气候变化领域集成服务门户(CCPortal): Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources

CCPortal

DOI	10.1016/j.atmosenv.2020.117832
	Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources
	Martín-Cruz Y.; Vera-Castellano A.; Gómez-Losada Á.
发表日期	2020
ISSN	1352-2310
卷号	243
英文摘要	The estimation of the background atmospheric concentration allows to assess local contributions and helping to the design of air quality improvement policies. Using clustering techniques and bivariate analysis, this study aims to characterize the background concentration of PM10 (particulate matter with an aerodynamic diameter less than or equal to 10 μm) and PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) in environments with heterogeneous emission sources. Background PM10 and PM2.5 pollution was characterized using Hidden Markov and Finite Mixture Models in four air quality monitoring stations, from 2011 to 2017. Average background concentrations in all stations were of 12.7 ± 2.2 μg m-3 for PM10 and 4.6 ± 0.4 μg m-3 for PM2.5. The contribution of background concentration to ambient pollution (both PM10 and PM2.5) was high (more than 40%) in all studied stations, being a 10% higher in background stations (Camping Temisas and Parque de San Juan) compared with stations influenced by an anthropogenic source (Castillo Romeral and San Agustín). Estimated background concentration showed significant differences among studied areas according to Kruskal-Wallis test (p < 0.001) and coefficients of divergence, which were greater than 0.2. PM10 and PM2.5 monthly profiles (concentration level) showed that the traffic urban station presented seasonality, probably due to the summer tourism, and daily profiles exhibited a differentiated bimodal distribution. The estimation of background concentrations in this study will allow to quantify local contributions from Saharan outbreaks and to study its possible effects on human health and marine biota. © 2020 Elsevier Ltd
关键词	Finite mixture models Hidden markov models Kruskal-wallis Particulate matter Representative background concentrations
语种	英语
scopus关键词	Aerodynamics; Air quality; Cluster analysis; Hidden Markov models; Particulate emissions; Tourism; Aerodynamic diameters; Air quality improvement; Air quality monitoring stations; Atmospheric concentration; Background concentration; Clustering techniques; Finite mixture models; Heterogeneous emissions; Particles (particulate matter); aerodynamics; air quality; anthropogenic source; atmospheric chemistry; cluster analysis; concentration (composition); estimation method; Markov chain; particle size; particulate matter; air quality; article; biota; bivariate analysis; camping; hidden Markov model; human; Kruskal Wallis test; particulate matter 10; particulate matter 2.5; quantitative analysis; seasonal variation; summer; tourism; Puerto Rico; San Juan [Puerto Rico]
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248891
作者单位	Department of Process Engineering, School of Industrial and Civil Engineering, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Gran Canaria, 35017, Spain; European Commission, Joint Research Centre (JRC), Seville, Spain
推荐引用方式 GB/T 7714	Martín-Cruz Y.,Vera-Castellano A.,Gómez-Losada Á.. Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources[J],2020,243.
APA	Martín-Cruz Y.,Vera-Castellano A.,&Gómez-Losada Á..(2020).Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources.ATMOSPHERIC ENVIRONMENT,243.
MLA	Martín-Cruz Y.,et al."Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources".ATMOSPHERIC ENVIRONMENT 243(2020).