Life Cycle Inventory and Impact Analysis Framework for Nonroad Construction Vehicles and Equipment Based Upon In-Use Measurements (TSE03-L)

工程车辆对国家排放量的贡献很大;然而，它们的重要性很难用现有工具量化。 关键假设是：（1）车辆/设备的能源使用和排放是偶发性的;（2）需要使用中的微尺度数据来量化这一点;（3）需要新的模型来解释这些事件并确定改进的机会;（4）与车辆/设备的直接能源使用和排放相比，燃料循环能源使用和排放不可忽略;（5）车辆之间的平均排放量存在很大的可变性和不确定性;以及（6）需要一个新的框架来设计施工方法，以便在不确定性面前防止排放和减少能源使用（从而节省成本）。 其目标是：（1）表征秒的非道路施工车辆和设备的使用中的排放和能源使用及其不确定性;（2）开发一个概率的传统非道路施工车辆和设备的生命周期清单;（3）识别和建议的方法，以减少能源使用，排放和影响。在任务1中，可用的秒的使用中的数据进行补充的实地研究，以填补数据空白。 在任务2中，开发了一个微型建筑在用车辆和设备排放和能源使用模型（CIVEM）。 在任务3中开发了建筑设备的概率生命周期清单（LCI）模型。 在任务4中通过详细的案例研究证明了这种方法的好处。 研究结果被纳入建筑和空气质量课程，并传播到区域规划组织。这项工作有利于建筑业，环境规划师和公众。 该行业可以更准确地估计其对排放的贡献，通过修改现行做法来确定减少能源使用（成本）和排放的机会，并为未来的监管做好更好的准备，减少违规或罚款的责任。 随着最近对臭氧（氮氧化物和碳氢化合物是其前体）和颗粒物实施新的严格的环境空气质量标准，这些污染物的排放者将面临更大的压力，以防止或控制其排放。 在这项工作中开发的改进排放估计的科学基础，使区域和国家有效的空气质量战略。

Construction vehicles contribute substantially to national emissions; however, their importance is difficult to quantify with existing tools. Key hypotheses are that: (1) vehicle/equipment energy use and emissions are episodic in nature; (2) in-use microscale data are required to quantify this; (3) new models are needed to explain such episodes and to identify opportunities for improvements; (4) fuel cycle energy use and emissions are not negligible compared to direct energy use and emissions of the vehicles/equipment; (5) there is substantial inter-vehicle variability and uncertainty in average emissions; and (6) there is a need for a new framework by which to design construction practices in order to prevent emissions and reduce energy use (thereby producing cost savings) in the face of uncertainty. The objectives are to:(1)Characterize second-by-second in-use emissions and energy use of nonroad construction vehicles and equipment and their uncertainty;(2)Develop a probabilistic life cycle inventory of conventional nonroad construction vehicles and equipment;(3)Identify and recommend methods for reducing energy use, emissions, and impacts.In Task 1, available second-by-second in-use data are supplemented by a field study to fill data gaps. In Task 2, a micro-scale Construction In-use Vehicle and Equipment emissions and energy use Model (CIVEM) is developed. A probabilistic life cycle inventory (LCI) model for construction equipment is developed in Task 3. The benefits of the approach are demonstrated via a detailed case study in Task 4. Results of the research are integrated into construction and air quality courses and disseminated to regional planning organizations.This work benefits the construction industry, environmental planners, and the general public. The industry can more accurately estimate its contribution to emissions, identify opportunities for reductions in energy use (cost) and emissions via modifications to current practices, and better prepare for future regulation and reduce exposure to liability for violations or fines. With the recent implementation of new stringent ambient air quality standards for ozone (for which NOx and hydrocarbons are precursors) and particulate matter, there will be more pressure on emitters of these pollutants to prevent or control their emissions. The scientific basis for improved emissions estimation developed in this work enables effective air quality strategies regionally and nationally.