Purchase College

Salt marshes in the northeastern United States support several specialized breeding bird species that are threatened by sea level rise (SLR) and coastal development, processes that drive habitat change and fragmentation. There have been rapid, widespread declines in some species, but mechanisms driving population change and whether declines continue remain unclear. We examined the influence of phenomena expected to modify salt marshes, including SLR, sediment delivery rates, and land use, on the population trajectories of saltmarsh breeding birds. We modeled population trajectories of 5 species with spatially extensive point count surveys conducted from Maine to Virginia from 2011 to 2022. We used Bayesian hierarchical abundance models and model selection to identify phenomena that had the strongest effect on population change. Clapper rails (Rallus crepitans) continued their long-term decline (-4.1%/year). Willets (Tringa semipalmata semipalmata) (2.6%/year) and saltmarsh sparrows (Ammospiza caudacuta) (4.1%/year) increased, and seaside (Ammospiza maritima) and Nelson's (Ammospiza nelsoni subvirgatus) sparrows exhibited no clear change in abundance. The estimated increase for saltmarsh sparrow was not consistent with trends over the previous 25years but aligned with prior demographic modeling, which predicted a short-term stabilization during the study years before an expected return to a long-term decline. Road density and other tidal restrictions near marshes were generally good predictors of abundance over the study period, as was marsh habitat composition. Local rates of SLR and sediment delivery were not as good predictors. During periods of relatively low rates of realized SLR, local-scale drivers of population trends had relatively stronger effects than global drivers on the persistence of several saltmarsh breeding birds. Conservation practitioners, however, should be attentive to global drivers, especially as rates of SLR accelerate in the future.

The rapid expansion of urban populations, coupled with growing epidemiological evidence that associates extreme temperature events with adverse health outcomes and elevated mortality rates underscores the critical role of Urban Green Areas (UGAs) in delivering ecosystem services that enhance human well-being. Among these services, the air temperature cooling potential (ΔT°C) driven by ecosystem evapotranspiration (ET) represents a key mechanism for mitigating heat-related health risks.This study investigates the capacity of various Machine Learning (ML) algorithms to predict the ΔT°C of UGAs, thereby supporting thermal regulation through ET and highlighting their importance in sustainable urban planning and climate adaptation strategies. We used multiple years of experimental Eddy Covariance (EC) observations of the ET the to train and validate a series of ML algorithms with the objective to simulate the cooling effect of the urban vegetation. A preliminary analysis of predictor variables was conducted to identify and rank their importance using the mean absolute Shapley (Sh) values. Results indicate that incoming shortwave solar radiation (Rg) was the most influential predictor (Sh = 0.45), followed by vapor pressure deficit (VPD, Sh = 0.20), relative humidity (RH, Sh = 0.075), air temperature (AirT, Sh = 0.065), friction velocity (u*, Sh = 0.02), and wind speed (WS, Sh = 0.01). The application of ML algorithms revealed that Bootstrap Aggregation (Bagging) and Least-Squares Boosting (LSBoost) performed best, achieving R² values of 0.89 and 0.83, respectively, during the training phase compared to observed data. Other algorithms, including Neural Networks (NN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), showed also similar, but slightly lower r2 , with values ranging from 0.80 (NN) to 0.79 (SVM). Ten-fold cross-validation confirmed robust generalization, as model performance remained consistent regardless of the data subset used to compute R² between modeled and observed values. Further evaluation using Taylor diagrams showed that the average normalized standard deviation (σn) and Pearson correlation coefficient of the models were 0.89 (±0.02) and 0.90 (±0.02), respectively, closely matching the observed data.During the testing phase we observed, as expected, a clear reduction of the ML performance compared to the training phase: however, over the three years of the testing phase, RG bagging and LSBOOST have confirmed their superiority, compared to the other algorithms, with an average r2 between observed and simulated data of 0.66 and 0.67 respectively. Discrepancies between predicted and observed ΔT°C during testing were most evident during midday hours, with an average overestimation of 0.31°C (±0.2).Overall, the investigated UGAs demonstrated an average capacity to reduce ambient air temperature during summer by approximately 2°C to 4°C.

Background: Manufactured chemicals are the second important classes as the natural chemicals mentioned above and the American Chemistry Council (ACC) projects global chemical production to rise by 3.4 % in 2024 and 3.5 % in 2025, after increasing just 0.3 % in 2023. Exist a variety of process in the industries that use a wide range of different chemicals accordingly their classes considering structure, polarity solubility for specific targeting. The common organic solvents are generally classified as an aliphatic hydrocarbon, aromatic hydrocarbon, cyclic hydrocarbon, halogenated hydrocarbon, amines, ketones, esters, ether, aldehyde, alcohols among others. Objective: This short-critical review will discuss throughout the literature background risk assessment, environmental and health hazard of two organic solvents as known Hexane (HEX) and Ethyl Acetate (EtoAc). Methods: The most significative databases regarding risk assessment, environmental, and health hazard was consulted and evaluated as American Chemistry Council (ACC); National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information in the NIH (National Library of Medicine); U.S. Environmental Protection Agency (EPA); U.S. Food and Drug Administration (FDA); European Chemicals Agency (ECHA-OECD); World Health Organization (WHO) and Department of Climate Change, Energy, the Environment and Water (AU). Conclusions and Future Outlook: In recent decades, various efforts have been made to replace toxic VOCs in chemical processes, according to the principles of Green Chemistry and Green Extraction One of the. However even for EtoAc still exist and susbtantial lack of background do be evaluated and properly described. Additionally the lack of accurated data, the number of unclear regulations and non-standarized protocols to proof risk assessment, environmental and health hazard of VOCs as HEX and EtoAc and many other chemicals are still a great challenge to be harmonized.

Degenerative diseases such as hypertension, diabetes mellitus, and hypercholesterolemia are major global health concerns with high prevalence in Indonesia, including in Kampung Biru, Banjarmasin. This community service initiative aimed to enhance public knowledge about the prevention and management of degenerative diseases, while also providing medication information through the Drug Information Service (DIS). The program involved 58 participants who underwent health screenings (blood pressure, blood glucose, cholesterol, and uric acid levels), disease-specific group education, and DIS sessions conducted by pharmacists. Results showed a 60.34% improvement in participants' knowledge, with a 98.27% satisfaction rate. Data analysis revealed a higher prevalence of these diseases among the elderly (aged 45 years and above), aligning with national epidemiological trends. The program demonstrated the effectiveness of a collaborative approach involving academics, healthcare professionals, and the community in promoting health literacy and preventive care.

Landscapes in the Making . Stephen Daniels and Dell Upton (eds). Dumbarton Oaks, Trustees for Harvard University, 2025. ![Figure][1]</img> This edited book is about landscape making from the perspectives of history, political ecology, economics, and cultural geography. As stated in the book’s

Abstract Sex is a term with multiple meanings depending on the contexts of its use. There are two primary definitions of sex, one referring to social identity grounded in biology and another to what people do with themselves or each other. These definitions are considered to be related, but it is important to note that sex as both social marker and erotic behavior is a highly contested term. Depending on who is defining sex and for what purposes, explanations can be value laden to indicate that a behavior or body is “normal” or “abnormal.” Therefore, it is necessary to understand how sex is always and everywhere a socially constructed category.

<strong class="journal-contentHeaderColor">Abstract.</strong> High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022, available at the Global Land Analysis and Discovery at the University of Maryland (<a href="https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states" target="_blank" rel="noopener">https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states</a>). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95 % with standard errors of less than 1 %. User&rsquo;s accuracies (UAs) and producer&rsquo;s accuracies (PAs) for maize were higher than 91 % and 84 % across the years, and UAs and PAs for soybean were greater than 88 % and 82 %, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1 % to 10 %, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 14 % and 16 %, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales. The dataset is also available at <a href="https://doi.org/10.6084/m9.figshare.28934993.v1" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.28934993.v1</a> (Li et al., 2025).