PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

Hang Zhang, Yong Liu (), Dongyang Yang and Guanpeng Dong ()
Additional contact information
Hang Zhang: Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
Yong Liu: Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
Dongyang Yang: Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
Guanpeng Dong: Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China

IJERPH, 2022, vol. 19, issue 17, 1-14

Abstract: Compiling fine-resolution geospatial PM 2.5 concentrations data is essential for precisely assessing the health risks of PM 2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM 2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM 2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM 2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM 2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran’s I statistic (a value of 0.136 with p -value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM 2.5 concentration, which would be beneficial for precise health risk assessment of PM 2.5 pollution exposure.

Keywords: spatial statistics; basis functions; heterogeneity; spatial correlation; PM 2.5 concentrations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/17/10811/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/17/10811/ (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:jijerp:v:19:y:2022:i:17:p:10811-:d:902003

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

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