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民用级气象站在木质结构火灾风险评估中的应用。

Consumer Grade Weather Stations for Wooden Structure Fire Risk Assessment.

机构信息

Department of Fire Safety and HSE Eng., Glö∂ R&D, Western Norway University of Applied Sciences, 5528 Haugesund, Norway.

出版信息

Sensors (Basel). 2018 Sep 27;18(10):3244. doi: 10.3390/s18103244.

Abstract

During January 2014, Norway experienced unusually cold and dry weather conditions leading to very low indoor relative humidity (RH) in inhabited (heated) wooden homes. The resulting dry wood played an important role in the two most severe accidental fires in Norway recorded since 1923. The present work describes testing of low cost consumer grade weather stations for recording temperature and relative humidity as a proxy for dry wood structural fire risk assessment. Calibration of the weather stations relative humidity (RH) sensors was done in an atmosphere stabilized by water saturated LiCl, MgCl₂ and NaCl solutions, i.e., in the range 11% RH to 75% RH. When calibrated, the weather station results were well within ±3% RH. During the winter 2015/2016 weather stations were placed in the living room in eight wooden buildings. A period of significantly increased fire risk was identified in January 2016. The results from the outdoor sensors compared favorably with the readings from a local meteorological station, and showed some interesting details, such as higher ambient relative humidity for a home close to a large and comparably warmer sea surface. It was also revealed that a forecast predicting low humidity content gave results close to the observed outdoor weather station data, at least for the first 48 h forecast.

摘要

2014 年 1 月,挪威遭遇异常寒冷干燥的天气,导致居住(供暖)木屋的室内相对湿度(RH)非常低。干燥的木材在挪威自 1923 年以来记录的两起最严重的意外火灾中起到了重要作用。本工作描述了低成本消费级气象站的测试,这些气象站用于记录温度和相对湿度,作为评估干木结构火灾风险的替代指标。气象站相对湿度(RH)传感器的校准是在由水饱和 LiCl、MgCl₂ 和 NaCl 溶液稳定的大气中进行的,即 11% RH 到 75% RH 的范围内。校准后,气象站的结果在±3% RH 以内。2015/2016 年冬季,气象站被放置在八栋木屋的客厅里。2016 年 1 月,确定了火灾风险显著增加的时期。户外传感器的结果与当地气象站的读数相当吻合,并显示了一些有趣的细节,例如靠近大型且相对温暖海面的房屋的环境相对湿度较高。结果还表明,预测低湿度含量的预报至少在前 48 小时的预报中,与观测到的户外气象站数据非常接近。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd17/6210543/47b82148ca65/sensors-18-03244-g001.jpg

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