Estimation of Air Pollutants using Time Series Model at Coalfield Site of India

A. Choudhary, P. Kumar
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Abstract

Assessment of air pollutants and quality is an intricate task because of dynamic nature, unpredictability and high inconsistency in space and time. In this study, a time series moving average (MA) model is employed to estimate air pollutants (PM2.5, PM10, NO2, NOX, O3, SO2 and CO) over the coalfield site of India. The estimated O3 with Adj. R2 = 0.958 was identified as the most accurate estimation followed by other estimated pollutants. Though, results for the estimated PM2.5 (Adj. R2 = 0.950) and NO2 (Adj. R2 = 0.949) were found almost similar to the results of O3 (Adj. R2 = 0.958). The estimated CO with Adj. R2 = 0.887 was identified lower among all the estimated pollutants was also found very well. The existing results of the study demonstrate that MA model permits us to precisely estimate daily basis pollutant concentrations, for the different sites of India.
利用时间序列模型估算印度煤田现场空气污染物
空气污染物和质量的评估是一项复杂的任务,因为它具有动态性、不可预测性和空间和时间上的高度不一致性。在本研究中,采用时间序列移动平均(MA)模型估计了印度煤田现场的空气污染物(PM2.5, PM10, NO2, NOX, O3, SO2和CO)。结果表明,臭氧的估计值最准确,相对值R2 = 0.958,其他污染物次之。PM2.5(相对值R2 = 0.950)和NO2(相对值R2 = 0.949)的估算结果与O3(相对值R2 = 0.958)的估算结果基本一致。在所有污染物中,相对值R2 = 0.887的CO估定值较低。现有的研究结果表明,MA模型允许我们精确地估计印度不同地点的日基础污染物浓度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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