Article
AIR QUALITY VS RESPIRATORY ILLNESS TREND COMPARISON
The Air Quality vs Respiratory Illness Trend Comparison system is a data analytics and visualization platform designed to study the relationship between airquality conditions and aggregate respiratory illness trends. Air pollution is influenced by pollutants such as particulate matter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide. Monitoring these environmental indicators alongside respiratoryhealth data can help researchers and public-health teams understand patterns that may require further investigation. The proposed system collects air-quality information such as PM2.5, PM10, NO₂, SO₂, CO, ozone, Air Quality Index (AQI), and measurement date and location. It also uses appropriately aggregated and de-identified respiratory-health information such as reported respiratory cases, hospital visits, or outpatient counts. The system processes these datasets and aligns them by location and time period for comparison. The dashboard presents the analyzed information through interactive charts, graphs, maps, tables, and Key Performance Indicators (KPIs). Users can compare air-quality measurements with respiratory illness trends across different months, seasons, locations, or other approved categories. Filters allow researchers and administrators to select specific pollutants, regions, time periods, and health indicators. The system can identify correlations, trends, and changes between environmental conditions and respiratory-health indicators. For example, users may observe whether periods with higher particulate pollution coincide with increased aggregate respiratory visits. However, observed relationships are presented as analytical associations rather than proof that air pollution directly caused a specific illness or individual health outcome. Overall, the Air Quality vs Respiratory Illness Trend Comparison system provides a centralized platform for combining environmental and public-health data. It can support research, environmental monitoring, public-health planning, and awareness activities. Future enhancements can include real-time air-quality feeds, advanced statistical analysis, predictive modeling, geographic risk visualization, anomaly detection, and integration with additional environmental and health datasets.
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