Advancements in Carbon Dioxide Modeling: An Algorithm Incorporating In-Situ and Satellite Data for Improved Understanding of pCO Dynamics in the Bay of Bengal<sub/>

Estimation of the partial pressure of carbon dioxide (pCO2) in the Bay of Bengal (BoB) region plays a crucial role in better understanding the air-sea CO2 fluxes. Complex physical and biogeochemical processes such as physical mixing, stratification, thermodynamic, and biological effects dominate the spatiotemporal variability of pCO2 concentration over the BoB. This is difficult to estimate through in-situ platforms alone due to the time-consuming, cost-effective, and intricacies involved in water sample collection during rough oceanic weather conditions. Alternatively, remote sensing technology provides governing control parameters with high spatiotemporal resolution over large synoptic scales. Since the BoB region is influenced by the Indian monsoon system and other complex processes, existing regional and global pCO2 algorithms are not adequate to estimate more accurate pCO2 fields. Hence, there is a need to develop a regional pCO2 algorithm over the BoB. To resolve this problem, in the present study, a Multi Parametric Regional Regression (MPRR) approach was developed over the BoB using satellite data such as sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll- ${a}$ (Chl $a$ ) concentration. To train and validate the MPRR approach, required in-situ measurements were obtained from the open and coastal waters of the BoB. The validation results revealed that the present MPRR approach showed better performance with significant low errors (mean relative error (MRE) &#x003D; 0.012, mean normalized bias (MNB) &#x003D; 0.022, and root mean square error (RMSE) &#x003D; 4.75 $\mu$ atm) and a high correlation coefficient (R2 &#x003D; 0.92). Furthermore, the study demonstrated the spatiotemporal variability of pCO2and generated monthly, seasonal, and annual pCO2 maps over the BoB

Quantifying engineered nanomaterial toxicity: comparison of common cytotoxicity and gene expression measurements

Abstract Background When evaluating the toxicity of engineered nanomaterials (ENMS) it is important to use multiple bioassays based on different mechanisms of action. In this regard we evaluated the use of gene expression and common cytotoxicity measurements using as test materials, two selected nanoparticles with known differences in toxicity, 5 nm mercaptoundecanoic acid (MUA)-capped InP and CdSe quantum dots (QDs). We tested the effects of these QDs at concentrations ranging from 0.5 to 160 µg/mL on cultured normal human bronchial epithelial (NHBE) cells using four common cytotoxicity assays: the dichlorofluorescein assay for reactive oxygen species (ROS), the lactate dehydrogenase assay for membrane viability (LDH), the mitochondrial dehydrogenase assay for mitochondrial function, and the Comet assay for DNA strand breaks. Results The cytotoxicity assays showed similar trends when exposed to nanoparticles for 24 h at 80 µg/mL with a threefold increase in ROS with exposure to CdSe QDs compared to an insignificant change in ROS levels after exposure to InP QDs, a twofold increase in the LDH necrosis assay in NHBE cells with exposure to CdSe QDs compared to a 50% decrease for InP QDs, a 60% decrease in the mitochondrial function assay upon exposure to CdSe QDs compared to a minimal increase in the case of InP and significant DNA strand breaks after exposure to CdSe QDs compared to no significant DNA strand breaks with InP. High-throughput quantitative real-time polymerase chain reaction (qRT-PCR) data for cells exposed for 6 h at a concentration of 80 µg/mL were consistent with the cytotoxicity assays showing major differences in DNA damage, DNA repair and mitochondrial function gene regulatory responses to the CdSe and InP QDs. The BRCA2, CYP1A1, CYP1B1, CDK1, SFN and VEGFA genes were observed to be upregulated specifically from increased CdSe exposure and suggests their possible utility as biomarkers for toxicity. Conclusions This study can serve as a model for comparing traditional cytotoxicity assays and gene expression measurements and to determine candidate biomarkers for assessing the biocompatibility of ENMs