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Outlier Detection of Air Quality for Two Indian Urban Cities Using Functional Data Analysis

DOI: 10.4236/ojap.2023.123005, PP. 79-91

Keywords: Functional Data Analysis, Outliers, Air Quality, Gas Emission, Classical Statistics

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Abstract:

Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.

References

[1]  Kumar, K. and Pande, B.P. (2023) Air Pollution Prediction with Machine Learning: A Case Study of Indian Cities. International Journal of Environmental Science and Technology, 20, 5333-5348.
https://doi.org/10.1007/s13762-022-04241-5
[2]  Persis, J. and Amar, A.B. (2023) Predictive Modeling and Analysis of Air Quality—Visualizing before and during COVID-19 Scenarios. Journal of Environmental Management, 327, Article ID: 116911.
https://doi.org/10.1016/j.jenvman.2022.116911
[3]  Ravindra, K., Singh, T., Singh, V., Chintalapati, S., Beig, G. and Mor, S. (2023) Understanding the Influence of Summer Biomass Burning on Air Quality in North India: Eight Cities Field Campaign Study. Science of the Total Environment, 861, Article ID: 160361.
https://doi.org/10.1016/j.scitotenv.2022.160361
[4]  Kumar, V., Gupta, S. and Jolli, V. (2022) Influence of Vehicular Frequency on Air Quality of Delhi, India. Ecological Chemistry and Engineering S, 29, 477-485.
https://doi.org/10.2478/eces-2022-0034
[5]  Ahmad, M., Haq, A., Kalam, A. and Shah, S.K. (2022) A Comparative Study of Outlier Detection of Yamuna River Delhi India by Classical Statistics and Statistical Quality Control. Reliability: Theory & Applications, 17, 430-438.
[6]  Haque, M.S. and Singh, R.B. (2017) Air Pollution and Human Health in Kolkata, India: A Case Study. Climate, 5, Article No. 77.
https://doi.org/10.3390/cli5040077
[7]  Rigueira, X., Araújo, M., Martínez, J., García-Nieto, P.J. and Ocarranza, I. (2022) Functional Data Analysis for the Detection of Outliers and Study of the Effects of the COVID-19 Pandemic on Air Quality: A Case Study in Gijón, Spain. Mathematics, 10, Article No. 2374.
https://doi.org/10.3390/math10142374
[8]  Febrero, M., Galeano, P. and González-Manteiga, W. (2008) Outlier Detection in Functional Data by Depth Measures, with Application to Identify Abnormal NOx Levels. Environmetrics, 19, 331-345.
https://doi.org/10.1002/env.878
[9]  Matías, J.M., Ordónez, C., Taboada, J. and Rivas, T. (2009) Functional Support Vector Machines and Generalized Linear Models for Glacier Geomorphology Analysis. International Journal of Computer Mathematics, 86, 275-285.
https://doi.org/10.1080/00207160801965305
[10]  Torres, J.M., Nieto, P.G., Alejano, L. and Reyes, A.N. (2011) Detection of Outliers in Gas Emissions from Urban Areas Using Functional Data Analysis. Journal of Hazardous Materials, 186, 144-149.
https://doi.org/10.1016/j.jhazmat.2010.10.091
[11]  Martínez, J., Saavedra, á., García-Nieto, P.J., Pineiro, J.I., Iglesias, C., Taboada, J., Sancho, J. and Pastor, J. (2014) Air Quality Parameters Outliers Detection Using Functional Data Analysis in the Langreo Urban Area (Northern Spain). Applied Mathematics and Computation, 241, 1-10.
https://doi.org/10.1016/j.amc.2014.05.004
[12]  Sancho, J., Iglesias, C., Pineiro, J., Martínez, J., Pastor, J.J., Araújo, M. and Taboada, J. (2016) Study of Water Quality in a Spanish River Based on Statistical Process Control and Functional Data Analysis. Mathematical Geosciences, 48, 163-186.
https://doi.org/10.1007/s11004-015-9605-y
[13]  Ordònez, C., Martìnez, J., Saavedra, à. and Mourelle, A. (2011) Intercomparison Exercise for Gases Emitted by a Cement Industry in Spain: A Functional Data Approach. Journal of the Air & Waste Management Association, 61, 135-141.
https://doi.org/10.3155/1047-3289.61.2.135
[14]  Sancho, J., Pastor, J.J., Martínez, J. and García, M.A. (2013) Evaluation of Harmonic Variability in Electrical Power Systems through Statistical Control of Quality and Functional Data Analysis. Procedia Engineering, 63, 295-302.
https://doi.org/10.1016/j.proeng.2013.08.224
[15]  Wu, D., Huang, S. and Xin, J. (2008) Dynamic Compensation for an Infrared Thermometer Sensor Using Least-Squares Support Vector Regression (LSSVR) Based Functional Link Artificial Neural Networks (FLANN). Measurement Science and Technology, 19, Article ID: 105202.
https://doi.org/10.1088/0957-0233/19/10/105202
[16]  Galán, C.O., Torres, J.M., de Cos, F.J. and Lasheras, F.S. (2011) Comparison of GPS Observations Made in a Forestry Setting Using Functional Data Analysis—CMMSE 2010. International Journal of Computer Mathematics, 89, 402-408.
[17]  Dombeck, D.A., Graziano, M.S. and Tank, D.W. (2009) Functional Clustering of Neurons in Motor Cortex Determined by Cellular Resolution Imaging in Awake Behaving Mice. Journal of Neuroscience, 29, 13751-1360.
https://doi.org/10.1523/JNEUROSCI.2985-09.2009
[18]  Dai, W. and Genton, M.G. (2018) Multivariate Functional Data Visualization and Outlier Detection. Journal of Computational and Graphical Statistics, 27, 923-934.
https://doi.org/10.1080/10618600.2018.1473781
[19]  Grubbs, F.E. (1969) Procedures for Detecting Outlying Observations in Samples. Technometrics, 11, 1-21.
https://doi.org/10.1080/00401706.1969.10490657
[20]  Jantschi, L. (2019) A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested. Symmetry, 11, Article No. 835.
https://doi.org/10.3390/sym11060835
[21]  Ramsay, J. and Silverman, B. (2005) Functional Data Analysis. Springer, New York.
https://doi.org/10.1007/b98888
[22]  Martínez Torres, J., Pastor Pérez, J., Sancho Val, J., McNabola, A., Martínez Comesana, M. and Gallagher, J. (2020) A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland. Mathematics, 8, Article No. 225.
https://doi.org/10.3390/math8020225
[23]  Cuevas, A. and Fraiman, R. (1997) A Plug-In Approach to Support Estimation. The Annals of Statistics, 25, 2300-2312.
https://doi.org/10.1214/aos/1030741073
[24]  Cuevas, A., Febrero, M. and Fraiman, R. (2006) On the Use of the Bootstrap for Estimating Functions with Functional Data. Computational Statistics & Data Analysis, 51, 1063-1074.
https://doi.org/10.1016/j.csda.2005.10.012
[25]  Febrero, M., Galeano, P. and González-Manteiga, W. (2007) A Functional Analysis of NOx Levels: Location and Scale Estimation and Outlier Detection. Computational Statistics, 22, 411-427.
https://doi.org/10.1007/s00180-007-0048-x
[26]  Peng, L. and Qi, Y. (2008) Bootstrap Approximation of Tail Dependence Function. Journal of Multivariate Analysis, 99, 1807-1824.
https://doi.org/10.1016/j.jmva.2008.01.018

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