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Plastic pellet spills are a major source of microplastic pollution, and pellets are found on beaches worldwide. However, the potential environmental impacts of these spills remain poorly understood. In December 2023, approximately 25,000 kg of polyethylene pellets containing high concentrations of the additive Tinuvin UV-622 were spilled during a shipping accident off the northern coast of Portugal. Pellets collected from an affected beach located in Galicia, Spain, along with solvent extracts and aqueous leachates, were subjected to both target and nontarget chemical analyses and tested in a battery of toxicity assays including a green microalga (Raphidocelis subcapitata), a marine copepod (Apocyclops royi), a fish model (Danio rerio), and a human cell line. Chemical screening identified on the order of 50 chemical substances in addition to Tinuvin UV-622, including a range of known plastic additives and nonintentionally added substances (NIAS). Toxicity assays revealed significant growth inhibition and stress-induced cell aggregation in R. subcapitata and acute toxicity causing immobilization in copepods, which could have potential implications in the environment via the disruption of primary producers and food web dynamics. In contrast, zebrafish embryos showed no significant developmental effects, while human cells exhibited modest, time-dependent reductions in viability. Our findings underscore the complex chemical burden associated with pellet spills and stress the need for policies and regulations to prevent them, reinforcing the importance of applying the precautionary principle in managing the environmental risks linked to plastic pellet production, transport, and accidental release.
2026
2001
In the present study, concentrations of legacy and emerging contaminants were determined in three non-destructive matrices (plasma, preen oil and body feathers) of northern goshawk (Accipiter gentilis) nestlings. Persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) and polybrominated diphenyl ethers (PBDEs), together with emerging pollutants, including per- and polyfluorinated alkyl substances (PFASs), novel brominated flame retardants (NBFRs), phosphorus flame retardants (PFRs) and Dechlorane Plus isomers (DPs) were targeted. Plasma, preen oil and feather samples were collected from 61 goshawk nestlings in Norway (Trøndelag and Troms) in 2015 and 2016, and pollutant concentrations were compared between the three matrices. In plasma, PFASs were detected in the highest concentrations, ranging between 1.37 and 36.0 ng/mL, which suggests that the nestlings were recently and continuously exposed to these emerging contaminants, likely through dietary input. In preen oil, OCPs (169–3560 ng/g) showed the highest concentrations among the investigated compounds, consistent with their high lipophilicity. PFRs (2.60–314 ng/g) were the dominant compounds in feathers and are thought to originate mainly from external deposition, as they were not detected in the other two matrices. NBFRs and DPs were generally not detected in the nestlings, suggesting low presence of these emerging contaminants in their environment and/or low absorption. Strong and significant correlations between matrices were found for all POPs (rs = 0.46–0.95, p < 0.001), except for hexachlorobenzene (HCB, rs = 0.20, p = 0.13). Correlations for PFASs were less conclusive: linear perfluorooctane sulfonate (PFOS), perfluoroundecanoate (PFUnA), perfluorododecanoate (PFDoA) and perfluorotetradecanoate (PFTeA) showed strong and significant correlations between plasma and feathers (rs = 0.42–0.72, p < 0.02), however no correlation was found for perfluorohexane sulfonate (PFHxS), perfluorononanoate (PFNA) and perfluorotridecanoate (PFTriA) (rs = 0.05–0.33, p = 0.09–0.85). A lack of consistency between the PFAS compounds (contrary to POPs), and between studies, prevents concluding on the suitability of the investigated matrices for PFAS biomonitoring.
2019
2005
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2007
2005
Integrated water vapor during rain and rain-free conditions above the Swiss Plateau
Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.
2021
2018
Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications
Low-cost air quality sensor systems (LCS) are emerging technologies for policy-relevant air quality analysis, including pollution levels, source identification, and forecasting. This report discusses LCS use in networks and alongside other data sources for comprehensive air quality applications, complementing other WMO publications on LCS operating principles, calibration, performance assessment, and data communication.
The LCS’s utility lies in their ability to provide new insights into air quality that existing data sources may not offer. While LCS data must be verified, their integration with other data sources can enhance understanding and management of air quality. In areas without reference-grade monitors, LCS can identify factors affecting local air quality and guide future monitoring efforts. Combining LCS data with satellite and other air quality systems can improve data reliability and establish corroborating evidence for observed trends. LCS can extend the spatial coverage of existing monitoring networks, offering localized insights and supporting effective air quality management policies. Co-locating LCS with reference-grade monitors helps quantify measurement uncertainties and apply LCS data appropriately for forecasting, source impact analysis, and community engagement.
World Meteorological Organization
2024
Urban areas experience elevated air pollution levels which pose significant health risks. Reducing exposure to poor air quality and mitigating the associated negative health impacts requires informed policy measures. This study advances urban air quality modelling by developing an air quality model (baseline model) and further integrating measurements from a network of low-cost sensors and regulatory monitors into the model output (data fusion model). The resulting data fusion model provides accurate air quality data in high spatiotemporal resolution. The data fusion model showed higher PM2.5 concentrations during evening hours and winter months, with a population-weighted exposure to PM2.5 almost twice as high as predicted by the baseline model during these months. The models exhibited different spatial patterns, with the data fusion model showing a shift in peak concentrations from the city centre to residential areas, where levels were up to 10 µg/m3 higher than the baseline model. These differences are likely attributable to an underestimation of residential emissions in the baseline model. While both models were FAIRMODE compliant, the data fusion model showed a reduced bias for most monitoring stations compared to the baseline model. The data fusion model enabled a more accurate assessment of existing policies, specifically those aimed at reducing urban air pollution from solid fuel burning. Moreover, by identifying locations and sectors which contribute significantly to high levels of PM2.5, the data fusion model supports the formation of targeted air quality policies. This enables cities to maximise reductions in air pollution and exposures, thereby safeguarding public health.
2026
2024
2023