Assessing the spatial transferability of calibration models across a low-cost sensors network

IF 3.9 3区 环境科学与生态学 Q2 ENGINEERING, CHEMICAL
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Abstract

Low-cost sensor networks (LCSNs) are expanding worldwide to gather high spatiotemporal resolution data due to their economic feasibility and compact size. The reliability of LCS-recorded data is limited due to their calibration dependencies in the field. Previous studies have focused on the development of LCS calibration models by co-location with the regulatory monitoring stations. However, it is challenging to calibrate LCS in the field for countries with limited infrastructure for air quality monitoring, pointing towards the need for transferable calibration models. Only a few studies have addressed this challenge and provide no information on the factors that may affect the performance of transferable calibration models. Here, we examined the spatial transferability of the calibration models developed using machine learning (ML) algorithms for an LCSN with twenty-two (22) sites in NCT-Delhi. The site-specific calibration models performed well at each site with high R2 and significantly low RMSE values. These models were transferred to the other sites, and the effect of distance between the sites (D), source composition, PM ratios, and particle size distribution (PSD) on the transferability of calibration models was investigated. The models developed at the Mundka (S10) and Punjabi Bagh (S16) sites complied with the evaluation criterion (R2 ≥ 0.70) for each site, irrespective of the distance between the sites. Furthermore, PM ratios reported by the LCSs did not significantly differ across sites, suggesting that the PMS algorithm provides a proxy of the size-resolved mass fractions. Evaluation of the PSD at different sites supported our findings. We also introduced the concept of selecting representative locations for LCS co-location by computing transferability scores using k-means clustering and presented a reference map for NCT-Delhi for developing scalable calibration models.

Abstract Image

评估校准模型在低成本传感器网络中的空间可转移性
低成本传感器网络(LCSN)因其经济可行性和体积小巧,正在全球范围内扩展,用于收集高时空分辨率数据。LCS 记录数据的可靠性因其在现场的校准依赖性而受到限制。以往的研究侧重于通过与监管监测站合用同一地点来开发 LCS 校准模型。然而,对于空气质量监测基础设施有限的国家来说,在现场校准 LCS 具有挑战性,因此需要可转移的校准模型。只有少数研究解决了这一难题,而且没有提供关于可能影响可转移校准模型性能的因素的信息。在此,我们对使用机器学习(ML)算法开发的校准模型的空间可转移性进行了研究,该模型适用于 NCT-Delhi 的 22 个站点的 LCSN。特定地点的校准模型在每个地点都表现良好,具有较高的 R 值和显著较低的 RMSE 值。这些模型被转移到其他站点,并研究了站点之间的距离(D)、源组成、可吸入颗粒物比率和粒径分布(PSD)对校准模型转移性的影响。在 Mundka(S10)和 Punjabi Bagh(S16)站点开发的模型符合每个站点的评估标准(R≥0.70),而与站点之间的距离无关。此外,LCS 报告的可吸入颗粒物比率在各站点之间并无显著差异,这表明 PMS 算法提供了粒度分辨质量分数的替代方法。对不同地点的 PSD 进行的评估支持了我们的发现。我们还引入了通过使用 k-means 聚类计算可转移性分数来为 LCS 共定位选择代表性地点的概念,并提出了用于开发可扩展校准模型的 NCT-Delhi 参考地图。
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来源期刊
Journal of Aerosol Science
Journal of Aerosol Science 环境科学-工程:化工
CiteScore
8.80
自引率
8.90%
发文量
127
审稿时长
35 days
期刊介绍: Founded in 1970, the Journal of Aerosol Science considers itself the prime vehicle for the publication of original work as well as reviews related to fundamental and applied aerosol research, as well as aerosol instrumentation. Its content is directed at scientists working in engineering disciplines, as well as physics, chemistry, and environmental sciences. The editors welcome submissions of papers describing recent experimental, numerical, and theoretical research related to the following topics: 1. Fundamental Aerosol Science. 2. Applied Aerosol Science. 3. Instrumentation & Measurement Methods.
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