“…To give a map representation of PM 10 from monitoring stations, we applied a geostatistical interpolated method (Empirical Bayesian kriging) that expands the monitor PM 10 point observations to the whole Lombardy territory (Figure 3A) [62]. Figure 3
Graphical representation of PM
10
concentration levels.…”
BackgroundDespite epidemiological findings showing increased air pollution related cardiovascular diseases (CVD), the knowledge of the involved molecular mechanisms remains moderate or weak. Particulate matter (PM) produces a local strong inflammatory reaction in the pulmonary environment but there is no final evidence that PM physically enters and deposits in blood vessels. Extracellular vesicles (EVs) and their miRNA cargo might be the ideal candidate to mediate the effects of PM, since they could be potentially produced by the respiratory system, reach the systemic circulation and lead to the development of cardiovascular effects.The SPHERE (“Susceptibility to Particle Health Effects, miRNAs and Exosomes”) project was granted by ERC-2011-StG 282413, to examine possible molecular mechanisms underlying the effects of PM exposure in relation to health outcomes.Methods/designThe study population will include 2000 overweight (25 < BMI < 30 kg/cm2) or obese (BMI ≥ 30 kg/cm2) subjects presenting at the Center for Obesity and Work (Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy).Each subject donates blood, urine and hair samples. Extensive epidemiological and clinical data are collected. Exposure to PM is assigned to each subject using both daily PM10 concentration series from air quality monitors and pollutant levels estimated by the FARM (Flexible air Quality Regional Model) modelling system and elaborated by the Regional Environmental Protection Agency.The recruitment period started in September 2010 and will continue until 2015. At December 31, 2013 we recruited 1250 subjects, of whom 87% lived in the province of Milan.Primary study outcomes include cardiometabolic and respiratory health effects. The main molecular mechanism we are investigating focuses on EV-associated microRNAs.DiscussionSPHERE is the first large study aimed to explore EVs as a novel potential mechanism of how air pollution exposure acts in a highly susceptible population. The rigorous study design, the availability of banked biological samples and the potential to integrate epidemiological, clinical and molecular data will also furnish a powerful base for investigating different complementary molecular mechanisms. Our findings, if confirmed, could lead to the identification of potentially reversible alterations that might be considered as possible targets for new diagnostic and therapeutic interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-1137) contains supplementary material, which is available to authorized users.
“…To give a map representation of PM 10 from monitoring stations, we applied a geostatistical interpolated method (Empirical Bayesian kriging) that expands the monitor PM 10 point observations to the whole Lombardy territory (Figure 3A) [62]. Figure 3
Graphical representation of PM
10
concentration levels.…”
BackgroundDespite epidemiological findings showing increased air pollution related cardiovascular diseases (CVD), the knowledge of the involved molecular mechanisms remains moderate or weak. Particulate matter (PM) produces a local strong inflammatory reaction in the pulmonary environment but there is no final evidence that PM physically enters and deposits in blood vessels. Extracellular vesicles (EVs) and their miRNA cargo might be the ideal candidate to mediate the effects of PM, since they could be potentially produced by the respiratory system, reach the systemic circulation and lead to the development of cardiovascular effects.The SPHERE (“Susceptibility to Particle Health Effects, miRNAs and Exosomes”) project was granted by ERC-2011-StG 282413, to examine possible molecular mechanisms underlying the effects of PM exposure in relation to health outcomes.Methods/designThe study population will include 2000 overweight (25 < BMI < 30 kg/cm2) or obese (BMI ≥ 30 kg/cm2) subjects presenting at the Center for Obesity and Work (Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy).Each subject donates blood, urine and hair samples. Extensive epidemiological and clinical data are collected. Exposure to PM is assigned to each subject using both daily PM10 concentration series from air quality monitors and pollutant levels estimated by the FARM (Flexible air Quality Regional Model) modelling system and elaborated by the Regional Environmental Protection Agency.The recruitment period started in September 2010 and will continue until 2015. At December 31, 2013 we recruited 1250 subjects, of whom 87% lived in the province of Milan.Primary study outcomes include cardiometabolic and respiratory health effects. The main molecular mechanism we are investigating focuses on EV-associated microRNAs.DiscussionSPHERE is the first large study aimed to explore EVs as a novel potential mechanism of how air pollution exposure acts in a highly susceptible population. The rigorous study design, the availability of banked biological samples and the potential to integrate epidemiological, clinical and molecular data will also furnish a powerful base for investigating different complementary molecular mechanisms. Our findings, if confirmed, could lead to the identification of potentially reversible alterations that might be considered as possible targets for new diagnostic and therapeutic interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-1137) contains supplementary material, which is available to authorized users.
“…Accordingly, a dense air pollution monitoring network must be employed if conventional geostatistical techniques are to be used. This is rarely available in urban areas [102][103][104][105], and interpolation using an inadequate number of monitoring stations can lead to highly biased and smoothed results [89,100].…”
Section: Spatial Prediction (Spatial Distribution) Of Pm 10 In Urban mentioning
confidence: 99%
“…Consequently, the number of published applications of Kriging on spatial prediction of air pollutants is relatively low [100,105].…”
Section: Spatial Prediction (Spatial Distribution) Of Pm 10 In Urban mentioning
PM 10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM 10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM 10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM 10 prediction. A review of the spatial predictions of PM 10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM 10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM 10 , only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ď 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.
“…In this paper, we focused on environmental statistics with the aim of estimating the concentration surface and related uncertainty of an air pollutant from air quality data recorded by a network of monitoring stations. We did so within a Bayesian framework to overcome difficulties in measuring prediction uncertainty (Diggle et al, 1998;Pilz and Spöck, 2008;Vicedo-Cabrera et al, 2013;Cecconi et al, 2016), which are usual when land-use regression (Hoek et al, 2008) or ordinary Kriging (Banerjee et al, 2004;Son et al, 2010) are used. However, it is important to acknowledge that, alternatively to statistical approaches, deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere are of great value [e.g.…”
In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.
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