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Groupe de Analysons l'instant

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Nicholas Hill
Nicholas Hill

X Particles 2.5 Serial Number 17


PM stands for particulate matter (also called particle pollution): the term for a mixture of solid particles and liquid droplets found in the air. Some particles, such as dust, dirt, soot, or smoke, are large or dark enough to be seen with the naked eye. Others are so small they can only be detected using an electron microscope.




x particles 2.5 serial number 17


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Most particles form in the atmosphere as a result of complex reactions of chemicals such as sulfur dioxide and nitrogen oxides, which are pollutants emitted from power plants, industries and automobiles.


Particulate matter contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. Some particles less than 10 micrometers in diameter can get deep into your lungs and some may even get into your bloodstream. Of these, particles less than 2.5 micrometers in diameter, also known as fine particles or PM2.5, pose the greatest risk to health.


AirNow: Every day the Air Quality Index (AQI) tells you how clean or polluted your outdoor air is, along with associated health effects that may be of concern. The AQI translates air quality data into numbers and colors that help people understand when to take action to protect their health.


Influenza data consisted of reports of daily number of patients seeking medical attention with influenza-like illness (ILI), defined as the one with body temperature more than 38 Celsius and cough or sore throat, from January 1, 2008 to December 31, 2014 in the capital city of China, Beijing. The data was retrieved from the surveillance system at the Beijing Centre of Disease Control [26]. The influenza surveillance system has been reported elsewhere [27]. In brief, the surveillance is conducted in 150 level two and level three hospitals in Beijing, which consists of hospitals from national, city and district level. The data has a reasonable representativeness given that the sentinel hospitals cover all the 16 districts in Beijing, and the data were from the all outpatients related to respiratory disease treatment. Health care system in each sentinel hospital reports the data to the Beijing Centre of Disease Control every day from online system, and staffs in the district Centre of Disease Controls are responsible for data validation.


In epidemiological research, one most frequently used model for modeling counts data is the Poisson regression. A severe limitation of the Poisson model is that the mean and variance of the dependent variable are assumed to be equal, conditional on any covariates [29]. In practice, a very common complication when modeling discrete responses is the presence of overdispersion, when the variance of the response is greater than the mean [30]. It is generally caused by positive correlation between responses or by an excess variation between response counts. If overdispersion is present in a dataset, the standard errors of the estimates could be underestimated (i.e. a variable may appear to be significant predictor when it is in fact not significant) [29]. Negative binomial (NB) regression has been suggested as an alternative to the Poisson, which accounts for overdispersion by adding an additional dispersion (variance) parameter to the Poisson model [31]. However, the negative binomial distribution also imposes some constraints on the mean and variance relationship, whose validation also needs to be seriously assessed. Over the past decades, the family of inverse Gaussian distributions [32, 33] has attracted the attention of many researchers in studying the number of event occurrences for a wide range of field. The inverse Gaussian distribution is particularly useful for dealing with data of considerable skewness [34]. In such cases, the choice is made upon the basis of goodness of fit and upon the ease of working with the distribution. As such, we carefully examined the Poisson, the negative binomial and the inverse Gaussian regression models to identify which model fits the data well and fits the data the best. In fact, all three types of distributions belong to the exponential family in a generalized linear modeling framework [35]; therefore, all the interpretation of the regression coefficients are the same, if the same link function is applied. Here, we used the most commonly used log link function for ease of interpretation [36].


The guidelines also offer qualitative statements on good practices for the management of certain types of particulate matter (PM), for example black carbon/elemental carbon, ultrafine particles, and particles originating from sand and dust storms, for which there is insufficient quantitative evidence to derive AQG levels.


A position paper published by ASHRAE looked at the claim that particle filtration has health benefits. According to this paper, there's a well established connection between higher concentrations of particles in outdoor air and poor health outcomes, so it would make sense that filtering these particles out of indoor air could lead to better health outcomes.


Air filters can be used as final filters or pre-filters. When used as final filters, they are the primary filters for an HVAC system. Final filters may be used alone in a single-filter system, or they may be used in combination with one or more pre-filters. When used in a multi-filter system, the pre-filters trap the dirt and large particles before the air reaches the final filters downstream, which then remove the smaller particles. This multi-filter system extends the life of the more expensive final filters, leading to overall cost savings.


A prospective panel study in a susceptible population was conducted in Erfurt, Germany, to study the effects of daily changes in ambient particles on various blood cells and soluble CD40ligand (sCD40L, also known as CD154), a marker for platelet activation that can cause increased coagulation and inflammation.


Recent toxicological studies have demonstrated a pro-thrombogenic effect of diesel exhaust particles in hamsters, as well as an activation of platelets [7], thus providing one possible explanation for the observed effects. Studies on rats additionally showed evidence for thrombus formation after the deposition of ultrafine particles (UFP, number concentration of particles from 0.01 to 0.1 μm in diameter,) in the respiratory tract [8] while a study in mice indicated a procoagulant effect, but no inflammation after intra-arterial infusion of UFP [9]. Nemmar et al. [10] demonstrated in hamster models that ultrafine polystyrene particles can modulate thrombus formation and Suwa et al. [11] observed a systemic inflammatory response and a progression of the atherosclerotic process in hyperlipidemic rabbits in association with PM10 (mass concentration of particles


The aim of this analysis was to study the effects of ambient air pollution on blood cells and sCD40L in a susceptible population in view of previous results. We hypothesised that sCD40L, as well as blood cell counts, would increase in tandem with increased ambient particles. For the analysis, repeated measurements of sCD40L, platelets, leukocytes, erythrocytes and haemoglobin were related to concurrent levels of air pollution in a panel of male patients with CHD.


Continuous UFP counts, accumulation mode particle counts and fine particle mass (PM2.5, particles


sCD40L needed to be log-transformed to fulfil the model assumption of residual normality. To explore the shape of the association between confounders and blood markers, non-parametric smooth functions based on locally weighted least squares were applied. Model fit was rated based on the Akaike Information Criterion (AIC). In the final model, non-parametric smooth functions were replaced by appropriate polynomials (degree 2 or 3) or natural splines with similar number of degrees of freedom. Parametric functions were used to avoid an underestimation of the standard errors which can occur if concurvity is present in the data, as is often the case in air pollution studies [21].


Time series of number concentrations of particles sized 0.01 to 0.1 μm (ultra fine particles, UFP) and mass concentrations of particles less than 2.5 μm in diameter (PM 2.5 ) together with air temperature in Erfurt, Germany between October 2000 and April 2001. UFP: ultrafine particles (number concentration of particles with a size range of 0.01 to 0.1 μm); PM2.5: mass concentration of particles less than 2.5 μm in diameter.


The results of the regression for sCD40L, platelets and leukocytes are given in Table 4. Linear regression suggested an increase in sCD40L in association with 24-h average ambient particles before the examination. The effects were significant for UFP and AP. The effect of the concentrations of UFP was supported by the result for NO, a gaseous marker for UFP, which also showed an increase with lag 0 that was borderline significant (%change from geometric mean: 3.2; CI: [-0.2; 6.8], p


Excluding eight patients whose data indicated possible autocorrelation did not change the results for erythrocytes significantly. Except for slightly larger confidence intervals, most probably due to the reduced number of data points, no major changes were found when comparing the whole dataset to the reduced one. The estimate for PM10 with lag2, for example showed a % change from the mean of -0.8, 95% CI: [-1.3; -0.2]. 350c69d7ab


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