Cluster analysis of five years of back trajectories arriving in Athens, Greece

Markou, M.T.; Kassomenos, Pavlos

doi:10.1016/j.atmosres.2010.08.006

Cited by 82 publications

(37 citation statements)

References 22 publications

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“…Centroid trajectory length was, subsequently, classified into one of a number of length classes, in accordance with the ranges proposed by Markou and Kassomenos (2010) and Dimitriou and Kassomenos (2014a). These are as follows: short-range cluster: 0 , D , 1000 (km), medium-range cluster: 1000.1 , D , 1800 (km), long-range cluster: 1800.1 , D , 3000 (km), and very long-range cluster: 3000.1 , D (km).…”

Section: Methodsmentioning

confidence: 99%

“…The time of every air parcel's arrival in all counties was set at 1200 UTC (Dimitriou and Kassomenos 2013). Once all trajectories had been calculated, the BT were grouped, according to their origin and path, in a similar fashion to that of Dorling et al (1992) and Dorling and Davies (1995) by using K-means cluster analysis based on the Euclidean distance (Borge et al 2007;Markou and Kassomenos 2010). The longitude and latitude of the trajectories over consecutive 1-h intervals were used as the clustering variables (Dimitriou and Kassomenos 2014a).…”

Section: Methodsmentioning

confidence: 99%

“…Once defined, the centroid trajectory's length was determined using the Haversine formula, which calculates the distance of the curvature D between two adjacent points [Equation (1)]. Total trajectory length was, therefore, calculated as the sum of the distances D i of each pair of hourly neighboring points, along the 96-h centroid trajectory, as expressed in Equation (2) (Markou and Kassomenos 2010;Dimitriou and Kassomenos 2014a). The application of this technique yields a better estimate of trajectory length, in comparison with the calculation of D as the distance between the first and last point of the entire centroid trajectory:…”

Section: Methodsmentioning

confidence: 99%

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Exploring Winter Mortality Variability in Five Regions of England Using Back Trajectory Analysis

et al. 2016

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACT: This paper aims to define atmospheric pathways related with the occurrence of daily winter low temperature episodes (LTE) in England, for the 26-yr period 1974-99, and to reveal possible associations with increased mortality rates. For this purpose, backward airmass trajectories, corresponding to LTE in five regions of England, were deployed. A statistically significant increase in mortality levels, at the 0.05 level, was found for LTE, compared to non-LTE days across all five regions. Seven categories of atmospheric trajectory patterns associated with LTE were identified: east, local, west, North Atlantic, Arctic, southwest, and Scandinavian. Consideration of the link between airmass trajectory patterns and mortality levels by region revealed a possible west-to-east split in the nature of air masses connected with elevated mortality. Specifically, for the West Midlands and northwest regions, relatively warm winter weather conditions from the west, most likely associated with the eastward progression of low pressure systems, are allied with the highest daily average mortality counts, whereas, for the northeast, Humberside/York, and southeast regions, cold continental air advection from northern or eastern Europe, which lasts for several days and is linked with either a blocking pattern over the western margins of Europe or an intense high pressure anomaly over eastern or northern Europe, appears important in mortality terms. This finding confirms that winter weather health associations are complex, such that climate setting and airmass climatology need to be taken into account when considering climate and health relationships.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Exploring Winter Mortality Variability in Five Regions of England Using Back Trajectory Analysis

et al. 2016

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“…This northern contribution reaches almost 85% during summer. On the other hand, Saharan dust transport events are very frequent in the area during spring and autumn contributing up to 25% of the prevailing air masses (Borge et al, 2007;Mihalopoulos et al, 2007;Kassomenos et al, 2010;Markou and Kassomenos, 2010;Gerasopoulos et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Composition and Mass Closure of PM2.5 in Urban Environment (Athens, Greece)

Remoundaki

Kassomenos

Mantas

et al. 2013

Aerosol Air Qual. Res.

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“…The coordinates (longitude/latitude) of the trajectory points are provided by HYSPLIT model. The length (L) of each atmospheric trajectory was calculated by an application of Haversine formula, as the sum of the 24 consecutive distances L i (km) between the neighboring trajectory points along the atmospheric pathway (Markou and Kassomenos, 2010). Long range trajectories describe the movement of fast moving air parcels, implying increased wind speed, while short range trajectories are responsible for stagnant conditions and recirculation of polluted air (Zawadzka et al, 2013;Beddows et al, 2015).…”

Section: Calculation Of Trajectory Lengthmentioning

confidence: 99%

Upgrading the Estimation of Daily PM10 Concentrations Utilizing Prediction Variables Reflecting Atmospheric Processes

Dimitriou

2016

Aerosol Air Qual. Res.

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This paper formulates a Multiple Linear Regression Model (MLRM), for the estimation of daily PM 10 concentrations in background urban areas. 24-hour backward air mass trajectories, NO 2 concentrations and gridded (1° × 1° resolution) Aerosol Optical Depth (AOD) observations from MODIS were used in order to compose the model's predictor variables. As a supplement to local combustion/non-combustion contributions, the suggested method intends to comprise and quantify the effect that transboundary PM sources and wind dispersion on particulate air pollution levels. The proposed technique was implemented at a background sampling site in Birmingham (United Kingdom) and the results were compared with the outcome of a Simple Linear Regression Model (SLRM) which contained only one predictor variable expressing local combustion. Various statistical indices signified the upgraded performance of the MLRM, in comparison with SLRM, thus the participation of long range transport and wind dispersion variables in the MLRM was successful. According to the MLRM's findings, anthropogenic combustion (traffic, heating) is the strongest source of PM 10 in the selected background urban area, followed by local non-combustion emissions and long range transport. Extreme PM 2.5 intrusions from continental Europe also emerged.

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Cluster analysis of five years of back trajectories arriving in Athens, Greece

Cited by 82 publications

References 22 publications

Exploring Winter Mortality Variability in Five Regions of England Using Back Trajectory Analysis

Exploring Winter Mortality Variability in Five Regions of England Using Back Trajectory Analysis

Composition and Mass Closure of PM2.5 in Urban Environment (Athens, Greece)

Upgrading the Estimation of Daily PM10 Concentrations Utilizing Prediction Variables Reflecting Atmospheric Processes

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