Suchergebnisse

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017-2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA Combining double low line 19.7 ± 11.3 μm2 cm-3) site and an urban background (UB, average LDSA Combining double low line 11.2 ± 7.1 μm2 cm-3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 μm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2Combining double low line0.80, MAE Combining double low line 3.7 μm2 cm-3) than at the UB site (R2Combining double low line0.77, MAE Combining double low line 2.3 μm2 cm-3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations. ; publishedVersion ; Peer reviewed

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µ m 2 cm −3 ) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µ m 2 cm −3 ) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination ( R 2 ), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µ m (PM 2.5 ), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM 2.5 , BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site ( R 2 =0.80 , MAE = 3.7 µ m 2 cm −3 ) than at the UB site ( R 2 =0.77 , MAE = 2.3 µ m 2 cm −3 ), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations. ; Peer reviewed

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO 2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95 th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m 3 room, no local ventilation, and NF ventilation of 1.6 m 3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the ...

Lung deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we develop a novel statistical model, named input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 μm 2 cm −3 ) site and an urban background (UB, average LDSA = 11.2 ± 7.1 μm 2 cm −3 ) site in Helsinki, Finland. For the continuous estimation of LDSA, IAME model is automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 sub-models were generated and ranked by the coefficient of determination ( R 2 ), mean absolute error ( MAE ) and centred root-mean-square differences ( cRMSD ) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 μm (PM 2.5 ), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site the LDSA concentrations were found to be correlated with PM 2.5 , BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site ( R 2 = 0.80, MAE = 3.7 μm 2 cm −3 ) than at the UB site ( R 2 = 0.77, MAE = 2.3 μm 2 cm −3 ) plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrate that the additional adjustment by taking random effects into account improves the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration. ; publishedVersion ; Peer reviewed

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration.

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration.

Abstract
STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

As the evidence for the adverse health effects of air pollution continues to increase, World Health Organization (WHO) recently published its latest edition of the Global Air Quality Guidelines. Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, European Union has stated its intent to revise the current Ambient Air Quality Directive (2008/50/EC) to resemble closer to that of the newly published WHO guidelines. This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about 1) air quality sensors as a part of hierarchical observation network, 2) number of minimum sampling points and their siting criteria, and 3) new target air pollution parameters for future consideration.

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration. ; Peer reviewed