Research on the Estimation of Air Pollution Models with Machine Learning in Urban Sustainable Development Based on Remote Sensing

Wenqian Chen (), Na Zhang, Xuesong Bai and Xiaoyi Cao
Additional contact information
Wenqian Chen: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Na Zhang: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Xuesong Bai: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Xiaoyi Cao: Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China

Sustainability, 2024, vol. 16, issue 24, 1-20

Abstract: Air quality is directly related to people’s health and quality of life and has a profound impact on the sustainable development of cities. Good air quality is the foundation of sustainable development. To solve the current problem of air quality for sustainable development, we used high-resolution (1 km) satellite-retrieved aerosol optical depth (AOD), meteorological, nighttime light and vegetation data to develop a spatiotemporal convolution feature random forest (SCRF) model to predict the PM 2.5 concentration in Shandong from 2016 to 2019. We evaluated the performance of the SCRF model and compared the results of other models, including neural network (BPNN), gradient boosting (GBDT), and random forest (RF) models. The results show that compared with the other models, the improved SCRF model performs best. The coefficient of determination (R 2 ) and root mean square error (RMSE) are 0.83 and 9.87 µg/m 3 , respectively. Moreover, we discovered that the characteristic variables AOD and air temperature (TEM) data improved the accuracy of the model in Shandong Province. The annual average PM 2.5 concentrations in Shandong Province from 2016 to 2019 were 74.44 µg/m 3 , 65.01 µg/m 3 , 58.32 µg/m 3 , and 59 µg/m 3 , respectively. The spatial distribution of air pollution increases from northeastern and southeastern to western Shandong inland. In general, our research has significant implications for the sustainable development of various cities in Shandong Province.

Keywords: PM 2.5; spatiotemporal convolutional feature random forest (SCRF); AOD; Shandong Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/24/10949/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/24/10949/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:24:p:10949-:d:1543303

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().