Traditional and new Approaches in Source Apportionment: A Critical Evaluation of Bias and Limitations

Source apportionment is a cornerstone in environmental sciences, providing essential insights into the contributions of diverse pollution sources to ambient air quality. Understanding these contributions is vital for designing effective regulatory and mitigation strategies. Among the most commonly employed techniques for source apportionment, Positive Matrix Factorization (PMF) has proven to be a powerful receptor model. PMF decomposes complex environmental datasets into source profiles and their corresponding contributions while accommodating measurement uncertainties Hopke [2016], Paatero and Tapper [1994]. Despite its advantages, PMF is not without limitations, such as rotational ambiguity, reliance on accurate input uncertainties, and potential biases in source attribution Reff et al. [2007].

In recent years, the emergence of artificial intelligence (AI)-based methodologies has opened new horizons for source apportionment. These approaches often build upon classical methods like PMF, aiming to enhance the interpretability and performance of source apportionment models Geng et al. [2020]. Machine learning techniques, including deep learning, leverage large datasets to identify patterns and relationships that may elude traditional approaches. Additionally, hybrid methods integrating classical models with AI frameworks have demonstrated potential for improved accuracy and robustness Wang et al. [2021].

A critical question remains unanswered: do the intrinsic limitations of PMF, such as biases and errors, propagate into these AI-driven alternatives? For instance,  AI methods are theoretically capable of overcoming such challenges, their reliance on data-driven training may introduce new sources of bias or amplify existing uncertainties if the underlying data or assumptions are flawed.
Moreover, how do these biases compare to those in other receptor models, such as the Chemical Mass Balance (CMB) model ?

This work aims to look on the extent to which the errors and biases inherent to PMF are inherited by alternative methods, including AI-based approaches and other receptor models. By critically assessing these methods, we seek to provide a comprehensive understanding of the strengths and limitations of emerging tools for source apportionment and their potential to overcome traditional challenges.

 

Xiaoyu Geng, Lin Zhang, and Yu Wang. Ai-based approaches for source apportionment in atmospheric science: A review and perspective. Atmospheric Environment, 223: 117276, 2020. doi: 10.1016/j.atmosenv.2020.117276.

 

Philip K. Hopke. Review of receptor modeling methods for source apportionment. Journal of the Air Waste Management Association, 66(3):237–259, 2016. doi: 10.1080/10962247.2016.1140693.

Pentti Paatero and Unto Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2): 111–126, 1994. doi: 10.1002/env.3170050203.

 

Adam Reff, Sierra I. Eberly, and Prakash V. Bhave. Receptor modeling of ambient particulate matter data using positive matrix factorization: Review of existing meth- ods. Journal of the Air Waste Management Association, 57(2):146–154, 2007. doi: 10.1080/10473289.2007.10465319.

Feifei Wang, Hui Zhang, and Xiaodong Li. Hybrid receptor models and machine learning for air pollution source apportionment: Advances and future directions. Environmental Research, 195:110827, 2021. doi: 10.1016/j.envres.2021.110827.

 

John G. Watson. Chemical mass balance receptor model methodology for assessing the sources of fine and coarse particulate matter. Environmental Software, 5(2):38–49, 1990. doi: 10.1016/0266-9838(90)90008-