scholarly journals Air pollution as a contributing factor of relapses and cases of multiplesclerosis

2020 ◽  
pp. 169-175
Author(s):  
C. Zhukovsky ◽  
◽  
M.-A. Bind ◽  
I. Boström ◽  
A.-M. Landtblom ◽  
...  

The role of air pollution exposure in multiple sclerosis (MS) incidence and relapse worldwide has not yielded a consensus; some studies have reported positive associations, which have failed to reject the null hypothesis. Potential reasons for these contradictory results can in part be explained by differences in study designs and their associated limitations. Of note, rat and canine studies in 2010 and 2013, respectively, have shown that expression of HO-1 enzyme and inflammatory factors increased due to PM10 and diesel engine exhaust (DEE) exposure. Of the eight non-null epidemiological studies scrutinized, the majority included a retrospective study design with air pollution monitoring data, which may be an advantage due to large number of study participants and a disadvantage with possible air pollution measurement error for personal exposure. The studies included analyses of PM10, PM2.5, SO2, NO2, NOx and/or O3 with PM10 as the common denominator between all of them. Studies from 2003, 2014–2019 from Finland, France, Iran, Italy, and Serbia all provide evidence of an association between PM10 and incidence or relapse of MS. Though one 2018 study likewise described associations between exposures to NO2, O3, and PM10 and MS relapses using a case-crossover design, the multi-pollutant model only associated O3. Of the epidemiological studies that fail to reject the null hypothesis, there was no evidence of an association between PM10 exposure and MS relapse or incidence. Though air pollution has not been conclusively proven to be a cause of MS, evidence from multiple studies have associated incidence and relapse with exposure to pollutants, particularly PM10.

2020 ◽  
pp. 169-175
Author(s):  
C. Zhukovsky ◽  
◽  
M.-A. Bind ◽  
I. Boström ◽  
A.-M. Landtblom ◽  
...  

The role of air pollution exposure in multiple sclerosis (MS) incidence and relapse worldwide has not yielded a consensus; some studies have reported positive associations, which have failed to reject the null hypothesis. Potential reasons for these contradictory results can in part be explained by differences in study designs and their associated limitations. Of note, rat and canine studies in 2010 and 2013, respectively, have shown that expression of HO-1 enzyme and inflammatory factors increased due to PM10 and diesel engine exhaust (DEE) exposure. Of the eight non-null epidemiological studies scrutinized, the majority included a retrospective study design with air pollution monitoring data, which may be an advantage due to large number of study participants and a disadvantage with possible air pollution measurement error for personal exposure. The studies included analyses of PM10, PM2.5, SO2, NO2, NOx and/or O3 with PM10 as the common denominator between all of them. Studies from 2003, 2014–2019 from Finland, France, Iran, Italy, and Serbia all provide evidence of an association between PM10 and incidence or relapse of MS. Though one 2018 study likewise described associations between exposures to NO2, O3, and PM10 and MS relapses using a case-crossover design, the multi-pollutant model only associated O3. Of the epidemiological studies that fail to reject the null hypothesis, there was no evidence of an association between PM10 exposure and MS relapse or incidence. Though air pollution has not been conclusively proven to be a cause of MS, evidence from multiple studies have associated incidence and relapse with exposure to pollutants, particularly PM10.


2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.


Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.


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