Air Quality Index Data Analysis for USA

• Shubham Banwal

• Dec, 2024

Air Quality Index (AQI) Data Analysis for the USA using Big Data

Problem:

Air quality data in the USA is large-scale, multi-source, and complex, making it difficult to extract meaningful insights for trend analysis, risk identification, and decision-making.

Approach:

Developed a big data analytics system to process and analyse large-scale AQI datasets across the USA, identifying patterns, trends, and regional variations in air pollution levels.

System Design:

• Large-scale AQI dataset ingestion and preprocessing pipeline
• Data cleaning and normalization layer
• Big data processing using distributed frameworks (e.g., Hadoop/Spark concepts)
• Analytical layer for trend analysis and pattern detection
• Visualization outputs for regional and temporal insights

Key Contributions:

• Built data pipelines to ingest, clean, and process large AQI datasets
• Performed trend analysis across time and geographic regions
• Identified pollution patterns, peak exposure periods, and high-risk areas
• Applied big data tools/concepts to handle high-volume environmental datasets
• Generated visual insights to support data-driven environmental understanding
• Ensured data consistency and accuracy through preprocessing and validation

Constraints & Tradeoffs:

• Data inconsistency across sources and time periods
• Handling missing or incomplete environmental data
• Tradeoff between processing speed and data volume
• Complexity in aggregating large-scale distributed datasets