Health Impact of PM10, PM2.5 and Black Carbon Exposure Due to Different Source Sectors in Stockholm, Gothenburg and Umea, Sweden

Segersson, David; Eneroth, Kristina; Gidhagen, Lars; Johansson, Christer; Omstedt, Gunnar; Nylén, Anders Engström; Forsberg, Bertil

doi:10.3390/ijerph14070742

Cited by 134 publications

(165 citation statements)

References 56 publications

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“…Regarding the health impact of air pollution, small health benefits within the general population and small health risks for active commuters (of only a few percent) due to increased cycling were found in most studies based on exposure-response functions from studies with comparisons of exposure between cities. Such exposure-response functions seem to underestimate the impact of vehicle exhaust exposure [108]. Three studies showed larger benefits for the general population, including the same benefits as associated with physical activity (50%) [50], larger than twice the benefits as associated with physical activity [40], and twice as large as the benefits associated with the introduction of a congestion tax in central Stockholm [37].…”

Section: Resultsmentioning

confidence: 99%

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

Raza

Forsberg

Johansson

et al. 2018

Global Health Action

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Background: Promotion of active commuting provides substantial health and environmental benefits by influencing air pollution, physical activity, accidents, and noise. However, studies evaluating intervention and policies on a mode shift from motorized transport to cycling have estimated health impacts with varying validity and precision. Objective: To review and discuss the estimation of air pollution exposure and its impacts in health impact assessment studies of a shift in transport from cars to bicycles in order to guide future assessments. Methods: A systematic database search of PubMed was done primarily for articles published from January 2000 to May 2016 according to PRISMA guidelines. Results: We identified 18 studies of health impact assessment of change in transport mode. Most studies investigated future hypothetical scenarios of increased cycling. The impact on the general population was estimated using a comparative risk assessment approach in the majority of these studies, whereas some used previously published cost estimates. Air pollution exposure during cycling was estimated based on the ventilation rate, the pollutant concentration, and the trip duration. Most studies employed exposure-response functions from studies comparing background levels of fine particles between cities to estimate the health impacts of local traffic emissions. The effect of air pollution associated with increased cycling contributed small health benefits for the general population, and also only slightly increased risks associated with fine particle exposure among those who shifted to cycling. However, studies calculating health impacts based on exposure-response functions for ozone, black carbon or nitrogen oxides found larger effects attributed to changes in air pollution exposure. Conclusion: A large discrepancy between studies was observed due to different health impact assessment approaches, different assumptions for calculation of inhaled dose and different selection of dose-response functions. This kind of assessments would improve from more holistic approaches using more specific exposure-response functions.

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Section: Resultsmentioning

confidence: 99%

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

Raza

Forsberg

Johansson

et al. 2018

Global Health Action

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“…CC BY 4.0 License. resolution (STEAM3, Johansson et al, 2017), more details are given in the next section. The STEAM model version used for the CMAQ simulations was, however, not including VOC emissions.…”

Section: Model Set-upmentioning

confidence: 99%

“…The decreased levels of NOx and NO2 during the period 1990-2015 in Gothenburg were estimated to increase the life expectancy by up to 12 months and 6 months respectively, and the slight increased trend of O3 and PM10 have relatively little impact on life expectancy (-2 month and -1 month) (Olstrup et al, 2018). In terms of exposure to PM10 and PM2.5 from different source categories in Gothenburg, Segersson et al (2017) calculated that the largest part was due to the 200 long-range transport and the dominating local sources were road traffic and residential wood combustion, while the contribution from local shipping was small, 0.04 µg/m 3 population weighted annual mean PM2.5. The exposure of PM2.5 from shipping in other harbour cities in Sweden are lower than in Gothenburg, with 0.02 µg/m 3 in Stockholm and 0.01 µg/ m 3 in Umea (Segersson et al, 2017).…”

mentioning

confidence: 98%

“…In terms of exposure to PM10 and PM2.5 from different source categories in Gothenburg, Segersson et al (2017) calculated that the largest part was due to the 200 long-range transport and the dominating local sources were road traffic and residential wood combustion, while the contribution from local shipping was small, 0.04 µg/m 3 population weighted annual mean PM2.5. The exposure of PM2.5 from shipping in other harbour cities in Sweden are lower than in Gothenburg, with 0.02 µg/m 3 in Stockholm and 0.01 µg/ m 3 in Umea (Segersson et al, 2017).…”

mentioning

confidence: 99%

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The impact of ship emissions on air quality and human health in the Gothenburg area – Part I: 2012 emissions

Tang¹,

Ramacher²,

Moldanová³

et al. 2020

Preprint

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Ship emissions in and around ports are of interest for urban air quality management in many harbour cities. We investigated the impact of regional and local ship emissions on urban air quality for 2012-year conditions in the city of 25 Gothenburg, Sweden, the largest cargo port in Scandinavia. In order to assess the effects of ship emissions, a coupled regional and local-scale model system has been set up, using ship emissions in the Baltic Sea and the North Sea, as well as in and around the port of Gothenburg. Ship emissions are calculated with the Ship Traffic Emission Assessment Model (STEAM) model taking into account individual vessel characteristics and vessel activity data. The calculated contributions from local and regional shipping to local air pollution in Gothenburg were found substantial, especially in areas around the city ports. The 30 local shipping contribution of NO2 to annual mean concentrations was up to 3.3 ppb, together with contribution from regional shipping at the North Sea and the Baltic Sea, the contribution was up to 4.3 ppb. In an area close to the city terminals, the contribution of NO2 from local shipping was higher than that of the road traffic, which indicates importance of controlling the local shipping emissions. The local shipping emissions of NOx decreased the summer mean O3 levels in the city by 0.5 ppb on annual mean. The regional shipping lead to a slight increase in the O3 concentrations, however, the overall effect of the regional 35 and the local shipping together was a small decrease of the summer mean O3 concentrations in the city. For PM2.5, the local ship emissions contributed with 0.1 µg m -3 to the annual mean concentrations on the city-domain average, regional shipping was under 2012 conditions a larger contributor to the local PM2.5 than the local shipping, with an annual mean contribution of 0.5 µg m -3 on the city-domain average. 40Based on the modelled local and regional shipping contributions, the health effects of PM2.5, NO2 and ozone were assessed using the ALPHA-RiskPoll (ARP) model. An effect of the shipping-associated PM2.5 exposure in the modelled area was a mean loss of the life expectancy by 0.015 years per person. The relative contribution of the local shipping to the impact of total PM2.5 was 2.2 % which can be compared to 5.3 % contribution from the local road traffic. The relative contribution of the regional shipping was 10.3 %. The mortalities due to the exposure to NO2 associated to shipping were calculated to be 2.6 45 premature deaths/year. The relative contribution of the local and the regional shipping to the total exposure to NO2 in the reference simulation was 14 % and 21 %, respectively. The shipping related ozone exposures were due to the NO titration effect, leading to negative number of premature deaths. Our study show that overall health impacts of regional shipping can be more important than those of local shipping, emphasising that abatement policy options on city-scale air pollution require close cooperation across governance level...

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“…The adverse health impacts from air pollution and especially from particulate matter (PM) is a well-documented problem (e.g. Keuken et al, 2011;REVIHAAP, 2013;Mukherjee and Agrawal, 2017;Segersson et al, 2017). Furthermore, it affects crops yields (e.g.…”

Section: Introductionmentioning

confidence: 99%

Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 – Part.1 The country contributions

Pommier

Fagerli

Schulz

et al. 2019

Preprint

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A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants 15 emitted in different European nations. In this study, we evaluate a source apportionment forecasting system aimed to assess the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0) which allows to consider differences in the source attribution.We also compared the PM10 concentrations and both models present satisfactory agreement in the 4day-forecasts of the surface 20 concentrations, since the hourly concentrations can be highly correlated with in-situ observations. The correlation coefficients reach values up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; and 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models under-predict the highest hourly concentrations measured by the urban stations (mean underestimation by 36%), predictable with the relatively coarse model resolution used (0.25° longitude × 0.125° latitude). 25For the source receptor calculations, the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions and then it is compared to a reference run where no changes are applied. Different percentages (5%, 15% and 50%) in the reduced emissions were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. 1 model grid cell, 9 grid cells and the grid cells covering the definition given by the Global Administrative Area -GADM). We found that by combining the use of the 15% factor and of a larger domain for 30 the city edges (9 grid cells or GADM), it helps to reduce the impact of non-linearity on the chemistry which is seen in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this non-linearity is observed in the 3 − , 4 + and H2O concentrations, which is related to gas-aerosol partitioning of the 2 species. The use of a 15% factor and of a larger city domain also gives a better agreement in the determination of the main country contributors between both country source receptor calculations. 35During the studied episode, dominated by the influence of the domestic emissions for the 34 European cities investigated and occurring from December 01 st to 09 th 2016, the two models agree 68% of the time (on hourly resolution) on the country, having been the dominant contributor to PM10 concentrations. 75% of the hourly predicted PM10 concentrations by both models, have the same top 5 main country contributors. Better results are found in the determination the dominant country contributor for the primary component (70% for POM and 80% for EC) than for the secondary inorganic aerosols (50%). 40 45 50 55 6...

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Health Impact of PM10, PM2.5 and Black Carbon Exposure Due to Different Source Sectors in Stockholm, Gothenburg and Umea, Sweden

Cited by 134 publications

References 56 publications

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

The impact of ship emissions on air quality and human health in the Gothenburg area – Part I: 2012 emissions

Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 – Part.1 The country contributions

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