Michael Biancardi , Yuye Zhou , Wei Kang , Ting Xiao , Tony Grubesic , Jake Nelson , Lu Liang
{"title":"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","authors":"Michael Biancardi , Yuye Zhou , Wei Kang , Ting Xiao , Tony Grubesic , Jake Nelson , Lu Liang","doi":"10.1016/j.apgeog.2024.103414","DOIUrl":null,"url":null,"abstract":"<div><p>Fine particulate matter (PM<sub>2.5</sub>) 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 PM<sub>2.5</sub> using LCS data. Results highlight distinctive PM<sub>2.5</sub> 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.</p></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"172 ","pages":"Article 103414"},"PeriodicalIF":4.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0143622824002194/pdfft?md5=b8c899f09d1dcddd75ec1a31f5263aa7&pid=1-s2.0-S0143622824002194-main.pdf","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Michael Biancardi , Yuye Zhou , Wei Kang , Ting Xiao , Tony Grubesic , Jake Nelson , Lu Liang\",\"doi\":\"10.1016/j.apgeog.2024.103414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Fine particulate matter (PM<sub>2.5</sub>) 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 PM<sub>2.5</sub> using LCS data. Results highlight distinctive PM<sub>2.5</sub> 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.</p></div>\",\"PeriodicalId\":48396,\"journal\":{\"name\":\"Applied Geography\",\"volume\":\"172 \",\"pages\":\"Article 103414\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0143622824002194/pdfft?md5=b8c899f09d1dcddd75ec1a31f5263aa7&pid=1-s2.0-S0143622824002194-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143622824002194\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143622824002194","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
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.
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.