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Development and Application of Regression Models for Predicting the Water Quality Performance of Permeable Pavement

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Abstract

The water quality performance of permeable pavement is influenced by many factors. The knowledge of the combined effect of multifactor on the performance of permeable pavement is vital to its design and construction. However, few quantitative relations are available in literature. Regression orthogonal composite design was adopted to develop models to predict the combined effects of multifactor on the performance of surface pavement layer and gravel layer of permeable pavement. The most commonly concerned factors, including rainfall intensity, inflow concentration of total suspended sediment (TSS), gradation of gravels, and thickness of the gravel layer, were selected. The interactions of these factors were also considered. The viability of the models was tested using analysis of variance (ANOVA), and the results showed the models for TSS removal rate of the surface pavement layer and gravel layer, Cd, Cu, Zn, TP, NH4-N, and NOx-N removal rate of the gravel layer were reliable and can be used for prediction purpose. More importantly, an integrated model was developed to predict the overall performance of permeable pavement, and a good performance was achieved (− 2.43% to approximately 1.85%) through comparison with the measured values, illustrating its promising application. Then, the integrated model was compiled as modeling tools based on EPA SWMM (modified SWMM) and applied to a campus renovation scenario (three scenarios) assessment. Scenario 1 with higher pollutant removal rates is better than others. The results obtained demonstrated that the modified SWMM can respond to the changes of influencing parameters and will be beneficial for both practitioners and decision makers in permeable pavement design and construction.

Highlights

  • Regression orthogonal composite design was employed to predict pollutant removals.

  • The relative importance and the most influential input variables were pointed.

  • An integrated model was developed to predict performance of permeable pavement.

  • The viability of model was tested and promising results were obtained.

  • The model was incorporated into SWMM and the application proved its effectiveness.

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Availability of Data and Material

The laboratory data generated during and/or analyzed during the current study are available in the manuscript and its supplementary information files. The study site data that support the findings of this study are available from local authorities but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

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Acknowledgements

The authors thank Ziyuan Liao, Kui Zhang, Wei Chen, Ruicheng Ji, and Ge Yang of the College of Environmental Science and Engineering at Tongji University for assistance with the experiments.

Funding

This work was partially supported by the National Natural Science Foundation of China (grant numbers 51978493, 51778452), the China Scholarship Council, and the Science and Technology program of the Ministry of Housing and Urban–Rural Development (grant number 2018-K4-022).

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Author notes

  1. Jia Liu and Hexiang Yan contributed equally to this work

Authors and Affiliations

  1. College of Environmental Science and Engineering, Tongji University, 1239 Si** Road, Shanghai, 200092, People’s Republic of China

    Jia Liu, Hexiang Yan, Kunlun ** Li & Tao Tao

  2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Jia Liu & Arthur R. Schmidt

  3. Institute of Analysis and Testing, Bei**g Academy of Science and Technology (Bei**g Center for Physical & Chemical Analysis), No. 27 North **sanhuan Road, Bei**g, 100089, People’s Republic of China

    Jia Liu

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Correspondence to Tao Tao.

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Liu, J., Yan, H., **n, K. et al. Development and Application of Regression Models for Predicting the Water Quality Performance of Permeable Pavement. Water Air Soil Pollut 233, 155 (2022). https://doi.org/10.1007/s11270-022-05517-9

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