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Adopting urban morphological indicators to land use regression modeling of seasonal mean PM2.5 concentrations for a high-density city

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Abstract

As a common air pollutant, particulate matter (PM2.5) can lead to serious health risks when inhaled over a long period of time, and it is therefore especially important for urban residents to understand the distribution characteristics of PM2.5 within the urban space. To predict long-term PM2.5 exposure at high resolution in high-density cities and quantify the influence of urban spatial morphological characteristics on the distribution of PM2.5 concentration produced under long-term conditions. The seasonal mean values of PM2.5 concentration from 23 monitoring stations in Shenzhen, China were used as dependent variables, and 6 categories of 502 potential predictor variables including urban/building spatial morphological indicators were used as independent variables. The seasonal land use regression models were developed by combining stepwise supervision and Akaike information criterion (AIC) selection with a leave-one-out-cross-validation (LOOCV) to test the prediction performance of the model. The adjusted R2 values were 0.86, 0.70, 0.70 and LOOCV R2 values were 0.83, 0.58, 0.68 in the first three seasons, respectively. In addition to the variables of land use category (i.e., the proportion of land for industrial and manufacturing, the proportion of land for green space and square, and the proportion of land for municipal utilities) and physical geography category (i.e., the count of parks and squares, average elevation), building coverage ratio and the diversity of building volume were also selected into the model as predictor variables. The R2 contributions of urban/building spatial morphology factors exceeded 10% for all three seasonal models. This study developed seasonal land use regression (LUR) models with good prediction performance for a high-density city and provided high-resolution spatial mapping of PM2.5 under steady-state conditions. The contribution of urban/building spatial morphological indicators to the interpretation of R2 for LUR models confirmed the effect of high-density urban morphology on the distribution of PM2.5 concentration. The quantitative correlation between PM2.5 concentration and spatial morphological factors such as building coverage and building volume diversity can provide a basis for incorporating air pollution dispersion considerations to urban planning at early stage.

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Data availability

All data analysed during this study are included in this published article [and its supplementary information files].

The datasets used during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would acknowledge the Meterorogical Bureau of Shenzhen Municipality for sharing relevant geographical data of Shenzhen, China. We also acknowledge the anonymous reviewers for their constructive comments on the early manuscript of this paper.

Funding

This study was supported by the National Key R&D Program of China (No. 2018YFC0704900), Science and Technology Planning Project of Shenzhen Municipality (CN) (No. JCYJ20180305125219726) and the innovative development fund for graduate students of Shenzhen University (No.315-0000470518).

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Authors and Affiliations

  1. School of Architecture and Urban Planning, Shenzhen University, No. 3688 Nanhai Road, Shenzhen, 518060, China

    Yang Wan & Lei Yuan

  2. Shenzhen Key Laboratory for Optimizing Design of Built Environment, No. 3688 Nanhai Road, Shenzhen, 518060, China

    Yang Wan, Lei Yuan & Xuesong Xu

  3. The Institute of Architecture Design & Research, Shenzhen University, No. 3688 Nanhai Road, Shenzhen, 518060, China

    Xuesong Xu

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  1. Yang Wan
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Lei Yuan planned, supervised and sought funding for this study. Yang Wan performed the data analysis and prepared the paper with contributions from all co-authors. Xuesong Xu checked and revised this paper.

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Wan, Y., Yuan, L. & Xu, X. Adopting urban morphological indicators to land use regression modeling of seasonal mean PM2.5 concentrations for a high-density city. Air Qual Atmos Health 15, 559–573 (2022). https://doi.org/10.1007/s11869-021-01134-3

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