🌍 EcoGuard AI – Industrial Pollution Compliance Monitoring System
🚀 A Multi-Agent AI System for Monitoring Industrial Water and Air Pollution Compliance using CPCB Standards
📌 Project Overview
EcoGuard AI is a Multi-Agent Artificial Intelligence system designed to monitor industrial wastewater quality and air emissions in real time. The system automatically evaluates environmental parameters against CPCB (Central Pollution Control Board) standards, detects violations, generates alerts, and produces compliance audit reports.
This project demonstrates how AI agents can collaborate to improve environmental monitoring, industrial safety, and regulatory compliance.
🎯 Developed for the Google x Kaggle AI Agents Capstone Project (Agents for Good Track).
❗ Problem Statement
Industrial facilities often struggle to continuously monitor environmental pollution levels and ensure compliance with CPCB regulations.
Common challenges include:
🌫️ Air pollution violations
💧 Wastewater contamination
⚠️ Delayed detection of environmental risks
📋 Manual compliance reporting
🚨 Slow incident response
EcoGuard AI addresses these challenges through automated monitoring, intelligent analysis, alert generation, and compliance reporting.
🎯 Project Objectives
✅ Monitor industrial wastewater quality
✅ Monitor industrial air emissions
✅ Detect CPCB compliance violations
✅ Generate automated SMS and Email alerts
✅ Demonstrate Multi-Agent AI collaboration
✅ Provide Explainable AI reasoning
✅ Generate compliance audit reports
🤖 Multi-Agent Architecture
EcoGuard AI uses a collaborative Multi-Agent architecture where specialized agents work together to perform environmental compliance monitoring.
🧠 EcoGuardMaster Agent Responsibilities Receives compliance requests Coordinates all agents Collects analysis results Makes final compliance decisions Generates audit workflow 💧 WaterMonitor Agent Responsibilities Monitors wastewater parameters Evaluates CPCB water quality limits Detects water pollution violations Parameters Monitored pH BOD (Biochemical Oxygen Demand) COD (Chemical Oxygen Demand) Heavy Metals 🌫️ AirMonitor Agent Responsibilities Monitors air emissions Evaluates CPCB air quality limits Detects air pollution violations Parameters Monitored SO₂ NOx PM2.5 CO₂ 🚨 AlertDispatch Agent Responsibilities
📱 SMS Alert Generation
📧 Email Notification Generation
🚨 Incident Response Activation
📢 Compliance Warning Dispatch
📄 ReportGen Agent Responsibilities
📋 Compliance Audit Report Generation
📊 Environmental Assessment Summary
📑 Regulatory Documentation
🧠 AI Concepts Implemented 🤖 1. Multi-Agent Systems
The project uses multiple AI agents that collaborate to solve environmental monitoring tasks.
Agent Workflow User ↓ EcoGuardMaster ↓ WaterMonitor ↓ AirMonitor ↓ AlertDispatch ↓ ReportGen ↓ User 🔄 2. Agent Communication
Agents communicate through delegated tasks and structured message passing.
Example
EcoGuardMaster → WaterMonitor
WaterMonitor → EcoGuardMaster
EcoGuardMaster → AirMonitor
AirMonitor → EcoGuardMaster
EcoGuardMaster → AlertDispatch
AlertDispatch → ReportGen
⚖️ 3. Rule-Based AI Decision Making
The system compares sensor values against CPCB limits.
Example BOD > 30 mg/L → Violation COD > 250 mg/L → Violation SO₂ > 80 µg/m³ → Violation NOx > 80 µg/m³ → Violation 🔍 4. Explainable AI
The Live Agent Reasoning panel explains:
✅ Which agent executed
✅ What analysis was performed
✅ Why violations occurred
✅ Why alerts were triggered
✅ How reports were generated
📢 5. Automated Alert Generation
When violations are detected:
📱 SMS alerts are generated
📧 Email alerts are generated
🚨 Incident response actions are triggered
📄 Compliance records are logged
📏 CPCB Parameters Used 💧 Wastewater Quality Parameters Parameter CPCB Limit pH 6.5 – 8.5 BOD ≤ 30 mg/L COD ≤ 250 mg/L Heavy Metals ≤ 0.1 mg/L 🌫️ Air Emission Parameters Parameter CPCB Limit SO₂ ≤ 80 µg/m³ NOx ≤ 80 µg/m³ PM2.5 ≤ 60 µg/m³ CO₂ ≤ 1000 ppm 🛠️ Technology Stack Programming Language
🐍 Python
Framework
🎨 Gradio
Libraries
📦 JSON
📝 Logging
⚙️ Python Standard Libraries
Development Tools
💻 Visual Studio Code
🌐 GitHub
🔄 System Workflow Step 1
👤 User enters sensor readings
⬇️
Step 2
🧠 EcoGuardMaster receives compliance request
⬇️
Step 3
💧 WaterMonitor analyzes wastewater quality
⬇️
Step 4
🌫️ AirMonitor analyzes emissions quality
⬇️
Step 5
⚠️ Violations are identified
⬇️
Step 6
🚨 AlertDispatch generates notifications
⬇️
Step 7
📄 ReportGen creates compliance report
⬇️
Step 8
✅ Results displayed to the user
📸 Screenshots 🌍 Figure 1 – EcoGuard AI Dashboard <img width="1272" height="656" alt="Image" src="https://github.com/user-attachments/assets/b37c4d80-e138-4116-aa18-756060573b77" /> Main dashboard displaying wastewater and air emission monitoring parameters.
<img width="1297" height="656" alt="Image" src="https://github.com/user-attachments/assets/4b13f2f3-a0c5-474f-b05f-3d6257b7fea4" />🚨 Figure 2 – CPCB Compliance Violation Detection
Automatic detection of pollution parameters exceeding CPCB limits. <img width="1296" height="640" alt="Image" src="https://github.com/user-attachments/assets/075799ff-f665-43ae-8372-362990a4b1a0" /> 📢 Figure 3 – Automated Alert Dispatch Queue
SMS and Email alerts generated after detecting violations. <img width="1296" height="640" alt="Image" src="https://github.com/user-attachments/assets/be10e588-eff6-4054-955c-527e746bb82a" />
🔄 Figure 4 – Agent Communication Trace
Communication between EcoGuardMaster, WaterMonitor, AirMonitor, AlertDispatch, and ReportGen. <img width="1300" height="663" alt="Image" src="https://github.com/user-attachments/assets/a9d79602-ca8e-4e01-903a-9417bf919ec1" /> 🧠 Figure 5 – Live Agent Reasoning
Explainable AI decision-making and compliance analysis. <img width="1257" height="647" alt="Image" src="https://github.com/user-attachments/assets/1882476a-a02a-4a2d-a944-da542237cacc" />
📄 Figure 6 – CPCB Industrial Compliance Audit Report
Final compliance report generated by ReportGen Agent. <img width="1257" height="647" alt="Image" src="https://github.com/user-attachments/assets/e07170a2-1c34-4d44-ab98-5933b795cc79" />
📋 Figure 7 – Air Quality Assessment Report <img width="1295" height="645" alt="Image" src="https://github.com/user-attachments/assets/b180451c-934d-4b97-bb37-60c717644deb" />
Detailed air quality compliance assessment.
✅ Results
The system successfully:
✅ Detected CPCB violations
✅ Evaluated wastewater compliance
✅ Evaluated air emission compliance
✅ Generated automated alerts
✅ Demonstrated multi-agent collaboration
✅ Provided explainable AI reasoning
✅ Generated compliance audit reports
🔮 Future Enhancements
📡 Real-time IoT sensor integration
☁️ CPCB API integration
📱 Mobile application support
🗺️ GIS-based pollution mapping
📈 Pollution forecasting using Machine Learning
🤖 Predictive environmental risk assessment
A complete screen-recorded demonstration video has been created showing:
✅ Dashboard Navigation
✅ Pollution Parameter Monitoring
✅ CPCB Compliance Evaluation
✅ Violation Detection
✅ Alert Generation
✅ Agent Communication
✅ Live AI Reasoning
✅ Audit Report Generation
🌱 Environmental Impact
EcoGuard AI helps industries:
🌍 Reduce environmental pollution
🏭 Improve regulatory compliance
⚠️ Detect risks earlier
📊 Improve environmental decision-making
📋 Maintain audit-ready records
🎉 Conclusion
EcoGuard AI demonstrates how Multi-Agent AI systems can be applied to environmental monitoring and industrial compliance management.
By combining environmental monitoring, automated decision-making, alert generation, explainable AI, and compliance reporting, the system provides a scalable solution for helping industries maintain CPCB compliance and reduce environmental risks.
👩💻 Author
Priyanka Hiraman Todavat
🎓 Diploma in Chemical Engineering (2020)
🏆 Google x Kaggle AI Agents Capstone Project
🌍 Project: EcoGuard AI – Industrial Pollution Compliance Monitoring System
🚀 AI for Environmental Sustainability & Industrial Safety 🌱
⭐ Key Features
✅ Multi-Agent AI Architecture
✅ CPCB Compliance Monitoring
✅ Water Quality Analysis
✅ Air Emission Analysis
✅ Automated Alert Generation
✅ Explainable AI Reasoning
✅ Compliance Audit Reporting
✅ Interactive Gradio Dashboard
⭐ If you like this project, please give it a star on GitHub! ⭐
