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Estimating black carbon levels using machine learning models in high-concentration regions.

Pratima Gupta¹, Pau Ferrer-Cid², Jose M Barcelo-Ordinas²

¹Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, India.

The Science of the Total Environment

|July 17, 2024

Summary

This summary is machine-generated.

Machine learning models can accurately estimate black carbon (BC) concentrations using common air pollution and meteorological data. The multilayer perceptron (MLP) shows the most promise for cost-effective BC monitoring in polluted regions.

Keywords:

Aethalometer Air pollution Air quality Black carbon Modelling Monitoring Prediction

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Area of Science:

Atmospheric Chemistry
Environmental Science
Data Science

Background:

Black carbon (BC) is a significant air pollutant from combustion, impacting radiative budgets.
Current BC monitoring is costly, limiting its widespread application.
Polluted regions, especially in Northern India, face challenges with high BC levels.

Purpose of the Study:

To develop cost-effective methods for estimating black carbon concentrations.
To assess the effectiveness of machine learning models for BC prediction.
To utilize existing air quality and meteorological networks for BC estimation.

Main Methods:

Employed machine learning algorithms: random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP).
Used data on nitrogen oxides (NOx), ozone (O3), PM2.5, relative humidity (RH), and solar radiation (SR).
Validated models in high-BC areas: Delhi and Agra, across different seasons.

Main Results:

Machine learning models demonstrated comparable effectiveness in predicting BC concentrations.
Multilayer perceptron (MLP) yielded the most promising results, with high correlations (R² up to 0.85) between estimated and monitored BC.
Consistent performance observed in both Delhi and Agra, indicating model reliability.

Conclusions:

Machine learning, particularly MLP, is a valuable tool for predicting BC concentrations.
This approach offers cost-effective urban air quality management and mitigation strategies.
The method is especially beneficial for megacities in medium- and low-income regions.