TOWARDS IMPROVED BIOAEROSOL MODEL VALIDATION AND VERIFICATION
TOWARDS IMPROVED BIOAEROSOL MODEL VALIDATION AND VERIFICATION
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DOI：
10.2495/air180041
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Ben Williams;E. Hayes;Z. Nasir;C. Rolph;S. Jackson;S. Khera;A. Bennett;T. Gladding;G. Drew;J. Longhurst;S. Tyrrel
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文献类型：
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作者：
Ben Williams;E. Hayes;Z. Nasir;C. Rolph;S. Jackson;S. Khera;A. Bennett;T. Gladding;G. Drew;J. Longhurst;S. Tyrrel
由细菌、真菌和病毒组成的生物气溶胶在环境空气中无处不在。已知生物气溶胶会对人类健康产生不利影响，其对人群的影响通常表现为退伍军人病和Q热等疾病的爆发，尽管人们对这些影响发生的浓度和环境条件还不太了解。生物气溶胶的浓度因来源而异，但特定的工业化人类活动，如水处理，集约化农业和露天堆肥，有助于产生比自然背景水平高出许多倍的生物气溶胶浓度。目前，生物气溶胶取样是根据环境署监管框架的要求进行的，其中最重要的是收集生物气溶胶，而不是长期测量。因此，取样装置常常根据风向的变化而在现场四处移动，取样间隔总是很短。堆肥设施的生物气溶胶的扩散模型通常依赖于代理污染物参数。此外，使用监测器随风向频繁移动的短期排放数据收集策略，不能提供稳健可靠和可重复的数据集来验证任何建模或验证其性能。新的采样方法，如光谱强度生物气溶胶传感器（SIBS）提供了一个机会，以解决生物气溶胶模型验证和验证的几个差距。在模型验证的背景下，本文从稳健建模要求的角度阐述了当前生物气溶胶监测的弱点
Bioaerosols, comprised of bacteria, fungi and viruses are ubiquitous in ambient air. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific industrialised human activities such as water treatment, intensive agriculture and open windrow composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosol sampling is currently undertaken according to the requirements of the Environment Agency’s regulatory framework, in which the collection of bioaerosols and not its long term measurement is of most importance. As a consequence, sampling devices are often moved around site according to changing wind direction and sampling intervals are invariably short-term. The dispersion modelling of bioaerosols from composting facilities typically relies on proxy pollutant parameters. In addition, the use of short term emission data gathering strategies in which monitors are moved frequently with wind direction, do not provide a robust reliable and repeatable dataset by which to validate any modelling or to verify its performance. New sampling methods such as the Spectral Intensity Bioaerosol Sensor (SIBS) provide an opportunity to address several gaps in bioaerosol model validation and verification. In the context of model validation, this paper sets out the current weaknesses in bioaerosol monitoring from the perspective of robust modelling requirements