Vector autoregressive clustering for redundancy analysis in air pollution monitoring networks at Türkiye

Yıl 2024, Cilt: 42 Sayı: 2, 399 - 406, 30.04.2024

Aytaç Pekmezci Muhammet Oğuzhan Yalçin

Öz

This study proposes a new approach to reduce the information redundancy at Air Pollution Monitoring Networks (APMNs) and costs required for monitoring them. Proposed approach is based on Vector Autoregressive (VAR) model which describes the relationship between multivariate time series and consists of three main steps: In the first step, VAR model between two or more than two time series consisting of air pollutant observations is estimated. This step is repeated as the number of monitoring stations (n) under study and thus, n parameter vectors are obtained. In the second step, parameters vectors are divided into homogenous groups by using clustering analysis. The objective of this step is to identify the similar mon-itoring stations in terms of the relationship. Last step is to calculate the reduced information redundancy and the monitoring costs. To evaluate the efficiency of proposed approach, data sets consisting of PM10 and SO2 time series obtained from 116 APMNs at Türkiye are used. Fuzzy K-Medoids (FKM) as clustering method Xie-Beni (XB) index as cluster validity index are preferred. Experimental results showed that information redundancy and monitoring cost in PM10 and SO2 stations can reduced at the rate of 63.36 by following proposed approach.

Anahtar Kelimeler

Air Pollution, Information Redundancy, Vector Autoregressive Models, Time Series Analysis

Kaynakça

• [1] Ghorani-Azam A, Riahi-Zanjani B, Balali-Mood M. Effects of air pollution on human health and practical measures for prevention in Iran. J Res Med Sci 2016:2165. [CrossRef]
• [2] Kurt Kar Ö, Zhang J, Pinkerton KE. Pulmonary health effects of air pollution. Curr Opin Pulm Med 2016;22:138143. [CrossRef]
• [3] Liu H, Liu S, Xue B, Lv Z, Meng Z, Yang X, et al. Gorund-level ozone pollution and its health impacts in China. Atmos Environ 2018;173:223230. [CrossRef]
• [4] Landrigan PJ, Fuller R, Fisher S, Suk WA, Sly P, Chiles TC, et al. Pollution and children’s health. Sci Total Environ 2019;650:23892394. [CrossRef]
• [5] Giri D, Murthy VK, Adhikary PR, Khanal SN. Cluster analysis applied to atmospheric PM10 concentration data for determination of sources and spatial patterns in ambient air-quality of Katmandu Valley. Res Commun 2006;93:684688.
• [6] Gramch E, Cereceda-Balic F, Oyola P, Von Baer D. Examination of pollution trends in Santiago De Chile with cluster analysis of PM10 and ozone data. Atmos Environ 2006;40:54645475. [CrossRef]
• [7] Lu HC, Chang CL, Hsieh JC. Classification of PM10 distributions in Taiwan. Atmos Environ, 2006;40:14531463. [CrossRef]
• [8] Morlini I. Searching for structure in measurements for air pollutant concentration. Environmetrics 2007;18:823840. [CrossRef]
• [9] Ignaccolo R, Ghigo S, Giovenali E. Analysis of monitoring networks by functional clustering. Environmetrics 2008;62:672686. [CrossRef]
• [10] Pires JCM, Sousa SIV, Pereira MC, Alvim-Ferraz MCM, Martins FG. Management of air quality monitoring using principal component and cluster analysis-Part I: SO2 and PM10. Atmos Environ 2008;42:12491260. [CrossRef]
• [11] D’Urso P, Maharaj EA. Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets Syst 2009;160:3565–3589. [CrossRef]
• [12] D’Urso P, Giovanni LD, Massari R. Time series clustering by a robust autoregressive metric with application to air pollution. Chemon Intell Lab Syst 2015;141:107124. [CrossRef]
• [13] Güler Dincer N, Yalçın MO. Revealing information and equipment redundancies in air pollution monitoring networks in Turkey. Int J Environ Sci Technol 2016a;13:29272938. [CrossRef]
• [14] Güler Dincer N, İşçi Güneri Ö, Yalçın MO. Time series clustering’s application to identifying information redundancy at air pollution monitoring stations in Turkey. Sakarya Univ J Sci 2016b;20:605616.
• [15] Cotta H, Reisen V, Bondon P, Prezotti P. Identification of redundant air quality monitoring stations using robust principal component analysis. Environ Model Assess 202;25:521530. [CrossRef]
• [16] Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root, J am Stat Assoc 1979;74:427431. [CrossRef]
• [17] Osterwald-Lenum M. A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics. Oxf Bull Econ Stat 1992;54:461472. [CrossRef]
• [18] Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969;37:424438. [CrossRef]
• [19] Xie XL, Beni GA. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 1991;13:841847. [CrossRef]
• [20] Joshi A, Krishnapuram LYR. A fuzzy relative of k-medoids algorithm with application to web document and snippet clustering. IEEE Int Fuzzy Syst Conf Proceed 1999. p. 12811286. [CrossRef]