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This study was conducted in northern New Jersey, USA, to estimate the nutrient fluxes from the Passaic River, the Hackensack River and other sources into Newark Bay and the nutrient residence time in Newark Bay. Bi-weekly total inorganic nitrogen (TIN) and orthophosphate concentration data in the Passaic River, the Hackensack River, and Newark Bay for over 15 years (2004–2019) were collected along with daily river discharge data from the public database. The annual TIN and orthophosphate (ortho-P) loading from the Passaic River ranged from 915 × 103 kg y−1 to 251 × 104 kg y−1 and 94 × 103 kg y−1to 372 × 103 kg y−1, respectively. The annual TIN and ortho-P loading from the Hackensack River ranged from 3.13 × 103 kg y−1 to 234 × 103 kg y−1 and 0.28 × 103 kg y−1 to 6.97 × 103 kg y−1, respectively. Seasonal variation results indicated that hurricane events highly increased TIN and ortho-P loading from riverine input and reduced residence time in Newark Bay. [Display omitted] • Hurricane event has impact on seasonal nutrient loading to Newark Bay. • Hurricane events highly increased nitrate and ortho-P loading from riverine input to Newark Bay. • Hurricane events reduced residence time of nutrient in Newark Bay. • The Passaic River is the major nutrient input source to Newark Bay. [ABSTRACT FROM AUTHOR]

Black patients were severely under-represented in the clinical trials that led to the approval of immune checkpoint inhibitors (ICIs) for all cancers. The aim of this study was to characterise the effectiveness and safety of ICIs in Black patients. We did a retrospective cohort study of patients in the US Veterans Health Administration (VHA) system's Corporate Data Warehouse containing electronic medical records for all patients who self-identified as non-Hispanic Black or African American (referred to as Black) or non-Hispanic White (referred to as White) and received PD-1, PD-L1, CTLA-4, or LAG-3 inhibitors between Jan 1, 2010, and Dec 31, 2023. Effectiveness outcomes were overall survival, time to treatment discontinuation, and time to next treatment. The safety outcome was the frequency of immune-related adverse events; assessed among a random sample of 1000 Black patients and 1000 White patients, 892 pairs were matched on the basis of baseline characteristics using 1:1 exact matching without replacement. After manual chart review, patients who did not receive ICI therapy or who had inadequate follow-up were excluded. The adjusted effect of race on each effectiveness outcome was assessed in the whole ICI-treated cohort with propensity-weighted Cox regression with robust standard errors. Immune-related adverse events outcomes were analysed in the random matched sample with multivariable Cox regression, adjusting for baseline characteristics. We identified 26 398 patients, of whom 4943 (18·7%) patients were Black, 21 455 (81·3%) were White, 895 (3·4%) were female, 25 503 (96·6%) were male, 11 859 (45%) had non-small-cell lung cancer, and 26 045 (98·7%) received PD-1 or PD-L1 inhibitors. As of data cutoff (Aug 28, 2024), median follow-up was 40·3 months (95% CI 38·3–42·3) for Black patients and 43·9 months (43·0–45·1) for White patients. Compared with White patients, Black patients had longer time to treatment discontinuation (2-year unadjusted rates 10·7% [95% CI 9·8–11·7] for Black patients vs 8·6% [8·2–9·0] for White patients; adjusted hazard ratio [HR] 0·91, 95% CI 0·87–0·95, p<0·0001), similar time to next treatment (23·5% [22·3–24·8] for Black patients vs 25·6% [25·0–26·2] for White patients; 1·00, 0·95–1·05, p=0·96), and slightly improved overall survival (36·5% [35·2–38·1] for Black patients vs 36·5% [35·8–37·1]; 0·95, 0·90–0·99, p=0·036). 1710 patients (n=862 Black and n=848 White) were analysed for safety outcomes. Compared with White patients, Black patients had a reduced risk of all-grade immune-related adverse events (unadjusted 2-year rate 33·1% [95% CI 28·9–37·1] vs 44·1% [95% CI 39·1–48·7]; adjusted HR 0·75, 95% CI 0·62–0·90, p=0·0026), immune-related adverse events requiring treatment with systemic steroids (0·61, 0·46–0·81, p=0·00051), and immune-related adverse events resulting in permanent ICI discontinuation (0·58, 0·44–0·78, p=0·00024). In exploratory analyses of irAE subtypes, a significant risk reduction in Black patients was found for colitis (0·46, 0·27–0·76, p=0·0026) and hyperthyroidism or hypothyroidism (0·63, 0·44–0·90, p=0·011), and no significant differences were found for any other immune-related adverse event subtypes analysed. Similar results were found in analyses using a steroid-based definition of immune-related adverse events among the entire ICI-treated cohort. Compared with White patients, Black patients had similar ICI effectiveness and lower toxicities among those treated in the national VHA system, potentially reflecting an important difference in the therapeutic ratio (ratio of benefit to harm) of ICIs. Our findings of decreased toxicity among Black patients require further investigation to assess their generalisability. Million Veteran Program, Office of Research and Development, Veterans Health Administration and the LUNGevity foundation. [ABSTRACT FROM AUTHOR]

• A hierarchy of physics-informed forecast models is evaluated under different cloud types and categories. • GHI and DNI performances exhibit a cloud-type order whereas DHI performance is less dependent on cloud types. • Cloud albedo and fraction play distinct roles in forecasts depending on radiative components and cloud types. • The improved accuracy demonstrates the effectiveness of incorporating physics into the models for various cloud types. • Further physical improvements are needed by directly integrating cloud type information into models. Cloud-type impacts present a great challenge to solar forecasting due to diverse and complex cloud-radiation interactions. This third part of our paper sequence seeks to address this challenge by quantifying the forecast accuracies under eight cloud types: cumulus (Cu), stratified clouds (St), altocumulus (Ac), altostratus (As), cirrostratus/anvil (Cr), cirrus (Ci), congestus (Co), deep convective clouds (Dc) across four physics-informed persistence models reported in Part I. To generalize the cloud impacts, the eight cloud types are further grouped into three cloud categories based on their common features: weak convective clouds, stratiform clouds, and strong convective clouds. The decade-long (2001 ∼ 2014) collocated measurements of irradiances and cloud types at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program South Great Plain (SGP) Central Facility site are used for model evaluation. Results reveal a clear performance hierarchy for global horizontal irradiance (GHI) and direct normal irradiance (DNI): best for weak convective clouds and cirrus, intermediate for stratiform clouds, and worst for strong convective clouds. Performance for diffuse horizontal irradiance (DHI) is less influenced by cloud types. Cloud albedo dominates all three irradiances for Dc, while both cloud albedo and cloud fraction are influential for other cloud types. A 12 %∼33 % improvement in accuracy at 6-hour lead time compared to the benchmark smart model confirms the effectiveness of incorporating physics into the models for various cloud types; further improvements are expected by directly integrating cloud type information into forecasting models by modifying the physical formulation of cloud-radiation interaction, and/or using more advanced machine learning models. [ABSTRACT FROM AUTHOR]

• A hierarchy of machine learning forecast systems is presented and compared with five persistence models. • Difference between hierarchical models enhance the interpretability of forecasts. • LSTM and XGBoost outperform five persistence models. • The introduction of cloud albedo and cloud fraction significantly improves solar forecasts. • Machine learning models can somewhat learn clear-sky irradiance, making the value of adding it limited. Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions. [ABSTRACT FROM AUTHOR]

• Physics-informed persistence forecast systems are presented. • New systems improve the forecast at long lead times. • Cloud-fraction and cloud-albedo-informed systems outperform the other models. • Model performance is dominated by the variability and error in cloud predictor. • Relationship between radiative and cloud properties can explain the model behavior. Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (<1 h). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM)'s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. The results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 h up to 6 h. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties. [ABSTRACT FROM AUTHOR]

The present study uses nanometer-scale synchrotron X-ray nanofluorescence to investigate season differences in concentrations and distributions of major (Ca, K, S and P) and trace elements (As, Cr, Cu, Fe and Zn) in the root system of Spartina alterniflora collected from Jamaica Bay, New York, in April and September 2015. The root samples were cross-sectioned at a thickness of 10 μm. Selected areas in the root epidermis and endodermis were mapped with a sampling resolution of 100 and 200 nm, varying with the mapping areas. The results indicate that trace element concentrations in the epidermis and endodermis vary among the elements measured, possibly because of their different chemical properties or their ability to act as micronutrients for the plants. Elemental concentrations (As, Ca, Cr, Cu, Fe, K, P, S and Zn) within each individual root sample and between the root samples collected during two different seasons are both significantly different ( p  < 0.01). Furthermore, this study indicates that the nonessential elements (As and Cr) are significantly correlated ( p  < 0.01) with Fe, with high concentrations in the root epidermis, while others are not, implying that Fe may be a barrier to nonessential element transport in the root system. Hierarchy cluster analysis shows two distinct groups, one including As, Cr and Fe and the other the rest of the elements measured. Factor analysis also indicates that the processes and mechanisms controlling element transport in the root system can be different between the nutrient and nonessential elements. [ABSTRACT FROM AUTHOR]

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. This information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can (1) robustly detect clouds and track layers, and (2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes. [ABSTRACT FROM AUTHOR]