Personal Exposure Estimates via Portable and Wireless Sensing and Reporting of Particulate Pollution
Personal Exposure Estimates via Portable and Wireless Sensing and Reporting of Particulate Pollution
复制标题
DOI：
10.3390/ijerph17030843
复制
发表时间：
2020-02-01
影响因子：
--
通讯作者：
Thompson, J. E.
中科院分区：
文献类型：
--
作者：
Agrawaal, Harsshit;Jones, Courtney;Thompson, J. E.
设计、制造了低成本便携式颗粒传感器（n = 3），并用于监测德克萨斯州Lubbock不同地点和时间的人体暴露于颗粒污染中。空气传感器由一个连接Arduino Uno R3的夏普GP2Y1010AU0F粉尘传感器和一个FONA808 3G通信模块组成。Arduino Uno用于接收来自校准的粉尘传感器的信号，以提供悬浮颗粒物的浓度（μ g/m(3)），并通过3G蜂窝网络协调数据的无线传输。在用于监测之前，根据独立运行的参考气溶胶监测仪（RAM-1）对粉尘传感器进行校准。在3.6 m(3)的混合室内产生氯化钠颗粒，同时RAM-1和每个粉尘传感器记录信号，并通过直接比较RAM-1的读数来独立地完成每个粉尘传感器的校准。为了提高数据流的质量，研究了夏普传感器在分析零空气时对重复的单个脉冲进行平均的影响。对n < 2000个平均值的所有传感器的数据点进行平均，可以指数地降低标准差，但在大约之后，平均产生的收益递减。2000年平均水平。当灰尘传感器LED的2000个脉冲在大约1 / 3的范围内平均时，传感器的重复测量标准偏差为3-6 μ g/m(3)，相应的3 σ检测限为9-18 μ g/m(3)。2分钟数据采集/传输周期。为了演示便携式监测，来自粉尘传感器的浓度值被实时无线发送到ThingSpeak频道，同时使用车载全球定位系统（GPS）传感器跟踪传感器的纬度和经度。在不同的地点和时间进行了室外和室内空气质量测量，由人类志愿者携带传感器。测量结果表明，步行经过餐馆和在家做饭会增加颗粒物的暴露量。灰尘传感器的构建和本研究收集的数据通过描述开源概念和提供初始测量值来增强当前的研究。原则上，传感器可以大规模复用，并用于生成给定位置周围颗粒物的实时地图。
Low-cost, portable particle sensors (n = 3) were designed, constructed, and used to monitor human exposure to particle pollution at various locations and times in Lubbock, TX. The air sensors consisted of a Sharp GP2Y1010AU0F dust sensor interfaced to an Arduino Uno R3, and a FONA808 3G communications module. The Arduino Uno was used to receive the signal from calibrated dust sensors to provide a concentration (mu g/m(3)) of suspended particulate matter and coordinate wireless transmission of data via the 3G cellular network. Prior to use for monitoring, dust sensors were calibrated against a reference aerosol monitor (RAM-1) operating independently. Sodium chloride particles were generated inside of a 3.6 m(3) mixing chamber while the RAM-1 and each dust sensor recorded signals and calibration was achieved for each dust sensor independently of others by direct comparison with the RAM-1 reading. In an effort to improve the quality of the data stream, the effect of averaging replicate individual pulses of the Sharp sensor when analyzing zero air has been studied. Averaging data points exponentially reduces standard deviation for all sensors with n < 2000 averages but averaging produced diminishing returns after approx. 2000 averages. The sensors exhibited standard deviations for replicate measurements of 3-6 mu g/m(3) and corresponding 3 sigma detection limits of 9-18 mu g/m(3) when 2000 pulses of the dust sensor LED were averaged over an approx. 2 min data collection/transmission cycle. To demonstrate portable monitoring, concentration values from the dust sensors were sent wirelessly in real time to a ThingSpeak channel, while tracking the sensor's latitude and longitude using an on-board Global Positioning System (GPS) sensor. Outdoor and indoor air quality measurements were made at different places and times while human volunteers carried sensors. The measurements indicated walking by restaurants and cooking at home increased the exposure to particulate matter. The construction of the dust sensors and data collected from this research enhance the current research by describing an open-source concept and providing initial measurements. In principle, sensors can be massively multiplexed and used to generate real-time maps of particulate matter around a given location.