Characterizing ammonia emission from light-duty gasoline vehicles under the influence of multiple factors and its correlation with conventional pollutants.

Ammonia (NH3), which is a precursor of secondary particulate matter (PM), can be produced through three-way catalyst (TWC) side reactions in light-duty gasoline vehicles (LDGVs), posing a threat to human health and air quality. To explore ammonia emission characteristics, 8 LDGVs and 1 hybrid electric light-duty vehicle (HEV) with various mileages traveled were analyzed with a chassis dynamometer system during regulation driving cycles. The emission factors of the adopted China VI in-use LDGVs were 7.04 ± 2.61 mg/km under cold-start conditions and 4.94 ± 1.69 mg/km under hot-start conditions. With increasing mileage traveled, the total ammonia emissions increased, and the difference between the cold/hot-start results decreased. The emissions of in-use LDGVs with bi-fuel engines were analyzed, and more ammonia was generated in the compressed natural gas (CNG) mode through the hydrocarbon (HC) reforming reaction. The relationship between the emissions of ammonia and conventional pollutants was established. During the initial cold-start phase, a delay in ammonia formation was observed, and the ammonia emissions conformed with the CO and HC emissions after exhaust heating. Vehicle specific power (VSP) analysis revealed that the interval of highest ammonia emissions corresponded to acceleration events at high speeds. For the HEV, the transition from motor to engine drive conditions contributed to ammonia emission occurrence because of the more pronounced cold-start events. The use of HEV technology could introduce additional uncertainties in controlling urban ammonia emissions. Detailed analysis of emission characteristics could provide data support for future research on ammonia emission standards and control strategies for LDGVs.

Chemodiversity of organic nitrogen emissions from light-duty gasoline vehicles is governed by engine displacements and driving speed.

Organic nitrogen emissions from light-duty gasoline vehicles (LDGVs) is believed to play a pivotal role in atmospheric particulate matter (PM) in urban environments. Here, the characterization of organic nitrogen emitted by LDGVs with varying engine displacements at different speed phases was analyzed using a Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at molecular level. For the LDGV with small engine displacements, the nitrogen-containing organic (CHON) compounds exhibit higher abundance, molecular weight, oxygen content and aromaticity in the extra-high-speed phase. Conversely, for the LDGV with big engine displacements, more CHON compounds with elevated abundance, molecular weight, oxygen content and aromaticity were observed in the low-speed phase. Our study assumed that the formation of CHON compounds emitted from LDGVs is mainly the oxidation reaction during fuel combustion, so the potential precursor-product pairs related to oxidation process were used to study the degree of combustion reaction. The results show that the highest proportion of oxidation occurs during extra-high-speed phase for LDGV with small engine displacements, and during low-speed phase for LDGV with big engine displacements. These results offer a novel perspective for comprehending the mechanism behind vehicle emissions formation and contribute valuable insights for crafting effective air pollution regulations.

Characterizing NOx emissions from diesel trucks with tampered aftertreatment systems and its implications for identifying high emitters.

Reducing the differences between real-world and certificated NOx emission levels is an important element of in-use emission surveillance programs. Therefore, investigating the characteristics of the vehicles which have much higher NOx emissions (i.e., high-emitters) and determining a reasonable cut-off point to identify high-emitters with a low false detection rate is important. In this study, six diesel trucks were tested under different aftertreatment conditions. The results showed that the discrepancies of fuel-specific NOx emissions between vehicles with functioning and tampered selective catalytic reduction (SCR) systems occur mainly from medium- to high-speed modes. This is because the SCR systems were at low conversion efficiencies when the exhaust temperature was low, including cold-start and urban creep conditions. By using binary classification, we selected fuel-specific NOx cut-off points for high-emitters from China V and China VI diesel trucks. The false detection rate of high-emitters can decrease by 33 % and 95 %, if only NOx emissions from medium- to high-speed modes were used for the chosen cut-off points, respectively. This work highlights the importance of in-use emission compliance programs. It also suggests that high-emitters can be more accurately identified at medium- to high-speed modes if using instantaneous emission data.

Assessment of identification performance for high emission heavy-duty diesel vehicles by means of remote sensing.

To improve the accuracy of detecting high NO (nitric oxide) emissions from heavy-duty diesel vehicles (HDDV) by remote sensing (RS), the emissions of one HDDV complied with China V regulation and one HDDV complied with China VI regulation at constant speeds, with and without after-treatment devices, are tested by a portable emission measurement system (PEMS) and RS. The optimized measurement procedures for detecting high NO emissions from China V and China VI HDDVs by RS are summarized. The correlation of RS and PEMS data shows that the ratio of NO to CO2 (carbon dioxide) is a more appropriate RS measurement than NO concentration alone for identifying high emitters, although NO concentrations of 600 ppm and 100 ppm can be used as a basis for distinguishing between China V and China VI HDDVs, respectively. When the NO/CO2 ratio is >200 × 10-4 and 25 × 10-4, identifying China V and China VI HDDV high emitters, respectively, is possible. Additionally considering the vehicle speed can reduce the high emitter identification error rate, and excluding data where vehicle acceleration is less than -0.1 m/s2 can further improve identification accuracy. Four new high-emitter identification methods based on different combinations of measurements are shown to improve identification efficiency with only small increases in identification error. This study provides evidence to support the future development of high-precision RS methodologies for identifying high-emission vehicles.

[Emission Factors of Carbonaceous Aerosol and Stable Carbon Isotope for In-use Vehicles].

Vehicle exhaust is an important anthropogenic source of atmospheric carbonaceous aerosols; of which, the emission factors and stable carbon isotope composition are important basic data. In-use motor vehicles of different types were selected to conduct dynamometer tests using different test cycles and under cold/hot start conditions. The exhaust of each test stage was collected to analyze the carbonaceous components and stable carbon isotopes and to discuss the influencing factors. The total carbon emission factors follow the order:heavy-duty diesel vehicles>light-duty diesel vehicles>light-duty gasoline vehicles. Although the emission factors of light-duty natural gas vehicles were very low at the low- and medium-speed stages, they were similar to those of heavy-duty diesel vehicles at the high-speed stage. The emission factors of cold start were higher than those of hot start, and the emission factors of the NEDC test cycle were lower than those of WLTC (which should be related to the driving speed). The emission factors of organic carbon (OC) of gasoline and natural gas vehicles were much higher than those of elemental carbon (EC) in every test stage. The emission factors of OC and EC of diesel vehicles were similar. The OC/EC of all types of vehicles increased with the increase in driving speed. Stable carbon isotopes in EC were higher than those in OC. The stable carbon isotope in different vehicles follow the order:light-duty gasoline vehicles

Exhaust aftertreatment device-derived ammonia emissions from conventional and hybrid light-duty gasoline vehicles over different driving cycles.

Ammonia emissions from motor vehicles have great effect on air pollution and human health in urban areas. Recently, many countries have focus on ammonia emission measurement and control technologies for light-duty gasoline vehicles (LDGVs). To analyze ammonia emission characteristics, three conventional LDGVs and one hybrid electric light-duty vehicle (HEV) were evaluated over different driving cycles. The average ammonia emission factor at 23℃ was 4.5 ± 1.6 mg/km over Worldwide harmonized light vehicles test cycle (WLTC). Most ammonia emissions mainly concentrated in low and medium speed sections at cold-start stage, which were related to rich burn conditions. The increasing ambient temperatures led to the decrease of ammonia emissions, but high load caused by extremely elevated ambient temperature led to obvious ammonia emissions. The ammonia formation is also related to three-way catalytic converter (TWC) temperatures, and underfloor TWC catalyst could eliminate ammonia partly. The ammonia emission from HEV, which are significant less than LDGV, corresponded to the engine working state. The large temperature difference in the catalysts caused by power source shifts were the main reason. Exploring the effects of various factors on the ammonia emission is beneficial for revealing the instinct formation conditions, providing theoretical support for the future regulations.

Impacts on real-world extra cold start emissions: Fuel injection, powertrain, aftertreatment and ambient temperature.

Vehicles emit substantial amounts of pollutants during start periods. Engine starts mainly occur in urban areas, causing serious harm to humans. To investigate the impacts on extra cold start emissions (ECSEs), eleven China 6 vehicles with various control technologies (fuel injection, powertrain, and aftertreatment) were monitored with a portable emission measurement system (PEMS) at different temperatures. For conventional internal combustion engine vehicles (ICEVs), the average ECSEs of CO2 increased by 24%, while the average ECSEs of NOx and particle number (PN) decreased by 38% and 39%, respectively, with air conditioning (AC) on. Gasoline direct injection (GDI) vehicles had 5% lower CO2 ECSEs, but 261% higher NOx ECSEs and 318% higher PN ECSEs than port fuel injection (PFI) vehicles at 23 °C. The average PN ECSEs were significantly reduced by gasoline particle filters (GPFs). The GPF filtration efficiency was higher in GDI than PFI vehicles due to particle size distribution. Hybrid electric vehicles (HEVs) generated excessive PN extra start emissions (ESEs), resulting in a 518% increase compared to ICEVs. The start times of the GDI-engine HEV accounted for 11% of the whole test time, but the proportion of PN ESEs relative to total emissions were 23%. Linear simulation based on the decrease in ECSEs with increasing temperature underestimated the PN ECSEs from PFI and GDI vehicles by 39% and 21%, respectively. For ICEVs, CO ECSEs varied with temperature in a U shape with a minimum at 27 °C; NOx ECSEs decreased as ambient temperature increased; PFI vehicles generated more PN ECSEs at 32 °C than GDI vehicles, stressing the significance of ECSEs at high temperature. These results are useful for improving emission models and assessing air pollution exposure in urban aeras.

Recent advances and perspectives towards emission inventories of mobile sources: Compilation approaches, data acquisition methods, and case studies.

In recent years, great efforts have been devoted to reducing emissions from mobile sources with the dramatic growth of motor vehicle and nonroad mobile source populations. Compilation of a mobile source emission inventory is conducive to the analysis of pollution emission characteristics and the formulation of emission reduction policies. This study summarizes the latest compilation approaches and data acquisition methods for mobile source emission inventories. For motor vehicles, a high-resolution emission inventory can be developed based on a bottom-up approach with a refined traffic flow model and real-world speed-coupled emission factors. The top-down approach has advantages when dealing with macroscale vehicle emission estimation without substantial traffic flow infrastructure. For nonroad mobile sources, nonroad machinery, inland river ships, locomotives, and civil aviation aircraft, a top-down approach based on fuel consumption or power is adopted. For ocean-going ships, a bottom-up approach based on automatic identification system (AIS) data is adopted. Three typical cases are studied, including emission reduction potential, a cost-benefit model, and marine shipping emission control. Outlooks and suggestions are given on future research directions for emission inventories for mobile sources: building localized emission models and factor databases, improving the dynamic updating capability of emission inventories, establishing a database of emission factors of unconventional pollutants and greenhouse gas from mobile sources, and establishing an urban high temporal-spatial resolution volatile organic compound (VOC) evaporation emission inventory.

Evaluation of real-world emissions of China V heavy-duty vehicles fueled by diesel, CNG and LNG on various road types.

Natural gas has been widely considered as an alternative fuel to diesel on heavy-duty vehicles (HDVs). To evaluate the real-world emissions, 19 diesel HDVs, 3 CNG HDVs and 9 LNG HDVs were tested using portable emission measurement system on urban, suburban and freeway roads . The results show that average emission rates of CO, HC and NOx from diesel, CNG and LNG HDVs tend to rise with the increasing of VSP. Due to different emission control strategies and complicated driving cycle, NOx reduction rates of real-world emission factors are lower than those of the corresponding limits from China II to China V diesel HDVs. CO and NOx emission factors from all tested HDVs on urban road are generally higher than those on suburban road in the same velocity intervals, which means driving behaviors on different road types have great influence on emission factors in the same velocity intervals. NOx emission factors from LNG HDVs are higher than those from diesel HDVs, indicating that using China V LNG HDVs instead of China V diesel HDVs could not be an ideal alternative for freeway transportation. As CO and NOx comprehensive emission factors from China V CNG HDVs are higher than those from China V diesel, even higher than China IV diesel HDVs, China V CNG HDVs are unable to be considered as cleaner vehicles when compared to China V diesel HDVs in the study. It is noticeable that HC emission factors from CNG and LNG HDVs are extremely higher compare with diesel HDVS. NO2 emission factors from LNG HDVs are 2.14-9.19 and 4.74-8.53 times than those from diesel and CNG HDVs with various road types. Our study can provide development road map for different fuel types of HDVs and the reference for new emission legislation of HDVs.

Study on real-world power-based emission factors from typical construction machinery.

Construction machinery accounts for a large share of the non-road machinery market and is an important pollutant source. In this study, real-world emission tests were undertaken on 16 excavators and 19 wheel loaders by using a portable emission measurement system (PEMS) to obtain their real-world engine performance and emission factors. The typical operating modes were categorized as idling, moving and working modes. The results show that in the working mode, the engine continuously operated at a high speed, high torque and high load factor, which led to sharp up and down CO, HC, NOx and particle number (PN) emissions. As the emission standards were strengthened, the CO, HC and NOx emissions clearly decreased, and the excavators and wheel loaders showed a higher emission level in the moving mode. A comparison of the excavator and wheel loader in China III displayed that the CO, HC, and NOx total emission factors of the wheel loader were 10.20, 2.70 and 1.79 times higher than those of the excavator, respectively, so the wheel loader contributed more seriously to environmental pollution. The CO and HC measured emission factors of the excavators and wheel loaders were 0.05-1.38 times and 0.13-0.58 times higher than the corresponding Limitation and the recommended values from the national emission inventory Guideline. However, the NOx emission factors were 1.20-3.25 times higher than the corresponding Limitation and the recommended value from the Guideline, which means that the recommended values overestimate CO and HC, and underestimate NOx. A comparison of the emission factors of the excavators and wheel loaders in this study with others demonstrate that the CO and HC power-based emission factors were generally lower, but the NOx emission factor was higher.

Modeling the emissions of rural vehicles based on real-world driving cycles.

Characterized by a high frequency of use, harsh working environments, poor maintenance, and low levels of emission controls, rural vehicles (RVs) are becoming an important source of air pollution. Our study used a portable emission measurement system (PEMS) to test the real-world emissions of 35 RVs on provincial, rural, and farm roads. The results show that high emission rates of carbon monoxide (CO) and hydrocarbons (HC) mainly occurred when accelerating at low speeds. However, high levels of nitrogen oxides (NOx) were emitted during high-speed acceleration. The particulate number (PN) of emissions was higher when the RVs were accelerating. According to the overall test results, the vehicle specific power (VSP) on the provincial road mostly ranged within (0, 3], accounting for 68.80% of the total. The VSP on rural and farm roads was concentrated within (0, 2] kW·ton-1, accounting for 67.09% and 76.64% of the total, respectively. We defined 14 bins based on the distribution of the VSP values and calculated the average emission rate of each bin. By comparing the average emission rate among the bins, we found that within Bins 1-7 (VSP < 0 kW·ton-1), CO, HC, and NOx emissions slowly increased as the VSP increased. In Bins 8-13 (VSP ≥ 0 kW·ton-1), the average emission rates of four pollutants increased as the VSP increased. However, all pollutants decreased in Bin 14 (VSP ≥ 6 kW·ton-1). We built a microscopic emission model according to the VSP distribution characteristics of RVs on different road types. We compared the measured and simulated emission factors and found that our emission model can greatly simulate the HC, NOx and PN emission factors of RVs.

Study on the real-world emissions of rural vehicles on different road types.

To better understand the real-world emissions of rural vehicles (RVs) in China, 8 China II RVs and 18 China III RVs were tested on a provincial road, rural road and farm road using a portable emissions measurement system. The results are illustrated in contour maps of the speed, acceleration and emission rates and show that CO, HC, NOx and PM emissions differ for the three road types; however, the peak emission points all occur on the provincial road. The average CO, HC, NOx and PM emission factors based on distance for the China II RVs are 9.21, 4.05, 1.68 and 2.58 times higher, respectively, than those of the China III RVs. However, the average NOx emission factors of the China II and III RVs are 2.21 and 1.65 times higher than the corresponding recommended values of national emission inventory guideline, resulting in underestimation of overall RVs' emissions. Distance-based emission factors of four pollutants ranked from high to low are farm road > rural road > provincial road. In contrast to the average emission factors of the China II RVs on the three road types, those of the China III RVs are significantly less in terms of distance and fuel consumption. The results of other researchers differ from those in this study: the CO emission factor of the China II RVs is 2.12 times higher than that of the China II light-duty diesel vehicles (LDDVs). The PM emission factor of the China III RVs is 2.67 times higher than that of the China III LDDVs. The NOx emission factors of the China II and III RVs are similar to those of the corresponding China II and III LDDVs. Our research increases the understanding of real-world emissions of RVs and can act as great references for policy makers developing RV emission baselines.

Source-Receptor Relationship Revealed by the Halted Traffic and Aggravated Haze in Beijing during the COVID-19 Lockdown.

The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles but coincided with Beijing experiencing "pandemic haze," raising the public concerns about the effectiveness of imposed traffic policies to improve the air quality. This paper explores the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown based on an integrated analysis framework, which combines a real-time on-road emission inventory, in situ air quality observations, and a localized numerical modeling system. We found that traffic emissions decreased substantially during the COVID-19 pandemic, but its imbalanced emission abatement of NOx (76%, 125.3 Mg/day) and volatile organic compounds (VOCs, 53%, 52.9 Mg/day) led to a significant rise of atmospheric oxidants in urban areas, resulting in a modest increase in secondary aerosols due to inadequate precursors, which still offset reduced primary emissions. Moreover, the enhanced oxidizing capacity in the surrounding regions greatly increased the secondary particles with relatively abundant precursors, which was transported into Beijing and mainly responsible for the aggravated haze pollution. We recommend that mitigation policies should focus on accelerating VOC emission reduction and synchronously controlling regional sources to release the benefits of local traffic emission control.

Intermediate-Volatility Organic Compound Emissions from Nonroad Construction Machinery under Different Operation Modes.

Intermediate-volatility organic compounds (IVOCs) have been found as important sources for secondary organic aerosol (SOA) formation. IVOC emissions from nonroad construction machineries (NRCMs), including two road rollers and three motor graders, were characterized under three operation modes using an improved portable emission measurement system. The fuel-based IVOC emission factors (EFs) of NRCMs varied from 245.85 to 1802.19 mg/kg·fuel, which were comparable at magnitudes to the reported results of an ocean-going ship and on-road diesel vehicles without filters. The discrepancy of IVOC EFs is significant within different operation modes. IVOC EFs under the idling mode were 1.24-3.28 times higher than those under moving/working modes. Unspeciated b-alkanes and cyclic compounds, which were the unresolved components in IVOCs at the molecular level, accounted for approximately 91% of total IVOCs from NRCMs. The SOA production potential analysis shows that IVOCs dominated SOA formation of NRCMs. Our results demonstrate that IVOC emissions from NRCMs are non-negligible. Thus, an accurate estimation of their IVOC emissions would benefit the understanding of SOA formation in the urban atmosphere.

DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation.

In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box's corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D⁻3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence⁻evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods.

An Occlusion-Aware Framework for Real-Time 3D Pose Tracking.

Random forest-based methods for 3D temporal tracking over an image sequence have gained increasing prominence in recent years. They do not require object's texture and only use the raw depth images and previous pose as input, which makes them especially suitable for textureless objects. These methods learn a built-in occlusion handling from predetermined occlusion patterns, which are not always able to model the real case. Besides, the input of random forest is mixed with more and more outliers as the occlusion deepens. In this paper, we propose an occlusion-aware framework capable of real-time and robust 3D pose tracking from RGB-D images. To this end, the proposed framework is anchored in the random forest-based learning strategy, referred to as RFtracker. We aim to enhance its performance from two aspects: integrated local refinement of random forest on one side, and online rendering based occlusion handling on the other. In order to eliminate the inconsistency between learning and prediction of RFtracker, a local refinement step is embedded to guide random forest towards the optimal regression. Furthermore, we present an online rendering-based occlusion handling to improve the robustness against dynamic occlusion. Meanwhile, a lightweight convolutional neural network-based motion-compensated (CMC) module is designed to cope with fast motion and inevitable physical delay caused by imaging frequency and data transmission. Finally, experiments show that our proposed framework can cope better with heavily-occluded scenes than RFtracker and preserve the real-time performance.

The impact of marine shipping and its DECA control on air quality in the Pearl River Delta, China.

Marine trade has significantly expanded over the past decades aiding to the economic development of the maritime countries, yet, this has been associated with a considerable increase in pollution emission from shipping operation. This study aims at considering both sides of the spectrum at the same time, which is including both public and shipping business. Of the key significance would be to optimize the operation of the shipping industry, such that its impact on air pollution is minimized, without, however, significant escalation of its cost, and therefore to protect the whole seaborne trade. To do this, we considered the impacts of three control strategies, including the current emission control area (ECA) design, as well two additional ones. Thus the first scenario (DECA1) was based on the China's domestic emission control area (DECA), which was set up in 2016. The DECA1 scale was only 12 nautical miles, which was much smaller than the emission control areas in US or Europe. We defined the second scenario (DECA2), by stretching the zone to 200 nautical miles towards the ocean, modeling it on the ECA in North America. The third scenario (DECA3), on the other hand, expanded the 12 nautical miles control zone along the whole coastline. To investigate the impact of shipping emissions on air quality, a shipping emission calculation model and an air quality simulation model were used, and Pearl River Delta (PRD), China was chosen to serve as a case study. The study demonstrated that in 2013 marine shipping emissions contributed on average 0.33 and 0.60µg·m-3, respectively to the land SO2 and PM2.5 concentrations in the PRD, and that the concentrations were high along the coastline. The DECA1 policy could effectively reduce SO2 and PM2.5 concentrations in the port regions, and the average reduction in the land area were 9.54% and 2.7%, respectively. Compared with DECA1, DECA2 would not measurably improve the air quality, while DECA3 would effectively decrease the pollution in the entire coast area. Thus, instead of expanding emission control area far to the ocean, it is more effective to control emissions along the coastline to secure the best air quality and lower the health impacts. By doing this, 19 million dollars of fuel cost could be saved per year. The saved cost could help the ship owners to endure, considering the current low profits of the seaborne trade, and thus to protect the overall growth of the economy.

Emission characteristics of offshore fishing ships in the Yellow Bo Sea, China.

Maritime transport has been playing a decisive role in global trade. Its contribution to the air pollution of the sea and coastal areas has been widely recognized. The air pollutant emission inventories of several harbors in China have already been established. However, the emission factors of local ships have not been addressed comprehensively, and thus are lacking from the emission inventories. In this study, on-board emission tests of eight diesel-powered offshore fishing ships were conducted near the coastal region of the northern Yellow Bo Sea fishing ground of Dalian, China. Results show that large amounts of fine particles (<0.5µm, 90%) were found in maneuvering mode, which were about five times higher than those during cruise mode. Emission rates as well as emission factors based on both distance and fuel were determined during the cruise and maneuvering modes (including departure and arrival). Average emission rates and distance-based emission factors of CO, HC and PM were much higher during the maneuvering mode as compared with the cruise mode. However, the average emission rate of Nitrous Oxide (NOx) was higher during the cruise mode as compared with the maneuvering modes. On the contrary, the average distance-based emission factors of NOx were lower during the cruise mode relative to the maneuvering mode due to the low sailing speed of the latter.

Shipping emission forecasts and cost-benefit analysis of China ports and key regions' control.

China established Domestic Emission Control Area (DECA) for sulphur since 2015 to constrain the increasing shipping emissions. However, future DECA policy-makings are not supported due to a lack of quantitive evaluations. To investigate the effects of current and possible Chinese DECAs policies, a model is presented for the forecast of shipping emissions and evaluation of potential costs and benefits of an DECA policy package set in 2020. It includes a port-level and regional-level projection accounting for shipping trade volume growth, share of ship types, and fuel consumption. The results show that without control measures, both SO2 and particulate matter (PM) emissions are expected to increase by 15.3-61.2% in Jing-Jin-Ji, the Yangtze River Delta, and the Pearl River Delta from 2013 to 2020. However, most emissions can be reduced annually by the establishment of a DECA that depends on the size of the control area and the fuel sulphur content limit. Costs range from 0.667 to 1.561 billion dollars (control regional shipping emissions) based on current fuel price. A social cost method shows the regional control scenarios benefit-cost ratios vary from 4.3 to 5.1 with large uncertainty. Chemical transportation model combined with health model method is used to get the monetary health benefits and then compared with the results from social cost method. This study suggests that Chinese DECAs will reduce the projected emissions at a favorable benefit-cost ratio, and furthermore proposes policy combinations that provide high cost-effective benefits as a reference for future policy-making.