A Chain Ratio Exponential-Type Compromised Imputation for Mean Estimation: Case Study on Ozone Pollution in Saraburi, Thailand

Kanisa Chodjuntug,Nuanpan Lawson,Aera Thavaneswaran

doi:10.1155/2020/8864412

Kanisa Chodjuntug, Nuanpan Lawson + Show 1 more

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https://doi.org/10.1155/2020/8864412

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Abstract

Due to its impact on health and quality of life, Thailand’s ozone pollution has become a major concern among public health investigators. Saraburi Province is one of the areas with high air pollution levels in Thailand as it is an important industrialized area in the country. Unfortunately, the August 2018 Pollution Control Department (PCD) report contained some missing values of the ozone concentrations in Saraburi Province. Missing data can significantly affect the data analysis process. We need to deal with missing data in a proper way before analysis using standard statistical techniques. In the presence of missing data, we focus on estimating ozone mean using an improved compromised imputation method that utilizes chain ratio exponential technique. Expressions for bias and mean square error (MSE) of an estimator obtained from the proposed imputation method are derived by Taylor series method. Theoretical finding is studied to compare the performance of the proposed estimator with existing estimators on the basis of MSE’s estimators. In this case study, the results in terms of the percent relative efficiencies indicate that the proposed estimator is the best under certain conditions, and it is then applied to the ozone mean estimation for Saraburi Province in August 2018.

Highlights

Air pollution is a global problem which results in negative effects on both the environment and human health
We propose to improve the compromised imputation method by using the chain ratio-type exponential technique and its corresponding estimator. e bias and the mean square error have been obtained to the first degree of approximation using the Taylor series method. e efficiency of the proposed estimator is compared with some existing estimators on the basis of MSE in order to obtain the certain conditions for application of proposed estimator
We found that the O3 concentration data had missing values, so it was taken as a variable of interest Y, and PM10 concentration data was taken as a variable of auxiliary X. e following values were obtained for the considered variables: N 744, n 580, r 545, yr 13.55, xn 69.33, xr 66.29, sy 12.45, sx 32.65, cy 0.92, and cx 0.49

Summary

Introduction

Air pollution is a global problem which results in negative effects on both the environment and human health. Many researchers have found that air pollution is associated with mortality and morbidity from lung cancer, respiratory, cardiovascular diseases, and exacerbation of chronic respiratory conditions [1, 2]. Moultion and Yang [3] have shown that air pollution is correlated with Alzheimer’s disease and other neurodegenerative disorders. From the World Health Organization’s report in 2018, air pollution caused approximately 4.2 million deaths [4]. Erefore, air pollution is a major public health concern; monitoring and measuring the quality of air is critical. As shown in the PCD’s Air Quality Management Division report in 2015, O3 and PM10concentrations were higher than standard levels in almost every province [5]

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