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Autoregressive Trend analysis & Hypercritical Air pollution Ranging & Visualisation Application

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ATHARVA : Autoregressive Trend analysis & Hypercritical Air pollution Ranging & Visualisation Application

Aim : To develop AI/ML-based software to identify/analyze: 1. Location of hot spots. 2. The long-term occurrence of hot spots and changes.

We have developed ATHARVA which is a user friendly Web application that can be used by common people to gain the knowledge about hotspots of India and their changes.

Link to PPT:

https://www.canva.com/design/DAEDV70XfpA/Qmn-R7Y6_iSq63JsldarnQ/view?utm_content=DAEDV70XfpA&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton#1

Idea Short Video:

https://drive.google.com/file/d/1jaEzWIA169S9Ln3C83RpgdI0CYdDdkm3/view?usp=sharing

Tweet Link:

https://twitter.com/darecoder/status/1285983011855454208

Features in our application Atharva:

  • District-wise plotting Heatmap for each pollutant analyzation and visualization
  • Coordinate basis Hotspots and Coldspots detection
  • Graphical Analysis of concentration of pollutants at each hotspot
  • Filters available for querying various pollutants on the map for any week.
  • Plotting shifts in HotSpots over a specific period of time
  • Analyzing those shifts by plotting backward trajectories and verifying it using meteorological data.
  • Two modes for visualization: Dark mode & Daylight

Steps to setup project(ATHARVA) locally

For Linux OS:

  1. NPM install
npm install
npm run clientinstall
  1. Install Postgres & Postgis using apt
  • Create new user and database
  • Switch to postgresql cli mode then, grant superuser privileges to your newly created user. (ALTER USER your_username WITH SUPERUSER;)
  • Start postgres by running: systemctl start postgresql
  • Replace user, password, database with the user, password, database (name) created above in knexfile.js.
  1. Run knex migrate:latest
knex seed:run
npm run dev

Updates on project till now

  • Fetched the sentinel-5p data.
  • Performed data mining and data preprocessing.
  • Plotted Hotspots district-wise using spatial auto-correlation.
  • Analysed the movement of Hotspots.
  • Plotted backward trajectory by using metrelogical dataset.
  • Depicted the shifts in hotspot over a period of time.
  • Trend analysis at each hotspot for concentration of every pollutant gas.
  • Multiple Heatmap plotted using MapBox for better visualization.

Tools and Technologies used

  • Sentinel-5P Pre-Operations Data Hub (data source)
  • HARP (for fetching Sentinel5p data)
  • PYSAL (for spatial relationship to detect hotspots, coldspots)
  • ESDA (for exploratory analysis of spatial data)
  • GEOPANDAAS (to work with geospatial data and geojson data)
  • METEX (for determining trajectories through backtracking analysis of metreological data)
  • NCEP/NCAR (data source for metreological data needed for trajectories)
  • React,Node.js,Express.js,Postgis,PostgreSQL (for our Web Application)

ATHARVA UI:

YouTube Video:

ATHARVA NM394_FreshlyBuilt

January, 2020 Clusters & Cluster centers:

February, 2020 Clusters & Cluster centers:

Change in HotSpots & Moving HotSpots:

HotSpot Trajectories:

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