Statistical Behavior of O3, OX, NO, NO2, and NOx in Urban Environment

Souza, Amaury de; Ozonur, Deniz

doi:10.1080/01919512.2019.1602468

Cited by 11 publications

(14 citation statements)

References 23 publications

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“…3 ). Since weather changes were taken into account in the statistical model, this phenomenon can be explained as a decline in anthropogenic-origin NO x levels in urban areas in VOC-limited environments, which leads to reduced local titration of NO x and thus to a more moderate increase of O 3 levels ( Souza and Ozonur, 2019 ; Tobías et al., 2020 ). It is important to mention that NO x concentrations can fall faster than those of other pollutants because they can also participate in other atmospheric reactions, among other causes ( Dadashi et al., 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study

Briz‐Redón¹,

Belenguer-Sapiña

Serrano‐Aroca

2021

Journal of Environmental Sciences

157

126

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Highlights Variations in air quality in terms of CO, SO 2 , PM 10 , O 3 and NO 2 levels were studied. The lockdown of Spain was not able to reduce severe air pollution in all its forms. Significant reductions of NO 2 levels were achieved in most cities. Increases of O3 pollution levels were found in several cities.

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Section: Resultsmentioning

confidence: 99%

Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study

Briz‐Redón¹,

Belenguer-Sapiña

Serrano‐Aroca

2021

Journal of Environmental Sciences

157

126

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“…When comparing this pattern with the burned indexes, it is observed an increase in respiratory attendance due to the fires, between August, September and October that compose the dry period of the year and intensification of the fires and higher concentration of fires [55][56][57].…”

Section: Hospital Admission For Springmentioning

confidence: 94%

Statistical Behavior of Hospital Admissions for Respiratory Diseases by Probability Distribution Functions

de¹,

Aristone²,

A³

et al. 2019

J Infect Dis Epidemiol

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Climate change has a high impact on health and morbidity/ mortality in respiratory system diseases and remains poorly investigated in probability distribution modeling. The objective of this study was to analyze the adjustments of Burr (Bu), Inv Gausian 3P (IG3P), Lognormal (LN), Pert (Pe), Rayleigh 2P (Ra 2P) and Weibull 3P (W3P) distributions of the historical series of hospitalizations for respiratory diseases (total hospital admissions) for the period from 2004 to 2018, in Campo Grande, MS. For the data series, the shape and scale parameters of the distributions were determined to verify the quality of fit of the observation data, the Goodness-of-Fit Tests (GOF): Kolmogorov-Smirnov Test, Anderson -Darling Test, Chi-square Test tests were used to verify an optimal estimate for the hospitalization data hospital.All PDFs are able to describe well the characteristics of hospitalizations. The results presented (total admissions), (summer) show that the functions Weibull 3P (W3P) and Inv Gausian 3P (IG3P); (fall) show that the functions Burr and Weibull 3P (W3P); (winter) shows that the Burr and Inv Gausian 3P (IG3P) and (spring) functions for lognormal and Rayleigh (2P) functions provided the best observed fit for hospital admissions.

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“…in construction of buildings, estimation of wind loads on buildings [4,5]. Wind speed is the most important factor in the dispersion of air pollutants in urban environment [6]. Wind speed is a complicated random phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

et al. 2022

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In the present paper, the Weibull distribution is used to analyse the wind speed data of Liptovský Mikuláš-Ondrašová (49°05′52″ N, 19°35′32″ E), situated in northern Slovakia. Analysed wind speed data were collected over the 11-year period (2005-2015) and they were recorded three times a day. The results show that the seasonal values of the shape parameter k range from 1.474 to 1.607, with yearly value of 1.546 while the seasonal values of the scale parameter c range from 2.488 to 3.010 m/s, with yearly value of 2.726 m/s. We find out that according to the coefficient of determination and root mean square error, the Weibull distribution performs well in fitting the wind speed data.

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Statistical Behavior of O₃, O_X, NO, NO₂, and NO_x in Urban Environment

Cited by 11 publications

References 23 publications

Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study

Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study

Statistical Behavior of Hospital Admissions for Respiratory Diseases by Probability Distribution Functions

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

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