Abstract
Air pollution has become one of the most serious environmental problems in the world. Considering Beijing and six surrounding cities as main research areas, this study takes the daily average pollutant concentrations and meteorological factors from 2 December 2013 to 30 June 2017 into account and studies the spatial and temporal distribution characteristics and the relevant relationship of particulate matter smaller than 2.5 μm (PM2.5) concentrations in Beijing. Based on correlation analysis and geo-statistics techniques, the inter-annual, seasonal, and diurnal variation trends and temporal spatial distribution characteristics of PM2.5 concentration in Beijing are studied. The study results demonstrate that the pollutant concentrations in Beijing exhibit obvious seasonal and cyclical fluctuation patterns. Air pollution is more serious in winter and spring and slightly better in summer and autumn, with the spatial distribution of pollutants fluctuating dramatically in different seasons. The pollution in southern Beijing areas is more serious and the air quality in northern areas is better in general. The diurnal variation of air quality shows a typical seasonal difference and the daily variation of PM2.5 concentrations present a “W” type of mode with twin peaks. Besides emission and accumulation of local pollutants, air quality is easily affected by the transport effect from the southwest. The PM2.5 and PM10 concentrations measured from the city of Langfang are taken as the most important factors of surrounding pollution factors to PM2.5 in Beijing. The concentrations of PM10 and carbon monoxide (CO) concentrations in Beijing are the most significant local influencing factors to PM2.5 in Beijing. Extreme wind speeds and maximal wind speeds are considered to be the most significant meteorological factors affecting the transport of pollutants across the region. When the wind direction is weak southwest wind, the probability of air pollution is greater and when the wind direction is north, the air quality is generally better.
Highlights
Ambient fine particulate matter smaller than 2.5 μm (PM2.5) is a major environmental problem and is harmful to human health [1,2]
Based on correlation analysis and geo-statistics techniques, this paper studies the inter-annual, seasonal, diurnal variation trends, and temporal spatial distribution characteristics of PM2.5 concentration in Beijing
The Spatial Center Statistics focused on depicting spatial distribution, which was mainly realized by calculating the basic parameters of the distribution, while the Exploratory Spatial Data Analysis emphasized the description of data, the identification of data statistical characteristics, and the preliminary judgment of the structure of the data through relevant assumptions, aimed at revealing spatial data characteristics, identifying outliers or regions, exploring spatial association patterns, recognizing accumulate or hotspot areas, implementing spatial zoning, and discovering spatial heterogeneity through geographical visualization
Summary
Ambient fine particulate matter smaller than 2.5 μm (PM2.5) is a major environmental problem and is harmful to human health [1,2]. Previous studies have shown that meteorological factors, including those of wind speed, wind direction, precipitation, relative humidity, and atmospheric pressure, have a very significant impact on air quality. In most studies on the temporal and spatial distribution characteristics and the relevant relationship of PM2.5 concentrations, only local pollutants and meteorological factors were considered [17,19]. We consider Beijing and six surrounding cities as main research areas, taking the daily average pollutant concentrations and meteorological elements from 2 December 2013 to 13 October 2017 into account and study the spatial and temporal distribution characteristics and the relevant relationship of PM2.5 concentrations in Beijing. The relevant relationships between PM2.5 and major local pollutants, surrounding pollutants, and meteorological factors are analyzed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
