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Title: Analyzing the impact of heating emissions on air quality index based on principal component regression
Authors: Li, H
You, S
Zhang, H
Zheng, W
Lee, WL 
Ye, T
Zou, L
Keywords: Air quality index
Heating emissions
Principal component regression
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Issue Date: 2018
Publisher: Elsevier Ltd
Source: Journal of cleaner production, 2018, v. 171, p. 1577-1592 How to cite?
Journal: Journal of cleaner production 
Abstract: The heat source types in China were not limited to the coal, other types of energy were introduced into the heating energy consumption in recent years, and therefore the specific regional impact of the heating emissions could not be obtained accurately according to the current achievement. Based on this, this study aimed to quantify the impact of the heating emissions on the air quality in an efficient and accurate way. A new method was proposed for investigating the quantitative impact of the heating emissions on the air quality index in this paper and its assumptions and accuracy were verified as well. The data employed in this method is extracted from Ministry of Environmental Protection Data Centre, Qingyue open source and Shanghai Central Meteorological Observatory. The key prerequisites of the proposed method were discussed in this study. By calculating, the averaged absolute difference of the meteorological parameters, namely Clo, WD, WS and P, is 3.58%, 2.40%, 3.21% and 3.94%, respectively. The difference ratio of the industrial emissions relative to the total emissions is as low as 4.3%. The optimal principal component number is six for the proposed method, which can satisfy the accuracy requirement with minimum workload. Compared with the results in previous similar studies, the proposed method showed excellent performance in analyzing the regional impact of the heating emissions on ambient air quality in China. Moreover, a total of 64 North cities in China were analyzed based on the proposed method to promote the knowledge on the influence of heating emissions on the environmental quality and the related parameters were provided in this study.
ISSN: 0959-6526
DOI: 10.1016/j.jclepro.2017.10.106
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