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Locally weighted total least-squares variance component estimation for modeling urban air pollution

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Abstract

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method’s efficiency in PM2.5 modeling.

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Data availability

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

Code availability

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

References

  • Almetwally, A. A., Bin-Jumah, M., & Allam, A. A. (2020). Ambient air pollution and its influence on human health and welfare: An overview. Environmental Science and Pollution Research, 27(20), 24815–24830.

    Article  Google Scholar 

  • Amini, H., Taghavi-Shahri, S. M., Henderson, S. B., Naddafi, K., Nabizadeh, R., & Yunesian, M. (2014). Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran Iran. Science of the Total Environment, 488, 343–353.

    Article  Google Scholar 

  • Amiri-Simkooei, A., & Jazaeri, S. (2012). Weighted total least squares formulated by standard least squares theory. Journal of Geodetic Science, 2(2), 113–124.

    Article  Google Scholar 

  • Amiri-Simkooei, A., Zangeneh-Nejad, F., & Asgari, J. (2013). Least-squares variance component estimation applied to GPS geometry-based observation model. Journal of Surveying Engineering, 139(4), 176–187.

    Article  Google Scholar 

  • Atkinson, R. W., Fuller, G. W., Anderson, H. R., Harrison, R. M., & Armstrong, B. (2010). Urban ambient particle metrics and health: A time-series analysis. Epidemiology, 501–511.

  • Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R., Brauer, M., Cohen, A. J., & Aggarwal, A. N. (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017. The Lancet Planetary Health, 3(1), e26–e39.

    Article  Google Scholar 

  • Bellander, T., Berglind, N., Gustavsson, P., Jonson, T., Nyberg, F., Pershagen, G., & Järup, L. (2001). Using geographic information systems to assess individual historical exposure to air pollution from traffic and house heating in Stockholm. Environmental Health Perspectives, 109(6), 633–639.

    Article  CAS  Google Scholar 

  • Chen, J., & Hoek, G. (2020). Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environment International, 105974.

  • Clougherty, J. E., Wright, R. J., Baxter, L. K., & Levy, J. I. (2008). Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants. Environmental Health, 7(1), 1–14.

    Article  Google Scholar 

  • Dastoorpoor, M., Sekhavatpour, Z., Masoumi, K., Mohammadi, M. J., Aghababaeian, H., Khanjani, N., & Vahedian, M. (2019). Air pollution and hospital admissions for cardiovascular diseases in Ahvaz Iran. Science of the Total Environment, 652, 1318–1330.

    Article  Google Scholar 

  • de Hoogh, K., Korek, M., Vienneau, D., Keuken, M., Kukkonen, J., Nieuwenhuijsen, M. J., & Cesaroni, G. (2014). Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies. Environment International, 73, 382–392.

    Article  Google Scholar 

  • Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., & Speizer, F. E. (1993). An association between air pollution and mortality in six US cities. New England Journal of Medicine, 329(24), 1753–1759.

    Article  CAS  Google Scholar 

  • Elliot, P., Wakefield, J. C., Best, N. G., & Briggs, D. J. (2000). Spatial epidemiology: methods and applications. Oxford University Press.

  • English, P., Neutra, R., Scalf, R., Sullivan, M., Waller, L., & Zhu, L. (1999). Examining associations between childhood asthma and traffic flow using a geographic information system. Environmental Health Perspectives, 107(9), 761–767.

    Article  CAS  Google Scholar 

  • Gao, J., & Li, S. (2011). Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Applied Geography, 31(1), 292–302.

    Article  Google Scholar 

  • Ghasemi, F. F., Dobaradaran, S., Saeedi, R., Nabipour, I., Nazmara, S., Abadi, D. R. V., & Mohammadi, M. J. (2020). Levels and ecological and health risk assessment of PM 2.5-bound heavy metals in the northern part of the Persian Gulf. Environmental Science and Pollution Research, 27(5), 5305–5313.

  • Goudarzi, G., Alavi, N., Geravandi, S., Yari, A. R., Alamdari, F. A., Dobaradaran, S., & Hashemzadeh, B. (2019). Ambient particulate matter concentration levels of Ahvaz, Iran, in 2017. Environmental Geochemistry and Health, 41(2), 841–849.

    Article  CAS  Google Scholar 

  • Gu, K., Zhou, Y., Sun, H., Dong, F., & Zhao, L. (2021). Spatial distribution and determinants of PM 2.5 in China’s cities: fresh evidence from IDW and GWR. Environmental Monitoring and Assessment, 193(1), 1–22.

  • Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environmental Science & Technology, 41(7), 2422–2428.

    Article  CAS  Google Scholar 

  • Hoek, G., Brunekreef, B., Goldbohm, S., Fischer, P., & van den Brandt, P. A. (2002). Association between mortality and indicators of traffic-related air pollution in the Netherlands: A cohort study. The Lancet, 360(9341), 1203–1209.

    Article  Google Scholar 

  • Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42(33), 7561–7578.

    Article  CAS  Google Scholar 

  • Huang, J., Pan, X., Guo, X., & Li, G. (2018). Health impact of China’s Air Pollution Prevention and Control Action Plan: An analysis of national air quality monitoring and mortality data. The Lancet Planetary Health, 2(7), e313–e323.

    Article  Google Scholar 

  • Jeong, C. H., McGuire, M. L., Herod, D., Dann, T., Dabek–Zlotorzynska, E., Wang, D., & Evans, G. (2011). Receptor model based identification of PM2.5 sources in Canadian cities. Atmospheric Pollution Research, 2(2), 158–171.

  • Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., & Giovis, C. (2005). A review and evaluation of intraurban air pollution exposure models. Journal of Exposure Science & Environmental Epidemiology, 15(2), 185–204.

    Article  CAS  Google Scholar 

  • Kanaroglou, P. S., Jerrett, M., Morrison, J., Beckerman, B., Arain, M. A., Gilbert, N. L., & Brook, J. R. (2005). Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach. Atmospheric Environment, 39(13), 2399–2409.

    Article  CAS  Google Scholar 

  • Kong, L., & Tian, G. (2020). Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China. Environmental monitoring and assessment, 192(2), 1–19.

  • Lane, K. J., Levy, J. I., Scammell, M. K., Peters, J. L., Patton, A. P., Reisner, E., & Brugge, D. (2016). Association of modeled long-term personal exposure to ultrafine particles with inflammatory and coagulation biomarkers. Environment International, 92, 173–182.

    Article  Google Scholar 

  • Lebret, E., Briggs, D., Van Reeuwijk, H., Fischer, P., Smallbone, K., Harssema, H., & Elliott, P. (2000). Small area variations in ambient NO2 concentrations in four European areas. Atmospheric Environment, 34(2), 177–185.

    Article  CAS  Google Scholar 

  • Liu, M., Peng, X., Meng, Z., Zhou, T., Long, L., & She, Q. (2019). Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model. Science of the Total Environment, 658, 51–61.

    Article  CAS  Google Scholar 

  • Lu, B., Charlton, M., Harris, P., & Fotheringham, A. S. (2014). Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4), 660–681.

    Article  Google Scholar 

  • Mölter, A., Lindley, S., De Vocht, F., Simpson, A., & Agius, R. (2010). Modelling air pollution for epidemiologic research—Part I: A novel approach combining land use regression and air dispersion. Science of the Total Environment, 408(23), 5862–5869.

  • Naghizadeh, A., Sharifzadeh, G., Tabatabaei, F., Afzali, A., Yari, A. R., Geravandi, S., & Mohammadi, M. J. (2019). Assessment of carbon monoxide concentration in indoor/outdoor air of Sarayan city, Khorasan Province of Iran. Environmental Geochemistry and Health, 41(5), 1875–1880.

    Article  CAS  Google Scholar 

  • Pisoni, E., Clappier, A., Degraeuwe, B., & Thunis, P. (2017). Adding spatial flexibility to source-receptor relationships for air quality modeling. Environmental Modelling & Software, 90, 68–77.

    Article  CAS  Google Scholar 

  • Samek, L., Stegowski, Z., Styszko, K., Furman, L., & Fiedor, J. (2018). Seasonal contribution of assessed sources to submicron and fine particulate matter in a Central European urban area. Environmental Pollution, 241, 406–411.

    Article  CAS  Google Scholar 

  • Son, Y., Osornio-Vargas, Á. R., O’Neill, M. S., Hystad, P., Texcalac-Sangrador, J. L., Ohman-Strickland, P., & Schwander, S. (2018). Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters. Science of the Total Environment, 639, 40–48.

    Article  CAS  Google Scholar 

  • Tashayo, B., & Alimohammadi, A. (2016). Modeling urban air pollution with optimized hierarchical fuzzy inference system. Environmental Science and Pollution Research, 23(19), 19417–19431.

    Article  CAS  Google Scholar 

  • Tashayo, B., Alimohammadi, A., & Sharif, M. (2017). A hybrid fuzzy inference system based on dispersion model for quantitative environmental health impact assessment of urban transportation planning. Sustainability, 9(1), 134.

    Article  Google Scholar 

  • Vizcaino, P., & Lavalle, C. (2018). Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis. Environmental Pollution, 240, 140–154.

    Article  CAS  Google Scholar 

  • Wang, R., Henderson, S. B., Sbihi, H., Allen, R. W., & Brauer, M. (2013). Temporal stability of land use regression models for traffic-related air pollution. Atmospheric Environment, 64, 312–319.

    Article  CAS  Google Scholar 

  • Wu, C. -D., Zeng, Y. -T., & Lung, S. -C. C. (2018). A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability. Science of the Total Environment, 645, 1456–1464.

    Article  CAS  Google Scholar 

  • Xu, S., Zou, B., Shafi, S., & Sternberg, T. (2018). A hybrid Grey-Markov/LUR model for PM10 concentration prediction under future urban scenarios. Atmospheric Environment, 187, 401–409.

    Article  CAS  Google Scholar 

  • Zarandi, S. M., Shahsavani, A., Nasiri, R., & Pradhan, B. (2021). A hybrid model of environmental impact assessment of PM 2.5 concentration using multi-criteria decision-making (MCDM) and geographical information system (GIS)—A case study. Arabian Journal of Geosciences, 14(3), 1–20.

  • Zarrabi, A., Mohammadi, J., & Abdollahi, A. (2010). Evaluation of mobile and stationary sources of Isfahan air pollution. Geography, 26, 151–164.

    Google Scholar 

  • Zhao, H., Geng, G., Zhang, Q., Davis, S. J., Li, X., Liu, Y., & Huo, H. (2019). Inequality of household consumption and air pollution-related deaths in China. Nature Communications, 10(1), 1–9.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran

    Arezoo Mokhtari & Behnam Tashayo

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  1. Arezoo Mokhtari
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  2. Behnam Tashayo
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Contributions

Behnam Tashayo: conceptualized, designed, and supervised the study, including experimental setup, model simulations and evaluation, and write—review and editing of the paper. Arezoo Mokhtari carried out model development and verification the models, and drafted the original version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Behnam Tashayo.

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Mokhtari, A., Tashayo, B. Locally weighted total least-squares variance component estimation for modeling urban air pollution. Environ Monit Assess 194, 840 (2022). https://doi.org/10.1007/s10661-022-10499-6

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