Challenges to Accurate Estimation of Methane Emission from Septic Tanks with Long Emptying Intervals.

Septic tanks in low- and middle-income countries are often not emptied for a long time, potentially resulting in poor pollutant removal efficiency and increased greenhouse gas emissions, including methane (CH4). We examined the impact of long emptying intervals (4.0-23 years) on the biochemical oxygen demand (BOD) removal efficiency of 15 blackwater septic tanks and the CH4 emission rates of 23 blackwater septic tanks in Hanoi. The average BOD removal efficiency was 37% (-2-65%), and the average CH4 emission rate was 10.9 (2.2-26.8) g/(cap·d). The emptying intervals were strongly negatively correlated with BOD removal efficiency (R = -0.676, p = 0.006) and positively correlated with CH4 emission rates (R = 0.614, p = 0.001). CH4 emission rates were positively correlated with sludge depth (R = 0.596, p = 0.002), but against expectation, negatively correlated with BOD removal efficiency (R = -0.219, p = 0.451). These results suggest that shortening the emptying interval improves the BOD removal efficiency and reduces the CH4 emission rate. Moreover, the CH4 emission estimation of the Intergovernmental Panel on Climate Change, which is a positive conversion of BOD removal, might be inaccurate for septic tanks with long emptying intervals. Our findings suggest that emptying intervals, sludge depth, and per-capita emission factors reflecting long emptying intervals are potential parameters for accurately estimating CH4 emissions from septic tanks.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea.

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea.

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Reversible and irreversible transgenerational effects of metal exposure on nine generations of a tropical micro-crustacean.

Micro-crustaceans are important grazers that control the algal blooms in eutrophic lakes. However, we know little about how these key species may be affected by long-term exposure to contaminants and when the transgenerational effects are reversible and irreversible. To address this, we investigated the effects of lead (Pb, 100 µg L-1) exposure on morphology and reproduction of Moina dubia for nine consecutive generations (F1-F9) in three treatments: control, Pb, and pPb (M. dubia from Pb-exposed parents returned to the control condition). In F1-F2, Pb did not affect morphology, and reproduction of M. dubia. In all later generations, Pb-exposed M. dubia had a smaller body and shorter antennae than those in control. In F3-F6, pPb-exposed animals showed no differences in body size and antennae compared to the control, suggesting recoverable effects. In F7-F9, the body size and antennae of pPb-exposed animals did not differ compared to Pb-exposed ones, and both were smaller than the control animals, suggesting irreversible effects. Pb exposure reduced the brood size, number of broods and total neonates per female in F3-F9, yet the reproduction could recover in pPb treatment until F7. No recovery of the brood size and number of broods per female was observed in pPb-exposed animals in the F8-F9. Our study suggests that long-term exposure to metals, here Pb, may cause irreversible impairments in morphology and reproduction of tropical urban micro-crustaceans that may lower the top-down control on algal blooms and functioning of eutrophic urban lakes.