一种数据驱动的方法来预测关键的TSP事件:来自巴西东南部矿业影响地区的见解
Camilo Bastos Ribeiro1, Leonardo Hoinaski2, Robson Will1
1Graduate Program of Environmental Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.
Environmental technology
|September 14, 2025
概括
采矿区的总悬浮颗粒 (TSP) 污染是由天气和表面条件驱动的. 确定土壤湿度,风力和干旱时期的关键值有助于预测高风险污染事件.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 公共卫生 公共卫生
背景情况:
- 由总悬浮颗粒 (TSP) 造成的空气污染对环境和健康构成重大风险,特别是在采矿地区.
- 由于气象和地表因素之间的复杂,非线性相互作用,控制TSP排放具有挑战性.
- 预测高风险TSP时期的数据驱动方法是有限的.
研究的目的:
- 描述一个采矿地区TSP的时间动态和关键驱动因素.
- 确定与关键TSP事件相关的驱动程序的值.
- 制定一个框架来预测TSP污染事件.
主要方法:
- 利用了2023年的观测数据.
- 应用内核回归 (KRR) 来分析每小时,每天和每月的TSP模式.
- 评估了气象和表面相关的驱动因素,划定了极端事件的门.
主要成果:
- 在干旱月份 (七月至九月),周日和高峰时段,TSP达到峰值.
- 在TSP和PM10之间发现了强烈的关联;干燥,低湿度条件加剧了污染.
- KRR模型解释了71%的TSP变异性,确定了土壤水分,风暴和干旱时期作为关键预测因素.
- 确定了关键值:土壤湿度<0.15 m3/m3,风速>16 m/s,连续干旱时期>470小时.
结论:
- 使用关键驱动因素为关键的TSP事件建立了一个预测框架.
- 调查结果使得矿业影响地区的信息化缓解策略成为可能.
- 强调气象和地表因素在TSP动态中的重要性.
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