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.     

Inexpensive substrate sampler for macroinvertebrates

Assessment of aquatic macroinvertebrates is a critical component of many watershed monitoring programs and passive samplers are often used to collect long-term site data, especially in environments where active sampling is not possible. However, standard passive samplers can be expensive and lost in extreme conditions. We developed a sampler using plastic soda bottles (PSB) filled with river rock and compared its effectiveness with standard Hester-Dendy samplers in both lotic and lentic environments. Abundance, taxa richness, and macroinvertebrate composition showed no significant differences between sampler types in either habitat type. PSB samplers, which can be constructed for less than one dollar each, collected the same number of organisms and represented the same diversity as Hester-Dendy devices that cost around $38 each. In studies where funds are limited, PSB samplers appear to be suitable for passive monitoring.     

The need for spatially explicit quantification of benefits in invasive‐species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive‐species control, and planning processes are needed to identify cost‐effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive‐species management. There is a need to improve understanding of how such assets are considered in invasive‐species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty‐four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty‐five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision‐making processes that guide invasive‐species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.     

Dynamic effects of fiscal and monetary policy instruments on environmental pollution in ASEAN

Environmental Science and Pollution Research - This study aims to re-examine the impacts of monetary and fiscal policy on environmental quality in ASEAN countries from 1990 to 2019. We utilized the...     

Analysing spectroscopy data using two-step group penalized partial least squares regression

Environmental and Ecological Statistics - A statistical challenge to analyse hyperspectral data is the multicollinearity between spectral bands. Partial least squares (PLS) has been extensively...     

Trace elements and microbiological parameters in farmed Nile tilapia with emphasis on muscle,water, sediment and feed

Fish may bioaccumulate contaminants from the aquatic environment and extend them to the food chain provoking risks to human health. This study evaluated the microbiological parameters of the pond´s water and trace elements concentrations in samples of water, sediment, feed and muscle of farmed Nile tilapia used for human consumption in southern Brazil. A total of 240 fish were collected from 12 tilapia farms. Sediment, tank water and dry ration used in the animals' diet were collected for analysis. Analysis were performed by Energy Dispersion X-ray Fluorescence (EDXRF), Inductively Coupled Plasma Mass Spectrometer (ICP-MS), Induced Coupled Plasma Optical Emission Spectrometer (ICP-OES), and Atomic Absorption Spectrometer (AAS-VGA). In addition, the microbiological analysis of the water was carried out. The concentrations of Se, I, Fe, Cu and Zn in fish muscle were higher than the recommended by the Brazilian legislation, considering the advised daily intake for adults. The arsenic element had concentrations above the limit stipulated by the present Brazilian legislation, observed in all samples of muscle, sediment and tank water highlighting a possible environmental and fish contamination by the toxic element. Moreover, the arsenic concentration in the water presented a positive correlation (ρ?=?0.33) with arsenic in the muscle, suggesting that tilapia is a good environmental bioindicator, once they properly reflect the levels of arsenic in the water. It is suggested to perform an arsenic speciation for quantification of the inorganic form and accurate assessment of the degree of toxicity in the muscle samples and risks it can bring to human health. Regarding the other potentially toxic elements (Hg, Pb and Cd), and microbiological analysis of water it was verified that the consumption of the fish in question does not raise risks, since the values are within a quality benchmark established by law. The concentration of total and fecal coliforms in pond´s water in the facilities was in agreement with the microbiological indexes required by the legislation of CONAMA class II. Western region presented the lowest concentrations of fecal coliforms when compared to the other regions. There was no significant difference in the microbiological counts of total heterotrophic bacteria, Vibrio spp. and Pseudomonas spp. among the regions.     

Modeling affective and cognitive responses to soft-target terrorism over time

Environment Systems and Decisions - There is great value in understanding the public’s reactions to terror attacks, though such reactions pose stark challenges for sound psychological...     

Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO₂), oxides of nitrogen (NO_X), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NO_X/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NO_X/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NO_X threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles.

Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.     

Freshwater fish diversity hotspots for conservation priorities in the Amazon Basin

Conserving freshwater habitats and their biodiversity in the Amazon Basin is a growing challenge in the face of rapid anthropogenic changes. We used the most comprehensive fish-occurrence database available (2355 valid species; 21,248 sampling points) and 3 ecological criteria (irreplaceability, representativeness, and vulnerability) to identify biodiversity hotspots based on 6 conservation templates (3 proactive, 1 reactive, 1 representative, and 1 balanced) to provide a set of alternative planning solutions for freshwater fish protection in the Amazon Basin. We identified empirically for each template the 17% of sub-basins that should be conserved and performed a prioritization analysis by identifying current and future (2050) threats (i.e., degree of deforestation and habitat fragmentation by dams). Two of our 3 proactive templates had around 65% of their surface covered by protected areas; high levels of irreplaceability (60% of endemics) and representativeness (71% of the Amazonian fish fauna); and low current and future vulnerability. These 2 templates, then, seemed more robust for conservation prioritization. The future of the selected sub-basins in these 2 proactive templates is not immediately threatened by human activities, and these sub-basins host the largest part of Amazonian biodiversity. They could easily be conserved if no additional threats occur between now and 2050.