Cloud-Native Air Pollution & Respiratory Death Analysis

Project Description

This project analyses the relationship between air pollution exposure and respiratory mortality using a cloud-native data processing workflow. It is designed as a public-health analytics system where pollution and mortality datasets are ingested, cleaned, transformed, modelled and visualised through dashboards. WHO identifies ambient air pollution as a major global health issue and estimates millions of premature deaths linked to outdoor air pollution, making this a strong real-world data project.

Problem

Environmental and health datasets are often large, distributed and difficult to interpret. Public-health decision makers need dashboards and analytical pipelines that can connect air quality indicators with respiratory death trends.

Solution

The project proposes a cloud-native analytics architecture using data lake storage, scalable Spark processing, transformed analytical datasets and interactive dashboards. Microsoft Azure Data Lake and Azure Synapse Spark are suitable for this type of scalable cloud data processing.

Main Features

FeatureDescription

Data Ingestion

Imports pollution and mortality datasets

Data Lake Storage

Stores raw and processed datasets

PySpark Processing

Cleans, joins and aggregates data

Analytical Modelling

Compares pollution exposure and health outcomes

Visual Dashboard

Shows trends by location, year and pollutant

Risk Interpretation

Identifies high-risk regions or periods

Documentation Angle

This project should be documented as a cloud data engineering and public health analytics case study. Include architecture diagram, data pipeline, ETL process, dataset table, dashboard screenshots, limitations and ethical use of public health data.