Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 they rose significantly to 62.18 µg/m³. PM_2.5 in 2022 averaged 30.37 µg/m³. These differences are likely influenced by meteorological variability, including increased transport of Saharan dust in mid-2022, and seasonal factors. Although local emission changes were not directly assessed, they may have also played a role in the observed trends. Statistical analyses revealed that aerosol optical depth (AOD), air pressure, and rainfall were significant predictors of PM₁₀ in 2022, explaining up to 75% of the variance. Correlations and regression models confirmed a robust association between AOD and PM levels on a weekly timescale. These findings highlight the importance of integrating remote sensing and meteorological data to improve air quality monitoring and inform environmental policy in Caribbean urban areas.

Intercomparison of FLEXPART and CALPUFF dispersion models. An application over a small tropical island

This study examines the concentrations of PM10 and PM2.5 in Santo Domingo by comparing data collected in 2019 and 2022. The research aims to identify temporal and spatial variations in particulate matter concentrations and to analyze the impact of meteorological and environmental variables on these concentrations. Methods include the collection of PM data at various urban sites and subsequent statistical analysis to assess the influence of factors like air temperature, wind speed, and aerosol optical depth (AOD). Results indicate significant fluctuations in PM levels, correlated with changes in meteorological conditions, seasonal variations, and urban activities. This study contributes to the understanding of air quality trends in Santo Domingo and provides insights into the efficacy of current environmental regulations and practices.

This study assessed the concentrations and spatial patterns of heavy metals in fine particulate matter with aerodynamic diameter below 2.5 $\mu$m and coarse particulate matter with aerodynamic diameter below 10 $\mu$m in Santo Domingo, Dominican Republic, during 2022. Thirty 24 h samples were collected using portable low-volume samplers across representative urban environments. Elemental concentrations of arsenic, cadmium, chromium, copper, iron, manganese, nickel, lead, vanadium, and zinc were quantified by energy-dispersive X-ray fluorescence. To address data below detection limits, regression on order statistics was applied. Copper and zinc exhibited the highest mean concentrations, pointing to strong anthropogenic inputs, while vanadium and iron showed pronounced spatial variability. Principal component analysis identified traffic and industrial activities as dominant sources. These findings provide baseline evidence for heavy metal pollution in Caribbean urban air and emphasize the need for continuous monitoring and effective regulatory strategies to mitigate potential health risks.

This study investigates the concentrations and spatial distribution of heavy metals in atmospheric particulate matter (PM2.5 and PM10) in Santo Domingo, Dominican Republic, during 2022. A total of 30 air samples were collected across diverse urban environments using portable low-volume samplers. Elemental analysis was performed via energy-dispersive X-ray fluorescence (EDXRF) to quantify levels of As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, V, and Zn. Due to a high proportion of left-censored values, regression on order statistics (ROS) was employed to impute data below detection limits. The highest mean concentrations in both PM fractions were observed for Cu and Zn, indicating significant anthropogenic contributions, while V and Fe displayed marked spatial variability. Principal component analysis (PCA) suggested potential source groupings linked to traffic and industrial emissions. This work provides critical baseline data for urban air quality management in the region and highlights the need for expanded environmental monitoring to mitigate health risks associated with airborne heavy metals.