Distribution patterns and characterization of outdoor fine and coarse particles

Mohammed, Mohammed O.A.; Song, Weihua; Liu, Li yan; Wan, Li; Li, Yi Fan; Wang, Feng yan; Ibrahim, Mohammed; Qi, Mei yun; Elzaki, Ahmed A.; Lv, Na

doi:10.1016/j.apr.2016.05.001

Cited by 8 publications

(2 citation statements)

References 63 publications

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“…Second, we conducted a discriminant analysis. This analysis allowed data explanation and prediction of disparities by describing group differences and discovering the predictors that discriminate or differentiate between groups [ 30 ]. Specifically, seeking to divide the records into two groups, the discriminant analysis focuses on maximizing the “between” sum of squares “SSbetween” (the variation between the two groups) relative to the “within” sum of squares “SSwithin” (the within-group variation).…”

Section: Methodsmentioning

confidence: 99%

Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States

Liu

Zhang

et al. 2021

IJGI

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Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas.

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Section: Methodsmentioning

confidence: 99%

Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States

Liu

Zhang

et al. 2021

IJGI

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“…Details of the sample pretreatment and analysis are provided in our previous work [50]. In brief, samples were collected during winter and summer at four receptor sites in Harbin, China, which represent typical residential urban areas, high-traffic roadsides, and low-traffic areas.…”

Section: Sampling and Chemical Analysismentioning

confidence: 99%

Multivariate Analysis for Characterization of Air Pollution Sources: Part 1 Prior Data Screening and Underlying Assumptions

Mohammed

2024

Pol. J. Environ. Stud.

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There is a real need for comparability and consistency of findings obtained from different multivariate methods, based on different assumptions and sensitivity to data errors. This study aims to investigate essential aspects of data screening prior to analysis, particularly the detection of outliers, communalities, multicollinearity, and Kaiser-Meyer-Olkin (KMO) and Bartlett's tests, and to examine the influence of changing test parameters such as the number of convergence, number of bootstrap runs, FPEAK value, and minimum value of coefficient of determination (R 2 ) on model results. Positive matrix factorization (PMF) and Unmix were applied to monitoring data collected from a receptor site. Findings of communalities estimate and multicollinearity indicated possible data errors in Ca, Cu, Na, and Mn, which affected the stability of source profiles. PMF detected biomass burning, coal combustion, traffic, industrial emissions, Mn-enriched sources, and secondary aerosols, while the Unmix model identified similar sources with comparable profiles, apart from profiles of vehicle exhaust and industrial emissions showing slight differences. Unmix was highly influenced by outliers, multicollinearity, and, to a lesser extent, change in sample size compared to PMF. We recommend interpreting the results of Bootstrapping, rather than basic runs for both PMF and Unmix. We also recommend data screening prior to further modeling. We suggest checking multicollinearity using more than one statistical measure, particularly VIF (Variance Inflation Factor) values together with tolerance values.

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Exposure to PM10 Aerosol Particles and Other Aerial Pollutants in the Capital City of Montenegro

Jovanović

Stevović

2018

Lecture Notes in Networks and Systems

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Distribution patterns and characterization of outdoor fine and coarse particles

Cited by 8 publications

References 63 publications

Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States

Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States

Multivariate Analysis for Characterization of Air Pollution Sources: Part 1 Prior Data Screening and Underlying Assumptions

Exposure to PM10 Aerosol Particles and Other Aerial Pollutants in the Capital City of Montenegro

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