Regional‐level prediction model with difference equation model and fine particulate matter (PM 2.5 ) concentration data

2021 ◽  
Author(s):  
Xiaoling Han ◽  
Ceyu Lei
Keyword(s):  
Particulate Matter ◽  
Difference Equation ◽  
Prediction Model ◽  
Fine Particulate Matter ◽  
Equation Model ◽  
Regional Level ◽  
Concentration Data ◽  
Pm 2.5 ◽  
Fine Particulate

Oxidative potential of on-road fine particulate matter (PM 2.5 ) measured on major freeways of Los Angeles, CA, and a 10-year comparison with earlier roadside studies

2017 ◽  
Vol 148 ◽  
pp. 102-114 ◽  
Author(s):  
Farimah Shirmohammadi ◽  
Dongbin Wang ◽  
Sina Hasheminassab ◽  
Vishal Verma ◽  
James J. Schauer ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Los Angeles ◽  
Fine Particulate Matter ◽  
Oxidative Potential ◽  
Pm 2.5 ◽  
Fine Particulate

scholarly journals Source apportionment of carbonaceous fine particulate matter (PM 2.5 ) in two contrasting cities across the Indo–Gangetic Plain

2015 ◽  
Vol 6 (3) ◽  
pp. 398-405 ◽  
Author(s):  
Ana M. Villalobos ◽  
Mansur O. Amonov ◽  
Martin M. Shafer ◽  
J. Jai Devi ◽  
Tarun Gupta ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Fine Particulate Matter ◽  
Gangetic Plain ◽  
Pm 2.5 ◽  
Fine Particulate ◽  
Indo Gangetic Plain

Elements in Fine Particulate Matter (PM $$_{2.5}$$ 2.5 ) from Indoor Air During Household Stoves Coal Combustion at Xuanwei, China

2017 ◽  
Vol 1 (1) ◽  
pp. 41-50 ◽  
Author(s):  
K. H. Lui ◽  
C. S. Chan ◽  
Linwei Tian ◽  
Bo-Fu Ning ◽  
Yiping Zhou ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Indoor Air ◽  
Coal Combustion ◽  
Fine Particulate Matter ◽  
Pm 2.5 ◽  
Fine Particulate

scholarly journals Correlation between exposure to fine particulate matter and hypertensive disorders of pregnancy in Shanghai, China

2020 ◽  
Author(s):  
Xiujuan Su ◽  
Yan Zhao ◽  
Yingying Yang ◽  
Jing Hua
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
First Trimester ◽  
Antenatal Clinic ◽  
Hypertensive Disorders Of Pregnancy ◽  
Singleton Pregnancy ◽  
Pm 2.5 ◽  
Hypertensive Disorders ◽  
Fine Particulate ◽  
Nulliparous Women

Abstract Background Association between fine particulate matter (PM 2.5 ) and hypertensive disorders of pregnancy (HDP) is inconsistent and appears to change in each trimester. We aim to investigate the association of exposure to ambient PM 2.5 in early pregnancy with HDP. A retrospective cohort study was performed among 8,776 women with singleton pregnancy who attended the antenatal clinic before 20 gestational weeks in a tertiary women’s hospital during 2014 - 2015. Land use regression models were used to predict individual levels of PM 2.5 exposure. Results The average PM 2.5 concentration during the first 20 gestational weeks ranged from 28.6 to 74.8 μg m -3 [median, 51.4 μg m -3 ; interquartile range, 47.3 - 57.8 μg m -3 ]. A total of 440 (5.0%) women was diagnosed with HDP. The restricted cubic spline showed an exposure-response relationship between the PM 2.5 concentration and risk of HDP. We observed an association between PM 2.5 exposure during the first trimester with HDP (RR = 3.89 per 10 μg m -3 , 95% CI: 1.45 - 10.43), but not during the second trimester (RR = 0.71 per 10 μg m -3 , 95% CI: 0.40 - 1.27). Compared with their counterparts, nulliparous women who were exposed to high levels of PM 2.5 in the index pregnancy had a higher risk of developing HDP [the relative excess risk due to interaction was 0.92 (0.46 - 1.38)]. Conclusion Our findings suggest that PM 2.5 exposure during the first trimester is associated with the development of HDP, and the association is modified by parity.

scholarly journals RELATIONSHIP BETWEEN FINE PARTICULATE MATTER, WEATHER CONDITIONS AND HOSPITALIZATION FOR STROKE AMONG ELDERLY PEOPLE AT SOME HOSPITALS IN DA NANG 2019

2020 ◽  
Vol 62 (1) ◽  
Author(s):  
Thị Khánh Linh Nguyễn ◽  
Văn Toàn Ngô ◽  
Tấn Dũng Nguyễn
Keyword(s):  
Particulate Matter ◽  
Hospital Admission ◽  
Elderly People ◽  
Diurnal Temperature Range ◽  
Fine Particulate Matter ◽  
Weather Conditions ◽  
Pm 2.5 ◽  
Diurnal Temperature ◽  
Admission Rates ◽  
Fine Particulate

Aims: To investigate the effect of fine particulate matter and weather condition on hospitalization of stroke among elderly people in Da Nang. Method: Cross- sectional study was used in this research, retrospective data were collected and analyzed by Stata 14.0. The data included the hospitalization date, diagnostic code I61, I63 (ICD-10) of each hospitalization, and the age and sex of the patient; weather conditions (temperature, humidity, air pressure, wind) and PM 2.5. Result: The number of patients admitted for stroke was 257. Hospital admission rates for ischemic stroke increased with the increases in PM 2.5 and diurnal temperature range or the decrease in humidity. Hospital admission rates for hemorrhagic stroke increased when the diurnal temperature range or the maximum temperature decreased. Conclusion: PM 2.5 levels and weather conditions impacted hospitalization for stroke.

scholarly journals Correlation and scaling behaviors of fine particulate matter (PM 2.5 ) concentration in China

2018 ◽  
Vol 122 (5) ◽  
pp. 58003 ◽  
Author(s):  
Yongwen Zhang ◽  
Dean Chen ◽  
Jingfang Fan ◽  
Shlomo Havlin ◽  
Xiaosong Chen
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Pm 2.5 ◽  
Fine Particulate

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.

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