Vehicle emissions inventory in high spatial–temporal resolution and emission reduction strategy in Harbin-Changchun Megalopolis

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Abstract

Harbin-Changchun Megalopolis (HCM), as one of the vehicle production centers in China, has rapidly increasing vehicles on road supported by people’s increasing purchasing power, having directly and indirectly led to air pollution. This study systematically analyzes tempo-spatial characteristics of vehicle emissions by combining International Vehicle Emissions (IVE) model with the Technical guidelines for road motor vehicle emission inventory of air pollutants (Guideline) in HCM. This research further analyzes emissions from various emissions sources and projected emissions based on scenarios. The results show that the CO and HC emissions from mini passenger cars (MiniPC) and ordinary motorcycles using gasoline account for 86.4 % and 82.3 % of total emissions. Diesel-fueled heavy-duty trucks (HDT) are the main sources of NOX and PM, accounting for 86.5 % and 89.7 % of the total emissions, respectively. Three emission reduction scenarios are developed to analyze the vehicle emissions in HCM in 2020. This study concluded that phasing out the old vehicles is an effective strategy to mitigate air pollutions in HCM, with reduction rates of pollutants CO, HC, NOX, PM2.5 and PM10 being 19.8 %, 19.6 %, 8.6 %, 18.3 % and 18.8 %, respectively.

Introduction

The rapid urbanization has catalyzed the development of low-carbon city and megalopolis projects in China. Megalopolis played a leading role in the economic, technological and population growth of China for the past 15 years. As of March 13, 2018, the State Council approved nine national-level megalopolises to affirm and support their important roles in socioeconomic development. Harbin-Changchun Megalopolis, as one of the nine national megapolises, serves as a window open to Russia, Japan and South Korea, as well as a significant contributor to the One Belt One Road strategy in Northeast China (Li et al., 2016a). However, air pollution in Northeastern China is getting worse in recent years and requires effective management strategies.

The most severe haze in records occurred on October 21, 2013 in many places in Heilongjiang Province, and PM2.5 reached over 1000 mg/m3 in some areas in Harbin (Yu and Li, 2014). The severe haze events are usually caused by compound pollutants, mainly sourcing from coal-burning and vehicle emissions. Research shows that vehicle exhaust contributed to 22.2 % of the sources of haze particulate matter in Beijing, and approximately 1/3 to the air pollutions in Guangdong Province, becoming an important source of air pollution (You et al., 2015; Chen, 2016). In terms of pollutants, carbon monoxide (CO) and total hydrocarbons (HC) emitted by vehicles account for 50 % of CO and HC in Jilin city (Lang, 2007). Vehicle emissions mainly include exhaust emissions and emissions from gasoline evaporative. The main pollutant emitted by evaporation was HC, which accounted for 20 % of the total HC emissions from vehicles (Meng, 2018).

Several factors potentially cause air pollution impacts from Northeastern China worse than other areas, including vehicle volume, climate factors, vehicle performance, and the industrial environment of the area. The HCM is led by two second-tier cities, Harbin and Changchun, and followed by several third-tier cities. The purchasing power and ownership of vehicles in HCM have been increasing rapidly, compared to the first-tier cities where the vehicle market reached saturated capacity (Liu et al., 2017). The increase of owning vehicles, increase of private vehicle usage, and the trend of the increase will lead to more vehicle exhausts. By the end of 2016, the total number of private vehicles in Heilongjiang and Jilin Provinces reached 7.518 million, an increase of 4.319 million vehicles compared to the number in 2009 (Heilongjiang Provincial Bureau of Statistics, 2017; Jilin Provincial Bureau of Statistics, 2017). Climate factors also affected emissions from vehicles. HCM had unique climatic characteristics, where the lowest temperature in winter can reach below −30 ℃. The low temperature could promote the generation of aerosols and the growth of particulate matters (Abdul-Khalek et al., 1999). In more detail, the low temperature not only facilitates the formation of aerosols but also increases vehicle usage. More frequent breaks and driving on slippery roads reduce the combustion efficiency and overall performance of vehicles. According to the China Motor Vehicle emission inventory, CO, NOX, NVOCs and PM10 in the Northeastern region account for 7.1 %, 9.2 %, 6.1 % and 6.4 % of the national emissions, respectively (Xie et al., 2006). As a core part of the Northeastern region, it is of great significance to analyze the characteristics of vehicle pollution in HCM and measure the emission reduction potential in Northeastern China.

Recent studies mainly concentrated on emission inventory of vehicles in China's fast-growing regions, such as the Pearl River Delta, the Yangtze River Delta and the Beijing-Tianjin-Hebei region (Zhou et al., 2011; Cheng et al., 2014; Li et al., 2014; Liu et al., 2015; Ma et al., 2015; Li et al., 2016b; Huang et al., 2017; Zhang et al., 2017; Che et al., 2009); nevertheless, only a few focused on the city-level vehicle emission inventory in Northeast China (Ma, 2005; Zhao et al., 2005; Wang, 2015; Liang et al., 2016; Yan, 2017; Wu et al., 2018), even fewer on urban agglomerations. It is necessary to carry out targeted research on such areas. Due to the long and cold winter in HCM, this study divided the year into heating season and non-heating season to better describe the seasonal emission characteristics.

The methods of compiling vehicle emission inventory mainly include emission factor method and field measurement method. The vehicle emission model has been widely used to obtain emission factors of vehicles in urban areas worldwide, including the COPERT model, the MOBILE model, the International Vehicle Emissions (IVE) model, and the MOVES model (Hao et al., 2017). The MOBILE model can be used to evaluate the emission factors of vehicle exhaust pollutants of the past, present and future (Rakha et al., 2003; Esteves-Booth et al., 2002). It also has a good user interface and portability, so it has been widely used. COPERT model is one of the most widely used road emission models in Europe (Ntziachristos et al., 2009). The principle of the model is similar to that of MOBILE model, and the average speed is used to characterize the driving characteristics of vehicles. The emission factors of this model include thermal emission, cold start emission and evaporation emission, which are all based on the function of average vehicle speed, and can be used to calculate the emission factors of a single vehicle or the pollutant emissions of a fleet in a year. The MOBILE model and the COPERT model were emission factor models based on the average velocity with the advantages of fewer parameters and easy access, but with lower accuracy (Liu et al., 2007). The MOVES model is a comprehensive, multi-scale vehicle emission model that represents the direction of model development (Park et al., 2015). Compared with other models, it has carried out detailed analysis and determination in more microscopic vehicle emission analysis, assessment and uncertainty evaluation of improved models. However, the MOVES model mainly targets mobile emissions in the United States, which is quite different from China's national conditions, and the parameters are solidified, which makes modification difficult.

The IVE model is a vehicle emission model based on the specific power of the vehicle engine. Through the operation of the model, the total amount of ordinary pollutants, greenhouse gas and toxic pollutants caused by vehicles in a region can be obtained. The IVE model introduced two parameters, vehicle specific power (VSP) and engine stress (ES), which significantly restored the real operation of the vehicle. At the same time, the effects of speed, acceleration, and road slope were comprehensively considered, so the emissions under different operating conditions can be simulated more accurately, making the IVE model applicable to regions with different operating conditions. The IVE model had been successfully tested in Brazil, Mexico, India, Iran and many other developing countries (Nesamani, 2010; Nagpure et al., 2011). Continuous model adjustment and improvement also greatly enriched the database. Coupled with the strong operability and outstanding micro-simulation ability, the IVE model was fully capable of meeting China's research needs, and had been applied in Beijing, Chongqing, Qingdao and other places (Yao et al., 2006; Feng et al., 2014; Li et al., 2017). Different from these regions, economic development and urban construction in Northeast China were relatively backward, resulting in severe traffic jams and more serious vehicle pollution. The influence of running characteristics was considered in the localization of emission factors. The current study follows the framework of IVE model with modified VOC and NH3 emission factors to present the emission patterns under its unique weather and operation condition in HCM. The CO, HC, NOX, PM2.5 and PM10 emission factors were adjusted based on the Technical guidelines for road motor vehicle emission inventory of air pollutants (Guideline). The combination of the IVE model and the Guideline provides emissions factors from vehicles with spatial heterogeneity in HCM.

For regional air quality models and atmospheric environment management, merely statistical analysis of air pollutant emissions from emission sources cannot meet the needs without high-resolution tempo-spatial analysis. The tempo-spatial analysis hereby helps to improve the simulation performance of the air quality model, and further informs local decision-makers with more tailored air pollution management strategies for transportation programs. This study combined the urban road network and traffic flow information in HCM. We analyzed spatial and temporal characteristics of vehicle pollutant emission based on the optimized road network weight method and spatial tool ArcGIS. Our study established a grid-based emission inventory with a high temporal and spatial resolution of vehicle emissions for research and policy references.

Section snippets

Investigation and collection of vehicle activity level data

The activity data of road vehicles mainly include the number of vehicles, vehicle type, vehicle age, mileage, and emission control technologies. In order to obtain data that could better represent traffic flows, different collection methods are adopted to get different activity dates. The amount of vehicle by different types and fuels in 2016 were derived from provincial statistical yearbooks (Liaoning Provincial Bureau of Statistics, 2017; Statistics Bureau of Heilongjiang Province, 2017). The

Vehicle emission inventory and emission sharing rate of different conditions

Fig. 2 shows that in 2016, the pollutant emissions of CO, HC, NOX, PM2.5, PM10, VOC and NH3 were 625.6 kt, 130.4 kt, 21.5 kt, 11.2 kt, 12.2 kt, 80.5 kt and 5.2 kt, respectively. The emission of pollutants varies greatly among cities, mainly due to the differences in vehicle population, the proportion of vehicle type, the type of vehicle fuel and emission standards in each city.

Conclusion

In 2016, the CO and HC (including gasoline vehicle evaporation), NOX, PM2.5, PM10, VOC, and NH3 emitted by vehicles in the HCM were 625.6 kt, 130.4 kt, 211.5 kt, 112.0 kt, 12.2 kt, 80.5 kt, and 5.2 kt, mainly from MiniPC, motorcycle and HDT. The vehicle emissions in the HCM had spatial heterogeneity. The pollution centered on cities with high vehicle population and dense road network was the most serious. The total amount of emissions in the urban center was much higher than in other regions,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded by the National Project of Key Research and Development Plan (2017YFC0212303-03), the Based Research Projects of National Natural Science Foundation of China (41871212, 41871204) and the Based Research Projects of Northeastern University (N172504031).

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