-py2.py3-none-any.whl (18 kB)\n",
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            "Collecting whitebox\n",
            "  Downloading whitebox-2.0.3-py2.py3-none-any.whl (70 kB)\n",
            "\u001b[K     |████████████████████████████████| 70 kB 7.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cffi>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from argon2-cffi->jupyter-server~=1.4->jupyterlab>=3->geemap) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0.0->argon2-cffi->jupyter-server~=1.4->jupyterlab>=3->geemap) (2.21)\n",
            "Collecting ratelim\n",
            "  Downloading ratelim-0.1.6-py2.py3-none-any.whl (4.0 kB)\n",
            "Requirement already satisfied: google-cloud-core<2.0dev,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage->earthengine-api>=0.1.230->geemap) (1.0.3)\n",
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            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (1.5.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.5.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.7.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (4.1.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.8.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.5.1)\n",
            "Collecting pyproj>=2\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 30.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from owslib->geemap) (3.13)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from sankee->geemap) (4.4.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->sankee->geemap) (1.3.3)\n",
            "Collecting nbconvert\n",
            "  Downloading nbconvert-6.3.0-py3-none-any.whl (556 kB)\n",
            "\u001b[K     |████████████████████████████████| 556 kB 55.6 MB/s \n",
            "\u001b[?25hCollecting jupyter-client\n",
            "  Downloading jupyter_client-6.1.12-py3-none-any.whl (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 66.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: nbclient<0.6,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from voila->geemap) (0.5.8)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.1.2)\n",
            "Building wheels for collected packages: folium, pyshp, ipynb-py-convert, pycrs\n",
            "  Building wheel for folium (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for folium: filename=folium-0.12.1.post1-py2.py3-none-any.whl size=95017 sha256=36baf5ee5a9de43611e0df1975d42a0de4756a8a2d4aae8e33df86cc17f33e28\n",
            "  Stored in directory: /root/.cache/pip/wheels/cf/d2/b1/569dd3ad4959f691dbd11987ac20ee2d5d58cb7d9f45968cae\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-2.1.3-py3-none-any.whl size=37325 sha256=005e1b127090ac5ad958f0cee18bc49cc87f70bbd99689349787f117d04dbd24\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/f8/87/53c8cd41545ba20e536ea29a8fcb5431b5f477ca50d5dffbbe\n",
            "  Building wheel for ipynb-py-convert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipynb-py-convert: filename=ipynb_py_convert-0.4.6-py3-none-any.whl size=4639 sha256=ef0f2a3222da120a8e676eedb096434f3b96be1f227367824daa51d50dc14a1d\n",
            "  Stored in directory: /root/.cache/pip/wheels/af/31/a9/761b134adbbca3c92d491eff3bad785c0a0c0079695d6f0504\n",
            "  Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycrs: filename=PyCRS-1.0.2-py3-none-any.whl size=32704 sha256=7f2627312eeba2b9bec8a5574c4fa98331edb6378a9c2d4a0a9ab2e54f1481bf\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/ce/32/1ec0aba6b9770681a423e82f0274c57d09ad2c20c2864901f9\n",
            "Successfully built folium pyshp ipynb-py-convert pycrs\n",
            "Installing collected packages: tornado, jsonschema, jupyter-client, nbconvert, sniffio, websocket-client, anyio, soupsieve, jupyter-server, json5, whitebox, traittypes, ratelim, pyproj, nbclassic, logzero, jupyterlab-server, ipytree, ipyfilechooser, beautifulsoup4, xyzservices, whiteboxgui, voila, sankee, python-box, pyshp, pycrs, owslib, mss, jupyterlab, ipynb-py-convert, ipyleaflet, ipyevents, geojson, geocoder, geeadd, folium, ffmpeg-python, colour, bqplot, geemap\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
            "      Successfully uninstalled jsonschema-2.6.0\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: nbconvert\n",
            "    Found existing installation: nbconvert 5.6.1\n",
            "    Uninstalling nbconvert-5.6.1:\n",
            "      Successfully uninstalled nbconvert-5.6.1\n",
            "  Attempting uninstall: beautifulsoup4\n",
            "    Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "  Attempting uninstall: folium\n",
            "    Found existing installation: folium 0.8.3\n",
            "    Uninstalling folium-0.8.3:\n",
            "      Successfully uninstalled folium-0.8.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.12.1.post1 which is incompatible.\u001b[0m\n",
            "Successfully installed anyio-3.3.4 beautifulsoup4-4.10.0 bqplot-0.12.31 colour-0.1.5 ffmpeg-python-0.2.0 folium-0.12.1.post1 geeadd-0.5.5 geemap-0.9.4 geocoder-1.38.1 geojson-2.5.0 ipyevents-2.0.1 ipyfilechooser-0.6.0 ipyleaflet-0.14.0 ipynb-py-convert-0.4.6 ipytree-0.2.1 json5-0.9.6 jsonschema-4.2.1 jupyter-client-6.1.12 jupyter-server-1.11.2 jupyterlab-3.2.4 jupyterlab-server-2.8.2 logzero-1.7.0 mss-6.1.0 nbclassic-0.3.4 nbconvert-6.3.0 owslib-0.25.0 pycrs-1.0.2 pyproj-3.2.1 pyshp-2.1.3 python-box-5.4.1 ratelim-0.1.6 sankee-0.0.5 sniffio-1.2.0 soupsieve-2.3.1 tornado-6.1 traittypes-0.2.1 voila-0.2.16 websocket-client-1.2.1 whitebox-2.0.3 whiteboxgui-0.6.0 xyzservices-2021.11.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "jupyter_client",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QU6SqXUrnqKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "7d694a7e-8e04-4c2c-e6c9-26b5f22c5600"
      },
      "source": [
        "import ee\n",
        "import geemap\n",
        "# Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the library.\n",
        "ee.Initialize()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://accounts.google.com/o/oauth2/auth?client_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fearthengine+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&code_challenge=PUmqMMAUF9AMJQe0jNbVc_xQegA9h6RRAU2g7tKNlIk&code_challenge_method=S256\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/1AX4XfWj-3Bqg5XdtJrMlBPHN7N1-SRgV6_u7JElSxacJAr-fUPNBNh9fLQ0\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFNOH-wmyz3o"
      },
      "source": [
        "admin = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level1\");\n",
        "maharashtra = admin.filter(ee.Filter.eq('ADM1_NAME', 'Maharashtra'))\n",
        "geometry = maharashtra.geometry()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgBUqW8Kubdx"
      },
      "source": [
        "# NRTI/L3_AER_AI\n",
        "This dataset provides near real-time high-resolution imagery of the UV Aerosol Index (UVAI), also called the Absorbing Aerosol Index (AAI).\n",
        "\n",
        "The AAI is based on wavelength-dependent changes in Rayleigh scattering in the UV spectral range for a pair of wavelengths. The difference between observed and modelled reflectance results in the AAI. When the AAI is positive, it indicates the presence of UV-absorbing aerosols like dust and smoke. It is useful for tracking the evolution of episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning.\n",
        "\n",
        "The wavelengths used have very low ozone absorption, so unlike aerosol optical thickness measurements, AAI can be calculated in the presence of clouds. Daily global coverage is therefore possible.\n",
        "\n",
        "For this L3 AER_AI product, the absorbing_aerosol_index is calculated with a pair of measurements at the 354 nm and 388 nm wavelengths.\n",
        "\n",
        "https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI?hl=en#bands"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOkBJvYerhMU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
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        "outputId": "f83e34aa-c113-4a34-b5fe-71bf34d3283a"
      },
      "source": [
        "Map2 = geemap.Map()\n",
        "\n",
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "\n",
        "absorbing_aerosol_index = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') \\\n",
        "          .select('absorbing_aerosol_index') \\\n",
        "          .filterDate('2021-10-01', '2021-10-31')\n",
        "#.filterDate('2018-07-10', '2021-11-19')\n",
        "\n",
        "band_viz = {\n",
        "  'min': -1,\n",
        "  'max': 2.0,\n",
        "  'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']\n",
        "};\n",
        "\n",
        "Map2.centerObject(point, 5)\n",
        "Map2.addLayer(absorbing_aerosol_index.mean().clip(geometry), band_viz, \"Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index\")\n",
        "#Map2.addLayer(absorbing_aerosol_index.mean(), band_viz, \"Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index\")\n",
        "\n",
        "Map2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ca87888028a4e588f0f412c9239fa1b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ok0KKAKLz-Kc"
      },
      "source": [
        "#NRTI/L3_CO\n",
        "This dataset provides near real-time high-resolution imagery of CO concentrations.\n",
        "\n",
        "Carbon monoxide (CO) is an important atmospheric trace gas for understanding tropospheric chemistry. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. Whereas fossil fuel combustion is the main source of CO at northern mid-latitudes, the oxidation of isoprene and biomass burning play an important role in the tropics. TROPOMI on the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance exploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 µm spectral range of the shortwave infrared (SWIR) part of the solar spectrum. TROPOMI clear sky observations provide CO total columns with sensitivity to the tropospheric boundary layer. For cloudy atmospheres, the column sensitivity changes according to the light path.\n",
        "\n",
        "https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO#description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2o02TY_yweY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
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            "4d62be93162641adb12baea2bd23db6e",
            "28393c4e107349cbbc5119826ed6ed0a",
            "ae053a60af234d349dcd39073e5e95cf",
            "1a6d81cdcdd24ae08c78588d9a7ee810",
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            "a79417ccc3ec494a981d16e984188050",
            "5b2ba2f4ae9042fb8e175b07978c1b61",
            "84d22d1e07614c418f8bce5f181a109b",
            "e24cc0ab4e1041719342fe45a6c1bd8a",
            "ef1f2276ee5449b38845ca2fc97f1271",
            "5b557cb602124344a0a341cd97a774dd",
            "af6b74903a6b4faeb707055d73e290af",
            "f4f4650f1bd74b6f9b849558c5bae7f3",
            "fa9c3dac320d4de480c20c159270888d",
            "766776ef04894f9185692b88b5579fb7",
            "fd1ce90ed221488e8535f698c6fa724a",
            "1dc487936a264941b55a184baeed6f6a",
            "2ac81a9a44524a7787f0a4c8e60a645a",
            "3cad63933bfc4fd1b8503b2e72079924",
            "c55623ec32a4401a8298d0b844eabd92",
            "10b64b459e16463f89738874c8cd1b73",
            "94a2029e861348be80f4c5b126789189"
          ]
        },
        "outputId": "3b6f44f6-00c3-45b7-8d36-5cb88263f5eb"
      },
      "source": [
        "Map3 = geemap.Map()\n",
        "\n",
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "\n",
        "carbon_monoxide = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') \\\n",
        "          .select('CO_column_number_density') \\\n",
        "          .filterDate('2021-10-01', '2021-10-31')\n",
        "\n",
        "band_viz = {\n",
        "  'min': 0,\n",
        "  'max': 0.05,\n",
        "  'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']\n",
        "};\n",
        "\n",
        "Map3.centerObject(point, 5)\n",
        "Map3.addLayer(carbon_monoxide.mean().clip(geometry), band_viz, \"Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide\")\n",
        "#Map3.addLayer(carbon_monoxide.mean(), band_viz, \"Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide\")\n",
        "\n",
        "Map3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ea4f45ab89e47bb83c662c5bd326341",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7oND3gO0Rvk"
      },
      "source": [
        "#NRTI/L3_NO2\n",
        "This dataset provides near real-time high-resolution imagery of NO2 concentrations.\n",
        "\n",
        "Nitrogen oxides (NO2 and NO) are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (notably fossil fuel combustion and biomass burning) and natural processes (wildfires, lightning, and microbiological processes in soils). Here, NO2 is used to represent concentrations of collective nitrogen oxides because during daytime, i.e. in the presence of sunlight, a photochemical cycle involving ozone (O3) converts NO into NO2 and vice versa on a timescale of minutes. The TROPOMI NO2 processing system is based on the algorithm developments for the DOMINO-2 product and for the EU QA4ECV NO2 reprocessed dataset for OMI, and has been adapted for TROPOMI. This retrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP chemistry transport model at a resolution of 1x1 degree as an essential element\n",
        "\n",
        "https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SknTciRN0JQM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "a9e88b5e24fb4e918be2dcaadd6efbf9",
            "882f76e216b04fd3a334e835cd573ce4",
            "e7750f1068df475ebf3e093fa49dd53d",
            "73b4cd8aca714c249cfd54696e7b92d1",
            "e496445d70554e58a49ef4fd0508a4b8",
            "c4c8045441f34cf58212336b28d38aad",
            "7cfe7c0e522448f4b92b825915ada1d1",
            "78a1bf21b9b7416c9c38e3dd0ec69373",
            "3ca5666059e64194b71d8f3ff9b30f07",
            "be459c144eac4fb1866c8da723847c2b",
            "211ad7e86ded48c3a7127e004a16514f",
            "838317b0586345d7adebe15c2b1a2713",
            "717f7e5d181a4fa29dc1bfd31cdf262f",
            "ef1f2276ee5449b38845ca2fc97f1271",
            "1b2cfe16d81f4413b5a44886a0137dcc",
            "99ef76353e244ac0b755c84fcf05d905",
            "00136c3f136f4a6cafa678036cb50581",
            "28b88874a091444493bf2d4c9827a1fb",
            "133d94442058437499b23a1b3bc1d920",
            "a8018c2f48bd4c6facad1bea7c988c06",
            "ced0e9ea5837426aa78c579238ed2f57",
            "40ed19de1d41466b9b15b8c95c8a07d8",
            "748f12aadf8f4579a650d9cadf3007fa",
            "b5f5b287bb934ab8890e3c15c55230fb",
            "51e60fc95acd4be8b5adf4e058e3f663",
            "b52d9771e3564e4aa6af7654f3028fc3"
          ]
        },
        "outputId": "2faa7a58-ae1a-42db-bc82-43cfb81cfac4"
      },
      "source": [
        "Map4 = geemap.Map()\n",
        "\n",
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "\n",
        "nitrogen_dioxide = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') \\\n",
        "          .select('NO2_column_number_density') \\\n",
        "          .filterDate('2021-10-01', '2021-10-31')\n",
        "\n",
        "band_viz = {\n",
        "  'min': 0,\n",
        "  'max': 0.0002,\n",
        "  'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']\n",
        "};\n",
        "\n",
        "Map4.centerObject(point, 5)\n",
        "Map4.addLayer(nitrogen_dioxide.mean().clip(geometry), band_viz, \"Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide\")\n",
        "#Map4.addLayer(nitrogen_dioxide.mean(), band_viz, \"Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide\")\n",
        "\n",
        "Map4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9e88b5e24fb4e918be2dcaadd6efbf9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKVgkllK1qgu"
      },
      "source": [
        "#NRTI/L3_HCHO\n",
        "This dataset provides near real-time high-resolution imagery of atmospheric formaldehyde (HCHO) concentrations.\n",
        "\n",
        "Formaldehyde is an intermediate gas in almost all oxidation chains of non-methane volatile organic compounds (NMVOC), leading eventually to CO2. Non-Methane Volatile Organic Compounds (NMVOCs) are, together with NOx, CO and CH4, among the most important precursors of tropospheric O3. The major HCHO source in the remote atmosphere is CH4 oxidation. Over the continents, the oxidation of higher NMVOCs emitted from vegetation, fires, traffic and industrial sources results in important and localized enhancements of the HCHO levels. The seasonal and inter-annual variations of the formaldehyde distribution are principally related to temperature changes and fire events, but also to changes in anthropogenic activities. HCHO concentrations in the boundary layer can be directly related to the release of short-lived hydrocarbons, which mostly cannot be observed directly from space.\n",
        "\n",
        "https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8VgVNsY1wpe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "6e383716700540ed9c1f79128d9d08ed",
            "a8b04019023840c38860fcb0cf80079c",
            "d119c0cdd0534c508551778f69be9d93",
            "a912fe884570471e963b1efa69d8bf6e",
            "b3ced38bc55a42faac2d053b53844bcf",
            "2e1857950dea48288f6efdeb10104aab",
            "535e309be983475a880e7aa218b75afa",
            "a3a8f2a881844bb99b3042e433f67f05",
            "9324764c0952432eaeca678f84ce418d",
            "556cfa1174394ffa84c96d9770b049d1",
            "f18fa579a5474d84b306a433b74f10d3",
            "c9946dd246a947d2bb14a958583da9fa",
            "1301f76b534f4da29da4ecdd49dbd8f9",
            "ef1f2276ee5449b38845ca2fc97f1271",
            "2eedcb83e2fd42d892ea8d243ea168cb",
            "346eb312187e403d8c9e4220f6fa320f",
            "1c1d63409d75462ea2284df49a1e0297",
            "ca49872096ff43d88f74d409ceec6f3d",
            "f711c13cf2d440549b8cf4112a1fc80e",
            "dd60c7de51fa4225a3089b69544ca340",
            "3502ee9d6e1246079182e0781b0576ca",
            "58f3d6404b5b45b3a516302c35bce34c",
            "5ee8be5f4a854f5693aae615f943a025",
            "903c0ec8f53c4e01a0ad0d3624a64398",
            "ae91d91fbb5940ffa207c96b72c90305",
            "a9a329169a5743a6ad4c366e5b7936a8"
          ]
        },
        "outputId": "6a52963d-c4bf-4139-b42d-4c1cf2765693"
      },
      "source": [
        "Map5 = geemap.Map()\n",
        "\n",
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "\n",
        "Formaldehyde = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') \\\n",
        "          .select('tropospheric_HCHO_column_number_density') \\\n",
        "          .filterDate('2021-10-01', '2021-10-31')\n",
        "\n",
        "band_viz = {\n",
        "  'min': 0.0,\n",
        "  'max': 0.0003,\n",
        "  'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']\n",
        "};\n",
        "\n",
        "\n",
        "Map5.centerObject(point, 5)\n",
        "Map5.addLayer(Formaldehyde.mean().clip(geometry), band_viz, \"Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde\")\n",
        "#Map5.addLayer(Formaldehyde.mean(), band_viz, \"Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde\")\n",
        "\n",
        "Map5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e383716700540ed9c1f79128d9d08ed",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fmmoe9JWgW32"
      },
      "source": [
        "#Graphical Analysis (AAI, CO, NO2, HCHO)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_ZRy5zVeERw"
      },
      "source": [
        "# !pip install ipygee\n",
        "# !pip install proplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdw92wltmEE1"
      },
      "source": [
        "# import numpy as np\n",
        "# import pandas as pd\n",
        "# import proplot as plot \n",
        "# import matplotlib.pyplot as plt \n",
        "# from ipygee import*\n",
        "# from pandas.plotting import register_matplotlib_converters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAkOfCdYhBIl"
      },
      "source": [
        "# admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
        "# Maharashtra = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Maharashtra'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBzksMvThCSP"
      },
      "source": [
        "# AAI = ee.FeatureCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') \\\n",
        "#         .select('absorbing_aerosol_index') \\\n",
        "#         .filterDate('2018-07-10', '2021-11-19')\n",
        "\n",
        "# NO = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') \\\n",
        "#         .select('NO2_column_number_density') \\\n",
        "#         .filterDate('2018-07-10', '2021-11-19')\n",
        "\n",
        "# CO = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') \\\n",
        "#       .select('CO_column_number_density') \\\n",
        "#       .filterDate('2018-11-22', '2021-11-19')\n",
        "\n",
        "# HCHO = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') \\\n",
        "#         .select('tropospheric_HCHO_column_number_density') \\\n",
        "#         .filterDate('2018-10-02', '2021-11-19')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}