# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
## 1 Introduction
The results of this paper emphasize the need for additional research to better analyze the influence of different prognostic weather models, such as MM5 and WRF models. Moreover, considering that CALPUFF has been widely used in the field of odor and accepted by regulatory agencies for environmental impact assessments, permitting, and compliance purposes, it is important to assess how the choice of meteorological data may affect simulated odor impact maps. In view of this, the present research discusses a comparative study of the results of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic meteorological data, and blended prognostic and measured data. These configurations will be referred to as CALPUFF \"OBS\" (surface and upper air meteorological observations), CALPUFF \"NO-OBS\" (WRF prognostic model for surface and upper air data), and CALPUFF \"HYBRID\" (wind fields generated by CALMET with blended prognostic and measured meteorological data). Odor exposure levels are simulated with each input meteorological setting for two different types of emission sources: a point source (i.e., a conveyed emission from an odor emission abatement system) and an area source (i.e., a wastewater tank), both in Italian and Cuban domains. The results are analyzed according to the Regional Guideline of Lombardy [33], which prescribes the computation of the 98 th percentile of odor peak concentration values on an annual basis. Despite the absence of a universal approach for defining short-term peak concentration, a constant factor of 2.3, to be multiplied by the hourly average concentration returned from the model, is suggested [33]. Due to the lack of a technical standard in Cuba in the field of odor, the aforementioned criterion has been considered in the current work for both Cuban and Italian domains. In summary, the purpose of this study is to compare the results of odor dispersion modeling by implementing three different input meteorological settings (i.e., CALPUFF \"NO-OBS\", CALPUFF \"OBS\", and CALPUFF \"HYBRID\") in two different simulation domains (northern Italy and Cuba) for two different types of emission sources. The novelty of this research is mainly related to the pollutant simulated. Indeed, in the case of \"traditional\" contaminants, concentrations are typically post-processed based on average or maximum concentrations. However, with odor, the main peculiarity compared to classical pollutants is that to characterize the instantaneous perception of the human nose, it is necessary to refer to short-term peak concentrations. Further details can be found in [PERSON] et al. (2020) [23]. Additionally, to the best of the authors' knowledge, this is the first investigation on the subject with two kinds of sources characterized by different emission mechanisms, exploring how they can be influenced by meteorology in different ways. Finally, the implementation of two distinct simulation domains provides a broad perspective on the research. Despite similar orography, these two domains have very different typical meteorological conditions: Cuba is characterized by a tropical climate with high temperatures and abundant rainfall year-round, while the investigated Italian region (i.e., Po Valley) has a temperate climate with moderate temperatures. Precipitation in Cuba is frequent and intense, particularly during the monsoon season, whereas precipitation in the Po Valley is more evenly distributed throughout the year with seasonal variations. Temperature variations are more pronounced in the Po Valley, with distinct seasonal changes, compared to Cuba, where temperatures are more consistent throughout the year. The typical climate of the Po Valley is characterized by low winds, which are particularly weak in the winter months; winds in Cuba are often more intense, with extreme weather phenomena such as hurricanes that are quite common.