4,572 research outputs found
Quantifying the health burden misclassification from the use of different PM2.5 exposure tier models: A case study of London
Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here we quantify the misclassification that occurs when using different exposure approaches to predict the mortality burden of a population using London as a case study. We develop a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors)in the Greater London Area (GLA), in 2017. We demonstrate that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, is used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimate the change in mortality on a fine resolution. Our results showed that using the outdoor concentration as a surrogate for the total population exposure but ignoring the different exposure concentration that occurs indoors and the time spent in transit, would lead to a misclassification of 1,174 predicted mortalities in GLA. Indoor exposure to PM2.5 is the largest contributor to total population exposure, accounting for 80% of total mortality, followed by the London Underground which contributes 15%, albeit the average percentage of time spent there by Londoners is only 0.4%. We generally confirmed that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification in health burden assessments
Spatial and temporal hot spots of Aedes albopictus abundance inside and outside a South European metropolitan area
Aedes albopictus is a tropical invasive species which in the last decades spread worldwide,
also colonizing temperate regions of Europe and US, where it has become a public health
concern due to its ability to transmit exotic arboviruses, as well as severe nuisance problems
due to its aggressive daytime outdoor biting behaviour. While several studies have
been carried out in order to predict the potential limits of the species expansions based on
eco-climatic parameters, few studies have so far focused on the specific effects of these
variables in shaping its micro-geographic abundance and dynamics. The present study
investigated eco-climatic factors affecting Ae. albopictus abundance and dynamics in metropolitan
and sub-urban/rural sites in Rome (Italy), which was colonized in 1997 and is nowadays
one of the most infested metropolitan areas in Southern Europe. To this aim,
longitudinal adult monitoring was carried out along a 70 km-transect across and beyond the
most urbanized and densely populated metropolitan area. Two fine scale spatiotemporal
datasets (one with reference to a 20m circular buffer around sticky traps used to collect
mosquitoes and the second to a 300m circular buffer within each sampling site) were
exploited to analyze the effect of climatic and socio-environmental variables on Ae. albopictus
abundance and dynamics along the transect. Results showed an association between
highly anthropized habitats and high adult abundance both in metropolitan and sub-urban/
rural areas, with “small green islands” corresponding to hot spots of abundance in the metropolitan
areas only, and a bimodal seasonal dynamics with a second peak of abundance in
autumn, due to heavy rains occurring in the preceding weeks in association with permissive
temperatures. The results provide useful indications to prioritize public mosquito control
measures in temperate urban areas where nuisance, human-mosquito contact and risk of
local arbovirus transmission are likely higher, and highlight potential public health risks also
after the summer months typically associated with high mosquito densities
A framework for exploration and cleaning of environmental data : Tehran air quality data experience
Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013. ; The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase.; The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR. The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data.; We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries
Permutation entropy and irreversibility in gait kinematic time series from patients with mild cognitive decline and early alzheimer’s dementia
Gait is a basic cognitive purposeful action that has been shown to be altered in late stages
of neurodegenerative dementias. Nevertheless, alterations are less clear in mild forms of dementia,
and the potential use of gait analysis as a biomarker of initial cognitive decline has hitherto mostly
been neglected. Herein, we report the results of a study of gait kinematic time series for two groups of
patients (mild cognitive impairment and mild Alzheimer’s disease) and a group of matched control
subjects. Two metrics based on permutation patterns are considered, respectively measuring the
complexity and irreversibility of the time series. Results indicate that kinematic disorganisation is
present in early phases of cognitive impairment; in addition, they depict a rich scenario, in which
some joint movements display an increased complexity and irreversibility, while others a marked
decrease. Beyond their potential use as biomarkers, complexity and irreversibility metrics can open a
new door to the understanding of the role of the nervous system in gait, as well as its adaptation and
compensatory mechanismsThis research was funded through the Premio del Ilustre Colegio Profesional de Fisioterapeutas de la
Comunidad De Madrid, prize number ICPFM-IX-201
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