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Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

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The northern Gulf of Mexico (GoM) is a region strongly influenced by river discharges of freshwater and nutrients, which promote a highly productive coastal ecosystem that host commercially valuable marine species. A variety of climate and weather processes could potentially influence the river discharges into the northern GoM. However, their impacts on the coastal ecosystem remain poorly described. By using a regional ocean-biogeochemical model, complemented with satellite and in situ observations, here we show that El Niño - Southern Oscillation (ENSO) is a main driver of the interannual variability in salinity and plankton biomass during winter and spring. Composite analysis of salinity and plankton biomass anomalies shows a strong asymmetry between El Niño and La Niña impacts, with much larger amplitude and broader areas affected during El Niño conditions. Further analysis of the model simulation reveals significant coastal circulation anomalies driven by changes in salinity and winds. The coastal circulation anomalies in turn largely determine the spatial extent and distribution of the ENSO-induced plankton biomass variability. These findings highlight that ENSO-induced changes in salinity, plankton biomass, and coastal circulation across the northern GoM are closely interlinked and may significantly impact the abundance and distribution of fish and invertebrates.

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Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

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Increased frequency and length of high heat episodes are leading to more cardiovascular issues and asthmatic responses among the population of San Juan, the capital of the island of Puerto Rico, USA. An urban heat island effect, which leads to foci of higher temperatures in some urban areas, can raise heat-related mortality. The objective of this research is to map the risk of high temperature in particular locations by creating heat maps of the city of San Juan. The heat vulnerability index (HVI) maps were developed using images collected by satellite-based remote sensing combined with census data. Land surface temperature was assessed using images from the Thermal Infrared Sensor flown on Landsat 8. Social determinants (e.g., age, unemployment, education and social isolation, and health insurance coverage) were analyzed by census tract. The data were examined in the context of land cover maps generated using products from the Puerto Rico Terrestrial Gap Analysis Project (USDA Forest Service). All variables were set in order to transform the indicators expressed in different units into indices between 0 and 1, and the HVI was calculated as sum of score. The tract with highest index was considered to be the most vulnerable and the lowest to be the least vulnerable. Five vulnerability classes were mapped (very high, high, moderate, low, and very low). The hottest and the most vulnerable tracts corresponded to highly built areas, including the Luis Munoz International Airport, seaports, parking lots, and high-density residential areas. Several variables contributed to increased vulnerability, including higher rates of the population living alone, disabilities, advanced age, and lack of health insurance coverage. Coolest areas corresponded to vegetated landscapes and urban water bodies. The urban HVI map will be useful to health officers, emergency preparedness personnel, the National Weather Service, and San Juan residents, as it helps to prepare for and to mitigate the potential effects of heat-related illnesses.

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Enterococci concentration variability at Escambron Beach, San Juan, Puerto Rico, was examined in the context of environmental conditions observed during 2005-2015. Satellite-derived sea surface temperature (SST), turbidity, direct normal irradiance, and dew point were combined with local precipitation, winds, and mean sea level (MSL) observations in a stepwise multiple regression analyses (Akaike Information Criteria model selection). Precipitation, MSL, irradiance, SST, and turbidity explained 20% of the variation in observed enterococci concentrations based upon these analyses. Changes in these parameters preceded increases in enterococci concentrations by 24 h up to 11 days, particularly during positive anomalies of turbidity, SST, and 480-960 mm of accumulated (4 days) precipitation, which relates to bacterial ecology. Weaker, yet still significant, increases in enterococci concentrations were also observed during positive dew point anomalies. Enterococci concentrations decreased with elevated irradiance and MSL anomalies. Unsafe enterococci concentrations per US EPA recreational water quality guidelines occurred when 4-day cumulative precipitation ranged 481-960 mm; irradiance < 667 W·m-2; daily average turbidity anomaly >0.005 sr-1; SST anomaly >0.8 °C; and 3-day average MSL anomaly <-18.8 cm. This case study shows that satellite-derived environmental data can be used to inform future water quality studies and protect human health.

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Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r2=0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence.

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Model projections indicate that climate change may dramatically restructure phytoplankton communities, with cascading consequences for marine food webs. It is currently not known whether evolutionary change is likely to be able to keep pace with the rate of climate change. For simplicity, and in the absence of evidence to the contrary, most model projections assume species have fixed environmental preferences and will not adapt to changing environmental conditions on the century scale. Using 15 y of observations from Station CARIACO (Carbon Retention in a Colored Ocean), we show that most of the dominant species from a marine phytoplankton community were able to adapt their realized niches to track average increases in water temperature and irradiance, but the majority of species exhibited a fixed niche for nitrate. We do not know the extent of this adaptive capacity, so we cannot conclude that phytoplankton will be able to adapt to the changes anticipated over the next century, but community ecosystem models can no longer assume that phytoplankton cannot adapt.

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We test the hypothesis that climate and environmental conditions are becoming favorable for dengue transmission in San Juan, Puerto Rico. Sea Level Pressure (SLP), Mean Sea Level (MSL), Wind, Sea Surface Temperature (SST), Air Surface Temperature (AST), Rainfall, and confirmed dengue cases were analyzed. We evaluated the dengue incidence and environmental data with Principal Component Analysis, Pearson correlation coefficient, Mann-Kendall trend test and logistic regressions. Results indicated that dry days are increasing and wet days are decreasing. MSL is increasing, posing higher risk of dengue as the perimeter of the San Juan Bay estuary expands and shorelines move inland. Warming is evident with both SST and AST. Maximum and minimum air surface temperature extremes have increased. Between 1992 and 2011, dengue transmission increased by a factor of 3.4 (95% CI: 1.9-6.1) for each 1 °C increase in SST. For the period 2007-2011 alone, dengue incidence reached a factor of 5.2 (95% CI: 1.9-13.9) for each 1 °C increase in SST. Teenagers are consistently the age group that suffers the most infections in San Juan. Results help understand possible impacts of different climate change scenarios in planning for social adaptation and public health interventions.

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Recent changes in ocean temperature have impacted marine ecosystem function globally. Nevertheless, the responses have depended upon the rate of change of temperature and the season when the changes occur, which are spatially variable. A rigorous statistical analysis of sea surface temperature observations over 25 years was used to examine spatial variability in overall and seasonal temperature trends within the wider Caribbean. The basin has experienced high spatial variability in rates of change of temperature. Most of the warming has been due to increases in summer rather than winter temperatures. However, warming was faster in winter in the Loop Current area and the south-eastern Caribbean, where the annual temperature ranges have contracted. Waters off Florida, Cuba and the Bahamas had a tendency towards cooling in winter, increasing the amplitude of annual temperature ranges. These detailed patterns can be used to elucidate ecological responses to climatic change in the region.

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