2020IOP Conference Series Earth and Environmental ScienceOpen access

Temporal-spatial Distribution Characteristics of Air Pollutants in the Pearl River Delta Region, China

Tao Jiang, Zhaoyang Liu, Jianping Wu, Wenpeng Zhao, Xunjiang Zhu

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Abstract

Abstract Studying the influences of meteorological factors on air pollutants (including SO2, NO2, O3 and PM10) and quantifying the relationships between them should enable further investigation on the variation of air pollutants in a region, especially their temporal-spatial distribution characteristics. We combined observed air pollutants data from ground-based monitoring sub-stations and relevant meteorological data in the Pearl River Delta region to investigate relationships between air pollutants and various meteorological factors, and then established regression models for each sub-station through principal component regression (PCR). According to these regression equations, the seasonal average temporal-spatial distribution of air pollutants is determined by inversion of the distance priority principle, and it provides strong support for the study of the spatial distribution and temporal variation of air pollutants in this region. Our conclusions are as follows: Different air pollutants have different spatial distribution characteristics with significant seasonal changes, and the concentration of each air pollutant in winter was higher than that in summer; PM10 levels in this region were higher than SO2, NO2 and O3.

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Abstract Studying the influences of meteorological factors on air pollutants (including SO2, NO2, O3 and PM10) and quantifying the relationships between them should enable further investigation on the variation of air pollutants in a region, especially their temporal-spatial distribution characteristics. We combined observed air pollutants data from ground-based monitoring sub-stations and relevant meteorological data in the Pearl River Delta region to investigate relationships between air pollutants and various meteorological factors, and then established regression models for each sub-station through principal component regression (PCR). According to these regression equations, the seasonal average temporal-spatial distribution of air pollutants is determined by inversion of the distance priority principle, and it provides strong support for the study of the spatial distribution and temporal variation of air pollutants in this region. Our conclusions are as follows: Different air pollutants have different spatial distribution characteristics with significant seasonal changes, and the concentration of each air pollutant in winter was higher than that in summer; PM10 levels in this region were higher than SO2, NO2 and O3.

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Available abstract

Abstract Studying the influences of meteorological factors on air pollutants (including SO2, NO2, O3 and PM10) and quantifying the relationships between them should enable further investigation on the variation of air pollutants in a region, especially their temporal-spatial distribution characteristics. We combined observed air pollutants data from ground-based monitoring sub-stations and relevant meteorological data in the Pearl River Delta region to investigate relationships between air pollutants and various meteorological factors, and then established regression models for each sub-station through principal component regression (PCR). According to these regression equations, the seasonal average temporal-spatial distribution of air pollutants is determined by inversion of the distance priority principle, and it provides strong support for the study of the spatial distribution and temporal variation of air pollutants in this region. Our conclusions are as follows: Different air pollutants have different spatial distribution characteristics with significant seasonal changes, and the concentration of each air pollutant in winter was higher than that in summer; PM10 levels in this region were higher than SO2, NO2 and O3.

Key concepts: Pollutant, Environmental science, Air pollutants, Spatial distribution, Air pollution, Geographically Weighted Regression, Criteria air contaminants, Spatial variability

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