Modeling airborne benzo(a)pyrene concentrations in the Czech Republic

Zalel, Amir; Yuval, .; Švecová, Vlasta; Šrám, Radìm J.; Bartoňová, Alena; Broday, David M.

doi:10.1016/j.atmosenv.2014.11.031

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…These results may be different for other pollutants or sensors. Zalel et al (2015), for example, found a good temporal stability and spatial transferability for benzo(a)pyrene.…”

Section: Discussionmentioning

confidence: 99%

Calibration of low-cost NO2 sensors in an urban air quality network

Zoest¹,

Osei²,

Stein³

et al. 2019

Atmospheric Environment

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“…These results may be different for other pollutants or sensors. Zalel et al (2015), for example, found a good temporal stability and spatial transferability for benzo(a)pyrene.…”

Section: Discussionmentioning

confidence: 99%

Calibration of low-cost NO2 sensors in an urban air quality network

Zoest¹,

Osei²,

Stein³

et al. 2019

Atmospheric Environment

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“…Nevertheless, to conduct such substitution for a considerable number of samples especially in a database modest in size could make a negative impact on the subsequent model performance. Following the approach proposed by Zalel et al (2015) to estimate airborne benzo(a)pyrene concentrations in the Czech Republic, a binary classification tree prior to model development has been applied to obtain, based on the observed independent and dependent variables, a set of rules that allow to estimate if the metal concentration of an specific sample is above or below a threshold value set as the method detection limit. The classification tree allow us identifying whether the combination of input variables corresponding to a certain sample represents conditions that are expected to be associated with a metal concentration above the method detection limit.…”

Section: Classification Treementioning

confidence: 99%

A proposed methodology for the assessment of arsenic, nickel, cadmium and lead levels in ambient air

Santos

Fernández-Olmo

2016

Science of The Total Environment

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Air quality assessment, required by the European Union (EU) Air Quality Directive, Directive 2008/50/EC, is part of the functions attributed to Environmental Management authorities. Based on the cost and time consumption associated with the experimental works required for the air quality assessment in relation to the EU-regulated metal and metalloids, other methods such as modelling or objective estimation arise as competitive alternatives when, in accordance with the Air Quality Directive, the levels of pollutants permit their use at a specific location. This work investigates the possibility of using statistical models based on Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANNs) to estimate the levels of arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb) in ambient air and their application for policy purposes. A methodology comprising the main steps that should be taken into consideration to prepare the input database, develop the model and evaluate their performance is proposed and applied to a case of study in Santander (Spain). It was observed that even though these approaches present some difficulties in estimating the individual sample concentrations, having an equivalent performance they can be considered valid for the estimation of the mean values - those to be compared with the limit/target values - fulfilling the uncertainty requirements in the context of the Air Quality Directive. Additionally, the influence of the consideration of input variables related to atmospheric stability on the performance of the studied statistical models has been determined. Although the consideration of these variables as additional inputs had no effect on As and Cd models, they did yield an improvement for Pb and Ni, especially with regard to ANN models.

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“…In the past decades, modeling approaches have been utilized to simulate ambient PAH concentrations to resolve the issue of sparse data [18][19][20][21][22][23][24]. However, only a handful of studies related to PAH predictions in the atmosphere have been demonstrated and no hourly intra-urban spatial data are available.…”

Section: Introductionmentioning

confidence: 99%

A new grid-scale model simulating the spatiotemporal distribution of PM2.5-PAHs for exposure assessment

Lee

Huang

Wang

et al. 2016

Journal of Hazardous Materials

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Exposure to polycyclic aromatic hydrocarbons (PAHs) associated with ambient air particulate matter (PM) poses significant health concerns. Several modeling approaches have been developed for simulating ambient PAHs, but no hourly intra-urban spatial data are currently available. The aim of this study is to develop a new modeling strategy in simulating, on an hourly basis, grid-scale PM2.5-PAH levels. PM and PAHs were collected over a one-year time frame through an established air quality monitoring network within a metropolitan area of Taiwan. Multivariate linear regression models, in combination with correlation analysis and PAH source identification by principal component analysis (PCA), were performed to simulate hourly grid-scale PM2.5-PAH concentrations, taking criteria pollutants and meteorological variables selected as possible predictors. The simulated levels of 72-h personal exposure were found to be significantly (R=0.729**, p<0.01) correlated with those analyzed from portable personal monitors. A geographic information system (GIS) was used to visualize spatially distributed PM2.5-PAH concentrations of the modeling results. This new grid-scale modeling strategy, incorporating the output of simulated data by GIS, provides a useful and versatile tool in personal exposure analysis and in the health risk assessment of air pollution.

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Modeling airborne benzo(a)pyrene concentrations in the Czech Republic

Cited by 6 publications

References 29 publications

Calibration of low-cost NO2 sensors in an urban air quality network

Calibration of low-cost NO2 sensors in an urban air quality network

A proposed methodology for the assessment of arsenic, nickel, cadmium and lead levels in ambient air

A new grid-scale model simulating the spatiotemporal distribution of PM2.5-PAHs for exposure assessment

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