A One Health Approach

Air pollution is a major global environmental health risk, causing an estimated seven million deaths across the globe annually. Your challenge is to take an interdisciplinary approach, using both Earth science and health science, and integrate different types of datasets and applications to study the effects of air pollution.

A ONE HEALTH APPROACH: Air pollution is a major global environmental health risk, causing an estima

Summary

Collected different data of the air pollution from the NASA resources and integrated those datasets. The integrated datasets are trained using the machine learning model to predict future precautions and required solutions to be taken for the cause of pollution.Collection of datasets contains of different gases responsible for the air pollution like NO2,SO2,O3,CO in different states of USA.The precautions and solutions for the air pollution are Afforestation, reusing pollutants from factories, using radioactive waste as energy, using diamond batteries, using electrical vehicles.Business model is using Air purifiers and when comes to affordable way is air purifying plants approved by NASA.

How We Addressed This Challenge

Developed a machine learning model by taking air pollution data to predict the future consequences and precautions.

Air pollution is the major problem in our world leading to lot of problems. Many people has been dying because of air pollution .So to save the lives of the people this is an important task.

It predicts the air quality index of the gases present in the air according to the previous data.

we hope to decrease the death of the lives caused by the air pollution.

How We Developed This Project

As a responsible citizen we need to save the earth.

We used machine learning.

Tools we used are python, tableau.

We faced problem in collecting data and training model ,finally we solved he problem.

How We Used Space Agency Data in This Project

We collected the data from CSA,CNES,JAXA and integrated them to use as a dataset for our machine learning model. From ESA we have taken sentinal 5p data which monitors CO,NO2,O3.

From CSA we used SCISAT data consisting of data of multiple air pollutants like NO,NO2,CO.

MOPITT consists of CO data.

Data & Resources

we have combined few data as mentioned above.

Tags
#AIR #POLLUTION #AIIRPOLLUTION #NASA
Judging
This project was submitted for consideration during the Space Apps Judging process.