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.     

A systematic review of animal personality in conservation science

Although animal personality research may have applied uses, this suggestion has yet to be evaluated by assessing empirical studies examining animal personality and conservation. To address this knowledge gap, we performed a systematic review of the peer-reviewed literature relating to conservation science and animal personality. Criteria for inclusion in our review included access to full text, primary research articles, and relevant animal conservation or personality focus (i.e., not human personality studies). Ninety-two articles met these criteria. We summarized the conservation contexts, testing procedures (including species and sample size), analytical approach, claimed personality traits (activity, aggression, boldness, exploration, and sociability), and each report's key findings and conservation-focused suggestions. Although providing evidence for repeatability in behavior is crucial for personality studies, repeatability quantification was implemented in only half of the reports. Nonetheless, each of the 5 personality traits were investigated to some extent in a range of conservations contexts. The most robust studies in the field showed variance in how personality relates to other ecologically important variables across species and contexts. Moreover, many studies were first attempts at using personality for conservation purposes in a given study system. Overall, it appears personality is not yet a fully realized tool for conservation. To apply personality research to conservation problems, we suggest researchers think about where individual differences in behavior may affect conservation outcomes in their system, assess where there are opportunities for repeated measures, and follow the most current methodological guides on quantifying personality.     

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.     

Spatial analysis of resources and environmental carrying capacity in Iran

Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land-use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.     

China’s carbon neutrality: an extensive and profound systemic reform

● China has pledged ambitious carbon peak and neutrality goals for mitigating global climate change. ● Major challenges to achieve carbon neutrality in China are summarized. ● The new opportunities along the pathway of China’s carbon neutrality are discussed from four aspects. ● Five policy suggestions for China are provided. China is the largest developing economy and carbon dioxide emitter in the world, the carbon neutrality goal of which will have a profound influence on the mitigation pathway of global climate change. The transition towards a carbon-neutral society is integrated into the construction of ecological civilization in China, and brings profound implications for China’s socioeconomic development. Here, we not only summarize the major challenges in achieving carbon neutrality in China, but also identify the four potential new opportunities: namely, the acceleration of technology innovations, narrowing regional disparity by reshaping the value of resources, transforming the industrial structure, and co-benefits of pollution and carbon mitigation. Finally, we provide five policy suggestions and highlight the importance of balancing economic growth and carbon mitigation, and the joint efforts among the government, the enterprises, and the residents.     

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.