Regulating greenhouse gas dynamics in tidal wetlands: Impacts of salinity gradients and water pollution.

Tidal wetlands play a critical role in emitting greenhouse gases (GHGs) into the atmosphere; our understanding of the intricate interplay between natural processes and human activities shaping their biogeochemistry and GHG emissions remains lacking. In this study, we delve into the spatiotemporal dynamics and key drivers of the GHG emissions from five tidal wetlands in the Scheldt Estuary by focusing on the interactive impacts of salinity and water pollution, two factors exhibiting contrasting gradients in this estuarine system: pollution escalates as salinity declines. Our findings reveal a marked escalation in GHG emissions when moving upstream, primarily attributed to increased concentrations of organic matter and nutrients, coupled with reduced levels of dissolved oxygen and pH. These low water quality conditions not only promote methanogenesis and denitrification to produce CH4 and N2O, respectively, but also shift the carbonate equilibria towards releasing more CO2. As a result, the most upstream freshwater wetland was the largest GHG emitter with a global warming potential around 35 to 70 times higher than the other wetlands. When moving seaward along a gradient of decreasing urbanization and increasing salinity, wetlands become less polluted and are characterized by lower concentrations of NO3-, TN and TOC, which induces stronger negative impact of elevated salinity on the GHG emissions from the saline wetlands. Consequently, these meso-to polyhaline wetlands released considerably smaller amounts of GHGs. These findings emphasize the importance of integrating management strategies, such as wetland restoration and pollution prevention, that address both natural salinity gradients and human-induced water pollution to effectively mitigate GHG emissions from tidal wetlands.

Performance of modified first-stage French Vertical Flow Constructed Wetlands under extreme operational conditions.

Operation conditions considerably affect the removal efficiency of wastewater treatment systems, and yet we still lack data on how these systems function under extreme dilution rates and climatic conditions at high altitudes. Here, we applied two modified First-Stage French Vertical Flow Constructed Wetlands (FS-FVFCWs) for sewage treatment in Northern Tropical Andes. Specifically, within 18 months, we conducted a pilot-scale experiment at two hydraulic loading rates (HLRs) of 0.94 and 0.56 m d-1, representing 2.5 and 1.5 times the recommended design values, with two different feeding/resting periods to investigate the impact of HLRs and operational strategy on system performance. We found that chemical oxygen demand (COD) and total suspended solids (TSS) removal was satisfactory, with average values of 53 ± 18 and 69 ± 16%, respectively. Moreover, reducing HLRs resulted in higher removal efficiency for COD, from 46 ± 15 to 64 ± 15%, but had no impact on TSS removal, with 3 days of feeding and 6 days of resting. For an equal time of feeding and resting, COD and TSS removals were not affected by the modified HLR. These findings suggest that high HLRs can be applied to FS-FVFCW without compromising the system operation and obtaining satisfactory results, leading to opportunities to reduce areas and costs.

Interpreting a recurrent neural network's predictions of ICU mortality risk.

Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Electronic Medical Records (EMR) introduce additional barriers to transparency because of the sequential processing of the RNN and the multi-modal nature of EMR data. This work seeks to improve transparency by: 1) introducing Learned Binary Masks (LBM) as a method for identifying which EMR variables contributed to an RNN model's risk of mortality (ROM) predictions for critically ill children; and 2) applying KernelSHAP for the same purpose. Given an individual patient, LBM and KernelSHAP both generate an attribution matrix that shows the contribution of each input feature to the RNN's sequence of predictions for that patient. Attribution matrices can be aggregated in many ways to facilitate different levels of analysis of the RNN model and its predictions. Presented are three methods of aggregations and analyses: 1) over volatile time periods within individual patient predictions, 2) over populations of ICU patients sharing specific diagnoses, and 3) across the general population of critically ill children.

Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset.

OBJECTIVES: Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child's risk of mortality throughout their ICU stay as a proxy measure of severity of illness. DESIGN: Retrospective cohort study. SETTING: PICU in a tertiary care academic children's hospital. PATIENTS/SUBJECTS: Twelve thousand five hundred sixteen episodes (9,070 children) admitted to the PICU between January 2010 and February 2019, partitioned into training (50%), validation (25%), and test (25%) sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: On 2,475 test set episodes lasting greater than or equal to 24 hours in the PICU, the area under the receiver operating characteristic curve of the recurrent neural network's 12th hour predictions was 0.94 (CI, 0.93-0.95), higher than those of Pediatric Index of Mortality 2 (0.88; CI, [0.85-0.91]; p < 0.02), Pediatric Risk of Mortality III (12th hr) (0.89; CI, [0.86-0.92]; p < 0.05), and Pediatric Logistic Organ Dysfunction day 1 (0.85; [0.81-0.89]; p < 0.002). The recurrent neural network's discrimination increased with more acquired data and smaller lead time, achieving a 0.99 area under the receiver operating characteristic curve 24 hours prior to discharge. Despite not having diagnostic information, the recurrent neural network performed well across different primary diagnostic categories, generally achieving higher area under the receiver operating characteristic curve for these groups than the other three scores. On 692 test set episodes lasting greater than or equal to 5 days in the PICU, the recurrent neural network area under the receiver operating characteristic curves significantly outperformed their daily Pediatric Logistic Organ Dysfunction counterparts (p < 0.005). CONCLUSIONS: The recurrent neural network model can process hundreds of input variables contained in a patient's electronic medical record and integrate them dynamically as measurements become available. Its high discrimination suggests the recurrent neural network's potential to provide an accurate, continuous, and real-time assessment of a child in the ICU.

Live birth after in vitro maturation in women with gonadotropin resistance ovary syndrome: report of two cases.

PURPOSE: Gonadotropin-resistant ovary syndrome (GROS) is a rare endocrine disorder that causes hypergonadotropic hypogonadism, amenorrhea, and infertility. This study reports live birth in two women with GROS who underwent fertility treatment with in vitro maturation (IVM). METHODS: Both patients had primary infertility, amenorrhea (primary and secondary), typical secondary sexual characters, elevated gonadotropin levels, normal ovarian reserve, normal chromosomal characteristics, and previous nonresponsiveness gonadotropin stimulations. One patient had polymorphism of the follicle-stimulating hormone receptor, which is a predictor of poor ovarian response. Given unresponsiveness to exogenous gonadotropin stimulations, IVM with human chorionic gonadotropin priming (hCG-IVM) was performed in both patients. All transferrable embryos were vitrified. RESULTS: Both patients achieved pregnancy after their first frozen embryos transfer, and each delivered a healthy baby boy. CONCLUSIONS: These results suggest that IVM should be a first-line therapeutic option for patients with GROS.

Integrated mechanistic and data-driven modeling for risk assessment of greenhouse gas production in an urbanized river system.

Surrounded by intense anthropogenic activities, urban polluted rivers have increasingly been reported as a significant source of greenhouse gases (GHGs). However, unlike pollution and climate change, no integrated urban water models have investigated the GHG production in urban rivers due to system complexity. In this study, we proposed a novel integrated framework of mechanistic and data-driven models to qualitatively assess the risks of GHG accumulation in an urban river system in different water management interventions. Particularly, the mechanistic model delivered elaborated insights into river states in four intervention scenarios in which the installation of a new wastewater treatment plant using two different technologies, together with new sewage systems and additional retention tanks, were assessed during dry and rainy seasons. From the insights, we applied fuzzy rule-based models as a decision support tool to predict the GHG accumulation risks and identify their driving factors in the scenarios. The obtained results indicated the important role of new discharge connection and additional storage capacity in decreasing pollutant concentrations, consequently, reducing the risks. Moreover, among the major variables explaining the GHG accumulation in the rivers, DO level was considerably affected by the reaeration capacity of the rivers that was strongly dependent on river slope and flow. Furthermore, river water quality emerged as the most critical variable explaining the pCO2 and N2O accumulation that implied that the more polluted and anaerobic the sites were, the higher were their GHG accumulation. Given its simplicity and transparency, the proposed modeling framework can be applied to other river basins as a decision support tool in setting up integrated urban water management plans.

Psychometric evaluation of the traditional Chinese version of the resilience Scale-14 and assessment of resilience in Hong Kong adolescents.

BACKGROUND: A reliable and valid instrument that accurately measures resilience is crucial for the development of interventions to enhance the resilience of adolescents and promote their positive mental well-being. However, there is a lack of adolescent resilience assessment tools with good psychometric properties suitable for use with Hong Kong participants. This study aimed to evaluate the psychometric properties of the traditional Chinese version of the Resilience Scale-14. METHODS: Between October 2017 and January 2018, a stratified random sample of 1816 Grade 7 (aged 11-15 years) students from all 18 districts of Hong Kong were invited to participate in the study. Subjects were asked to respond to the traditional Chinese version of the Resilience Scale-14, the Center for Epidemiologic Studies Depression Scale for children, and Rosenberg's Self-Esteem Scale. The psychometric properties, including the internal consistency, content validity, convergent and discriminant validity, exploratory and confirmatory factor analyses, and test-retest reliability of the Resilience Scale-14 were assessed. RESULTS: The translated scale demonstrated good internal consistency and test-retest reliability, excellent content validity, and appropriate convergent and discriminant validity. The results of the confirmatory factor analysis supported the two-factor structure of the traditional Chinese version of the Resilience Scale-14. CONCLUSIONS: Results suggest that the translated scale is a reliable and valid tool to assess the resilience of young Hong Kong Chinese adolescents. Healthcare professionals could use the newly translated scale to assess resilience levels among Hong Kong adolescents and develop interventions that can help them combat mental health problems and lead healthier lives. TRIAL REGISTRATION: Clinicaltrials.gov ID NCT03538145 (retrospectively registered on May 15, 2018).

The impact of extraneous features on the performance of recurrent neural network models in clinical tasks.

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input features on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous features that were randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR features only; EMR and extraneous features; extraneous features only) were trained to predict three clinical outcomes: in-ICU mortality, 72-h ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the inclusion of extraneous features to EMR variables were negligible.

Quitting trajectories of Chinese women smokers following telephone smoking cessation counselling: A longitudinal study.

AIMS AND OBJECTIVES: This study mapped the quitting patterns (trajectories) of Hong Kong Chinese women smokers who had received counselling via a quitline service and examined factors correlated with different trajectories. BACKGROUND: Quitting smoking is always a gradual and progressive process. However, most existing studies on smoking cessation have adopted a cross-sectional approach to conduct evaluation. Little is known about the quitting trajectories of smokers, particularly those who are women after receiving smoking cessation counselling. METHODS: We used a retrospective longitudinal design and analysed 474 women smokers who had called the quitline. Quitting trajectories were mapped using latent growth modelling. Multinomial logistic regression was performed to identify factors associated with class membership. A STROBE checklist was completed. RESULTS: We identified three trajectory groups: 'quitters' who quit smoking at 6 months and abstained from cigarettes up to 6 years; 'reducers' who cut down cigarette consumption ≥50% at 3 years and maintained reduced levels up to 6 years; and 'increasers' who increased smoking ≥20% at 3 years and continued smoking up to 6 years. Participants who perceived more difficulties in quitting were more likely to be increasers. Those with higher daily cigarette consumption at baseline were more likely to be reducers. CONCLUSIONS: We clarified three trajectory groups of women smokers. The results indicate that existing cessation services need to be improved, especially for women smokers who do not quit after receiving telephone counselling. RELEVANCE TO CLINICAL PRACTICE: Existing cessation services need to be improved, especially for women smokers who do not quit after receiving telephone counselling. For those who reduce smoking but fail to quit, quit plans should be developed that provide step-by-step guidance in achieving abstinence through smoking reduction. Instant messages may complement telephone counselling to deliver cessation support for those who increase their cigarette consumption.

Exploring the influence of meteorological conditions on the performance of a waste stabilization pond at high altitude with structural equation modeling.

Algal photosynthesis plays a key role in the removal mechanisms of waste stabilization ponds (WSPs), which is indicated in the variations of three parameters, dissolved oxygen, pH, and chlorophyll a. These variations can be considerably affected by extreme climatic conditions at high altitude. To investigate these effects, three sampling campaigns were conducted in a high-altitude WSP in Cuenca (Ecuador). From the collected data, the first application of structure equation modeling (SEM) on a pond system was fitted to analyze the influence of high-altitude characteristics on pond performance, especially on the three indicators. Noticeably, air temperature appeared as the highest influencing factors as low temperature at high altitude can greatly decrease the growth rate of microorganisms. Strong wind and large diurnal variations of temperature, 7-20 °C, enhanced flow efficiency by improving mixing inside the ponds. Intense solar radiation brought both advantages and disadvantages as it boosted oxygen level during the day but promoted algal overgrowth causing oxygen depletion during the night. From these findings, the authors proposed insightful recommendations for future design, monitoring, and operation of high-altitude WSPs. Moreover, we also recommended SEM to pond engineers as an effective tool for better simulation of such complex systems like WSPs.

Modeling greenhouse gas emissions from riverine systems: A review.

Despite the recognized importance of flowing waters in global greenhouse gas (GHG) budgets, riverine GHG models remain oversimplified, consequently restraining the development of effective prediction for riverine GHG emissions feedbacks. Here we elucidate the state of the art of riverine GHG models by investigating 148 models from 122 papers published from 2010 to 2021. Our findings indicate that riverine GHG models have been mostly data-driven models (83%), while mechanistic and hybrid models were uncommonly applied (12% and 5%, respectively). Overall, riverine GHG models were mainly used to explain relationships between GHG emissions and biochemical factors, while the role of hydrological, geomorphic, land use and cover factors remains missing. The development of complex and advanced models has been limited by data scarcity issues; hence, efforts should focus on developing affordable automatic monitoring methods to improve data quality and quantity. For future research, we request for basin-scale studies explaining river and land-surface interactions for which hybrid models are recommended given their flexibility. Such a holistic understanding of GHG dynamics would facilitate scaling-up efforts, thereby reducing uncertainties in global GHG estimates. Lastly, we propose an application framework for model selection based on three main criteria, including model purpose, model scale and the spatiotemporal characteristics of GHG data, by which optimal models can be applied in various study conditions.

Epigenetic analytical approaches in ecotoxicological aquatic research.

Environmental epigenetics has become a key research focus in global climate change studies and environmental pollutant investigations impacting aquatic ecosystems. Specifically, triggered by environmental stress conditions, intergenerational DNA methylation changes contribute to biological adaptive responses and survival of organisms to increase their tolerance towards these conditions. To critically review epigenetic analytical approaches in ecotoxicological aquatic research, we evaluated 78 publications reported over the past five years (2016-2021) that applied these methods to investigate the responses of aquatic organisms to environmental changes and pollution. The results show that DNA methylation appears to be the most robust epigenetic regulatory mark studied in aquatic animals. As such, multiple DNA methylation analysis methods have been developed in aquatic organisms, including enzyme restriction digestion-based and methyl-specific immunoprecipitation methods, and bisulfite (in)dependent sequencing strategies. In contrast, only a handful of aquatic studies, i.e. about 15%, have been focusing on histone variants and post-translational modifications due to the lack of species-specific affinity based immunological reagents, such as specific antibodies for chromatin immunoprecipitation applications. Similarly, ncRNA regulation remains as the least popular method used in the field of environmental epigenetics. Insights into the opportunities and challenges of the DNA methylation and histone variant analysis methods as well as decreasing costs of next generation sequencing approaches suggest that large-scale epigenetic environmental studies in model and non-model organisms will soon become available in the near future. Moreover, antibody-dependent and independent methods, such as mass spectrometry-based methods, can be used as an alternative epigenetic approach to characterize global changes of chromatin histone modifications in future aquatic research. Finally, a systematic guide for DNA methylation and histone variant methods is offered for ecotoxicological aquatic researchers to select the most relevant epigenetic analytical approach in their research.

Impact of salinity gradient, water pollution and land use types on greenhouse gas emissions from an urbanized estuary.

Estuaries have been recognized as one of the major sources of greenhouse gases (GHGs) in aquatic systems; yet we still lack insights into the impact of both anthropogenic and natural factors on the dynamics of GHG emissions. Here, we assessed the spatiotemporal dynamics and underlying drivers of the GHG emissions from the Scheldt Estuary with a focus on the effects of salinity gradient, water pollution, and land use types, together with their interaction. Overall, we found a negative impact of salinity on carbon dioxide (CO2) and nitrous oxide (N2O) emissions which can be due to the decrease of both salinity and water quality when moving upstream. Stronger impact of water pollution on the GHG emissions was found at the freshwater sites upstream compared to saline sites downstream. In particular, when water quality of the sites reduced from good, mainly located in the mouth and surrounded by arable sites, to polluted, mainly located in the upstream and surrounded by urban sites, CO2 emissions from the sites doubled while N2O emissions tripled. Similarly, the effects of water pollution on methane (CH4) emissions became much stronger in the freshwater sites compared to the saline sites. These decreasing effects from upstream to the mouth were associated with the increase in urbanization as sites surrounded by urban areas released on average almost two times more CO2 and N2O than sites surrounded by nature and industry areas. Applied machine learning methods also revealed that, in addition to salinity effects, nutrient and organic enrichment stimulated the GHG emissions from the Scheldt Estuary. These findings highlight the importance of the interaction between salinity, water pollution, and land use in order to understand their influences on GHG emissions from dynamic estuarine systems.

A randomized controlled trial of a peer-facilitated self-management program for people with recent-onset psychosis.

Early patient-centered interventions can improve mental health and prevent psychotic relapse in people with recent-onset psychosis (ROP). However, limited effective peer-facilitated early interventions are found worldwide. We aimed to test the effects of a four-month peer-facilitated self-management intervention (PFSMI) for Chinese patients with ROP compared with a psychoeducation group (PEG) and treatment-as-usual (TAU) group. A randomized controlled trial was conducted at six Integrated Community Centers for Mental Wellness in Hong Kong. The primary outcome was level of recovery. Secondary outcomes were improvement of problem-solving ability, insight into illness/treatment, and functioning, and reducing psychotic symptoms and re-hospitalization rates. Overall, 180 ROP patients were randomly selected, and after collecting baseline data, randomly assigned to the PFSMI, PEG or TAU (60 per group). Their outcomes were measured at 1-week and 6-month post-intervention. One hundred and sixty-one patients (89.4 %) completed their interventions, with an overall attrition rate of 7.8 % (n = 14). Based on intention-to-treat principle, results of generalized estimating equation test indicated that the PFSMI group reported significantly greater improvements in levels of recovery, functioning and insight into illness/treatment and reductions in psychotic symptoms and duration of re-hospitalizations (p = 0.0007-0.02, with moderate to large effect sizes) than the TAU group at 1-week post-intervention, and both the TAU and PEG at 6-month post-intervention. Significantly fewer PFSMI participants were hospitalized than the TAU and PEG over 6-month follow-up (p = 0.003). The findings support that PFSMI can produce medium-term positive effects on the mental health and functioning of patients with ROP.

Greenhouse gas dynamics in an urbanized river system: influence of water quality and land use.

Rivers act as a natural source of greenhouse gases (GHGs). However, anthropogenic activities can largely alter the chemical composition and microbial communities of rivers, consequently affecting their GHG production. To investigate these impacts, we assessed the accumulation of CO2, CH4, and N2O in an urban river system (Cuenca, Ecuador). High variation of dissolved GHG concentrations was found among river tributaries that mainly depended on water quality and land use. By using Prati and Oregon water quality indices, we observed a clear pattern between water quality and the dissolved GHG concentration: the more polluted the sites were, the higher were their dissolved GHG concentrations. When river water quality deteriorated from acceptable to very heavily polluted, the mean value of pCO2 and dissolved CH4 increased by up to ten times while N2O concentrations boosted by 15 times. Furthermore, surrounding land-use types, i.e., urban, roads, and agriculture, could considerably affect the GHG production in the rivers. Particularly, the average pCO2 and dissolved N2O of the sites close to urban areas were almost four times higher than those of the natural sites while this ratio was 25 times in case of CH4, reflecting the finding that urban areas had the worst water quality with almost 70% of their sites being polluted while this proportion of nature areas was only 12.5%. Lastly, we identified dissolved oxygen, ammonium, and flow characteristics as the main important factors to the GHG production by applying statistical analysis and random forests. These results highlighted the impacts of land-use types on the production of GHGs in rivers contaminated by sewage discharges and surface runoff.

Sampling errors and variability in video transects for assessment of reef fish assemblage structure and diversity.

Video monitoring is a rapidly evolving tool in aquatic ecological research because of its non-destructive ability to assess fish assemblages. Nevertheless, methodological considerations of video monitoring techniques are often overlooked, especially in more complex sampling designs, causing inefficient data collection, processing, and interpretation. In this study, we discuss how video transect sampling designs could be assessed and how the inter-observer variability, design errors and sampling variability should be quantified and accounted for. The study took place in the coastal areas of the Galapagos archipelago and consisted of a hierarchical repeated-observations sampling design with multiple observers. Although observer bias was negligible for the assessment of fish assemblage structure, diversity and counts of individual species, sampling variability caused by simple counting/detection errors, observer effects and instantaneous fish displacement was often important. Especially for the counts of individual species, sampling variability most often exceeded the variability of the transects and sites. An extensive part of the variability in the fish assemblage structure was explained by the different transects (13%), suggesting that a sufficiently high number of transects is required to account for the within-location variability. Longer transect lengths allowed a better representation of the fish assemblages as sampling variability decreased by 33% if transect length was increased from 10 to 50 meters. However, to increase precision, including more repeats was typically more efficient than using longer transect lengths. The results confirm the suitability of the technique to study reef fish assemblages, but also highlight the importance of a sound methodological assessment since different biological responses and sampling designs are associated with different levels of sampling variability, precision and ecological relevance. Therefore, besides the direct usefulness of the results, the procedures to establish them may be just as valuable for researchers aiming to optimize their own sampling technique and design.

The Contribution of Thai Fisheries to Sustainable Seafood Consumption: National Trends and Future Projections.

Sustainably feeding a growing human population is one of the greatest food system challenges of the 21st century. Seafood plays a vital role in supporting human wellbeing, by providing bioavailable and nutrient-dense animal-source food. In Thailand, seafood demand is increasing, and wild capture fishery yields have plateaued, due to oceanic ecosystem degradation and fishery stock exploitation. In this study, we investigated the supply trend of fishery products and subsequent seafood-derived nutrient availability over the last decade. In addition, we explored the possibility of predicting seafood availability and consumption levels, including adherence to Thailand's national food guide and global dietary recommendations for sustainable seafood consumption. Our findings indicate that, at national-level, fishery products supplied between 19% and 35% of the Thai populations recommended dietary protein intake, 4-6% of calcium, 6-11% of iron, and 2-4% of zinc from 1995 to 2015. Nevertheless, our research also reports that if Thailand's wild-caught seafood production were to decrease by 13%, as is highly likely, by 2030, the country might face a per capita supply deficit of fish and shellfish to meet healthy and sustainable dietary recommendations (28-30 g/day), let alone the current Thai average intake (32 g/day). Although a 1% per year increase in aquaculture production might bridge this supply gap, policymakers and relevant fishery stakeholders must consider the long-term environmental impacts of such an approach in Thailand.

Spatial and temporal variations of greenhouse gas emissions from a waste stabilization pond: Effects of sludge distribution and accumulation.

Due to regular influx of organic matter and nutrients, waste stabilization ponds (WSPs) can release considerable quantities of greenhouse gases (GHGs). To investigate the spatiotemporal variations of GHG emissions from WSPs with a focus on the effects of sludge accumulation and distribution, we conducted a bathymetry survey and two sampling campaigns in Ucubamba WSP (Cuenca, Ecuador). The results indicated that spatial variation of GHG emissions was strongly dependent on sludge distribution. Thick sludge layers in aerated ponds and facultative ponds caused substantial CO2 and CH4 emissions which accounted for 21.3% and 78.7% of the total emissions from the plant. Conversely, the prevalence of anoxic conditions stimulated the N2O consumption via complete denitrification leading to a net uptake from the atmosphere, i.e. up to 1.4±0.2 mg-N m-2 d-1. Double emission rates of CO2 were found in the facultative and maturation ponds during the day compared to night-time emissions, indicating the important role of algal respiration, while no diel variation of the CH4 and N2O emissions was found. Despite the uptake of N2O, the total GHG emissions of the WSP was higher than constructed wetlands and conventional centralized wastewater treatment facilities. Hence, it is recommended that sludge management with proper desludging regulation should be included as an important mitigation measure to reduce the carbon footprint of pond treatment facilities.

An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis.

Imitating natural lakes, pond treatment systems inherit a high complexity with interconnected web of biochemical reactions and complex hydraulic processes. As such, its simulation requires a large and integrated model, which has been a challenge for pond engineers. In this study, we develop an all-encompassing model to gain a quantitative and comprehensive understanding of the hydraulic, physicochemical and microbiological conversion processes in the most common pond, a facultative pond. Moreover, to deal with an evitable issue of large mechanistic models as overparameterization leading to poor identifiability, a systematic parameter estimation was implemented. The application of sensitivity analysis reveals the most influential parameters on pond performance. Particularly, physical parameters, such as vertical eddy diffusivity, water temperature, and maximum growth rate of heterotrophs induce the most changes of organic matters while microbial assimilation and ammonia volatilization appear to be main processes for nutrient removal. In contrast, the efficiency of phosphate precipitation and nutrient biological removal via polyphosphate accumulating organisms and denitrifying bacteria is limited. Identifiability problems are addressed mainly by the characterization of light dependence of algal growth, interaction between water temperature and its coefficient, and the growth of autotrophic bacteria while based on the determinant measures, the most important parameter subsets affecting model outputs are related to physical processes and algal activity. After the establishment of the influential and identifiable parameter subset, an automatic calibration with the data collected from Ucubamba pond system (Ecuador) demonstrates the effect of high-altitude climatic conditions on pond behaviors. An aerobic prevailing condition is observed as a result of high light intensity causing accelerated algal activities, hence, leading to the limitation of hydrolysis, anaerobic processes, and the growth of anoxic heterotrophs for denitrification. Furthermore, the output of uncertainty analysis indicates that a large avoidable uncertainty as a result of vast complexity of the applied model can be reduced greatly via a systematic approach for parameter estimation.