➠From Analysis to App: Building an Interactive Air Quality Dashboard with Streamlit

This is Part 2 of my series on India’s Air Quality. Check out Part 1 here to see the full analysis and visualisations.

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📌 Introduction

In Part 1, I explored India’s air quality data—cleaning, analysing, and visualising trends across pollutants and cities. In this post, I take the insights one step further by turning them into an interactive dashboard using Streamlit.

The goal: to make air quality data accessible, explorable, and insightful for a wider audience.

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💡 Why Build a Dashboard?

While static visuals are powerful, dashboards allow:

• Real-time interaction with data

• City and pollutant-level filtering

• Forecasting and exploratory analysis for all users, technical or not

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🔍 Dashboard Features

✅ City and Pollutant Selector
Users can choose specific cities (e.g. Delhi, Mumbai) and pollutants (PM2.5, NO₂, etc.)

✅ Dynamic Visualisations
Interactive time-series plots and pollutant comparisons update instantly

✅ Clean, User-Friendly Layout
Simple interface designed in Streamlit for intuitive navigation

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🛠️ Tech Stack

• Python

• Pandas

• Streamlit

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🚧 Challenges Faced

• Managing large time-series data interactively

• Handling missing values in forecasting models

• Designing a clean, user-friendly dashboard layout

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🚀 Try the Dashboard Live

👉 Click here to explore the dashboard

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🧾 Conclusion

This project transformed a static analysis into an interactive tool accessible to anyone interested in India’s air quality. Dashboards like this make complex data stories easier to explore and understand.

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👩‍💻 About Me

I’m Arpita Garg, a data storyteller passionate about turning complex datasets into engaging narratives and practical tools.
📂 Check out more of my work on Arpitext Data Stories