scholarly journals PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach

2020 ◽  
Vol 28 (3) ◽  
pp. 2804-2809
Author(s):  
Roberto Bergamaschi ◽  
Maria Cristina Monti ◽  
Leonardo Trivelli ◽  
Giulia Mallucci ◽  
Leonardo Gerosa ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Air Pollution ◽  
Ecological Study ◽  
Ground Level ◽  
Limit Value ◽  
Ecological Regression ◽  
Increased Risk ◽  
Bayesian Mapping ◽  
Pm2.5 Exposure ◽  
Particulate Matter 2.5

AbstractSome environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010–2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 μg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.

Air pollution is associated to the multiple sclerosis inflammatory activity as measured by brain MRI

2017 ◽  
Vol 24 (12) ◽  
pp. 1578-1584 ◽  
Author(s):  
Roberto Bergamaschi ◽  
Andrea Cortese ◽  
Anna Pichiecchio ◽  
Francesca Gigli Berzolari ◽  
Paola Borrelli ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Air Pollution ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Inflammatory Activity ◽  
Resonance Imaging ◽  
Increased Risk ◽  
Immune Therapies ◽  
Magnetic Resonance Imaging Mri ◽  
The Relationship

Background: Some environmental factors have been already associated to increased risk of multiple sclerosis (MS), but it is plausible that additional factors might play a role. Objective: To investigate in MS patients the relationship between inflammatory activity, detected by brain magnetic resonance imaging (MRI) with gadolinium (Gd), and air pollution, namely, particulate matters with diameter less than 10 μm (PM10). Methods: We analyzed from 52 remitting MS patients 226 brain MRIs, 34% with (Gd+MRI) and 66% without (Gd-MRI) T1-Gd-enhancing lesions. Daily recording of PM10 in the 30 days before MRI examination was obtained by monitors depending on the residence of subjects. Results: PM10 levels in the 5, 10, 15, 20, and 25 days before brain MRIs were higher (plus 16%, 21%, 24%, 25%, and 21%, respectively) with reference to Gd+MRI versus Gd-MRI. There was a significant association between Gd+MRI and PM10 levels ( p = 0.013), independent of immune therapies, smoker status, and season. In patients who had two repeated MRIs with opposite outcomes (Gd-MRI and Gd+MRI), PM10 levels were strongly higher in concurrence with Gd+MRI ( p < 0.0001). Conclusion: Our findings suggest that air pollution may be a risk factor for MS favoring inflammatory exacerbations.

2284Sex and age differences in short-term exposure to ambient fine particulate matter and out-of-hospital cardiac arrest: a nationwide case-crossover study in Japan

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
B Zhao ◽  
F H Johnston ◽  
F Salimi ◽  
K Negishi
Keyword(s):  
Air Pollution ◽  
Cardiac Arrest ◽  
Age Differences ◽  
Ambient Air ◽  
Short Term ◽  
Case Crossover ◽  
Short Term Exposure ◽  
Increased Risk ◽  
Pm2.5 Exposure ◽  
Hospital Cardiac Arrest

Abstract Introduction Accumulating evidence has shown the elevated risk for cardiovascular diseases (CVD) with exposure to air pollution, such as fine particles <2.5μm in aerodynamic diameter (PM2.5). A bi-directional relationship exists between air pollution and traditional CV risk factors like obesity, diabetes, and hypertension. However, little is known about the effect of age and sex on association between ambient air pollution and out-of-hospital cardiac arrest (OHCA). Purpose This study aimed to identify sex and age differences in the associations between exposure to PM2.5 and OHCA in Japan. Methods A case-crossover design was used to determine the odds ratio (OR) of OHCA across Japan with daily PM2.5 exposure on the day of the arrest or 1–3 days before it (lag 0–3). OHCA cases were identified through the All-Japan Utstein registry of the Fire and Disaster Management Agency from January 1, 2014 to December 31, 2015. OHCAs were investigated by conditional logistic regression adjusted for daily temperature and relative humidity with stratification by sex and age. Results A total of 249,372 OHCAs were included during study period. Their mean age was 75 years and 57% were male. Each 10 μg/m3 increase in daily PM2.5 exposure over 4 days was associated with all cause OHCA risk for male (lag 0: OR 1.022, 95% confidence interval (CI) 1.013, 1.031; lag 1: OR 1.016, 95% CI 1.007, 1.025; lag 2: OR 1.016, 95% CI 1.007, 1.026; lag 3: OR 1.017, 95% CI 1.008, 1.027; lag 0–1: OR 1.025, 95% CI 1.015, 1.036). Increased risk in OHCA was also found with lag 1 to lag 3 PM2.5 exposure among women. Lag 0 to lag 3 PM2.5 exposures were significantly associated with OHCA among patients older than 65 years. Among 35 to 64 years, only lag 3 PM2.5 exposure was associated with an increased risk in OHCA. No significant association was observed between PM2.5 exposure and OHCA among patients less than 35 years. Conclusions Short-term exposure to PM2.5 is associated with an increased risk of OHCA in both sexes. Patients older than 65 years were more susceptible to PM2.5 exposure than younger age group.

Association between particulate matter air pollution and lung cancer

Thorax ◽  
2019 ◽  
Vol 75 (1) ◽  
pp. 85-87 ◽  
Author(s):  
Zhenyu Zhang ◽  
Dawei Zhu ◽  
Bin Cui ◽  
Ruoxi Ding ◽  
Xuefeng Shi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Air Pollution ◽  
Particulate Matter ◽  
Low Income ◽  
Population Attributable Risk ◽  
Health Benefit ◽  
Middle Income ◽  
Increased Risk ◽  
Significant Public Health ◽  
Particulate Matter 2.5

Long-term exposure to particulate matter 2.5 μm (PM2.5) air pollution is associated with an increased risk of lung cancer. However, the evidence is limited in low-income and middle-income countries. We estimated the association between the incidence of lung cancer and PM2.5 air pollution exposure in the Urban Employee Basic Medical Insurance (UEBMI) beneficiaries in China. A total of 16 483 new lung cancer cases diagnosed from 12 966 137 UEBMI beneficiaries from 36 cities between 2013 and 2016. The relative risk for lung cancer associated with a 10 µg/m3 increase in 3-year PM2.5 exposure was 1.12 (95% CI 1.00 to 1.26). The population attributable risk estimated for a reduction in PM2.5 concentration to 35 µg/m3 corresponded to a decrease of 14% in cases of lung cancer. Reducing PM2.5 air pollution has a significant public health benefit.

scholarly journals Subtle Changes or Dramatic Perceptions of Air Pollution in Sydney during COVID-19

Environments ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Peter Brimblecombe ◽  
Yonghang Lai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
New South ◽  
Human Mobility ◽  
Ground Level ◽  
Physical Reality ◽  
Social Perceptions ◽  
Search Terms ◽  
South Wales ◽  
Significant Difference

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.

scholarly journals Environmental and Air Pollution Factors Affecting Allergic Eye Disease in Children and Adolescents in India

2021 ◽  
Vol 18 (11) ◽  
pp. 5611
Author(s):  
Anthony Vipin Das ◽  
Sayan Basu
Keyword(s):  
Air Pollution ◽  
Children And Adolescents ◽  
Temporal Pattern ◽  
Environmental Parameters ◽  
Ground Level ◽  
Significant Negative Correlation ◽  
Eye Disease ◽  
Cross Sectional ◽  
Recent Onset ◽  
Ground Level Ozone

The aim of this study was to describe the correlation between the meteorological and air pollution parameters with the temporal pattern of presentation of recent onset allergic eye disease (AED). This cross-sectional hospital-based study included new patients (≤21 years of age) presenting between January 2016 and August 2018 from the district of Hyderabad with a clinical diagnosis of AED and an acute exacerbation of recent onset of symptoms of less than 3 months duration. Correlation analysis was performed with the local environmental rainfall, temperature, humidity, windspeed, and air pollution. Of the 25,354 new patients hailing from the district of Hyderabad, 2494 (9.84%) patients were diagnosed with AED, of which 1062 (4.19%) patients had recent onset of symptoms. The mean monthly prevalence in this cohort was 4.13%, and the month of May (6.09%) showed the highest levels. The environmental parameters of humidity (r2 = 0.83/p = < 0.0001) and rainfall (r2 = 0.41/p = 0.0232) showed significant negative correlation, while temperature (r2 = 0.43/p = 0.0206) and ground-level ozone (r2 = 0.41/p = 0.0005) showed significant positive correlation with the temporal pattern of AED in the population. An increase in rainfall and humidity was associated with a lower prevalence, and an increase of temperature and ground-level ozone was associated with a higher prevalence of AED cases during the year among children and adolescents.

scholarly journals Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Wind Shear ◽  
Dispersion Model ◽  
Near Field ◽  
Ground Level ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Mobile Sources ◽  
Input Variables

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

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