Abstract
With the acceleration of economic development and urbanization, urban air pollution is becoming increasingly serious and has already affected the environmental and human health. Therefore, the detection and management of air quality is imperative. An accurate air quality evaluation can help people better understand and improve the air quality. In this work, a new multi-pollutant weighted comprehensive air quality assessment method combined with the relative entropy theory and improved order preference by similarity to the ideal solution (iTOPSIS) method is proposed. First, two objective weight methods are adopted to determine the degree of influence of pollutants on air quality. Then, a comprehensive weight is calculated using the relative entropy, which can measure the degree of similarity between the two probability distributions. Based on the calculated comprehensive weights, the air quality level is evaluated using the iTOPSIS method. Finally, the effectiveness of the proposed method is evaluated using an example. The results show that the proposed method can not only directly express the air quality level but also provide important and effective information for managers to use to control air pollution.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal A, Toshniwal D (2019) Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods. J Air Waste Manage Assoc 69:805–822. https://doi.org/10.1080/10962247.2019.1577314
Ahmadian Marj A, Mobasheri MR, Matkan AA (2017) Quantitative assessment of different air pollutants (QADAP) using daily MODIS images. Int J Environ Res 11:523–534. https://doi.org/10.1007/s41742-017-0046-y
Barchielli A, Gregoratti M, Toigo A (2018) Measurement uncertainty relations for discrete observables: relative entropy formulation. CMaPh 357:1253–1304. https://doi.org/10.1007/s00220-017-3075-7
Caramagna A, Famoso F, Lanzafame R, Monforte P (2015) Analysis of vertical profile of particulates dispersion in function of the aerodynamic diameter at a congested road in Catania. Energy Procedia 82:702–707. https://doi.org/10.1016/j.egypro.2015.11.795
Chakraborty S & Chatterjee P (2017). A developed meta-model for selection of cotton fabrics using design of experiments and TOPSIS method. J Inst Eng (India): Series E 98:79-90. https://doi.org/10.1007/s40034-017-0108-x
CMEP (2012) Ambient air quality standards (GB 3095-2012). Beijing, China, CMEP
Dai W, Yi L (2016) Spatio-temporal differences and driving forces of air quality in Chinese cities. J Resour Ecol 7:77–85. https://doi.org/10.5814/j.issn.1674-764x.2016.02.001
Desarkar A & Das A (2018) Implementing decision tree in air pollution reduction framework. In, Singapore, Smart Computing and Informatics. Springer Singapore, pp 105-113. doi:https://doi.org/10.1007/978-981-10-5544-7_11
Dimitriou K, Liakakou E, Lianou M, Psiloglou B, Kassomenos P, Mihalopoulos N, Gerasopoulos E (2020) Implementation of an aggregate index to elucidate the influence of atmospheric synoptic conditions on air quality in Athens, Greece. Air Qual Atmos Health 13:447–458. https://doi.org/10.1007/s11869-020-00810-0
El-Nadry M, Li W, El-Askary H, Awad MA, Mostafa AR (2019) Urban health related air quality indicators over the middle east and north Africa countries using multiple satellites and AERONET data. Remote Sens 11:2096. https://doi.org/10.3390/rs11182096
van Erp AM, Kelly FJ, Demerjian KL, Pope CA, Cohen AJ (2012) Progress in research to assess the effectiveness of air quality interventions towards improving public health. Air Qual Atmos Health 5:217–230. https://doi.org/10.1007/s11869-010-0127-y
Esteki K, Prakash N, Li Y, Mu C, Du K (2017) Seasonal variation of CO2 vertical distribution in the atmospheric boundary layer and impact of meteorological parameters. Int J Environ Res 11:707–721. https://doi.org/10.1007/s41742-017-0062-y
Freeman B, McBean E, Gharabaghi B, Thé J (2017) Evaluation of air quality zone classification methods based on ambient air concentration exposure. J Air Waste Manage Assoc 67:550–564. https://doi.org/10.1080/10962247.2016.1263585
Ghasemi A, Amanollahi J (2019) Integration of ANFIS model and forward selection method for air quality forecasting. Air Qual Atmos Health 12:59–72. https://doi.org/10.1007/s11869-018-0630-0
Jodeh S, Hasan AR, Amarah J, Judeh F, Salghi R, Lgaz H, Jodeh W (2018) Indoor and outdoor air quality analysis for the city of Nablus in Palestine: seasonal trends of PM10, PM5.0, PM2.5, and PM1.0 of residential homes. Air Qual Atmos Health 11:229–237. https://doi.org/10.1007/s11869-017-0533-5
Kobza J, Geremek M, Dul L (2018) Characteristics of air quality and sources affecting high levels of PM10 and PM2.5 in Poland, Upper Silesia urban area. Environ Monit Assess 190:515. https://doi.org/10.1007/s10661-018-6797-x
Krishan M, Jha S, Das J, Singh A, Goyal MK, Sekar C (2019) Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Qual Atmos Health 12:899–908. https://doi.org/10.1007/s11869-019-00696-7
Kumar A, Goyal P (2013) Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl Geophys 170:711–722. https://doi.org/10.1007/s00024-012-0583-4
Lee S, Kang D (2019) Development of interval-valued fuzzy GRA with SERVPERF based on subjective and objective weights for evaluation of airline service quality: a case study of Korea low-cost carriers. PLoS One 14:e0219739. https://doi.org/10.1371/journal.pone.0219739
Li Q, Meng X, Liu Y, Pang L (2019) Risk assessment of floor water inrush using entropy weight and variation coefficient model. Geotech Geol Eng 37:1493–1501. https://doi.org/10.1007/s10706-018-0702-9
Monforte P, Ragusa MA (2018) Evaluation of the air pollution in a Mediterranean region by the air quality index. Environ Monit Assess 190:625–635. https://doi.org/10.1007/s10661-018-7006-7
network Ttq (2019) Daily historical data of Zhenjiang air quality index in April 2019. https://www.aqistudy.cn/historydata/daydata.php?city=%E9%95%87%E6%B1%9F&month=201904. Accessed 20 May 2019
Nieto PG, García-Gonzalo E, Sánchez AB, Miranda AR (2018) Air quality modeling using the PSO-SVM-based approach, MLP neural network, and M5 model tree in the metropolitan area of Oviedo (Northern Spain). Environ Model Assess 23:229–247. https://doi.org/10.1007/s10666-017-9578-y
Ning M, Guan J, Liu P, Zhang Z, O’Hare GM (2019) GA-BP air quality evaluation method based on fuzzy theory. Comput Mater Con 58:215–227. https://doi.org/10.32604/cmc.2019.03763
Niu D, Li S, Dai S (2018) Comprehensive evaluation for operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm from the view of sustainable development. Sustainability 10:860–886. https://doi.org/10.3390/su10030860
Pak U, Kim C, Ryu U, Sok K, Pak S (2018) A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Qual Atmos Health 11:883–895. https://doi.org/10.1007/s11869-018-0585-1
Post T, Potì V (2016) Portfolio analysis using stochastic dominance, relative entropy, and empirical likelihood. Manag Sci 63:153–165. https://doi.org/10.1287/mnsc.2015.2325
Sahoo MM, Patra K, Swain J, Khatua K (2017) Evaluation of water quality with application of Bayes' rule and entropy weight method. Eur J Environ Civ Eng 21:730–752. https://doi.org/10.1080/19648189.2016.1150895
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:623–656. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Sharma R, Kumar R, Sharma DK, Son LH, Priyadarshini I, Pham BT, Tien Bui D, Rai S (2019) Inferring air pollution from air quality index by different geographical areas: case study in India. Air Qual Atmos Health 12:1347–1357. https://doi.org/10.1007/s11869-019-00749-x
Sheikh V, Kornejady A, Ownegh M (2019) Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran. Nat Hazards 96:1335–1365. https://doi.org/10.1007/s11069-019-03617-0
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 189:373. https://doi.org/10.1007/s10661-017-6075-3
Tang H, Fang F (2018) A novel improvement on rank reversal in TOPSIS based on the efficacy coefficient method. Int J Internet Manuf Serv 5:67–84. https://doi.org/10.1504/IJIMS.2018.090591
Turrini E, Vlachokostas C, Volta M (2019) Combining a multi-objective approach and multi-criteria decision analysis to include the socio-economic dimension in an air quality management problem. Atmos 10:381–400. https://doi.org/10.3390/atmos10070381
Wang X, Wang Z, Guo M, Chen W & Zhang H (2018) Research on air quality evaluation based on principal component analysis. In: IOP Conference Series: Earth and Environmental Science. vol 4. IOP Publishing, pp 042030-042037. https://doi.org/10.1088/1755-1315/108/4/042030
Zhang X (2016) The research on dynamic operating TOPSIS comprehensive evaluation method based on panel data. Ph.D. Thesis, Hunan University
Zhou Y & Xu J (2011) Evaluation of enterprises liquidity value based on the relative entropy-topsis. In: 2011 International Conference on E-Business and E-Government (ICEE), IEEE, pp 1-4. https://doi.org/10.1109/ICEBEG.2011.5881514
Funding
This work is supported by the Key R&D Program of Jiangsu Province, China, under Grant BE2018370 and Key R&D Program of Zhenjiang, China, under Grant GY2018013.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lin, H., Pan, T. & Chen, S. Comprehensive evaluation of urban air quality using the relative entropy theory and improved TOPSIS method. Air Qual Atmos Health 14, 251–258 (2021). https://doi.org/10.1007/s11869-020-00930-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11869-020-00930-7