Dream Makers - Space Apps Challenge

Dream Makers| 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 Quality Data Path Finder

Summary

We are developing a low cost Air quality path finder designed to detect the harmful particulate matters and gasses & even purifies them when attached on helmet through an external attachment. This tool uses the sensor data as well as the Space Agencie's datasets to make aM2M process to give a accurate data in real time to our smart phones. It can be even attached to masks as well as on clothes to detect the harmful pollutants. Asthma as well as Allergic patients and Air quality research communities will surely find this device usefulOur system consists of several devices that serve different purposes:•Mesh Network•Laser Particle Sensor PM2012•Microcontroller/ Arduino •Mobile App

How We Addressed This Challenge

https://ibb.co/HVYLNp1

This Device will take Sensor data as well as the Datasets data from the Satellite through the cloud . The Device will use ML to do a trial and error and give the end User a very Accurate result of the surrounding gases and particles.

Suppose the smartphone is not taking in the processed data for some reasons ,the data will be sent to the cloud and the cloud will send it to the smartphone when the network is available.

So thus this Device works in Online as well as in Offline mode.

Future Plans

We aim to provide additional features which will be provided by this same device & thus make the device more user friendly.We will Integrate Water quality sensors to detect the harmful pollutants in water as well,Bio-Sensors in future to detect the viruses with the pollutants. Also integrate other necessary sensors like Electrochemical sensors if cost is ok to be in the higher end,to meet the customer requirement.

To conclude this Device will give accurate results using data from 2 sources , from the sensors as well as the Datasets available to train the ML.

How We Developed This Project

As we all know the air pollution is increasing day by day as a result people are suffering from different kind of diseases because they have no idea on what kind of air they are breathing.

Unknowingly a person will breathe harmful particulate matters and gases unless he is aware of his surrounding pollutants. The common people cannot get accurate real time data whenever required.

So we came up this idea which is accessible by anyone .

We used hardware tools such asmicro controllers(Arm cortex-M)/Arduino(Nodemcu) with sensors( Laser PM 2012) which will detect both PM2.5mm and PM10mm .

The micro controller is trained using ML.

If Arduino is used then we can use C++.

Technologies such as System-on-Chip (SoC) and chip scale packages (CSP) help to shrink the size. For example, Cypress offers PSoC (Programmable System-on-Chip) devices in multiple packaging options including WLCSP

Features

• Smallest & ultra-thin

• High accuracy

• High sensitive and quick response(≤8s)

• Signal output optional: UART, I²C

• Compact structure, light weight, easy to install

Applications

▪ Air purifier

▪ Air quality monitor

▪ Air conditioner

▪ Ventilation system

How We Used Space Agency Data in This Project

We are using the NASA ,ESA,JAXA,CSA,CNES datasets to train the ML with the real time sensor data and give accurate results about the surrounding air to the end User without any Human intervention.