Refinement of a model for evaluating the population exposure in an urban area

Soares, Joana; Kousa, Anu; Kukkonen, Jaakko; Matilainen, L.; Kangas, Leena; Kauhaniemi, Mari; Riikonen, Kari; Jalkanen, Jukka-Pekka; Rasila, T.; Hänninen, Otto; Koskentalo, Tarja; Aarnio, Mia; Hendriks, Carlijn; Karppinen, Ari

doi:10.5194/gmd-7-1855-2014

Cited by 57 publications

(72 citation statements)

References 35 publications

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“…With the increase in international maritime trade, shipping emissions and their impacts have attracted increased attention globally over the past decades (Capaldo et al, 1999;Cooper, 2003;Eyring et al, 2010;Sofiev et al, 2018). Shipping emits air pollutants that contribute to adverse impacts on climate, on air quality and on the health of people living near ports .…”

Section: Introductionmentioning

confidence: 99%

The influence of spatiality on shipping emissions, air quality and potential human exposure in the Yangtze River Delta/Shanghai, China

Feng

Zhang

et al. 2019

Atmos. Chem. Phys.

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Abstract. The Yangtze River Delta (YRD) and the megacity of Shanghai are host to one of the busiest port clusters in the world; the region also suffers from high levels of air pollution. The goal of this study was to estimate the contributions of shipping to regional emissions, air quality, and population exposure and to characterize the importance of the geographic spatiality of shipping lanes and different types of ship-related sources for the baseline year of 2015, which was prior to the implementation of China's Domestic Emission Control Areas (DECAs) in 2016. The WRF-CMAQ model, which combines the Weather Research and Forecasting model (WRF) and the Community Multi-scale Air Quality (CMAQ) model, was used to simulate the influence of coastal and inland-water shipping, port emissions and ship-related cargo transport on air quality and on the population-weighted concentrations (which is a measure of human exposure). Our results showed that the impact of shipping on air quality in the YRD was primarily attributable to shipping emissions within 12 NM (nautical miles) of shore, but emissions coming from the coastal area between 24 and 96 NM still contributed substantially to ship-related PM2.5 concentrations in the YRD. The overall contribution of ships to the PM2.5 concentration in the YRD could reach 4.62 µg m−3 in summer when monsoon winds transport shipping emissions onshore. In Shanghai city, inland-water going ships were major contributors (40 %–80 %) to the shipping impact on urban air quality. Given the proximity of inland-water ships to the urban populations of Shanghai, the emissions of inland-water ships contributed more to population-weighted concentrations. These research results provide scientific evidence to inform policies for controlling future shipping emissions; in particular, in the YRD region, expanding the boundary of 12 NM from shore in China's current DECA policy to around 100 NM from shore would include most of shipping emissions affecting air pollutant exposure, and stricter fuel standards could be considered for the ships on inland rivers and other waterways close to residential regions.

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Section: Introductionmentioning

confidence: 99%

The influence of spatiality on shipping emissions, air quality and potential human exposure in the Yangtze River Delta/Shanghai, China

Feng

Zhang

et al. 2019

Atmos. Chem. Phys.

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“…For Helsinki, the shipping emissions have not been included in the PNC map shown in Fig. 8a; however, it was separately evaluated that their influences can be notable near the main harbour areas (Soares et al, 2014).…”

Section: Predicted Concentration Distributions In the Target Citiesmentioning

confidence: 99%

“…All the models included the emissions from vehicular traffic. The shipping emissions were explicitly included in the computations of Oslo, Rotterdam, and Athens, and the importance of primary shipping emissions was separately evaluated for Helsinki (Soares et al, 2014). For London, the local-scale shipping emissions were not taken into account, as its importance was found to be negligible.…”

Section: Overview Of the Pnc Computation In The Target Citiesmentioning

confidence: 99%

“…The importance of the shipping emissions was evaluated based on Soares et al (2014). They showed using the STEAM2 shipping emission modelling (Jalkanen et al, 2012;Johansson et al, 2013) that the contribution of primary shipping emissions of to the concentrations of PM 2.5 are only 3 % on the average in the Helsinki metropolitan area.…”

Section: Emission Inventory For Helsinkimentioning

confidence: 99%

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Modelling the dispersion of particle numbers in five European cities

et al. 2016

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Abstract. We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximately ≤ 26 % (measured as fractional biases), except for one traffic station in London. This study indicates that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remain in the evaluation of both the emissions and atmospheric transformation of PNCs.

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“…In the Helsinki metropolitan area, vehicular traffic is the most significant local particle source affecting urban air quality (Pirjola et al, 2012;Soares et al, 2014). Also, the effects of wood combustion on local air quality can be considerable, especially during periods of low vertical mixing due to stagnant weather conditions, as the release height of the emissions is typically low and the combustion in domestic heating appliances is incomplete (Saarikoski et al, 2008;Saarnio et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Chemical and Source Characterization of Submicron Particles at Residential and Traffic Sites in the Helsinki Metropolitan Area, Finland

Aurela

Saarikoski

Niemi

et al. 2015

Aerosol Air Qual. Res.

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Chemical characterization of non-refractory submicron particles (NR-PM 1 ) and source apportionment of organic aerosols (OA) were carried out at four different sites in the Helsinki metropolitan area, Finland, using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). Two of the sites represented suburban residential areas, whereas the other two were traffic sites, one in a curbside in downtown and the other one in a suburban highway edge. The residential and the curbside measurements were conducted during the winter, but the highway campaign was carried out in the autumn. NR-PM 1 were composed mainly of organics (40-68% in the different sites), followed by sulphate (11-34%), nitrate (12-16%), ammonium (7.8-8.5%) and chloride (0.24-1.3%). The mean concentrations of NR-PM 1 were quite similar during the winter campaigns (10.1-12.5 µg/m 3 ), but NR-PM 1 was clearly lower during the autumn campaign at the highway site (6.0 µg/m 3 ) due to the meteorology (favourable mixing conditions), small concentrations of long-range transported particles and non-intensive heating period locally and regionally. Using a multilinear engine algorithm (ME-2) and the custom software tool Source Finder (SoFI), the organic fraction was divided into two or three types of OA representing hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and in three sites, biomass burning organic aerosol (BBOA). At the downtown traffic site (Curbside), BBOA could not be found, probably because most of the local wood burning occurs in the suburban areas of the Helsinki region. OOA had the largest contribution to OA at all the sites (50-67%). The contribution of HOA was higher at the traffic sites (25-32%) than at the residential sites (15-18%). At the suburban residential and highway sites, the contribution of BBOA was high (25-30%). Especially during cold periods, very high BBOA contributions (~50%) were observed at the residential sites.

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Refinement of a model for evaluating the population exposure in an urban area

Cited by 57 publications

References 35 publications

The influence of spatiality on shipping emissions, air quality and potential human exposure in the Yangtze River Delta/Shanghai, China

The influence of spatiality on shipping emissions, air quality and potential human exposure in the Yangtze River Delta/Shanghai, China

Modelling the dispersion of particle numbers in five European cities

Chemical and Source Characterization of Submicron Particles at Residential and Traffic Sites in the Helsinki Metropolitan Area, Finland

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