HealthyPapitas - Space Apps Challenge

HealthyPapitas| A One Health Approach

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.

Air N Health

Summary

The project addresses the problem of air quality. It uses the labelled dataset provided on the SpaceApps official GitHub repository. The dataset received has various scales for different fields. The PM 2.5 data was converted to categorical data with four values from 0(low PM 2.5 levels) to 3(very high concentration of PM 2.5 particles).Tensor Flow with Keras was used as the framework. A 4-layered NN was used as the architecture of the model. The weather API is used in the front-end to fetch the required data which is processed and the PM2.5 value is given to the user.

How We Addressed This Challenge

The project is intended to deal with the decreasing air quality. It will help people become aware of the air quality , encouraging them to take proper measures before they step out . This will be helpful to the people suffering from respiratory diseases. It would be useful for everyone.

How We Developed This Project

As the quality of air is decreasing day by day because of the pollutants present in the air , and many people even youngsters are suffering from respiratory problems.

We considered various factors which are responsible for the degradation of the air quality and based on that we found the value of PM2.5 in the air which is directly related to the AQI(Air Quality Index).

We built the front-end (Android App) using Java and our backend using python language.

For front-end we used Android Studio and for backend we used Tensor Flow for building the model.

We were able to get good accuracy when we tested our model. We faced problems when we tried to connect frontend and backend.

How We Used Space Agency Data in This Project

The data provided by NASA and other partner agencies was quite helpful. The sources were quite useful to understand the problem of air quality and its impact on our health.

It helped us in understanding the relation between different parameters of air quality like PM2.5 , Aerosol Optical Density , Relative Humidity, Wind Speed, HPBL. It helped us to understand how the model should work.

The data was used to create the model which further when processed gave the desired output.