Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2017.04.036 |
Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area | |
Rahman, Md Mahmudur1; Mazaheri, Mandana1; Clifford, Sam1,2; Morawska, Lidia1 | |
2017-09-15 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2017 |
卷号 | 194 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | Quantifying and apportioning the contribution of a range of sources to ultrafine particles (UFPs, D < 100 nm) is a challenge due to the complex nature of the urban environments. Although vehicular emissions have long been considered one of the major sources of ultrafine particles is urban areas, the contribution of other major urban sources is not yet fully understood. This paper aims to determine and quantify the contribution of local ground traffic, nucleated particle (NP) formation and distant non-traffic (e.g. airport, oil refineries, and seaport) sources to the total ambient particle number concentration (PNC) in a busy, inner-city area in Brisbane, Australia using Bayesian statistical modelling and other exploratory tools. The Bayesian model was trained on the PNC data on days where NP formations were known to have not occurred, hourly traffic counts, solar radiation data, and smooth daily trend. The model was applied to apportion and quantify the contribution of NP formations and local traffic and non-traffic sources to UFPs. The data analysis incorporated long-term measured time-series of total PNC (D 6 nm), particle number size distributions (PSD, D = 8 to 400 mn), PM2.5, PM10, NOx, CO, meteorological parameters and traffic counts at a stationary monitoring site. The developed Bayesian model showed reliable predictive performances in quantifying the contribution of NP formation events to UFPs (up to 4 x 10(4) particles cm(-3)), with a significant day to day variability. The model identified potential NP formation and no-formations days based on PNC data and quantified the sources contribution to UFPs. Exploratory statistical analyses show that total mean PNC during the middle of the day was up to 32% higher than during peak morning and evening traffic periods, which were associated with NP formation events. The majority of UFPs measured during the peak traffic and NP formation periods were between 30-100 nm and smaller than 30 mn, respectively. To date, this is the first application of Bayesian model to apportion different sources contribution to UFPs, and therefore the importance of this study is not only in its modelling outcomes but in demonstrating the applicability and advantages of this statistical approach to air pollution studies. |
英文关键词 | Ambient ultrafine particles Bayesian statistical model Non-traffic sources Nucleated particle formation Urban area |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000405043700015 |
WOS关键词 | SIZE DISTRIBUTION ; NUCLEATION EVENTS ; EMISSION FACTORS ; AIR-POLLUTION ; SULFURIC-ACID ; EXPOSURE ; GROWTH ; POLLUTANTS ; DEPENDENCE ; QUALITY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/38530 |
专题 | 地球科学 |
作者单位 | 1.Queensland Univ Technol, Inst Hlth & Biomed Innovat, Int Lab Air Qual & Hlth, GPO Box 2434, Brisbane, Qld 4001, Australia; 2.Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, GPO Box 2434, Brisbane, Qld 4001, Australia |
推荐引用方式 GB/T 7714 | Rahman, Md Mahmudur,Mazaheri, Mandana,Clifford, Sam,et al. Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area[J]. ATMOSPHERIC RESEARCH,2017,194. |
APA | Rahman, Md Mahmudur,Mazaheri, Mandana,Clifford, Sam,&Morawska, Lidia.(2017).Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area.ATMOSPHERIC RESEARCH,194. |
MLA | Rahman, Md Mahmudur,et al."Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area".ATMOSPHERIC RESEARCH 194(2017). |
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