AI World 🌍

Here’s a conceptual framework and code outline for an AI-driven environmental gas detection system with a companion app. This system uses sensors, data analysis, and machine learning to measure gas concentrations and air quality.

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System Architecture

1. Hardware:

• Gas sensors (MQ-135 for air quality, MQ-7 for CO, SDS011 for PM2.5/PM10).
• Microcontroller (Arduino/Raspberry Pi) for data collection.
• GPS module for location tracking.
• IoT connectivity (Wi-Fi/Bluetooth).

1. Software:

• Sensor calibration algorithms.
• ML model for gas type classification.
• Air Quality Index (AQI) calculator.
• Mobile/Web app for visualization.

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Step 1: Sensor Data Collection & Calibration

Arduino Code (Gas Concentration Measurement) #include <MQUnifiedsensor.h> // MQ-135 for NH3, CO, and alcohol detection #define Board "Arduino UNO" #define Pin A0 MQUnifiedsensor MQ135(Board, Pin); void setup() { Serial.begin(9600); MQ135.setRegressionMethod(1); // Exponential regression MQ135.init(); MQ135.setRL(10); // Load resistance (kOhm) MQ135.setR0(9.83); // Calibrated in clean air } void loop() { MQ135.update(); float NH3_ppm = MQ135.readSensor("NH3"); float CO_ppm = MQ135.readSensor("CO"); Serial.print("NH3: "); Serial.print(NH3_ppm); Serial.print(" ppm | CO: "); Serial.print(CO_ppm); Serial.println(" ppm"); delay(2000); }

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Step 2: Air Quality Index (AQI) Calculation

Python Code (AQI Logic) def calculate_aqi(pm25, pm10, co, nh3): # AQI thresholds (WHO standards) pm25_breakpoints = [0, 12, 35.4, 55.4, 150.4, 250.4] pm10_breakpoints = [0, 54, 154, 254, 354, 424] co_breakpoints = [0, 4.4, 9.4, 12.4, 15.4, 30.4] nh3_breakpoints = [0, 200, 400, 800, 1200, 1800] # Calculate sub-indices aqi_pm25 = calculate_subindex(pm25, pm25_breakpoints) aqi_pm10 = calculate_subindex(pm10, pm10_breakpoints) aqi_co = calculate_subindex(co, co_breakpoints) aqi_nh3 = calculate_subindex(nh3, nh3_breakpoints) # Final AQI = max of sub-indices return max(aqi_pm25, aqi_pm10, aqi_co, aqi_nh3) def calculate_subindex(value, breakpoints): aqi_ranges = [0, 50, 100, 150, 200, 300] for i in range(1, len(breakpoints)): if value <= breakpoints[i]: return ((aqi_ranges[i] - aqi_ranges[i-1]) / (breakpoints[i] - breakpoints[i-1])) * (value - breakpoints[i-1]) + aqi_ranges[i-1] return 300 # Hazardous

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Step 3: Gas Classification with Machine Learning

Python Code (TensorFlow/Keras Model) import tensorflow as tf from sklearn.preprocessing import StandardScaler # Sample dataset: [NH3, CO, PM2.5, PM10, Temperature, Humidity] X_train = [...] # Sensor data y_train = [...] # Labels (e.g., 0=Clean, 1=Moderate, 2=Unhealthy) # Model architecture model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(6,)), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(3, activation='softmax') # 3 air quality classes ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, epochs=50, batch_size=32) # Save model for app integration model.save('gas_classifier.h5')

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Step 4: Mobile App (Flutter Example)

Flutter Code (UI & API Integration) import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; class AirQualityApp extends StatefulWidget { @override _AirQualityAppState createState() => _AirQualityAppState(); } class _AirQualityAppState extends State<AirQualityApp> { double aqi = 0.0; String gasType = "Unknown"; Future<void> fetchData() async { final response = await http.get(Uri.parse('http://<RASPBERRY_PI_IP>/sensor-data')); if (response.statusCode == 200) { setState(() { aqi = json.decode(response.body)['aqi']; gasType = json.decode(response.body)['gas_type']; }); } } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar(title: Text("Air Quality Monitor")), body: Center( child: Column( mainAxisAlignment: MainAxisAlignment.center, children: [ Text("AQI: ${aqi.toStringAsFixed(1)}", style: TextStyle(fontSize: 40, color: _getAqiColor(aqi))), SizedBox(height: 20), Text("Dominant Gas: $gasType", style: TextStyle(fontSize: 24)), ElevatedButton(onPressed: fetchData, child: Text("Refresh")) ], ), ), ); } Color _getAqiColor(double aqi) { if (aqi <= 50) return Colors.green; else if (aqi <= 100) return Colors.yellow; else if (aqi <= 150) return Colors.orange; else return Colors.red; } }

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Step 5: Deployment

1. IoT Setup:

• Connect sensors to Raspberry Pi.
• Use Python Flask to create an API: from flask import Flask, jsonify app = Flask(__name__) @app.route('/sensor-data') def get_sensor_data(): # Read sensor values via GPIO aqi = calculate_aqi(pm25, pm10, co, nh3) gas_type = model.predict([[nh3, co, pm25, pm10, temp, hum]]) return jsonify({"aqi": aqi, "gas_type": gas_type}) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000)

1. App Integration:

• Deploy the Flutter/Django app.
• Use Firebase for real-time alerts.

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Key Features

• Real-time gas concentration monitoring.
• AQI visualization with color coding.
• Historical data graphs.
• Push notifications for unsafe levels.

Example Output: AQI: 85 (Moderate) Dominant Gas: CO (12 ppm) Recommendation: Limit outdoor activities.

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Tools & Libraries

• Hardware: Raspberry Pi, Arduino, MQ sensors.
• Software: TensorFlow, Flask, Flutter.
• APIs: Google Maps API for pollution heatmaps.

This system can be extended with satellite data integration or blockchain for tamper-proof environmental reporting.