IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8520-d860787.html
   My bibliography  Save this article

Random Forest Estimation and Trend Analysis of PM 2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

Author

Listed:
  • Xingyu Li

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China
    University of the Chinese Academy of Sciences, Beijing 100049, China)

  • Long Li

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China
    Department of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Longgao Chen

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Ting Zhang

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Jianying Xiao

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Longqian Chen

    (School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
    Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
    Collaborative Innovation Center for Territorial Space Safety & Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM 2.5 ). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM 2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM 2.5 . By averaging the modeled daily PM 2.5 concentration, we produced a yearly PM 2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM 2.5 with observational data, the coefficient of determination ( R 2 ) of the modeling was 0.85, the root means square error ( R M S E ) was 14.63 μg/m 3 , and the mean absolute error ( M A E ) was 10.03 μg/m 3 . The quality assessment of the synthesized yearly PM 2.5 concentration dataset shows that R 2 = 0.77, R M S E = 6.92 μg/m 3 , and M A E = 5.42 μg/m 3 . Despite different trends from 2000–2010 and from 2010–2020, the trend of PM 2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM 2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM 2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

Suggested Citation

  • Xingyu Li & Long Li & Longgao Chen & Ting Zhang & Jianying Xiao & Longqian Chen, 2022. "Random Forest Estimation and Trend Analysis of PM 2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8520-:d:860787
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8520/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8520/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ziyun Jing & Pengfei Liu & Tuanhui Wang & Hongquan Song & Jay Lee & Tao Xu & Yu Xing, 2020. "Effects of Meteorological Factors and Anthropogenic Precursors on PM 2.5 Concentrations in Cities in China," Sustainability, MDPI, vol. 12(9), pages 1-13, April.
    2. Dieu Tien Bui & Biswajeet Pradhan & Owe Lofman & Inge Revhaug, 2012. "Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-26, July.
    3. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
    Full references (including those not matched with items on IDEAS)

    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. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    2. Shabnam Mehrnoor & Maryam Robati & Mir Masoud Kheirkhah Zarkesh & Forough Farsad & Shahram Baikpour, 2023. "Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 1997-2030, February.
    3. Yu Duan & Junnan Xiong & Weiming Cheng & Nan Wang & Yi Li & Yufeng He & Jun Liu & Wen He & Gang Yang, 2022. "Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 269-294, January.
    4. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1515-1543, September.
    5. Nicola Baldo & Matteo Miani & Fabio Rondinella & Clara Celauro, 2021. "A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    6. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
    7. Sheela Bhuvanendran Bhagya & Anita Saji Sumi & Sankaran Balaji & Jean Homian Danumah & Romulus Costache & Ambujendran Rajaneesh & Ajayakumar Gokul & Chandini Padmanabhapanicker Chandrasenan & Renata P, 2023. "Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps," Land, MDPI, vol. 12(2), pages 1-29, February.
    8. Uzodigwe Emmanuel Nnanwuba & Shengwu Qin & Oluwafemi Adewole Adeyeye & Ndichie Chinemelu Cosmas & Jingyu Yao & Shuangshuang Qiao & Sun Jingbo & Ekene Mathew Egwuonwu, 2022. "Prediction of Spatial Likelihood of Shallow Landslide Using GIS-Based Machine Learning in Awgu, Southeast/Nigeria," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    9. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
    10. Jiachuang Wang & Haoji Ma & Xianhang Yan, 2023. "Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms," Mathematics, MDPI, vol. 11(4), pages 1-29, February.
    11. Huaiting Luo & Wei Zhou & Izhar Mithal Jiskani & Zhiming Wang, 2021. "Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining," Energies, MDPI, vol. 14(9), pages 1-19, May.
    12. Ram, Pappu Kalyan & Pandey, Neeraj & Persis, Jinil, 2024. "Modeling social coupon redemption decisions of consumers in food industry: A machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    13. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    14. Hülya Yürekli & Öyküm Esra Yiğit & Okan Bulut & Min Lu & Ersoy Öz, 2022. "Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    15. Moon-Jung Kim & Yu-Sang Chang & Su-Min Kim, 2021. "Impact of Income, Density, and Population Size on PM 2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries," IJERPH, MDPI, vol. 18(17), pages 1-30, August.
    16. Shengwu Qin & Shuangshuang Qiao & Jingyu Yao & Lingshuai Zhang & Xiaowei Liu & Xu Guo & Yang Chen & Jingbo Sun, 2022. "Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2709-2738, December.
    17. Thao Nguyen-Da & Yi-Min Li & Chi-Lu Peng & Ming-Yuan Cho & Phuong Nguyen-Thanh, 2023. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    18. Jiejun Zhang & Pengfei Liu & Hongquan Song & Changhong Miao & Jie Yang & Longlong Zhang & Junwu Dong & Yi Liu & Yunlong Zhang & Bingchen Li, 2022. "Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    19. Deliang Sun & Danlu Chen & Jialan Zhang & Changlin Mi & Qingyu Gu & Haijia Wen, 2023. "Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation," Land, MDPI, vol. 12(5), pages 1-37, May.
    20. Rui-Si Ma & Si-Ioi Ng & Tan Lee & Yi-Jian Yang & Raymond Kim-Wai Sum, 2022. "Validation of a Speech Database for Assessing College Students’ Physical Competence under the Concept of Physical Literacy," IJERPH, MDPI, vol. 19(12), pages 1-11, June.

    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:jsusta:v:14:y:2022:i:14:p:8520-:d:860787. 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.