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首页> 外文期刊>International Journal of Environmental Research and Public Health >Empirical Model for Evaluating PM 10 Concentration Caused by River Dust Episodes
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Empirical Model for Evaluating PM 10 Concentration Caused by River Dust Episodes

机译：河尘事件引起的PM 10浓度评估的经验模型

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Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM 10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM 10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM 10 concentration, but daily maximum PM 10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM 10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.
机译：在台湾的S水河河口附近，冬季水域消退，导致裸地面积增加，并暴露了沉积在河床上的大量细土和沙粒。在现场观察发现，当东北季风吹过没有植被或没有水覆盖的裸露土地时，细小颗粒很容易被风吹起，形成河尘，极大地危害了附近居民的健康。因此，在研究和开发具有河粉尘排放风险的地区的预警系统原型中，确定影响河粉尘的因素并构建预测河粉尘浓度的模型至关重要。在这项研究中，选择the水河河口周围的区域（从自强桥到西宾桥）作为研究区域。来自附近空气质量监测站的数据被用于筛选连续数天的河尘事件。在不同的时间尺度上分析了PM 10浓度与气象因子或裸地面积之间的关系，以探讨影响河流扬尘排放的因素。研究结果表明，没有任何一个单独的因素具有足够的能力来解释每日平均或每日最大PM 10浓度。多因素逐步回归分析表明，该模型不能有效预测日平均PM 10浓度，但可以通过风速，温度和裸地面积的组合预测日最高PM 10浓度。该模型的确定系数为0.67。据推测，河尘事件是多种因素共同作用的结果。此外，研究数据还显示了气象因素与每小时PM 10浓度之间的时滞效应。此特征已应用于构建预测模型，并可用于当地居民的预警系统。

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