Collection Time

Abstract: The optimization of municipal waste collection can reduce management costs and negative impacts on the environment This article analyzes municipal waste collection in Churriana de la Vega (Granada, Spain), and describes a way to improve waste collection service, based on the information provided by Geographic Information Systems The results of our study showed that the town had an excessive number of containers for organic matter and rest-waste fraction This made waste collection less efficient and raised costs related to the purchase of containers, collection time, personnel costs, collection route length, and vehicle maintenance In the case of recyclable fraction collection, our results showed that waste collection could be improved by increasing the number of containers and optimizing their location The solutions proposed could improve the percentage of selective waste collection and raise environmental awareness although this action should be accompanied by public awareness campaigns

Abstract: Stop-and-copy garbage collection has been preferred to mark-and-sweep collection in the last decade because its collection time is proportional to the size of reachable data and not to the memory size. This paper compares the CPU overhead and the memory requirements of the two collection algorithms extended with generations, and finds that mark-and-sweep collection requires at most a small amount of additional CPU overhead (3-6%) but, requires an average of 20% (and up to 40%) less memory to achieve the same page fault rate. The comparison is based on results obtained using trace-driven simulation with large Common Lisp programs.

Abstract: Solid waste collection contributes to the cost, emissions, and fossil fuel required to manage municipal solid waste. Mechanistic models to estimate these parameters are necessary to perform integrated assessments of solid waste management alternatives using a life-cycle approach; however, models are only as good as their parameterization. This study presents operational waste collection data that can be used in life-cycle models for areas with similar collection systems, and provides illustrative results from a collection process model using operational data. Fuel use and times associated with various aspects of waste collection were obtained for vehicles collecting mixed residential (residual) waste, recyclables, and yard waste from single-family residences in selected municipalities. The total average fuel economy for similarly-sized diesel collection vehicles was 0.6-1.4 km/L (1.4–3.3 mpg (miles per gallon)) for residual waste and 0.8–1 km/L (1.9–2.4 mpg) for recyclables. For residual waste and recyclables collection stops, the average time to collect at each residence using automated collection was 11–12 s and 13–17 s, respectively. The average time between stops was 11–12 s and 10–13 for residuals and recyclables, respectively. A single yard waste route was observed, and all collection times were longer than those measured for either recycling or residual waste. Unload or tip times were obtained or measured at a landfill, transfer station, and material recovery facility (MRF). Average time to unload was 7–9 min at a MRF, 14–22 min at a landfill, and 11 min at a transfer station. Commercial and multi-family collection vehicles tend to have longer stops and spend more time between stops than single-family collection, and a larger portion of fuel is used while driving relative to single-family collection. Roll-off vehicles, which collect more waste per stop, spend longer at each stop and drive longer distances between stops than front-loader vehicles. Diesel roll-offs averaged 2.4 km/L (5.7 mpg) and front-loaders averaged 1.4 km/L (3.3 mpg).

Abstract: Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results. In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing. Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze–thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.