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The on-board identification of ore minerals during a cruise is often postponed until long after the cruise is over. During the M127 cruise, 21 cores with deep-seafloor sediments were recovered in the Trans-Atlantic Geotraverse (TAG) field along the Mid Atlantic Ridge (MAR). Sediments were analyzed on-board for physicochemical properties such as lightness (L*), pH and Eh. Selected samples were studied for mineral composition by X-ray powder diffraction (XRD). Based on XRD data, sediment samples were separated into high-, low- and non-carbonated. Removal of carbonates is a common technique in mineralogical studies in which HCl is used as the extraction agent. In the present study, sequential extraction was performed with sodium acetate buffer (pH 5.0) to remove carbonates. The ratio between the highest calcite XRD reflection in the original samples (Iorig) vs its XRD-reflection in samples after their treatment with the buffer (Itreat) was used as a quantitative parameter of calcite removal, as well as to identify minor minerals in carbonated samples (when Iorig/Itreat > 24). It was found that the lightness parameter (L*) showed a positive correlation with calcite XRD reflection in selected TAG samples, and this could be applied to the preliminary on-board determination of extraction steps with acetate buffer (pH 5.0) in carbonated sediment samples. The most abundant minerals detected in carbonated samples were quartz and Al- and Fe-rich clays. Other silicates were also detected (e.g., calcic plagioclase, montmorillonite, nontronite). In non-carbonated samples, Fe oxides and hydroxides (goethite and hematite, respectively) were detected. Pyrite was the dominant hydrothermal mineral and Cu sulfides (chalcopyrite, covellite) and hydrothermal Mn oxides (birnessite and todorokite) were mineral phases identified in few samples, whereas paratacamite was detected in the top 20 cm of the core. The present study demonstrates that portable XRD analysis makes it possible to characterize mineralogy at cored sites, in particular in both low- and high-carbonated samples, before the end of most cruises, thus enabling the quick modification of exploration strategies in light of new information as it becomes available in near-real time.

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Exposure to continuous moderate noise levels is known to impair the auditory system leading to Noise-Induced Hearing Loss (NIHL) in animals including humans. The mechanism underlying noise-dependent auditory Temporary Threshold Shifts (TTS) is not fully understood. In fact, only limited information is available on vertebrates such as fishes, which share homologous inner ear structures to mammals and have the ability to regenerate hair cells. The zebrafish Danio rerio is a well-established model in hearing research providing an unmatched opportunity to investigate the molecular and physiological mechanisms of NIHL at the sensory receptor level. Here we investigated for the first time the effects of noise exposure on TTS and functional recovery in zebrafish, as well as the associated morphological damage and regeneration of the inner ear saccular hair cells. Adult specimens were exposed for 24h to white noise at various amplitudes (130, 140 and 150 dB re. 1 μPa) and their auditory sensitivity was subsequently measured with the Auditory Evoked Potential (AEP) recording technique. Sensory recovery was tested at different times post-treatment (after 3, 7 and 14 days) and compared to individuals kept under quiet lab conditions. Results revealed noise level-dependent TTS up to 33 dB and increase in response latency. Recovery of hearing function occurred within 7 days for fish exposed to 130 and 140 dB noise levels, while fish subject to 150 dB only returned to baseline thresholds after 14 days. Hearing impairment was accompanied by significant loss of hair cells only at the highest noise treatment. Full regeneration of the sensory tissue (number of hair cell receptors) occurred within 7 days, which was prior to functional recovery. We provide first baseline data of NIHL in zebrafish and validate this species as an effective vertebrate model to investigate the impact of noise exposure on the structure and function of the adult inner ear and its recovery process.