A study of landfill content and its chemical evolution using historical, geographic and site monitoring data

Historical landfills present a paradoxical position where, on the one hand, their impact on the environment is overtly negative and, on the other are seen as potential stores of value. Intrusive investigation to identify a landfill’s resource potential is expensive and, due to the heterogeneity of content, has significant uncertainty. This thesis proposes a solution using historical data to identify the quantities and types of materials entering landfills. However, it is accepted that recording of waste data was limited and exactly how much or what data exists is typically answered anecdotally. This study considers the problem from a formal research perspective by collecting and reviewing historical waste data. Data collection focussed on 4 areas: i) contemporary landfill emissions monitoring and waste import data, ii) historical data leading to waste generation, iii) historical disposal data and iv) historical municipal waste composition data. In each case, a significant quantity of data was discovered and reviewed. Initially, using historical data, 4 discrete epochs were identified where contemporary determinants impacted on i) the materials forming waste ii) the quantities of waste generated iii) waste disposal and iv) regulatory controls. For the first two epochs, waste flows into landfills were determined by waste composition. The third epoch witnessed a transition from composition to regulation with regulation becoming the controlling factor in the fourth. Such classification enabled the data for use i) as surrogates for missing data and ii) as inputs to a system dynamics model that estimates weights for generated and landfilled MSW for England (1.051 billion tonnes and 885 million tonnes respectively) and provides quantities of MSW components disposed to landfill. Contemporary data included leachate monitoring data for 2 landfills operational since 2005 and 2007 and regulated by the 1999 Landfill Directive whereby the European Union proposed diversion of putrescible waste away from landfill. When decomposing, putrescible MSW reduces the pH of a landfill’s environment. Landfill decomposition progresses through a series of phases where this low pH phase or acetogenesis has been shown to inhibit the development of methanogenic bacteria. Analysis of the data identified this low pH phase did not occur at these landfills whereby they provided a possibly singular opportunity to report this anticipated phenomenon to the research community

Altered chemical evolution in landfill leachate post implementation of biodegradable waste diversion

Within the UK implementation of the European Union Landfill Directive (1999) has led to the diversion of biodegradable waste (BW) from municipal solid wastes away from landfills. It has been widely anticipated, but thus far not verified, that the diversion of BW and consequent reduction in BW reaching landfill would lead to a change in the degradation processes occurring within landfills and that this would be reflected in an altered evolution in leachate chemistry compared to pre-Directive landfills. This paper provides evidence based on leachate chemistry from two operational landfills together with calculations of the reduced BW content, that demonstrate the acetogenic phase that characterised pre-Directive landfill leachates is missing and is now more typical of methanogenic phase leachate. The paper demonstrates how data from national datasets and detailed landfill records can be used to constrain likely and upper estimates of the amount of BW going into post-Directive landfills, and the observed change in the evolution of leachate chemistry which has resulted from a decrease in BW content from typical values of BW (pre-Landfill Directive) of 22% to an inferred 12% in the case-study landfills. Data provided here add to the growing literature that estimates the amount of BW in recent post-Directive landfills which importantly allow the quantitative linkage between a decrease in landfilled BW and observed changes in leachate chemistry to be established such that future landfill operators can increase confidence in the effect of Directive implementation on landfill operational parameters

Enhancing Museum Narratives: Tales of Things and UCL’s Grant Museum

Emergent mobile technologies offer museum professionals new ways of engaging visitors with their collections. Museums are powerful learning environments and mobile technology can enable visitors to experience the narratives in museum objects and galleries and integrate them with their own personal reflections and interpretations. UCL‟s QRator project is exploring how handheld mobile devices and interactive digital labels can create new models for public engagement, personal meaning making and the construction of narrative opportunities inside museum spaces. The use of narrative in museums has long been recognised as a powerful communication technique to engage visitors and to explore the different kinds of learning and participation that result. Many museums make extensive use of narrative, or storytelling, as a learning, interpretive, and meaning making tool. This chapter discusses the potential for mobile technologies to connect museums to audiences through co-creation of narratives, taking the QRator project as a case study. The QRator project aims to stress the necessity of engaging visitors actively in the creation of their own interpretations of museum collections through the integration of QR codes, iPhone, iPad, and Android apps into UCL‟s Grant Museum of Zoology. Although this chapter will concentrate on mobile technology created for a natural history museum, issues of meaning making and narrative creation through mobile technology are applicable to any discipline. In the first instance, the concern is with the development of mobile media in museums followed by a discussion of the QRator project which stresses the opportunities and challenges in utilizing mobile technology to enhance visitor meaning making and narrative construction. Finally, this chapter discusses the extent to which mobile technologies might be used purposefully to transform institutional cultures, practices and relationships with visitors

Trajectories of Inflammation in Youth and Risk of Mental and Cardiometabolic Disorders in Adulthood

IMPORTANCE: Research suggests that low-grade, nonresolving inflammation may predate adult mental and physical illness. However, evidence to date is largely cross-sectional or focuses on single disorder outcomes.OBJECTIVES: To examine trajectories of inflammation as measured by C-reactive protein (CRP) levels in a large sample of children and adolescents, and to explore associations between different identified trajectories and mental and related cardiometabolic health outcomes in early adulthood.DESIGN, SETTING, AND PARTICIPANTS: In a longitudinal cohort study using data from the large UK-based Avon Longitudinal Study of Parents and Children (ALSPAC), latent class growth analysis (LCGA) was used to explore different trajectories of inflammation, with logistic regression exploring association with mental and physical health outcomes. Participants with measurable CRP data and associated mental and cardiometabolic health outcomes recorded were included in the analysis. Data analysis was performed from May 1, 2023, to March 30, 2024.EXPOSURES: Inflammation was assessed via CRP levels at ages 9, 15, and 17 years. LCGA was used to identify different trajectories of inflammation.MAIN OUTCOMES AND MEASURES: Outcomes assessed at age 24 years included psychotic disorders, depressive disorders, anxiety disorders, hypomania, and, as a measure of insulin resistance, Homeostasis Model Assessment (HOMA2) score.RESULTS: A total of 6556 participants (3303 [50.4%] female) were included. Three classes of inflammation were identified: persistently low CRP levels (reference class, n = 6109); persistently raised CRP levels, peaking at age 9 years (early peak, n = 197); and persistently raised CRP levels, peaking at age 17 years (late peak, n = 250). Participants in the early peak group were associated with a higher risk of psychotic disorder (odds ratio [OR], 4.60; 95% CI, 1.81-11.70; P = .008), a higher risk of severe depression (OR, 4.37; 95% CI, 1.64-11.63; P = .02), and higher HOMA2 scores (β = 0.05; 95% CI, 0.01-0.62, P = .04) compared with participants with persistently low CRP. The late peak group was not associated with any outcomes at age 24 years.CONCLUSIONS AND RELEVANCE: Low-grade systemic inflammation peaking in midchildhood was associated with specific mental and cardiometabolic disorders in young adulthood. These findings suggest that low-grade persistent inflammation in early life may be an important shared common factor for mental-physical comorbidity and so could be relevant to future efforts of patient stratification and risk profiling.</p

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

Grazing management for soil carbon in Australia: A review

The livestock industry accounts for a considerable proportion of agricultural greenhouse gas emissions, and in response, the Australian red meat industry has committed to an aspirational target of net-zero emissions by 2030. Increasing soil carbon storage in grazing lands has been identified as one method to help achieve this, while also potentially improving production and provision of other ecosystem services. This review examined the effects of grazing management on soil carbon and factors that drive soil carbon sequestration in Australia. A systematic literature search and meta-analysis was used to compare effects of stocking intensity (stocking rate or utilisation) and stocking method (i.e, continuous, rotational or seasonal grazing systems) on soil organic carbon, pasture herbage mass, plant growth and ground cover. Impacts on below ground biomass, soil nitrogen and soil structure are also discussed. Overall, no significant impact of stocking intensity or method on soil carbon sequestration in Australia was found, although lower stocking intensity and incorporating periods of rest into grazing systems (rotational grazing) had positive effects on herbage mass and ground cover compared with higher stocking intensity or continuous grazing. Minimal impact of grazing management on pasture growth rate and below-ground biomass has been reported in Australia. However, these factors improved with grazing intensity or rotational grazing in some circumstances. While there is a lack of evidence in Australia that grazing management directly increases soil carbon, this meta-analysis indicated that grazing management practices have potential to benefit the drivers of soil carbon sequestration by increasing above and below-ground plant production, maintaining a higher residual biomass, and promoting productive perennial pasture species. Specific recommendations for future research and management are provided in the paper

Metabolomics enables precision medicine: “A White Paper, Community Perspective”

Introduction: Background to metabolomics: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studie