Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM2.5 concentrations. In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

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Mapping the geogenic radon potential and radon risk by using Empirical Bayesian Kriging regression: A case study from a volcanic area of central Italy

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.01.146 ◽

2019 ◽

Vol 661 ◽

pp. 449-464 ◽

Cited By ~ 14

Author(s):

Francesca Giustini ◽

Giancarlo Ciotoli ◽

Alessio Rinaldini ◽

Livio Ruggiero ◽

Mario Voltaggio

Keyword(s):

Central Italy ◽

Volcanic Area ◽

Empirical Bayesian ◽

Bayesian Kriging ◽

Empirical Bayesian Kriging ◽

Radon Potential ◽

Radon Risk

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How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176274 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6274 ◽

Cited By ~ 2

Author(s):

Zhiyu Fan ◽

Qingming Zhan ◽

Chen Yang ◽

Huimin Liu ◽

Meng Zhan

Keyword(s):

Air Pollution ◽

Regression Analysis ◽

Particulate Matter ◽

Socioeconomic Factors ◽

Influencing Factors ◽

Geographically Weighted Regression ◽

Meteorological Factors ◽

Central China ◽

Weighted Regression ◽

Pm2.5 And Pm10

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

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Testing statistical methods to predict pesticide drift deposition

10.5194/egusphere-egu2020-11014 ◽

2020 ◽

Author(s):

Glenda Garcia-Santos ◽

Michael Scheiber ◽

Juergen Pilz

Keyword(s):

Risk Assessment ◽

Spatial Interpolation ◽

Soil Surface ◽

Farming Systems ◽

Linear Interpolation ◽

Empirical Bayesian ◽

Bayesian Kriging ◽

Empirical Bayesian Kriging ◽

Spray Technique ◽

Pesticide Drift

We studied the case of the Andean region in Colombia as example of non-mechanized small farming systems in which farmers&#160;use handheld sprayers to spray pesticides. This is the most common technique to spray pesticide in developing countries. To better understand the spatial distribution of airborne pesticide drift deposits on the soil surface using that spray technique, nine different spatial interpolation&#160;methods were tested using a surrogate tracer substance (Uranine) i.e. classical approaches&#160;like the linear interpolation and kriging, and some advanced methods like spatial vine copulas, the Karhunen-Lo&#232;ve expansion of the underlying random field, the integrated&#160;nested Laplace approximation and the Empirical Bayesian Kriging used in ArcMap (GIS).&#160;This study contributes to future&#160;studies on mass balance and risk assessment related to environmental drift pollution in developing&#160;countries.

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A new species of Music frog (Anura, Ranidae, Nidirana) from Mt Daming, Guangxi, China

ZooKeys ◽

10.3897/zookeys.1059.68140 ◽

2021 ◽

Vol 1059 ◽

pp. 35-56

Author(s):

Zhi-Tong Lyu ◽

Zhong Huang ◽

Xiao-Wen Liao ◽

Li Lin ◽

Yong Huang ◽

...

Keyword(s):

New Species ◽

Southern China ◽

Morphological Characteristics ◽

New Music ◽

Central China ◽

Advertisement Call ◽

South Central ◽

New Information ◽

Frog Species ◽

A New Species

Nidirana guangxiensissp. nov., a new music frog species, is proposed, based on a series of specimens collected from Mt Daming, Guangxi, southern China. The new species is close to N. yeae, N. daunchina, N. yaoica, and N. chapaensis from southwestern and south-central China and northern Indochina, while the relationships among these species remain unresolved. Nidirana guangxiensis sp. nov. can be distinguished from all known congeners by the genetic divergences in the mitochondrial 16S and COI genes, the behavior of nest construction, the advertisement call containing 6–11 rapidly repeated regular notes, and a combination of morphological characteristics. Furthermore, the Nidirana populations recorded in Guangxi are clarified in this work, providing valuable new information on the knowledge of the genus Nidirana.

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Detecting Orobanche minor Seeds in Soil Using PCR

Plant Health Progress ◽

10.1094/php-2003-0702-01-rs ◽

2003 ◽

Vol 4 (1) ◽

pp. 4 ◽

Cited By ~ 1

Author(s):

Megan E. Patzoldt ◽

Weidong Chen ◽

Brian W. Diers

Keyword(s):

Southern China ◽

Germplasm Collection ◽

Central China ◽

Stem Rot ◽

Brown Stem Rot ◽

Phialophora Gregata ◽

South Central ◽

Resistant Sources ◽

Preliminary Study ◽

Orobanche Minor

A new set of soybean accessions from south-central China were added to the USDA germplasm collection in 1996. Previous studies have shown that accessions with high levels of resistance to brown stem rot (BSR) can be found in germplasm collected from central and southern China. The objective of this study was to screen these accessions and identify those with resistance to BSR. In a preliminary study, 85 of 623 accessions tested were identified as resistant to BSR. In the second study, these 85 accessions were challenged with multiple biotypes of Phialophora gregata f. sp. sojae to identify those accessions with the strongest resistance. From these two studies, ten accessions were identified that had BSR resistance equal to or greater than the current resistant sources. Accepted for publication 10 June 2003. Published 1 July 2003.

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Empirical Bayesian kriging implementation and usage

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.137290 ◽

2020 ◽

Vol 722 ◽

pp. 137290 ◽

Cited By ~ 6

Author(s):

Alexander Gribov ◽

Konstantin Krivoruchko

Keyword(s):

Empirical Bayesian ◽

Bayesian Kriging ◽

Empirical Bayesian Kriging

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A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong

Sustainability ◽

10.3390/su12187512 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7512

Author(s):

Yuhao Jin ◽

Han Zhang ◽

Yuchao Yan ◽

Peitong Cong

Keyword(s):

Geographically Weighted Regression ◽

Vegetation Cover ◽

Southern China ◽

Ordinary Least Squares ◽

Driving Factors ◽

Weighted Regression ◽

Impact Factors ◽

Negative Effects ◽

Vegetation Growth ◽

Fractional Vegetation Cover

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

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Geospatial Situation Analysis for the Prediction of Possible Cases of Suicide Using EBK

Handbook of Research on Natural Language Processing and Smart Service Systems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-4730-4.ch015 ◽

2021 ◽

pp. 327-346

Author(s):

Carlos Manuel Ramirez López ◽

Martín Montes Rivera ◽

Alberto Ochoa ◽

Julio César Ponce Gallegos ◽

José Eder Guzmán Mendoza

Keyword(s):

Suicide Prevention ◽

Interpolation Method ◽

Error Prediction ◽

Empirical Bayesian ◽

Bayesian Kriging ◽

Empirical Bayesian Kriging ◽

Completed Suicide ◽

Geostatistical Interpolation ◽

Mexican State

This research presents the application of Empirical Bayesian Kriging, a geostatistical interpolation method. The case study is about suicide prevention. The dataset is composed of more than one million records, obtained from the report database of the Emergency Service 911 of the Mexican State of Aguascalientes. The purpose is to get prediction surfaces, probability, and standard error prediction for completed suicide cases. Here, the variations in the environment of suicide cases are relative to and dependent on economic, social, and cultural phenomena.

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