The document outlines a project to develop a real-time air quality prediction system in India, addressing severe air pollution that causes 1.7 million premature deaths annually. It highlights the importance of predicting air quality index (AQI) levels using various data sources and machine learning models. The system aims to promote public health, community awareness, and proactive measures against poor air quality.

1. REAL TIME AIR
QUAILTY
PREDICTION

2. INTRODUCTION
• The Air Quality Index (AQI) is a standardized system used to
communicate the quality of air in a specific area and its
potential impact on public health.
• It provides a numerical score that reflects the concentration of
various air pollutants, including particulate matter (PM10 and
PM2.5), ground-level ozone (O3), nitrogen dioxide (NO2),
sulfur dioxide (SO2), and carbon monoxide (CO).

3. PROBLEM STATEMENT
India faces a severe air pollution crisis, with
hazardous AQI levels causing approximately 1.7
million premature deaths annually due to health
issues. This project aims to develop a real-time
air quality prediction system using Python to
forecast AQI levels, enabling proactive public
health measures and increased community
awareness.

4. MAIN POLLUTING GASES
SULFUR
DIOXIDE
NITROGEN
DIOXIDE
CARBON
MONOXIDE
CARBON
DIOXIDE
1 2 4
3

5. OBJECTIVES
 Data Acquisition
 Data Processing
 Feature Development
 Model Development
 Real-Time Prediction

6. DATA SOURCES
 GOVERNMENT AND ENVIRONMENTAL AGENCIES
 WORLD AIR QUALITY INDEX
 OPEN DATA PLATFORMS
 OPENAQ
 DATA.GOV
 NASA EARTH DATA

7. MODEL IMPLEMENTATION
LINEAR REGRESSION
SARIMA (Seasonal ARIMA)
LOGISTIC REGRESSION
ANN (Artificial Neural Networks)

8. ADVANTAGES
PUBLIC HEALTH PROTECTION
DATA-DRIVEN DECISION MAKING
ENHANCED COMMUNITY AWARENESS
PROACTIVE MEASURES
RESOURCE OPTIMIZATION
LONG-TERM HEALTH BENEFITS

9. CHALLENGES FACED FOR
IMPLEMENTATION
 DATA QUALITY AND AVAILABILITY
 COMPLEXITY OF AIR QUALITY FACTORS
 MODEL SELECTION AND TUNING
 COMPUTATIONAL RESOURCES
 REAL-TIME PROCESSING
 INTEROPERABILITY

10. FEATURES
 REAL-TIME DATA MONITORING
 PREDICTIVE MODELING
 POLLUTANT TRACKING
 API INTEGRATION
 HISTORICAL DATA COMPARISON
 FORECAST VISUALIZATION

11. The development of a real-time air quality index
prediction system is a vital step toward addressing the
growing concerns of air pollution and its adverse
effects on public health. By leveraging historical data,
meteorological factors, and advanced machine
learning techniques, this system can provide accurate
forecasts of air quality levels, enabling proactive
measures to protect community health.
CONCLUSION

12. CREDITS: This presentation template was created by Slidesgo, and
includes icons by Flaticon, and infographics & images by Freepik
THANKS
PAVAN KUMAR 4AL22AI036
YASHWANTH 4AL22AI010
HEMANTH KUMAR 4AL22AI019
MADHUSUDHAN G 4AL22AI026
TEAM-06