Are empirical equations an appropriate tool to assess separation distances to avoid odour annoyance?

Annoyance due to environmental odour exposure is in many jurisdictions evaluated by a yes/no decision. Such a binary decision has been typically achieved via odour impact criteria (OIC) and, when applicable, the resultant separation distances between emission sources and residential areas. If the receptors lie inside the required separation distance, odour exposure is characterised with the potential of causing excessive annoyance. The state-of-the-art methodology to determine separation distances is based on two general steps: (i) calculation of the odour exposure (time series of ambient odour concentrations) using dispersion models and (ii) determination of separation distances through the evaluation of this odour exposure by OIC. Regarding meteorological input data, dispersion models need standard meteorological observations and/or atmospheric stability typically on an hourly basis, which requires expertise in this field. In the planning phase, and as a screening tool, an educated guess of the necessary separation distances to avoid annoyance is in some cases sufficient. Therefore, empirical equations (EQs) are in use to substitute the more time-consuming and costly application of dispersion models. Because the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches. Otherwise, the resultant separation distance shape is simply given by a circle around the emission source. Here, an outline of selected empirical equations is given, and it is shown that only a few of them properly reflect the meteorological situation of a site. Furthermore, for three case studies, separation distances as calculated from empirical equations were compared against those from Gaussian plume and Lagrangian particle dispersion models. Overall, our results suggest that some empirical equations reach their limitation in the sense that they are not successful in capturing the inherent complexity of dispersion models. However, empirical equations, developed for Germany and Austria, have the potential to deliver reasonable results, especially if used within the conditions for which they were designed. The main advantage of empirical equations lies in the simplification of the meteorological input data and their use in a fast and straightforward approach

Generalised joint regression for count data: a penalty extension for competitive settings

We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by competitive settings, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal’s empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting competitive settings. Finally, the method is applied to football data, showing its benefits compared to the standard approach with regard to predictive performance

Postpartum behaviour as predictor of weight change from before pregnancy to one year postpartum

<p>Abstract</p> <p>Background</p> <p>Postpartum weight retention affects many women and increases the risk of becoming overweight. The research objective was to study modifiable factors contributing to weight change at one year postpartum.</p> <p>Methods</p> <p>In this prospective cohort, postpartum behavior, such as physical activity, sedentary behavior, sleep, and intake of total energy, total fat and saturated fatty acids of 118 Dutch women were assessed in 2003/2004 by self-report at 6 weeks, 6 and 12 months postpartum. Mean postpartum scores were computed for the behavioral measures. In linear regression models it was determined which factors were associated with average weight change from before pregnancy to one year postpartum. Furthermore, factors associated with substantial postpartum weight retention (≥ 5 kg) were also studied in logistic regression models.</p> <p>Results</p> <p>At one year postpartum, the average weight of participants had increased by 0.9 kg (SD 4.4). Moreover, 20% of the women retained ≥ 5 kg. Women who perceived themselves more physically active than others were almost ten times less likely to retain ≥ 5 kg than women who perceived themselves equally active (OR = 0.11, 95%CI: 0.02 - 0.66). Exceeding the guideline for saturated fatty acid intake (OR = 3.40, 95%CI: 1.04 - 11.11), total gestational weight gain (OR = 1.14/kg, 95%CI: 1.01 - 1.27), and not having completed post high school education (OR = 5.13, 95%CI: 1.66 - 15.90) increased the odds of retaining ≥ 5 kg.</p> <p>Conclusions</p> <p>Since one in five women had substantial weight retention postpartum, effective interventions for the prevention of weight retention are much needed. Future studies should evaluate whether interventions focusing on the identified modifiable postpartum factors are successful in reducing weight retention after childbirth.</p

Technische Zusammenfassung

Die Technische Zusammenfassung des APCC-Sonderberichts ″Landnutzung und Klimawandel in Österreich″ umfasst die Kernbotschaften der Kapitel 1–9. In ihr sind die Hauptaussagen zu den sozioökonomischen und klimatischen Treibern der Landnutzungsänderungen, zu den Auswirkungen von Landnutzung und -bewirtschaftung auf den Klimawandel, zu Minderungs- und Anpassungsoptionen im Kontext nachhaltiger Entwicklungsziele sowie zu Synergien, Zielkonflikten und Umsetzungsbarrieren von Klimamaßnahmen enthalten

Odour impact assessment by considering short-term ambient concentrations: A multi-model and two-site comparison

Short-term events are one of the specific aspects that differentiate odour nuisance problems from conventional air quality pollutants. Atmospheric dispersion modelling has been considered the gold standard to realise odour impact assessments and to calculate separation distances. Most of these models provide predictions of concentrations of a pollutant in ambient air on an hourly basis. Even when the hourly mean odour concentration is lower than the perception threshold, concentration peaks above the threshold may occur during this period. The constant peak-to-mean factor is nowadays the most widespread method for evaluating short-term concentrations from the long-term ones. Different approaches have been proposed in the scientific literature to consider non-constant peak-to-mean factors. Two prominent approaches to do so are the i) variable peak-to-mean factor which considers the distance from the source and atmospheric stability and the ii) concentration-variance transport. In this sense, the aim of this work is to compare the results of three different freely available dispersion models (namely, CALPUFF, LAPMOD and GRAL), which implement three distinct ways to evaluate the short-term concentration values. Two sites, one in Austria and the other in Italy, were selected for the investigation. Dispersion model results were compared and discussed both in terms of long-term (hourly) concentrations and short-term. An important outcome of this work is that the dispersion models provided more equivalent results for hourly mean concentrations, in particular in the far-field. On the contrary, the method to evaluate short-term concentrations can deliver disparate results, thereby revealing a potential risk of poor assessment conclusions. The utilistion of a multiangle methodological approach (dispersion models, study site locations, algorithms to incorporate short-term concentrations) allowed providing useful information for future studies and policymaking in this field. Accordingly, our findings call for awareness on how the use of a particular dispersion model and its sub-hourly peak calculation method can affect odour impact assessment conclusions and compliance demonstrations

New international handbook on the assessment of odour exposure using dispersion modelling

A new development towards the first worldwide guideline on the assessment of odour exposure by using dispersion modelling is taking its first steps. At this stage, there are many initiatives around the world related to odour dispersion modelling but there is no specific handbook or guidance document for odour modelling to our knowledge. Modelling odours is complex and many of the guidelines on modelling published around the world fall short in treating this vector. Odour modelling often requires forgetting traditional dispersion modelling operating modes and focusing on exposure. Odours are perceived in seconds or minutes, not hours, and this is key in calculating their impact in the ambient air. Most odour incidents are generated during calm or very low wind speeds which do not facilitate the dispersion of an odour and that makes modelling extremely challenging. Development of this guideline is an initiative promoted by over 50 experts around the globe in the area of modelling odours. The group is led by Carlos Diaz (Spain), Jennifer Barclay (New Zealand) and Günther Schauberger (Austria). The first meeting took place in August 2020, and there are planned monthly meetings. The aim of this paper is to report on the advances being made for this initiative