IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1934-d1042256.html
   My bibliography  Save this article

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

Author

Listed:
  • Asrah Heintzelman

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
    Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA)

  • Gabriel M. Filippelli

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
    Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA)

  • Max J. Moreno-Madriñan

    (Department of Global Health, DePauw University, Greencastle, IN 46135, USA)

  • Jeffrey S. Wilson

    (Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Lixin Wang

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Gregory K. Druschel

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Vijay O. Lulla

    (Independent Researcher, Indianapolis, IN 46214, USA)

Abstract

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM 2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM 2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM 2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM 2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m 3 decrease in PM 2.5 , and a 1% increase in “heavy industry” results in a 0.07 µg/m 3 increase in PM 2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Suggested Citation

  • Asrah Heintzelman & Gabriel M. Filippelli & Max J. Moreno-Madriñan & Jeffrey S. Wilson & Lixin Wang & Gregory K. Druschel & Vijay O. Lulla, 2023. "Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments," IJERPH, MDPI, vol. 20(3), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1934-:d:1042256
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1934/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/3/1934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ronan Hart & Lu Liang & Pinliang Dong, 2020. "Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
    2. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Patrick Johnson & Niranjan Ravi & Gabriel Filippelli & Asrah Heintzelman, 2024. "A Novel Hybrid Approach: Integrating Bayesian SPDE and Deep Learning for Enhanced Spatiotemporal Modeling of PM 2.5 Concentrations in Urban Airsheds for Sustainable Climate Action and Public Health," Sustainability, MDPI, vol. 16(23), pages 1-28, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun Bai & Shixiang Li & Nan Wang & Jianru Shi & Xianmin Li, 2020. "Spatial Spillover Effect of New Energy Development on Economic Growth in Developing Areas of China—An Empirical Test Based on the Spatial Dubin Model," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    2. Wang, Xiaomin & Tian, Guanghui & Yang, Dongyang & Zhang, Wenxin & Lu, Debin & Liu, Zhongmei, 2018. "Responses of PM2.5 pollution to urbanization in China," Energy Policy, Elsevier, vol. 123(C), pages 602-610.
    3. Hansol Mun & Mengying Li & Juchul Jung, 2022. "Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach," Land, MDPI, vol. 11(12), pages 1-26, December.
    4. Zeng, Jingjing & Liu, Ting & Feiock, Richard & Li, Fei, 2019. "The impacts of China's provincial energy policies on major air pollutants: A spatial econometric analysis," Energy Policy, Elsevier, vol. 132(C), pages 392-403.
    5. Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
    6. Enkhjargal Enkhbat & Yong Geng & Xi Zhang & Huijuan Jiang & Jingyu Liu & Dong Wu, 2020. "Driving Forces of Air Pollution in Ulaanbaatar City Between 2005 and 2015: An Index Decomposition Analysis," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    7. Weiwei Xie & Hongbing Deng & Zhaohui Chong, 2019. "The Spatial and Heterogeneity Impacts of Population Urbanization on Fine Particulate (PM 2.5 ) in the Yangtze River Economic Belt, China," IJERPH, MDPI, vol. 16(6), pages 1-17, March.
    8. Lan, Jing & Wei, Yiming & Guo, Jie & Li, Qiuming & Liu, Zhen, 2023. "The effect of green finance on industrial pollution emissions: Evidence from China," Resources Policy, Elsevier, vol. 80(C).
    9. Liu, Yang & Du, Junying & Wang, Kun, 2024. "Towards common prosperity: The role of mitigating energy inequality," Energy Policy, Elsevier, vol. 195(C).
    10. Ekaterina Alekhanova & Kate Foreman & Maya Papineau & Reid Stevens, 2023. "One Size Does Not Fit All: Co-Benefits of Congestion Pricing in the San Francisco Bay Area," Carleton Economic Papers 23-07, Carleton University, Department of Economics.
    11. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    12. Pengcheng Lv & Haoyu Zhang & Xiaodong Li, 2023. "Spatio-Temporal Distribution Characteristics and Drivers of PM 2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic," IJERPH, MDPI, vol. 20(6), pages 1-14, March.
    13. Lingyan Xu & Dandan Wang & Jianguo Du, 2021. "The Heterogeneous Influence of Infrastructure Construction on China’s Urban Green and Smart Development—The Threshold Effect of Urban Scale," Land, MDPI, vol. 10(10), pages 1-17, September.
    14. Huiping Wang & Qi Ge, 2022. "Analysis of the Spatial Association Network of PM 2.5 and Its Influencing Factors in China," IJERPH, MDPI, vol. 19(19), pages 1-15, October.
    15. Yu Sang Chang & Byong-Jin You & Hann Earl Kim, 2020. "Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights," Sustainability, MDPI, vol. 12(7), pages 1-34, April.
    16. Bazyli Czyżewski & Anna Matuszczak & Łukasz Kryszak & Andrzej Czyżewski, 2019. "Efficiency of the EU Environmental Policy in Struggling with Fine Particulate Matter (PM 2.5 ): How Agriculture Makes a Difference?," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    17. Li Yang & Chunyan Qin & Ke Li & Chuxiong Deng & Yaojun Liu, 2023. "Quantifying the Spatiotemporal Heterogeneity of PM 2.5 Pollution and Its Determinants in 273 Cities in China," IJERPH, MDPI, vol. 20(2), pages 1-17, January.
    18. Wenqin Gong & Yu Kong, 2022. "Nonlinear Influence of Chinese Real Estate Development on Environmental Pollution: New Evidence from Spatial Econometric Model," IJERPH, MDPI, vol. 19(1), pages 1-22, January.
    19. Ye Yang & Haifeng Lan & Jing Li, 2019. "Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM 2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone," IJERPH, MDPI, vol. 17(1), pages 1-19, December.
    20. Hu, Qianlin & Mijit, Razia & Xu, Jingxuan & Miao, Shan, 2024. "Can government-led urban expansion simultaneously alleviate pollution and carbon emissions? Staggered difference-in-differences evidence from Chinese firms," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1-25.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1934-:d:1042256. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.