On-road vehicle emissions are a major contributor to
elevated air pollution levels in populous metropolitan areas. We developed a
link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet),
based on multiple datasets extracted from the extensive road traffic
monitoring network that covers the entire municipality of Beijing, China
(16 400 km2). We employed the EMBEV-Link model under various traffic
scenarios to capture the significant variability in vehicle emissions,
temporally and spatially, due to the real-world traffic dynamics and the
traffic restrictions implemented by the local government. The results
revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in
the urban area (i.e., within the Fifth Ring Road) and during rush hours,
both associated with the passenger vehicle traffic. By contrast,
considerable fractions of nitrogen oxides (NOx), fine particulate
matter (PM2.5) and black carbon (BC) emissions were present beyond the
urban area, as heavy-duty trucks (HDTs) were not allowed to drive through
the urban area during daytime. The EMBEV-Link model indicates that nonlocal
HDTs could account for 29 % and 38 % of estimated total on-road emissions of
NOx and PM2.5, which were ignored in previous conventional
emission inventories. We further combined the EMBEV-Link emission inventory
and a computationally efficient dispersion model, RapidAir®,
to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the
hotspots). The simulated results indicated a close agreement with ground
observations and captured sharp concentration gradients from line sources to
ambient areas. During the nighttime when the HDT traffic restrictions are
lifted, HDTs could be responsible for approximately 10 µg m-3 of
NOx in the urban area. The uncertainties of conventional top-down
allocation methods, which were widely used to enhance the spatial resolution
of vehicle emissions, are also discussed by comparison with the EMBEV-Link
emission inventory.
Introduction
The rapid growth in vehicle use associated with socioeconomic development
has triggered serious atmospheric pollution and adverse health impacts
(Anenberg et al., 2017; Guo et al., 2014; Huang et al., 2014). Serious air
pollution problems, which are seen as high ambient concentration levels of
major air pollutants, have attracted substantial public attention in populous
metropolitan areas. Beijing demonstrates two profound aspects in one single city:
an obvious achievement in city development accompanied by substantial
pressure to mitigate air pollution episodes (UNEP, 2016). Many other
megacities are facing similar environmental challenges after decades of
rapid economic development. Beijing's annual concentration of fine
particulate matter (PM2.5) in 2017 was 58 µg m-3. Although
this value was reduced by 35 % as opposed to that in 2013, it still
significantly exceeded the limit of China's national ambient air quality
standard (35 µg m-3) by 66 % (Beijing MEEB, 2018a). Recent
official source apportionment results indicated that vehicle emissions
remained one of the most important pollution contributors, responsible
for an average of 45 % of total PM2.5 concentrations from local
sources (Beijing MEEB, 2018b). The exceedance of ambient nitrogen dioxide
(NO2) concentrations represents another air quality problem in Beijing
(UNEP, 2016; Beijing MEEB, 2018a), where nitrate aerosols have become one of
the most important PM2.5 components, with an average mass fraction of
up to 40 % (Beijing MEEB, 2018b; Li et al., 2018). Therefore, controlling
vehicle emissions is one of the prioritized tasks remaining for local
environmental protection authorities.
Beijing has been playing the role of a pioneer in controlling vehicle emissions
within China over the past two decades (Zhang et al., 2014b). So far,
emission standards for new vehicles in Beijing have been tightened to the
fifth generation (China 5/V standards), and ultra-low sulfur gasoline and
diesel fuels have been fully delivered upon. In addition, after witnessing the
effectiveness of driving restrictions (i.e., the “odd–even” policy) to
control vehicle emissions during the 2008 Olympic Games, transportation
management has been substantially implemented for environmental purposes,
notably through license control and driving restriction policies. Currently,
traffic measures are increasingly important in the “vehicle–fuel–road”
integrated emission mitigation strategies (Wu et al., 2017). For example,
the Beijing municipal government has finalized an emergency plan for
extreme air pollution in Beijing, which requires issuance of a red alert
when a severe pollution episode (e.g., 24 h average concentration of
PM2.5 above 250 µg m-3) lasting over 3 d is reported.
During the red alert periods, private vehicles are prohibited from roads
every other day based on the last digit of the license plate, namely,
according to the odd–even policy.
Although previous testing results could convincingly support the decreasing
trend in fleet-average emission factors for local vehicles (Zhang et al.,
2014b; Wu et al., 2012), some major limitations have not yet been adequately
addressed. First, a major aspect of previous assessment tools, known as
emission inventories (Lang et al., 2012; Zhang et al., 2014b), was
developed based on vehicle registration data lacking temporal and spatial
associations with real-world traffic patterns. Only a few studies have
attempted to establish cell-gridded or link-based emission inventories that
limited their study domains to the urban area (e.g., within the Fifth or
Sixth Ring Road) and/or limited vehicle categories (e.g., light-duty
passenger vehicles) (Huo et al., 2009; Wang et al., 2009; Jing et al.,
2016). Nevertheless, the total municipal area of Beijing is approximately
16 400 km2, and vehicular emissions in the outskirts should be
evaluated. As a regional transportation hub, it is known that a considerable
number of freight trucks registered in other regions operate within the city
boundaries of Beijing. All previous studies have not quantified on-road
emissions from nonlocal trucks. On-road measurement studies using a plume
chasing method indicated that nonlocal trucks were highly likely to be
gross emitters of primary PM2.5 and black carbon (BC) (Wang et al.,
2012), since their original registration regions usually were less strict
with respect to environmental oversights (e.g., type-approval conformity
check, in-use compliance inspection) than Beijing (Zheng et al., 2015).
Driven by the rapid development of intelligent transportation systems (ITSs)
in many cities during recent decades, we are able to collect real-world
traffic data using multiple ITS approaches (Barth et al., 2003; Gately et al., 2017;
Zhang et al., 2018). These ITS-informed datasets are capable of capturing
the dynamic traffic conditions in congested urban areas as well as actual
driving patterns of diesel trucks, which could contribute large fractions of
NOx and PM2.5 despite small vehicle numbers (Dallmann et al.,
2013; Gately et al., 2017). In this study, we established a high-resolution
emission inventory of on-road vehicles (EMBEV-Link, Link-level Emission factor Model for the BEijing Vehicle fleet) based on large-scale,
real-world traffic datasets (e.g., traffic count, hourly speed, fleet
configuration), which covered the entire road network of the municipality of
Beijing. This tool enabled us to elucidate the temporal and spatial emission
patterns and to detail the emission burden from nonlocal trucks. This paper
presents an example to conduct fine-grained emission modeling at the
megacity scale and can directly support local emission mitigation
strategies.
Methodology and dataResearch domain and emission calculation
The entire municipality of Beijing, with a total area of 16 400 km2,
comprises 16 urban, suburban and rural districts. The present city
progressively spreads outwards in concentric ring expressways (i.e., Second
to Sixth Ring Road). The urban area is typically referred to as the region
within the Fifth Ring Road, wherein the municipal government has intensively
implemented driving restrictions since 2008. Emissions of primary vehicular
pollutants (carbon monoxide, CO; total hydrocarbon, THC; nitrogen oxide,
NOx; PM2.5; and black carbon, BC) were calculated with a
high-resolution method in a temporal and spatial framework, namely, the
Link-level Emission factor Model for the BEijing Vehicle fleet (EMBEV-Link). Figure 1 is a
flow diagram to illustrate the overall modeling methodology for the
EMBEV-Link system. The traffic data acquisition and further modeling to the
entire road network will be introduced and detailed in Sect. 2.2. For each
road link, hourly emissions are the product of traffic volume, link length
and speed-dependent emission factors (see Eq. 1) (Zhang et al., 2016).
Eh,j,l=∑tEFc,jv×TVc,h,l×Ll,
where Eh,j,l is the total emission of pollutant j on road link l at hour
h, in units of grams per hour (g h-1); EFc,j(v) is the average emission factor of
pollutant j for vehicle category c at speed v, in units of grams per kilometer (g km-1);
TVc,h,j is the traffic volume of vehicle category c on road link l at hour
h, in units of vehicles per hour (veh h-1); Ll is the length of road link l, in units of kilometers (km).
Eight vehicle categories were defined, namely, light-duty passenger vehicles
(LDPVs), medium-duty passenger vehicle (MDPVs), heavy-duty passenger vehicles
(HDPVs), light-duty truck (LDTs), heavy-duty truck (HDTs), public buses, taxis and
motorcycles (MCs) (see Table S1 in the Supplement). For HDTs, we further classified into local
HDTs and nonlocal HDTs according to the registration location.
Significantly higher BC emission factors were identified from nonlocal HDTs
than from local HDTs because Beijing has more stringent conformity
enforcement requirements (Wang et al., 2012).
A system diagram of the modeling methodology for EMBEV-Link.
The speed-dependent emission factors for each vehicle category were
developed based on the official Emission factor Model for the BEijing
Vehicle fleet version 2.0 (EMBEV 2.0). The EMBEV model was developed based on
thousands of in-lab dynamometer tests and hundreds of on-road tests (Zhang
et al., 2014b). The EMBEV methodology and key parameters have been
described in China's National Emission Inventory Guidebook (Wu
et al., 2016, 2017). Figure S1 in the Supplement presents speed-dependent emission
factors of CO, NOx and BC for LDPV and HDT categories, representing
average environmental conditions, fleet configurations (e.g., fuel type,
emission standard and vehicle size) and fuel quality (e.g., sulfur content).
To match the traffic data, we utilized 2013–2014 as the calendar year to
estimate emission factors. The original EMBEV model included evaporative THC
emissions for gasoline vehicles (Zhang et al., 2014b). Later on, we revised
the diurnal and hot soak emission rates based on local SHED tests (Liu et
al., 2015) and estimated that the evaporative THC emissions could be
responsible for approximately 30 % of total THC emissions in Beijing. In
the current EMBEV-Link work, evaporative THC emissions were not included
because we are limited to spatially specifying the evaporative off-network
emissions. Furthermore, air quality simulations often require non-methane
volatile organic compounds (NMVOCs) as emission input to simulate secondary
pollutant formation (e.g., ozone and secondary organic aerosol). Based on
local dynamometer measurements, NMVOCs could approximately account for 90 %
of tailpipe THC emissions. The detailed species-resolved measurement
profiles are more sensitive to vehicle technologies, fuel properties and
environmental conditions, which should be developed based on advanced
measurements.
Generating dynamic traffic profiles based on real-time congestion
information
High-resolution congestion mapping was developed based on densely
distributed taxis (more than 60 000 vehicles) in Beijing, known as “floating”
cars (Cai and Xu, 2013). The municipal traffic commission used numerous
trajectory data of GPS-instrumented taxis to estimate color-coded
congestion levels (red: serious congestion; orange: moderate congestion;
grey: not congested; see Fig. S2 for example). The congestion level was
defined by real-time speed and was updated every 5 min. Nevertheless,
although dynamic traffic conditions were visualized by congestion maps, most
required data such as link-level speeds were not available. From the
official website (http://www.bjtrc.org.cn/, last access: 30 June 2019), the only
open-access data in addition to real-time congestion maps were hourly
speeds and congestion indexes for ring-expressway-defined traffic regions.
To improve the spatial resolution, we developed an image recognition program
to parameterize the congestion level based on available congestion maps (141
available days annually in this study). Furthermore, link-level hourly
speeds were calculated based on the relationship between congestion index
and average speed. The calculation method is documented in the Supplement,
Sect. S2. On the aggregate level, the biases of rush hour speeds between
estimated results and reported data were within ±5 % for all
districts. It is noted that link-level speeds for public buses were
corrected due to their frequent stops for discharging and picking up
passengers (Zhang et al., 2014a).
We further used congestion-map-informed road speeds to improve the temporal
resolution of traffic volumes, which were originally investigated on an
annual basis (BJTU and Beijing EPB, 2014). Traffic density modeling was used
to express the relationship between total volume and speed in this study.
The Underwood-style traffic density models (see Eq. 2) were used for
expressways and arterial roads, which better fit the local
traffic profiles than Greenshield's model (Hooper et al., 2013; Wang et
al., 2013).
q=kmulnufu,
where q is the lane-specific traffic volume at speed u, in units of vehicles per hour (veh h-1); u is the
hourly average traffic speed, in units of kilometers per hour (km h-1); and km is the best
fitting traffic density, in units of vehicles per kilometer (veh km-1). The model coefficients, km
and uf, are determined through linear least squares fitting based
on annual-average hourly volume and speed profiles for urban major roads
(see the Supplement, Sect. S2). We applied the Underwood model to estimate
the relative change of hourly traffic volume in response to the speed
variation.
Traffic video records were collected at more than 30 major urban roads to
develop traffic mixes by hour, road type and district (see Fig. S3). In
particular, we manually counted the number of nonlocal HDTs at the
representative road sites and distinguished volume allocation for local and
nonlocal traffic during different hours during the night (Zhang et al.,
2017). The suburban and rural areas outside of the Fifth Ring Road were
scarcely covered by municipal floating cars and traffic investigations. The
Ministry of Transport has established a nationwide networking to monitor
intercity traffic conditions (Zhang et al., 2018). Twenty-four-hour diurnal
traffic profiles including volume, speed and fleet mix were obtained from 70
highway sites in Beijing, leading to an improved understanding of traffic
patterns in the outlying districts beyond the Fifth Ring Road. Taking G-101
as an in-depth example (see Fig. S4), apparent morning and evening peaks
were observed at one site (Site A) close to the North Sixth Ring Road,
representing urban passenger travel demand. By contrast, HDTs were
responsible for nearly half of the total volume at one remote site
approaching the municipal border (Site B) and peaked around noon. Section S2 in the Supplement also includes the technical details regarding estimating traffic
profiles for nonmonitored roads (e.g., residential roads).
Traffic scenarios under various transportation management schemes
In this study, four scenarios were generated as inputs for the EMBEV-Link to
observe the impacts from major transportation management schemes. Table S2
details the traffic management schemes enforced for major vehicle categories
during various traffic scenarios. The weekday scenario (S1) estimated
annual-average traffic patterns during weekdays (Monday to Friday) with
regular driving restriction rules on personal car use. The weekend scenario
(S2) estimated average traffic patterns during weekends (Saturday and Sunday)
without regular driving restrictions, when urban residents tend to reduce
commutes but increase casual trips. The congestion scenario (S3) reflected the
most congested conditions that occasionally existed during the weekends
prior to some statutory holidays (e.g., Workers' Day on 1 May and
National Day on 1 October). During these special weekends, the scheduling
program was adjusted according to normal weekdays, but the driving
restrictions were not implemented. The APEC scenario (S4) estimated the traffic
patterns during the Asia-Pacific Economic Cooperation summit, with much
stricter traffic limitations than normal situations. Half of all personal
vehicles were restricted from roads by the odd–even policy, and nonlocal
trucks were also strictly prohibited from journeying into the city.
Dispersion mapping for vehicular pollutants
The RapidAir® model developed by Ricardo Energy &
Environment was combined with EMBEV-Link to simulate vehicular
concentrations of NOx for the entire domain and typical hotspot areas.
RapidAir® combines the EPA's Gaussian plume dispersion model
(AERMOD) and open-source computing algorithms by using a kernel convolution
that creates millions of overlapping plumes from emission sources and sums
distance-weighted concentrations at each receptor cell (Masey et al., 2018).
Using unified emissions and meteorological inputs, RapidAir®
can produce concentration results in strong agreement with other Gaussian
dispersion models (e.g., AERMOD, ADMS) while greatly improving computational
efficiency (e.g., 5 min for each hotspot). This study selected NOx as
the simulated pollutant category due to the high contribution from traffic
emissions. For the entire municipality, hourly NOx concentrations
contributed by vehicle emissions were simulated at a spatial resolution of
10 m × 10 m, which used the annual-average hourly meteorological
data (e.g., temperature, wind speed, wind direction) as modeling inputs. Two
typical hotspots in the central business district (Guomao) and along a major
suburban freeway (Xisanqi) were selected for more fine-grained simulations.
The receptor cells in the hotspot areas were meshed into 1 m × 1 m
in order to visualize the NOx concentration gradients from road, to
curbside and thus throughout the ambient urban zone. Detailed data sources
and key parameters of meteorological and terrain input profiles are
described in the Supplement, Sect. S3.
Results and discussionTraffic and emission patterns under various scenarios
The daily traffic activities during weekdays (S1, 258 million veh km) and weekends (S2, 259 million veh km) are estimated to be close
to each other, representing comparable effects from the increased commute
travel demand during weekdays and the absence of regular driving
restrictions during weekends (see Fig. S5). However, the diurnal
fluctuations of average speeds depict different travel characteristics
between weekdays and weekends. The two most congested periods with lowest
traffic speeds (below 23 km h-1) clearly occurred during the mornings
(08:00 and 09:00 GMT+8; note that 08:00 hereafter represents the entire hour from
08:00 to 08:59 GMT+8) and evenings (18:00 and 19:00 GMT+8) of weekdays. By
contrast, we could not observe similar morning traffic peaks during
weekends, but traffic conditions deteriorated throughout the afternoon
(15:00 to 18:00 GMT+8), reflecting frequent casual travel. Combined with
the daily traffic activity of S1 and S2, we could calculate the annual vehicle
kilometers traveled (VKT) in Beijing. For all vehicle categories except
HDPVs, EMBEV-Link indicated that VKT data showed good agreement (i.e.,
relative bias within ±6 %) with the results derived from the
official vehicle inspection database (see Fig. S6). The remaining excess of
estimated annual VKT of the HDPVs is probably contributed by nonlocal
HDPVs, whose emissions are not estimated in a separate vehicle category.
Two certain scenarios (S3 and S4) indicate the substantial impacts from
municipal transportation management on traffic activities in Beijing.
Without strict driving restrictions, the 24 h average speed within the Fifth
Ring Road decreased to merely 23 km h-1 under S3 (see Fig. S7),
indicating that the daily level of congestion was almost comparable to the
rush hours of normal weekdays. The daily traffic activity then increased
by 8 % versus that of normal weekdays. By contrast, the odd–even policy
was implemented during the APEC summit week and played an effective role in
reducing traffic demand and alleviating road congestion. The daily traffic
activity under S4 was lowered by 12 %, while the average speed rose to 35 km h-1. It is noted that additional controls were simultaneously
enforced upon heavy-duty trucks during the APEC period, which did not
significantly change overall traffic patterns compared with the strictly
controlled LDPV fleet but greatly contributed to emission reductions (see
next section).
Total daily emissions estimated by the EMBEV-Link model are 823 t for CO,
84.4 t for THC, 326 t for NOx, 10.6 t for PM2.5 and 5.5 t for BC during weekdays (S1; see Fig. 2). During weekends
(S2), total vehicle emissions decreased by small percentages (e.g., 3 % for
CO and THC and 5 % to 7 % for NOx, PM2.5 and BC). Greater traffic
demand and more serious congestion under S3 combined to trigger increased
vehicle emissions, e.g., 12 % for CO and THC and 6 % for NOx,
PM2.5 and BC in the entire municipality. The CO and THC emission
enhancements were more significant in the urban areas, increased by 17 %
compared with S1, representing the effect from the increasing number of
on-road LDPVs during the more congested period. The recent traffic monitoring data
indicate the overall congestion in the urban area has not changed
significantly, which is owing to the stringent restrictions on the
registration of new vehicles in Beijing (BTI, 2018). On the other hand,
average emission factors have decreased significantly due to the
implementation of newer emission standards and the subsidized scrappage of
older vehicles. As a result, we estimated that the total daily emissions
would be 523 t for CO, 62.5 t for THC, 256 t for NOx, 8.33 t for PM2.5 and 4.18 t for BC in 2017. The
significant reductions are primarily attributed to the improvements in
average vehicle emission factors.
Estimated total emissions under various traffic scenarios, S1 to
S4: (a) CO, (b) THC, (c)NOx, (d) PM2.5 and (e) BC.
Comprehensive traffic controls are estimated to greatly reduce total vehicle
emissions by 43 % for CO, 44 % for THC, 28 % for NOx, 37 % for
PM2.5 and 35 % for BC under S4 relative to S1. The greater reductions in
CO and THC resulted from the greatly increased average speeds of urban LDPVs
and taxis, resulting in lower emission factors. However, diesel freight
trucks were responsible for a major part of NOx, PM2.5 and BC
emissions. More than 80 % of total traffic activities of HDTs were
distributed beyond the Fifth Ring Road, where traffic congestion was less
serious and emission factors were less sensitive. However, the Beijing
municipal government dispatched more public buses for transportation
services during the APEC period (Beijing Municipal Government, 2014), which
would increase NOx emissions as opposed to the normal bus fleet.
Overall, the average concentration of NO2 during the APEC period was 46 µg m-3, representing a reduction of 31 % compared with the same
period of the prior year (Beijing EPB, 2014). The air quality benefit with
respect to ambient NO2 concentrations was in line with the comparative
results between S1 and S4.
Temporal and spatial characteristics of vehicle emissions
The major temporal difference in emission patterns between S1 and S2 is higher
emissions during weekday rush hours. We thus refer to the weekday scenario
(S1) to elucidate temporal and spatial emission patterns (see Figs. 3 and 4).
For CO and THC, their emission peaks during morning (07:00 to 09:00 GMT+8)
and evening (17:00 to 19:00 GMT+8) rush hours seem to be associated
with diurnal fluctuations in passenger travel demand. For example, the
highest hourly emissions of CO and THC were estimated during the morning
rush hour (07:00 GMT+8), higher than the 24 h averages by approximately
90 %. As Fig. 4a and b illustrate, CO emission intensity in the urban
area is significantly higher than that in the outlying area during both peak
and nighttime periods. Table 1 summarizes the emission allocation by
vehicle categories and regions according to EMBEV-Link. The emission
allocation shows a high resemblance between CO and THC: 55 %–60 % of
city-total emissions are estimated to exist within the urban area, where
LDPVs and taxis dominate the contributions. CO and THC emissions also
exhibit heterogeneously diurnal fluctuations in various traffic regions (see
Fig. 3) because they are both primarily contributed by LDPVs which show typical two-peak patterns on the whole.
Estimated hourly emissions by vehicle category under S1: (a) CO,
(b) THC, (c)NOx, (d) PM2.5 and (e) BC.
Daily emission allocation by vehicle category and region under the
weekday traffic scenario (S1).
Air pollutantsRegionDaily emissions (t d-1)Emission allocation by vehicle category group LDPVs andMDPVs, HDPVsLocalNonlocalOtherstaxisand busestruckstrucksCO emissionsWithin the Fifth Ring Roada45877.7 %11.9 %7.6 %1.2 %1.7 %Between the Fifth and Sixth Ring Roadb23336.1 %28.1 %22.6 %9.7 %3.6 %Outside the Sixth Ring Road14226.5 %29.0 %24.6 %12.5 %7.4 %THC emissionsWithin the Fifth Ring Road49.078.8 %10.5 %6.7 %1.5 %0.5 %Between the Fifth and Sixth Ring Road21.237.6 %24.1 %19.0 %14.2 %5.1 %Outside the Sixth Ring Road14.227.9 %24.3 %19.6 %17.3 %10.9 %NOx emissionsWithin the Fifth Ring Road104.122.5 %38.4 %29.0 %10.0 %0.1 %Between the Fifth and Sixth Ring Road130.85.3 %24.3 %35.2 %35.2 %0.1 %Outside the Sixth Ring Road91.03.5 %17.0 %37.8 %41.5 %0.2 %PM2.5 emissionsWithin the Fifth Ring Road3.1925.1 %31.1 %28.5 %15.0 %0.4 %Between the Fifth and Sixth Ring Road4.175.7 %15.6 %31.1 %47.4 %0.3 %Outside the Sixth Ring Road3.303.7 %16.4 %29.6 %49.8 %0.5 %BC emissionsWithin the Fifth Ring Road1.3410.2 %19.4 %47.6 %22.7 %0.2 %Between the Fifth and Sixth Ring Road2.351.7 %7.3 %37.5 %53.4 %0.1 %Outside the Sixth Ring Road1.841.1 %7.1 %34.9 %56.7 %0.2 %
a Including the emissions on the Fifth Ring Road. b Including the emissions on the Sixth Ring Road but excluding the emissions on the Fifth Ring Road.
Link-based emission intensity of CO (a, b) and BC (c, d) during the midnight hour (00:00 GMT+8) and the morning rush
hour (07:00 GMT+8).
Note that dark blue indicates the area within the Fifth Ring Road, while light
blue indicates the area between the Fifth and Sixth Ring Road.
Diesel fleets (e.g., HDTs, HDPVs, buses) are responsible for much greater
shares of the vehicle emissions of NOx, PM2.5 and BC compared with
their contributions to CO and THC. Consequently, distinctive traffic
behaviors of these diesel fleets would result in disparate temporal and
spatial emission patterns than those for CO and THC, which are more
significantly influenced by gasoline fleets. First, we could additionally
observe elevated total emissions of NOx, PM2.5 and BC after 23:00 and during the nighttime period (02:00 to 04:00 GMT+8; Fig. 3), which are
not discerned from CO and THC emission patterns. These elevated emissions
are caused by the local traffic restrictions on HDTs during the daytime,
which result in the HDT traffic during nighttime hours (after 23:00 GMT+8). Second, nearly 70 % of NOx, PM2.5 and BC emissions
occur outside the urban area (see Fig. 2), and the emission contributions of
local HDTs and nonlocal HDTs account for the largest proportion
(approximately 70 % to 80 %; see Table 1). By contrast, the public buses
contribute 16 % of the total NOx emissions and 7 % of the total
PM2.5 emissions in the entire city; in the urban area, buses contribute
30 % of NOx emissions (see Table 1). The EMBEV-Link emission maps
indicate that many HDTs would likely flood into the urban area during the
midnight period, leading to higher emissions on major urban roads (e.g.,
urban ring expressways) (Fig. 4c); however, these HDTs would travel between
the Fifth and Sixth Ring Road or on other outlying expressways during the
daytime period (Fig. 4d). In Sect. 3.5, we further explore the
environmental impacts contributed by these diesel trucks.
Intra-day variability of traffic conditions during the same hour has been
observed based on available traffic monitoring data, which can further
impact traffic emissions. Using two urban roads as examples (West Third Ring
Rd. and Zizhuqiao Rd.), we developed the distributions of inter-day hourly
speeds during a typical morning rush duration (08:00 GMT+8) (see Fig. S8).
Although the speed distributions for the expressway (West Third Ring Rd.)
and sub-arterial (Zizhuqiao Rd.) representatives show various patterns, the
input data applied in S1 are close to the mean values of speed distributions.
Furthermore, despite inter-day variabilities (within ±15 % for
95 % variation intervals), the estimated emission factors and emission
intensities in S1 also approximate to the mean values of the results during
various workdays (bias less than 4 %).
High-resolution simulation of vehicular NOx concentrations
Figure 5a illustrates the spatial distribution of annual-average NOx
concentrations for each cell (meshed into 10 m × 10 m) contributed
by vehicle emissions. Clearly, the spatial variations in the simulated
concentrations highly resemble the emission patterns. The cell-average
NOx concentrations within the Sixth Ring Road are simulated as 46.1 µg m-3, significantly higher than the level of outlying areas.
Beyond the Sixth Ring Road, moderate impacts could also be observed in
proximity to several intercity expressways with considerable traffic
fractions of HDTs. Two hotspots in close proximity to busy roads, Guomao
(Fig. 5b) and Xisanqi (Fig. 5c), each have average NOx concentrations
above 100 µg m-3. The RapidAir® model is capable of visualizing the
NOx decline gradients from the road to near-road ambient zone at the two
hotspots, as well as the areas surrounded by densely packed buildings
influenced by street canyon effects. Extremely high NOx concentrations
are observed in the road environments, which would substantially influence
up to 50 m across the expressways (over 200 µg m-3) and up to 20 m for the arterial roads (over 100 µg m-3) (see the Supplement,
Sect. S3).
In China, the environmental protection authorities only report the NO2
concentrations measured at the official air quality monitoring sites, which
do not include NO concentrations. We referred to the approximate
photostationary state (i.e., chemical equilibrium between the NO2
photolysis and the O3 depletion) to estimate total NO concentrations
for the official sites (Yang et al., 2018). In this study, we only used the
tropospheric NOx chemistry to estimate NO concentrations during the
daytime (approximately 06:00 to 18:00 GMT+8 as the annual average) and
derived the total NOx as the sum of observed NO2 and estimated NO
(see the Supplement, Sect. S3). In Fig. S9, we compared the simulated
NOx concentrations contributed only by vehicle emissions and the total
NOx concentrations for 17 official air quality sites (12 urban sites
and 5 traffic sites) (see the Supplement, Sect. S3). First, the
significantly strong correlation (R2=0.89) between vehicular NOx
and total NOx indicates that the EMBEV-Link inventory has reasonably
captured the spatial distribution of vehicular NOx emissions.
Furthermore, the average ratios of vehicular NOx within total NOx
suggest substantial contributions from on-road vehicles: 76 % for urban
sites and 87 % for traffic sites (i.e., daytime annual average). The
remaining portion of NOx concentrations could be attributed to regional
background and other local sources, which account for a minor part compared
with traffic emissions. We acknowledge that the daytime concentration of
other reactive oxides of nitrogen (i.e., NOz, including HNO3 and
HONO) could be approximately 10 % of concurrent NOx concentrations by
analyzing the air quality simulation outputs of Zheng et al. (2019). Further
studies would be needed to couple dispersion and advanced atmospheric
chemistry to better resolve urban pollution.
High-resolution simulation of annual-average vehicular NOx
concentrations for (a) the entire municipality, (b) Guomao and (c) Xisanqi.
In this study, we estimated the NOx emissions and concentrations, both
based on annual-average environmental conditions. In addition to seasonal
changes of dispersion conditions (e.g., wind speed, wind direction),
NOx emissions could probably be affected by ambient temperature
conditions. Of note, by analyzing more than hundreds of thousands European
vehicles by using remote sensing measurements, a strong temperature dependence
for NOx emissions of diesel cars has been identified (Grange et al.,
2019; Borken-Kleefled and Dallmann, 2018). NOx emission factors from
diesel cars significantly increase during wintertime, which has not been
sophisticatedly characterized by many emission models. NOx emissions
from heavy-duty diesel vehicles in Beijing during wintertime could
probably be elevated because of similar temperature impacts, which should be
carefully characterized by analyzing local measurement data in the future.
The environmental impacts from heavy-duty trucks (HDTs)
Conventional emission inventories were developed based on the registered
vehicle population to support on-road emission management in Beijing (Zhang
et al., 2014b). However, the significant nonlocal truck traffic was not
reflected by the static registration data. The EMBEV-Link shows that
nonlocal HDTs emitted 2.46 t of NOx, 1.07 t of PM2.5 and
0.68 t of BC annually, which were responsible for 29 %,
38 % and 47 % of estimated total emissions in 2013. The greatest
discrepancy of BC further represents higher BC emission factors of nonlocal
HDTs than those for local HDTs. In other words, the previous conventional
emission inventory (Zhang et al., 2014b) underestimated the emissions of
NOx and PM2.5 by 45.2 % and 45.1 %, respectively, which was
primarily due to the missing contributions from nonlocal HDTs.
Stringent transportation management of HDTs in Beijing caused their travel
behaviors and air pollutant emissions to sharply vary from other vehicle
categories, both temporally and spatially. During the daytime with urban
restrictions (before 23:00 GMT+8), we could scarcely observe on-road HDTs
other than certain municipal vehicles within the Fifth Ring Road.
Consequently, the total HDT emissions (local and nonlocal combined)
predominantly appeared beyond the Fifth Ring Road (68 % of NOx,
70 % of PM2.5 and 76 % of BC), including a considerable fraction
in the area between the Fifth Ring Road and Sixth Ring Road (40 % of
NOx, 39 % of PM2.5, and 42 % of BC). By contrast, many HDTs
drove into the urban area during the nighttime without such restrictions.
Therefore, we could clearly observe a significant elevation of HDT emissions
within the Fifth Ring Road beginning at precisely 23:00 (GMT+8). The
RapidAir® model is applied to visualize NOx
concentrations contributed by HDTs exclusively. During the daytime (06:00 to
22:00 GMT+8), HDTs primarily contributed to high concentration spots
scattered near major expressways between the Fifth Ring Road and Sixth Ring
Road (see Fig. S10). Nevertheless, the nighttime impact (23:00 to 05:00 GMT+8) was more significant due to the concentrated urban truck activity
and more unfavorable dispersion conditions (e.g., lower stable boundary
layer). Total HDTs could contribute 9.8±1.6µg m-3 of
NOx during the night period, including 6.3±1.0µg m-3
from nonlocal HDTs. This study has quantified results regarding the air
quality impacts from nonlocal trucks, which is an important issue of air
quality management which has been neglected in previous studies (Li et al.,
2015). Future studies utilizing this improved emission inventory could
include the characterization of secondary air pollutants contributed by
nonlocal traffic. Managing road freight transportation in Beijing is a
regional task, which is controlled even beyond the Jing–Jin–Ji region and is
highly relevant to other coal-rich provincial areas (e.g., Shanxi, Inner
Mongolia). We suggest that the research domain be enlarged to include these
surrounding provincial areas as more traffic data become available in the
future.
A comparative discussion on various methods to construct link-based emission inventories
Traffic data availability is a significant challenge in characterizing
real-world spatial and temporal distributions of on-road vehicle emissions.
As high-resolution emissions are essentially required by air quality
simulations, other accessible spatial surrogates are used to artificially
allocate total vehicle emissions to fine spatial cells. Population density
and/or road length density are two typical varieties of spatial indicators
to allocate vehicle emissions by assuming linear relationships between
vehicle emissions and spatial surrogates (Zheng et al., 2009, 2014). However, such top-down allocation is often questioned with respect
to the accurate representation of real-world traffic activity. We compare
three methods of developing emission inventories with spatial resolutions
of 1 km × 1 km. M1 denotes this study (EMBEV-Link)
using link-level traffic data and reflects real-world emission patterns. M2
and M3 denote two top-down allocation methods based on population density
and road length density, respectively (see the Supplement, Sect. S4). To
observe only the effect from using spatial surrogates, estimated total
emissions of M1 are also used by M2 and M3 allocation. For M2 allocation,
the GIS-based population density is obtained from the LandScan 2012
population database (ORNL, 2012). Standard road length (Zheng et al., 2009),
one proxy parameter to further take account of traffic flow distinctions
between urban and rural areas, is applied in M3 allocation instead of actual
road length. CO and BC are discussed, as they represent gasoline and diesel
featured emissions, respectively.
As Fig. 6 illustrates, M2 generates many scattered emission hotspots in
accordance with highly populous communities in both urban and suburb/rural
areas but weakly represents the topology of road networks. Compared with
M1, M2 tends to underestimate CO emissions in the urban area but
overestimates for the outlying areas because the static population
distribution differs significantly from actual travel activities. Many people reside
outside the Fifth Ring Road, where housing costs are relatively lower, but they must travel into the urban area for employment or casual purposes. However,
M2 artificially estimates a number of urban hotspots regarding BC emissions (72 % of the cells within the Fifth Ring Road are
overestimated) while substantially underestimating the emission density
between the Fifth and Sixth Ring Road (68 % of the cells in that region
are underestimated). Such distortion is caused by the simple assumption of a
proportional relationship between BC emissions and population density, as
well as the absent accounting of HDT driving restrictions within M2.
Comparison of link-level emission intensity of (a) CO and (b) BC
developed by various methods.
M3 reflects the topology of traffic emission as line sources to some extent
but underestimates CO emissions within the Fifth Ring Road compared with M1
by 28 %. This is because although M3 considers the traffic volume
characteristics according to region and road type, increased emission
factors due to traffic congestion are not accounted for. CO emission density
between the Fifth and Sixth Ring Road is overestimated by M3 because the
same coefficients as for the urban area are applied to calculate standard
road lengths. In contrast, M3 overestimates BC emissions in the urban area
but underestimates emissions between the Fifth and Sixth Ring Road, due to
the absent consideration of local traffic restrictions on HDTs. In addition,
we also observe that M3 tends to overestimate both CO and BC emissions in
the northern areas with intercity expressways; however, underestimations in
M3 are identified in the southern areas. This is because M3 considers
unified traffic volume weights for all of the intercity highways outside the
Sixth Ring Road. In reality, the traffic monitoring data reveal that
outlying expressways in the southern areas have greater traffic volumes than
the northern expressways, which connect to hilly and less populous regions.
Currently, secondary aerosols (e.g., nitrate and secondary organic aerosols)
are the leading chemical components of PM2.5 concentrations in Beijing.
Air quality management in this megacity requires fine-grained air quality
simulations by improving emission patterns and including chemical transport
simulations. As discussed above, the EMBEV-Link model enables us to
characterize the spatial heterogeneity in real-world traffic emissions to
support small-scale simulations (e.g., down to 1 km scale), which are
typically completed at approximately 3 to 4 km scales in current studies
(e.g., Zheng et al., 2019). To better fulfill this function, the seasonal
variability and species-resolved NMVOC profiles are also required to be
improved in the EMBEV-Link model.
Conclusions
This study presents the development of a high-resolution emission inventory of
vehicle emissions in Beijing (EMBEV-Link) by using multiple large-scale
traffic monitoring datasets. Real-time traffic congestion index maps,
intercity highway monitoring and manual traffic investigations were applied
to estimate link-level and hourly profiles for traffic volume, fleet
composition and road speed. We applied the EMBEV-Link model to four typical
traffic scenarios in order to elucidate spatial and temporal patterns of
vehicle emissions in association with different transportation management
schemes in Beijing. The vehicular NOx concentrations were simulated by
using the RapidAir® model at high spatial resolutions, meshed
into 10 m × 10 m cells in the entire municipality and further 1 m × 1 m cells in the hotspots.
The EMBEV-Link results indicate significant impacts on temporal and spatial
patterns of vehicle emissions caused by the traffic restrictions in Beijing.
Total vehicle emissions were estimated as 823 t for CO, 84.4 t for
THC, 326 t for NOx, 10.6 t for PM2.5 and 5.5 t for BC during an average weekday (S1) of 2013. CO and THC emissions are
featured as pollutants contributed by gasoline vehicles, whose peaks were
identified in the urban area and during traffic rush hours. By contrast,
NOx, PM2.5 and BC were considerably contributed by diesel fleets,
whose emissions peaked between the Fifth and Sixth Ring Road during the daytime
and then flooded within the Fifth Ring Road when truck restrictions were not
implemented. The overall emissions during weekends (S2) were close to the
weekday levels because the urban traffic restrictions on LDPVs were not
enforced during weekends. The absence of regular restrictions on LDPVs would
trigger serious congestion and lead to 12 % increases of CO and THC
emissions in the entire municipality (S3), in comparison with the normal
weekday levels. On the other hand, the stringent traffic controls
implemented during the APEC summit period (S4) could reduce vehicle emissions
by approximately 30 % to 40 %, varying by pollutant category.
We further demonstrated a few major improvements by EMBEV-Link compared with
previous emission inventory methods. First, the EMBEV estimated that
nonlocal HDTs contributed 2.46 t of NOx, 1.07 t of PM2.5
and 0.68 t of BC annually, which were responsible for
29 %, 38 % and 47 % of estimated total emissions in 2013.
Nevertheless, these emissions from nonlocal HDTs were missing from the
registration-based emission estimates. A considerable fraction of truck
traffic floods into the urban area after 23:00 GMT+8, resulting in
approximately 10 µg m-3 of nighttime NOx concentrations
there. Second, combined with the RapidAir® model, the
link-level emissions could represent a valuable asset to map high-resolution
concentrations of vehicular pollutants over a large geographical area. The
case study of NOx dispersion indicated a large contribution from
traffic emissions, with strong agreement with observation data in the urban
area and sharp elevation gradients from ambient areas to roads in the
hotspots. Finally, we also revealed that the conventional top-down
allocation methods according to population or road density could cause
significant uncertainties in the spatial distributions of vehicle emissions
because these allocation methods were limited to considering both the real
traffic patterns and the effects of local traffic restrictions.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8831-2019-supplement.
Author contributions
DY and SZ contributed equally to this research. SZ and YeW
conceived the research idea; DY, SZ and HX prepared the traffic
dataset; DY contributed to the new emission inventory model; TN
conducted the dispersion simulation; DY, SZ, YuW, KMZ and YeW analyzed the data; DY and TN drew the figures; and SZ, DY and YeW wrote the paper with contributions from all the authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was supported by the National Natural Science Foundation of China
(91544222) and the National Key Research and Development Program of China
(2017YFC0212100). Shaojun Zhang is supported by Cornell University's David R
Atkinson Center for a Sustainable Future (ACSF Postdoctoral Fellowship).
K. Max Zhang acknowledges support from the National Science Foundation (NSF)
through grant no. 1605407. The contents of this paper are solely the
responsibility of the authors and do not necessarily represent official
views of the sponsors or companies.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 91544222), the National Key Research and Development Program of China (grant no. 2017YFC0212100) and the National Science Foundation (grant no. 1605407).
Review statement
This paper was edited by Chul Han Song and reviewed by three anonymous referees.
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