The 2019 northeast Brazil oil spill: scenarios.

During the last quarter of 2019, the beaches, mangroves, and estuaries of Northeast Brazil received an unprecedented volume of crude oil from the sea, which became the worst environmental disaster ever to reach the Brazilian coast. The oil, having reached the shores completely unnoticed, left both society and government agents completely clueless on (i) where the oil was coming from; (ii) how much oil was still in the ocean to reach the shorelines; and (iii) which beaches were going to be affected next! By exploring remote sensing data and ocean numerical modeling, along with oil dispersion chemistry on sea water, this study investigates the possible origin and path of the spill and whether it could have been detected from space. The oil dispersion modeling simulations performed for this investigation revealed a possible region and timing of the oil spill, also indicating the likelihood of it being advected toward the shoreline under the ocean surface.

Sociodemographic and spatiotemporal profiles of hepatitis-A in the state of Pará, Brazil, based on reported notified cases.

Hepatitis-A virus is a worldwide healthcare problem, mainly affecting countries with poor sanitary and socioeconomic conditions. This communication evaluates the spatiotemporal variability of the disease's socioepidemiological profile in one of the endemic Brazilian regions (Pará State) prior to (2008-2013) and after (2014-2017) the launch of the national public vaccination programme. Hepatitis-A epidemiological reports concerning Pará State - Brazil - were used for this study including municipalitylevel data of the disease's reported positive notification cases (PNCs). The analyses involved socioepidemiological profiling and space-time scan statistics. A total of 5500 PNCs were reported in the study period. On average, PNCs decreased over time throughout the state, with strongest drops after 2015. The PNCs were specific for gender, race/ethnic origin and age group. The predominant gender and race/ethnic groups was male and brown, respectively. While children were the most susceptible age group prior to 2015, there was a shift towards older ages (young and adults) in later years. Those found to be the most affected by the disease, as shown by space-time scan statistics, were people in densely populated municipalities with unsatisfactory sanitary conditions and also less well covered by the public vaccination programme. Despite drops in the number of hepatitis-A PNCs, thanks to the national vaccination programme, the disease still persists in Pará State and elsewhere in Brazil. The present study reinforces the need of continuous prevention and control strategies for effective control and erradication of hepatitis-A.

Associations Between Environmental and Sociodemographic Data and Hepatitis-A Transmission in Pará State (Brazil).

Hepatitis-A is a waterborne infectious disease transmitted by the eponymous hepatitis-A virus (HAV). Due to the disease's sociodemographic and environmental characteristics, this study applied public census and remote sensing data to assess risk factors for hepatitis-A transmission. Municipality-level data were obtained for the state of Pará, Brazil. Generalized linear and nonlinear models were evaluated as alternative predictors for hepatitis-A transmission in Pará. The Histogram Gradient Boost (HGB) regression model was deemed the best choice ( R M S E = 2.36, and higher R 2  = 0.95) among the tested models. Partial dependence analysis and permutation feature importance analysis were used to investigate the partial dependence and the relative importance values of the independent variables in the disease transmission prediction model. Results indicated a complex relationship between the disease transmission and the sociodemographic and environmental characteristics of the study area. Population size, lack of sanitation, urban clustering, year of notification, insufficient public vaccination programs, household proximity to open-air dumpsites and storm-drains, and lack of access to healthcare facilities and hospitals were sociodemographic parameters related to HAV transmission. Turbidity and precipitation were the environmental parameters closest related to disease transmission. Based on HGB model, a hepatitis-A risk map was built for Pará state. The obtained risk map can be thought of as an auxiliary tool for public health strategies. This study reinforces the need to incorporate remote sensing data in epidemiological modelling and surveillance plans for the development of early prevention strategies for hepatitis-A.

Satellite Ocean Colour: Current Status and Future Perspective.

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

Using CDOM optical properties for estimating DOC concentrations and pCO2 in the Lower Amazon River.

Coloured dissolved organic matter (CDOM) is one of the major contributors to the absorption budget of most freshwaters and can be used as a proxy to assess non-optical carbon fractions such as dissolved organic carbon (DOC) and the partial pressure of carbon dioxide (pCO2). Nevertheless, riverine studies that explore the former relationships are still relatively scarce, especially within tropical regions. Here we document the spatial-seasonal variability of CDOM, DOC and pCO2, and assess the potential of CDOM absorption coefficient (aCDOM(412)) for estimating DOC concentration and pCO2 along the Lower Amazon River. Our results revealed differences in the dissolved organic matter (DOM) quality between clearwater (CW) tributaries and the Amazon River mainstream. A linear relationship between DOC and CDOM was observed when tributaries and mainstream are evaluated separately (Amazon River: N = 42, R2 = 0.74, p<0.05; CW: N = 13, R2 = 0.57, p<0.05). However, this linear relationship was not observed during periods of higher rainfall and river discharge, requiring a specific model for these time periods to be developed (N = 25, R2 = 0.58, p<0.05). A strong linear positive relation was found between aCDOM(412) and pCO2(N = 69, R2 = 0.65, p<0.05) along the lower river. pCO2 was less affected by the optical difference between tributaries and mainstream waters or by the discharge conditions when compared to CDOM to DOC relationships. Including the river water temperature in the model improves our ability to estimate pCO2 (N = 69; R2 = 0.80, p<0.05). The ability to assess both DOC and pCO2 from CDOM optical properties opens further perspectives on the use of ocean colour remote sensing data for monitoring carbon dynamics in large running water systems worldwide.

Water column correction for coral reef studies by remote sensing.

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic.

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3M(RAD)), Ocean Chlorophyll 4 bands (OC4v4(RAD)), and Ocean Chlorophyll 2 bands (OC2v4(RAD)). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3M(SAT), and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01(SAT)), and Carder(SAT). In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg/m(3). In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m(3) (OC2v4(RAD)). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m(3)) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m(3), respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of R(RS) by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.