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Delhi generates about 8360 tons of municipal solid waste per day, and there is low compliance to rules regarding waste management. The objective of this paper was to understand the situation in Delhi with respect to the segregation, storage, collection, and disposal of household waste, and to assess the knowledge of the residents of Delhi, and their willingness to engage in solid-waste management. A stratified random sample, comprising 3047 respondents, was chosen for a questionnaire survey, covering all municipalities of Delhi, with socio-economic classification as the stratifying variable. Survey results indicate that 60% of residents do not know the difference between biodegradable and non-biodegradable waste, and only 2% of them segregate waste. Fifty-eight percent of respondents reported that the waste collector mixes the segregated waste, 97% of respondents reported that they sold items to an itinerant waste buyer, and 87% of households are covered by doorstep waste collection services. Abstract knowledge (general knowledge about waste management) is seen to have a significant correlation with willingness to engage in waste management. Differences between the socio-economic groups indicate that the highest (most educated and wealthy), as well as the lowest socio-economic category (least educated and poor), older age-groups, and women, have greater abstract knowledge. Socio-economic categories having higher abstract knowledge can be active participants in decentralized models of waste management.

\"The clutter filling our spaces impacts on our productivity, stresses us out and keeps us stuck. Or stuff stands in the way of the lives we dream about... Decluttering is great for our mental wellbeing, and when done right, it can be good for the planet too. When we rehome, repurpose or recycle the things we no longer need, we free up existing resources for others and reclaim our homes with less guilt. Less Stuff is a guide for people who find it difficult to declutter and who don't want to see things go to waste. Step by step, you'll explore finding your 'enough', learn how to let go of your old possessions without sending them to landfill, and eventually break the cycle of stuff. The end result is a planet with less strain, a home with more peace and a life with more meaning.\"--Page 4 of cover.

Jurassic sedimentary sequences suitable for nuclear waste storage in northern Germany consist of organic-lean claystone and were uplifted to < 100 m depth in the Hils Syncline area (southern Lower Saxony Basin). This Hils Syncline, showcasing a northwestward increase in thermal maturity, facilitates the study of shale petrophysical properties influenced by burial history. This study introduces a 3D-thermally calibrated numerical model of the Hils Syncline area to analyze its geodynamic evolution and maturity variations. It provides new vitrinite reflectance and sonic velocity data for modeling calibration and erosion estimation. The Hils Syncline area has undergone continuous subsidence, interrupted by a Cretaceous uplift documented by an erosional unconformity. During the latest Early Cretaceous, Jurassic rocks underwent maximum burial reaching up to several thousand meters depth and temperatures up to 160 °C in the northwest. The Late Cretaceous inversion caused stronger erosion towards the northwest removing up to 3300 m of sediment compared to about 1300 m in the south, according to vitrinite reflectance-based estimations. Numerical modeling results along the study area indicate decreasing porosity and permeability northwestward with increasing thermal maturity. Porosity and vertical permeability decreased to 5–14% and 2.8 × 10 –23 to 1.5 × 10 –19 m 2 [1 mD = 10 −15 m 2 ], respectively, while vertical thermal conductivity increased to 1.30–2.12 (W/m/K). These trends of porosity/permeability and thermal conductivity with burial align with sonic velocity and published experimental porosity data, except for the thermally most mature region (Haddessen). This anomaly is tentatively attributed here to localized overpressure generation in the Posidonia Shale during maximum burial, affecting both the underlying Pliensbachian and overlying Doggerian units. Graphical abstract 3D numerical model of the Hils Syncline and surrounding area revealing that a northwestward increase in maximum burial resulted in higher temperatures and varying maturity levels. While most locations align well with calibration data (i.e. measured vitrinite reflectance and porosity), discrepancies arise in the Haddessen/Bensen area. The mismatch between porosity, vitrinite reflectance, and sonic velocity response indicates local overpressure in the northernmost region mainly during the Cretaceous. It was likely caused by gas generation in the Posidonia Shale affecting nearby Lower and Middle Jurassic units.

A project-oriented book for the DIY enthusiast presenting what one needs to know to reclaim and reuse pallets in innovative, useful ways.

This study assessed the bioconversion efficiency of Hermetia illucens larvae (BSFL) fed on food waste stored under different conditions, focusing on the nutritional and microbial quality of the resulting larval biomass. Food waste was prepared as a fresh diet (FD) or naturally contaminated and stored at 20–22 °C (OS-T, opened storage-tempered) or under refrigeration, at 5–8 °C (CS-C, closed storage-cooled). Refrigerated, closed storage (CS-C) led to the highest rates of waste reduction (91.0%) and bioconversion efficiency (30.2%), with larvae exhibiting the highest protein content (36.83%) compared to the FD (35.5%) and OS-T (34.71%) groups. Microbiome analysis revealed that the CS-C condition promoted beneficial yeasts like Pichia and Diutia, which correlated positively with improved protein content and microbial safety. In contrast, OS-T storage supported spoilage fungi (Mucor, Rhizopus) and elevated total aerobic counts (7.28 log CFU/g), indicating higher microbial risks. The observed trends in waste reduction and protein content most probably relate to differences in microbial profiles, as controlled cooling affected microbial dynamics, preserving substrate quality and supporting larval growth. These findings emphasize the importance of refrigerated, closed storage to optimize bioconversion, improve larval nutritional value, and minimize microbiological hazards.

\"Create useful products out of reclaimed wood from shipping pallets: 30 step-by-step projects include raised garden beds; herb and rail planters; wine, spice and coat racks; book shelves; benches; water barrel stands; small compost bins; and more\"-- Provided by publisher.

The Lac du Bonnet batholith (LDBB) is an Archean pluton in the Superior Province in southeastern Manitoba and formerly the site of the Underground Research Laboratory operated by Atomic Energy of Canada Limited. Batholith-scale investigations were conducted there between the late 1970s and 2005 into the Canadian concept of used nuclear fuel disposal in crystalline rocks of the Canadian Shield. A review of legacy data from this period has revealed that the distribution of the fracture network can be related directly to the thickness of the intrusion, as inferred from the model of the base of the batholith. Fractures have preferentially developed in the shallow and thinner sill-like western remnant of the batholith and, in comparison, they are suppressed within and over its deep root. Fracture orientations, frequency and continuity as seen at the surface are not characteristic of the fractures within the batholith interior, even at relatively shallow depths. A substantial volume of unfractured granite has been preserved despite the LDBB’s great age and its exposure to several cycles of loading and unloading, as revealed by the regional stratigraphic record and a unique apatite fission track dating profile. The LDBB is not a one-of-a-kind anomaly, and similar fracture/host rock relationships can be found in the literature. Based on the data reported here, it can be recommended that site models for any continuance of underground nuclear waste storage define the volume distribution of the host rock and its internal structure as these may serve as guides for subsurface exploration and the development and incremental testing of a conceptual fracture model.

Zero Waste Home is the ultimate guide to simplified, sustainable living from Bea Johnson, author of the popular blog of the same name. Bea Johnson transformed her family's health, finances, and relationships for the better by reducing their waste to an astonishing half litre per year. It's all down to the 5 Rs: Refuse, Reduce, Reuse, Recycle, Rot (and only in that order!). Zero Waste Home shows how these key principles can be applied to every area of your house from the kitchen to the kids' room, and it's packed with easy tips for all of us: from buying in bulk and clever meal planning to simply refusing unwanted freebies and using your plants as air fresheners. Bea Johnson shows, by inspiring example, what green living looks like and offers a practical, step-by-step guide to diminishing your environmental footprint and improving your life.

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected \"signal\"; (5) using several machine learning methods to test the \"signal's\" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation.