References
- Castro, M., and J. C. M. Pires. 2019. Decision support tool to improve the spatial distribution of air quality monitoring sites. Atmos. Pollut. Res. 10 (3):827–34. doi:https://doi.org/10.1016/j.apr.2018.12.011.
- Chen, Y., R. Shi, S. Shu, and W. Gao. 2013. Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis. Atmos. Environ. 74:346–59. doi:https://doi.org/10.1016/j.atmosenv.2013.04.002.
- Galán-Madruga, D. 2021. A methodological framework for improving air quality monitoring network layout. Applications to environment management. J. Environ. Sci. 102:138–47. doi:https://doi.org/10.1016/j.jes.2020.09.009.
- Gryech, I., M. Ghogho, H. Elhammouti, N. Sbihi, and A. Kobbane. 2020. Machine learning for air quality prediction using meteorological and traffic related features. J. Ambient. Intell. Smart Environ. 12 (5):379–91. doi:https://doi.org/10.3233/AIS-200572.
- Hao, Y., and S. Xie. 2018. Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm. Atmos. Environ. 177:222–33. doi:https://doi.org/10.1016/j.atmosenv.2018.01.011.
- Hwang, J.-S., and -C.-C. Chan. 2012. Redundant measurements of urban air monitoring networks in air quality reporting. J. Air Waste Manage. Assoc. 47 (5):614–19. doi:https://doi.org/10.1080/10473289.1997.10463682.
- Kim, D., Z. Chen, L. F. Zhou, and S. X. Huang. 2018. Air pollutants and early origins of respiratory diseases. Chronic Dis. Transl. Med. 4 (2):75–94. doi:https://doi.org/10.1016/j.cdtm.2018.03.003.
- Li, J., H. Zhang, Y. Luo, X. Deng, M. L. Grieneisen, F. Yang, B. Di, and Y. Zhan. 2019. Stepwise genetic algorithm for adaptive management: Application to air quality monitoring network optimization. Atmos. Environ. 215:116894. doi:https://doi.org/10.1016/j.atmosenv.2019.116894.
- Liu, B., Q. Zhao, Y. Jin, et al. 2021. Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector. Sci. Rep. 11:3247. doi:https://doi.org/10.1038/s41598-021-82871-4:3247.
- Liu, Y., J. Wu, D. Yu, and R. Hao. 2018. Understanding the patterns and drivers of air pollution on multiple time scales: The case of Northern China. Environ. Manage. 61 (6):1048–61. doi:https://doi.org/10.1007/s00267-018-1026-5.
- Lu, W.-Z., H.-D. He, and L.-Y. Dong. 2011. Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Build. Environ. 46 (3):577–83. doi:https://doi.org/10.1016/j.buildenv.2010.09.004.
- Luo, Y., S. Liu, L. Che, and Y. Yu. 2021. Analysis of temporal spatial distribution characteristics of PM2.5 pollution and the influential meteorological factors using Big Data in Harbin, China. J. Air Waste Manage. Assoc. 71 (8):964–73. doi:https://doi.org/10.1080/10962247.2021.1902423.
- Macpherson, A. J., H. Simon, R. Langdon, and D. Misenheimer. 2017. A mixed integer programming model for National Ambient Air Quality Standards (NAAQS) attainment strategy analysis. Environ. Model. Softw. 91:13–27. doi:https://doi.org/10.1016/j.envsoft.2017.01.008.
- Malinovic-Milicevic, S., Y. Vyklyuk, G. Stanojevic, M. M. Radovanovic, D. Doljak, and N. B. Curcic. 2021. Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia. Environ. Monit. Assess. 193 (2):84. doi:https://doi.org/10.1007/s10661-020-08821-1.
- Mofarrah, A., and T. Husain. 2010. A holistic approach for optimal design of air quality monitoring network expansion in an urban area. Atmos. Environ. 44 (3):432–40. doi:https://doi.org/10.1016/j.atmosenv.2009.07.045.
- Pope, R., and J. Wu. 2014. A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models. J. Air Waste Manage. Assoc. 64 (6):721–37. doi:https://doi.org/10.1080/10962247.2014.888378.
- Sahu, S. P., and A. K. Patra. 2021. Assessment of dispersion of respirable particles emitted from opencast mining operations: Development and validation of stepwise regression models. Environ. Dev. Sustain. doi:https://doi.org/10.1007/s10668-021-01816-z.
- Shi, Y., K. K. Lau, and E. Ng. 2017. Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment. Environ. Res. 157:17–29. doi:https://doi.org/10.1016/j.envres.2017.05.007.
- Stolz, T., M. E. Huertas, and A. Mendoza. 2020. Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico. Atmos. Pollut. Res. 11 (8):1271–80. doi:https://doi.org/10.1016/j.apr.2020.05.005.
- Su, X., J. An, Y. Zhang, P. Zhu, and B. Zhu. 2020. Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods. Atmos. Pollut. Res. 11 (6):51–60. doi:https://doi.org/10.1016/j.apr.2020.02.024.
- Vlachokostas, C., C. Achillas, E. Chourdakis, and N. Moussiopoulos. 2011. Combining regression analysis and air quality modelling to predict benzene concentration levels. Atmos. Environ. 45 (15):2585–92. doi:https://doi.org/10.1016/j.atmosenv.2010.11.042.
- Wang, C., L. Zhao, W. Sun, J. Xue, and Y. Xie. 2018. Identifying redundant monitoring stations in an air quality monitoring network. Atmos. Environ. 190:256–68. doi:https://doi.org/10.1016/j.atmosenv.2018.07.040.
- Xing, Y. F., Y. H. Xu, M. H. Shi, and Y. X. Lian. 2016. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 8 (1):E69–74. doi:https://doi.org/10.3978/j.2072-1439.2016.01.19.
- Yidana, S. M., D. Ophori, and B. Banoeng-Yakubo. 2008. A multivariate statistical analysis of surface water chemistry data–the Ankobra Basin, Ghana. J. Environ. Manage. 86 (1):80–87. doi:https://doi.org/10.1016/j.jenvman.2006.11.023.
- Zaman, N. A. F. K., K. D. Kanniah, D. G. Kaskaoutis, and M. T. Latif. 2021. Evaluation of machine learning models for estimating PM2.5 concentrations across Malaysia. Appl. Sci. 11 (16):7326. doi:https://doi.org/10.3390/app11167326.
- Zhang, H., S. Zhang, P. Wang, Y. Qin, and H. Wang. 2017. Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. J. Air Waste Manage. Assoc. 67 (7):776–88. doi:https://doi.org/10.1080/10962247.2017.1292968.
- Zhao, L., Y. Xie, J. Wang, and X. Xu. 2015. A performance assessment and adjustment program for air quality monitoring networks in Shanghai. Atmos. Environ. 122:382–92. doi:https://doi.org/10.1016/j.atmosenv.2015.09.069.
- Zhu, F., R. Ding, R. Lei, H. Cheng, J. Liu, C. Shen, C. Zhang, Y. Xu, C. Xiao, X. Li, et al. 2019. The short-term effects of air pollution on respiratory diseases and lung cancer mortality in Hefei: A time-series analysis. Respir. Med. 146:57–65. doi:https://doi.org/10.1016/j.rmed.2018.11.019.