Environmental Pollution Detection and Monitoring Systems

Module: Sustainable Tech | Difficulty: Premium

Pollutant Dispersion

Exposure Assessment

Comparison

Python Implementation

import numpy as np
from sklearn.ensemble import IsolationForest
from scipy.spatial.distance import cdist

class PollutionMonitor:
    def __init__(self, sensor_locations):
        self.locations = sensor_locations
        self.detector = IsolationForest(contamination=0.05)

    def spatial_interpolation(self, readings, targets):
        distances = cdist(targets, self.locations)
        weights = 1 / (distances**2 + 1e-10)
        return np.dot(weights / weights.sum(axis=1, keepdims=True), readings)

    def detect_anomalies(self, data):
        self.detector.fit(data.reshape(-1, 1))
        return self.detector.predict(data.reshape(-1, 1)) == -1

    def source_apportionment(self, receptor_data, source_profiles):
        from scipy.optimize import nnls
        A = np.array([source_profiles[s] for s in source_profiles]).T
        x, _ = nnls(A, receptor_data)
        return dict(zip(source_profiles.keys(), x))

Research Insight: Dense IoT sensor networks with 100+ nodes per km2 resolve pollution plumes at street-level resolution.