RESUMO
This study aimed to form a basis for future predictive modeling efforts in support of the harmful algal blooms (HAB) surveillance program currently in force in the Brazilian State of Santa Catarina (SC). Data from monitoring toxin-producing algae were merged with both meteorological and oceanographic data and analyzed. Data from four sources were used in this study: climate reanalysis (air temperature, pressure, cloud cover, precipitation, radiation, U and V winds); remote sensing (chlorophyll concentration and sea surface temperature); Oceanic Niño Index; and HAB monitoring data (phytoplankton counts and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline). This study analyzed the period from 2007-01-01 to 2019-12-31 (7035 records in the HAB database) and used descriptive, bivariate and multivariate analyses to draw correlations among environmental parameters and the occurrence of algal blooms (AB), HAB and toxic events. Dinophysis spp. AB were the most registered type of event and tended to occur during the late autumn and winter months. These events were associated with high atmospheric pressure, predominance of westerly and southerly winds, low solar radiation and low sea and air temperature. An inverted pattern was observed for Pseudo-nitzschia spp. AB, which were mostly registered during the summer and early autumn months. These results give evidence that the patterns of occurrence of highly prevalent toxin-producing microalgae reported worldwide, such as the Dinophysis AB during the summer, differ along the coast of SC. Our findings also show that meteorological data, such as wind direction and speed, atmospheric pressure, solar radiation and air temperature, might all be key predictive modeling input parameters, whereas remote sensing estimates of chlorophyll, which are currently used as a proxy for the occurrence of AB, seem to be a poor predictor of HAB in this geographic area.
Assuntos
Dinoflagellida , Proliferação Nociva de Algas , Brasil , Tecnologia de Sensoriamento Remoto , FitoplânctonRESUMO
This study developed, cross-validated and applied a regression-based model to predict concentrations of faecal indicator organisms (FIOs) under different environmental conditions in the North and South bays of Santa Catarina, South of Brazil. The model was developed using a database of FIO concentrations in seawater sampled at 50 sites and the validation was performed using a different database by comparing 288 pairs of measured and modelled results for 15 sites. The index of agreement between the model outputs and the FIO concentrations measured during the validation period was 66%; the mean average error was 0.43 log10 and the root mean square error was 0.58 log10 MPN.100mL-1. These validation results indicate that the model provides a fair representation of the FIO contamination in the bays for the meteorological conditions under which the model was trained. The simulation of different scenarios showed that under typical levels of resident human population in the catchments and median rainfall and solar radiation conditions, the median FIO concentration in the bays is 0.4 MPN.100mL-1. Under extreme meteorological conditions, the combined effect of high rainfall and low solar radiation increased FIO concentrations up to 5 log10 MPN.100mL-1. The simulated scenarios also show that increases in resident population during the summer tourist season and average rainfall concentrations do not increase median FIO concentrations in the bays relative to periods of time with average population, possibly because of higher bacterial die-off in the waters. The models can be an effective tool for management of human health risks in bathing and shellfish waters impacted by sewage pollution.