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首页> 外文期刊>IEEE sensors journal >A Sensor-Based Data Driven Framework to Investigate PM2.5 in the Greater Detroit Area
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A Sensor-Based Data Driven Framework to Investigate PM2.5 in the Greater Detroit Area

机译：基于传感器的数据驱动框架，用于调查大底特律区域的PM 2.5

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摘要

PM2.5 are inhalable particles with aerodynamic diameters of 2.5micrometers or smaller. PM2.5 concentrations require close monitoring as they impose negative effects on both human health and air quality. Monitoring PM2.5 concentrations in the metropolitan Detroit Area is increasingly important as its residents are being disproportionately exposed to harmful air pollution due to health inequities through economic divestment, limited educational and employment opportunities. The relations between PM2.5 and meteorological factors can be critical in understanding how particulate matter affects humans and the environment. This study utilizes PurpleAir sensors to measure PM2.5 along with some methodological factors such as humidity and applies temporal analysis of the impact of meteorological factors on PM2.5 concentrations and spatiotemporal analysis of PM2.5 distributions at different locations over the Greater Detroit Area via Long Short Term Memory (LSTM) neural networks and Dynamic Time Warping (DTW) algorithms, respectively. Our findings show that although LSTMs with exogenous variables (i.e., the current values of PM2.5 concentration, meteorological features, and weather conditions) can accurately (i.e., average RMSE of 3.2 mu g/m(3)) predict levels of PM2.5, but there is no significant relation between the mentioned meteorological factors and PM2.5 concentrations over the Greater Detroit Area. Furthermore, DTW analysis portraits the similarity of PM2.5 behavioral patterns over the Greater Detroit Area.
机译：PM2.5是可吸入的颗粒，空气动力学直径为2.5m，或更小。 PM2.5浓度需要密切监测，因为它们对人类健康和空气质量施加负面影响。监测PM2.5在大都市底特律地区的浓度越来越重要，因为由于经济撤资，有限的教育和就业机会，其居民因健康不公平而暴露于有害的空气污染。 PM2.5与气象因素之间的关系对于了解微粒物质如何影响人类和环境，这是至关重要的。该研究利用P350.5的Pureeair传感器以及湿度的一些方法因素，并适用气象因素对PM2.5浓度和PM2.5在不同地区的不同地区的PM2.5分布的时空分析的时间分析。长期内记忆（LSTM）神经网络和动态时间翘曲（DTW）算法。我们的研究结果表明，尽管具有外源变量的LSTM（即PM2.5浓度，气象特征和天气条件的当前值）可以准确地（即，平均RMSE为3.2μg/ m（3））预测PM2水平。 5，但上述气象因素和PM2.5浓度在大底特律区域之间没有显着关系。此外，DTW分析肖像在大底特律区域上的PM2.5行为模式的相似性。

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