Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of linear, nonlinear, and hybrid models for predicting PM2.5 based on a GTWR model and MODIS AOD data

  • Published:
Air Quality, Atmosphere & Health Aims and scope Submit manuscript

Abstract

Ground monitoring station data of PM2.5 are not available for each day and all places in urban areas. In this research, taking Tehran as an example, a geographically and temporally weighted regression (GTWR) model was utilized to investigate the spatial and temporal variability relationship between PM2.5 concentrations measured at ground monitoring stations and satellite aerosol optical depth (AOD) data. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor produced AOD values with 3-km spatial resolution. Using meteorological variables and land use information as additional predictors utilized in the GTWR model, the AOD was converted to PM2.5 at ground level for warm (October to March) and cold seasons (April to September) from 2011 to 2017. To improve the accuracy of the correlation coefficient between converted PM2.5 from the GTWR model and PM2.5 concentrations measured at ground monitoring station, the results of a linear model (LR), a nonlinear model (artificial neural network (ANN)), and hybrid models including general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were compared. The results of the linear, nonlinear, and hybrid models for the cold season display higher accuracy compared with the results for the warm season. Among the used models, the GRNN model has higher accuracy compared with the other models. This study reveals that AOD conversion to particulate matter by the GTWR model and its simulation to PM2.5 at ground level using a hybrid model such as the GRNN can be used to study air quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213

    Article  CAS  Google Scholar 

  • Amanollahi J, Kaboodvanpour S, Abdullah AM, Ramli MF (2011) Accuracy assessment of moderate resolution image spectroradiometer products for dust storms in semiarid environment. Int J Environ Sci Technol 8(2):373–380

    Article  Google Scholar 

  • Amanollahi J, Tzanis C, Abdullah AM, Ramli MF, Pirasteh S (2013) Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper. Int J Environ Sci Technol 10(6):1245–1254

    Article  CAS  Google Scholar 

  • Amanollahi J, Kaboodvandpour SH, Qhavami S, Mohammadi B (2015) Effect of the temperature variation between Mediterranean Sea and Syrian deserts on the dust storm occurrence in the western half of Iran. Atmos Res 154:116–125

    Article  Google Scholar 

  • Amirkhani S, Nasirivatan S, Kasaeian AB, Hajinezhad A (2015) ANN and ANFIS models to predict the performance of solar chimney power plants. Renew Energy 83:597–607

    Article  Google Scholar 

  • Ansari M, Ehrampoush MH (2019) Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran. Iran Environ Res 170:141–150

    Article  CAS  Google Scholar 

  • Antonopoulos VS, Papamichail DM, Aschonitis VG, Antonopoulos AV (2019) Solar radiation estimation methods using ANN and empirical models. Comput Electron Agric 160:160–167

    Article  Google Scholar 

  • Anusree A, Varrghese KO (2016) Stream flow prediction of Karuvannur river basin using ANFIS, ANN, and MNLR models. Proc Technol 24:101–108

    Article  Google Scholar 

  • Arora S, Keshari A (2018) Estimation of re-aeration coefficient using MLR for modelling water quality of rivers in urban environment. Ground Water Sustain Dev 7:430–435

    Article  Google Scholar 

  • Ausati S, Amanollahi J (2016) Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5. Atmos Environ 142:465–474

    Article  CAS  Google Scholar 

  • Brooks N, Legrand M (2000) Dust variability over northern Africa and rainfall in the Sahel. In: McLaren S, Kniveton D (eds) Linking climate change to land surface change. Springer, New York, pp 1–25

    Google Scholar 

  • Delle Monache L, Perry KD, Cederwall RT (2002) Comparison of aerosol properties within and above the ABL at the ARM program’s SGP site. Proceedings AMS conference on the application of air pollution meteorology 2002, Norfolk, Virginia, pp. 78–80

  • Faridi S, Shamsipour M, Krzyzanowski M, Künzli N, Amini H, Azimi F, Malkawi M, Momeniha F, Gholampour A, Hassanvand MS, Naddafi K (2018) Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006–2015. Environ Int 114:37–49. https://doi.org/10.1016/j.envint.2018.02.026

    Article  CAS  Google Scholar 

  • Fernando HJ, Mammarella MC, Grandoni G, Fedele P, Di Marco R, Dimitrova R et al (2012) Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environ Pollut 163:62–67

    Article  CAS  Google Scholar 

  • Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmos Pollut Res 9(5):912–922

    Article  CAS  Google Scholar 

  • Ghasemi A, Amanollahi J (2019) Integration of ANFIS model and forward selection method for air quality forecasting. Air Qual Atmos Health 12(1):59–72

    Article  CAS  Google Scholar 

  • Guneri AF, Ertay T, Yücel A (2011) An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Syst Appl 38(12):14907–14917

    Article  Google Scholar 

  • Guo Y, Tang Q, Gong D-Y, Zhang Z (2017) Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sens Environ 198:140–149

    Article  Google Scholar 

  • He Q, Huang B (2018a) Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sens Environ 206:72–83

    Article  Google Scholar 

  • He Q, Huang B (2018b) Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model. Environ Pollut 236:1027–1037

    Article  CAS  Google Scholar 

  • Hu X, Waller LA, Al-Hamdan MZ, Crosson WL, Estes MG, Estes SM et al (2013) Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression. Environ Res 121:1–10

    Article  CAS  Google Scholar 

  • Hu X, Waller LA, Lyapustin A, Wang Y, Al-Hamdan MZ, Crosson WL et al (2014a) Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sens Environ 140:220–232

    Article  Google Scholar 

  • Hu X, Waller LA, Lyapustin A, Wang Y, Liu Y (2014b) 10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data. Atmos Chem Phys 14:6301–6314

    Article  CAS  Google Scholar 

  • Huang M, Peng G, Zhang J, Zhang S (2006) Application of artificial neural networks to the prediction of dust storms in Northwest China. Glob Planet 52(1-4):216–224

    Article  Google Scholar 

  • Huang R, Zhai X, Lvey CE, Friberg MD, Hu X, Liu Y, Di Q, Schwartz J, Moulholland JA, Russell AG (2018) Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA. Air Qual Atmos Health 11(1):11–22

    Article  CAS  Google Scholar 

  • IPCC (2007) IPCC Fourth Assessment Reports (AR4): Working Group I Report: Climate Change 2007, the Physical Basis (WMO/UNEP Report)

  • Kaboodvandpour S, Amanollahi J, Qhavami S, Mohammadi B (2015) Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran. Nat Hazards 78(2):879–893

    Article  Google Scholar 

  • Karagulian F, Temimi M, Ghebreyesus D, Weston M, Kondapalli NK, Valappli VK, Aldabesh A, Lyapustin A, Chaouch N, Hammadi FA, Abdooli AA (2019) Analysis of a severe dust storm and its impact on air quality condition using WRF-Chen modeling, satellite imagery, and ground observations. Air Qual Atmos Health 12(4):453–470

    Article  CAS  Google Scholar 

  • Kaufman YJ, Tanre D, Boucher O (2002) A satellite view of aerosols in climate systems. Nature 419:215–223

    Article  CAS  Google Scholar 

  • Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fi” measures in hydrologic and hydroclimatic model validation. Water Res 35(1):233–241

    Article  Google Scholar 

  • Leng X, Wang J, Ji H, Wang Q, Li H, Qian X, Li F, Yang M (2017) Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses. Chemosphere 180:513–522

    Article  CAS  Google Scholar 

  • Leung MT, Daock H, Chen A (2000) Forecasting stock indices: a comparison of classification and level estimation models. Int J Forecast 16(2):173–190

    Article  Google Scholar 

  • Li T, Shen H, Zeng C, Yuan Q, Zhang L (2017) Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment. Atmos Environ 152:477–489

    Article  CAS  Google Scholar 

  • Lin J, Sheng G, Yan Y, Dai J, Jiang X (2018) Prediction of dissolved gas concentrations in transformer oil based on the KPCA-FFOA-GRNN model. Energies 11(1):255. https://doi.org/10.3390/en11010225

    Article  CAS  Google Scholar 

  • Liu Y (2008) The application of satellite remote sensing in estimating fine particle concentration. PhD thesis, Harvard University, 113 pp

  • Ma Z, Hu X, Huang L, Bi J, Liu Y (2014) Estimating ground-level PM2.5 in China using satellite remote sensing. Environ Sci Technol 48(13):7436–7444

    Article  CAS  Google Scholar 

  • MATLAB (2019) ANFIS and the ANFIS Editor, Available at: https://www.mathworks.com/help/fuzzy/index.html

  • Mirzaei R, Mesdaghinia A, Hoseini SS, Yunesian M (2019) Antibiotics in urban wastewater and rivers of Tehran, Iran: consumption, mass load, occurrence, and ecological risk. Chemosphere 221:55–66

    Article  CAS  Google Scholar 

  • Nizar S, Dodamani BM (2019) Spatiotemporal distribution of aerosols over the Indian subcontinent and its dependence on prevailing meteorological conditions. Air Qual Atmos Health 12(4):503–517

    Article  CAS  Google Scholar 

  • Noori R, Hoshyaripour G, Ashrafi K, NadjarArrabi B (2010) Uncertainty analysis of developed ANN and ANFIS model in prediction of carbon monoxide daily concentration. Atmos Environ 44:476–482

    Article  CAS  Google Scholar 

  • Park S, Kim M, Kim M, Namgung H-G, Kim K-T, Cho KH, Kwon S-B (2018) Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN). J Hazard Mater 341:75–82

    Article  CAS  Google Scholar 

  • Patra AK, Gautam S, Majumdar S, Kumar P (2016) Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model. Air Qual Atmos Health 9(6):697–711

    Article  CAS  Google Scholar 

  • Quan J, Zhang X, Zhang Q, Guo J, Vogt RD (2008) Importance of sulfate emission to sulfur deposition at urban and rural sites in China. Atmos Res 89(3):283–288

    Article  CAS  Google Scholar 

  • Radojevic D, Antanasijevic D, Peric-Grujic A, Ristic M, Pocajt V (2019) The significance of periodic parameters for ANN modeling of daily SO2 and NOX concentrations: a case study of Belgrade, Serbia. Atmos Pollut Res 10(2):621–628

    Article  CAS  Google Scholar 

  • Seinfeld JH, Pandis SN (1998) Atmospheric chemistry and physics-from air pollution to climate change. John Wiley and Sons 1326p

  • Shahbazi H, Hassani A, Hosseini V (2019) Evaluation of Tehran clean air action plan using emission inventory approach. Urban Clim 27:446–456

    Article  Google Scholar 

  • Song W, Jia H, Huang J, Zhang Y (2014) A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China. Remote Sens Environ 154:1–7

    Article  Google Scholar 

  • Suleiman A, Tight MR, Quinn AD (2019) Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2.5). Atmos Pollut Res 10(1):134–144

    Article  CAS  Google Scholar 

  • Taghvaee S, Sowlat MH, Diapouli E, Manouskas ML, Vasilatou V, Eleftheriadis K, Sioutas C (2019) Source apportionment of the oxidative potential of fine ambient particulate matter (PM2.5) in Athens, Greece. Sci Total Environ 653:1407–1416

    Article  CAS  Google Scholar 

  • Titidezh V, Arefi M, Taghaddosinejad F, Behnoush B, Akbarpour S, Mahboobi M (2019) Epidemiologic profile of deaths due to drug and chemical poisoning in patients referred to Baharloo hospital of Tehran, 2011 to 2014. J Forensic Legal Med 64:31–33

    Article  Google Scholar 

  • Tzanis CG, Alimissis A, Philippopoulos K, Deligiorgi D (2019) Applying linear and nonlinear models for the estimation of particulate matter variability. Environ Pollut 246:89–98

    Article  CAS  Google Scholar 

  • Ventura LMB, Pinto FO, Soares LM, Luna AS, Gioda A (2019) Forecast of daily PM2.5 concentrations applying artificial neural networks and Holt-Winters models. Air Qual Atmos Health 2(3):317–325

    Article  CAS  Google Scholar 

  • Vlachogianni A, Kassomenos P, Karppinen A, Karakitsios S, Kukkonen J (2011) Evaluation of a multiple regression model for the forecasting of the concentration of NOx and PM10 Athens and Helsinki. Sci Total Environ 409(8):15559–15571

    Article  CAS  Google Scholar 

  • Voukantsis D, Karatzas K, Kukkonen J, Rasanen T, Karppinen A, Kolehmainen M (2011) Inter-comparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci Total Environ 409(7):1266–1276

    Article  CAS  Google Scholar 

  • Wang J, Christopher SA (2003) Inter-comparison between satellite-derived aerosol optical thickness and PM 2.5 mass: implication for air quality studies. Geophys Res Lett 30:2095

    Article  CAS  Google Scholar 

  • Wang T, Li S, Jiang F, Gao L (2006) Investigations of main factors affecting tropospheric nitrate aerosol using a coupling model. China Particuol 4(6):336–341

    Article  CAS  Google Scholar 

  • Wang Y, Wang J, Zhao G, Dong Y (2012) Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China. Energ Policy 48:284–294

    Article  Google Scholar 

  • Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inf Sci 177(20):4445–4461

    Article  Google Scholar 

  • Wendish MS, Mertes MW, Ruggaber A, Nakajima T (1996) Vertical profiles and radiation and the influence of a temperature inversion: measurements and radiative transfer calculations. J Appl Meteorol 35(10):1703–1715

    Article  Google Scholar 

  • Xin J, Zhang Q, Wang L, Gong C, Wang Y, Liu Z, Gao W (2014) The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011. Atmos Res 138:179–188

    Article  CAS  Google Scholar 

  • Yarahmadi M, Hadei M, Nazari SSH, Conti GO, Alipour MR, Ferrante M, Shahsavani A (2018) Mortality assessment attributed to long-term exposure to fine particles in ambient air of the megacity of Tehran, Iran. Environ Sci Pollut Res Int 25(14):14354–14262

    Article  CAS  Google Scholar 

  • Yeganeh B, Hewson MG, Samuel C, Knibbs LD, Morawska L (2017) A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques. Environ Modell Softw 88:84–92

    Article  Google Scholar 

  • You W, Zang Z, Zhang L, Zhang M, Pan X, Li Y (2016) A nonlinear model for estimating ground-level PM10 concentration in Xian using MODIS aerosol optical depth retrieval. Atmos Res 168:169–179

    Article  CAS  Google Scholar 

  • Zaman NAFK, Kanniah KD, Kaskaoutis D (2017) Estimating particulate matter using satellite based aerosol optical depth and meteorological variables in Malaysia. Atmos Res 193:142–162

    Article  CAS  Google Scholar 

  • Zang L, Mao F, Gou J, Wang W, Pan Z, Shen H, Zhu B, Wang Z (2019) Estimation of spatiotemporal PM1 distributions in China by combining PM2.5 observations with satellite aerosol optical depth. Sci Total Environ 658:1256–1264

    Article  CAS  Google Scholar 

  • Zhou Q, Jiang H, Wang J, Zhou J (2014) A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci Total Environ 496:264–274

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The researchers are grateful to Mr. Farzad Zandi from the Department of Environment, Iran, for his provision of the PM2.5 data.

Author information

Authors and Affiliations

  1. Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Pasdaran Street, P.O. Box 416, Sanandaj, 6617715175, Iran

    Mahin Mirzaei & Jamil Amanollahi

  2. Department of Physics, Section of Environmental Physics and Meteorology, University Campus, National and Kapodistrian University of Athens, Bldg. Phys-5, 157 84, Athens, Greece

    Chris G. Tzanis

Authors
  1. Mahin Mirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamil Amanollahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chris G. Tzanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamil Amanollahi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirzaei, M., Amanollahi, J. & Tzanis, C.G. Evaluation of linear, nonlinear, and hybrid models for predicting PM2.5 based on a GTWR model and MODIS AOD data. Air Qual Atmos Health 12, 1215–1224 (2019). https://doi.org/10.1007/s11869-019-00739-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11869-019-00739-z

Keywords

Search

Navigation