scholarly journals Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches

2015 ◽  
Vol 26 (4) ◽  
pp. 405-414 ◽  
Author(s):  
Yuddy Ramos ◽  
Benoît St-Onge ◽  
Jean-Pierre Blanchet ◽  
Audrey Smargiassi
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Inverse Distance Weighted ◽  
Fine Particulate ◽  
Kriging With External Drift ◽  
Temporal Models ◽  
Distance Weighted ◽  
Spatio Temporal ◽  
Inverse Distance ◽  
External Drift

scholarly journals Spatio-Temporal Analysis of PM10 in Southern Peninsular Malaysia

2018 ◽  
Vol 7 (3.9) ◽  
pp. 27
Author(s):  
Mohamad Saiful Mohamad Khir ◽  
Khalida Muda ◽  
Norelyza Hussein ◽  
Mohd Faisal Abdul Khanan ◽  
Mohd Nor Othman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Pollutant Dispersion ◽  
Temporal Analysis ◽  
Peninsular Malaysia ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Haze Episode ◽  
Spatio Temporal ◽  
Inverse Distance

In this study, the particulate matter with diameter less than 10 micrometers (PM10) is being observed. Other factors that influenced the pollutant dispersion are also being studied prior to identification of their relationship. The aim of this study is to identify the trend of PM10 concentrations in the Southern Peninsular of Malaysia during the period 2005 to 2015 by using spatio-temporal analysis in regards to air pollution. The inverse distance weighted (IDW) is used for the spatio interpolation data and mapping. The trends of the PM10 concentration are illustrated via map which indicates the affected and vulnerable area of Southern Peninsular Malaysia especially during Haze episode.  

scholarly journals Analysis of Spatio-Temporal Heterogeneity and Socioeconomic Driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1324
Author(s):  
Ju Wang ◽  
Ran Li ◽  
Kexin Xue ◽  
Chunsheng Fang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Shandong Province ◽  
Temporal Heterogeneity ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Spatio Temporal ◽  
Surrounding Areas ◽  
Pm2.5 Concentration

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

scholarly journals Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hongwu Zhou ◽  
Haidong Pan ◽  
Shuang Li ◽  
Xianqing Lv
Keyword(s):  
Particulate Matter ◽  
Missing Data ◽  
Empirical Orthogonal Function ◽  
Fine Particulate Matter ◽  
Spatial Mode ◽  
Concentration Data ◽  
Orthogonal Function ◽  
Fine Particulate ◽  
Inverse Distance ◽  
Pm2.5 Concentration

Fine particulate matter with diameters less than 2.5 μm (PM2.5) concentration monitoring is closely related to public health, outdoor activities, environmental protection, and other fields. However, the incomplete PM2.5 observation records provided by ground-based PM2.5 concentration monitoring stations pose a challenge to the study of PM2.5 propagation and evolution model. Consequently, PM2.5 concentration data imputation has been widely studied. Based on empirical orthogonal function (EOF), a new spatiotemporal interpolation method, EOF interpolation (EOFI) is introduced in this paper, and then, EOFI is applied to reconstruct the hourly PM2.5 concentration records of two stations in the first half of the year. The main steps of EOFI here are to firstly decompose the spatiotemporal data matrix of the original observation site into mutually orthogonal temporal and spatial modes with EOF method. Secondly, the spatial mode of the missing data station is estimated by inverse distance weighting interpolation of the spatial mode of the observation sites. After that, the records of the missing data station can be reconstructed by multiplying the estimated spatial mode and the corresponding temporal mode. The optimal mode number for EOFI is determined by minimizing the root mean square error (RMSE) between reconstructed records and corresponding valid records. Finally, six evaluation indices (mean absolute error (MAE), RMSE, correlation coefficient (Corr), deviation rate bias, Nash–Sutcliffe efficiency (NSE), and index of agreement (IA)) are calculated. The results show that EOFI performs better than the other three interpolation methods, namely, inverse distance weight interpolation, thin plate spline, and surface spline interpolation. The EOFI has the advantages of less computation, less parameter selection, and ease of implementation, it is an alternative method when the number of observation stations is rare, and the proportion of missing value at some stations is large. Moreover, it can also be applied to other spatiotemporal variables interpolation and imputation.

scholarly journals Air pollution and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA) air–lung study

2017 ◽  
Vol 50 (6) ◽  
pp. 1700559 ◽  
Author(s):  
Coralynn Sack ◽  
Sverre Vedal ◽  
Lianne Sheppard ◽  
Ganesh Raghu ◽  
R. Graham Barr ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Ethnic Study ◽  
Fine Particulate ◽  
Temporal Models

We studied whether ambient air pollution is associated with interstitial lung abnormalities (ILAs) and high attenuation areas (HAAs), which are qualitative and quantitative measurements of subclinical interstitial lung disease (ILD) on computed tomography (CT).We performed analyses of community-based dwellers enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) study. We used cohort-specific spatio-temporal models to estimate ambient pollution (fine particulate matter (PM2.5), nitrogen oxides (NOx), nitrogen dioxide (NO2) and ozone (O3)) at each home. A total of 5495 participants underwent serial assessment of HAAs by cardiac CT; 2671 participants were assessed for ILAs using full lung CT at the 10-year follow-up. We used multivariable logistic regression and linear mixed models adjusted for age, sex, ethnicity, education, tobacco use, scanner technology and study site.The odds of ILAs increased 1.77-fold per 40 ppb increment in NOx (95% CI 1.06 to 2.95, p = 0.03). There was an overall trend towards an association between higher exposure to NOx and greater progression of HAAs (0.45% annual increase in HAAs per 40 ppb increment in NOx; 95% CI −0.02 to 0.92, p = 0.06). Associations of ambient fine particulate matter (PM2.5), NOx and NO2 concentrations with progression of HAAs varied by race/ethnicity (p = 0.002, 0.007, 0.04, respectively, for interaction) and were strongest among non-Hispanic white people.We conclude that ambient air pollution exposures were associated with subclinical ILD.

Spatio-temporal variation of wind influence on distribution of fine particulate matter and its precursor gases

2019 ◽  
Vol 10 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Hamed Karimian ◽  
Qi Li ◽  
Chengcai Li ◽  
Gong Chen ◽  
Yuqin Mo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Temporal Variation ◽  
Fine Particulate Matter ◽  
Fine Particulate ◽  
Wind Influence ◽  
Spatio Temporal
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