Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021

Dai, Hongbin; Huang, Guangqiu; Wang, Jingjing; Zeng, Huibin; Zhou, Fangyu

doi:10.3390/ijerph19106292

Cited by 18 publications

(14 citation statements)

References 43 publications

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“…Furthermore, the performance of the interpolation algorithms was assessed using indicators commonly employed in atmospheric sciences (Karunasingha 2022 ). These indicators include root mean square error (RMSE), mean prediction error (MPE), and mean absolute percentage errors (MAPE), which are calculated according to the equations provided by Dai et al ( 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

de Lourdes Berrios Cintrón,

Broomandi,

Cárdenas-Escudero

et al. 2023

Bull Environ Contam Toxicol

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The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.

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Section: Methodsmentioning

confidence: 99%

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

de Lourdes Berrios Cintrón,

Broomandi,

Cárdenas-Escudero

et al. 2023

Bull Environ Contam Toxicol

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“…The seasonal average statistics according to the climatic conditions of the Chinese region were defined for spring (March to May), summer (June to August), autumn (September to November), and winter (December to February) [13,28,29]. The average concentrations of particulate matter in different seasons are shown in Figure 2.…”

Section: Seasonal Concentrations Of Particulate Mattermentioning

confidence: 99%

Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

Hong

Dong

Yang

et al. 2023

IJERPH

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With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi’an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM10 and PM2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM2.5 decreased by 20.3 μg/m3, and the annual concentrations of PM10 decreased by 47.3 μg/m3. The days with concentrations of PM10 exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM10 and PM2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi’an and even worldwide.

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“…George Matheron further provided a detailed theoretical framework, initially proposed by Krige, on linear estimators for interpolation through the theory of regionalized variables published in [2]. Geostatistics techniques have been widely employed in various applications, including mining engineering [3][4][5][6], environmental sciences [7][8][9][10], and meteorology [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation

Supajaidee,

Chutsagulprom,

Moonchai

2024

Algorithms

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Ordinary kriging (OK) is a popular interpolation method for its ability to simultaneously minimize error variance and deliver statistically optimal and unbiased predictions. In this work, the adaptive moving window kriging with K-means clustering (AMWKK) technique is developed to improve the estimation obtained from the moving window kriging based on the K-means clustering proposed by Abedini et al. This technique specifically addresses the challenge of selecting appropriate windows for target points located near the borders, which can potentially be the source of errors. The AMWKK algorithm introduces a dynamic clustering approach within the moving window kriging, where each target site sequentially serves as a cluster centroid. The OK is then applied within the cluster encompassing the target point, ensuring localized and adaptive interpolation. The proposed method is compared with ordinary kriging and other moving window kriging variant approaches to estimate Thailand’s mean annual pressure and humidity in 2018. The results indicate superior estimation capabilities of the AMWKK approach in terms of distinct quantitative performance statistics. The advantage of using the AMWKK method for spatial interpolation can be attributed to the fact that it facilitates the automatic tuning of the window size at any estimation point. The algorithm is particularly effective when observations in the same cluster as target points are sparse.

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Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021

Cited by 18 publications

References 43 publications

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation

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