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One of the intriguing characteristics of the 2003 severe acute respiratory syndrome (SARS) epidemics was the occurrence of super spreading events (SSEs). Here, the authors report the results of identifying the occurrence of SSEs in the Hong Kong and Singapore epidemics using mathematical and statistical analysis. Their predicted occurrence of SSEs agreed well with the reported occurrence of all seven super spreaders in the two cities. Additional unidentified SSEs were also found to exist. It was found that 71.1% and 74.8% of the infections were attributable to SSEs in Hong Kong and Singapore, respectively. There also seemed to be 'synchronized' occurrences of infection peaks in both the community and the hospitals in Hong Kong. The results strongly suggested that the infection did not depend on the total number of symptomatic cases, with only a very small proportion of symptomatic individuals being shown to be infectious (i.e., able to infect other individuals). The authors found that the daily infection rate did not correlate with the daily total number of symptomatic cases but with the daily number of symptomatic cases who were not admitted to a hospital within 4 days of the onset of symptoms. [ABSTRACT FROM AUTHOR]

Algal blooms have been identified as one major threat to coastal safety and marine ecosystem functioning, but the dominant mechanism regulating the formation of algal blooms remains controversial, ranging from physical control (via water column stability), the chemical control (via coastal nutrients) to joint control. Here we leveraged the unique data collected in the Hong Kong water over the annual cycle and past three decades, including direct observations of algal blooms and coastal nutrients and process model output of water column stability, and evaluated the differential competing hypotheses in regulating algal blooms. Our results demonstrate that the joint mechanism rather than the single mechanism effectively predicts all algal blooms. Meanwhile, we observed that the adequate nutrients (phosphate, PO 4 3- ) significantly originate from coastal groundwater. The production and fluctuation of PO 4 3- in beach aquifers are primarily governed by groundwater temperature, leading to a sustained and sufficient supply of PO 4 3- in a low groundwater temperature environment. Furthermore, along with submarine groundwater discharge (SGD), the ongoing release of PO 4 3- in groundwater enters coastal waters and serves as sufficient nourishment for promoting algal blooms in coastal areas. These results highlight the importance of both physical and chemical mechanisms, as well as SGD, in regulating coastal algal blooms. These findings have practical implications for the prevention of coastal algal blooms and provide insights into mariculture, water security, and the sustainability of coastal ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Dairy cattle are often restrained for veterinary procedures, but restraint can cause fear responses that can make the procedure challenging for both the animal and the human handler. Positive reinforcement training (PRT) is used in other species to reduce fear responses and there is now evidence that this can also facilitate handling in cattle. The objectives of this study were to test the effect of PRT on anticipatory and play behavior in dairy heifers. We predicted that heifers trained with PRT would show more anticipatory and play behaviors than control heifers in the period before gaining access to a chute. We used 20 heifers (5 ± 0.6 mo old) that had been habituated to the chute area and had previous experience with handling. Heifers were randomly assigned to 2 treatments: control (n = 10) and PRT (n = 10). Positive reinforcement training heifers were subjected to a training protocol that included standard farm handling techniques, as well as target training with food reinforcement. Control heifers were moved to the chute using standard farm handling techniques only. As predicted, PRT heifers performed more behavioral transitions (7.6 ± 0.77 vs. 4.4 ± 0.57 transitions for control heifers; F 1,9 = 21.99, P < 0.01), and specifically performed more locomotory play such as jumping (2.1 ± 0.30 vs. 0.4 ± 0.19 jumps; F 1,9 = 57.18, P < 0.01) and running (2.0 ± 0.40 s vs. 0.5 ± 0.16 s; F 1,9 = 20.73, P < 0.01). These results indicate that PRT results in heifers having a more positive emotional state in anticipation of handling, and support the use of training to improve the welfare of dairy cattle., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Harmful Algal Blooms (HAB) are damaging to ecosystem functions and pose challenges to environmental and fisheries management. The key to HAB management and understanding the complex algal growth dynamics is the development of robust systems for real-time monitoring of algae populations and species. Previous algae classification studies mainly rely on the combination of an in-situ imaging flow cytometer and an off-site lab-based algae classification model such as Random Forest (RF) for the analysis of high-throughput images. An on-site AI algae monitoring system on top of an edge AI chip embedded with the proposed Algal Morphology Deep Neural Network (AMDNN) model is developed to achieve real-time algae species classification and HAB prediction. Based on a detailed examination of real-world algae images, dataset augmentation is first performed: consisting of orientation, flipping, blurring, and Resizing with Aspect ratio Preserved (RAP). The dataset augmentation is shown to significantly improve classification performance which is superior to that of the competitive RF model. And the attention heatmaps show that for relatively regular-shaped algal species (e.g., Vicicitus), the model weights the color and texture information heavily; while the shape-related features are more important for complex-shaped algae (e.g., Chaetoceros). The AMDNN is tested on a dataset of 11,250 algae images containing the 25 most common HAB classes in Hong Kong subtropical waters with 99.87% test accuracy. Based on the fast and accurate algae classification, the AI-chip-based on-site system is applied to a one-month dataset in February 2020; the predicted trends of total cell counts and targeted HAB species counts are in good agreement with observations. The proposed edge AI algae monitoring system provides a platform for the development of practical HAB early warning systems that can effectively support environmental risk and fisheries management., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

The Secchi disk depth (SD) is an important parameter in aquatic ecosystem monitoring. As algal growth depends on solar irradiation, the SD - a measure of light extinction - gives an indirect indication of the chlorophyll concentration. However, most SD measurements are manually based and too sparse to resolve water quality variations during algal blooms. A remotely controlled automatic system for field measurement of light extinction has been developed and installed in three marine fish culture zones in Hong Kong. The visual images of the disk at different prescribed depths and the surrounding water are taken. Based on the contrast theory and image analysis, the recorded light intensity distributions can be analyzed to give the SD and the light extinction coefficient. The method has been extensively verified by field data over a wide range of water quality and hydro-meteorological conditions. The proposed system enables high frequency SD measurements on demand for environmental management and emergency response., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Ecosystem based water quality models are important tools for prognostic site assessment and evaluation of ecosystem-performance of marine fish farms. We present the development and application of a comprehensive Fish Culture Zone Water Quality Model using continuous bi-weekly field data over a six-year period (2012-2017). The model simulates five interacting subsystems: phytoplankton, phosphorus and nitrogen cycles, and the dissolved oxygen (DO) and particulate organic carbon balance. The application of the model to two fish culture zones in Hong Kong shows the model captures the trends of nutrient and DO variation and the performance in quantitative prediction of algal biomass is challenging. The effect of errors in the specification of primary model inputs are evaluated using dimensionless sensitivity coefficients based on First Order Variance Analysis reveals the relative importance of fish stock (loading), physical size (volume), tidal flushing rate and boundary conditions in the prediction of key water quality variables., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Galvanic corrosion is one of the main reasons for pipe degradation and lead contamination in drinking water systems. The electrical connection of dissimilar metals in corrosive tap water accelerates the dissolution rate of lead from leaded materials. This paper reports an electrochemistry based model to predict lead leaching from a copper pipe fitted with leaded connections. The corrosion of lead at the metal-electrolyte interface depends on the charge transfer and the electric field across the interface. The electric potential field and the mass transport process are dynamically coupled for corrosion propagation in stagnant water; they are respectively governed by the conservation of charge and reactant mass. Using polarization parameters for the electrodes as a function of concentration of oxidizing agents, a dynamic electrochemical model is developed to predict lead leaching from galvanic corrosion. The predicted lead and copper leaching curves are in good agreement with the experimental data for a lead-soldered coupled copper pipe, a brass valve coupled copper pipe, and a pure copper pipe. The findings offer a quantitative understanding on galvanic corrosion in drinking water supply systems and a practical modeling framework for prediction of lead leaching in tap water as a function of stagnation time., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Statistical water quality forecast models are useful tools to assist with beach management. In particular, multiple linear regression (MLR) models have been successfully developed for prediction of fecal indicator bacteria concentrations for beaches in river, lake, and marine environments. Nevertheless, an unresolved challenging issue is the reliable prediction of infrequent events of high bacterial concentrations to inform beach closure decisions to protect public health. The number of field data available for the infrequent events is typically an order of magnitude less than that for days when the water quality criterion is met-MLR models often perform poorly in predicting bacterial concentrations on days when the beaches should be closed. For beach management in Hong Kong, MLR models have been developed to predict beach water quality indices in terms of four gradings (BWQI-1 to 4) based on Escherichia coli ( E. coli ) concentrations. In this study, we propose an artificial intelligence (AI)-based binary classification (EasyEnsemble) model using class-imbalance learning to predict "very poor" occasions (BWQI-4)-when E. coli concentration exceeds 610 counts/100 mL. Models are developed for three marine beaches with different hydrographic and pollution characteristics using a 30 year data set spanning three periods with different water quality status. The model-data comparison over a wide range of conditions shows that the proposed method results in a significant improvement in the prediction of "very poor" water quality. The proposed class-imbalance method for predicting rare events has an F-score of 0.84, and it significantly outperforms MLR and classification tree (CT) models with corresponding F-scores of 0.39 and 0.69. A robust beach water quality forecast system can hence be developed using hybrid MLR-binary classification modeling.

In eutrophic coastal waters, harmful algal blooms (HAB) often occur and present challenges to environmental and fisheries management. Despite decades of research on HAB early warning systems, the field validation of algal bloom forecast models have received scant attention. We propose a daily algal bloom risk forecast system based on: (i) a vertical stability theory verified against 191 past algal bloom events; and (ii) a data-driven artificial neural network (ANN) model that assimilates high frequency data to predict sea surface temperature (SST), vertical temperature and salinity differential with an accuracy of 0.35 o C, 0.51 o C, and 0.58 psu respectively. The model does not rely on past chlorophyll measurements and has been validated against extensive field data. Operational forecasts are illustrated for representative algal bloom events at a marine fish farm in Tolo Harbour, Hong Kong. The robust model can assist with traditional onsite monitoring as well as artificial-intelligence (AI) based methods., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Excess concentrations of lead (Pb) were found in tap water from drinking water supply systems of high-rise buildings in 11 public rental housing (PRH) estates in Hong Kong, posing threats to public health. The copper supply lines are fitted with lead-soldered connections and brass fixtures and faucets. The causes of excess lead are studied through field sampling on occupied households, experiments on prototype supply chains, and 3D numerical modeling. The tap water lead concentration of 129 households in the PRH estates was sampled using a specially designed protocol, revealing the highly variable lead concentration variations induced by sources along the supply chain. Lead concentration variation at consumer tap and its relation with various lead sources are studied in a full-scale test rig. A 3D computational fluid dynamics (CFD) model is successfully developed to interpret the time variation of lead concentrations at the consumer tap. Model predictions of the complex variation of dissolved lead are in good agreement with data and confirm lead solder in copper pipe connections as a major cause of the "lead water" episode in Hong Kong. The CFD calculations demonstrate the importance of turbulent diffusion and shear flow dispersion in the modeling of lead; the use of a "plug flow" approximation can result in significant overestimation of lead concentration. The findings provide a basis for lead risk assessment of different water sampling strategies in densely populated high-rise buildings in Megacities.

The explosive growth of phytoplankton under favorable conditions in subtropical coastal waters can lead to water discolouration and massive fish kills. Traditional water quality monitoring relies on manual field sampling and laboratory analysis of chlorophyll-a (Chl-a) concentration, which is resources intensive and time consuming. The cloudy weather of Hong Kong also precludes using satellite images for algal blooms monitoring. This study for the first time demonstrates the use of an Unmanned Aerial Vehicle (UAVs) to quantitatively map surface water Chl-a distribution in coastal waters from a low altitude. An estimation model for Chl-a concentration from visible images taken by a digital camera on a UAV has been developed and validated against one-year field data. The cost-effective and robust technology is able to map the spatial and temporal variations of Chl-a concentration during an algal bloom. The proposed method offers a useful complement to traditional field monitoring for fisheries management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Stonecutters Island Sewage Treatment Works is one of the largest sewage treatment plants in the world and consists mainly of a chemically enhanced primary treatment (CEPT) unit and a disinfection unit. It has long been realized that most of the dosed chlorine (15 mg/L) is lost at the beginning part of the disinfection unit during disinfection of the CEPT effluent. Lab-scale tests were therefore conducted in this study to determine the causes. Because ferric chloride is used in CEPT, ferrous iron in the CEPT effluent (from the reduction of ferric iron) was initially thought to be the main chlorine consumer. However, the chlorine consumption by ferrous iron was found to be 1.2 mg/L at most. Suspended solids and sulfide also did not contribute significantly to the chlorine consumption. Batch tests were therefore conducted to evaluate the effects of mixing condition and chlorine stock solution concentration on the chlorine consumption. Less chlorine was consumed upon increased mixing. Using a high-concentration chlorine stock solution (25000 mg/L) resulted in a 3-times-higher chlorine consumption in the absence of mixing than using a low-concentration chlorine stock solution (2500 mg/L). By correlating the losses of ammonia and total nitrogen with the chlorine consumption, we hypothesized that the use of a high-concentration chlorine stock solution under poor mixing leads to a localized high ratio of chlorine to ammonia, resulting in breakpoint chlorination and an unusually excessive chlorine consumption. A novel apparatus was developed to quantify the nitrogen gas generated during chlorination of a simulated wastewater, and the mass balance of nitrogen-containing species (i.e., ammonia, nitrogen gas, nitrite and nitrate) during the chlorination was inspected. The good fit between the measured chlorine consumption and that back-calculated from nitrogen-containing species verified our hypothesis. Finally, it needs mentioning that the high chlorine consumption and the breakpoint chlorination may occur during chlorine disinfection of any sewage effluents with relatively high ammonia levels; thus it is suggested that either not-too-high concentrations of chlorine stock solutions or sufficient mixing should be applied during disinfection of the sewage effluents., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Many of the world's large coastal cities discharge partially treated wastewater effluents containing various endocrine disrupting chemicals (EDCs) to coastal environments. Nonylphenols (NP) and bisphenol A (BPA) were found to be the most abundant EDCs in sewage effluents in Hong Kong. The environmental fate and ecological risk of these two EDCs remains largely unknown, particular for coastal systems with complex hydrodynamic flows. Based on a validated three-dimensional (3D) multiple-scale hydrodynamic model, a field-based study was conducted to track the two EDCs from potential sources to the only marine reserve in Hong Kong. The two compounds were detected in all seawater, suspended particle, and sediment samples, with higher aqueous concentrations in wet season than in dry season. High concentrations in sediments suggest sediment is a sink, posing an ecological risk to the benthos. The fate and transport of the two EDCs was predicted using a 3D near-field Lagrangian jet model seamlessly coupled with a 3D shallow water circulation model. The results suggested the NP and BPA in the marine reserve cannot be solely attributed to the nearby submarine sewage outfall, but likely concurrently contributed by other sources. This study calls for more effective measures of reducing the use and release of these EDCs, and research to investigate their impacts on the marine benthos., (Copyright © 2017 Elsevier Ltd. All rights reserved.)