This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

Long-term exposure to poor air quality has been associated with respiratory viral infections such as influenza and measles. Some epidemiological studies in North America, Europe, and Asia also suggest that exposure to air pollution is associated with increased SARS-CoV-2 infection and mortality associated with COVID-19. However, few studies have been conducted on this topic in South America. In this study, we investigate the association between ambient PM_2.5, NO₂, and O₃ and their interaction with COVID-19-associated deaths in Brazil, the largest country in South America. We apply a negative binomial mixed model with zero inflation using municipality-level COVID-19 deaths as the endpoint and the long-term average of PM_2.5, NO₂, and O₃ as the exposure. We added a random intercept by state to account for potential correlations between communities within the same state. After adjusting the model for several potential confounders, including meteorological variables, demographic characteristics, socioeconomic aspects, and healthcare conditions, we found a positive association between PM_2.5 and COVID-19 deaths in Brazil, where a 1 μg/m³ increase in the long-term average of PM_2.5 was associated with a 10.22% (95% CI: 9.35; 11.09) increase in COVID-19 deaths. In contrast, O₃ and NO₂ showed negative associations. As suggested by the literature in other countries, our results suggest that air pollution is an important cofactor that increases the risk of death from COVID-19 in Brazil. The effects of air pollution in Brazil are pronounced, indicating the need for improved air quality control policies.