Building AirAware: A real-time air quality dashboard
Development of my project called AirAware, which is a website app that will gather information about air pollution together with real-time weather forecast.
AirAware: Making Air Quality Easier to Understand with AWS
Before going out, we check the weather. But how often do we check the air we are breathing?
Air pollution is present in our daily lives, yet it is not always easy to understand the air quality data. Numbers such as AQI, PM2.5, PM10, and others may be hard to comprehend when we are interested in knowing about the current conditions.
This thought motivated me to create AirAware, a simple environmental dashboard that aggregates air quality and weather information.
What Is AirAware?
AirAware allows users to search for a city and view its environmental information through a simple dashboard. It shows air quality data, PM2.5 and PM10 levels, weather conditions, UV index, and health recommendations.
The idea is to make the environmental data accessible and understandable for everyone, including students, commuters, families, and other users.
Instead of showing data as a set of numbers, AirAware tries to make it applicable.
How I Built It
I created the web interface using Replit and AWS serverless services for the backend. Open-Meteo APIs provide the environmental data.
Here is how the components work together.
Amazon API Gateway creates an HTTP API that links the frontend and the backend. It receives application requests and redirects them to the corresponding Lambda function.
AWS Lambda executes the Node.js backend. My Lambda function, airaware-environment-api, searches for cities, provides weather forecasts and air quality information. It validates user input, queries the external APIs, and gives JSON responses to the frontend.
Open-Meteo APIs provide the underlying information. The Geocoding API identifies the location, the Weather Forecast API provides weather information, and the Air Quality API provides the pollutant information.
The process is pretty simple: a user searches for a city, the frontend sends a request to API Gateway, Lambda gets the required information, and the dashboard displays the response.
Architecture: User → AirAware Frontend → Amazon API Gateway → AWS Lambda → Open-Meteo APIs → Dashboard.
Challenges and Lessons Learned
The most valuable part of this project was gaining experience connecting all the pieces together into a functional application.
Connecting frontend and backend entailed working on API requests, CORS, and response management. Input validation, timeout, and error handling were other aspects that had to be taken care of in order to make requests more stable.
This project has shown me that creating an application requires more than developing its features. Each component must interact properly to create a proper user experience.
Work with API Gateway and Lambda has also expanded my knowledge about the capabilities of serverless architecture for backend development without having to deal with continuously running servers.
What's Next?
AirAware is already a good start and I would like to improve it even further by providing more detailed explanation of the AQI measurement, exploring historical air quality tendencies, adding accessibility support, and refining recommendations.
Future improvements should be made taking into account the clarity, reliability, and accuracy of the provided information.
A Small Step Towards a Healthier Future
Developing AirAware not only gave me practical experience with AWS Lambda, Amazon API Gateway, Node.js, and API integration. But also has shown me how technology can help to make environment-related information easier to understand.
I think that truly meaningful projects do not have to solve all the problems at once. Sometimes, helping people to understand the problem more is a good first step.
AirAware is my step towards a future when understanding of the air we breathe is as easy as checking the weather forecast.
Built for WeMakeDevs × AWS Environmental Hacks.
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