评估室内低成本粒子传感器:算法见解和校准方法

IF 8.1 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Nan Ma, Ye Kang, Weiduo Gan, Jin Zhou
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引用次数: 0

摘要

低成本颗粒物(PM)传感器由于价格合理且易于部署,越来越多地用于室内空气质量监测。然而,对其内置处理功能的可靠性和数据的准确性的关注仍然存在。本研究评估了30个Plantower PMS5003传感器在三种不同室内环境下的性能:ex_normal(典型办公空间)、Ex_Incense(有人为颗粒排放的空间)和Ex_Bushfire(受室外空气污染影响的空间)。主要目的是通过检查传感器的内部处理算法和识别有效的校准模型来提高数据的可靠性。最近的发现spiecwise线性回归分析揭示了传感器内部的两个关键功能:一个用于将粒子数转换为质量,另一个用于根据粒子类型进行调整。对对数线性(LN)、非对数线性(nLN)和随机森林(RF)三种校准模型进行了评估。所有模型在决定系数(r2)、均方根误差(RMSE)、平均归一化偏差(MNB)和变异系数(CV)方面都比原始传感器数据有所改善,其中RMSE(高达64%)、MNB(高达70%)和CV(超过50%)的增强尤为显著。虽然这三种校准模型都显著提高了数据质量,但它们之间没有显著差异。推荐使用LN模型,因为它简单且性能相当。这些发现有助于改进低成本传感器的算法处理,并为寻求提高室内空气质量监测应用中传感器可靠性的最终用户提供实用指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Indoor Low-Cost Particle Sensors: Algorithmic Insights and Calibration Approaches

Purpose of Review

Low-cost particulate matter (PM) sensors are increasingly used for indoor air quality monitoring due to their affordability and ease of deployment. However, concerns persist regarding the reliability of their built-in processing functions and the accuracy of their data. This study evaluates the performance of 30 Plantower PMS5003 sensors across three distinct indoor environments—Ex_Normal (typical occupied office space), Ex_Incense (space with anthropogenic particle emissions), and Ex_Bushfire (space affected by outdoor air pollution). The primary aim is to improve data reliability by examining the sensors’ internal processing algorithms and identifying effective calibration models.

Recent Findings

Piecewise linear regression analysis revealed two key internal functions within the sensor: one for converting particle number to mass and another for adjusting based on particle type. Three calibration models—Log-Linear (LN), non-Log-Linear (nLN), and Random Forest (RF)—were evaluated. All models showed improvements over raw sensor data in terms of coefficient of determination (r2), root mean square error (RMSE), mean normalized bias (MNB), and coefficient of variation (CV), with particularly notable enhancements in RMSE (up to 64%), MNB (up to 70%), and CV (over 50%).

Summary

Although all three calibration models significantly improved data quality, no substantial differences were observed among them. The LN model is recommended for its simplicity and comparable performance. These findings contribute to improving algorithmic processing in low-cost sensors and offer practical guidance for end-users seeking to enhance sensor reliability in indoor air quality monitoring applications.

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来源期刊
Current Pollution Reports
Current Pollution Reports Environmental Science-Water Science and Technology
CiteScore
12.10
自引率
1.40%
发文量
31
期刊介绍: Current Pollution Reports provides in-depth review articles contributed by international experts on the most significant developments in the field of environmental pollution.By presenting clear, insightful, balanced reviews that emphasize recently published papers of major importance, the journal elucidates current and emerging approaches to identification, characterization, treatment, management of pollutants and much more.
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