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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]

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals of increasing concern to human health. PFAS contamination in water systems has been linked to a variety of sources including hydrocarbon fire suppression activities, industrial and military land uses, agricultural applications of biosolids, and consumer products. To assess PFAS in California tap water, we collected 60 water samples from inside homes in four different geographic regions, both urban and rural. We selected mostly small water systems with known history of industrial chemical or pesticide contamination and that served socioeconomically disadvantaged communities. Thirty percent of the tap water samples (18) had a detection of at least one of the 32 targeted PFAS and most detections (89 %) occurred in heavily industrialized Southeast Los Angeles (SELA). The residents of SELA are predominately Latino and low-income. Concentrations of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) ranged from 6.8 to 13.6 ng/L and 9.4-17.8 ng/L, respectively in SELA and were higher than State (PFOA: 0.007 ng/L; PFOS: 1.0 ng/L) and national health-based goals (zero). To look for geographic patterns, we mapped potential sources of PFAS contamination, such as chrome plating facilities, airports, landfills, and refineries, located near the SELA water systems; consistent with the multiple potential sources in the area, no clear spatial associations were observed. The results indicate the importance of systematic testing of PFAS in tap water, continued development of PFAS regulatory standards and advisories for a greater number of compounds, improved drinking-water treatments to mitigate potential health threats to communities, especially in socioeconomically disadvantaged and industrialized areas.

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.

Coastal upwelling supplies nutrients supporting primary production while also adding the toxic trace metal mercury (Hg) to the mixed layer of the ocean. This could be a concern for human and environmental health if it results in the enhanced bioaccumulation of monomethylmercury (MMHg). Here, we explore how upwelling influences Hg cycling in the California Current System (CCS) biome through particle scavenging and sea-air exchange. We collected suspended and sinking particle samples from a coastal upwelled water parcel and an offshore non-upwelled water parcel and observed higher total particulate Hg and sinking flux in the upwelling region compared to open ocean. To further investigate the full dynamics of Hg cycling, we modeled Hg inventories and fluxes in the upper ocean under upwelling and non-upwelling scenarios. The model simulations confirmed and quantified that upwelling enhances sinking fluxes of Hg by 41% through elevated primary production. Such an enhanced sinking flux of Hg is biogeochemically important to understand in upwelling regions, as it increases the delivery of Hg to the deep ocean where net conversion to MMHg may take place.

BACKGROUND: Diesel exhaust (DE) exposures pose concerns for serious health effects, including asthma and lung cancer, in California communities burdened by multiple stressors. OBJECTIVE: To evaluate DE exposures in disproportionately impacted communities using biomonitoring and compare results for adults and children within and between families. METHODS: We recruited 40 families in the San Francisco East Bay area. Two metabolites of 1-nitropyrene (1-NP), a marker for DE exposures, were measured in urine samples from parent-child pairs. For 25 families, we collected single-day spot urine samples during two sampling rounds separated by an average of four months. For the 15 other families, we collected daily spot urine samples over four consecutive days during the two sampling rounds. We also measured 1-NP in household dust and indoor air. Associations between urinary metabolite levels and participant demographics, season, and 1-NP levels in dust and air were evaluated. RESULTS: At least one 1-NP metabolite was present in 96.6% of the urine samples. Detection frequencies for 1-NP in dust and indoor air were 97% and 74%, respectively. Results from random effect models indicated that levels of the 1-NP metabolite 6-hydroxy-1-nitropyrene (6-OHNP) were significantly higher in parents compared with their children (p-value = 0.005). Urinary 1-NP metabolite levels were generally higher during the fall and winter months. Within-subject variability was higher than between-subject variability (~60% of total variance versus ~40%, respectively), indicating high short-term temporal variability. IMPACT: Biomonitoring, coupled with air monitoring, improves understanding of hyperlocal air pollution impacts. Results from these studies will inform the design of effective exposure mitigation strategies in disproportionately affected communities.

BACKGROUND: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions. OBJECTIVE: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics. METHODS: We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed localized difference. We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition. RESULTS: We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations. IMPACT: Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

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]

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]

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.

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.

Monomethylmercury (MMHg) is a neurotoxicant that biomagnifies in marine food webs, reaching high concentrations in apex predators. To predict changes in oceanic MMHg concentrations, it is important to quantify the sources and sinks of MMHg. Here, we study mercury speciation in the California Current System through cruise sampling and modeling. Previous work in the California Current System has found that upwelling transports mercury-enriched deep waters to productive surface waters. These upwelled waters originate within the California Undercurrent water mass and are subsequently advected as a surface water parcel to the California Current. Between the two major water masses, we find that compared to the California Current, the California Undercurrent contains elevated dissolved total mercury (THg) and dimethylmercury (DMHg) concentrations by 59 and 69%, respectively. We explain that these differences result from losses during advection, specifically scavenging of THg and DMHg demethylation. We calculate a net DMHg demethylation rate of 2.0 ± 1.1% d-1 and build an empirically constrained mass budget model to demonstrate that net DMHg demethylation accounts for 61% of surface MMHg sources. These findings illustrate that DMHg is a significant source of MMHg in this region, challenging the current understanding of the major sources of marine MMHg.

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.

Motor vehicles are among the major sources of pollutants and greenhouse gases in urban areas and a transition to "zero emission vehicles" is underway worldwide. However, emissions associated with brake and tire wear will remain. We show here that previously unrecognized volatile and semi-volatile organic compounds, which have a similarity to biomass burning emissions are emitted during braking. These include greenhouse gases or, these classified as Hazardous Air Pollutants, as well as nitrogen-containing organics, nitrogen oxides and ammonia. The distribution and reactivity of these gaseous emissions are such that they can react in air to form ozone and other secondary pollutants with adverse health and climate consequences. Some of the compounds may prove to be unique markers of brake emissions. At higher temperatures, nucleation and growth of nanoparticles is also observed. Regions with high traffic, which are often disadvantaged communities, as well as commuters can be impacted by these emissions even after combustion-powered vehicles are phased out.

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.

The use of bi-temporal remote sensing images for detecting changes in land cover is an important means of obtaining surface change information, thus contributing to urban governance and ecological environment monitoring. In this article, we propose a deep learning model named Siam-UNet3+ for high-resolution remote sensing image change detection. This model integrates the full-scale skip connections and full-scale deep supervision of the network UNet3+, which can achieve the multi-scale feature fusion of remote sensing images, effectively avoiding the locality disadvantage of convolution operations. Different from UNet3+, Siam-UNet3+ has made major improvements, including the following: (1) incorporating a Siamese network in the encoder, which can process bi-temporal remote sensing images in parallel; (2) leveraging the residual module as the backbone, which can avoid gradient vanishing (or exploding) and model degradation problems; (3) adding a Triplet Attention module to the decoder, which can avoid information redundancy that may occur in full-scale skip connections and increase the ability to focus on changing patterns; and (4) designing a hybrid loss function consisting of focal loss and dice loss, which is more suitable for remote sensing image change detection tasks. In this study, we conducted change detection experiments using the publicly available LEVIR-CD dataset, as well as two local datasets in Beijing. Through comparative experiments with five other models and ablation experiments, the proposed model Siam-UNet3+ in this article demonstrated significant advantages and improvements in four evaluation metrics, namely, precision, recall, F1-score, and overall accuracy (OA), proving to have great potential in the application to highresolution remote sensing image change detection tasks. [ABSTRACT FROM AUTHOR]

To enhance the effects of some functional soft drinks, drugs, especially metronidazole (MNZ) and ibuprofen (IBF), are often illegally added. This poses a serious threat to the health of consumers. Therefore, developing simple and rapid detection methods for these additives is crucial. In this study, DNA aptamers of metronidazole and ibuprofen were selected using the library-immobilized method. The best aptamer for metronidazole, named MNZ-1, has a dissociation constant (Kd) value of 4.9 μM and the aptamer for ibuprofen, named IBF-1, shows a Kd of 9.3 μM, as determined by the thioflavin T (ThT) fluorescence assay. The Kd values measured using isothermal titration calorimetry (ITC) were 17.0 μM and 66.7 μM for these two aptamers, respectively. Selectivity experiments indicate that MNZ-1 demonstrates very weak binding to structurally similar drugs, whereas IBF-1 exhibits binding capability to some structurally similar compounds comparable to ibuprofen, enabling the simultaneous detection of these types of drugs. Neither MNZ-1 nor IBF-1 binds to other common drugs. Using ThT, a label-free fluorescent detection method was developed for metronidazole and ibuprofen in soft drinks, showing limits of detection (LODs) of 0.6 μM and 4.7 μM, respectively. Owing to their small size and well-defined secondary structures, these aptamers are expected to be utilized in analytical applications for food and environmental monitoring. [ABSTRACT FROM AUTHOR]

Laccase is an enzyme known for its eco-friendly uses in environmental cleanup and biotechnology. However, it has limitations such as low stability, high cost, and complex recycling. So, there is a need for laccase mimics that can effectively imitate its properties. Herein, we created copper formate–lysine nanoparticles (Cuf-Lys) that mimic laccase's activity. The developed Cuf-Lys demonstrated remarkable polyphenol oxidase-like activity, stability, and recyclability, making them suitable for the fabrication of efficient colorimetric sensors for the detection of epinephrine. These sensors had a specific response and could accurately measure epinephrine concentrations ranging from 2.5 to 50 μM, with a detection limit as low as 1 μM. Furthermore, the biosensor demonstrated high sensitivity and selectivity when applied to the detection of rutin. The limit of detection for rutin was determined to be 0.16 μM while in the linear concentration range of 0.25 to 150.0 μM. We believe that Cuf-Lys provide a new route for the design of laccase mimics, showing potential applications for biomedical diagnosis and environmental monitoring. [ABSTRACT FROM AUTHOR]

Nanozyme technology has gained significant regard and been successfully implemented in various applications including chemical sensing, bio-medicine, and environmental monitoring. Fe-CDs were synthesized and characterized well in this study. As compared to HRP (3.7 mM), the Fe-CDs exhibited a higher affinity towards H2O2 (0.2 mM) using the steady-state kinetic assay and stronger catalytic capability by changing the color of TMB to the blue color of the oxidized state, oxTMB. Additionally, an efficient peroxidase mimic, Fe-CDs/GOx, based on the hybrid cascade system to produce in situ H2O2 for the visual detection of glucose (color change: colorless to blue, and then to green), has been developed in detail, with limits of detection (LODs) for H2O2 and glucose of 0.33 μM and 1.17 μM, respectively. The changes further demonstrate a linear relationship between absorbance and H2O2 concentration, ranging from 10 to 60 μM, and for glucose (1 to 60 μM). To assess the accuracy and detection capability of the Fe-CDs/GOx system, we evaluated a real human serum sample obtained from adult males in a local hospital. In conclusion, Fe-CDs serving as a peroxidase mimic have the potential for various applications in the fields of biomedicine and nanozymes. [ABSTRACT FROM AUTHOR]

Clouds play a pivotal role in determining Earth's climate, impacting several atmospheric processes influencing the weather. The study explores the temporal variations in cloud base height (CBH) over Pune, a tropical station, from 2017 to 2023 using a Laser-Based Ceilometer with a range of 0–7500 m. Our analysis shows seasonal trends in CBH, with the monsoon season exhibiting unique characteristics showing the lowest average CBH and minimal diurnal changes during the study period. Conversely, the pre-monsoon, post-monsoon, and winter season show a gradual rise of CBH in the morning, maintaining higher levels until late afternoon, followed by a decline in the evening. Analysis of the diurnal frequency of cloud occurrence at 500 m intervals reveals the monsoon season's dominance in the 500 m to 1000 m layer. Throughout the year, low cloud bases (CBH <2000 m) are most frequent, outpacing the frequency of mid and high cloud bases. High cloud base (7500 < CBH > 6000 m) is the least common, showing a semi-diurnal pattern in winter with peaks around noon and midnight. These findings not only enhance our understanding of Pune's atmospheric behaviour but also offer valuable information for weather prediction, climate modelling, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices. [ABSTRACT FROM AUTHOR]

Bimetallic Au@Ag nano-sea urchins (Au@Ag NSUs) functionalized with methimazole were synthesized and combined with paper tips as efficient surface-enhanced Raman scattering (SERS) substrates for ultrasensitive Cr(VI) detection. The redox reaction between methimazole and Cr(VI) ensured the reliability of quantitative Cr(VI) analysis. When droplets were continuously dropped on the tip of the paper, based on the gravity effect, the nanoparticles achieved enrichment at the front end of the tip, which further improved the sensitivity of detection. The detection limit is as low as 0.956 ng L−1, demonstrating its excellent performance in ultrarace Cr(VI) detection. Additionally, the SERS sensor was successfully applied to the detection of Cr(VI) in real water samples, such as lake water and tap water, indicating its wide application potential in environmental monitoring. This study not only provides an efficient and reliable new method for detecting Cr(VI) but also shows the great prospect of paper-based SERS sensors in practical applications. [ABSTRACT FROM AUTHOR]

Humidity sensing, as a promising approach for detecting environmental humidity and respiration patterns, is garnering widespread attention. Here, we present a quartz crystal microbalance (QCM) humidity sensor modified with hollow NiCo-LDH polyhedrons for real-time monitoring of both environmental humidity and human respiration. The hollow NiCo-LDH polyhedrons possess a large surface area and loose stacking, which contribute to its excellent humidity sensing performance. In practical applications, our device demonstrates outstanding performance when the relative humidity (RH) ranges from 11% to 97%, exhibiting high sensitivity, rapid response and recovery time (all within 1 s), and high selectivity. Furthermore, the sensor is capable of effectively distinguishing different breathing patterns, such as normal breathing, breath after exercising, and holding the breath, as well as detecting humidity changes before and after drinking water. This demonstrates its potential application prospects in monitoring and analyzing human health conditions. [ABSTRACT FROM AUTHOR]

Artemesia tridentata is a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected by Artemisia species. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring. [ABSTRACT FROM AUTHOR]

Accurate extraction of grassland sample coverage is crucial for regional ecological environment monitoring. Due to the strong feature learning capability, high flexibility, and scalability of deep learning methods, they have great potential in grassland sample extraction modelling. However, we still lack a model that can achieve both lightweight structure and effective performance for small object segmentation to considering the small target characteristics of grassland vegetation and the requirements for model deployment in later stages. Here, we combined the UNet model, which performs well in small target segmentation, with the lightweight network Shufflenetv2 model, proposing an improved UNet neural network, Shufflenetv2UNet, for grassland sample coverage extraction. The core of Shufflenetv2UNet is the removal of maximum pooling and double‐layer convolution modules from downsampling in the UNet neural network. In addition, the Inverted Residual Block structure module from Shufflenetv2 was added to achieve a lightweight model and improved extraction accuracy. The Shufflenetv2UNet achieves an accuracy of 98.23%, with a parameter size of 50.74 M, and a model inference speed of 0.004 s. Compared to existing extraction methods, this model has advantages in prediction accuracy, parameter size, and model inference speed. Moreover, Shufflenetv2UNet achieved different types of grassland sample coverage extractions, with good robustness, generalization, and universality, enabling investigators to quickly and accurately obtain grassland sample coverage. This allows more dynamic and accurate ground measurement data for regional grassland environmental monitoring. [ABSTRACT FROM AUTHOR]

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results--existing solutions are either slow; or bulky, expensive, and power-intensive--limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond. [ABSTRACT FROM AUTHOR]

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet. [ABSTRACT FROM AUTHOR]

Detecting ammonia at low concentrations is crucial in various fields, including environmental monitoring, industrial processes, and healthcare. This study explores the development and performance of an ultra-sensitive ammonia sensor using carboxylic group-functionalized multi-walled carbon nanotubes (f-MWCNTs) overlaid on polyvinyl acetate nanofibers coated on a quartz crystal microbalance (QCM). The sensor demonstrates high responsiveness, with a frequency shift response of over 120 Hz when exposed to 1.5 ppm ammonia, a sensitivity of 23.3 Hz ppm−1 over a concentration range of 1.5–7.5 ppm, and a detection limit of 50 ppb. Additionally, the sensor exhibits a rapid response time of only 14 s, excellent selectivity against other gases, such as acetic acid, formaldehyde, methanol, ethanol, propanol, benzene, toluene, and xylene, and good stability in daily use. These characteristics make the sensor a promising tool for real-time ammonia detection in diverse applications. [ABSTRACT FROM AUTHOR]

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources. [ABSTRACT FROM AUTHOR]

We present a terahertz (THz) sensing chip capable of detecting trace biomolecules. Its operating bandwidth stretches from 0.2 to 1.35 THz, capable of signal strengths exceeding 38 dB above noise levels, achieved by the mitigation of mode competition issues. The high sensitivity of the sensing chip is used to observe the absorption fingerprints of α-lactose monohydrate, L-tyrosine, and L-histidine powders at their characteristic frequencies for sample masses as low as 0.3 mg. Using a solution titration technique, we demonstrate detection of the absorption fingerprint of α-lactose monohydrate for concentrations of 72 μg/μl, adding just 57.6 μg of the powder onto the chip after evaporation of the ethanol solvent, representing a twofold advancement over prior work by providing a significantly broader operating spectrum and higher sensitivity. The relatively easy manufacturability, low cost, and scalability of the sensing chip paves the way for studies of enhanced light–matter interaction between THz waves and trace analytes, while also unlocking innovative potential applications in biomolecular sensing, medical diagnostics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

This article presents a 45-year data series (from 1978 to 2023) acquired under the IGA ("Impact des Grands Aménagements" in French; Impacts of Major Developments) program, conducted by Ifremer for EDF (Électricité de France, the French multinational electricity utility company). The IGA program was established to monitor the ecological and environmental quality of the coastal area surrounding the Gravelines Nuclear Power Plant (NPP), located in the southern bight of the North Sea. The main objective of this program is to assess medium- and long-term environmental evolution by providing means to identify possible changes in the local marine habitats. Since 1978, the IGA program has measured key parameters, including temperature, salinity, nutrient concentrations, oxygen levels, chlorophyll- a concentrations, as well as the abundance of phytoplankton and zooplankton species. These measurements have been taken at different sampling stations around the NPP, including the "Canal d'amenée" sampling station, for which hydrological and biological characteristics are considered as representative of broader coastal area of the southern bight of the North Sea. This data paper provides an overview of the main statistical characteristics of the time series, including long-term trends and shifts analysis. Despite the importance and length of this dataset, one of the longest available for this region, its application in advancing knowledge of hydrological and biological processes has been surprisingly limited. The aim of this paper is to make this valuable dataset available to the scientific community, stakeholders, and society to help decipher the local and global influence of anthropogenic activities in a world increasingly affected by climate change. Since all the main statistics and patterns are still available thanks to our analysis, the user should be able to use this data and combine it with other sources (in situ , satellite, modelling), in order to dive into deeper analyses, and to investigate new key scientific challenges as well as more specific ones. [ABSTRACT FROM AUTHOR]

The interaction of substrate surfaces with the environment (moisture, heat, pressure, chemicals) has cascade effects on their efficiency, performance, durability due to their susceptibility towards microbial adhesion, spontaneous leaching, impairment, corrosion etc. Many scientific, technological innovations such as smart coating have been discovered for the protection of these surfaces. It is a high‐tech, high value coating capable of responding to environmental impulses. Incorporation of nanofillers, nanomatrices in smart coating improve surface performance by extending service life, provide high durability, high physical coverage, adhesive strength etc. Real‐time monitoring of environmental conditions, structural health could be made possible by sensing coatings. This review predominantly highlights the essential features, design, advancements, applications of smart nanocoatings over elementary nanocoatings. To cover the whole study, drawbacks of traditional coating methods are also discussed. Finally, the research gaps, future perspectives of smart nanocoatings will be overlooked. [ABSTRACT FROM AUTHOR]

Introduction: The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency. Methods: This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures. Results: In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model's ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the "Permanent pasture" class. The F1-score was 94%, indicating a balanced measure of the model's performance. Discussion: The integration of advanced technologies in the CAP's control mechanisms, as demonstrated, has the potential to automate the verification of farmers' declarations and subsequent subsidy payments. [ABSTRACT FROM AUTHOR]

The production of home and personal care products demands stringent contamination control to ensure safety and quality. Microbial contamination poses significant risks to both product integrity and consumer safety, leading to potential recalls and brand damage. This article explores the critical role of environmental monitoring (EM) programs in identifying, tracking, and trending sources of microbial contamination. A robust contamination control strategy includes species-level microbial identifications, early warning systems, and adherence to regulatory standards such as the Modernization of Cosmetics Regulation Act (MoCRA) and USP chapters <60>, <61>, and <62>. These regulations emphasize the importance of tracking and trending microorganisms to increase visibility and improve the state of control of your processes and facility. Drawing on the analogy of a railway system, we illustrate how undetected microbial contamination can spread across production lines. By implementing proactive contamination controls, utilizing advanced identification techniques like MALDI-TOF and DNA sequencing, and managing microbial risks through accurate data analysis, manufacturers can stay ahead of potential threats. The article underscores the value of harmonized EM programs, which shift manufacturers from reactive to proactive responses, minimizing contamination risks and ensuring product safety. Investing in such programs ultimately protects consumers, ensures regulatory compliance, and preserves the brand's reputation. [ABSTRACT FROM AUTHOR]

This work extends recent developments in diacetylene-based, sprayable sensors by identification and assessment of formulations which facilitate their use for wearable sensing. Diacetylene-based spray-on sensors have the potential to be a widely deployed sensing technology, as they require no power and can be applied as thin coatings onto surfaces to provide a colorimetric response to target exposure. In responding to radiation, liquid-phase targets, or gas-phase targets specifically determined by the formulation of the sprayable sensor used, this technology is amenable to wearable sensors for measuring exposure to different environmental risks. Here, we provide the means to improve wear resistance, reduce false-positive signals due to wetting, and enhance color fastness for coatings of sprayable, diacetylene-based sensor formulations on cotton fabric. These sensor formulations possess polymethyl methacrylate (PMMA), which enhances the coating stability to only 8% color loss due to wear compared to 18–25% without PMMA, while maintaining the inherent ability of diacetylene-component formulations to detect radiation as well as gas or liquid phase analytes. This represents a significant step toward the use of diacetylene-based sensing formulations for wearable sensing. In the future, the form of spray-on sensor materials demonstrated here may find use in wearable sensing applications for detection of cumulative exposure to UV radiation, hydrogen peroxide vapors, or solvent exposure. We expect trends toward applications toward other wearable sensors for environmental monitoring given the well-known customizability in target response of diacetylene-containing monomers by modifying their headgroup chemistry. [ABSTRACT FROM AUTHOR]

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics. [ABSTRACT FROM AUTHOR]

The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation. [ABSTRACT FROM AUTHOR]

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends. [ABSTRACT FROM AUTHOR]

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand. [ABSTRACT FROM AUTHOR]

Semantic change detection (SCD) is a newly important topic in the field of remote sensing (RS) image interpretation since it provides semantic comprehension for bi-temporal RS images via predicting change regions and change types and has great significance for urban planning and ecological monitoring. With the availability of large scale bi-temporal RS datasets, various models based on deep learning (DL) have been widely applied in SCD. Since convolution operators in DL extracts two-dimensional feature matrices in the spatial dimension of images and stack feature matrices in the dimension termed the channel, feature maps of images are tri-dimensional. However, recent SCD models usually overlook the stereoscopic property of feature maps. Firstly, recent SCD models are usually limited in capturing spatial global features in the process of bi-temporal global feature extraction and overlook the global channel features. Meanwhile, recent SCD models only focus on spatial cross-temporal interaction in the process of change feature perception and ignore the channel interaction. Thus, to address above two challenges, a novel fine-grained feature perception network (FFPNet) is proposed in this paper, which employs the Omni Transformer (OiT) module to capture bi-temporal channel–spatial global features before utilizing the Omni Cross-Perception (OCP) module to achieve channel–spatial interaction between cross-temporal features. According to the experiments on the SECOND dataset and the LandsatSCD dataset, our FFPNet reaches competitive performance on both countryside and urban scenes compared with recent typical SCD models. [ABSTRACT FROM AUTHOR]

Farmstead dairy processing facilities may be particularly susceptible to Listeria spp. contamination due to the close physical proximity of their processing environments (PE) to associated dairy farm environments (FE). In this case study, we supported the implementation of interventions focused on improving (1) cleaning and sanitation efficacy, (2) hygienic zoning, and (3) sanitary equipment and facility design and maintenance in a farmstead dairy processing facility, and evaluated their effect on Listeria spp. detection in the farmstead's PE over 1 yr. Detection of Listeria spp. in the farmstead's PE was numerically reduced from 50% to 7.5% after 1 yr of intervention implementation, suggesting that these interventions were effective at improving Listeria spp. control. In addition, environmental samples were also collected from the farmstead's FE to evaluate the risk of the FE as a potential source of Listeria spp. in the PE. Overall, detection of Listeria spp. was higher in samples collected from the FE (75%, 27/36) compared with samples collected from the PE (24%, 29/120). Whole-genome sequencing performed on select isolates collected from the PE and FE supported the identification of 6 clusters (range of 3 to 15 isolates per cluster) that showed ≤ 50 high-quality single nucleotide polymorphism differences. Of these 6 clusters, 3 (i.e., clusters 2, 4, and 5) contained isolates that were collected from both the PE and FE, suggesting that transmission between these 2 environments was likely. Moreover, all cluster 2 isolates represented a clonal complex of L. monocytogenes commonly associated with dairy farm environmental reservoirs (i.e., CC666), which may support that the farmstead's FE represented an upstream source of the cluster 2 isolates that were found in the PE. Overall, our data underscore that although the FE can represent a potential upstream source of Listeria spp. contamination in a farmstead dairy processing facility, implementation of targeted interventions can help effectively minimize Listeria spp. contamination in the PE. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]