Skip to main content

Advertisement

SpringerLink
Log in

Can fuzzy set theory bring complex issues in sizing air quality monitoring network into focus?

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

This paper presents an objective methodology for determining the optimum number of ambient air quality monitoring stations in a air quality monitoring network that will give maximum information of present air quality. The suggested two fold approach is a combination of fuzzy similarity measures and fuzzy c-mean (FCM) clustering. In the first stage, cosine amplitude—one of the fuzzy similarity measures is used to classify or to regroup monitoring stations for the most critical air pollutant PM10 and NO2 for the defined possibility (α-cut) levels. In the second stage, average values of PM10, NO2, SO2, CO and SPM for the winter months being a worst case scenario are used in FCM clustering. Finally, the optimal number of air quality monitoring locations is selected as those (1) which form single station partition (unique partition) in crisp relational matrix for PM10 and NO2 and (2) have highest membership in fuzzy clustering. The methodology has been demonstrated by applying to case study on Delhi Metro City in India. The air quality data of criteria pollutants was collected for 15 months from the installed 41 stations. The outcome of the study reveals that the city of Delhi needs only 16 monitoring stations which will result into sizable reduction in the capital cost and the recurring expenses in ambient air quality monitoring in Delhi.

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

Similar content being viewed by others

References

  • Baldauf RW, Lane DD, Marote GA (1999) Ambient air quality monitoring network design for assessing human health impacts from exposures to airborne contaminants. Environ Monit Assess 66:63–76

    Article  Google Scholar 

  • Bezdek, J. C. (1976). Feature selection for binary data—medical diagnosis with fuzzy sets. In Proceedings of the June 7–10, 1976, National Computer Conference and Exposition on—AFIPS’76 (pp. 1057–1068). New York, NY: ACM Press. doi:10.1145/1499799.1499946

  • Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York

    Book  MATH  Google Scholar 

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203. doi:10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  • Cannon RL, Dave JV, Bezdek JC (1986) Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans Pattern Anal Mach Intell 8(2):248–255

    Article  MATH  Google Scholar 

  • Census of India (2011) Office of the Register General and Census Commissioner, http://censusindia.gov.in. Accessed 14 Jun 2014

  • Chang NB, Tseng CC (1999) Optimal evaluation of expansion alternatives for existing air quality monitoring network by grey compromise programing. J Environ Manag 56:61–77

    Article  Google Scholar 

  • Dembele D, Kastner P (2003) Fuzzy C-means method for clustering microarray data. Bioinformatics 19(8):973–980. doi:10.1093/bioinformatics/btg119

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250. doi:10.1016/j.enggeo.2004.06.001

    Article  Google Scholar 

  • Ercanoglu M, Weber K, Langille J, Neves R (2006) Modeling wildland fire susceptibility using fuzzy systems. GIScience Remote Sens 43(3):268–282. doi:10.2747/1548-1603.43.3.268

    Article  Google Scholar 

  • Hanesch M, Scholger R, Dekkers MJ (2001) The application of fuzzy c-means cluster analysis and non-linear mapping to a soil data set for the detection of polluted sites. Phys Chem Earth Part A 26(11–12):885–891. doi:10.1016/S1464-1895(01)00137-5

    Article  Google Scholar 

  • Husain T, Khan SM (1983) Air monitoring network design using Fisher’s information measures—a case study. Atmos Environ 17(12):2591–2598. doi:10.1016/0004-6981(83)90087-2

    Article  Google Scholar 

  • ISI (1985) Methods for measurement of air quality: Part 14 Guidelines for Planning the sampling of atmosphere and location of monitoring stations. IS: 5182 (Part 14), Indian Standards Institute, New Delhi

  • Kainuma Y, Shiozawa K, Okamoto S (1990) Study of the optimal allocation of ambient air monitoring stations. Atmos Environ 24(3):395–406. doi:10.1016/0957-1272(90)90047-X

    Article  Google Scholar 

  • Khan FI, Sadiq R (2005) Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique. Environ Monit Assess 105(1–3):261–283. doi:10.1007/s10661-005-3852-1

    Article  Google Scholar 

  • Liu MK, Avrin J, Pollack RI, Behar JV, McElroy JL (1986) Methodology for designing air quality monitoring networks: I. Theoretical aspects. Environ Monit Assess 6(1):1–11. doi:10.1007/BF00394284

    Article  Google Scholar 

  • Lozano A, Usero J, Vanderlinden E, Raez J, Contreras J, Navarrete B (2009) Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain. Microchem J 93(2):164–172. doi:10.1016/j.microc.2009.06.005

    Article  Google Scholar 

  • Maina J, Venus V, McClanahan TR, Ateweberhan M (2008) Modelling susceptibility of coral reefs to environmental stress using remote sensing data and GIS models. Ecol Model 212(3–4):180–199. doi:10.1016/j.ecolmodel.2007.10.033

    Article  Google Scholar 

  • Mazzeo NA, Venegas LE (2008) Design of an air-quality surveillance system for Buenos Aires City integrated by a NOx monitoring network and atmospheric dispersion models. Environ Model Assess 13(3):349–356. doi:10.1007/s10666-007-9101-y

    Article  Google Scholar 

  • McElroy JL, Behar JV, Meyers TC, Liu MK (1986) Methodology for designing air quality monitoring networks: II. Application to Las Vegas, Nevada, for carbon monoxide. Environ Monit Assess 6(1):13–34. doi:10.1007/BF00394285

    Article  Google Scholar 

  • Modak PM, Lohani BN (1985a) Optimization of ambient air quality monitoring networks: (Part I). Environ Monit Assess 5(1):1–19. doi:10.1007/BF00396391

    Article  Google Scholar 

  • Modak PM, Lohani BN (1985b) Optimization of ambient air quality monitoring networks: (Part II). Environ Monit Assess 5(1):21–38. doi:10.1007/BF00396392

    Article  Google Scholar 

  • Modak PM, Lohani BN (1985c) Optimization of ambient air quality monitoring networks: (Part III). Environ Monit Assess 5(1):39–53. doi:10.1007/BF00396393

    Article  Google Scholar 

  • Mofarrah A, Husain T (2010) A holistic approach for optimal design of air quality monitoring network expansion in an urban area. Atmos Environ 44(3):432–440. doi:10.1016/j.atmosenv.2009.07.045

    Article  Google Scholar 

  • Munn RE (1981) The design of air quality monitoring network. MacMillan Publishers Ltd, London

    Book  Google Scholar 

  • Nakamori Y, Sawaragi Y (1984) Interactive design of urban level monitoring network. Atmos Environ 18(4):793–799

    Article  Google Scholar 

  • Nakamori Yoshiteru, Ikeda S, Sawaragi Y (1979) Design of air pollutant monitoring system by spatial sample stratification. Atmos Environ 13(1):97–103

    Article  Google Scholar 

  • Peterson JT (1970) Distribution of sulfur dioxide over metropolitan St. Louis, as described by empirical Eigenvectors, and its relation to meteorological parameters. Atmos Environ 4(5):501–518. doi:10.1016/0004-6981(70)90020-X

    Article  Google Scholar 

  • Ross TJ (2004) Fuzzy logic with engineering applications, 2nd edn. Wiley, Chichester

    MATH  Google Scholar 

  • SAD (Statistical Abstract of Delhi) (2012) Directorate of Economics and Statistics, Government of National Capital Territory of Delhi (GNCTD), New Delhi, http://delhi.gov.in/DoIT/DES/Publication/abstract/SA2012.pdf. Accessed 23 June 2014

  • Saksena S, Joshi V, Patil RS (2003) Cluster analysis of Delhi’s ambient air quality data. J Environ Monit 5(3):491. doi:10.1039/b210172f

    Article  Google Scholar 

  • Sarigiannis DA, Saisana M (2008) Multi-objective optimization of air quality monitoring. Environ Monit Assess 136(1–3):87–99. doi:10.1007/s10661-007-9725-z

    Google Scholar 

  • Saxena P, Bhardwaj R, Ghosh C (2012) Status of air pollutants after implementation of CNG in Delhi. Curr World Environ 7(1):109–115

  • Stalker WW, Dickerson RC (1962) Sampling station and time requirements for urban air pollution surveys. J Air Pollut Control Assoc 12(3):111–128. doi:10.1080/00022470.1962.10468055

    Article  Google Scholar 

  • Tseng C, Chang NB (2001) Assessing relocation strategies of urban air quality monitoring stations by GA-based compromise programming. Environ Int 26(7–8):523–541. doi:10.1016/S0160-4120(01)00036-8

    Article  Google Scholar 

  • U.S. EPA (U.S. Environmental Protection Agency) (1971) Guidelines: air quality surveillance networks. AP-98, http://nepis.epa.gov/Exe/ZyPDF.cgi/9100K9L3.PDF?Dockey=9100K9L3.PDF. Accessed 23 June 2014

  • Vriend SP, Van Gaans PFM, Middelburg J, De Nijs A (1988) The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal. Appl Geochem 3(2):213–224. doi:10.1016/0883-2927(88)90009-1

    Article  Google Scholar 

  • WHO (World Health Organization) (1977) Air monitoring programme design for urban and industrial areas, Global Environmental Monitoring System, WHO Offset Publication No. 38, http://whqlibdoc.who.int/offset/WHO_OFFSET_33.pdf. Accessed 23 June 2014

  • Zadeh LA (1971) Similarity relations and fuzzy orderings. Inf Sci 3(2):177–200. doi:10.1016/S0020-0255(71)80005-1

    Article  MathSciNet  MATH  Google Scholar 

  • Zimmermann HJ (2001) Fuzzy set theory—and its applications, 4th edn. Kluwer Academic Publishers, Boston, Dordrecht, London

    Book  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge to the Delhi Pollution Control Committee (DPCC) authorities in India for giving the permission to use air quality parametric data in the case study. The first author thanks Council of Scientific and Industrial Research (CSIR), New Delhi, India for providing the scholarship to carry out research at Centre for Environmental Science and Engineering (CESE), IIT Bombay, India.

Author information

Authors and Affiliations

  1. Center for Environmental Science and Engineering (CESE), Indian Institute of Technology Bombay, Mumbai, 400076, Maharashtra, India

    Kamal Jyoti Maji & Anil Kumar Dikshit

  2. School of Civil Engineering, Survey and Construction, University of KwaZulu-Natal, Durban, 4041, South Africa

    Anil Kumar Dikshit

  3. Berkeley Initiative in Soft Computing (BISC)–Special Interest Group (SIG)–Environment Management Systems (EMS), Berkeley, CA, USA

    Ashok Deshpande

Authors
  1. Kamal Jyoti Maji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Kumar Dikshit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashok Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anil Kumar Dikshit.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maji, K.J., Dikshit, A.K. & Deshpande, A. Can fuzzy set theory bring complex issues in sizing air quality monitoring network into focus?. Int J Syst Assur Eng Manag 8 (Suppl 4), 2118–2128 (2017). https://doi.org/10.1007/s13198-014-0327-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-014-0327-1

Keywords

Search

Navigation