High-resolution mapping of vehicle emissions in China in 2008

Abstract

Abstract. This study is the first in a series of papers that aim to develop high-resolution emission databases for different anthropogenic sources in China. Here we focus on on-road transportation. Because of the increasing impact of on-road transportation on regional air quality, developing an accurate and high-resolution vehicle emission inventory is important for both the research community and air quality management. This work proposes a new inventory methodology to improve the spatial and temporal accuracy and resolution of vehicle emissions in China. We calculate, for the first time, the monthly vehicle emissions for 2008 in 2364 counties (an administrative unit one level lower than city) by developing a set of approaches to estimate vehicle stock and monthly emission factors at county-level, and technology distribution at provincial level. We then introduce allocation weights for the vehicle kilometers traveled to assign the county-level emissions onto 0.05° × 0.05° grids based on the China Digital Road-network Map (CDRM). The new methodology overcomes the common shortcomings of previous inventory methods, including neglecting the geographical differences between key parameters and using surrogates that are weakly related to vehicle activities to allocate vehicle emissions. The new method has great advantages over previous methods in depicting the spatial distribution characteristics of vehicle activities and emissions. This work provides a better understanding of the spatial representation of vehicle emissions in China and can benefit both air quality modeling and management with improved spatial accuracy.

Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> 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. The methods, parameters and assumptions of GAINS and MEIC are always referred to by inventory developers (e.g., Lu et al, 2010;Fu et al, 2013;Kurokawa et al, 2013;Zhao et al, 2013).…”

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

“…While applied emission factors are comparable for most categories, there is large discrepancy for light duty vehicles where MIX value is four times larger than ECLIPSE. One possible explanation is that there might be an issue with assumptions about fuel efficiency that was applied when converting the native MEIC values which are kilometers driven for activity and gram per kilometer for emission factors (Zheng et al, 2014) …”

“…While ECLIPSE inventory includes province specific fleet characteristics , MIX emissions were developed at a county level by modeling the vehicle stock following the Gompertz function, technology distributions in accordance with emission standards, and emission factors using IVE (international vehicle emission) model, as documented by Zheng et al (2014). 30 Table 2 combustion sources (on-road and off-road) are the main contributor to emission discrepancies.…”

“…MIX (MEIC) uses Google Earth in verifying the locations for each power plant. As 15 described in Zheng et al (2014), for the transport sector, the China Digital Road-network Map is used for emission distribution. Other proxies are mainly population-based, which are in general consistent with ECLIPSE .…”

“…Power plants are treated as 25 point sources with emissions estimated by fuel type considering actual combustion technology and installed control measures such as FGD (Flue-gas Desulfurization Devices); this information is derived from CPED (China coal-fired Power plant Emissions Database) as described by Liu et al (2015). Following the methodology of Zheng et al (2014), emissions of the transport sector in MEIC are estimated at county level based on comprehensive parameterization of vehicle ownership, fuel consumption, temporal evolution of emission factors, and implementation of new environmental standards. VOCs are 30 speciated to more than 1000 species and lumped to GEOS-Chem configured mechanism based on source-specific composite profiles and mapping tables in Li et al (2014).…”