Agricultural fertilisers contribute substantially to microplastic concentrations in UK soils

Since their invention, plastics have driven a revolution in behavior in all aspects of our lives, including agriculture. In-use and as a waste material, plastics degrade and accumulate in agricultural systems. Accumulation of plastic pollution in agricultural systems has negative impacts on human health and agricultural productivity but little is known about concentrations of microplastics in soils. Here we used a historical time series to examine changes to microplastic concentrations in agricultural soils over time. Microplastics were stained with Nile Red and quantified using fluorescence microscopy. We demonstrate that microplastic concentrations increased at higher rates in soils that are amended with either organic or inorganic fertiliser between 1966 and 2022, suggesting that agricultural fertilisers are an important contributor to microplastic concentrations in agricultural soils over time. This study provides evidence that agricultural soils are receptors and reservoirs of microplastic pollution, a legacy which is growing over time

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.</p

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.</p

Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane

[EN] We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods for inferring emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision (< 1 %) instruments with 0.1-10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel (< 60 m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009-present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018-present), which provides global continuous daily mapping to quantify emissions on regional scales. These instruments already provide a powerful resource to quantify national methane emissions in support of the Paris Agreement. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100-10 000 kg h(-1) range that enable monitoring of large point sources. Future area flux mappers, including MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the capability to quantify emissions at high resolution, and the MERLIN lidar will improve observation of the Arctic. The averaging times required by area flux mappers to quantify regional emissions depend on pixel size, retrieval precision, observation density, fraction of successful retrievals, and return times in a way that varies with the spatial resolution desired. A similar interplay applies to point source imagers between detection threshold, spatial coverage, and return time, defining an observing system completeness. Expanding constellations of point source imagers including GHGSat and Carbon Mapper over the coming years will greatly improve observing system completeness for point sources through dense spatial coverage and frequent return times.This research has been supported by the Collaboratory to Advance Methane Science (CAMS) and the National Aeronautics and Space Administration, Earth Sciences Division (grant no. NNH20ZDA001N-CMS).Jacob, DJ.; Varon, DJ.; Cusworth, DH.; Dennision, PE.; Frankenberg, C.; Gautam, R.; Guanter-Palomar, LM.... (2022). Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane. ATMOSPHERIC CHEMISTRY AND PHYSICS. 14:9617-9646. https://doi.org/10.5194/acp-22-9617-2022961796461

Automated detection and monitoring of methane super-emitters using satellite data

A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.</p

The thermal Sunyaev-Zel'dovich effect power spectrum in light of Planck

The amplitude of the thermal Sunyaev–Zel'dovich effect (tSZ) power spectrum is extremely sensitive to the abundance of the most massive dark matter haloes (galaxy clusters) and therefore to fundamental cosmological parameters that control their growth, such as σ8 and Ωm. Here we explore the sensitivity of the tSZ power spectrum to important non-gravitational (‘subgrid’) physics by employing the cosmo-OWLS suite of large-volume cosmological hydrodynamical simulations, run in both the Planck and 7-year Wilkinson Microwave Anisotropy Probe (WMAP7) best-fitting cosmologies. On intermediate and small angular scales (ℓ ≳ 1000, or θ≲10 arcmin), accessible with the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT), the predicted tSZ power spectrum is highly model dependent, with gas ejection due to active galactic nuclei (AGN) feedback having a particularly large effect. However, at large scales, observable with the Planck telescope, the effects of subgrid physics are minor. Comparing the simulated tSZ power spectra with observations, we find a significant amplitude offset on all measured angular scales (including large scales), if the Planck best-fitting cosmology is assumed by the simulations. This is shown to be a generic result for all current models of the tSZ power spectrum. By contrast, if the WMAP7 cosmology is adopted, there is full consistency with the Planck tSZ power spectrum measurements on large scales and agreement at the 2σ level with the SPT and ACT measurements at intermediate scales for our fiducial AGN model, which Le Brun et al. have shown reproduces the ‘resolved’ properties of the Local Group and cluster population remarkably well. These findings strongly suggest that there are significantly fewer massive galaxy clusters than expected for the Planck best-fitting cosmology, which is consistent with recent measurements of the tSZ number counts. Our findings therefore pose a significant challenge to the cosmological parameter values preferred (and/or the model adopted) by the Planck primary cosmic microwave background analyses

The future of library and information services How Hertfordshire consulted in a Best Value context

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