Spatial interpolation of PM2.5 concentrations during holidays in south-central China considering multiple factors

Spatial interpolation of regional PM2.5 concentrations in China during COVID-19 incorporating multivariate data

“…For model applications related to PM 2.5 during COVID-19, Fan et al (2020) combined meteorological and socioeconomic factors to develop multi-scale geographically weighted regression (MGWR), ordinary least squares (OLS) regression and GWR models to combine PM 2.5 and PM 10 for the period from January 20 to April 8, 2020 in China, and the results showed that the R 2 of the MGWR model was better than the other two types of models. For the MGWR model, it has been shown that the interpolation accuracy of the combined GWR model is better than that of the MGWR model in the numerical calculation of PM 2.5 regions after the residuals have been interpolated and optimized ( Wei et al, 2021 , 2022 ).…”

“…4 also shows that the national PM 2.5 concentrations were higher from both January–February 2019 and January–February 2020, mainly because there are more areas with indoor air heating in winter, especially the northern heating system, which could increase particle pollution, and the northern winter climate is dry and the inverse temperature phenomenon is obvious, which is not conducive to PM 2.5 diffusion. Another reason is that January–February is accompanied by the Chinese New Year, generating a holiday effect, such as increased population travel and fireworks, which could be the cause of the higher PM 2.5 concentrations ( Wei et al, 2022 ). During the Chinese New Year period in February 2020, due to the impact of the COVID-19 outbreak, reduced travel guidelines were issued across the whole country, so February 2020 was subject to a less severe holiday effect, and the PM 2.5 values were relatively low.…”

“…Various factors were considered to accomplish the study of the interpolation and spatial distribution of regional PM 2.5 concentrations, and one of the most common models is the geographically weighted regression (GWR) model ( Wei et al, 2022 ). Narayan et al (2022) used the GWR model to predict the spatial distribution of PM 2.5 in Jharkhand, eastern India, with good results.…”

Downscaling MERRA-2 Reanalysis PM2.5 Series over the Arabian Gulf by Inverse Distance Weighting, Bicubic Spline Smoothing, and Spatio-Temporal Kriging

“…Various methods have been used for this goal. They include Inverse Distance Weighting (IDW) or some of its versions for PM 2.5 trend evaluation [ 19 , 20 ], kriging [ 21 , 22 ], neural networks [ 23 , 24 , 25 , 26 ], generalized additive mixed models [ 27 ], Multiscale Geographically Weighted Regression (MGWR) [ 28 ], Bayesian Kriging, and Tensor Spline Function [ 29 ], Random-forest-spatio-temporal kriging [ 30 ], hierarchical modeling [ 31 ], hybrid models [ 32 ], data fusion [ 33 ], optimal interpolation [ 34 ], exponentially smoothing [ 35 ], etc. Each one addressed some issues of the classical interpolation techniques, intending to increase the forecast accuracy.…”

Sensitivity Analysis of the Inverse Distance Weighting and Bicubic Spline Smoothing Models for MERRA-2 Reanalysis PM2.5 Series in the Persian Gulf Region

“…A comparison of different geostatistical methods for evaluating exposure to PM2.5 was presented by Lee et al [26]. Spatial interpolation and spatio-temporal interpolation of large data series are presented [27][28][29][30][31].…”