Regulatory Compliance Monitoring and Reporting

Module: Sustainable Tech | Difficulty: Premium

Compliance Index

Comparison

| Pollutant | US EPA Limit | EU Limit | Monitoring Frequency | |-----------|-------------|----------|---------------------| | SO2 | 75 ppb (1h) | 350 ug/m3 (1h) | Continuous | | NOx | 100 ppb (1h) | 200 ug/m3 (1h) | Continuous | | PM2.5 | 35 ug/m3 (24h) | 25 ug/m3 (24h) | Daily | | CO | 9 ppm (8h) | 10 mg/m3 (8h) | Continuous |

Python Implementation

import numpy as np
from datetime import datetime

class ComplianceMonitor:
    def __init__(self):
        self.standards = {'SO2': 75, 'NOx': 100, 'PM2.5': 35, 'CO': 9}

    def check_compliance(self, measurements, pollutant):
        limit = self.standards[pollutant]
        violations = measurements > limit
        return 1 - violations.mean(), violations

    def generate_report(self, data, period_start, period_end):
        report = {'period': (period_start, period_end), 'params': {}}
        for param, values in data.items():
            if param in self.standards:
                rate, viols = self.check_compliance(values, param)
                report['params'][param] = {'compliance': rate, 'violations': viols.sum()}
        return report

    def trend_analysis(self, historical, months=12):
        recent = historical[-months:]
        return np.polyfit(range(len(recent)), recent, 1)[0] * 12

Research Insight: NLP models extract compliance requirements from regulatory documents with 92% accuracy.