Long-term sensor measurements of lung deposited surface area of particulate matter emitted from local vehicular and residential wood combustion sources

Abstract

Lung deposited surface area (LDSA) is a relatively new metric that has been argued to be more accurate at predicting health effects from aerosol exposure. For typical atmospheric aerosol, the LDSA concentration depends mainly on the concentration of ultrafine particles (e.g. vehicular exhaust emissions and residential wood combustion) and therefore optical methods cannot be used to measure and quantify it. The objective of this study was to investigate and describe typical characteristics of LDSA under different urban environments and evaluate how a diffusion charging-based Pegasor AQ Urban sensor (Pegasor Ltd., Finland) can be used as an alternative to optical sensors when assessing local combustion emissions and respective LDSA concentrations. Long-term (12?months) sensor measurements of LDSA were carried out at three distinctly different measurement sites (four sensor nodes) in the Helsinki metropolitan area, Finland. The sites were affected mainly by vehicular exhaust emission (street canyon and urban background stations) and by residential wood combustion (two detached housing area stations). The results showed that the accuracy of the AQ Urban was good (R² = 0.90) for the measurement of LDSA when compared to differential mobility particle sizer. The mean concentrations of LDSA were more than twice as high at the street canyon (mean 22 µm² cm^?3) site when compared to the urban background site (mean 9.4 µm² cm^?3). In the detached housing area, the mean concentrations were 12 µm² cm^?3, and wood combustion typically caused high LDSA peaks in the evenings. High correlations and similar diurnal cycles were observed for the LDSA and black carbon at street canyon and urban background stations. The utilization of a small-scale sensor network (four nodes) showed that the cross-station variability in hourly LDSA concentrations was significant in every site, even within the same detached housing area (distance between the two sites ～670?m).     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.