A software application for mapping livestock waste odour dispersion

Danuso, Francesco; Rocca, Alvaro; Ceccon, P.; Ginaldi, Fabrizio

doi:10.1016/j.envsoft.2015.03.016

Cited by 22 publications

(3 citation statements)

References 18 publications

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“…Some studies have used field-sampled measurements of odors like the ones we made to validate mathematical dispersion models like CALPUFF and ISC3/ISCST3 at places such as beef cattle feedlots [ 36 ] and commercial pig units [ 37 ], and typically find good agreement between models and field measurements [ 38 ]. New odor models continue to appear [ 39 ], and existing odor models are frequently refined [ 40 ], so better procedures and more datasets from gathering/analyzing odor data such as through studies like ours should lead to improved validation and thus better models. Odor measurements following VDI 3940 methods have also been statistically analyzed with Kriging to assess the spatial extent of odor nuisance [ 17 ], and our study employs Kriging for statistical analysis as well, but provides information about temporal variability in Kriging results (Figs 2 – 13 ) that is not presented by some other studies [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon

et al. 2018

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This study combines Ordinary Kriging, odor monitoring, and wind direction data to demonstrate how these elements can be applied to identify the source of an industrial odor. The specific case study used as an example of how to address this issue was the University Park neighborhood of Portland, Oregon (USA) where residents frequently complain about industrial odors, and suspect the main source to be a nearby Daimler Trucks North America LLC manufacturing plant. We collected 19,665 odor observations plus 105,120 wind measurements, using an automated weather station to measure winds in the area at five-minute intervals, logging continuously from December 2014 through November 2015, while we also measured odors at 19 locations, three times per day, using methods from the American Society of the International Association for Testing and Materials. Our results quantify how winds vary with season and time of day when industrial odors were observed versus when they were not observed, while also mapping spatiotemporal patterns in these odors using Ordinary Kriging. Our analyses show that industrial odors were detected most frequently to the northwest of the Daimler plant, mostly when winds blew from the southeast, suggesting Daimler’s facility is a likely source for much of this odor.

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Section: Discussionmentioning

confidence: 99%

Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon

et al. 2018

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“…These are mainly associated with agricultural, industrial and waste management sectors. Examples vary from wastewater collection and treatment (Zarra et al, 2019), to municipal solid waste management (Sarkar et al, 2003) but also food industries (Brancher and De Melo Lisboa, 2014), and livestock production, alongside many others (Danuso et al, 2015). Despite odors often are not a direct cause of toxicity, as they reach the population in concentration below the toxicity threshold (Piccardo et al, 2022;Blanes-Vidal et al, 2014), several studies correlated exposure to malodorous substances with negative physiological responses (Baldacci et al, 2015;Blanes-Vidal, 2015;Hooiveld et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The screening evaluation of environmental odors: a new dispersion modelling-based tool

Pelliconi,

Andretta,

Righi

2024

Preprint

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Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian Dispersion Models (GDM), which can be executed through user-friendly software that doesn’t need high computational power. However, their outputs tend to be excessively conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes, are based on Lagrangian Particle Dispersion Models (LPDM). These grant a more accurate modelling of the pollutants’ dispersion but are very demanding regarding both the needed users’ technical skills and high computing power. The present study aims to increase the accuracy of screening odor impact assessment, by identifying the correlation function of the outputs derived from the two simulation models. The case-study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The identified correlation functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This grants an accurate tool which can be used to address citizens’ concerns while saving workforce and technical resources.

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“…For instance, the sensation of smell is perceived mostly in hours when the PBL turbulence is low and therefore the atmospheric stability is high, i.e., at evening and early morning [17,28]. In these situations, the use of an atmospheric dispersion model appears to be crucial [29][30][31]. As a matter of fact, the majority of environmental odor legislations around the world lean upon models [32,33].…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment

Invernizzi

Capra

Sozzi³

et al. 2021

Applied Sciences

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For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, R90, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, σC, and concentration intensity, Ic, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of R90 is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6.

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A software application for mapping livestock waste odour dispersion

Cited by 22 publications

References 18 publications

Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon

Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon

The screening evaluation of environmental odors: a new dispersion modelling-based tool

Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment

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