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Fuzzy environmental analogy index to develop environmental similarity maps for designing air quality monitoring networks on a large-scale

Stochastic Environmental Research and Risk Assessment volume 33, pages 1793–1813 (2019)Cite this article

Abstract

All activities aimed at studying the primary causes and effects of air pollution cannot disregard the fact that it is necessary to have an optimal air quality monitoring network for assessing population exposure to air pollution and predicting the magnitude of the health risks. In the framework of a cooperation between the ARPA Sicilia Organization and the Department of Engineering, University of Palermo, research was performed to develop an innovative methodology useful for defining environmental similarity maps aimed at supporting the design of air quality monitoring networks at the regional scale. This approach is based on a new index called the fuzzy environmental analogy index (FEAI) based on fuzzy theory. FEAI is deduced by combining two indexes: meteorological pressure indicator (MPI) and anthropic pressure indicator (API). MPI allows us to investigate, for the examined territory, analogies relevant to meteorological conditions, and API emphasizes the importance of impacts related to anthropogenic or natural sources at the regional scale. Finally, FEAI applications in a case study related to the Sicily region in Italy are also described. The obtained results confirm the capability of the FEAI to investigate similarities between neighboring areas in terms of environmental pressures due to anthropic and natural sources and to identify gaps in the monitoring network used to define existing air quality conditions.

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References

  • Amar BF, Elamouri M (2011) Wind energy assessment of the Sidi Daoud Wind Farm—Tunisia, Wind Farm—Technical Regulations, Potential Estimation and Siting Assessment, Gastón O. Suvire, IntechOpen. https://doi.org/10.5772/16536. https://www.intechopen.com/books/wind-farm-technical-regulations-potential-estimation-and-siting-assessment/wind-energy-assessment-of-the-sidi-daoud-wind-farm-tunisia

    Google Scholar 

  • ARPA (2015) L’inventario delle emissioni in atmosfera della regione Sicilia. Report ARPA Sicilia 2012. http://www.arpa.sicilia.it/wp-content/uploads/2015/08/Relazione-Inventario-Emissioni.pdf

  • ARPA (2018) Relazione annuale sullo stato della qualità dell’aria nella Regione Siciliana anno 2017. https://www.arpa.sicilia.it/wp-content/uploads/2017/08/Relazione_QA_2017_.pdf

  • Ashrafi K, Ghader S, Motesadi S, Esfahanian V (2008) Site locating of air quality monitoring stations over great Tehran. J Environ Stud 33:1–10

    Google Scholar 

  • Barua DK (2005) Beaufort wind scale. In: Schwartz ML (ed) Encyclopedia of coastal science Encyclopedia of earth science series. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3880-1_45

    Chapter  Google Scholar 

  • Brock G, Pihur V, Datta S, Datta S (2008) clValid: an R Package for cluster validation. J Stat Softw 25(4):1–21

    Article  Google Scholar 

  • Buchholz S, Junk J et al (2010) Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmos Environ 44(39):5183–5190

    Article  CAS  Google Scholar 

  • Carbajal-Hernández JJ, Sánchez-Fernández LP, Carrasco-Ochoa JA, Martínez-Trinidad JF (2012) Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos Environ 60(2012):37–50

    Article  CAS  Google Scholar 

  • Carta JA, Ramírez P, Velázquez S (2009) A review of wind speed probability distributions used in wind energy analysis. Renew Sustain Energy Rev 13(5):933–955. https://doi.org/10.1016/j.rser.2008.05.005

    Article  Google Scholar 

  • Casamirra M, Castiglia F, Giardina M, Tomarchio E (2009) Fuzzy modelling of HEART methodology: application in safety analyses of accidental exposure in irradiation plants. Radiat Eff Defects Solids 164(5–6):291–296

    Article  CAS  Google Scholar 

  • Castiglia F, Giardina M (2011) Fuzzy risk analysis of a modern γ-ray industrial irradiator. Health Phys 100(6):622–631

    Article  CAS  Google Scholar 

  • Castiglia F, Giardina M, Caravello FP (2008) Fuzzy fault tree analysis in modern γ-ray industrial irradiator: use of fuzzy version of HEART and CREAM techniques for human error evaluation. In: 9th international conference on probabilistic safety assessment and management 2008, PSAM 2008

  • Castiglia F, Giardina M, Tomarchio E (2010) Risk analysis using fuzzy set theory of the accidental exposure of medical staff during brachytherapy procedures. J Radiol Prot 30(1):49–62

    Article  CAS  Google Scholar 

  • Castiglia F, Giardina M, Tomarchio E (2015) THERP and HEART integrated methodology for human error assessment. Radiat Phys Chem 116:262–266

    Article  CAS  Google Scholar 

  • Connan O, Pellerin G, Maro D, Damay P, Hébert D, Roupsard P, Rozet M, Laguionie P (2018) Dry deposition velocities of particles on grass: field experimental data and comparison with models. J Aerosol Sci 2018:58–67

    Article  CAS  Google Scholar 

  • Debnath J, Majumder D, Biswas A (2018) Air quality assessment using weighted interval type-2 fuzzy inference system. Ecol Inform 46:133–146

    Article  Google Scholar 

  • Di Nardo A, Bortone I, Chianese S, Di Natale M, Erto A, Santonastaso GF, Musmarra D (2018) Odorous emission reduction from a waste landfill with an optimal protection system based on fuzzy logic. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-018-2514-0)

    Article  Google Scholar 

  • Duncan OD, Duncan B (1955) A methodological analysis of segregation indexes. Am Sociol Rev 20:210–217

    Article  Google Scholar 

  • Duyzer J, van den Hout D, Zandveld P, van Ratingen S (2015) Representativeness of air quality monitoring networks. Atmos Environ 104:88–101

    Article  CAS  Google Scholar 

  • Elshout S, Karine L, Hermann H (2014) CAQI Common Air Quality Index—update with PM2.5 and sensitivity analysis. Sci Total Environ 488–489:461–468

    Article  CAS  Google Scholar 

  • EPA (2003) Framework for cumulative risk assessment, U.S. Environmental Protection Agency, EPA/630/P-02/001F

  • EPA (2006) Guideline for Reporting of Daily Air Quality—Air Quality Index (AQI), U.S. Environmental Protection Agency, EPA-454/B-06-001

  • Essa KSM, Embaby M (2005) Statistical evaluation of wind energy at Inshas, Egypt. Wind Eng 29(1):83–88. https://doi.org/10.1260/0309524054353692

    Article  Google Scholar 

  • Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis. Wiley, New York

    Book  Google Scholar 

  • Florent R, Didier S (2014) Measuring territorial vulnerability? An attempt of qualification and quantification computational science and its applications. In: International conference on computational science and its applications, ICCSA 2014, pp 331–343

  • Giardina M, Buffa P (2018) A new approach for modeling dry deposition velocity of particles. Atmos Environ 180:11–22

    Article  CAS  Google Scholar 

  • Giardina M, Castiglia F, Tomarchio E (2014) Risk assessment of component failure modes and human errors using a new FMECA approach: application in the safety analysis of HDR brachytherapy. J Radiol Prot 34(4):891–914. https://doi.org/10.1088/0952-4746/34/4/891

    Article  CAS  Google Scholar 

  • Giardina M, Tomarchio E, Greco D (2015) Analysis of radionuclide concentration in air released through the stack of a radiopharmaceutical production facility based on a medical cyclotron. Radiat Phys Chem 116:368–372

    Article  CAS  Google Scholar 

  • Giardina M, Buffa P, Cervone A, De Rosa F, Lombardo C, Casamirra M (2017) Dry deposition models for radionuclides dispersed in air: a new approach for deposition velocity evaluation schema. J Phys Conf Ser 923:012057

    Article  CAS  Google Scholar 

  • Giardina M, Buffa P, Cervone A, Lombardo C (2019a) Dry deposition of particle on urban areas. IOP Conf Ser J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1224/1/012050

    Article  Google Scholar 

  • Giardina M, Donateo A, Buffa P, Contini D, Cervone A, Lombardo C, Rocchi F (2019b) Atmospheric dry deposition processes of particles on urban and suburban surfaces: modelling and validation works. Atmos Environ 214:1–16. https://doi.org/10.1016/j.atmosenv.2019.116857

    Article  CAS  Google Scholar 

  • Gómez-Navarro T, García-Melón M, Acuña-Dutra S, Díaz-Martín D (2009) An environmental pressure index proposal for urban development planning based on the analytic network process. Environ Impact Assess Rev 29(5):319–329

    Article  Google Scholar 

  • Hartigan JA, Wong MA (1979) K-means clustering algorithm. Appl Stat 28:100–108

    Article  Google Scholar 

  • Hellendoorn H, Thomas C (1993) Defuzzification in fuzzy controllers. Intell Fuzzy Syst 1:109–123

    Google Scholar 

  • Hong T, Leeb C (1996) Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst 84:33–47

    Article  Google Scholar 

  • Hout D, Voogt M, Moosmann L, Nagl C, Spangl W (2012) Survey of views of stakeholders, experts and citizens on the review of the EU Air Policy. TNO, Netherlands. TNO report TNO-060-UT2012-00714. http://ec.europa.eu/environment/air/pdf/Survey_AQD_review_PartI_Mainresults.pdf

  • Kazemi-Beydokhti M, Abbaspour RA, Kheradmandi M, Bozorgi-Amiri A (2019) Determination of the physical domain for air quality monitoring stations using the ANP-OWA method in GIS. Environ Monit Assess 191(S2):5. https://doi.org/10.1007/s10661-019-7422-3

    Article  CAS  Google Scholar 

  • Kentel E, Aral MM (2004) Probabilistic-fuzzy health risk modeling. Stoch Env Res Risk Assess 18(5):324–338

    Article  Google Scholar 

  • Kentel E, Aral MM (2007) Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective. Stoch Env Res Risk Assess 21(4):405–417

    Article  Google Scholar 

  • Kundu S (1998) The min-max composition rule and its superiority over the usual max-min composition rule. Fuzzy Sets Syst 93(3):319–329

    Article  Google Scholar 

  • Li L, Qian J, Ou CQ, Zhou YX, Guo C, Guo Y (2014) Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011. Environ Pollut 190:75–81

    Article  CAS  Google Scholar 

  • Li L, Guo-Zhen L, Hua-Zhang L, Yuming G, Chun-Quan O, Ping-Yan C (2015) Can the Air Pollution Index be used to communicate the health risks of air pollution. Environ Pollut 205(2015):153–160

    Article  CAS  Google Scholar 

  • Mahmoudi M, Amoozad Mahdiraji H, Jafarnejad A, Safari H (2019) Dynamic prioritization of equipment and critical failure modes: an interval-valued intuitionistic fuzzy condition-based model. Kybernetes (in press)

  • Mofarrah A, Husain T (2010) A holistic approach for optimal design of air quality monitoring network expansion in an urban area. Atmos Environ 44:432–440

    Article  CAS  Google Scholar 

  • OECD (2008) Handbook on constructing composite indicators: methodology and user guide. Organisation for Economic Co-operation and Development, Paris

    Book  Google Scholar 

  • Olvera-García MÁ, Carbajal-Hernández JJ, Sánchez-Fernández LP, Hernández-Bautista I (2016) Air quality assessment using a weighted fuzzy inference system. Ecol Inform 33(2016):57–74

    Article  Google Scholar 

  • Pedrycz W (1996) Fuzzy modelling. Paradigms and practice. Kluwer, Dordrecht

    Book  Google Scholar 

  • Plaia A, Ruggieri M (2011) Air quality indices: a review. Rev Environ Sci Biotechnol 10:165–179

    Article  Google Scholar 

  • Pope RL, Wu J (2014) Characterizing air pollution patterns on multiple time scales in urban areas: a landscape ecological approach. Urban Ecosyst. https://doi.org/10.1007/s11252-014-0357-0

    Article  Google Scholar 

  • Ramli N, Mohamad D (2009) A comparative analysis of centroid methods in ranking fuzzy numbers. Eur J Sci Res 28:492–501

    Google Scholar 

  • Renjith VR, Jose Kalathil M, Kumar PH, Madhavan D (2018) Fuzzy FMECA (failure mode effect and criticality analysis) of LNG storage facility. J Loss Prev Process Ind 56:537–547. https://doi.org/10.1016/j.jlp.2018.01.002

    Article  CAS  Google Scholar 

  • Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7

    Article  Google Scholar 

  • Sadiq R, Tesfamariam S (2009) Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stoch Environ Res Risk Assess 23:75–91

    Article  Google Scholar 

  • Saib M, Caudeville J, Beauchamp M, Carré F, Ganry O, Trugeon A, Cicolella A (2015) Building spatial composite indicators to analyze environmental health inequalities on a regional scale. Environ Health 14:68. https://doi.org/10.1186/s12940-015-0054-3

    Article  Google Scholar 

  • Scirè JS, Robe FR, Fermau ME, Yamartino RJ (1999) A user’s guide for the CALMET meteorological model (version 5.0). Earth Tech Inc., Concord

    Google Scholar 

  • Singh AP, Chakrabarti S, Kumar S, Singh A (2017) Assessment of air quality in Haora River basin using fuzzy multiple-attribute decision making techniques. Environ Monit Assess 189(8):5. https://doi.org/10.1007/s10661-017-6075-3

    Article  CAS  Google Scholar 

  • Tang Y, Zheng J (2006) Linguistic modelling based on semantic similarity relation among linguistic labels. Fuzzy Set Syst 157:1662–1673

    Article  Google Scholar 

  • Xu Z (2012) Linguistic decision making: theory and methods, 2012th edn. Springer, Berlin

    Book  Google Scholar 

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci Part I II 8:199–249. https://doi.org/10.1016/0020-0255(75)90036-5

    Article  Google Scholar 

  • Zadeh LA (1992) The calculus of fuzzy if/then rules. Al Expert 7:23–27

    Google Scholar 

  • Zamonin (2006) https://en.wikipedia.org/wiki/File:Topography_of_Sicily.png#filehistory

  • ZoroufchiBenis K, Fatehifar E, Ahmadi J, Rouhi A (2015) Optimal design of air quality monitoring network and its application in an oil refinery plant: an approach to keep health status of workers. Health Promot Perspect 5(4):269–279

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy

    Mariarosa Giardina & Pietro Buffa

  2. Agenzia Regionale per la Protezione dell’Ambiente - ARPA Sicilia, Via S. Lorenzo, 312/G, Palermo, Italy

    Anna Maria Abita & Giuseppe Madonia

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  1. Mariarosa Giardina
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  2. Pietro Buffa
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  3. Anna Maria Abita
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  4. Giuseppe Madonia
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Correspondence to Mariarosa Giardina.

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Giardina, M., Buffa, P., Abita, A.M. et al. Fuzzy environmental analogy index to develop environmental similarity maps for designing air quality monitoring networks on a large-scale. Stoch Environ Res Risk Assess 33, 1793–1813 (2019). https://doi.org/10.1007/s00477-019-01723-w

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