# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
## 4 Conclusions  
The present investigation provides information on how different input meteorological models may affect the results of odor dispersion modeling. In this regard, the study focuses on comparing CALPUFF results by setting different CALMET input meteorological data: surface and upper air observational meteorological data (\"OBS\" mode), 3D prognostic meteorological data (\"NO-OBS\" mode), and blended prognostic and measured single station observational meteorological data (\"HYBRID\" mode). For the simulations, two different domains (northern Italy and Cuba) and two different types of emission sources were investigated. The elaboration of the results refers to the odor impact criteria in force in some Italian regions by computing the 98 th percentile of odor peak concentration values on an annual basis. From the model results, it turns out that in the case of an area source, \"OBS\" and \"HYBRID\" simulations are very similar, while the \"NO-OBS\" mode slightly underestimates odor impact compared to the other simulations. For both Cuban and Italian domains, this evidence may be attributable to the lower percentage of wind calms in the \"NO-OBS\" mode. On the contrary, for a point source, all the simulation runs, \"NO-OBS\", \"OBS\", and \"HYBRID\", are comparable in the Italian and Cuban domains. The different behavior, compared to an area source, may be due to the different emissive and dispersion mechanism, characterized by the plume rise in the case of a point source. As previously mentioned, observational data from the Cuban meteorological station show a significant percentage of invalid data (i.e., almost 20%). Given the difficulty of running and maintaining the current network of Cuban weather stations, it is expected that the quality and the volume of data may be restricted. To enhance and optimize the performance of the \"NO-OBS\" model, it may be helpful to implement additional surface stations in the same domain and investigate how to properly set the parameters R1 and R2 under these conditions. Another crucial variable for the \"NO-OBS\" simulation is the cloud cover. Unfortunately, many surface stations do not directly provide this parameter. Several approaches, including theoretical ones, have been suggested in the literature for cloud cover estimation. Future research could investigate these approaches and assess CALPUFF's sensitivity to this variable. As this work specifically focused on domains with flat orography, it would be interesting to evaluate different meteorological models in a complex orography domain since 3D reconstruction of wind fields is more difficult in non-flat terrain. Finally, the present investigation provides information on how the WRF prognostic model can affect the conclusions of an odor impact assessment. Future research could explore alternative forecast models like MPAS, reanalysis data such as ERA5, or investigate techniques like nudging or data assimilation of observations within the WRF model. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14062457/s1](https://www.mdpi.com/article/10.3390/app14062457/s1)]([https://www.mdpi.com/article/10.3390/app14062457/s1](https://www.mdpi.com/article/10.3390/app14062457/s1)), Table S1: Odor concentration values--98 th percentile on annual basis for Point source (Italy); Table S2: Odor concentration values--98 th percentile on annual basis for Point source (Cuba); Table S3: Odor concentration values--98 th percentile on annual basis for Area source (Italy); Table S4: Odor concentration values--98 th percentile on annual basis for Area source (Cuba). Conceptualization, F.T., L.F., M.I., A.H.-G. and S.S.; methodology, F.T., M.I. and A.H.-G.; software, F.T., L.F., A.L.F.H. and A.H.-G.; investigation, F.T., L.F., M.I. and A.H.-G.; data curation, F.T., M.I. and A.L.F.H. ; visualization, F.T., M.I., A.L.F.H. and S.S.; writing--original draft, F.T.