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The present study is the first assessment of occupational risk associated with artisanal cashew nut roasting using exposure and effect biomarkers, as well as a characterization and dispersion analysis of the released particulate matter (PM). A real-time particle monitor was used to quantify PM, PM and PM. Furthermore, the PM was sampled using a Handi-vol sampler, and the physicochemical characteristics were determined by SEM-EDS analysis. Trajectories, dispersion and deposition of the emitted material were calculated using the NOAA-HYSPLIT model. Urinary 1-hydroxypyrene (1-OHP) levels were analyzed by HPLC. DNA damage, chromosomal instability and cell death were measured by a buccal micronucleus cytome assay (BMCyt). The PM concentrations for all measurements in the exposed area were higher than in the non-exposed area. SEM-EDS analyses exhibited a wide variety of particles, and K, Cl, S and Ca biomass burning tracers were the major inorganic compounds. In addition, atmospheric modeling analysis suggested that these particles can reach regions farther away than 40 kilometers. Occupational polycyclic aromatic hydrocarbon exposure was confirmed by increases in 1-OHP levels in cashew nut workers. Frequencies of BMCyt biomarkers of genotoxicity (micronuclei and nuclear bud) and cytotoxicity (pyknosis, karyolysis, karyorrhexis and condensed chromatin) were higher in the exposed group compared with the controls. The influence of factors, such as age, on the micronuclei frequencies was demonstrated, and a correlation between 1-OHP and micronuclei was observed. To the best of our knowledge, no other study has demonstrated a correlation between these types of biomarkers. The use of exposure (1-OHP) and effect (BMCyt) biomarkers were therefore efficient in assessing the occupational risk associated with artisanal cashew nut roasting, and the high rates of PM are considered to be a potential contributor to this effect.

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Wildfires expose populations to increased morbidity and mortality due to increased air pollutant concentrations. Data included burned area, particulate matter (PM 10 , PM 2.5 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), temperature, relative humidity, wind-speed, aerosol optical depth (AOD) and mortality rates due to Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD), and Asthma (ASMA).Only the months of the 2011-2020 wildfire season (June-July-August-September-October) with burned area greater than 1000 ha were considered. Multivariate statistical methods were used to reduce the dimensionality of the data to create two fire-pollutionmeteorology indices (PBI, API), which allow us to understand how the combination of these variables affect cardio-respiratory mortality. Cluster analysis applied to PBI-API-Mortality divided the data into two Clusters. Cluster 1 included the months with lower temperatures, higher relative humidity, and high PM 10 , PM 2.5 , and NO 2 concentrations. Cluster 2 included the months with more extreme weather conditions such as higher temperatures, lower relative humidity, larger forest fires, high PM 10 , PM 2.5 , O 3 , and CO concentrations, and high AOD. The two clusters were subjected to linear regression analysis to better understand the relationship between mortality and the PBI and API indices. The results showed statistically significant (p-value < 0.05) correlation (r) in Cluster 1 between RSDxPBI (r RSD = 0.539), PNEUxPBI (r PNEU = 0.644). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD = 0.464), PNEUxPBI (r PNEU = 0.442), COPDxPBI (r COPD = 0.456), CSDxAPI (r CSD = 0.705), RSDxAPI (r CSD = 0.716), PNEUxAPI (r PNEU = 0.493), COPDxAPI (r PNEU = 0.619).

In Part II of this chapter, we intend to show the significant advances and results concerning aerosols’ tropospheric monitoring in South America. The tropospheric lidar monitoring is also supported by the Latin American Lidar Network (LALINET). It is concerned about aerosols originating from urban pollution, biomass burning, desert dust, sea spray, and other primary sources. Cloud studies and their impact on radiative transfer using tropospheric lidar measurements are also presented.