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A pruned feed‑forward neural network (pruned‑FNN) approach to measure air pollution exposure

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journal contribution
posted on 2025-01-20, 12:11 authored by Xi Gong, Lin Liu, Yanhong Huang, Bin Zou, Yeran SunYeran Sun, Li Luo, Yan Lin

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modelling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feedforward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model’s performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman’s rank correlation coefcients for tenfold cross-validation (mean±standard deviation: 0.906±0.028) and for random cross-validation (0.913±0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufciently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a fexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and efective approach for exposure assessment that covers a large geographic area over a long period of time.

History

School affiliated with

  • Department of Geography (Research Outputs)

Publication Title

Environmental Monitoring and Assessment

Volume

195

Pages/Article Number

1183

Publisher

Springer

ISSN

0167-6369

eISSN

1573-2959

Date Accepted

2023-08-30

Date of Final Publication

2023-09-11

Relevant SDGs

  • SDG 11 - Sustainable Cities and Communities
  • SDG 3 - Good Health and Well-being

Open Access Status

  • Not Open Access

Publisher statement

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10661-023-11814-5.

Will your conference paper be published in proceedings?

  • N/A