Suchergebnisse

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[ a ]pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ng m −3 ) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area.

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[a]pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ng m−3) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area.

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[a] pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ngm(-3) ) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area. ; 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 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

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.

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