The Scottish Environment Protection Agency (SEPA; Scottish Gaelic: Buidheann Dìon Àrainneachd na h-Alba) is Scotland's environmental regulator and national flood forecasting, flood warning and strategic flood risk management authority. Its main role is to protect and improve Scotland's environment. SEPA does this by helping business and industry to understand their environmental responsibilities, enabling customers to comply with legislation and good practice and to realise the economic benefits of good environmental practice. One of the ways SEPA does this is through the NetRegs environmental guidance service. It protects communities by regulating activities that can cause harmful pollution and by monitoring the quality of Scotland's air, land and water. The regulations it implements also cover the storage, transport and disposal of radioactive materials.SEPA is an executive non-departmental public body (Executive NDPB, often known as a Quango) of the Scottish Government. SEPA was established in 1996 by the Environment Act 1995 and is responsible for the protection of the natural environment in Scotland. SEPA is a member of SEARS (Scotland's Environmental and Rural Services).On 24 December 2020 SEPA was subject to a serious and complex cyber-attack that significantly impacted their contact center, internal systems, processes and communications. Hackers stole over 4,000 digital files. SEPA refused to pay the ransom, and on 21 January 2021 learned that the information stolen had been published online illegally; some of the information was already publicly available, while some was not.
Ordovician–Devonian intrusions of the Scottish Northern Highlands Terrane have piecemeal geochronology and contested geodynamic significance. We review existing geochronological data and present new in situ U–Pb zircon results from seven of these intrusions. Zircon growth predating emplacement is nearly ubiquitous, related to magma addition to the lower crust during Early Scandian compression. Emplacement ages, structures and petrology vary in relation to major geodynamic processes on the Laurentian margin in the context of Baltica, Ganderia and Avalonia accretion: (1) c . 450–432 Ma Iapetus subduction and back-arc magmatism; (2) c . 432–423 Ma Ganderia accretion, lithospheric foundering, Scandian metamorphism and sinistral transpression; (3) c . 423–415 Ma Late Scandian exhumation and decompression; (4) c . 415–380 Ma Acadian subduction and collision processes. Only c . 423–415 Ma magmatism resulted in production of high radiogenic heat production intrusions of geothermal exploration interest. There is considerable potential in constraining the extent and timing of pre-emplacement zircon growth to relate crustal tectonics and magmatism during orogenesis.
Antimicrobial resistance (AMR) is a global public health threat as it reduces the effectiveness of drug treatments and therefore our ability to combat infections. For the first time, this study used high temporal resolution data of antimicrobial resistance genes (ARG) in two livestock-dominated agricultural study catchments in south-west Scotland to understand ‘background’ ARG loadings in the absence of pollution point-sources such as sewage treatment works and health care facilities. Daily composite samples were collected twice a week over a 2-year period 2017–2019, resulting in 124 samples in Cessnock (22 km2) and 93 in the Mein (12 km2) catchment. We found a seasonal pattern in ARG relative abundance, with highest abundance in winter inversely related to the highest bacterial abundance in summer. This could reflect seasonal input from faecal pollution during winter high flows, indicated by a relationship between E. coli and terrestrial dissolved organic matter (DOM) sources. However, faecal indicator organisms (FIOs) ranked as less important predictors, so the seasonal pattern could be caused by microbial stress during cold periods and/or competition with susceptible bacteria during the warm periods. These natural fluctuations need to be considered when inferring potential drivers of AMR prevalence in surface waters. Therefore, in future monitoring, we recommend higher temporal resolution and longer-term monitoring (at least one year) to improve our understanding of natural seasonal variation in ARG abundance and evaluate effectiveness of mitigation measures (e.g., waste management).
Policy developments for a sustainable Blue Economy require scientific advice. An important component of the Blue Economy is salmon (Salmo salar) aquaculture, the sustainability of which is limited by salmon louse (Lepeophtheirus salmonis) infection. This parasite impacts both farmed and wild salmonid fish. Modelling is a valuable source for advice, but inevitable uncertainties exist. Here we develop an approach we call “Knowledge Strength” to maximise our confidence in model results; this is aimed at reducing uncertainties in model outputs and understanding the remaining uncertainty in these outputs, to maximise our confidence in model results, so that we can give policy makers the best advice to support informed decision making. The approach consists of addressing five questions: (1) What is the objective addressed by the model? (2) What are the causes of uncertainty in model outputs? We describe uncertainties due to (a) limitations of computing, (b) model building and (c) parameters, and (d) forcing data. (3) What is the statistical nature of uncertainty? Noise and bias are qualitatively different. (4) How can knowledge strength be maximised given those uncertainties? Approaches of resourcing (“power”) and analysis (“wisdom”) are considered. (5) How can information, including uncertainties, be communicated to different audiences? Policy makers/managers define and resource the objective for question 1, modellers address questions 2 to 4 - but their solutions are made transparent, and the communication question 5 is a two-way process with outputs transparent to immediate decision makers and external stakeholders. Examples of the policy environment behind salmon lice management are detailed in the Supplementary Material covering Scotland, Norway and the Faroe Islands.
Abstract The cumulative impacts of future climatic and socioeconomic change threaten the ability of freshwater catchments to provide essential ecosystem services. Stakeholders who manage freshwaters require decision-support tools that increase their understanding of catchment system resilience and support the appraisal of adaptive management options to inform decision-making. Our research aims to test the ability of a Bayesian Network model to identify adaptive management scenarios and test their effectiveness across future pathways to 2050. Using the predominantly arable river Eden catchment (320 km2) in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water quality issues and achieving good ecological status in the catchment both now and in the future. Our participatory methods helped stakeholders overcome multiple layers of complexity and uncertainty associated with future-focused water management. Outputs of a Bayesian Network model simulated both current and future catchment resilience to inform the identification of six management scenarios. The effectiveness of each management scenario was tested using the Bayesian Network model. Two adaptive management scenarios increased catchment resilience and helped achieve good ecological status; a ‘Best Available Technology’ scenario, including aerobic granular sludge treatment, and a management scenario focused on ‘Resource Centre’, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Stakeholders were interested in a 'Nature Based' management scenario including options such as wetland wastewater treatment methods and rural sustainable drainage systems, which improved water quality in the catchment, but had lower certainty in achieving desired outcome. Findings led to a recognition that innovative and collaborative action was required to improve current and future freshwater conditions.
PM2.5 (fine particulate matter <= 2.5 mu m in diameter) is a key pollutant that can produce acute asthma exacerbations and longer-term deterioration of respiratory health. Individual exposure to PM2.5 is unique and varies across microenvironments. Low-cost sensors (LCS) can collect data at a spatiotemporal resolution previously unattainable, allowing the study of exposures across microenvironments. The aim of this study is to investigate the acute effects of personal exposure to PM2.5 on self-reported asthma-related health. Twenty-eight non-smoking adults with asthma living in Scotland collected PM2.5 personal exposure data using LCS. Measurements were made at a 2-min time resolution for a period of 7 days as participants conducted their typical daily routines. Concurrently, participants were asked to keep a detailed time-activity diary, logging their activities and microenvironments, along with hourly information on their respiratory health and medication use. Health outcomes were modelled as a function of hourly PM2.5 concentration (plus 1- and 2-h lag) using generalized mixed-effects models adjusted for temperature and relative humidity. Personal exposures to PM2.5 varied across microenvironments, with the largest average microenvironmental exposure observed in private residences (11.5 +/- 48.6 mu g/m3) and lowest in the work microenvironment (2.9 +/- 11.3 mu g/m3). The most frequently reported asthma symptoms, wheezing, chest tightness and cough, were reported on 3.4%, 1.6% and 1.6% of participant-hours, respectively. The odds of reporting asthma symptoms increased per interquartile range (IQR) in PM2.5 exposure (odds ratio (OR) 1.29, 95% CI 1.07-1.54) for samehour exposure. Despite this, no association was observed between reliever inhaler use (non-routine, nonexercise related) and PM2.5 exposure (OR 1.02, 95% CI 0.71-1.48). Current air quality monitoring practices are inadequate to detect acute asthma symptom prevalence resulting from PM2.5 exposure; to detect these requires high-resolution air quality data and health information collected in situ. Personal exposure monitoring could have significant implications for asthma self-management and clinical practice.
