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Exploiting two quasi-natural experiments, we find that firms increase emissions of toxic pollution following decreases in analyst coverage. The effects are stronger for firms with low initial analyst coverage, poor corporate governance, and firms subject to less stringent monitoring by environmental regulators. Decreases in environmental-related questions raised in conference calls, an increased cost of monitoring to institutional shareholders, reductions in pollution abatement investment, and the weakening of internal governance related to environmental performance are channels through which reduced analyst coverage contributes to increases in firm pollution. Our study highlights the monitoring role analysts play in shaping corporate environmental policies. [ABSTRACT FROM AUTHOR]

Purpose: Precooling (PreC) may only benefit performance when thermal strain experienced by an individual is sufficiently high. We explored the effect of mixed-method PreC on 20-km cycling time-trial (CTT) performance under 3 different apparent temperatures (AT). Methods: On separate days, 12 trained or highly trained male cyclists/triathletes completed six 20-km CTTs in 3 different ATs: hot-dry (35 °C AT), moderately hot-humid (40 °C AT), and hot-humid (46 °C AT). All trials were preceded by 30 minutes of mixed-method PreC or no PreC (control [CON]). Results: Faster 2.5-km-split completion times occurred in PreC compared with CON in 46 °C AT (P =.02), but not in 40 °C AT (P =.62) or 35 °C AT (P =.57). PreC did not affect rectal and body temperature during the 20-km CTT. Skin temperature was lower throughout the CTT in PreC compared with CON in 46 °C AT (P =.01), but not in 40 °C AT (P = 1.00) and 35 °C AT (P = 1.00). Heart rate had a greater rate of increase during the CTT for PreC compared with CON in 46 °C AT (P =.01), but not in 40 °C AT (P =.57) and 35 °C AT (P = 1.00). Ratings of perceived exertion (P <.001) and thermal comfort (P =.04) were lower for PreC compared with CON in 46 °C AT only, while thermal sensation was not different between PreC and CON. Conclusion: Mixed-method PreC should be applied prior to 20-km CTTs conducted in hot-humid conditions (≥46 °C AT). Alternatively, mixed-method PreC may be a priority in moderately hot-humid (∼40 °C AT) conditions but should not be in hot-dry (∼35 °C AT) conditions for 20-km CTT. [ABSTRACT FROM AUTHOR]

Environmentally persistent free radicals (EPFRs) play an important role in aerosol effects on air quality and public health, but their atmospheric abundance and sources are poorly understood. We measured EPFRs contained in PM2.5 collected in Fairbanks, Alaska, in winter 2022. We find that EPFR concentrations were enhanced during surface-based inversion and correlate strongly with incomplete combustion markers, including carbon monoxide and elemental carbon (R2 > 0.75). EPFRs exhibit moderately good correlations with PAHs, biomass burning organic aerosols, and potassium (R2 > 0.4). We also observe strong correlations of EPFRs with hydrocarbon-like organic aerosols, Fe and Ti (R2 > 0.6), and single-particle mass spectrometry measurements reveal internal mixing of PAHs, with potassium and iron. These results suggest that residential wood burning and vehicle tailpipes are major sources of EPFRs and nontailpipe emissions, such as brake wear and road dust, may contribute to the stabilization of EPFRs. Exposure to the observed EPFR concentrations (18 ± 12 pmol m-3) would be equivalent to smoking ∼0.4-1 cigarette daily. Very strong correlations (R2 > 0.8) of EPFR with hydroxyl radical formation in surrogate lung fluid indicate that exposure to EPFRs may induce oxidative stress in the human respiratory tract.

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

The accurate detection and quantification of biological species that are rarely present but potentially devastating is of paramount importance for the life sciences, biosecurity, food safety, and environmental monitoring. Consequently, there has been significant interest in the sensitive and accurate detection of nucleic acids, leveraging both chemical and biological methods. Among these, quantitative polymerase chain reaction (qPCR) is regarded as the gold standard due to its sensitivity and precision in identifying specific nucleic acid targets. Despite the widespread adoption of qPCR for nucleic acid detection, the analysis of qPCR data typically depends on the use of calibrated standard curves and a threshold method to interpret signal measurements. In this study, we use a stochastic simulation to show the limitations of the threshold method due to its assumptions on amplification kinetics. We propose a new approach for the absolute quantification of nucleic acids that overcomes these limitations by reconstructing the efficiency profile across amplification cycles and using cumulative amplification folds to build a standard curve, thus avoiding the constant efficiency assumption. Our method, validated through experiments with nucleic acid amplification in the presence of potent inhibitors, demonstrates improved accuracy in quantifying nucleic acids, avoiding the systematic errors of the threshold method. This innovation enhances the reliability of nucleic acid quantification, especially where traditional methods struggle with kinetic variability. [ABSTRACT FROM AUTHOR]

The precise detection of target substances through refractive-index sensing is essential in various fields such as environmental monitoring, food assessment, and optical applications. This study demonstrates a real-time refractive index sensor based on a liquid-infiltrated metal-insulator-metal island (MIMi) structure in a lithography-free manner. In the MIMi structure, the top layer of the metal islands is formed through solid-state dewetting, which serves as an etching mask to form nanopillars in the insulator layer. This allows for the infiltration of external substances, resulting in different structural colors depending on the refractive index. This design results in a redshift of the resonance peak as the infiltrated refractive index increases. Furthermore, by adjusting the area fraction of the metal-island layer and etching depth of the insulator layer, the resonance-peak position can be controlled within the visible range according to the target refractive index. The advantages of our design are the use of cost-effective materials and the ability to implement scalable fabrication. Our proposed method offers a convenient method to sense the target refractive index in real time. [ABSTRACT FROM AUTHOR]

Harmful cyanobacteria blooms are a growing threat in estuarine waters as upstream blooms are exported into coastal environments. Cyanobacteria can produce potent toxins, one of which-hepatotoxic microcystins (MCs)-can persist and accumulate within the food web. Filter-feeding invertebrates may biomagnify toxins up to 100× ambient concentrations. As such, bivalves can be used as an environmentally relevant and highly sensitive sentinel for MC monitoring. To date there has been little research on cyanotoxin bioaccumulation in estuaries. The Sacramento-San Joaquin Delta (Delta) aquatic food web has undergone a profound change in response to widespread colonization of aquatic invasive species such as Asian clams (Corbicula fluminea) in the freshwater portion of the Delta. These clams are prolific-blanketing areas of the Delta at densities up to 1000 clams/m2 and are directly implicated in the pelagic organism decline of threatened and endangered fishes. We hypothesized that Asian clams accumulate MCs which may act as an additional stressor to the food web and MCs would seasonally be in exceedance of public health advisory levels. MCs accumulation in Delta Asian clams and signal crayfish (Pacifastacus leniusculus) were studied over a two-year period. ELISA and LC-MS analytical methods were used to measure free and protein-bound MCs in clam and crayfish tissues. We describe an improved MC extraction method for use when analyzing these taxa by LC-MS. MCs were found to accumulate in Asian clams across all months and at all study sites, with seasonal maxima occurring during the summer. Although MC concentrations rarely exceeded public health advisory levels, the persistence of MCs year-round still poses a chronic risk to consumers. Crayfish at times also accumulated high concentrations of MCs. Our results highlight the utility of shellfish as sentinel organisms for monitoring in estuarine areas.

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Bottlenose dolphins (Tursiops truncatus) are keystone and sentinel species in the world's oceans. We studied correlations between per- and polyfluoroalkyl substances (PFAS) and their stress axis. We investigated associations between plasma biomarkers of 12 different PFAS variants and three cortisol pools (total, bound, and free) in wild T. truncatus from estuarine waters of Charleston, South Carolina (n = 115) and Indian River Lagoon, Florida (n = 178) from 2003 to 2006, 2010-2013, and 2015. All PFAS and total cortisol levels for these dolphins were previously reported; bound cortisol levels and free cortisol calculations have not been previously reported. We tested null hypotheses that levels of each PFAS were not correlated with those of each cortisol pool. Free cortisol levels were lower when PFOS, PFOA, and PFHxS biomarker levels were higher, but free cortisol levels were higher when PFTriA was higher. Bound cortisol levels were higher when there were higher PFDA, PFDoDA, PFDS, PFTeA, and PFUnDA biomarkers. Total cortisol was higher when PFOA was lower, but total cortisol was higher when PFDA, PFDoDA, PFTeA, and PFTriA were higher. Additional analyses indicated sex and age trends, as well as heterogeneity of effects from the covariates carbon chain length and PFAS class. Although this is a cross-sectional observational study and, therefore, could reflect cortisol impacts on PFAS toxicokinetics, these correlations are suggestive that PFAS impacts the stress axis in T. truncatus. However, if PFAS do impact the stress axis of dolphins, it is specific to the chemical structure, and could affect the individual pools of cortisol differently. It is critical to conduct long-term studies on these dolphins and to compare them to populations that have no or little expose to PFAS.

Because humans spend about one-third of their time asleep in their bedrooms and are themselves emission sources of volatile organic compounds (VOCs), it is important to specifically characterize the composition of the bedroom air that they experience during sleep. This work uses real-time indoor and outdoor measurements of volatile organic compounds (VOCs) to examine concentration enhancements in bedroom air during sleep and to calculate VOC emission rates associated with sleeping occupants. Gaseous VOCs were measured with proton-transfer reaction time-of-flight mass spectrometry during a multiweek residential monitoring campaign under normal occupancy conditions. Results indicate high emissions of nearly 100 VOCs and other species in the bedroom during sleeping periods as compared to the levels in other rooms of the same residence. Air change rates for the bedroom and, correspondingly, emission rates of sleeping-associated VOCs were determined for two bounding conditions: (1) air exchange between the bedroom and outdoors only and (2) air exchange between the bedroom and other indoor spaces only (as represented by measurements in the kitchen). VOCs from skin oil oxidation and personal care products were present, revealing that many emission pathways can be important occupant-associated emission factors affecting bedroom air composition in addition to direct emissions from building materials and furnishings.

To understand the extent of anthropogenic noise in the ocean, it is essential to compare the differences between modern noise environments and their pre-industrial equivalents. The Santa Barbara Channel, off the coast of Southern California, is a corridor for the transportation of goods to and from the busiest shipping ports in the Western hemisphere. Commercial ships introduce high levels of underwater noise into the marine environment. To quantify the extent of noise in the region, we modeled pre-industrial ocean noise levels, driven by wind, and modern ocean noise levels, resulting from the presence of both ships and wind. By comparing pre-industrial and modern underwater noise levels, the low-frequency (50 Hz) acoustic environment was found to be degraded by more than 15 dB. These results can be used to identify regions for noise reduction efforts, as well as to model scenarios to identify those with the greatest potential to support marine conservation efforts.

Wildfires are increasing in duration and intensity across the United States' Pacific West region, resulting in heightened particulate matter from smoke in the atmosphere. Levels of peak particulate matter are concurrent to peak visitor attendance at National Parks, given seasonal alignment with summer vacation travel and heightened forest fire conditions. Particulate matter threatens visitor health and safety and contributes to poor visibility and a deteriorated visitor experience. To assess visitation response to diminished air quality, we utilized wildfire-generated particulate matter (PM2.5) data in conjunction with monthly attendance records for three ecoregions containing eight national parks in Washington, Oregon, and California from 2009 to 2019. We analyzed daily PM2.5 levels from data gridded at the 10 km scale for National Park Service units by Level III forest ecoregions within the National Park Service's Pacific West Unit. Data were then compared to normalized monthly visitation trends for each of the ecoregions using two statistical methods Kendall's Tau and Analysis of Variance (ANOVA) with post-hoc Tukey tests. Results demonstrate that attendance at these national parks does not decrease in response to increased PM2.5 levels. Instead, we see several statistically significant increases in attendance across these ecoregions during periods of reduced air quality. Of 115 shifts between air quality categories during the busy season of July to September, there are no significant decreases in attendance as air quality worsens. These findings suggest that visitors are willing to tolerate reduced air quality compared to other factors such as temperature or precipitation. Given that park units within each ecoregion feature diverse historical contexts, varied built environments, and unique ecological systems, our discussion specifically addresses managerial concerns associated with maintained high levels of visitation during suboptimal, and potentially dangerous, conditions. There is substantial need for specific, scalable approaches to mitigate adverse health and experiential impacts as visitors are exposed to increased risks during a range of exertional activities associated with diverse settings.

Protected areas are typically managed as a network of sites exposed to varying anthropogenic conditions. Managing these networks benefits from monitoring of conditions across sites to help prioritize coordinated efforts. Monitoring marine vessel activity and related underwater radiated noise impacts across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of marine species. Here, we use underwater acoustic detections of vessels to quantify different characteristics of vessel noise at 25 locations within eight marine sanctuaries including the Hawaiian Archipelago and the U.S. east and west coasts. Vessel noise metrics, including temporal presence and sound levels, were paired with Automatic Identification System (AIS) vessel tracking data to derive a suite of robust vessel noise indicators for use across the network of marine protected areas. Network-wide comparisons revealed a spectrum of vessel noise conditions that closely matched AIS vessel traffic composition. Shifts in vessel noise were correlated with the decrease in vessel activity early in the COVID-19 pandemic, and vessel speed reduction management initiatives. Improving our understanding of vessel noise conditions in these protected areas can help direct opportunities for reducing vessel noise, such as establishing and maintaining noise-free periods, enhancing port efficiency, engaging with regional and international vessel quieting initiatives, and leveraging co-benefits of management actions for reducing ocean noise.

Ocean disposal of industrial waste from technical DDT [mainly 1,1-(2,2,2-trichloroethane-1,1-diyl)bis(4-chlorobenzene), or 4,4-DDT] manufacture occurred historically in the Southern California Bight. However, the paucity of historical records highlights uncertainties as to the mode, location, and timing of disposal or ongoing ecological effects of these wastes. This study combines sampling, chemical analysis, and numerical modeling of deep San Pedro Basin sediments revealing substantial DDT contamination that extends at least 25 km from the mainland. These findings narrate bulk DDT waste disposal to the offshore that peaked in the 1950s, prior to the onset of formal regulations; was agnostic to later-designated disposal sites; and has experienced sluggish transformation. Our findings further indicate an attenuating secondary source for the DDT daughter product, 1-chloro-4-[2,2-dichloro-1-(4-chlorophenyl)ethenyl]benzene (4,4-DDE), which still deposits into deep San Pedro Basin sediments. While demonstrating the severity of DDT contamination to the region, these findings further define the burial potential of DDT wastes and inform the past, present, and future contamination potential that is needed to understand and predict ecological consequences. This work also points firmly to bulk, not containerized, disposal of DDT waste and to potential alternative contents of collocated waste.

The Santa Barbara Basin is an extraordinary archive of environmental and ecological change, where varved sediments preserve microfossils that provide an annual to decadal record of the dynamics of surrounding ecosystems. Of the microfossils preserved in these sediments, benthic foraminifera are the most abundant seafloor-dwelling organisms. While they have been extensively utilized for geochemical and paleoceanographic work, studies of their morphology are lacking. Here we use a high-throughput imaging method (AutoMorph) designed to extract 2D data from photographic images of fossils to produce a large image and 2D shape dataset of recent benthic foraminifera from two core records sampled from the center of the Santa Barbara Basin that span an ~800-year-long interval during the Common Era (1249-2008 CE). Information on more than 36,000 objects is included, of which more than 22,000 are complete or partially-damaged benthic foraminifera. The dataset also includes other biogenic microfossils including ostracods, pteropods, diatoms, radiolarians, fish teeth, and shark dermal denticles. We describe our sample preparation, imaging, and identification techniques, and outline potential data uses.

Electrochemiluminescence resonance energy transfer (ECL-RET) is a versatile signal transduction strategy widely used in the fabrication of chem/biosensors. However, this technique has not yet been applied in visualized imaging analysis of intracellular species due to the insulating nature of the cell membrane. Here, we construct a ratiometric ECL-RET analytical method for hypochlorite ions (ClO−) by ECL luminophore, with a luminol derivative (L-012) as the donor and a fluorescence probe (fluorescein hydrazide) as the acceptor. L-012 can emit a strong blue ECL signal and fluorescein hydrazide has negligible absorbance and fluorescence signal in the absence of ClO−. Thus, the ECL-RET process is turned off at this time. In the presence of ClO−, however, the closed-loop hydrazide structure in fluorescein hydrazide is opened via specific recognition with ClO−, accompanied with intensified absorbance and fluorescence signal. Thanks to the spectral overlap between the ECL spectrum of L-012 and the absorption spectrum of fluorescein, the ECL-RET effect is gradually recovered with the addition of ClO−. Furthermore, the ECL-RET system has been successfully applied to image intracellular ClO−. Although the insulating nature of the cell itself can generate a shadow ECL pattern in the cellular region, extracellular ECL emission penetrates the cell membrane and excites intracellular fluorescein generated by the reactions between fluorescein hydrazide and ClO−. The cell imaging strategy via ECL-RET circumvents the blocking of the cell membrane and enables assays of intracellular species. The importance of the ECL-RET platform lies in calibrating the fluctuation from the external environment and improving the selectivity by using fluorescent probes. Therefore, this ratiometric ECL sensor has shown broad application prospects in the identification of targets in clinical diagnosis and environmental monitoring. [ABSTRACT FROM AUTHOR]

In recent years, heavy metals derived from several anthropogenic sources have both direct and indirect detrimental effects on the health of the environment and living organisms. Whole-cell bioreporters (WCBs) that can be used to monitor the levels of heavy metals in drinking and natural spring waters are important. In this study, whole-cell arsenic bacterial bioreporters were immobilized using polycaprolactone (PCL) electrospun fibers as the support material. The aim is to determine the properties of this immobilized bioreporter system by evaluating its performance in arsenic detection. Within the scope of the study, different growth media and fiber immobilization times were tested to determine the parameters affecting the fluorescent signals emitted by the immobilized bioreporter system in the presence of two dominant forms of arsenic, namely arsenite (As(III)) and arsenate (As(V)). In addition, the sensitivity, selectivity, response time, and shelf-life of the developed bioreporter system were evaluated. As far as the literature is concerned, this is the first study to investigate the potential of using PCL-electrospun fiber-immobilized fluorescent bacterial bioreporter for arsenic detection. This study will open new avenues in environmental arsenic monitoring. [ABSTRACT FROM AUTHOR]

Localization is a vital task in underwater wireless sensor networks (UWSNs). Accurate location information is critical for applications such as environmental monitoring and military operations. Received signal strength (RSS)-based localization schemes are commonly employed because of their cost-effectiveness and performance in terrestrial environments. However, underwater acoustic environments are affected by not only path loss, but also absorption bias in which case these schemes perform poorly with large delays. This makes real-time underwater acoustic localization based on RSS challenging and unable to meet the localization requirements of practical applications. Thus, a real-time RSS-based target localization (RRTL) scheme for UWSNs is proposed. Since the derived closed-form expression is complex and nonconvex, an intelligent localization technique based on a backpropagation (BP) neural network is considered which employs improved differential evolution (IDE) to obtain the network weights and biases. First, improved opposition-based learning is used to obtain a robust population. Then an adaptive mutation strategy is utilized to improve global searching and convergence. Finally, optimized network parameters are obtained via IDE to train the BP neural network. Performance results are presented which demonstrate that the proposed method is superior to conventional state-of-the-art methods and is robust to absorption bias. For example, for an error cumulative distribution function (CDF) of 0.8 with N = 6, the proposed method provides an improvement of 69.7% in accuracy and 78.4% in computation time over the well-known WLS technique. [ABSTRACT FROM AUTHOR]

A sensitive platform was constructed for on-site detection of chlorpyrifos by immobilizing a core–shell structured NaYbF 4 @NaYF 4 : Yb3+, Tm3+ UCNPs/MnO 2 composite in calcium alginate hydrogel kit. Specifically, the designed UCNPs fluorescent probe (NaYbF 4 @NaYF 4 : Yb3+, Tm3+) has the blue-violet emission. After mixing with MnO 2 nanoflakes, the blue-violet upconversion (UC) emission is quenched. Thiocholine (TCh), formed through acetylcholinesterase (AChE)-mediated catalysis, reduces MnO 2 nanoflakes to Mn2+ ions, thereby restoring the system's fluorescence. The introduction of chlorpyrifos inhibits AChE activity, accompanied by the quenching of the fluorescence signal. Based on the blue-violet UC emission signal response to chlorpyrifos, fluorescent photos were taken by smartphones. Utilizing a Python-assisted analysis platform, a "one-run" operation simultaneously captures image grayscale values, gray images, and differentiated pseudo-color images related to pesticide concentrations, enabling real-time and rapid quantitative analysis of pesticides. [Display omitted] On-site quantitative analysis of pesticide residues is crucial for monitoring environmental quality and ensuring food safety. Herein, we have developed a reliable hydrogel portable kit using NaYbF 4 @NaYF 4 : Yb, Tm upconversion nanoparticles (UCNPs) combined with MnO 2 nanoflakes. This portable kit is integrated with a smartphone reader and Python-assisted analysis platform to enable sample-to-result analysis for chlorpyrifos. The novel UCNPs maximizes energy donation to MnO 2 acceptor by employing 100 % of activator Yb3+ in the nucleus for NIR excitation energy collection and confining emitter Tm3+ to the surface layer to shorten energy transfer distance. Under NIR excitation, efficient quenching of upconversion blue-violet emission by MnO 2 nanoflakes occurs, and the quenched emission is recovered with acetylcholinesterase-mediated reactions. This process allows for the determination of chlorpyrifos by inhibiting enzymatic activity. The UCNPs/MnO 2 were embedded to fabricate a hydrogel portable kit, the blue-violet emission images captured by smartphone were converted into corresponding gray values by Python-assisted superiority chart algorithm which achieves a real-time rapid quantitative analysis of chlorpyrifos with a detection limit of 0.17 ng mL−1. At the same time, pseudo-color images were also added by Python in "one run" to distinguish images clearly. This sensor detection with Python-assisted analysis platform provides a new perspective on pesticide monitoring and broadens the application prospects in bioanalysis. [ABSTRACT FROM AUTHOR]

Summary: Wireless Sensor Networks (WSNs) find applications in diverse fields such as environmental monitoring, healthcare, and military surveillance. Nonetheless, one of the primary challenges encountered by WSNs is congestion. Congestion arises in WSNs when there is a high volume of traffic on the network, leading to significant repercussions. These repercussions encompass packet loss, heightened latency durations, and diminished network efficiency. This paper presents an innovative congestion control mechanism named Dual Indicator Random Early Detection (DIRED). DIRED leverages two indicators, namely queue length and packet loss rate, to dynamically adjust the dropping probability of packets, thereby mitigating congestion and enhancing network performance. For the successful execution of the DIRED model, a congestion control algorithm is introduced. This strategy efficiently prevents the deterioration of network performance that can commonly arise from extremely low packet drop probabilities. Simulations are conducted to evaluate the proposed DIRED model, comparing it with the KACO and KFOA techniques. The results demonstrate that DIRED outperforms KACO and KFOA in terms of network performance, achieving a more optimal balance between performance metrics and packet‐dropping probability. [ABSTRACT FROM AUTHOR]

Applied nucleation and other spatially patterned restoration methods are promising approaches for scaling up projects to meet ambitious international restoration commitments in an ecologically and economically sound manner. Much of the corresponding literature to date, however, has centered around theoretical discussions and small‐scale studies that are largely divorced from constraints faced by restoration practitioners. We briefly review recent academic literature about applied nucleation and other spatially patterned restoration methods and discuss practical challenges to their implementation. We offer several recommendations to move spatially patterned restoration from an academic conversation to scalable application, including: (1) comparing different planting designs and natural regeneration within the same system at an appropriate scale; (2) monitoring ecological outcomes throughout the restored area over sufficient time to evaluate recovery; (3) quantifying costs and documenting other logistical constraints to implementation; and (4) exploring methods for using unplanted areas to provide benefits to landholders until planted vegetation establishes. [ABSTRACT FROM AUTHOR]

The Anil River estuary (ARE), which is crucial for São Luís Island's socioeconomic well-being, faces substantial human pressure due to its urban location, hastening the eutrophication process. This study, which was conducted from 2022 to 2023, aimed to assess trophic levels across the ARE. Using a multiparametric probe, we analysed the pH, temperature, dissolved oxygen, oxygen saturation, salinity, and total dissolved solids quarterly during the rainy and dry seasons at six sampling points. The nutrient analysis included nitrite, nitrate, ammonium ions, phosphate, total nitrogen (TN), and total phosphorus (TP). Trophic index (TRIX) values categorised the ARE as eutrophic (sector 1) or hypereutrophic (sector 2). The dry season exhibited relatively high trophic levels, indicating that the ARE was hypereutrophic. Sector 2, influenced by concentrated nutrients and chlorophyll-a, exhibited increased trophic status from domestic and industrial effluent discharge. Rainy season data at the downstream from the point 3 recorded the maximum DIN:DIP ratio, indicating phosphorus limitation. Monitoring estuarine trophic states is vital for kerbing eutrophication and preserving local biodiversity in the anil river estuary. [ABSTRACT FROM AUTHOR]

The re-evaluation of animals, plants, and microorganisms for green synthesis has revolutionized the fabrication of metallic nanoparticles (MNPs). Green synthesis provides more biocompatibility compared to chemically synthesized MNPs, which make them ideal for diverse biological applications, especially in biomedicine. Various organisms have been extensively studied for green synthesis. Interestingly, angiosperms, algae, and animal-derived biomaterials like chitin and silk have shown a prominent role in synthesizing these nanoparticles. Moreover, bacteria, viruses, and fungi serve as sources of reducing agents, further expanding green synthesis possibilities. Despite progress, research on natural reducing agents remains relatively limited, with only a few exceptions such as tea and neem plants receiving attention. Green-synthesized nanoparticles have diverse applications in various fields. In biomedicine, they enable drug delivery, targeted therapies, and bio-imaging due to their enhanced biocompatibility. Some MNPs also exhibit potent antimicrobial properties, aiding in disease control and eco-friendly disinfection. Furthermore, green nanoparticles contribute to environmental remediation by purifying water and serve as sensitive biosensors for diagnostics and environmental monitoring. This review will provide the recent progress and advancements in the field of green synthesis (GS) of nanoparticles. It will also analyze the key characteristics and evaluate the advantages and disadvantages of GS compared to chemical synthesis. [ABSTRACT FROM AUTHOR]

Wireless sensor nodes (WSNs) play an important role in many fields, including environmental monitoring. However, unattended WSNs face challenges in consuming power continuously even in the absence of useful information, which makes energy supply the bottleneck of WSNs. Here, we realized zero-power infrared switches, which consist of a metasurface and two-phase microfluidic flow. The metasurface can recognize the infrared signal from the target and convert it into heat, which triggers the two-phase microfluidic flow switch. As the target is not present, the switch is turned off. The graphene/MoS2/graphene 2D material heterostructure (thickness <2 nm) demonstrates an exceptionally high thermal resistance of 4.2 K/W due to strong phonon scattering and reduces the heat flow from the metasurface to the supporting substrate, significantly increasing the device sensitivity (the displacement of the two-phase microfluidic flow increases from ~1500 to ~3000 µm). The infrared switch with a pair of symmetric two-phase microfluidic flows can avoid spurious triggering resulting from environmental temperature changes. We realized WSNs with near-zero standby power consumption by integrating the infrared switch, sensors, and wireless communication module. When the target infrared signal appears, the WSNs are woken and show superb visual/auditory sensing performance. This work provides a novel approach for greatly lengthening the lifespan of unattended WSNs. [ABSTRACT FROM AUTHOR]

Objective: To estimate, in a cohort of young Portuguese adults, the environmental impact (greenhouse gas (GHG) emissions, land use, energy used, acidification and potential eutrophication) of diet according to adherence to the Mediterranean Diet (MD). Methods: Data from 1554 participants of the Epidemiologic Health Investigation of Teenagers in Porto (EPITeen) were analysed. Food intake and MD adherence were determined using validated questionnaires. The environmental impact was evaluated with the EAT-Lancet Commission tables, and the link between MD adherence and environmental impact was calculated using adjusted multivariate linear regression models. Results: Higher adherence (high vs. low) to the MD was associated with lower environmental impact in terms of land use (7.8 vs. 8.5 m2, p = 0.002), potential acidification (57.8 vs. 62.4 g SO2-eq, p = 0.001) and eutrophication (21.7 vs. 23.5 g PO4-eq, p < 0.001). Energy use decreased only in the calorie-adjusted model (9689.5 vs. 10,265.9 kJ, p < 0.001), and GHG emissions were reduced only in a complementary model where fish consumption was eliminated (3035.3 vs. 3281.2 g CO2-eq, p < 0.001). Meat products had the greatest environmental impact for all five environmental factors analysed: 35.7% in GHG emissions, 60.9% in energy use, 72.8% in land use, 70% in acidification and 61.8% in eutrophication. Conclusions: Higher adherence to the MD is associated with lower environmental impact, particularly in terms of acidification, eutrophication, and land use. Reducing meat consumption can contribute to greater environmental sustainability. [ABSTRACT FROM AUTHOR]

Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future. [ABSTRACT FROM AUTHOR]

Heavy metals ions as metallic pollutants are a growing global issue due to their adverse effects on the aquatic ecosystem, and human health. Unfortunately, conventional detection methods such as atomic absorption spectrometry exhibit a relatively low limit of detection and hold numerous disadvantages, and therefore, the development of an efficient method for in-situ and real-time detection of heavy metal residues is of great importance. The aptamer-based sensors offer distinct advantages over antibodies and emerged as a robust sensing platform against various heavy metals due to their high sensitivity, ease of production, simple operations, excellent specificity, better stability, low immunogenicity, and cost-effectiveness. The nucleic acid aptamers in conjugation with nanomaterials can bind to the metal ions with good specificity/selectivity and can be used for on-site monitoring of metal ion residues. This review aimed to provide background information about nanomaterials-based aptasensor, recent advancements in aptamer conjunction on nanomaterials surface, the role of nanomaterials in improving signal transduction, recent progress of nanomaterials-based aptasening procedures (from 2010 to 2022), and future perspectives toward the practical applications of nanomaterials-based aptasensors against hazardous metal ions for food safety and environmental monitoring. [ABSTRACT FROM AUTHOR]

Community-engaged research (CEnR) is becoming more prevalent in environmental health. With a spectrum of community involvement characterizing this research, it has the potential to foster a diverse set of collaborative dynamics. Our research team has facilitated a regional CEnR effort focusing on the effects of drought driven by climate change. Specifically, the CEnR is focusing on regional water quality by analyzing samples from privately owned groundwater wells and examining the samples for heavy metal contaminants. This special report outlines lessons learned and project management strategies used to facilitate largescale community-engaged environmental science. Recruitment methods evolved in response to obstacles and included direct mailings, listservs and social media promotion, direct recruitment by local leaders, and local newspaper coverage. Participant onboarding evolved as new recruitment methods were implemented but was standardized to achieve process effeciency. External data sources were used to align contextual information of sampled wells to the submitted samples. Open-source tools were used to streamline reporting of results to participants. Scalability, project management, bidirectional involvement with community members and organizations, and accountability are important themes to consider when facilitating environmental CEnR. [ABSTRACT FROM AUTHOR]

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be insufficient resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Community drivers shift over time in response to changes in landscape and environmental conditions. Although dams alter the landscape (e.g., by flooding areas) and environment (e.g., by increasing transparency), their impact on the influence of community drivers remains unclear. We investigated how the construction of a large Amazonian River dam altered the influence of environment and space on planktonic communities. From 2005 to 2015, we monitored environmental parameters and collected phytoplankton and zooplankton from 15 locations along the Madeira River and its tributaries. We conducted twenty-nine samplings upstream of the Jirau dam before, during filling, and after its construction, evaluating the influence of environment and space on communities and assessing any changes with damming. We documented the frequency of significant variables to determine if their importance (frequency) changed post-damming. Following damming, environmental heterogeneity decreased and communities comprised species adapted to lentic habitats, with the environment exerting the main influence. Damming did not significantly alter environmental influence but shifted specific drivers, with some factors decreasing (e.g., nutrients) and others increasing (e.g., oxygen) in importance, indicating changes in the environmental mechanisms underlying community assemblage. Monitoring programs in dam-affected regions should be regularly evaluated due to the temporal variability of aquatic community drivers. [ABSTRACT FROM AUTHOR]

Humidity sensors have gained tremendous attention due to their practical applications in the pharmaceutical industry, food processing, biomedical applications, microelectronics, and meteorology. Herein, a polymer-based multifunctional humidity sensor (PANI-Bi-ZnO) has been fabricated. This study describes a polymer-based capacitive-type humidity sensor that has excellent repeatability, negligible hysteresis (0.5% RH at 60% RH), quick response (8 s) and relaxation times (20 s), high stability (30 days), high sensitivity, and good linearity in a wide humidity detection range of 10–90% RH and has become increasingly significant for applications involving industrial control and environmental monitoring. Moreover, the fabricated sensor possesses a highly reliable/stable output performance that remains constant after multiple test cycles. The current work also provides a cost-effective method to fabricate high-performance humidity sensors. The designed humidity sensor would be an excellent choice for measuring a variety of applications in the field of humidity. [ABSTRACT FROM AUTHOR]

There is increased call to demonstrate the benefits of EU Member States' river basin management plans, whose implementation has been delayed largely due to insufficient funding. This paper applies a contingent valuation study to investigate the benefit value of improved ecological status in river basins and the discrepancy between the quality of waters as perceived by river basin residents and as monitored under the Water Framework Directive. Respondents often reported worse quality for their focal water body than the monitored status indicated, a tendency established in a GIS analysis. The likelihood of such divergence increased most with degree of perceived deterioration of surface waters. Observed deterioration in waters, official status of one's focal water body and divergence between two quality measures had implications for welfare estimates. Describing water quality according to ecological criteria and as uniformly as possible would facilitate the use of valuation results in future benefit transfers. [ABSTRACT FROM AUTHOR]

The article explores the importance of wastewater-based surveillance (WBS) to public health. Topics discussed include the prominence of WBS in tracking diseases and monitoring viruses, the challenges presented by the methodologies adopted for WBS, and the advantages of WBS for public health authorities.

High-sensitivity solutions for real-time and on-line multi-component gas detection are required in coal mine safety, petrochemical and storage and transportation industries, atmospheric and environmental monitoring, combustion diagnosis, and industrial process monitoring and control. In optical technology, Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been proven to be an advanced technology for detection of multi-component gas, which benefits from the high sensitivity, good selectivity, fast response and anti-disturbance of TDLAS sensors. In detection of multi-component gas, the presence of non-negligible cross-interference, cost and performance requirements become a primary concern in the design of many detection schemes. In this review article, the application of TDLAS technology in multi-component gas detection will be discussed in two categories: (I) Multiple lasers for detection of multi-component gas without cross-interference. (II) A single laser for detection of multi-component gas. This article outlines the advantages and disadvantages of applying various schemes in the two categories when TDLAS is applied to multi-component detection, with the aiming of providing ready-made guidance for multi-component detection under various applications. [ABSTRACT FROM AUTHOR]

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct sub-tasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy. [ABSTRACT FROM AUTHOR]

Mauritania is a fishing country. However, the Mauritanian coast is increasingly exposed to environmental issues mainly due to anthropogenic activities such as the mining, gas, oil, and fishing industries, as well as new agricultural practices that unreasonably use inputs. Environmental monitoring of the Mauritanian coast faces several challenges; thus, improving the fisheries sector begins with enhancing the state of marine ecosystems and implementing environmental monitoring adapted to climatic conditions and local needs. This study aims to evaluate the quality of the sediments of the "Baie du Repos" in the town of Nouadhibou, Mauritania, through the study of organic matter and the quantification of trace metallic elements in the Quaternary sediments of the Bay. Six samples deemed representative of this Bay were taken and transported to the laboratory. The physicochemical analysis of these samples shows that the superficial horizons of 30 cm depth have overall organic matter contents higher than the average threshold value proposed by the literature for 4 out of 6 of the points studied. The contents recorded for the different metallic trace elements indicate that point 1 is the most exposed to contamination, with the highest concentrations of cadmium, lead, copper, iron, and zinc. The ACP (Principal Component Analysis) showed that the metallic trace elements Pb, Cu, Fe, Cd, and Zn are closely related and evolve positively in the same direction. Additionally, it was found that the points studied are divided into three groups: Group 1 contains only point 1, which is the most exposed to contamination by these toxic elements (Pb, Cu, Zn, Fe, and Cd). Group 2 contains points 3, 5, and 6, which are moderately contaminated by metallic elements with a significant dominance of organic matter (OM). Finally, Group 3 is the least contaminated, with a very high content of organic matter (OM). [ABSTRACT FROM AUTHOR]

As the global urbanization process is further accelerating, the number of urban people is also steadily increasing. With the continuous growth of the urban population, the use of various functional facilities in the city is gradually becoming saturated, which seriously restricts the development of the city. Therefore, real-time perception and prediction of the operational status of various functional urban facilities, as well as environmental safety monitoring in the city, are of great significance for improving the functionality and livability of the city. In complex urban environments, multi-source data is interrelated, and the noise reduction of multi-source data and the integration of this correlation still face great challenges. At the same time, how to apply urban intelligent monitoring in the reconstruction of old cities is rarely mentioned. Based on the above problems, this paper proposes a data noise reduction model based on a wavelet algorithm and combines it with the Macroscopic Fundamental Diagram (MFD) multi-source data fusion structure proposed in this paper to provide a theoretical basis for the construction of an urban intelligent monitoring model. Then, the author constructed a data-driven urban safety environment monitoring model and a multi-source data-based urban congestion monitoring model, which have good experimental results and certain practical value. At the end of the paper, the author briefly discussed the application of the smart city monitoring model in the reconstruction of old cities, hoping to provide certain guidance for further integration in the future. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures. [ABSTRACT FROM AUTHOR]

The fact that sensor nodes are situated in specific places is most obvious in wireless sensor networks. A wireless sensor network has been designed to address node localization through several solutions. Environmental monitoring, healthcare, and industrial automation are just some of the fields where wireless sensor networks (WSNs) find extensive application. This sort of WSN application is fundamentally dependent on the accurate localization of sensor nodes. The purpose of this paper is to propose an enhanced received signal strength indicator (RSSI) technique for determining unlocalized nodes using anchor nodes. An optimal communication range, aligning with the application needs, can be determined by assessing the dynamic correlation between communication distance and the RSSI throughout the localization procedure. The advanced RSSI-based node localization method proposed in this study involves two distinct stages: the distance determination phase and the computation phase. In this work, we assess the outcome using the Castalia simulation. The precision of wireless sensor network node location is greatly improved by integrating mobile anchor nodes with RSSI-based dynamic node localization. The results of the simulations and the subsequent analysis attest to the excellent accuracy and low energy consumption of the suggested method during the localization procedure. [ABSTRACT FROM AUTHOR]

Recent changes in climate and environment, influenced by both global and local factors, have had profound impacts on coastal ecosystem functioning and trajectories. By examining archived samples from ongoing ecological monitoring efforts, particularly focusing on bivalves like mussels and oysters, we gain a valuable long-term perspective on how ecosystems are responding at various scales. We conducted analyses on carbon and nitrogen elemental and isotopic ratios (C:N, δ13C, δ15N) of mussel and oyster soft tissues collected annually at 33 sites along the French coast from 1981 to 2021. This extensive dataset (https://doi.org/10.17882/100583 , Liénart et al., 2024a) offers a comprehensive view spanning multiple decades and ecosystems, allowing to track how coastal ecosystems and marine species record changing climate, physical-chemical environments and organic matter cycles. Additionally, these data are crucial for establishing isotope baselines for studying food webs. Ultimately, this data set provide valuable information for more effective ecosystem conservation and management strategies in our rapidly changing world. [ABSTRACT FROM AUTHOR]

Reptiles make up one‐third of tetrapods, however they are often omitted from global conservation analyses. Understanding the determinants of reptile distribution is the foundation for reptile conservation research. We assembled a dataset on the distribution of 231 reptile species (nearly 50% of recorded species in China). We then investigated the association of species range filling (the proportion of observed ranges compared to species potential climate distributions) with climate, range size, topography and human activity, using three regression methods. At the species level, we found the most primary factors influencing the recent distribution pattern of reptiles across China were the mean annual precipitation (MAP) and the mean annual temperature (MAT). In contrast, human activity came in last. Similarly, at a spatial level, MAP and MAT were still the most important factors. Geographically, the south and east of China support the highest reptile diversity, partially due to high precipitation. Contrary to the prevailing notion that reptile distributions are primarily shaped by human activities, our study re‐emphasizes the importance of climate in determining reptile distribution. It accentuates the necessity of integrating climate variables into future animal conservation strategies in China. Our findings also offer valuable insights for informing conservation practices, including the site planning of natural reserves and national parks, as well as monitoring of environmental factors in protected areas. [ABSTRACT FROM AUTHOR]

Negative Air Ions (NAIs), essential for environmental and human health, facilitate air purification and offer antimicrobial benefits. Our study achieves hourly estimations of NAIs using a machine learning framework, developed from a multi‐layer selection pipeline of over 200 variables, to identify the key determinants critical for adapting to high‐resolution NAIs dynamics. Addressing site sparsity and NAIs volatility, we introduced a hybrid stacking incorporating pseudo sites generated from Kriging Spatiotemporal Augmentation (KSTA) to mitigate spatial overfitting. Our approach, validated in Zhejiang, China, demonstrates exceptional accuracy, achieving R2 values of 0.90 (sample‐based), 0.85 (temporal‐based), and 0.79 (site‐based). This work not only sheds light on NAIs behavior in relation to diurnal shifts, land use, and environmental events, but also integrates a health grading system, enhancing public health strategies through precise air quality assessment. Plain Language Summary: Our study advances the environmental science by developing a machine‐learning model capable of estimation the hourly concentrations of negative air ions (NAIs) across Zhejiang Province, China. NAIs, vital for air purification and human health, are significantly related to environmental conditions. Taking advantage of a novel approach that reduces errors commonly seen in similar models, we provide a highly accurate, hour‐by‐hour analysis of air quality. Our research goes beyond mere prediction; it investigates how various factors like vegetation, soil type, and weather events influence NAI levels. The insights gained offer a comprehensive view of how NAI distribution changes over time and across different landscapes, informing environmental policy and public health strategies with precise air quality assessments. This study contributes significantly to the field by introducing an essential tool for continuous environmental monitoring, thereby facilitating more effective decision‐making in environmental and public health sectors. Key Points: A hybrid stacking machine learning framework shows superior accuracy (R2 = 0.90), combing a filter pipeline dealing with more than 200 variablesKriging Spatiotemporal Augmentation (KSTA) as a pseudo‐label strategy to resolve spatial overfitting due to sparse sitesAn hourly estimate of NAIs reveals NAIs dynamics correlations with environmental factors and land use, supporting public health policy [ABSTRACT FROM AUTHOR]

The AquaFly project is a research collaboration between the Flight Systems and Control (FSC) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) and Queen’s University Biology Department in Canada to offer a fast, reliable, and efficient method of sampling water bodies by using autonomous Unmanned Aerial Systems (UAS). To this end, two UAS are custom-designed and flown for autonomous water sampling, incorporating an octocopter configuration and a ratchet and pawl mechanism for retracting and releasing payloads. This design offers easy-to-use and safe deployment and retraction without continuous motor engagement while eliminating load swinging and the risk of instability by positioning the sampler close to the drone’s center of mass. Furthermore, the system enables autonomous water sampling in remote or inaccessible areas with pre-programmed flight paths and sampling locations, ensuring consistent data collection. The sampling mechanism allows controlled retrieval and release of payloads and further facilitates multi-drone operations to support a variety of sampling missions. This innovative UAS has the potential to revolutionize water sampling and enhance efficiency and safety in environmental monitoring, research, and resource management. Flight tests over several water bodies demonstrate successful simultaneous water sampling missions with two UAS. To the best of the authors’ knowledge, this is the first work to include multiple UAS, each equipped with a payload deployment and retraction mechanism, for autonomous water sampling. [ABSTRACT FROM AUTHOR]

Ulva green tides have adversely affected coastal ecosystems. In June 2023 in Changhua County, Taiwan, an unprecedented floating tubular Ulva bloom spanning about 30 km of coastline caused about 30 % reduction in oyster yield as reported by local farmers. Understanding its taxonomic and ecological basis is crucial for preemptive and remedial measures. Based on molecular (rbcL and tufA) and morphological evidence, Ulva meridionalis was the cause of this green tide. Haplotype network analysis (based on ITS) suggests that this green tide originated from northern China via the China Coastal Current. Historical survey data indicate that U. meridionalis arrived in Taoyuan Algal Reef (about 150 km north of the bloom area) as far back as 2018. Our ecophysiological experiments revealed that U. meridionalis exhibited a rapid daily growth rate with biomass increment up to 13–21 % when subjected to local nutrient-rich waters under lower salinity and spring-like conditions. Although historical ecological and poultry/livestock data analyses showed no noticeable change in sea surface temperature and rainfall over the past decade, a gradual rise in agricultural nitrogen and phosphorus output was observed. This taxonomic and ecological background lays the groundwork for long-term ecological monitoring. Moreover, this study exemplifies the detrimental impact of an unforeseen Ulva bloom on oyster farming. [ABSTRACT FROM AUTHOR]

Sensing technology is gaining attention and continuously advancing, making it a recommended element of individualized healthcare management. This is due to the powers exhibited by organometallic compounds, which are further enhanced by the field of bioengineering. Organometallic compounds have a wide range of biological activity and find uses in industrial and material science fields. Their unique ability to specifically target and overcome constraints faced by traditional counterparts makes them potential contenders for sensor technology. These compounds are highly sensitive to changes in their environment, allowing them to be utilized as sensors for detecting various chemicals or conditions. Additionally, the versatility of organometallic compounds enables their integration into different sensor platforms, making them suitable for environmental monitoring, medical diagnostics, and industrial quality control. This article provides a comprehensive summary of recent advancements in the design and synthesis of organometallic compounds, with a specific emphasis on their potential use as sensors. It also discusses the changes made to the structure, the processes used for functionalization, the incorporation of microfluidics, and the resulting impact on the materials’ sensing capabilities. These biologically derived methods align with sustainability goals and enhance the affordability, applicability, and effectiveness of sensing. [ABSTRACT FROM AUTHOR]

Insects play crucial roles in nearly every ecosystem and provide a wide array of ecosystem services. However, both managed and wild insect populations face threats from parasites and pathogens, which require surveillance to mitigate. Current infectious disease surveillance methods for insects often involve invasive, time‐consuming and occasionally destructive techniques, such as manual inspections and molecular detection. Volatile organic compound (VOC) surveillance provides a real‐time, accurate and non‐invasive alternative for disease detection and has been well‐established in humans and livestock. Recent advances in sensor technology now allow for the development of in‐field VOC surveillance devices. This review explores the need for disease surveillance in insects and highlights recent advances of using VOCs for this purpose, focusing on honey bees as an example. We outline potential applications, challenges and future prospects of using VOCs for insect disease surveillance, providing examples of how this technology could be globally applied to mitigate the impacts of disease in a range of insect systems. [ABSTRACT FROM AUTHOR]

Introduction: Companion animals offer a unique opportunity to investigate risk factors and exposures in our shared environment. Passive sampling techniques have proven effective in capturing environmental exposures in dogs and humans. Methods: In a pilot study, we deployed silicone monitoring devices (tags) on the collars of a sample of 15 dogs from the Dog Aging Project Pack cohort for a period of 120h (5days). We extracted and analyzed the tags via gas chromatography--mass spectrometry for 119 chemical compounds in and around participants' homes. Results: Analytes belonging to the following chemical classes were detected: brominated flame retardants (BFRs), organophosphate esters (OPEs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides, phthalates, and personal care products. The types and amounts of analytes detected varied substantially among participants. Discussion: Data from this pilot study indicate that silicone dog tags are an effective means to detect and measure chemical exposure in and around pet dogs' households. Having created a sound methodological infrastructure, we will deploy tags to a geographically diverse and larger sample size of Dog Aging Project participants with a goal of further assessing geographic variation in exposures. [ABSTRACT FROM AUTHOR]

Summary Wireless sensor networks (WSNs) consist of numerous sensor nodes with limited battery life, computational power, and network capabilities. These sensors are deployed in specific areas to monitor environmental physical parameters. Once the data are collected, it is processed and transmitted to a base station (BS) via designated routes. The processes of sensing and transmitting consume significant energy, leading to rapid depletion of node batteries and the occurrence of hot spot problems. Consequently, relying on a single route for data transmission can result in network overhead issues. Enhancing the energy efficiency of WSNs is a persistent challenge. To address this, improvements in processes, such as routing and clustering are necessary. Implementing dynamic cluster head (CH) selection is a key approach for optimal path selection and energy conservation. Accordingly, in this work, a novel multiobjective CH selection and routing method for providing energy‐aware data transmission in WSN is presented. Here, CH selection is carried out using the proposed chronological wild geese optimization (CWGO) technique based on multiple constraints, such as delay, intercluster distance, intracluster distance, Link Life Time (LLT), and predicted energy. Further, the nodes' energy is determined by the deep recurrent neural network (DRNN). Then, the ideal path from the node to the BS is identified by the CWGO considering constraints, like predicted energy, delay, distance, and trust. Moreover, the proposed CWGO is examined considering metrics, like energy, trust, distance, and delay and is found to have attained superior values of 0.963 J, 0.700, 19.468 m, and 0.252 s, respectively. [ABSTRACT FROM AUTHOR]

The use of environmental DNA (eDNA) is becoming prevalent as a novel method of ecological monitoring. Although eDNA can provide critical information on the distribution and biomass of particular taxa, the DNA sequences of an organism remain unaltered throughout its existence, which complicates the accurate identification of crucial events, including spawning. Therefore, we examined DNA methylation as a novel source of information from eDNA, considering that the methylation patterns in eggs and sperm released during spawning differ from those of somatic tissues. Despite its potential applications, little is known about eDNA methylation, including its stability and methods for detection and quantification. Therefore, we conducted tank experiments and performed methylation analysis targeting 18S rDNA through bisulphite amplicon sequencing. In the target region, eDNA methylation was not affected by degradation and was equivalent to the methylation rate of genomic DNA from somatic tissues. Unmethylated DNA, abundant in the ovaries, was detected in the eDNA released during fish spawning. These results indicate that eDNA methylation is a stable signal reflecting targeted gene methylation and further demonstrate that germ cell‐specific methylation patterns can be used as markers for detecting fish spawning. [ABSTRACT FROM AUTHOR]