GIS-based modelling of odour emitted from the waste processing plant: case study

Sówka, Izabela; Grzelka, Agnieszka; Bezyk, Yaroslav; Miller, Urszula

doi:10.1051/e3sconf/20171700085

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…The basic assumption in the IDW method is that each sample affects the environment. In the IDW method, the value of the variable at the interpolation point is determined as the weighted average of the surrounding sampling points (Sówka et al, 2017). Thus, smell dispersion maps were developed with the IDW method.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Odor Distribution in Urban Landscape With GIS

Çeven

Belkayalı

Aydin

2022

Preprint

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A component of urban landscape, smell sources affect the urban atmosphere based on various activities. In this atmosphere, several unpleasant odors and pleasant smells are present and dispersion of these smells vary based on spatial attributes. Thus, in the present study, urban smellscapes in Kastamonu province urban center with various smellscapes and diverse physical structure were determined with human evaluator and smell measurement device to determine whether urban physical structure and climatic factors were effective on the dispersion modeled with the IDW method in GIS. Since the odor density changes depending on temperature and humidity, the study was carried out in all seasons for 1 year and serial measurements were analyzed for 1 month in August in the most suitable climatic period. The study findings demonstrated that smell sources and intensity varied based on spatial and climatic properties on a route with different occupancy types; thus, smell did not affect the space at different point but as an area. Therefore, the element of smell should be integrated into planning and design work based on the dispersion property of smell.

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Section: Methodsmentioning

confidence: 99%

Analysis of Odor Distribution in Urban Landscape With GIS

Çeven

Belkayalı

Aydin

2022

Preprint

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“…With this method, values at unknown measurement points are calculated as a weighted average of known measurement points. Its main assumption is that each point has a certain impact on its surroundings (Sówka et al, 2017a). This impact decreases with distance.…”

Section: Methodology and Research Areamentioning

confidence: 99%

“…One of the most commonly used are sensory methods, including dynamic olfactometry and fi eld olfactometry (Korczyński et al, 2011;Gębicki, Byliński & Namieśnik, 2016;Sówka, Pachurka, Bezyk, Grzelka & Miller, 2017b). In the latter case, the test results obtained at the measuring points can be used in the analysis of spatial distributions, which allow, among others assessment of variability of pollution concentrations and assessment of air quality (Wong, Yuan & Perlin, 2004;Sówka, Grzelka, Bezyk & Miller, 2017a;Núñez-Alonso, Pérez-Arribas, Manzoor & Cáceres, 2019). GIS-based model tools are used, among others, in the study of odour dispersion based on measurement data obtained by dynamic olfactometry and measurement data from the so-called fi eld inspections (Sówka et al, 2017a(Sówka et al, , 2017b.…”

Section: Introductionmentioning

confidence: 99%

“…In the latter case, the test results obtained at the measuring points can be used in the analysis of spatial distributions, which allow, among others assessment of variability of pollution concentrations and assessment of air quality (Wong, Yuan & Perlin, 2004;Sówka, Grzelka, Bezyk & Miller, 2017a;Núñez-Alonso, Pérez-Arribas, Manzoor & Cáceres, 2019). GIS-based model tools are used, among others, in the study of odour dispersion based on measurement data obtained by dynamic olfactometry and measurement data from the so-called fi eld inspections (Sówka et al, 2017a(Sówka et al, , 2017b. However, there have been no attempts to perform geostatistical analyses based on the results of odour concentration measurements by fi eld olfactometry.…”

Section: Introductionmentioning

confidence: 99%

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The use of ordinary kriging and inverse distance weighted interpolation to assess the odour impact of a poultry farming

Sówka¹,

Grzelka²,

Pawnuk³

et al. 2020

srees

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The aim of the study was to determine the usefulness of spatial data interpolation methods in analyses of the odour impact of animal husbandry facilities. The interpolation methods of data obtained from measurements using the field olfactometry technique were the ordinary kriging method (OK) and the inverse distance weighted method (IDW). The quality of the analyses that have been obtained indicates the potentially better use of the OK method in the presentation of spatial odour concentration distributions.

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“…The spatial analysis of odor intensity data collected from lab-based (dynamic) olfactometry [23] and field panels/assessors (human receptors) [24] have proved valuable in evaluating spatial pattern. Sówka et al [24] contend that the use of GIS for the analysis of odor data recorded using a field olfactometer is one of the most promising methodologies for the study of odor, yet few studies have employed these methods together. Similarly, Intarakosit [25] invoked spatial distribution of perceptible odors related to the agricultural application of biosolids.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Urban Odor with Field Olfactometry in Camden, NJ

et al. 2019

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Odor annoyance negatively impacts residents of communities adjacent to persistent nuisance industries. These residents, often with a high percentage of minority or otherwise marginalized residents, experience subjective and objective impacts on health and well-being; yet, reliable methods for quantifying and categorizing odors have been elusive. Field olfactometry is integral to the study of odor annoyance experienced by communities as it includes both qualitative (human perception) and quantitative (intensity measurement) dimensions of human odor experience and has been employed by municipalities in the U.S. to evaluate odor pollution levels. Cartographic visualization of odor data recorded using a field olfactometer offers further opportunity to evaluate potential patterns of odor annoyance, yet the use of field olfactometry and geographic information systems have not been frequently employed by geographers. By employing a mixed-methods approach to evaluate odor pollution, this study addresses the environmental justice context by quantifying and categorizing the presence of odor pollution in Waterfront South, a neighborhood in Camden, NJ previously identified for its disproportionate malodor burden. This study offers support to mixed methods research and the need for monitoring subjective and objective impacts in communities with compounding odor nuisance industries.

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GIS-based modelling of odour emitted from the waste processing plant: case study

Cited by 4 publications

References 6 publications

Analysis of Odor Distribution in Urban Landscape With GIS

Analysis of Odor Distribution in Urban Landscape With GIS

The use of ordinary kriging and inverse distance weighted interpolation to assess the odour impact of a poultry farming

Evaluating Urban Odor with Field Olfactometry in Camden, NJ

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