Remarkable dynamics of nanoparticles in the urban atmosphere

Nanoparticles emitted from road traffic are the largest source of respiratory exposure for the general public living in urban areas. It has been suggested that the adverse health effects of airborne particles may scale with the airborne particle number, which if correct, focuses attention on the nanoparticle (less than 100 nm) size range which dominates the number count in urban areas. Urban measurements of particle size distributions have tended to show a broadly similar pattern dominated by a mode centred on 20–30 nm diameter particles emitted by diesel engine exhaust. In this paper we report the results of measurements of particle number concentration and size distribution made in a major London park as well as on the BT Tower, 160 m high. These measurements taken during the REPARTEE project (Regents Park and BT Tower experiment) show a remarkable shift in particle size distributions with major losses of the smallest particle class as particles are advected away from the traffic source. In the Park, the traffic related mode at 20–30 nm diameter is much reduced with a new mode at <10 nm. Size distribution measurements also revealed higher number concentrations of sub-50 nm particles at the BT Tower during days affected by higher turbulence as determined by Doppler Lidar measurements and indicate a loss of nanoparticles from air aged during less turbulent conditions. These results suggest that nanoparticles are lost by evaporation, rather than coagulation processes. The results have major implications for understanding the impacts of traffic-generated particulate matter on human health

An Assessment of Remote Sensing Applications in Transportation

Remote sensing is an innovative science and technology that is aiding in numerous modes of transportation. Almost every aspect of transportation can benefit from utilizing imagery and data. Specifically, these technologies can be applied to planning, environmental impact assessment, hazard and disaster response, infrastructure management, traffic assessment, and homeland security planning (“Transportation and Remote Sensing,” 1999). The United States transportation system is a critical component of our economy and mobility (Williamson, Morain, Budge, & Hepner, 2002). There are millions of miles of roadways and bridges to monitor and maintain. In addition, remote sensing can be utilized towards the development and planning of new infrastructure and transportation systems. Remote sensing provides the unique ability to detect changes in our transportation system on a real-time basis. Imagery can be collected from multiple platforms, including satellite, aircraft-based, and ground-based, which allows data collection to be tailored to a particular transportation application. This paper will provide an overview of some of the potential applications of remote sensing in transportation. Due to the broad scope of this topic, several modes will not be discussed including aviation and marine. The main focus will be on ground transportation, infrastructure, and homeland security as it relates to transportation applications. Emerging technologies, such as hyperspectral remote sensing and LIDAR, will also be discussed. In addition, the Nebraska Airborne Remote Sensing Facility, one of only a few operating in the United States will be described. Two tribal communities in Nebraska are utilizing the data collected from the facility to address transportation issues

Cooperative and non-cooperative sense-and-avoid in the CNS+A context: a unified methodology

A unified approach to cooperative and noncooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into nonsegregated airspace. In this paper, state-of-the-art sensor/system technologies for cooperative and noncooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems

Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment

Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes

In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level

Atmospheric Air Pollution and Monitoring

Indoor air quality (IAQ) is an important aspect in building design due to its effect on human health and wellbeing. Generally, people spend about 90% of their time indoors where they are exposed to chemicals, particulate matters, biological contaminants and possibly carcinogens. In particular, the air quality at hospitals carries with it risks for serious health consequences for medical staff as well as patients and visitors. This book is a study of atmospheric air pollution and presents ways we can reduce its impacts on human health. It discusses tools for measuring IAQ as well as analyzes IAQ in closed buildings. It is an important documentation of air quality and its impact on human health