Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality.
Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality.
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低成本室内空气质量传感器和采样平台的设计和测试。
DOI：
10.1016/j.buildenv.2021.108398
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发表时间：
2021
影响因子：
7.4
通讯作者：
Volckens,John
中科院分区：
文献类型：
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作者：
Tryner,Jessica;Phillips,Mollie;Quinn,Casey;Neymark,Gabe;Wilson,Ander;Jathar,ShantanuH;Carter,Ellison;Volckens,John
美国人大部分时间都呆在室内，但对室内空气污染的全面表征受到参考质量监测器的成本和尺寸的限制。我们组装了小型“家庭健康盒”（HHB），使用过滤器采样器和低成本传感器测量室内PM2.5、PM10、CO2、CO、NO2和O3浓度。在美国科罗拉多的柯林斯堡，在野火烟雾影响当地空气质量的同时，将9个HHB与参考监测器一起放置在被占用房屋的厨房中168 h。当使用气体传感器制造商的校准来解释HHB数据时，HHB和参考监测器（a）将每种气体污染物的水平相似地分类（相对于空气质量标准为低、高或高），并且（B）都表明燃气烹饪燃烧器是CO和NO2污染的主要来源;然而，HHB和参考O3数据不相关。当HHB气体传感器数据解释使用线性混合校准模型通过搭配与参考监测器，均方根误差降低CO2（从408到58 ppm），CO（645至572 ppb），NO2（22至14 ppb）和O3（21至7 ppb）;此外，HHB和参考O3数据之间的相关性得到改善（Pearson r从0.02增加到0.75）。从9个过滤器样品中获得的168小时平均PM2.5和PM10浓度分别为19.4 μg m−3（6.1%相对标准偏差[RSD]）和40.1 μg m−3（7.6% RSD）。PMS 5003传感器高估了168小时的PM2.5浓度（传感器/过滤器比值中位数= 1.7），而SPS 30传感器略微低估了168小时的PM2.5浓度（传感器/过滤器比值中位数= 0.91）。
Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small “Home Health Boxes” (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2pollution; however, HHB and reference O3data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2(from 408 to 58 ppm), CO (645 to 572 ppb), NO2(22 to 14 ppb), and O3(21 to 7 ppb); additionally, correlation between HHB and reference O3data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5and PM10concentrations derived from nine filter samples were 19.4 μg m−3(6.1% relative standard deviation [RSD]) and 40.1 μg m−3(7.6% RSD). The 168-h PM2.5concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).