The Italian pilot for the HORIZON 2020 D-NOSES Project: combining citizen science and dispersion modelling to identify odour sources in the municipal area of Castellanza

    This work aims to describe the Italian pilot study carried out within the frame of the H2020 D-NOSES project and discuss the opportunity to integrate different approaches in order to deal with complex situations. The pilot study was carried out in the municipal area between Castellanza, Marnate, and Olgiate Olona, where an odour problem has been lamented for years.

    As a first step of the project, 4 industries were identified as potential odour sources: a chemical plant, the municipal WWTP, a textile and dyeing industry, and the WWTP connected to the dyeing industry. In order to monitor the odour problem, it was proposed to combine the application of the D-NOSES methodology with the “traditional” way involving olfactometric measurements and dispersion modelling, the latter being the approach foreseen by the Regional guideline. The comparison of the citizens’ observations and the model outcomes allowed to verify the compatibility of the odour observations with the emissions from the two WWTPs.

B.J. Lotesoriere*, L. Capelli

Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”; P.za Leonardo da Vinci 32, 20133 Milano, Italy. *Beatrice Lotesoriere

 

   Competing interests: The author has declared that no competing interests exist.

   Academic editor:  Carlos N. Díaz.

   Content quality: This paper has been peer-reviewed by at least two reviewers. See the scientific committee here.

   Citation:  B.J. Lotesoriere, L. Capelli, The Italian pilot for the HORIZON 2020 D-NOSES Project: combining citizen science and dispersion modelling to identify odour sources in the municipal area of Castellanza, 9th IWA Odour& VOC/Air Emission Conference, Bilbao, Spain, www.olores.org.

   Copyright:  2022 Olores.org. Open Content  Creative Commons license. It is allowed to download, reuse, reprint, modify, distribute, and/or copy articles on Olores.org website, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

   ISBN: 978-84-09-37032-0

   Keyword: Odour impact assessment, dynamic olfactometry, multiple sources, integrated approach, odour observations.

 

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Abstract

   This work has the aim to describe the Italian pilot study carried out within the frame of the H2020 D-NOSES project, and discuss the opportunity to integrate different approaches in order to deal with complex situations. The pilot study was carried out in the municipal area between Castellanza, Marnate, and Olgiate Olona, where an odour problem has been lamented for years. Politecnico di Milano was involved with the purpose of identifying the origin of the odours perceived by the citizens. As a first step of the project, 4 industries were identified as potential odour sources: a chemical plant, the municipal WWTP, a textile and dyeing industry, and the WWTP connected to the dyeing industry. In order to monitor the odour problem, it was proposed to combine the application of the D-NOSES methodology with the “traditional” way involving olfactometric measurements and dispersion modelling, the latter being the approach foreseen by the Regional guideline. The comparison of the citizens’ observations and the model outcomes allowed to verify the compatibility of the odour observations with the emissions from the two WWTPs. Moreover, both the results of the olfactometric measurements and the analysis of the odour observations pointed out the absence of a biunivocal correlation between the presence of odours in the studied area and a specific production by the chemical plant, which had been suspected to be the origin of the odour problems.

 

  1. Introduction

   In recent years, citizens have become more sensitive to problems related to air quality and odours deriving from industrial and human activities because of the evercloser proximity between industrial and residential areas. Monitoring odours is particularly complex, because it entails the need to objectify a sensation (Bax et al., 2020; Conti et al., 2020). In this context, the overall aim of the H2020 D-NOSES project is to develop and validate a methodology for odour pollution management based on a bottom-up approach involving “Extreme citizen science”, to engage diverse groups of citizens, irrespective of their literacy or education level, in every step of the monitoring process (Paleco et al., 2021). This approach focuses on using participatory strategies for citizens involvement, and the adoption of a “quadruple-helix” model, thus encouraging collaboration between all stakeholders (e.g., citizens, local governments,
industries and experts in odours), and the co-creation of practical and balanced solutions (Arias et al., 2018). 

   Within the frame of this project, 10 pilot studies are being carried out in 10 different countries in order to develop and validate the methodology.

   In the case of Italy, the pilot study involved 3 small Municipalities located in Northern Italy: Castellanza, Olgiate Olona, Marnate, which belong to the Olona valley crossed by the Olona river, and which comprise a total of ca. 35’000 inhabitants. Because of the high industrialization of the area and the consequent pollution of the Olona river, the problem of odours in the study area has been a known problem for years. A non-organized collection of complaints started already in 2016: the number of complaints clearly shows that there is an unsolved (a non-identified) odour problem on the territory.

   Based on a preliminary analysis of the collected odour complaints, it was possible to identify 4 plants as potential sources of the lamented odour nuisance: a chemical plant, the municipal WWTP, a textile and dyeing industry, and the WWTP connected to the dyeing industry. The chemical plant has been deemed particularly worthy of attention because of the common belief that a particular product of the chemical plant is the principal responsible of the presence of malodours in the area, releasing odorous chemical substances in the wastewaters treated by the municipal WWTP.

   Regarding the design of the project, it has to be considered that, although in Italy there is not a national regulation on odours, in the region of Lombardy (where the 3 municipalities are located) there is a specific regional guideline regarding the management of odour issues since 2012. This guideline foresees the execution of olfactometric analysis followed by dispersion modelling in order to quantify odour emissions and their impact on the territory.

   Thus, for the purpose of investigating the cause of the odour nuisance, it was decided to adopt an integrated approach, involving the new D-NOSES methodology with the “traditional” top-down approach involving olfactometric measurements and dispersion modelling (Capelli et al., 2011), as it is foreseen by the specific guideline about odours of the region of Lombardy (Cusano et al., 2010), where the 3 Municipalities are located. This is very important on one hand to follow the principles of the guideline, but also to provide scientific sound experimental data to validate the D-NOSES methodology.

  This work thus aims to describe the Italian pilot study, thereby pointing out the original aspects of the proposed integrated approach.

 

 2. Materials and methods

   In this case, the research question to be answered was: “Where does the odour come from?”. The pilot was thus designed with the aim to answer this question, by combining the “top-down” approach with the “bottom-up” approach.

   The top-down approach is based on the quantification of the odour emissions by means of dynamic olfactometry according to EN 13725:2003, and the simulation of their diffusion into the atmosphere by dispersion modelling (Cusano et al., 2010).

   In this study, olfactometric campaigns were repeated in different conditions and seasons, to also characterize the variability of the emission sources according to different weather conditions (i.e. higher/lower temperatures). All the sampling campaigns were carried out when the production of the suspected product related to the chemical plant was active to investigate the hypothesis of its influence on the odour impact on the territory. The choice of the most significant sources to be implemented in the dispersion modelling has been based on the guideline of the Lombardy region, which states that a source is relevant if the odour concentration is higher than 80 ouE/m3 and the odour emission rate is higher than 500 ouE/s.

   For the dispersion modelling we used CALPUFF, processing the emission data derived from the olfactometric campaigns together with local meteorological data and soil profile data.

   The bottom-up approach related to the D-NOSES methodology lies on the citizen science approach, which has the aim to assess odours based on citizens’ perceptions. The approach used for the Italian pilot highlights the importance of a training phase before starting the data collection, with the purpose of empowering citizens and providing them with the necessary background to make the odour reports correctly. Firstly, a public meeting was promoted to collect volunteers’ adhesions to the project. Then, three theoretical and practical trainings were organized, in order to engage citizens and increase their sense of responsibility towards the project as well as their knowledge of odour pollution problems and, consequently, the reliability of their observations. After this training phase, we started the collection of the citizens’ observations by means the Odour Collect app, which allows to record the position, the type, and the intensity of the odours perceived. The data collection phase lasted from May, 14th to December, 31st, 2020. The χ2 test was implemented to investigate the influence of external factors in the distribution of the citizens’ observations, comparing the observed distribution with the expected one, and thus looking for the most relevant differences, which need to be further investigated (Franke et al., 2012).

   Finally, the odour observations were compared punctually with the outcomes of the odour dispersion models, thereby verifying their reciprocal compatibility, with the purpose of investigating if, by falling within the odour plume of one (or more) of the investigated plants, the odour observation was attributable to its emissions. The odour observations were considered attributable to the emissions of one plant (“yes”) if they fell within the modelled odour plume, whereas they were considered as not related to any of the plants under investigation if the modelled plume was in a different direction. Moreover, there are situations in which the observations were slightly out of the modelled plume, but still considered as attributable to it (“maybe”), since odour perceptions can be related to fluctuations that are not accounted for in the model outputs, which are based on hourly averages.

  

 3. Results and discussion

The olfactometric analyses carried out on the 4 plants under investigation enabled the quantification of their odour emissions in terms both of odour concentration and of odour emission rate (OER). Based on the previously mentioned criteria fixed by the regional guideline, it was possible to identify the most significant odour sources of each plant, to be implemented in the dispersion model (Lotesoriere et al., 2021).

  According to these criteria, the odour emissions from the textile and dyeing industry turned out to be negligible. The chemical plant has one main odour emission, corresponding to the chimney conveying all the emissions from the plant process units, which has an OER of ca. 1800 ouE/s, and is thus not negligible. Regarding the industrial WWTP connected to the textile industry, the only non-negligible odour emissions are the ones from the homogenization tank (with an odour concentration ranging from 130 ouE/m3 in winter to 1200 ouE/m3 in summer) and those from the chemical-physical reaction tank (ca. 20’000 ouE/m3 in both seasons). However, due to its small surface of 3.5 m2, the OER of the latter is just close to 500 ouE/s.

   Finally, for the municipal WWTP, the main odour sources correspond to the wastewater arrival, the fine screening, and the sand removal. It should be highlighted that these tanks are all enclosed in sheds, so they were implemented as volume sources in the dispersion model. Moreover, it is important to highlight that the olfactometric results point out two clearly distinguishable conditions: a first condition of low odour emissions, with odour concentration values in the range of dozens or hundreds of odour units (July and December 2020), and a second condition with measured odour concentration values being at least one order of magnitude higher (September 2020). This variability cannot be explained by the meteorological conditions, since the campaigns of July and September, which were both characterized by high temperatures (> 30°C), produced very different results. Also, the common belief regarding a correlation between a specific production by the chemical plant and the increase of odour emissions at the WWTP is not supported by these results, since the “incriminated” production was active during all the olfactometric campaigns.

   The absence of a two-way correlation between the activities of the chemical plant and the presence of malodours on the territory is also proven by the analysis of the citizens’ observations acquired during the data collection phase: 400 odour observations were registered between May, 14th to December, 31st, 2020.

Indeed, as can be seen from Figure 1, left, there is no correlation between the number of observations and the concentration of aldehydes in the wastewater of the chemical plant, which is directly related to the “suspected” product. There are several situations in which the production at the chemical plant was active, but no odours were reported by the citizens. However, if the same analysis is carried out on a weekly basis (Figure 1, right), a certain correlation can be observed: during the weeks with a higher number of odour observations, the production at the chemical plant was active. This unexpected behaviour could be explained by the presence of another contributing factor, which has not been identified yet, which, together with the wastewater of the chemical plant, causes the generation of intense and unpleasant odours at the municipal WWTP.

Lotesoriere 01 Fig. 1.: Correlation between the number of odour observations and the concentration of aldehydes in the wastewaters of the chemical plant on a daily basis (left) and on a weekly basis (right)

   The punctual comparison between single odour observations and model outputs shows that there are 141 observations out of 400 (i.e. ca. 36% of the total) not correlated with the emissions of any of the considered plant. An example of this condition is shown in Figure 2, left. This absence of correlation may be explained hypothesizing the presence of other odour sources, different from the 4 plants investigated, causing some odour events.

   By critically analysing the odour observations collected, it was possible to highlight the presence of 34 “anomalous” observations, occurring always from the same position, and attributed always to the category “wastewater”, despite being not compatible with the emissions of any of the WWTP involved in the study. These observations were thus discarded for further data analysis. Finally, the odour reports falling within or close to the plumes of the considered plants allowed to determine the provenance of the observed odours. Figure 2, right, shows that 43% of the odour observations are attributable to the municipal WWTP, 41% to the industrial WWTP connected to the textile industry, and 11% to both of them. These results clearly identify the two WWTP as the main sources of the odours on the study area.

41 Lotesoriere 02  41 Lotesoriere 02

Fig. 2.: Example of odour observation not attributable to the odour emissions from the studied plants (left), and results of the comparison of the odour observations with the dispersion model, as percent attributions of the observations falling within or close to the odour plumes of the studied plants (right).

 

 4. Conclusions

   The Italian pilot study carried out within the D-NOSES project allowed to deeply investigate the causes of the presence of malodours in the area of Castellanza, Olgiate
Olona and Marnate, where an odour problem has been lamented for years.

   The situation of the odour emissions in the study area is particularly complex, involving different plants having variable emissions over time. In such a complex situation, the integration of the approach foreseen by the Regional guideline (dynamic olfactometry + dispersion modelling) with the citizen science approach proposed by the D-NOSES project, allowed to obtain a more complete picture of the odour problem, and to investigate its causes.

   The comparison of the citizens’ observations with the model results enabled to identify the two WWTP as the main sources of the odours on the study area.

   Moreover, the results of the study prove that the “suspected” product of the chemical plant is not strictly correlated with the citizens’ odour perceptions, thus pointing out the possible presence of another – not yet identified – factor, which, together with the wastewaters of the chemical plant, causes the generation of intense and unpleasant odours at the municipal WWTP. Currently, the research activity is going on with the aim of identifying this unknown factor.

 

 5. Acknowledgements

This project has received funding from the European Union’s HORIZON 2020 research and innovation programme under grant agreement No 789315.

 

 6. References

   Arias, R., Capelli, L., Diaz, C. 2018. A new methodology based on citizen science to improve environmental odour management. Chemical Engineering Transactions 68, 7-12.

   Bax, C., Sironi, S., & Capelli, L. 2020. How can odors be measured? An overview of methods and their applications. Atmosphere, 11, 92.

   Capelli, L., Sironi, S., Del Rosso, R., Céntola, P., Rossi, A., Austeri, C. 2011. Olfactometric approach for the evaluation of citizens’ exposure to industrial emissions in the city of Terni, Italy. Sci Total Environ 409, 595-603.

   Conti, C., Guarino, M., & Bacenetti, J. 2020. Measurements techniques and models to assess odor annoyance: A review. Environ Int 134, 105261.

   Cusano, G., Licotti, C., Sironi, S., Capelli, L., Rossi, A.N., Il Grande, M. 2010, Odour regulation in Italy: the regional guidelines on odour emissions in Lombardia, Chemical Engineering Transactions 23, 19-24.

   Franke, T.M., Ho T., Christie, C.A. 2012. The chi-square test: Often used and more often misinterpreted. Am J Eval 33, 448-458.

   Lotesoriere, B. J., Giacomello, A. D., Bax, C., & Capelli, L. 2021. The Italian Pilot Study of the D-noses Project: an Integrated Approach Involving Citizen Science and Olfactometry to Identify Odour Sources in the Area of Castellanza (VA). Chemical Engineering Transactions 85, 145-150.

   Paleco, C., García Peter, S., Salas Seoane, N., Kaufmann, J., Argyri, P. 2021. Chapt. 14: Inclusiveness and Diversity in Citizen Science. In: The Science of Citizen science. Springer, Switzerland. 

 

 
 

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