Future air pollution in the Shared Socio-economic Pathways

Abstract

Emissions of air pollutants such as sulfur and nitrogen oxides and particulates have significant health impacts as well as effects on natural and anthropogenic ecosystems. These same emissions also can change atmospheric chemistry and the planetary energy balance, thereby impacting global and regional climate. Long-term scenarios for air pollutant emissions are needed as inputs to global climate and chemistry models, and for analysis linking air pollutant impacts across sectors. In this paper we present methodology and results for air pollutant emissions in Shared Socioeconomic Pathways (SSP) scenarios. We first present a set of three air pollution narratives that describe high, central, and low pollution control ambitions over the 21st century. These narratives are then translated into quantitative guidance for use in integrated assessment models. The resulting pollutant emission trajectories under the SSP scenarios cover a wider range than the scenarios used in previous international climate model comparisons. In the SSP3 and SSP4 scenarios, where economic, institutional and technological limitations slow air quality improvements, global pollutant emissions over the 21st century can be comparable to current levels. Pollutant emissions in the SSP1 scenarios fall to low levels due to the assumption of technological advances and successful global action to control emissions

Global anthropogenic emissions of particulate matter including black carbon

“…As a matter of fact, the GAINS model and the ECLIPSE dataset and scenarios have already been used as a starting point to develop emission data and mitigation strategies for the recently published International Energy Agency (IEA) World Energy Outlook special report on air pollution (IEA, 2016). Furthermore, elements of the ECLIPSE data have been part of the contribution towards improved representation of carbonaceous aerosols in the large-scale integrated assessment models used in the development of the shared socio-economic pathways (SSPs) (O'Neill et al, 2014;Rao et al, 2017;.…”

AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6

“…Weak pollution controls assume delays to the implementation of CLE and make less progress towards MTFR than the medium scenario. For more details, see Rao et al (2017).…”

“…To detect the largest signal we choose the reference scenario to be SSP3-7.0 "Regional Rivalry" without climate policy (7.0 W m −2 at 2100, experiment ssp370), see , as this has the highest levels of short-lived climate pollutants and "Weak" levels of air quality control measures (O'Neill et al, 2016;Rao et al, 2017). The ssp370 ScenarioMIP simulation will need to have been run with the AerChemMIP setup and diagnostics, or repeated here.…”

Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> over China

“…To further improve the accuracy of emission estimation, we compared and evaluated the global ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants; Klimont et al, 2017) and MIX Asian inventories due to the following reasons: (a) Up to the time of paper preparation, ECLIPSE and MIX were the only publicly accessible gridded emission datasets that include both SO 2 and NO x covering China for the period of 2005 and 2010; (b) both inventories have been widely applied in atmospheric modeling and policy discussions (e.g., Stohl et al, 2015;Duan et al, 2016;Galmarini et al, 2017;Rao et al, 2017); (c) the technology-based framework and compiling parameters by source categories are obtained for ECLIPSE and MIX through international collaboration, which is not accessible for other inventories over China. The methods and data were extensively described by a series of papers (Zheng et al, 2014;Liu et al, 2015;Klimont et al, 2017;, supporting us for explicit comparisons and analyses; (d) ECLIPSE (GAINS model, Greenhouse gas-Air pollution Interactions and Synergies model; Amann et al, 2011) can be representative of the state-of-science global emission inventory covering China, and MIX (MEIC model, Multi-resolution Emission Inventory for China; available at www.meicmodel.org) represents the regional inventory compiled with advanced methods and local data.…”