Air Pollution Forecasting and Emission Monitoring

Module: Sustainable Tech | Difficulty: Premium

Gaussian Dispersion Model

AQI Calculation

Comparison

Python Implementation

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor

class AirQualityPredictor:
    def __init__(self):
        self.model = GradientBoostingRegressor(n_estimators=100)

    def gaussian_plume(self, Q, u, H, x, y, z, sigma_y, sigma_z):
        term1 = Q / (2 * np.pi * u * sigma_y * sigma_z)
        term2 = np.exp(-y**2 / (2 * sigma_y**2))
        term3 = np.exp(-(z - H)**2 / (2 * sigma_z**2)) + np.exp(-(z + H)**2 / (2 * sigma_z**2))
        return term1 * term2 * term3

    def calculate_aqi(self, concentration, pollutant):
        bp = {'PM2.5': [(0, 12, 0, 50), (12.1, 35.4, 51, 100)]}
        for low, high, aqi_low, aqi_high in bp.get(pollutant, bp['PM2.5']):
            if low <= concentration <= high:
                return (aqi_high - aqi_low) / (high - low) * (concentration - low) + aqi_low
        return 500

Research Insight: Satellite-based NO2 monitoring from TROPOMI provides daily global coverage at 5.5 km resolution.