The weekly cycle of ambient concentrations and traffic emissions of coarse (PM10–PM2.5) atmospheric particles

Barmpadimos, I.; Nufer, M.; Oderbolz, D.; Keller, Johannes; Aksoyoğlu, Şebnem; Hueglin, Christoph; Baltensperger, Urs; Prévôt, Andrê S. H.

doi:10.1016/j.atmosenv.2011.05.068

Cited by 44 publications

(26 citation statements)

References 27 publications

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“…Epidemiological studies show different associations between adverse health effects and particles with aerodynamic diameters of less than 2.5 μm (PM 2.5 ) and less than 10 μm (PM 10 ) (Barmpadimos et al, 2011;Mcbride et al, 2011). The sources of PM 2.5 and PM 10 are different, and include a wide range of natural phenomena and human activities.…”

Section: Introductionmentioning

confidence: 99%

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Gugamsetty

Han

Liu

et al. 2012

Aerosol Air Qual. Res.

204

117

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Ambient Particulate Matters (PM 10 , PM 2.5 and PM 0.1 ) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM 10 , PM 2.5 and PM 0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, including soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.

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Section: Introductionmentioning

confidence: 99%

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Gugamsetty

Han

Liu

et al. 2012

Aerosol Air Qual. Res.

204

117

View full text Add to dashboard Cite

show abstract

“…The current interest in PM is mainly due to its effect on human health [2][3][4] and its potential role in climate change [5]. Epidemiological studies show different associations between adverse health effects and particles with aerodynamic diameters lower than 2.5 µm (PM 2.5 ) and lower than 10 µm (PM 10 ) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

et al. 2014

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Samples of PM 10 and PM 2.5 were collected discontinuously between 2003 and 2010 at fifteen monitoring sites (urban, background, industrial) in the south-eastern part of Italy using a mobile laboratory. In total, 483 PM 10 and 154 PM 2.5 samples were collected and chemically analyzed for the determination of metal content. Data were used to investigate concentration differences among the typologies of sites, the seasonal patterns, and the influence of advection of Saharan dust (SD). PM 10 and PM 2.5 average concentrations increase from background to industrial and urban sites but the ratio PM 2.5 /PM 10 is significantly lower (0.61 ± 0.10) in background sites. The average metals concentrations in PM 10 and in PM 2.5 do not show a clear dependence on site typology apart an increase in crustal elements in background sites and an increase in the enrichment factors of Ni and of Cr in PM 10 in industrial sites. Urban sites show a statistically significant increase of PM 10 average concentration during the cold seasons (autumn and winter), likely associated with the anthropogenic urban emissions, instead, the background sites show a decrease in concentrations during the cold seasons. This could be due to more frequent cases of SD observed in spring and summer periods that mainly influence background sites. The seasonal difference on the average concentration for industrial sites is not statistically significant. The SD cases influence both PM 10 and PM 2.5 concentrations but their effect is significantly larger on PM 10 . Over the studied area, the effect is relatively limited on long-term average PM 10 (estimated increase of 3.2%) and PM 2.5 (estimated increase of 1.5%) concentrations but it OPEN ACCESSAtmosphere 2014, 5 436 is significant on daily concentrations. It is estimated an increase of 22% of the probability to overcome the air quality standard daily threshold for PM 10 .

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“…The MAPE of these values was measured as a model performance criterion. The Step 9 rolling prediction obtainedx (0) (22) compared with the validation data; we calculated the APE of the predicted values for 4 November. Table 1 shows the comparative analysis of the RDGM(1,1) and RSDGM(1,1) models for 16 different cross-sectional data intervals.…”

Section: Analysis Of Rsdgm(11) Model Prediction Resultsmentioning

confidence: 99%

“…Zou et al [21] considered the cyclical characteristics of freeway speed data by introducing a trigonometric regression function to capture the periodic component. Furthermore, the weekly cycle of traffic emissions revealed by Barmpadimos et al [22] also reflect the weekly seasonality of traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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The weekly cycle of ambient concentrations and traffic emissions of coarse (PM10–PM2.5) atmospheric particles

Cited by 44 publications

References 27 publications

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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