# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
## 1 Introduction  
Due to rapid population and industrial growth in recent decades, the number of contaminants released into the atmosphere is rapidly growing [1; 2]. In this context, complaints about annoyance due to environmental odors, typically emitted by industrial and agricultural activities, are a common way for people to express dissatisfaction to authorities [3; 4; 5]. Determining the extent of chemical exposure, particularly odor [6], becomes a crucial concern as we learn more about atmospheric pollution and its effects on citizens' health, the ecosystem, and well-being [7; 8]. Odors are currently monitored and regulated in many nations [9]. The need to control odor impacts involves the development and application of specific methods to assess ground-level odor concentration [4; 10; 11; 12; 13]. In this sense, dispersion modeling is a valuable technique for replicating the spatial-temporal distribution of contaminants released by a single source to estimate areas of population exposure as well as ground-level concentrations of contaminants [14; 15; 16; 17; 18; 19; 20; 21; 22]. The Lagrangian Gaussian puff model CALPUFF, listed by the U.S. Environmental Protection Agency (EPA) as an alternative model for assessing the near-field transport of pollutants, has been widely used and accepted by regulatory agencies for environmental impact assessments [23]. In the CALMET/CALPUFF modeling system, the meteorological pre-processor CALMET is used to produce 3D meteorological data (e.g., wind and temperature fields) through advanced elaborations of available surface and upper air measurements, prognostic data from meso-scale models (e.g., WRF model), and geophysical data [24; 25; 26]. The trustworthiness of dispersion models is influenced by various sources of uncertainty. First, the dispersion model and its parametrizations (e.g., internal model parameters, dispersion parameterizations, deposition, etc.) are important sources of uncertainty. Moreover, different studies focus on models' sensitivity [6; 23; 27; 28; 29], highlighting the influence of different parameters (e.g., stack temperature, source diameter) related to the emission source. Another significant source of uncertainty is represented by the input meteorological data used to run the simulations. Indeed, the dispersion of pollutants in the atmosphere is intricately linked to various meteorological parameters. Wind speed and direction play a significant role in determining the transport of pollutants. Higher wind speeds facilitate the dispersion of pollutants over larger distances, while wind direction determines the trajectory and potential impact areas. Atmospheric stability is another critical factor. Under stable conditions, where the air near the surface is cooler and denser than aloft, pollutants may be trapped near the ground, leading to higher concentrations and localized pollution hotspots. In contrast, unstable conditions promote vertical mixing, allowing pollutants to disperse more evenly throughout the atmosphere. Temperature and humidity also influence pollutant dispersion. Temperature inversions, where warmer air sits above cooler air near the surface, can hinder vertical mixing and trap pollutants beneath the inversion layer. Humidity affects the solubility of pollutants and can influence their removal from the atmosphere through processes like wet deposition. Until now, the existing literature has documented some studies about the influence of the choice of meteorological data on atmospheric dispersion for classical pollutants [30; 31], revealing that atmospheric model predictions are significantly over/underestimated depending on the choice of input meteorological settings. However, as far as an odor impact assessment is concerned, to the best knowledge of the authors, only one study partially addresses this topic [32]. [PERSON] et al. [32] evaluated the effect of three different meteorological inputs on odor impact while considering a single type of source.