J Air Waste Manag Assoc. 2014 Mar;64(3):272-9. doi: 10.1080/10962247.2013.856817.
Vale Canada Limited owns and operates a large nickel smelting facility located in Sudbury, Ontario. This is a complex facility with many sources of SO2 emissions, including a mix of source types ranging from passive building roof vents to North America's tallest stack. In addition, as this facility performs batch operations, there is significant variability in the emission rates depending on the operations that are occurring. Although SO2 emission rates for many of the sources have been measured by source testing, the reliability of these emission rates has not been tested from a dispersion modeling perspective. This facility is a significant source of SO2 in the local region, making it critical that when modeling the emissions from this facility for regulatory or other purposes, that the resulting concentrations are representative of what would actually be measured or otherwise observed. To assess the accuracy of the modeling, a detailed analysis of modeled and monitored data for SO2 at the facility was performed. A mobile SO2 monitor sampled at five locations downwind of different source groups for different wind directions resulting in a total of 168 hr of valid data that could be used for the modeled to monitored results comparison. The facility was modeled in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model) using site-specific meteorological data such that the modeled periods coincided with the same times as the monitored events. In addition, great effort was invested into estimating the actual SO2 emission rates that would likely be occurring during each of the monitoring events. SO2 concentrations were modeled for receptors around each monitoring location so that the modeled data could be directly compared with the monitored data. The modeled and monitored concentrations were compared and showed that there were no systematic biases in the modeled concentrations.
This paper is a case study of a Combined Analysis of Modelled and Monitored Data (CAMM), which is an approach promulgated within air quality regulations in the Province of Ontario, Canada. Although combining dispersion models and monitoring data to estimate or refine estimates of source emission rates is not a new technique, this study shows how, with a high degree of rigor in the design of the monitoring and filtering of the data, it can be applied to a large industrial facility, with a variety of emission sources. The comparison of modeled and monitored SO2 concentrations in this case study also provides an illustration of the AERMOD model performance for a large industrial complex with many sources, at short time scales in comparison with monitored data. Overall, this analysis demonstrated that the AERMOD model performed well.
Vale 加拿大有限公司拥有并运营一家位于安大略省萨德伯里的大型镍冶炼厂。这是一个复杂的设施，有许多二氧化硫排放源，包括从被动式建筑物屋顶通风口到北美最高烟囱的各种源类型的组合。此外，由于该设施进行分批操作，因此排放率取决于正在进行的操作而有很大的变化。尽管许多源的二氧化硫排放率已经通过源测试进行了测量，但从分散建模的角度来看，这些排放率的可靠性尚未经过测试。该设施是当地地区二氧化硫的重要来源，因此在为监管或其他目的对该设施的排放进行建模时，至关重要的是，所得到的浓度是实际测量或其他观察到的浓度的代表。为了评估建模的准确性，对该设施的二氧化硫进行了建模和监测数据的详细分析。一个移动的二氧化硫监测器在不同的源组下风方向的五个位置进行采样，总共获得了 168 小时有效的数据，可用于对监测结果进行建模比较。该设施在 AERMOD（美国气象学会/美国环境保护署监管模型）中进行建模，使用了特定于站点的气象数据，以便模型化的时间段与监测事件的时间段相吻合。此外，还投入了大量精力来估计在每次监测事件中可能发生的实际二氧化硫排放率。为每个监测地点周围的受体模拟了二氧化硫浓度，以便可以直接将模型化数据与监测数据进行比较。对模型化和监测浓度进行了比较，结果表明模型化浓度没有系统偏差。
本文是安大略省加拿大空气质量法规中规定的综合分析模型和监测数据（CAMM）方法的案例研究。尽管将分散模型和监测数据相结合以估算或改进源排放率的估算并不是一种新技术，但本研究表明，通过在监测设计和数据筛选方面具有高度的严谨性，该技术可应用于具有多种排放源的大型工业设施。在本案例研究中，对模型化和监测的二氧化硫浓度进行比较，也说明了 AERMOD 模型在与监测数据相比时间尺度较短的情况下，对具有许多排放源的大型工业综合体的性能。总体而言，该分析表明 AERMOD 模型表现良好。
