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Wastewater treatment plants are large non-linear systems subject to large perturbations in wastewater flow rate, load and composition. Nevertheless these plants have to be operated continuously, meeting stricter and stricter regulations. Many control strategies have been proposed in the literature for improved and more efficient operation of wastewater treatment plants. Unfortunately, their evaluation and comparison – either practical or based on simulation – is difficult. This is partly due to the variability of the influent, to the complexity of the biological and biochemical phenomena and to the large range of time constants (from a few minutes to several days). The lack of standard evaluation criteria is also a tremendous disadvantage. To really enhance the acceptance of innovative control strategies, such an evaluation needs to be based on a rigorous methodology including a simulation model, plant layout, controllers, sensors, performance criteria and test procedures, i.e. a complete benchmarking protocol.  This book is a Scientific and Technical Report produced by the IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants. The goal of the Task Group includes developing models and simulation tools that encompass the most typical unit processes within a wastewater treatment system (primary treatment, activated sludge, sludge treatment, etc.), as well as tools that will enable the evaluation of long-term control strategies and monitoring tasks (i.e. automatic detection of sensor and process faults). Work on these extensions has been carried out by the Task Group during the past five years, and the main results are summarized in Benchmarking of Control Strategies for Wastewater Treatment Plants. Besides a description of the final version of the already well-known Benchmark Simulation Model no. 1 (BSM1), the book includes the Benchmark Simulation Model no. 1 Long-Term (BSM1_LT) – with focus on benchmarking of process monitoring tasks – and the plant-wide Benchmark Simulation Model no. 2 (BSM2). 

Rainfall-induced landslides are a common and significant source of damages and fatalities worldwide. Still, we have little understanding of the quantity and properties of landsliding that can be expected for a given storm and a given landscape, mostly because we have few inventories of rainfall-induced landslides caused by single storms. Here we present six new comprehensive landslide event inventories coincident with well identified rainfall events. Combining these datasets, with two previously published datasets, we study their statistical properties and their relations to topographic slope distribution and storm properties. Landslide metrics (such as total landsliding, peak landslide density, or landslide distribution area) vary across 2 to 3 orders of magnitude but strongly correlate with the storm total rainfall, varying over almost 2 orders of magnitude for these events. Applying a normalization on the landslide run-out distances increases these correlations and also reveals a positive influence of total rainfall on the proportion of large landslides. The nonlinear scaling of landslide density with total rainfall should be further constrained with additional cases and incorporation of landscape properties such as regolith depth, typical strength or permeability estimates. We also observe that rainfall-induced landslides do not occur preferentially on the steepest slopes of the landscape, contrary to observations from earthquake-induced landslides. This may be due to the preferential failures of larger drainage area patches with intermediate slopes or due to the lower pore-water pressure accumulation in fast-draining steep slopes. The database could be used for further comparison with spatially resolved rainfall estimates and with empirical or mechanistic landslide event modeling.

Synthetic nitrogen (N) fertilizer has played a key role in enhancing food production and keeping half of the world's population adequately fed. However, decades of N fertilizer overuse in many parts of the world have contributed to soil, water, and air pollution; reducing excessive N losses and emissions is a central environmental challenge in the 21st century. China's participation is essential to global efforts in reducing N-related greenhouse gas (GHG) emissions because China is the largest producer and consumer of fertilizer N. To evaluate the impact of China's use of N fertilizer, we quantify the carbon footprint of China's N fertilizer production and consumption chain using life cycle analysis. For every ton of N fertilizer manufactured and used, 13.5 tons of CO₂-equivalent (eq) (tCO₂-eq) is emitted, compared with 9.7 t CO₂-eq in Europe. Emissions in China tripled from 1980 [131 terrogram (Tg) of CO₂-eq (Tg CO₂-eq)] to 2010 (452 Tg CO₂-eq). N fertilizer-related emissions constitute about 7% of GHG emissions from the entire Chinese economy and exceed soil carbon gain resulting from N fertilizer use by several-fold. We identified potential emission reductions by comparing prevailing technologies and management practices in China with more advanced options worldwide. Mitigation opportunities indude improving methane recovery during coal mining, enhancing energy efficiency in fertilizer manufacture, and minimizing N overuse in field-level crop production. We find that use of advanced technologies could cut N fertilizer-related emissions by 20-63%, amounting to 102-357 Tg CO₂-eq annually. Such reduction would decrease China's total GHG emissions by 2-6%, which is significant on a global scale.

Biochar - Solid Carbon for Sustainable Agriculture explores the potential of biochar, a form of charcoal produced from organic materials, to improve soil health, increase crop yields, and mitigate climate change. This book offers a comprehensive overview of biochar and its applications in sustainable agriculture. The book begins by introducing the concept of biochar and its historical use in agriculture. Next, the content deals with the production methods and properties of biochar, providing insights into its chemical composition and physical characteristics. Subsequent chapters explore the diverse applications of biochar in agriculture, including its role in soil fertility improvement, carbon sequestration, and pollution remediation. Case studies and practical examples illustrate the effectiveness of biochar across different agricultural settings. The authors also discuss the potential challenges and future directions of biochar research and application. This book is essential reading for agronomists, soil scientists, environmental scientists, farmers, policymakers, and anyone interested in sustainable agriculture and climate change mitigation strategies. Readership Agronomists, soil scientists, environmental scientists, farmers, policymakers, and anyone interested in sustainable agriculture and climate change initiatives.

Adequate wastewater treatment in low to medium income cities worldwide has largely been a failure despite decades of funding.The still dominant end-of-pipe paradigm of treatment for surface water discharge, focusing principally on removal of organic matter, has not addressed the well-published problems of pathogen and nutrient release with.

Increasing diffuse nitrate loading of surface waters and groundwater has emerged as a major problem in many agricultural areas of the world, resulting in contamination of drinking water resources in aquifers as well as eutrophication of freshwaters and coastal marine ecosystems. Although empirical correlations between application rates of N fertilizers to agricultural soils and nitrate contamination of adjacent hydrological systems have been demonstrated, the transit times of fertilizer N in the pedosphere-hydrosphere system are poorly understood. We investigated the fate of isotopically labeled nitrogen fertilizers in a three-decade-long in situ tracer experiment that quantified not only fertilizer N uptake by plants and retention in soils, but also determined to which extent and over which time periods fertilizer N stored in soil organic matter is rereleased for either uptake in crops or export into the hydrosphere.We found that 61-65% of the applied fertilizers N were taken up by plants,whereas 12-15% of the labeled fertilizer Nwere still residing in the soil organic matter more than a quarter century after tracer application. Between 8-12% of the applied fertilizer had leaked toward the hydrosphere during the 30-y observation period. We predict that additional exports of 15N-labeled nitrate from the tracer application in 1982 toward the hydrosphere will continue for at least another five decades. Therefore, attempts to reduce agricultural nitrate contamination of aquatic systems must consider the long-term legacy of past applications of synthetic fertilizers in agricultural systems and the nitrogen retention capacity of agricultural soils.

Purpose of ReviewThe incidence of allergic diseases such as asthma, rhinitis and atopic dermatitis has risen at an alarming rate over the last century. Thus, there is a clear need to understand the critical factors that drive such pathologic immune responses. Peroxisome proliferator-activated receptor-γ (PPAR-γ) is a nuclear receptor that has emerged as an important regulator of multiple cell types involved in the inflammatory response to allergens; from airway epithelial cells to T Helper (TH) cells.Recent FindingsInitial studies suggested that agonists of PPAR-γ could be employed to temper allergic inflammation, suppressing pro-inflammatory gene expression programs in epithelial cells. Several lines of work now suggest that PPAR-γ plays an essential in promoting ‘type 2’ immune responses that are typically associated with allergic disease. PPAR-γ has been found to promote the functions of TH2 cells, type 2 innate lymphoid cells, M2 macrophages and dendritic cells, regulating lipid metabolism and directly inducing effector gene expression. Moreover, preclinical models of allergy in gene-targeted mice have increasingly implicated PPAR-γ in driving allergic inflammation.SummaryHerein, we highlight the contrasting roles of PPAR-γ in allergic inflammation and hypothesize that the availability of environmental ligands for PPAR-γ may be at the heart of the rise in allergic diseases worldwide.

A framework is developed in which new connections to a growing network optimize geometric trade-offs between popularity and similarity, instead of simply preferring popular nodes; this approach accurately describes the large-scale evolution of various networks. Networks driven by the liked and alike Preferential attachment is a mechanism that attempts to explain the emergence of scaling in growing networks. If new connections are preferentially established with more popular nodes in a network, then the network is scale-free. So, because 'popularity is attractive', does preferential attachment predict network evolution? This study shows that popularity is a strong force in shaping complex network structure and dynamics, but so too is similarity. The authors develop a model that increases the accuracy of network-evolution predictions by considering the trade-offs between popularity and similarity. The model accurately describes large-scale evolution of technological (Internet), social and metabolic networks, predicting the probability of new links with high precision. The principle 1 that ‘popularity is attractive’ underlies preferential attachment 2 , which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws 3 , 4 , as observed in many real networks 5 , 6 . Preferential attachment has been directly validated for some real networks (including the Internet 7 , 8 ), and can be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks or duplication 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . Here we show that popularity is just one dimension of attractiveness; another dimension is similarity 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 . We develop a framework in which new connections optimize certain trade-offs between popularity and similarity, instead of simply preferring popular nodes. The framework has a geometric interpretation in which popularity preference emerges from local optimization. As opposed to preferential attachment, our optimization framework accurately describes the large-scale evolution of technological (the Internet), social (trust relationships between people) and biological ( Escherichia coli metabolic) networks, predicting the probability of new links with high precision. The framework that we have developed can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.