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Microplastics have been considered as contaminants of emerging concern due to ubiquity in the environment; however, the occurrence of microplastics in river estuaries is scarcely investigated. The Klang River estuary is an important ecosystem that receives various contaminants from urbanised, highly populated areas and the busiest maritime centre in Selangor, Malaysia. This study investigates the abundance and characteristics of microplastics in surface water of the Klang River estuary. The abundance of microplastics ranged from 0.5 to 4.5 particles L–1 with a mean abundance of 2.47 particles L–1. There is no correlation between the abundance of microplastics and physicochemical properties, while there is a strong correlation between salinity and conductivity. The microplastics were characterised with a stereomicroscope and attenuated total reflection–Fourier transform infrared spectroscopy to analyse size, shape, colour, and polymer composition. The microplastics in the surface water were predominantly in the 300–1000 μm size class, followed by > 1000 μm and < 300 μm, and were mostly transparent fibres, fragments, and pellets. Polyamide and polyethylene were the main polymer types in the composition of the microplastics, suggesting that the microplastics originated from heavily urbanised and industrial locations such as the port, jetty, and residential areas. The widespread occurrence of microplastics in the environment and subsequent penetration of aquatic food webs may pose a serious threat to organisms. This study provides baseline data and a framework for further investigation of microplastic contamination in estuaries.

The Clarion-Clipperton Zone (CCZ) in the tropical East Pacific is a region of interest for deep-sea mining due to its underwater deposits of polymetallic nodules containing economically important metals such as nickel, copper, and cobalt. It is also a region of extensive baseline studies aiming to describe the state of the environment, including the biodiversity of the benthic fauna. An abundant component of the abyssal plain ecosystem consists of sessile fauna which encrusts polymetallic nodules and are vulnerable to potential impacts arising from exploitation activities, particularly removal of substrate. Therefore, this fauna is often considered to have key species whose genetic connectivity should be studied to assess their ecological resilience. One such species is Abyssoprimnoa gemina Cairns, 2015, a deep-sea coral from the CCZ whose presence in the Interoceanmetal Joint Organization (IOM) claim area has been confirmed during samplings. In this study, we used next-generation sequencing (NGS) to obtain the 18S nuclear rRNA gene and the complete mitochondrial genome of A. gemina from IOM exploration area. The mitogenome is 18,825 bp long and encodes for 14 protein coding genes, 2 rRNAs, and a single tRNA. The two phylogeny reconstructions derived from these data confirm previous studies and display A. gemina within a highly supported cluster of seven species whose mitogenomes are all colinear and of comparable size. This study also demonstrates the suitability of NGS for DNA barcoding of the benthic megafauna of the CCZ, which could become part of the IOM protocol for the assessment of population diversity and genetic connectivity in its claim area.

Background Oral health-related quality of life (OHRQoL) could be affected not only by oral health but also by demographic and ecosocial factors. This research aimed to analyze the sociodemographic and clinical factors that may influence the OHRQoL of 12-year-old children. Methods A representative sample was selected from Hong Kong. Periodontal status and caries were examined according to WHO criteria. Four orthodontic indices were used to assess malocclusion. Child Perception Questionnaires (CPQ 11–14 -ISF:8 and CPQ 11–14 -RSF:8) including four domains, namely oral symptoms (OS), functional limitations (FL), emotional well-being (EWB), and social well-being (SWB), were used to measure OHRQoL. Adjusted OR was calculated by ordinal logistic regression. Results Totally 589 eligible subjects (305 females, 284 males) were recruited. Males tended to rank higher in OS domain but lower in EWB domain (adjusted OR = 1.89 and 0.67). Mother’s education was linked more closely with children’s CPQ scores. Higher education levels were associated with better quality of life (adjusted OR = 0.45 and 0.37). Household income showed no effect on CPQ scores. Unhealthy periodontal conditions had a negative effect on EWB and total CPQ (adjusted OR = 1.61 and 1.63). High caries experience only had a negative effect on SWB (adjusted OR = 1.60). Malocclusion affected FL, EWB, SWB and total CPQ: all malocclusion severities affected SWB; only severe malocclusions affected FL, EWB and total CPQ. Conclusion Males were more tolerant of oral symptoms than females were. Higher levels of mother’s education led to better OHRQoL of their children. Unhealthy periodontal conditions affected emotional well-being, while high caries experience affected social well-being. All malocclusion severities had an effect on social well-being; severe malocclusion further caused functional limitations, worse emotional well-being, and hence worse OHRQoL.

Indoor and outdoor ovitraps were placed in 15 randomly selected houses in two rural villages in Chiapas, southern Mexico. In addition, ovitraps were placed in five transects surrounding each village, with three traps per transect, one at the edge, one at 50 m, and another at 100 m from the edge of the village. All traps were inspected weekly. A transect with eight traps along a road between the two villages was also included. Population fluctuations of Aedes aegypti and Ae. albopictus were examined during 2016–2018 by counting egg numbers. A higher number of Aedes spp. eggs was recorded at Hidalgo village with 257,712 eggs (60.9%), of which 58.1% were present in outdoor ovitraps and 41.9% in indoor ovitraps, compared with 165,623 eggs (39.1%) collected in the village of Río Florido, 49.0% in outdoor and 51.0% in indoor ovitraps. A total of 84,047 eggs was collected from ovitraps placed along transects around Río Florido, compared to 67,542 eggs recorded from transects around Hidalgo. Fluctuations in egg counts were associated with annual variation in precipitation, with 2.3 to 3.2-fold more eggs collected from ovitraps placed in houses and 4.8 to 5.1-fold more eggs in ovitraps from the surrounding transects during the rainy season than in the dry season, respectively. Aedes aegypti was the dominant species during the dry season and at the start of the rainy season in both villages. Aedes albopictus populations were lower for most of the dry season, but increased during the rainy season and predominated at the end of the rainy season in both villages. Aedes albopictus was also the dominant species in the zones surrounding both villages. The numbers of eggs collected from intradomiciliary ovitraps were strongly correlated with the numbers of eggs in peridomiciliary ovitraps in both Río Florido (R2adj = 0.92) and Hidalgo (R2adj = 0.94), suggesting that peridomiciliary sampling could provide an accurate estimate of intradomiciliary oviposition by Aedes spp. in future studies in these villages. We conclude that the feasibility of sterile insect technique (SIT)-based program of vector control could be evaluated in the isolated Ae. aegypti populations in the rural villages of our baseline study.

We present the evolution of the mass–metallicity (MZ) relation at z = 4–10 derived with 135 galaxies identified in JWST/NIRSpec data taken from the three major public spectroscopy programs of ERO, GLASS, and CEERS. Because there are many discrepancies between the flux measurements reported by the early ERO studies, we first establish our NIRSpec data reduction procedure for reliable emission-line flux measurements and errors, successfully explaining Balmer decrements with no statistical tensions thorough comparisons with the early ERO studies. Applying the reduction procedure to the 135 galaxies, we obtain emission-line fluxes for physical property measurements. We confirm that 10 out of the 135 galaxies with [O iii] λ4363 lines have electron temperatures of ≃(1.1–2.3) × 104 K, similar to lower-z star-forming galaxies, which can be explained by heating by young massive stars. We derive the metallicities of the 10 galaxies by a direct method and the rest of the galaxies with strong lines using the metallicity calibrations of Nakajima et al. applicable for these low-mass metal-poor galaxies, anchoring the metallicities with the direct-method measurements. We thus obtain the MZ relation and star formation rate (SFR)–MZ relation over z = 4–10. We find that there is a small evolution of the MZ relation from z ∼ 2–3 to z = 4–10, while interestingly the SFR–MZ relation shows no evolution up to z ∼ 8 but a significant decrease at z > 8 beyond the errors This SFR–MZ relation decrease at z > 8 may suggest a break of the metallicity equilibrium state via star formation, inflow, and outflow, while further statistical and local-baseline studies are needed for a conclusion.

Climate change poses a substantial threat to the stability of the Arctic terrestrial carbon (C) pool as warmer air temperatures thaw permafrost and deepen the seasonally-thawed active layer of soils and sediments. Enhanced water flow through this layer may accelerate the transport of C and major cations and anions to streams and lakes. These act as important conduits and reactors for dissolved C within the terrestrial C cycle. It is important for studies to consider these processes in small headwater catchments, which have been identified as hotspots of rapid mineralisation of C sourced from ancient permafrost thaw. In order to better understand the role of inland waters in terrestrial C cycling we characterised the biogeochemistry of the freshwater systems in a c. 14 km² study area in the western Canadian Arctic. Sampling took place during the snow-free seasons of 2013 and 2014 for major inorganic solutes, dissolved organic and inorganic C (DOC and DIC, respectively), carbon dioxide (CO₂) and methane (CH₄) concentrations from three water type groups: lakes, polygonal pools and streams. These groups displayed differing biogeochemical signatures, indicative of contrasting biogeochemical controls. However, none of the groups showed strong signals of enhanced permafrost thaw during the study seasons. The mean annual air temperature in the region has increased by more than 2.5°C since 1970, and continued warming will likely affect the aquatic biogeochemistry. This study provides important baseline data for comparison with future studies in a warming Arctic.

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting. Plain Language Summary Traditionally, weather forecasts are made by models that attempt to replicate the physical processes of the atmosphere. This has been very successful over the last few decades as better computers, better observations and model upgrades have lead to steadily improving weather forecasts. However, with rapid advances in artificial intelligence (AI), the question can be asked whether one can simply learn a weather model from past observations or reanalyzes. In the last couple of years, we have seen tremendous progress with state‐of‐the‐art AI models rivaling the best “traditional” weather models in skill. WeatherBench 2 is a benchmark data set designed to evaluate and compare the quality of AI and traditional models. By setting a standard for evaluation, alongside providing open‐source data and code, this project aims to accelerate this research direction and lead to better weather prediction. Key Points WeatherBench 2 is a framework for evaluating and comparing data‐driven and traditional numerical weather forecasting models It provides an evaluation framework, publicly available data sets and a website to assess the state‐of‐the‐art weather models The evaluation protocol has been designed following best practices established in the operational weather forecasting community

Environmental risk assessment is one of the key tools in environmental engineering. This risk assessment can be qualitative or quantitative and it is based on preliminary studies i.e., baseline study for waste disposal sites. Even though the literature exists on baseline study in general, still there is a lack of guidance regarding development of a site-specific baseline study model for a waste disposal site. This study has two-fold aim, firstly, how to develop site-specific baseline study model for a selected dumping site, and secondly, how this site-specific baseline study can support the environmental engineering via mathematical risk estimation. Mahmood Booti Open Dumping Site (MBODS) is selected to demonstrate the development and application of site-specific baseline study model. This is followed by building a framework that shows how the output of the baseline study can lead to environmental engineering via mathematical risk estimation. The paper provides a mechanism of how to construct a bespoke baseline-study model that is readily useable, avoiding procurement of expensive computer software and yet smoothly connecting with the follow-on stages of the risk assessment. The work presented in this paper can be reproduced repeatedly to create site-specific baseline study models for risk assessment of other waste disposal sites in a cost-effective, consistent and cohesive manner.

A baseline study is a means of and for acquiring, organising, cleansing, presenting, and analysing all the data and/or information of preliminary investigation for hazard and risk assessment. This output of baseline study can be regarded as a conceptual site model (CSM), which has wide-ranging aspects that the literature to date does not appear to have captured a detailed account of, thereby limiting the full exploitation of CSM capacity in environmental communication between varying stakeholders. This knowledge-gap is focused upon by bringing out some new insights regarding CSM and creating an account of features of CSM for the first time. To start with, this study introduces CSM as an “intermediary” between a baseline study and the follow-on stages of the associated environmental risk assessment, and this is an innovative idea in its own right. Furthermore, light is torched upon CSM in several other new ways to show how CSM can serve as a live and “organic” foundation of an environmental risk assessment. It is depicted how the eight modules of a baseline study – geology, hydrology, hydrogeology, meteorology, geography, topography, anthropology and site management – can inform to develop a CSM. Also, a CSM could be descriptive and/or schematic which could still be computer-aided or non-computer aided. Another implication is that even though CSM contains the word “site” in the phrase, it does not mean that the model includes only the geographical or physical extent of the site, rather it also includes off-site, i.e., site-surroundings. This is where the aforesaid eight modules can cover both on-site and off-site characteristics of a given site being assessed. The innovative account of CSM parameters, advantages and uses would pave the way for further research and ignite debates among a diverse range of researchers, consultants, environmental regulators, decision-makers and other stakeholders.

Since the weather is chaotic, it is necessary to forecast an ensemble of future states. Recently, multiple AI weather models have emerged claiming breakthroughs in deterministic skill. Unfortunately, it is hard to fairly compare ensembles of AI forecasts because variations in ensembling methodology become confounding and the baseline data volume is immense. We address this by scoring lagged initial condition ensembles—whereby an ensemble can be constructed from a library of deterministic hindcasts. This allows the first parameter‐free intercomparison of leading AI weather models' probabilistic skill against an operational baseline. Lagged ensembles of the two leading AI weather models, GraphCast and Pangu, perform similarly even though the former outperforms the latter in deterministic scoring. These results are elaborated upon by sensitivity tests showing that commonly used multiple time‐step loss functions damage ensemble calibration. Plain Language Summary 2023 was a seminal year for data‐driven weather forecasts with several high‐profile publications claiming that AI outperformed traditional physics‐based approaches to weather forecasts. These claims are mostly supported by scoring deterministic forecasts, even though it is widely known that forecasting is a probabilistic problem. Probabilistic intercomparisons have proved challenging because of the data volumes involved and because they are confounded by particulars of how probabilistic forecasts are built. As a workaround, we propose benchmarking weather forecasts using lagged ensemble forecasting where forecasts initialized at different times are considered independent samples. When benchmarked in this way, we show that some AI models achieve better deterministic scores by reducing the variance of their forecasts at the cost of physical realism. Key Points Lagged ensembling is a practical, quantitative, and parameter‐free framework for benchmarking weather models Lagged ensembles of some recent data‐driven forecasts are under‐dispersive despite claims of “state of the art” deterministic skill Training data‐driven models with multi‐step loss functions damages ensemble calibration