预测造行业的空气污染物排放:基于电力大数据
Xiangyu Chi1, Zheng Li1, Hanqing Liu1
1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
The Science of the total environment
|January 26, 2024
概括
本研究引入了一种使用电力数据预测工业空气污染的新型模型,提高了造厂的准确性和成本效益. 这些发现有助于环境监管和排放清单优化.
科学领域:
- 环境科学 环境科学
- 工业生态学 工业生态学
- 空气质量监测 空气质量监测
背景情况:
- 工业企业是空气污染的重要来源.
- 目前的排放监测方法范围有限,成本高昂,不准确.
- 造行业需要改进的空气污染物排放评估工具.
研究的目的:
- 开发一个预测模型,用于来自造厂的空气污染物排放.
- 利用高精度的电力消耗数据和持续排放监测系统 (CEMS).
- 为没有CEMS和时间分配因子的设施优化排放计算.
主要方法:
- 开发了一个预测模型,整合了电力消耗数据和CEMS.
- 将模型应用于缺乏CEMS的造厂,用于排放计算.
- 建立了一个立方多项式模型,将电力消耗和烟气流量相关联 (R2 = 0.85).
主要成果:
- 在2022年北京冬季奥运会期间,电力消耗和颗粒物 (PM) 排放呈现同步趋势.
- 与排放因子方法相比,模型计算的颗粒物排放显示出较小的相对误差 (-17.09-24.12%),具有很强的相关性 (r=0.93).
- 造厂PM排放在春季和冬季达到峰值,周末排放较低.
结论:
- 开发的模型为监测工业空气污染物排放提供了更准确和更具成本效益的方法.
- 调查结果为环境监管和炼金造行业排放库存提供了有价值的数据.
- 季节性和每周排放模式为有针对性的污染控制策略提供了洞察力.
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