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.     

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.     

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 the Distribution of Broadleaf Tree Species in the Central Part of the Southern Urals since the 1970s

Russian Journal of Ecology - Changes in the distribution of broadleaf tree species—Tilia cordata Mill., Quercus robur L., Acer platanoides L., and Ulmus glabra Huds.—in the central part...     

Materials,fuels, upgrading,economy, and life cycle assessment of the pyrolysis of algal and lignocellulosic biomass: a review

Climate change issues are calling for advanced methods to produce materials and fuels in a carbon–neutral and circular way. For instance, biomass pyrolysis has been intensely investigated during the last years. Here we review the pyrolysis of algal and lignocellulosic biomass with focus on pyrolysis products and mechanisms, oil upgrading, combining pyrolysis and anaerobic digestion, economy, and life cycle assessment. Products include oil, gas, and biochar. Upgrading techniques comprise hot vapor filtration, solvent addition, emulsification, esterification and transesterification, hydrotreatment, steam reforming, and the use of supercritical fluids. We examined the economic viability in terms of profitability, internal rate of return, return on investment, carbon removal service, product pricing, and net present value. We also reviewed 20 recent studies of life cycle assessment. We found that the pyrolysis method highly influenced product yield, ranging from 9.07 to 40.59% for oil, from 10.1 to 41.25% for biochar, and from 11.93 to 28.16% for syngas. Feedstock type, pyrolytic temperature, heating rate, and reaction retention time were the main factors controlling the distribution of pyrolysis products. Pyrolysis mechanisms include bond breaking, cracking, polymerization and re-polymerization, and fragmentation. Biochar from residual forestry could sequester 2.74 tons of carbon dioxide equivalent per ton biochar when applied to the soil and has thus the potential to remove 0.2–2.75 gigatons of atmospheric carbon dioxide annually. The generation of biochar and bio-oil from the pyrolysis process is estimated to be economically feasible.

     

Water,sanitation, hygiene and waste disposal practices as COVID-19 response strategy: insights from Bangladesh

Environment, Development and Sustainability - The COVID-19 pandemic has caused a global emergence, and the absence of a proven vaccine or medicine has led to the implementation of measures to...     

Institutional quality,financial development and sustainable economic growth among lower income countries

This article postulates strong endogenous relationships in lower income countries between institutional quality, financial development and sustained economic growth. These associations were investigated using the vector-error correction model (VECM) and Granger causality method for a sample of 79 countries from 2005 to 2022. The findings show that (1) these variables reinforce each other in the short run. (2) In the long run, both institutional quality and financial development can fuel economic growth. (3) The positive effect of institutional quality on economic growth is greater than that of financial development. Policy implications of these findings are that careful attention should be paid to co-development policies to enhance the institutional quality and the financial system in these economies. Policies should also consider economic growth strategies to enable sustainable economic growth rates.     

Between Active Seekers and Non-Users: Segments of Science-related Media Usage in Switzerland and Germany

ABSTRACT

Citizens’ attitudes toward science are related to their use of science-related information from various sources. Evidence is scarce regarding citizens’ individual media repertoires for staying informed about science as segmentation studies so far have primarily focused on scientific attitudes. In this paper, we explore audience segments regarding their science-related information behavior and whether such segments are comparable or vary between two countries with similar information environments. Based on two surveys in Switzerland and Germany, we identify national audience segments that differ in their science-related information repertoires, and analyze their sociodemographic characteristics and science-related attitudes. In both countries, we find very comparable information user segments ranging from those who inform themselves frequently about science (“Active Seekers”/“Science Consumers”) to those who hardly get in contact with any information about science and research (“Non-Users”). Those segments which get in contact with information about science frequently show generally more positive attitudes.     

Temporal variation in total phosphorus concentrations revealed from a multidecadal monitoring program on Big Platte Lake,Michigan

Effective water quality management depends on enactment of appropriately designed monitoring programs to reveal current and forecasted conditions. Because water quality conditions are influenced by numerous factors, commonly measured attributes such as total phosphorus (TP) can be highly temporally varying. For highly varying processes, monitoring programs should be long-term and periodic quantitative analyses are needed so that temporal trends can be distinguished from stochastic variation, which can yield insights into potential modifications to the program. Using generalized additive mixed modeling, we assessed temporal (yearly and monthly) trends and quantified other sources of variation (daily and subsampling) in TP concentrations from a multidecadal depth-specific monitoring program on Big Platte Lake, Michigan. Yearly TP concentrations decreased from the late 1980s to late 1990s before rebounding through the early 2000s. At depths of 2.29 to 13.72 m, TP concentrations have cycled around stationary points since the early 2000s, while at the surface and depths ≥?18.29 concentrations have continued declining. Summer and fall peaks in TP concentrations were observed at most depths, with the fall peak at deeper depths occurring 1 month earlier than shallower depths. Daily sampling variation (i.e., variation within a given month and year) was greatest at shallowest and deepest depths. Variation in subsamples collected from depth-specific water samples constituted a small fraction of total variation. Based on model results, cost-saving measures to consider for the monitoring program include reducing subsampling of depth-specific concentrations and reducing the number of sampling depths given observed consistencies across the program period.