气候变化领域集成服务门户(CCPortal): Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling

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DOI	10.1016/j.aeaoa.2019.100054
	Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling
	Brancher M.; Knauder W.; Piringer M.; Schauberger G.
发表日期	2020
ISSN	25901621
卷号	5
英文摘要	The annoyance potential of odours can be assessed using dispersion modelling, thereby delineating separation distances between pollution sources and nearby communities. It is common practice in this context to assume constant emissions over time, although odour emissions are often characterised by temporal variability. Here we show that the assumption of constant emissions can bias the separation distances towards underestimation, as compared to more realistic scenarios incorporating time-varying emissions. We identify three primary factors driving the level of such underestimation: wind direction frequency, degree of emission variability and percentile compliance level of odour impact criteria. Accordingly, the underestimation was more significant in the prevailing wind directions. With greater variability of the odour emission rate, the separation distances tended to be larger. The higher the percentile, the greater the underestimation of separation distances. In particular, the 90th percentile showed superior skill in counteracting the source emission variability when compared to the 98th, 99th and 99.5th percentiles. The findings are achieved using a Lagrangian particle dispersion model. Meteorological input data are due to locally-derived wind and turbulence measurements at a site in Central Europe (Austria). Discrete representation of odour emissions over time (hourly resolution) was accounted for by employing a Monte Carlo-based method (inverse transform sampling). This work provides a better understanding of the extent to which accounting for temporal variability in odour emissions can be most useful. © 2019 The Authors
英文关键词	Dispersion model; Environmental odours; Impact assessment; Odorous air pollution; Temporal variability; Variable emission rate
学科领域	Atmospheric movements; Dispersions; Inverse transforms; Monte Carlo methods; Odors; Dispersion modeling; Impact assessments; Lagrangian particle dispersion model; Odour emission rates; Prevailing wind directions; Temporal variability; Turbulence measurements; Variable emission rates; Air pollution; atmospheric pollution; dispersion; emission; environmental impact assessment; odor; temporal variation; turbulence; wind direction; air pollution; annoyance; article; Austria; odor; skill; Austria
语种	英语
scopus关键词	Atmospheric movements; Dispersions; Inverse transforms; Monte Carlo methods; Odors; Dispersion modeling; Impact assessments; Lagrangian particle dispersion model; Odour emission rates; Prevailing wind directions; Temporal variability; Turbulence measurements; Variable emission rates; Air pollution; atmospheric pollution; dispersion; emission; environmental impact assessment; odor; temporal variation; turbulence; wind direction; air pollution; annoyance; article; Austria; odor; skill; Austria
来源期刊	Atmospheric Environment: X
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120909
作者单位	WG Environmental Health, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Veterinärplatz 1, Vienna, A-1210, Austria; Central Institute for Meteorology and Geodynamics, Hohe Warte 38, Vienna, A-1190, Austria
推荐引用方式 GB/T 7714	Brancher M.,Knauder W.,Piringer M.,et al. Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling[J],2020,5.
APA	Brancher M.,Knauder W.,Piringer M.,&Schauberger G..(2020).Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling.Atmospheric Environment: X,5.
MLA	Brancher M.,et al."Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling".Atmospheric Environment: X 5(2020).