利用低成本传感器网络探索 PM2.5 污染的时空动态、季节性和天时趋势:经典马尔可夫链和空间显式马尔可夫链的启示

IF 4 2区 地球科学 Q1 GEOGRAPHY
Michael Biancardi , Yuye Zhou , Wei Kang , Ting Xiao , Tony Grubesic , Jake Nelson , Lu Liang
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引用次数: 0

摘要

细颗粒物(PM2.5)是一个主要的健康和环境问题,在城市地区具有显著的时空动态变化。低成本的空气质量传感器(LCS)网络为获取高时空分辨率数据提供了一个改变模式的机会,它能以足够的细节揭示城市污染状况,从而进行有效的政策制定和健康评估。本研究使用经典的空间马尔可夫链,利用 LCS 数据分析 PM2.5 的季节性和日内变化,推进了地理空间空气质量研究。研究结果表明,PM2.5 具有明显的季节性,"良好 "状态在夏季占主导地位,而在冬季则最少见。中午是 "良好 "状态的高峰期,而早晨和夜晚的状态较差,这表明在晚间交通高峰时段需要更严格的污染控制。值得注意的是,时间尺度对空间马尔可夫分析的影响很大,与每日评估相比,时间间隔内的空气污染状态范围更广,稳定性更高,变化更小。站点层面的分析表明,农村站点更有可能保持 "良好 "状态,而较少可能脱离 "良好 "状态。总之,这项研究强调了高时空分辨率数据的有效性,并展示了马尔可夫链揭示空气污染等现象细微差别的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network: Insights from classic and spatially explicit Markov chains

Fine particulate matter (PM2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM2.5 using LCS data. Results highlight distinctive PM2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.

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来源期刊
Applied Geography
Applied Geography GEOGRAPHY-
CiteScore
8.00
自引率
2.00%
发文量
134
期刊介绍: Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.
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