Harmful algal blooms in the Chesapeake Bay and coastal bays of Maryland, USA, are not a new phenomenon, but may be increasing in frequency and diversity. Outbreaks ofPfiesteria piscicida (Dinophyceae) were observed during 1997 in several Chesapeake Bay tributaries, while in 1998,Pfiesteria-related events were not found but massive blooms ofProrocentrum minimum (Dinophyceae) occurred. In 1999,Aureococcus anophagefferens (Pelagophyceae) developed in the coastal bays in early summer in sufficient densities to cause a brown tide. In 1997, toxicPfiesteria was responsible for fish kills at relatively low cell densities. In 1998 and 1999, the blooms ofP. minimum andA. anophagefferens were not toxic, but reached sufficiently high densities to have ecological consequences. These years differed in the amount and timing of rainfall events and resulting nutrient loading from the largely agricultural watershed. Nutrient loading to the eastern tributaries of Chesapeake Bay has been increasing over the past decade. Much of this nutrient delivery is in organic form. The sites of thePfiesteria outbreaks ranked among those with the highest organic loading of all sites monitored bay-wide. The availability of dissolved organic carbon and phosphorus were also higher at sites experiencingA. anophagefferens blooms than at those without blooms. The ability to supplement photosynthesis with grazing or organic substrates and to use a diversity of organic nutrients may play a role in the development and maintenance of these species. ForP. minimum andA. anophagefferens, urea is used preferentially over nitrate.Pfiesteria is a grazer, but also has the ability to take up nutrients directly. The timing of nutrient delivery may also be of critical importance in determining the success of certain species.

Polyethylene terephthalate has been widely used in the packaging industry. Degraded PET micro(nano)plastics could pose public health concerns following release into various environments. This study focuses on PET degradation under ultraviolet radiation using the NIST SPHERE facility at the National Institute of Standards and Technology in saturated humidity (i.e., ≥95% relative humidity) and dry conditions (i.e., ≤5% relative humidity) with varying temperatures (30 °C, 40 °C, and 50 °C) for up 20 days. ATR-FTIR was used to characterize the chemical composition change of degraded PET as a function of UV exposure time. The results showed that the cleavage of the ester bond at peak 1713 cm⁻¹ and the formation of the carboxylic acid at peak 1685 cm⁻¹ were significantly influenced by UV radiation. Furthermore, the formation of carboxylic acid was considerably higher at saturated humidity and 50 °C conditions compared with dry conditions. The ester bond cleavage was also more pronounced in saturated humidity conditions. The novelty of this study is to provide insights into the chemical degradation of PET under environmental conditions, including UV radiation, humidity, and temperature. The results can be used to develop strategies to reduce the environmental impact of plastic pollution.

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.