Dataset Information

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

ABSTRACT: 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.

SUBMITTER: Laureano-Rosario AE

PROVIDER: S-EPMC5751019 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

Laureano-Rosario Abdiel E AE Symonds Erin M EM Rueda-Roa Digna D Otis Daniel D Muller-Karger Frank E FE

International journal of environmental research and public health 20171219 12

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 ...[more]

PMID: 29257092

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Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Project description:Tropical mangrove forests have been described as "coastal kidneys," promoting sediment deposition and filtering contaminants, including excess nutrients. Coastal areas throughout the world are experiencing increased human activities, resulting in altered geomorphology, hydrology, and nutrient inputs. To effectively manage and sustain coastal mangroves, it is important to understand nitrogen (N) storage and accumulation in systems where human activities are causing rapid changes in N inputs and cycling. We examined N storage and accumulation rates in recent (1970 - 2016) and historic (1930 - 1970) decades in the context of urbanization in the San Juan Bay Estuary (SJBE, Puerto Rico), using mangrove soil cores that were radiometrically dated. Local anthropogenic stressors can alter N storage rates in peri-urban mangrove systems either directly by increasing N soil fertility or indirectly by altering hydrology (e.g., dredging, filling, and canalization). Nitrogen accumulation rates were greater in recent decades than historic decades at Piñones Forest and Martin Peña East. Martin Peña East was characterized by high urbanization, and Piñones, by the least urbanization in the SJBE. The mangrove forest at Martin Peña East fringed a poorly drained canal and often received raw sewage inputs, with N accumulation rates ranging from 17.7 to 37.9 g -2 y-1 in recent decades. The Piñones Forest was isolated and had low flushing, possibly exacerbated by river damming, with N accumulation rates ranging from 18.6 to 24.2 g -2 y-1 in recent decades. Nearly all (96.3%) of the estuary-wide mangrove N (9.4 Mg ha-1) was stored in the soils with 7.1 Mg ha-1 sequestered during 1970-2017 (0-18 cm) and 2.3 Mg ha-1 during 1930-1970 (19-28 cm). Estuary-wide mangrove soil N accumulation rates were over twice as great in recent decades (0.18 ± 0.002 Mg ha-1y-1) than historically (0.08 ± 0.001 Mg ha-1y-1). Nitrogen accumulation rates in SJBE mangrove soils in recent times were twofold larger than the rate of human-consumed food N that is exported as wastewater (0.08 Mg ha-1 y-1), suggesting the potential for mangroves to sequester human-derived N. Conservation and effective management of mangrove forests and their surrounding watersheds in the Anthropocene are important for maintaining water quality in coastal communities throughout tropical regions.

Dataset Information

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

Publications

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

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