YC-CPW PM2.5/PM10传感器
PM2.5传感器介绍:
PM2.5传感器也叫粉尘传感器、灰尘传感器。PM2.5传感器可以用来监测周围空气中的粉尘浓度,即PM2.5值大小。
PM2.5 、PM10设备采用先进的激光防衰减技术,保证设备长期稳定性0-1000ug/m3
测量范围,双频数据采集及自动标定技术,一致性可达±10%
同系列产品:
YC-PF空气质量控制器
YC-KT空气质量控制器
YC-CMW一氧化碳传感器
YC-CDW二氧化碳传感器
YC-THI温度探测器
YC-CJW甲醛探测器
YC-CPW PM2.5传感器
YC-DPW多合一传感器
PM10传感器
YCX-3600空气质量监控主机
服务信息:
![](data:image/png;base64,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)
技术参数:
测量范围:0~1000ug/m³
精 度:±10%
相应时间:T90<5s
分 辨 率:1ug/m³
通讯方式:二总线 或 RS-485
供 电:12~24VDC
功能特点:
测试精度高、性能稳定、多功能性强、 操作简单方便的特点、适用于公共场所环境及大气环境的监测、空气质量监测。
应用场景:
可用于室内、图书馆、档案馆、学校教室、会议厅、商业综合体、办公大楼、工业厂房等需要PM2.5或PM10浓度监测的场所。