A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ42 protein

● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ₄₂. ● β-content of the peptide was significantly enhanced in the presence of the PAHs. ● Nicotine made stable cluster with Aβ₄₂ peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ₄₂ monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ₄₂ peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ₄₂ peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Investigation of long-term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

This study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite-nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann-Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.     

Mowers versus growers: Riparian buffer management in the Southern Blue Ridge Mountains,USA

Despite long-standing knowledge of the benefits of riparian buffers for mitigating nonpoint source pollution, many streams are unprotected by buffers. Even landowners who understand ecological values of buffers mow riparian vegetation to the streambank. Do trends in rural riparian conditions reflect the development of riparian forest science? What motivates residential riparian management actions? Using high-resolution orthoimagery, we quantified riparian conditions and trends between 1998 and 2015 in the rural upper Little Tennessee River basin in Macon County, North Carolina and explored how landowners view riparian zone management and riparian restoration programs. Buffer composition in 2015 was as follows: no buffer (32.5%), narrow (19.3%), forested (26.7%), shrub (7.2%), and intermediate (7.0%). Relative to 1998, the greatest decrease occurred in the no buffer class (−17.7%, 46 km) and the largest increases occurred in the shrub (+72.5%, 20 km) and narrow (12.6%, 14 km) classes. Forested buffer marginally increased. Semi-structured interview data suggest that landowners prioritize recreational and scenic aspects of riparian buffers over ecological functions such as filtration and bank stabilization. Riparian restoration programs might be made more enticing to non-adopters if outreach language appealed to landowner priorities, design elements demonstrated intentional management, and program managers highlighted areas where ecological goals and landowner values align.     

Five-wheel framework for system-based monitoring of heavy metal pollution in rivers

Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.     

Changes in electrical generator cooling systems: Are they cost-effective sources of water now and under climate change?

In the United States, thermal power plant electrical generators (EGs) are large water diverters and consumptive users who need water for cooling. Retrofitting existing cooling systems to dry cooling and building new facilities with dry cooling can save water and reduce EG's vulnerability to drought. However, this can be an expensive source of water. We estimate that the cost of water saved by retrofitting cooling in existing EGs ranges from $0.04/m³ to $18/m³ depending on facility characteristics. Also water savings from building new EGs with dry cooling ranges in cost per unit water from $1.29/m³ to $2.24/m³. We compare costs with that for water development projects identified in the Texas State Water Plan. We find the water cost from converting to dry cooling is lower than many of the water development possibilities. We then estimate the impact of climate change on the cost of water saved, finding climate change can increase EG water use by up to 9.3% and lower the costs of water saved. Generally, it appears that water planners might consider cooling alterations as a cost competitive water development alternative whose cost would be further decreased by climate change.     

Co-development of an integrated assessment framework to evaluate the effectiveness and impact of selected nature-based water treatment technologies in Sri Lanka,The Philippines,and Vietnam

Water quality is a critical challenge in Asia in the context of growing industrialization, urbanization, and climate change. Nature-based solutions (NbS) could play an important role in reducing urban water pollution, while generating multiple co-benefits that could make cities more liveable and resilient. In this regard, a number of pilot and demonstration projects have been set up to explore their potential across cities in Asia. Their effectiveness and impacts, however, have not been adequately documented, thus how they can be sustained, replicated and up-scaled remain poorly understood. This study aims to contribute to addressing this challenge by co-developing an integrated assessment framework and employing it to understand how existing evaluations of NbS in the region can be improved. It focuses specifically on a set of nature-based solutions that have been employed for water treatment across six cities in Southeast Asia (two in each Sri Lanka, the Philippines, and Vietnam), namely, floating wetlands, constructed wetlands and maturation ponds. The study also suggests specific methodologies for capturing a set of core indicators considered relevant for assessing the effectiveness and capturing the multi-faceted impacts of the examined NbS.     

财政分权对环境污染的空间效应

利用2006—2017年我国30个省级行政单位的面板数据,采用空间计量模型对财政分权与环境污染之间的关系进行实证分析,并通过效应分解分别计算出财政收入分权和财政支出分权对环境污染的直接效应、空间溢出效应和总效应。模型估计的结果表明:①财政收入分权与财政支出分权通过直接效应和空间溢出效应两种途径对环境污染产生显著影响,但收入分权与支出分权对环境污染的空间效应具有异质性。②财政收入分权对环境污染具有显著的负向直接效应和负向空间溢出效应,影响系数分别为-5.429和-17.572。提高财政收入分权度不仅有利于减轻本省份环境污染,也对邻近省份的环境污染起到抑制作用。而财政支出分权对环境污染具有显著的负向直接效应和正向的空间溢出效应,影响程度分别为-3.345和1.173。提高财政支出分权虽然有利于减少本省份污染的排放,但在一定程度上引起省际的污染外溢效应,提高邻近省份的污染程度。③从总效应来看,财政收入分权和财政支出分权对环境污染均呈现出显著的负向相关性,影响程度分别为-23.001和-2.172。提高财政分权可降低环境污染水平,有利于环境质量提升。④通过构建不同空间权重矩阵,检验模型的稳健性,结果表明模型估计是稳健可靠的。根据分析结论,可能的启示与建议有:①合理划分中央政府和地方政府之间的财政事权和支出责任,在提高财政支出分权度的同时提高地方政府财政收入分权度,充分发挥地方政府环境治理的积极性和主动性。②继续加大生态环境保护力度,完善绿色经济考核机制,引导地方政府加大对污染治理和环境改善的支出水平及效率。     

Critical review for the quantification of leachate production: Evaluation example for the “Oum Azza” public landfill in Rabat city,Morocco

The estimation of leachate quantities produced in landfills is necessary to dimension the treatment plants allowing to reduce the polluting load of these effluents and consequently avoid their negative impacts on the environment. Different leachate quantification methods were used in this study to assess the leachate volume produced at the Oum Azza landfill. The water balance method give comparable estimations of leachate production to the Ouled Berjal landfill ratio. The first method showed average values between 487 and 495 m³/day for 2015, 2018, and 2019, and at the same time, the second method gave values between 470 and 477 m³/day for the same years. In contrast, the World Bank ratio showed high values that vary between 2260 and 2295 m³/day for 2015, 2018, and 2019. The on-site data and the statistical analysis showed us that the World Bank ratio is not adapted for the estimation of the leachates produced in Oum Azza landfill, while the water balance and the ratio of Ouled Berjal landfill allowed to give comparable results to reality.