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Python script downloading NO2 pollution datas from the European satellite Sentinel5 and aggregating by country, regions, states, cities. This work is carried out within the framework of the juanporrasl/AMSECovid19 project.

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Sentinel5_NO2

Downloading, cleaning and aggregation of NO2 (Nitrogen Dioxide) datas from the European satellite Sentinel5. This work is being carried out as part of the AMSECovid19 project.

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├── LICENSE
├── README.md          <- The top-level README for developers using this project.
│
├── datas_sentinel5    <- You have to look here if you are only interested in downloading the data.
│   ├─ archives            <- File in which the files are recorded and definitively processed
│   ├─ cleaned             <- Folder where cleaned, non-aggregated records are temporarily stored.
│   ├─ csv                 <- Data file extracted from the ONDA DIAS API to obtain the links to
│   │                         download the files.
│   └─ tracking_files.csv  <- Automatically generated file, allows you to record the days processed,
│                             with the number of files downloaded. This makes it possible to detect
│                             the presence of new files for a day that has already been downloaded.
│
├── notebooks          <- Notebooks folders
│   ├── confidential
│   │    └──secrets.py        <- Secret script where ID and password are saved to connect to ONDA DIAS
│   │                            with following elements: user="<your_username"
│   │                            password = "<your_password>"
│   ├── analysis.ipynb        <- Give a small analysis of our datas
│   ├── country_code.py       <- Got all ISO-2 country codes for analysis
│   └── main.py               <- Main script to run. Download and clean the datas from ESA.
│
└── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
                          generated with `pip freeze > requirements.txt`

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Python script downloading NO2 pollution datas from the European satellite Sentinel5 and aggregating by country, regions, states, cities. This work is carried out within the framework of the juanporrasl/AMSECovid19 project.

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