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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.
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Air pollution is a major concern issue on Macao since the concentration levels of several of the most common pollutants are frequently above the internationally recommended values. The low air quality episodes impacts on human health paired with highly populated urban areas are important motivations to develop forecast methodologies in order to anticipate pollution episodes, allowing establishing warnings to the local community to take precautionary measures and avoid outdoor activities during this period. Using statistical methods (multiple linear regression (MLR) and classification and regression tree (CART) analysis) we were able to develop forecasting models for the main pollutants (NO2, PM2.5, and O3) enabling us to know the next day concentrations with a good skill, translated by high coefficients of determination (0.82–0.90) on a 95% confidence level. The model development was based on six years of historical data, 2013 to 2018, consisting of surface and upper-air meteorological observations and surface air quality observations. The year of 2019 was used for model validation. From an initially large group of meteorological and air quality variables only a few were identified as significant dependent variables in the model. The selected meteorological variables included geopotential height, relative humidity and air temperature at different altitude levels and atmospheric stability characterization parameters. The air quality predictors used included recent past hourly levels of mean concentrations for NO2 and PM2.5 and maximum concentrations for O3. The application of the obtained models provides the expected daily mean concentrations for NO2 and PM2.5 and maximum hourly concentrations O3 for the next day in Taipa Ambient air quality monitoring stations. The described methodology is now operational, in Macao, since 2020.
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Distinct patterns of gene expression often underlie intra- and intersexual differences, and the study of this set of coregulated genes is essential to understand the emergence of complex behavioural phenotypes. Here, we describe the development of a de novo transcriptome and brain gene expression profiles of wild-caught peacock blenny, Salaria pavo, an intertidal fish with sex-role reversal in courtship behaviour (i.e., females are the courting sex) and sequential alternative reproductive tactics in males (i.e., larger and older nest-holder males and smaller and younger sneaker males occur). Sneakers mimic both female's courtship behaviour and nuptial coloration to get access to nests and sneak fertilizations, and later in life transition into nest-holder males. Thus, this species offers the unique opportunity to study how the regulation of gene expression can contribute to intersex phenotypes and to the sequential expression of male and female behavioural phenotypes by the same individual. We found that at the whole brain level, expression of the sneaker tactic was paralleled by broader and divergent gene expression when compared to either females or nest-holder males, which were more similar between themselves. When looking at sex-biased transcripts, sneaker males are intersex rather than being either nest-holder or female-like, and their transcriptome is simultaneously demasculinized for nest-holder-biased transcripts and feminized for female-biased transcripts. These results indicate that evolutionary changes in reproductive plasticity can be achieved through regulation of gene expression, and in particular by varying the magnitude of expression of sex-biased genes, throughout the lifetime of the same individual.
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Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.
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The recently explored inactive Tianzuo hydrothermal field, in the amagmatic segment of the ultraslow-spreading Southwest Indian Ridge (SWIR), is closely associated with detachment faults. In this site, sulfide minerals are hosted by serpentine-bearing ultramafic rocks and include high-temperature (isocubanite, sphalerite, and minor pyrrhotite) and low-temperature (pyrite I, marcasite, pyrite II, and covellite) phases. In this study, trace-element concentrations of isocubanite and pyrite II were used to elucidate mineralization processes in ultramafic rocks hosting sulfides. Results show that isocubanite is enriched in metals such as Cu, Co, Sn, Te, Zn, Se, Pb, Bi, Cd, Ag, In, and Mn, and pyrite II is enriched in Mo and Tl. The marked enrichment in Te, Cu, Co, and In in isocubanite (compared with Se, Zn, Ni, and Sn, respectively) is most likely due to the contribution of magmatic fluids from gabbroic intrusions beneath the hydrothermal field. The intrusion of gabbroic magmas would have enhanced serpentinization reactions and provided a relatively oxidizing environment through the dissolution of anhydrite precipitated previously in the reaction zone, within high temperature and low pH conditions. This might have facilitated the extraction of metals by initial hydrothermal fluids, leading to the general enrichment of most metals in isocubanite. Metals in pyrite II have compositions similar to those of isocubanite, except for strong depletion in magmatically derived Te, Cu, Co, and In. This means that serpentinization processes had a dominating role in pyrite II precipitation as well. The enrichment of pyrite II in Mo and Tl is also indicative of seawater contribution in its composition. The study concludes that serpentinization reactions contribute effectively both to high- and low-temperature sulfide mineralization at Tianzuo hydrothermal field, with gabbroic intrusions further promoting high-temperature sulfide mineralization, providing additional metals, fluids and heat. In contrast, low-temperature sulfide mineralization occurred during the cooling of gabbroic intrusions, with decreasing rates of serpentinization reactions and a significant influence of seawater.
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Limited special education and related services are available for children with autism spectrum disorder (ASD) in Macau, especially those who are educated in general education classrooms. No intervention study has been conducted on these children. This study was conducted to explore the relationship between a board game play intervention and board game play behaviors and social communication of children with ASD educated in general education classrooms in Macau. A repeated measures design was used and the results of this study showed the mean occurrence of unprompted board game play behaviors per session during intervention was not significantly different from that during pre- or post-intervention. The mean occurrence of social communication per session during intervention was significantly higher than that during pre- and post-intervention. These findings suggest a positive relationship existed between the board game intervention used in this study and social communication of children with ASD.
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Uptake and depuration kinetics of 4,4′-dichlorobenzophenone (main metabolite of dicofol) in the edible clam Meretrix meretrix were evaluated through a mesocosm experiment. M. meretrix was exposed to different dicofol concentrations (environmental concentration, D1 = 50 ng/L; supra-environmental concentration, D2 = 500 ng/L) for 15 days, followed by the same depuration period. To accomplish this goal, an analytical method was successfully optimized for 4,4′-DCBP using QuEChERS as extraction method with a range of concentrations 0.3–76.8 ng/g ww quantified by gas chromatography coupled to tandem mass spectrometry. Our results demonstrated different kinetics of accumulation depending on the two dicofol treatments. For D1, the uptake kinetic was best fitted using a plateau followed by one phase association kinetic model, while for D2 a one phase association kinetic model suited better. Similar bioconcentration factors were obtained for both concentrations but only animals exposed to D2, showed 4,4′-DCBP levels above the limits of quantification after 24 h exposure. These animals also showed lower uptake rate (ku) than organisms exposed to D1. During the depuration period, only organisms exposed to D1 successfully depurated after 24 h. On the other hand, although animals exposed to D2 presented higher elimination factor, they did not reach the original levels after depuration. Moreover, values detected in these clams were higher than the Maximum Residue Level (10 ng/g) established by the European legislation. This indicates that longer periods of depuration time than the ones used in this study, may be needed in order to reach safe levels for human consumption. This work also demonstrated that studies on metabolite kinetics during uptake/depuration experiments, could be a new alternative to understand the impact and metabolism of pesticides in the marine environment.
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In this study, components of the food-web in Macao wetlands were quantified using stable isotope ratio techniques based on carbon and nitrogen values. The δ13C and δ15N values of particulate organic matter (δ13CPOM and δ15NPOM, respectively) ranged from −30.64 ± 1.0 to −28.1 ± 0.7 ‰, and from −1.11 ± 0.8 to 3.98 ± 0.7 ‰, respectively. The δ13C values of consumer species ranged from −33.94 to −16.92 ‰, showing a wide range from lower values in a freshwater lake and inner bay to higher values in a mangrove forest. The distinct dietary habits of consumer species and the location-specific food source composition were the main factors affecting the δ13C values. The consumer 15N-isotope enrichment values suggested that there were three trophic levels; primary, secondary, and tertiary. The primary consumer trophic level was represented by freshwater herbivorous gastropods, filter-feeding bivalves, and plankton-feeding fish, with a mean δ15N value of 5.052 ‰. The secondary consumer level included four deposit-feeding fish species distributed in Fai Chi Kei Bay and deposit-feeding gastropods in the Lotus Flower Bridge flat, with a mean δ15N value of 6.794 ‰. The tertiary consumers group consisted of four crab species, one shrimp species, and four fish species in the Lotus Flower Bridge Flat, with a mean δ15N value of 13.473 ‰. Their diet mainly comprised organic debris, bottom fauna, and rotten animal tissues. This study confirms the applicability of the isotopic approach in food web studies.
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Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.
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