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Accurate predictions of coastal ocean chlorophyll -a (Chl -a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl -a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl -a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl -a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl -a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl -a and the importance of considering POC in modeling Chl -a concentrations. • The Gaussian process regression (GPR) model outperformed the deep learning (DL) model in estimating coastal Chlorophyll- a (Chl- a). • Taking the particulate carbon as model inputs can estimate the Chl- a successfully. • The Chl- a concentration was higher in the coastal areas than in the open ocean. • Particulate organic carbon (POC) is the main driver of POC/Chl- a change. [ABSTRACT FROM AUTHOR]

Abstract: Seasonal and inter-annual variability in satellite-derived estimates of near-surface chlorophyll-a concentration off the central east coast of India from 1998 to 2003 is examined. Wind-induced upwelling predominates in late spring and winter, coinciding with the maximum in solar radiation, leading to increased accumulations of phytoplankton biomass. Chlorophyll concentrations varied from 2 to 10mg/m3 over the central east coast of India and were generally lower in June and maximal in March. Chlorophyll concentrations along the coast followed a similar seasonal pattern (ranging from 0.5 to 6mg/m3); however, concentrations were always greater on the Machilipatnam and Nellore compared with the Visakhapatnam and Chennai. The lack of upwelling favorable conditions results in the majority of the southern side of the central east coast of India waters being insufficient, which is reflected in low or moderate productivity. The possible reasons and observed correlations between chlorophyll-a and upwelling index during the study period was discussed. [Copyright &y& Elsevier]

• Artificial lights in caves allow the establishment of photosynthetic organisms. • We tested the response of lampenflora in two show caves with different tourist flux. • Lampenflora growth was higher in the cave with longer lighting duration. • Higher light intensity corresponds to higher proliferation of lampenflora. • Modulating the lighting regime can effectively help in controlling lampenflora growth. When artificial lighting systems are installed in show caves, alien photosynthetic epilithic prokaryotic and eukaryotic microorganisms —the so-called 'lampenflora'— form extended biofilms on cave speleothems, causing structural and aesthetic damage. Among the environmental drivers influencing their proliferation, light intensity and duration of illumination are expected to play a key role. To unravel which environmental variables determine the proliferation of lampenflora, we designed a specific study in two Italian show caves with similar characteristics and management practices, but differing in terms of tourist use and thus in terms of duration of illumination. In each cave, we selected multiple illuminated speleothems at progressive distance from the cave entrance where we measured the chlorophyll-a concentration of the three main microorganism groups constituting phototrophic biofilms, namely cyanobacteria, diatoms and green algae, by means of the portable fluorimeter BenthoTorch®. In each sampling plot we also recorded the intensity of artificial lights and we visually evaluated the presence of seeping water on speleothems. By adopting advanced regression techniques, we revealed that the total chlorophyll-a and the concentration of the three groups differ between caves, increasing in the cave with higher number of visitors. Regarding the other environmental factors, light intensity positively influenced the total chlorophyll-a and the concentration of cyanobacteria, while humidity increased diatom concentration. We then provided suggestions to implement appropriate management actions addressed to the modulation of the lighting regime in show caves, especially in the areas most exposed to the proliferation of lampenflora. [ABSTRACT FROM AUTHOR]