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

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

Impact of school-based health center use on academic outcomes

Abstract Purpose: The purpose of this study was twofold: (1) to examine the effects of School-Based Health Center (SBHC) use on academic outcomes for high school students, using a well-controlled, longitudinal model, and (2) to examine whether SBHC medical and mental health service use differentially impacts academic outcomes. Methods: Analyses used a latent variable growth curve modeling approach to examine longitudinal outcomes over five school semesters for ninth grade SBHC users and nonusers from Fall 2005 to Fall 2007 (n ¼ 2,306). Propensity score analysis was used to control for self-selection factors in the SBHC user and nonuser groups. Results: Results indicated a significant increase in attendance for SBHC medical users compared to nonusers. Grade point average increases over time were observed for mental health users compared to nonusers. Discipline incidents were not found to be associated with SBHC use. Conclusions: SBHC use was associated with academic improvements over time for a high-risk group of users. The moderating effect of type of use (medical and mental health) reinforces the importance of looking at subgroups when determining the impact of SBHC use on outcomes.