Regionalized life cycle impact assessment of air pollution on the global scale: Damage to human health and vegetation
Graphical abstract
Introduction
Air pollution causing primary and secondary aerosols and ozone in the atmosphere can have a substantial negative impact on human health, ranging from respiratory symptoms to hospital admissions and death (Bell et al., 2005, WHO, 2006, Friedrich et al., 2011, Jerrett et al., 2009, Burnett et al., 2014, Lelieveld et al., 2015). Additionally, ozone can have a negative impact on vegetation, including reduction of growth and seed production, acceleration of leaf senescence and a reduced ability to withstand stressors (see e.g., Ashmore, 2005, Gerosa et al., 2015).
To quantify the damage per unit of emission of a certain air pollutant, so called characterization factors (CFs) can be used. These factors can then be applied in the impact assessment phase of an environmental life cycle assessment (LCA) to quantify the damage caused by emissions due to activities connected to a product or service. This study will focus on human health damage due to fine particulate matter (PM2.5) and photochemical ozone formation (in years of life lost per kg of substance emitted), as well as ecosystem damage due to photochemical ozone formation (in area- and time-weighted potentially affected fraction of species per kg of substance emitted). For the calculation of CFs, information on the intake of (human) or exposure to (vegetation) a pollutant is needed, as well as the damage related to that intake or exposure.
The characteristics of the emission location (source region) determines where and in which concentration a pollutant ends up (receptor regions), and thereby influences the exposure of receptors in the corresponding regions. The characteristics of the receptors in each region, such as age distribution, influence the total human health damage followed by intake or exposure. The differences in vegetation types at the receptor region cause variation in the damage to vegetation from ozone exposure.
The intake of a pollutant by the population is described by intake fractions (iF, in kg intake per kg emission) that quantify the relationship between an emission and intake (Van Zelm et al., 2008). Humbert et al. (2011) provide iFs for three different emission location archetypes (urban, rural, remote), making a distinction between three different stack heights (high-stack, low-stack and ground-level). Furthermore, Apte et al. (2012) developed globally applicable iFs that were relevant for the urban environment and pollutants emitted at ground level. However, using actual local characteristics for weather conditions and population instead of archetypes will provide more precise results. Van Zelm et al. (2008) used a source receptor model for this purpose. However, the developed intake fractions were for Europe only, with no distinction between countries. Human effect factors are generally developed for one continent only, e.g. Europe (Hofstetter, 1998, Krewitt et al., 2001, Van Zelm et al., 2008) or North America (Gronlund et al., 2015), while ecosystem effect factors for plant species were developed for Europe (Van Goethem et al., 2013b). Finally (Tang et al., 2016a, Tang et al., 2016b), derived human health characterization factors for PM2.5 and photochemical ozone, respectively, based on a global chemistry transport model. The world was divided into 10 regions only and the effect factor was based on one world-generic concentration-response function, while ammonia (NH3) was not included as a precursor substance.
To provide more spatial detail on the global scale for both damage to human health and vegetation of air pollution, the aim of this paper was to develop a set of globally applicable and spatially explicit characterization factors for human health damage from particulate matter and ozone, and for damage to vegetation from ozone. For this, we consistently applied one global chemical transport model and determined human intake fractions and ecosystem fate factors for 56 emission and receptor regions. Region-specific mortality rates, background concentrations and years of life lost were used to determine human health effect factors. We included cardiopulmonary and lung cancer mortality due to PM2.5, and respiratory mortality due to ozone.
Section snippets
Characterization factor
CFs for human health damage caused by substance x emitted in world region i (CFk,x,i in yr kton−1) are defined as the yearly change in years of life lost (YLL) of all inhabitants (dYLL in yr yr−1) caused by pollutant k due to a change in emission of substance x in source region i (dMx,i in kton yr−1). This CF for human health damage is composed of a dimensionless intake fraction (iFk,x, i→j), providing the population intake of pollutant k in receptor region j (in kg/year) following an emission
Human health
Fig. 1 shows the region-specific characterization factors for human health for fine dust precursor emissions. It can be seen that lowest factors were obtained for emissions of NOx on the Southern Hemisphere, while largest factors were obtained for primary PM2.5 emissions in Central Asia. Emissions in Australia and New Zealand lead to relatively low damages for all substances. For Chili, however, SO2 and NOx emissions lead to relatively low impacts, while PM2.5 and NH3 emissions lead to
Fate and intake factors
In this section model assumptions and uncertainties in the calculation of intake fractions and fate factors are discussed. Results of the global chemistry transport model TM5 were applied in this research to derive intake fractions and fate factors for PM2.5 and ozone exposure. TM5 was evaluated (Huijnen et al., 2010, Textor et al., 2006) and validated in a model intercomparison study (Van Loon et al., 2007). Textor et al. (2006) analyzed the atmospheric fate of sulfate, black carbon, and
Acknowledgements
The authors would like to thank Frank Dentener for providing the source-receptor runs of TM5-FASST, and Gea Stam, Joachim Roos, and Zoran Steinmann for their technical support, and Alexis Laurent and Francesca Verones for emission data compilation. This research was funded by the European Commission under the 7th framework program on environment; ENV.2009.3.3.2.1: LC-IMPACT – Improved Life Cycle Impact Assessment methods (LCIA) for better sustainability assessment of technologies, grant
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