Interactive comment on “ A new vehicle emission inventory for China with high spatial and temporal resolution ”

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Introduction
Quantifying the magnitude and trend of anthropogenic air pollutants and greenhouse gas (GHG) emissions from China is of great importance because of their negative impact on the environment and their significant contribution to global emission budgets.The other method estimates emissions at provincial level and allocates total emissions to counties or grids based on surrogates, such as GDP (Cai and Xie, 2007), population density (Ohara et al., 2007), or road density (Streets et al., 2003;Zhang et al., 2009), by assuming a linear relationship between the surrogates and vehicle emissions of counties or grids within a province.However, these studies often apply national averages for key parameters (such as, technology distributions and vehicle emission factors) to estimate provincial emissions, which can introduce significant errors in the spatial distribution of emissions.Furthermore, many surrogates, such as GDP and population density, are not directly related to vehicle activity.While road density is directly related, it cannot reflect the variation of traffic flow between different roads and, therefore, this allocation method has been considered to have significant uncertainties at city level (Tuia et al., 2007;Ossés de Eicker et al., 2008;Saide et al., 2009).Some studies have improved on this method by using an aggregated surrogate that combines population density, road density, and traffic flow (Saide et al., 2009;Zheng et al., 2009).However, this method can only be applied for a few provinces with good data availability because data, such as traffic flow road by road, are not available for the whole of China.Therefore, previous inventory methods are applicable either for a few specific cities, or for provinces and the country but with significant uncertainties resulting from the exclusion of geographical differences in key parameters and the choice of spatial surrogates that are weakly related to vehicle activity.Therefore, existing methods are not able to establish an accurate, high-resolution vehicle emission inventory for China.

The community has put tremendous effort into quantifying anthropogenic emissions in Figures
There are two important objectives to improve the accuracy and resolution of the vehicle emission inventory of China: (1) to increase the spatial resolution of the key influencing factors of emissions; (2) to develop a gridding method in which the surrogates are strongly related to vehicle activity.
With these two aims in mind, this work developed a new methodology of highresolution vehicle emission inventory for China.We first developed a county-level vehicle emission inventory that covered 2364 counties in China (county is an administrative Introduction

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Full unit one level lower than city).To calculate the vehicle emissions for each county, we simulated county-level vehicle stock, province-level technology distribution, and countylevel emission factors that took into account the geographic differences in local meteorological factors (e.g.temperature and humidity).We then allocated the county-level vehicle emissions onto 0.05 • × 0.05 • grids based on the latest available electronic road map of China compiled in 2010.In this step, the total vehicle kilometers traveled (VKT) of each vehicle type in a county was allocated to roads according to the road type.
Because the new method differentiated the traffic load on different types of roads, it had advantages over previous allocation methods in depicting the spatial distribution of vehicle activities.
This study focused on CO, NMHC, NO x , and PM 2.5 emissions generated from running, starting and evaporative processes of passenger vehicles and trucks in China in 2008.The article is organized as follows: in Sect. 2 we describe the methods to determine the county-level parameters for calculating county-level vehicle emissions and to allocate the emissions onto grids; in Sect. 3 we analyze the results of key parameters, county-level vehicle emissions, and gridded emissions; in Sect. 4 we evaluate the new allocation method by comparing with previous methods and by conducting sensitivity analyses for key assumptions; finally in Sect. 5 we discuss the main uncertainties of the inventory method and the next step of future work.

General methodology description
To develop a high-resolution vehicle emission inventory for China, we estimated vehicle emissions at county-level by exploring the geographic differences in the key parameters as fully as possible, and allocated the county-level emissions onto 0.05 • × 0.05 • grids with a new allocation method which could better reflect the spatial distribution characteristics of vehicle activities.Introduction

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The research included all counties of the 31 provinces in China, except for Hong Kong, Macau and Taiwan.As Eq. (1) shows, to establish an accurate vehicle emission inventory at county level, it was important to understand the differences in major parameters between counties.
By extensive application of available statistical data and existing model tools, we improved the spatial resolution and accuracy of three critical parameters -vehicle population, technology distributions, and emission factors.
Different approaches were developed for these parameters: (1) county-level vehicle population was estimated by city-level Gompertz functions, which were adjusted by county-level socio-economic status; (2) province-level technology distribution was calculated by provincial vehicle stock and survival functions; (3) monthly county-level emission factors were simulated by the International Vehicle Emission (IVE) model using China's on-road vehicle emission corrections and county-level meteorological corrections.Sections 2.2-2.4 present the three approaches in detail.
We allocated the county-level emissions calculated by Eq. ( 1) to different road types (highways, national, provincial, and county roads, as defined in Table 1) on the basis of Introduction

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Full VKT allocation weights generated from our field surveys.We then allocated the emissions onto 0.05 • × 0.05 • grids according to road densities (for hot-stabilized emissions) and urban populations (for start and evaporation emissions).Details of the emission allocation approach are provided in Sect.2.5.

Modeling vehicle population at county level
In China, the administrative tiers from high to low are province, city, and county, and no statistics are available on county-level vehicle populations.We estimated the vehicle ownership for each county according to the socio-economic status of the county and the progress of motorization taking place in the city that this county belongs to.The vehicle growth of a region is highly correlated to its economic development (e.g.percapita GDP), and the Gompertz function (an S-shaped curve with three phases of slow, fast, and, finally, saturated growth) is often used to establish the relationship between per-capita GDP and vehicle ownership (Dargay and Gately, 1999;Dargay et al., 2007;Huo and Wang, 2012).In this study, we used the Gompertz function to calculate countylevel vehicle ownership as follows: Gompertz Function: where V represents vehicle ownership (vehicles 1000 people −1 ); V * represents the saturation level of vehicle ownership (vehicles 1000 people −1 ); E represents an economic factor (here, per-capita GDP); and α and β are two negative parameters that determine the shape of the curve.
According to Eq. ( 2), three key parameters must be determined to estimate the vehicle population of a county: (1) the saturation level (V * ), assumed to be 500 vehicles per 1000 people for all counties in in this study, which is a moderate vehicle growth scenario for China (Wang et al., 2006); (2) per-capita GDP (E ) of the county, which is obtained from China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2002-2011), and; (3) parameters α and β of the county, which are determined by the Introduction

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Full α and β values of the city that this county belongs to and a county-specific adjustment factor.City-level α and β values are derived from historical socio-economic data and vehicle ownership of the city, as shown in Eq. (3), which is converted from Eq. (2) ln − ln where i represents the city that the county belongs to.City-level per-capita GDP (E i ) and vehicle ownership data (V i ) were available from 2001 to 2010 from China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2002-2011a).
As shown by Eq. (3), ln(−α) and β were linearly related, and could be regressed from the 10 pairs (data from 2001 to 2010) of known ln(− ln(V i /V * )) and E i .According to the regression results, the mean value of R-square (R 2 ) of the linear regression for all the cities was 0.92 and the median value was 0.96, indicating that the Gompertz function was reliable for simulating city-level vehicle growth patterns in China.A few cities (e.g.Qiqihar and Jiamusi City) showed a poor R 2 (< 0.5).For these cities, as well as those in Tibet, Qinghai, and Xinjiang where the statistics are largely incomplete, we used their provincial α and β regression parameters instead.In total provincial regression parameters were used for 14 % of the cities.
As vehicle growth patterns may vary between counties in the same city, the city-level α and β derived from Eq. (3) had to be adjusted to reflect the county-level growth pattern.The adjustment factor was related to the socioeconomic status of the county relative to that of its city.To evaluate this relationship, we examined the β values of each province and those of its cities (β was selected because it reflects the vehicle growth rate as the per-GDP increases).Taking the Hebei Province as an example, Fig. 1a compares the β values of Hebei and its three cities.As shown in the figure, the three cities had different β values from the provincial one.Of the three cities, the richer city tended to have a lower vehicle growth rate because the Gompertz function is S-shaped and the vehicle growth rate slowed down close to the saturation level.Figure 1b shows Introduction

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Full that the β values of the Hebei province and all its cities had a strong inverse correlation with their per-capita GDP.Based on this correlation, we assumed that the β values between a city and its counties followed the same trend, and introduced an adjustment factor k to calculate county-level β from city-level β, as follows: where i represents city; j represents county; E i ,min and E i ,max are the minimum and maximum per-capita GDP, respectively, used to regress the city-level Gompertz function.If the per-capita GDP of a county was out of the range of E i ,min and E i ,max , the adjustment factor was calculated as the ratio of the minimum or maximum per-capita GDP of the Gompertz curve to the county per-capita GDP.We assumed the same α for all counties in the same city.The county-level α and β could be calculated from Eq. ( 5) and Eq. ( 6).

Modeling technology distributions
In this study, the vehicle technology distributions were derived from the age distribution of the fleet and the implementation year of each stage of emission standard, based Introduction

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Full on the assumption that vehicles registered in a given year comply with the up-to-date emission standards.Because the parameters necessary for the calculation were available only at the provincial level, we simulated province-level vehicle technology distributions and assumed that all counties in one province had the same vehicle technology distribution.
The age distribution of the fleet in 2008 was calculated for each province, as follows: where A i ,j represents the number of model year i vehicles that survived in target year j (j is 2008); R i represents the number of newly registered vehicles in year i (model year i vehicles), i = 1994-2008; S i ,j −i represents the survival rate of model year i vehicles at age (j − i ).
We obtained data for province-level newly registered vehicles (R i ) from 2002 to 2008 from the China Statistical Yearbook (National Bureau of Statistics, 2003-2009).For the period of 1994-2001, where many statistics were missing, we used a back-calculation method to get newly registered vehicle data for each province, as shown in Eq. ( 8).This method has been applied previously to calculate future projections of the vehicle population of China at the national level (Wang et al., 2006).1994, 1995, . . . , 2008) (8) where i represents model year; j represents target year; R i is the number of newly registered vehicles in year i ; P j is the province-level vehicle population in year j , which were available from China Statistical Yearbook (National Bureau of Statistics, 1995Statistics, -2009)); S i ,j −i is the survival rates of model year i vehicles at age (j − i ), which were calculated separately for passenger vehicles and trucks using the following function:

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Full where T is associated with vehicle life; b is associated with survival curve decline rate.
T and b were determined based on our previous estimate (Huo and Wang, 2012).Note that survival rates were also used in Eq. ( 7) to calculate the age distribution of the fleet.

Modeling emission factors at county level
Vehicle emissions are influenced by many factors, including technology, fuels, local meteorological conditions, and local driving patterns.In the vehicle emission models that are applied worldwide (e.g. the MOBILE model in the United States and the COP-ERT model in Europe), vehicle emissions are usually estimated using base emission factors measured in a standard environment, and applying correction parameters that can reflect the impact of these influencing factors.In this study, we applied the same method to estimate county-level emission factors in China, by coupling the IVE model developed by the International Sustainable Systems Research Center (Davis et al., 2005), local meteorological correction factors, and correction factors based on on-road measurement, as shown in Eq. ( 10): where i represents the county; j represents the pollutant (CO, NMHC, NO x and PM 2. • F, relative humidity of 60 %, and under the US Federal Test Procedure (FTP) driving cycle.BEFs are built into the IVE model; K j ,k represents driving pattern correction factors, which are simulated in the IVE model using driving bin distributions (Davis et al., 2005).
The main parameters include local driving patterns (to calculate K in IVE), local meteorological correction factors (η), and correction factors (ϕ), as shown by Eq. ( 10).
Local driving patterns were obtained from surveys that we conducted in several Chinese cities, details of the data collection techniques are presented in our previous work (Liu et al., 2007;Yao et al., 2007;Wang et al., 2008).We used the same driving patterns for all counties.
Local meteorological parameters include atmospheric pressure, temperature and humidity, which can significantly affect emission levels (Bishop et al., 2001;Nam et al., 2008;Weilenmann et al., 2009).To use the most recent research findings, we applied the US Environmental Protection Agency's latest model, MOVES (MOtor Vehicle Emission Simulator), to generate monthly county-specific meteorological correction factors (η), in which county-level altitude was obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) land use map (Schneider et al., 2009) and countylevel monthly mean temperature and humidity from the aggregation of the WRF model v3.3.1 output at 36 km horizontal resolution.
Correction factors (ϕ) were included because the base emission factors embedded in IVE may not be able to reflect real emission levels in China.The correction factor ϕ is the ratio of the real-world emission factor to the IVE result under the same conditions.We determined the ϕ values based on the real-world emission measurements we conducted in several Chinese cities during the past ten years using the portable emissions measurement system (PEMS) technique (Wang et al., 2005;Yao et al., 2007Yao et al., , 2011;;Liu et al., 2009;Huo et al., 2012a, b).For the vehicle types that we did not measure, ϕ was set as 1.Introduction

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Spatial allocation
The spatial allocation of vehicle emissions was processed in two steps.First, we used the VKT allocation weights on different types of roads (highway, national, provincial and county roads) to split vehicle activity.Second, we divided the county-level emissions according to road type, then plotted the results onto 0.05 • × 0.05 • grids based on road density for hot-stabilized emissions and urban population distributions for start and evaporation emissions.
The truck VKT allocation weights were obtained from a survey conducted in Beijing and Shandong using GPS devices with data acquired over 278 h.The results are presented in Table 1.Heavy duty trucks run more frequently on inter-county than on county roads, because they are generally used for long-distance transportation.For passenger vehicles, we assumed that they are used more often in urban than in non-urban areas, given that the major purpose of passenger vehicles is to meet people's routine travel needs.Because the VKT survey data of passenger cars were absent, we assumed that 80 % of passenger vehicle VKT were driven on county roads and the remaining 20 % on inter-county roads (including highways, national and provincial roads), based on previous estimates (Tuia et al., 2007).To investigate the effect of these VKT weight assumptions on gridded emissions, we performed a sensitivity analysis with different weighting factors (presented in Sect.4.2).
We assumed that all use of passenger vehicles occurred within the city boundary and the use of trucks within the province boundary.This assumption for trucks may have introduced errors because a proportion of trucks travel between provinces.Unfortunately, the number of trucks used for inter-province transportation is unknown.This issue can be addressed once such traffic flow data become available in China.
The VKT levels of individual vehicles, presented in Table 2, were estimated on the basis of the Fuel Economy and Environmental Impact (FEEI) model (Huo et al., 2012c).We assumed that VKT will decline as a vehicle ages and that the VKT of new vehicles varies with the model year (Huo et al., 2012c).Introduction

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Full Hot-stabilized, start, and evaporative emissions were assigned onto grids by different allocation approaches.Hot-stabilized emissions that were split into highway, national, provincial and county roads were allocated onto 0.05 • × 0.05 • grids based on road density.We used the China Digital Road-network Map (CDRM) data, a set of new road network data developed in 2010 by National Administration of Surveying, Mapping and Geoinformation of China, instead of the DCW data (Digital Chart of the World), which has been widely applied in previous work (Streets et al., 2003;Ohara et al., 2007;Zhang et al., 2009).The CDRM data is better at representing the road network in urban areas than the DCW data, because it includes more detailed city roads.Start and evaporation emissions were allocated based on the urban population density (ORNL, 2006) given that most vehicle journeys start at parking lots that are close to where people live and work.

County-level vehicle activity
The spatial distribution of vehicle population represented by county in 2008 is shown in Fig. 2. We observed significant spatial differences in vehicle population and ownership between the counties.Developed cities, such as provincial capitals, industrial and coastal cities, had higher vehicle numbers than less developed cities. most other counties had just begun the fast growth phase and thus had a much lower vehicle ownership.Figure 3 compares the simulated and statistical vehicle population for 665 counties and 311 cities for which statistics were available.As shown in the figure, the simulated vehicle population shows good agreement with the statistical data with an R 2 greater than 0.9.Note that the method we established to estimate county-level vehicle ownership is less accurate for counties with small vehicle populations, because the number of required vehicles in a country (those used to maintain the basic functioning of society) is not strongly related to economic growth and thus cannot be simulated by the Gompertz function.A large vehicle ownership can reduce the influence of this proportion of vehicles, but for counties with a low vehicle population, the basic need for vehicles accounts for a significant share and can therefore reduce the accuracy of the calculation.

Technology distributions at provincial level
Vehicle technology distribution differs significantly between regions, as shown by able uncertainties.Therefore, estimating technology distribution at provincial level will improve the accuracy of vehicle emission inventories significantly.
As shown in Fig. 4b, shares of pre-Euro 1 vehicles of the provinces were inversely related to their vehicle growth rates, but Shanghai is an outlier point.With a vehicle growth rate of only 13 %, Shanghai had a low share of pre-Euro 1 vehicles equivalent to that of the provinces that have a vehicle growth rate of 23 %.The main reason for this is that old vehicles in Shanghai are scrapped at a much faster rate than in other provinces.As Fig. 5 shows, of the 261 counties examined, Shanghai counties had the greatest differences between the number of newly-registered vehicles and the net vehicle increase (9 % in Shanghai vs. 2 % on average in other counties).The fast vehicle scrapping in Shanghai is attributable to its license plate auction policy, which began in 1994 and limits the number of new license plates available each year, making new license plates expensive.As the per-capita GDP grows in Shanghai, people will have the capability to purchase better cars, however, because of the license plate policy, they will have to scrap their old cars before they are able to purchase new ones.
Figure 6 evaluates the back-calculation method by comparing the simulated new vehicle results with statistical records from 2002 to 2010 for 30 provinces (270 data points in total, the Hebei Province is not included because of irregularities in the data).As can be seen from Fig. 6, the simulated results showed good agreement with the statistical records, especially for passenger vehicles (R 2 = 0.98).This indicates that the technology distribution calculated in this study is reliable, and the vehicle survival functions chosen for the provinces can accurately depict the vehicle scrapping patterns.

Meteorological correction factors (η)
The versely, NO x running and start emissions increased as the temperature decreased.In addition, start emissions were more sensitive to environmental temperature because, when starting the vehicle, the catalytic converters need longer/shorter time to reach the working temperature in a colder/warmer climate.For example, from summer (July) to winter (January), the NO x start emission factors for HDT-D increased by 5-20 times while the NO x running emissions increased by only 1.1-1.3times.The spatial distribution of the correction factors for CO emissions of LDB-G are presented in Fig. 8.The correction factors varied considerably between northern and southern regions, because the regions differed significantly in temperature.In July, the CO running emission factors in the southern regions were approximately 30 % higher than in the northern regions; while in January, the north had CO start emission factors 3.5 times higher than the south.Figure 8 also reveals the remarkable differences in meteorological correction factors between the western and eastern regions, which were caused not only by their different temperatures but also by their different altitudes.In general, western China is at a higher altitude than eastern China (e.g.1900 m in Gansu vs. 12 m in Jiangsu, which are both located at similar latitudes).Higher altitudes can result in more incomplete combustion products (e.g.CO and NMHC) because of the low concentration of oxygen in the atmosphere.Therefore, vehicles operated in the western regions had approximately 9-20 % higher CO emission factors than those in the eastern regions under the same temperature.The analysis of meteorological correction factors suggests that vehicles with the same control technology may have very different emission factors in different regions.Therefore, the regions with weather conditions that increase vehicle emissions should take stricter control measures.The significant disparity in seasonal and regional correction factors also emphasizes the importance and necessity to calculate emission factors by region in order to improve the spatial and temporal resolution of the inventory.Introduction

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Total vehicle emissions in 2008
The on-road CO, NMHC, NO x and PM 2.5 emissions by vehicle and technology type are summarized in Table 3 (Huo et al., 2012b), and new vehicles tended to be used more often than old ones.As demand for long-distance transportation is growing rapidly and heavy duty vehicle numbers are increasing, more stringent control measures should be taken for heavy-duty diesel vehicles in order to control on-road NO x emissions.

Monthly variation of vehicle emissions
Monthly vehicle emissions are plotted in Fig. 9.The total emissions, as well as the contributions from different processes (e.g.running the vehicle, starting and evaporation) vary significantly between months.During winter months (December to February) vehicles produce 19 % more CO, 11 % more NMHC, and 21 % more NO x emissions than in the summer (June to August).The monthly PM 2.5 emissions did not vary significantly because MOVES assumes that the PM 2.5 emission factors of diesel trucks change very little with temperature.Hot-stabilized processes accounted for the largest proportion of emissions, with 79 % CO, 80 % NMHC, 97 % NO x , and 87 % PM 2.5 emissions in the summer, and 52 % CO, 69 % NMHC, 88 % NO x , and 86 % PM 2.5 in winter.The share of CO and NMHC start emissions was much higher in winter (48 % for CO and 30 % for NMHC), because when Introduction

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Full The monthly variability in vehicle emissions at different latitudes is shown in Fig. 10.The monthly pattern of variability of the CO and NMHC emissions differed remarkably from region to region, with the greatest variability at higher latitudes where low winter temperatures can significantly increase the start emissions.For NO x and PM 2.5 emissions, monthly variability was similar at different latitudes, because the NO x and PM 2.5 emissions were dominated by running emissions, which are not as dependent on temperature.

Spatial variation of vehicle emissions
The county and gridded emissions of CO and NO x are depicted in Fig. 11.The NMHC and PM 2.5 emission maps are similar to those for CO and NO x , respectively, and they are therefore not shown.
Vehicle emissions were distributed unevenly throughout China.The majority of emissions were concentrated in a few counties.Emission hot-spots could be identified, as shown in Fig. 11a.The counties shown in red accounted for less than 1 % of the total counties, but contributed approximately 20 % of the CO emissions in 2008.Most of these counties are the urban centers of the province capitals, which can be considered as the most developed areas in China.
Urban areas have the highest vehicle emission levels, in terms of both total amount and emission intensity (defined as emissions per unit area).In 2008, urban areas in China accounted for only 11 % of the total land area and 28 % of the total population.However, they contributed 42 %, 39 %, 32 % and 32 % to the total vehicle CO, NMHC, NO x , and PM 2.5 emissions, respectively.The share of urban NO x and PM 2.5 emissions was a little lower because their major contributors, trucks, run less often in urban areas.
On average, the urban vehicle emission intensity was 2.9-3.8 times the national average.The differences were even more dramatic in developed areas.Taking Beijing as an example, the six urban districts (including Dongcheng, Xicheng, Haidian, Chaoyang, Introduction

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Full Fengtai, and Shijingshan) accounted for only 8 % of the Beijing surface area, but contributed 53-64 % of the total vehicle emissions for Beijing.The emission intensities of these six districts were 6.3-7.7 times the average of the entire city.Beijing, Shanghai, Guangzhou and Tianjin had the highest vehicle emissions in China.For example, the vehicle CO emission intensity was 45, 34, 27 and 17 times higher, respectively, than the average urban emission intensity for the country.Beijing, Shanghai and Guangzhou have implemented restriction policies on car purchases to contain the excessive vehicle growth, address traffic congestion, and reduce vehicle emissions.Similar measures are planned for Tianjin.
Gridded CO and NO x emissions are presented in Fig. 11b and d.The majority of vehicle emissions were concentrated in urban areas and on inter-county highways connecting major cities.However, the spatial distribution of CO and NO x emissions had notable differences.CO (NMHC) emissions were highly concentrated in urban areas, while much of the NO x (PM 2.5 ) emissions were distributed on highways.This difference can be attributed to the fact that light-duty vehicles, the major contributor of CO and NMHC, are operated more frequently on county roads.On the other hand, heavy duty vehicles (HDBs and HDTs), the major NO x and PM 2.5 contributors, are used extensively on inter-county roads.
4 Evaluation of the spatial allocation method

Spatial surrogates
Spatial surrogates are important because the extent to which they can represent the spatial distribution of emissions directly determines the accuracy of an emission inventory.The major differences between the spatial proxies used in this study and those applied in previous studies are: (1) VKT weight factors for different road types were used to allocate county emissions, which were usually neglected in previous work (Streets Introduction

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To evaluate the improvement provided by the new allocation method developed in this study, we compared the new method with three existing allocation methods: (1) the population-based allocation method (M1); (2) the road-length-based allocation method using DCW data (M2); and (3) the road-length-based allocation method using the CDRM data (M3) to explore the effect of road data quality.Details on the four methods are provided in Table 4.
The differences in grid vehicle emissions between our method and the other three methods are illustrated in Fig. 12.Compared with M1, this study generated higher emissions for rich counties with small populations, and lower emissions for less-developed counties with large populations.This is a more reasonable result than that of M1 where the ratio of vehicle activities or emissions was assumed to be proportional to population size.As mentioned in Sect.2.2, vehicle population is determined by both per-capita GDP and total population.The population-based allocation method (M1) neglects the effect from per-capita GDP on vehicle ownership.More importantly, our work improves the estimates for super-large counties with a population over 2 million.Super-large cities are usually the most industrialized and developed cities in China (e.g.megacities, provincial capitals and coastal cities) and have much higher percentage of vehicle ownership than the national average, and therefore the population-based method could underestimate their emissions.As shown in Fig. 12b and c, the road-lengthbased methods (M2 and M3) significantly underestimated the emissions for counties with high population or per-capita GDP, and thus failed to identify emission hotspots.When compared with the method developed in this study, the relative differences in M3 were smaller than those in M2, because the new CDRM data has more detailed information on urban roads that can improve spatial allocation in urban areas.However, the underestimation of emissions for urban areas is not addressed completely.
The comparison of gridded emissions at different spatial resolutions is presented in vehicle emissions as area sources, it failed to depict their spatial characteristics as line sources.M2 was not able to identify emission hotspots in big cities, because city roads are not included in DCW and few emissions could be allocated to urban areas.M3 could identify emission hotspots in cities but had less emissions allocated to major roads (e.g.inter-county highways) compared with our new method.The road-lengthbased method assumed a proportional relationship between emissions and the road length regardless the road type.As a result, major roads that carry a higher traffic load than smaller roads were allocated less emissions than they should have been.
The allocation method developed in this work was able to reflect the characteristics of vehicle emissions as line sources and could identify emission hotspots in cities, because of improvements in three aspects: (1) emissions are estimated at county level, (2) detailed road network data was used, and (3) spatial distribution features of traffic activities were taken into consideration.
As the grid resolution became coarser, differences between the four methods became less significant because the spatial surrogates tended to have similar spatial distribution characteristics at a large spatial scale.As Fig. 13 shows, when the grid resolution was 0.5 • , which is greater than most counties in eastern China, the spatial distributions generated from the four methods had similar characteristics.
Figure 14 further explores the differences in gridded emissions between the methods at different resolutions.If the grid size was smaller than 0.2 • (equivalent to 20 km × 20 km), as shown in Fig. 14b and c, the population-based and DCW-based methods may have caused significant bias in the emissions in most grids.In particular, the population-based method can underestimate the emissions of low-populated grids, which have few people but have highways passing through them.The DCW-based method substantially underestimated the emissions of highly-populated grids.
If the grid size was increased, the differences in the overall gridded emissions between the three methods were reduced.However, as Fig. 14d and e show, both M1 and M2 methods may significantly underestimate the emissions of some grids with large populations (e.g.grids that cover Beijing, PRD and YRD), even though the grid Introduction

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Full size was enlarged to 1.0 • (equivalent to 100 km × 100 km).These highly-populated regions are usually the key objective and focus of air quality modeling studies.Therefore, the allocation method developed in this study can provide better accuracy at both high and low resolution.

VKT allocation weights
We introduced the concept of VKT allocation weights to improve the accuracy of the gridded emission inventory.However, due to a lack of sufficient traffic survey data, the assumptions that we made for VKT weights may have created uncertainties in the gridded emission results.Therefore, we conducted a sensitivity analysis to quantify the sensitivity of the gridded emissions to the VKT allocation weights.Two scenarios (denoted as S1 and S2) were designed to represent the extreme values of VKT allocation weights for passenger vehicles and trucks, respectively, as shown in Table 5.
The results of the sensitivity analysis for NMHC and NO x emissions are presented in Fig. 15.As the CO result was similar to that of NMHC, and the PM 2.5 result to that of NO x , this data is therefore not shown.As can be seen in Fig. 15a and b, on average, the difference in gridded emissions between this work and S1 ranged from −1 to 7 %, which suggests that the overall results were not very sensitive to the VKT weights of passenger vehicles.For each individual grid, the sensitivity of the emissions was dependent on the grid length ratio of county to inter-county roads (C/I road ratio).If a grid had the same C/I road ratio with the county where the grid was located, the emissions of this grid had zero sensitivity to the VKT weights of passenger vehicles.The greater the difference in the C/I road ratios between a grid and its county, the more sensitive the gridded emissions were to the VKT weights.As shown in Fig. 15, compared with S1, this work allocated greater emissions to a few highly-populated grids, because grids with a high population were more likely to have a higher C/I road ratio than the county average.For a similar reason, this work allocated lower emissions than S1 for some grids with low populations.If a grid had 100 % county roads and no inter-county roads, and its county had a C/I road ratio of 1.7 (the national average in China), which 32027 Introduction

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Full is an extreme and rare case, the change of the VKT weights for county roads from 80 % to 50 % could cause a maximal reduction of 60 % in the gridded emissions of passenger vehicles.Under a normal scenario, the emission change would have been much smaller.As Fig. 15c and d shows, the sensitivity of emissions to the VKT weights of trucks was small, given that the average difference in the gridded NMHC and NO x emissions between this work and S2 ranged from −2 to 2 %.Furthermore, for individual grids, the sensitivity of emissions to the VKT weight of trucks was related to the grid C/I road ratio, as was the case with the VKT weights of passenger vehicles.Increasing the VKT weights of trucks from 8 ∼ 25 % (this work) to 63 % (S2) allocated more truck emissions to highly-populated grids because these grids tended to have higher C/I road ratios, and vice versa for grids with low populations.However, as shown by Fig. 15c, the NMHC emissions of highly-populated grids were observed to have little sensitivity to VKT weights of trucks, because light-duty passenger cars usually dominated the NMHC emissions in highly-populated grids and trucks played only a very limited role.

Discussion
This work proposes a new inventory methodology to improve the spatial and temporal accuracy and resolution of vehicle emissions for China.By developing a set of approaches to estimate, for the first time, the vehicle emissions for each county, and introducing the VKT allocation weights to assign county emissions into grids, our proposed methodology overcomes the common weakness of previous methods, such as, neglecting the geographical differences in crucial parameters of vehicle emissions and using surrogates that are weakly related to vehicle activities to allocate vehicle emissions.
Compared with previous methods, the new methodology has great advantages in The uncertainty in these emission factors lies in the representativeness of the selected measured vehicles.To lower this uncertainty, more measurements are required and eventually a vehicle emission model needs to be developed for China.On the other hand, the vehicle activities are determined based on surveys conducted in a few cities and on several assumptions, which could involve uncertainties because of the disparity in vehicle activities between cities.To improve the data quality, dynamic traffic flow should be integrated into the inventory, which will require collaboration with traffic management research groups.
Addressing these uncertainties requires long-term efforts from the research community and concrete support from various governmental sectors for data availability and sharing.In the meantime, we will continue to improve the methodology by addressing the remaining key issues, including VKT by county, different technology distributions within the same province, base emission factors by road type, and more reliable VKT weights.We also plan to extend this methodology from 2008 onwards to perform a multi-year analysis.Introduction

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Full  b National roads are defined as main roads connecting provincial capitals, economically developed cities and traffic hub cities.The CDRM data separated a proportion of roads from national roads and categorized them as "Highways".c Provincial roads are defined as main roads connecting cities within a province.The provincial government is responsible for the construction, maintenance and management of provincial roads.The CDRM data separated a proportion of roads from provincial roads, and categorized them as "Highways".d County roads are defined as roads used mainly for transportation within a city.The municipal government is responsible for the construction, maintenance and management of these roads.Introduction

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Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), county-vehicle population was calculated by multiplying the county-level vehicle ownership and population (National Bureau of Statistics, 2009a).The county vehicle population was further broken down into different vehicle types (HDBs, MDBs, etc.) based on the province-level shares of each vehicle type (National Bureau of Statistics, 2009b).
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 5 ); k represents the vehicle type (e.g.HDBs, MDBs etc.); m represents the IVE vehicle categories, which are categorized by fuels (gasoline and diesel), emission control technologies (e.g., Euro I, and Euro II, etc.) and accumulative mileage (< 80 000 km, 80 000-160 000 km, and > 160 000 km, because emissions deteriorate as the mileage increases); EF i ,j ,k,m is the on-road emission factor of pollutant j of vehicle type k and IVE category m in county i ; EF IVE j ,k,m is the emission factors simulated by IVE; η i ,j ,k is the local meteorological correction factor, which reflects the effect of local meteorology on vehicle emissions; ϕ j ,k is the emission correction factor, which takes into account the difference in on-road vehicle emission levels in China from those generated by the Introduction Discussion Paper | Discussion Paper | Discussion Paper | IVE model; BEF IVE j ,k,m represents the base emission factors of the vehicle category k measured at an altitude of 500 feet, a temperature of 75 Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | For example, counties in the three most economically developed regions -North China Plain (NCP), Yangtze River Delta (YRD) and Pearl River Delta (PRD) -had 100 to 200 vehicles per 1000 people in 2008; whereas the median value in other counties was 23 and 84 % of them had a vehicle ownership level lower than 55 vehicles per 1000 people.The economic development level affects vehicle ownership significantly.The large difference between counties suggests that they are at different stages of economic growth.Counties in developed regions (e.g.NCP, YRD, and PRD) had already entered into the fast growth period, the second growth phase of the Gompertz function, while Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 4a .
Fig.4a.Provinces where emission standards were implemented 1-3 yr earlier than the country (e.g.Beijing and Shanghai) tended to have a more technologically advanced fleet.For provinces with the same standard implementation schedule, a larger new vehicle fleet may lead to a smaller share of old vehicles in the future.As shown in Fig.4b, provinces with higher vehicle growth rates tended to have a lower fraction of pre-Euro 1 vehicles.For example, vehicle numbers grew fastest in Zhejiang, which had 12 % pre-Euro 1 vehicles, and slowest in Xinjiang with 31 % pre-Euro 1 vehicles.Because the emission factors of vehicles compliant with different standards can vary significantly (e.g.CO, HC and NO x emission factors of pre-Euro 1 gasoline cars are 15, 40 and 8 times those of their Euro 3 counterparts, respectively)(Huo et al., 2012a), the assumption generally made in previous studies that all provinces (except Beijing and Shanghai) had the same vehicle technology distribution as the national average(Streets et al., 2003;Zhang et al., 2009;Huo et al., 2011) may have involved consider-32019 seasonal meteorological correction factors for NMHC and CO in light-duty gasoline passenger vehicles (LDB-G), and for NO x in heavy-duty diesel trucks (HDT-D) are shown in Fig. 7. LDB-G and HDT-D were selected as examples because they are the largest contributors to total on-road emissions of NMHC, CO and NO x .In general, NMHC and CO running emissions increased as the temperature increased.Con-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the temperature decreased the CO and NMHC start emission factors increased while their running emission factors decreased.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Fig. 13.As shown in the figure, because the population-based method (M1) treats Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | portraying the spatial distribution characteristics of vehicle activities and emissions.However, uncertainties still exist in two aspects -vehicle emission factors and vehicle Discussion Paper | Discussion Paper | Discussion Paper | activities.In this work, vehicle emission factors were simulated by a US IVE model that was adjusted with hundreds of on-road vehicle emission measurements in China.
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Fig. 1 .Fig. 4 .
Fig. 1.Gompertz regression of the Hebei Province and its cities: (a) Gompertz function fitting of Hebei vs. three selected cities within it (Cangzhou, Tangshan and Hengshui) from 2002 to 2010; (b) β values of all cities in Hebei.
sents the control technologies (corresponding to pre-Euro I, Euro I, Euro II, Euro III and Euro IV standards); k represents pollutant type (CO, NMHC, NO x and PM 2.5 in this work); E k is the vehicle emissions of pollutant k (Mg); VP i is the vehicle popula- tion (million); X i ,j is the share of vehicles with control technology j in the vehicle type i; VKT i is the average vehicle mileage traveled of vehicle type i (km yr −1 ); EF i ,j ,k is the emission factor of pollutant k of vehicle type i with control technology j (g km −1 . In 2008; China's vehicles emitted 16.37 Tg CO, 1.53 Tg NMHC, 4.57 Tg NO x , and 0.245 Tg PM 2.5 .As shown in Table3, older vehicles (e.g.pre-Euro 1 and Euro 1 vehicles) contributed significantly to on-road emissions.Pre-Euro 1 vehicles contributed 24-26 % of CO and NMHC emissions, but only 13-14 % of NO x and PM 2.5 emissions, and this was because CO and NMHC emission factors decreased faster than those for NO x and PM 2.5 from pre-Euro 1 to Euro 1 standards.Euro 3 vehicles contributed more significantly (17 %) to NO x than to other pollutant types, because the reduction in the real-world NO x emission factors from Euro 2 to Euro 3 vehicles was very small

Table 1 .
Vehicle kilometers traveled allocation weights for trucks.Highways a National roads b Provincial roads c County roads d a The China Digital Road-network Map (CDRM), which was applied in this study, classified roads into four types: highways, national roads, provincial roads, and county roads.