Characterizing Operating Condition-Based Formaldehyde Emissions of Light-Duty Diesel Trucks in China Using a PEMS-HCHO System.

Formaldehyde (HCHO) plays a critical role in atmospheric photochemistry and public health. While existing studies have suggested that vehicular exhaust is an important source of HCHO, the operating condition-based diesel truck HCHO emission measurements remain severely limited due to the limited temporal resolution and accuracy of measurement techniques. In this study, we characterized the second-by-second HCHO emissions from 29 light-duty diesel trucks (LDDTs) in China over dynamometer and real-world driving tests using a portable online HCHO emission measurement system (PEMS-HCHO), considering various operating conditions. Our results suggested that the HCHO emissions from LDDTs might be underestimated by the widely used offline DNPH-HPLC method. The HCHO emissions at a 200 s cold start from China V LDDT can be up to 50 mg/start. Different driving conditions over dynamometer and real-world driving tests led to a 2-4 times difference in the HCHO emission factors (EFs). Under real-world hot-running conditions, the HCHO EFs of China III, IV, V, and VI LDDTs were 43.5 ± 35.7, 10.6 ± 14.2, 8.8 ± 5.1, and 3.2 ± 1.2 mg/km, respectively, which significantly exceeded the latest California low emission vehicle III HCHO emission standard (2.5 mg/km). These findings highlighted the significant impact of vehicle operating conditions on HCHO emissions and the urgency of regulating HCHO emissions from LDDTs in China.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China.

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

High Gaseous Nitrous Acid (HONO) Emissions from Light-Duty Diesel Vehicles.

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (•OH) in the atmosphere. Vehicular emissions are a crucial primary source of atmospheric HONO, yet remain poorly investigated, especially for diesel trucks. In this study, we developed a novel portable online vehicular HONO exhaust measurement system featuring an innovative dilution technique. Using this system coupled with a chassis dynamometer, we for the first time investigated the HONO emission characteristics of 17 light-duty diesel trucks (LDDTs) and 16 light-duty gasoline vehicles in China. Emissions of HONO from LDDTs were found to be significantly higher than previous studies and gasoline vehicles tested in this study. The HONO emission factors of LDDTs decrease significantly with stringent control standards: 1.85 ± 1.17, 0.59 ± 0.25, and 0.15 ± 0.14 g/kg for China III, China IV, and China V, respectively. In addition, we found poor correlations between HONO and NOx emissions, which indicate that using the ratio of HONO to NOx emissions to infer HONO emissions might lead to high uncertainty of HONO source budget in previous studies. Lastly, the HONO emissions are found to be influenced by driving conditions, highlighting the importance of conducting on-road measurements of HONO emissions under real-world driving conditions. More direct measurements of the HONO emissions are needed to improve the understanding of the HONO emissions from mobile and other primary sources.

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models.

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two  pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol.

Origin and Radiative Forcing of Black Carbon Aerosol: Production and Consumption Perspectives.

Air pollution, a threat to air quality and human health, has attracted ever-increasing attention in recent years. In addition to having local influence, air pollutants can also travel the globe via atmospheric circulation and international trade. Black carbon (BC), emitted from incomplete combustion, is a unique but representative particulate pollutant. This study tracked down the BC aerosol and its direct radiative forcing to the emission sources and final consumers using the global chemical transport model (MOZART-4), the rapid radiative transfer model for general circulation simulations (RRTM), and a multiregional input-output analysis (MRIO). BC was physically transported (i.e., atmospheric transport) from western to eastern countries in the midlatitude westerlies, but its magnitude is near an order of magnitude higher if the virtual flow embodied in international trade is considered. The transboundary effects on East and South Asia by other regions increased from about 3% (physical transport only) to 10% when considering both physical and virtual transport. The influence efficiency on East Asia was also large because of the comparatively large emission intensity and emission-intensive exports (e.g., machinery and equipment). The radiative forcing in Africa imposed by consumption from Europe, North America, and East Asia (0.01 Wm-2) was even larger than the total forcing in North America. Understanding the supply chain and incorporating both atmospheric and virtual transport may improve multilateral cooperation on air pollutant mitigation both domestically and internationally.

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM2.5 Simulations.

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM2.5 boundary conditions and primary PM2.5 emissions are found to be the two major uncertainty sources in PM2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

Ambient Ozone Control in a Photochemically Active Region: Short-Term Despiking or Long-Term Attainment?

China has made significant progress decreasing the ambient concentrations of most air pollutants, but ozone (O3) is an exception. O3 mixing ratios during pollution episodes are far higher than the national standard in the Pearl River Delta (PRD), thus greater evidence-based control efforts are needed for O3 attainment. By using a validated O3 modeling system and the latest regional emission inventory, this study illustrates that control strategies for short-term O3 despiking and long-term attainment in the PRD may be contradictory. VOC-focused controls are more efficient for O3 despiking in urban and industrial areas, but significant NOx emission reductions and a subsequent transition to a NOx-limited regime are required for O3 attainment. By tracking O3 changes along the entire path toward long-term attainment, this study recommends to put a greater focus on NOx emission controls region-wide. Parallel VOC reductions around the Nansha port are necessary in summertime and should be extended to the urban and industrial areas in fall with a flexibility to be strengthened on days forecasted to have elevated O3. Contingent VOC-focused controls on top of regular NOx-focused controls would lay the groundwork for striking a balance between short-term despiking and long-term attainment of O3 concentrations in the PRD.

Leveraging opportunity of low carbon transition by super-emitter cities in China.

Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

Hidden dependence of spreading vulnerability on topological complexity.

Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time-commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its "spreading vulnerability," is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the "entropy of temporal entanglement" as a quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological, and engineered systems.

Quantifying agent impacts on contact sequences in social interactions.

Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions-since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time-analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual's behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential 'behavioral super-spreaders'. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.

Near-real-time estimation of hourly open biomass burning emissions in China using multiple satellite retrievals.

Open biomass burning (OBB) is an important source of air pollutants and greenhouse gases, but its dynamic emission estimation remains challenging. Existing OBB emission datasets normally provide daily estimates based upon Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals but tend to underestimate the emissions due to the coarse spatial resolution and sparse observation frequency. In this study, we proposed a novel approach to improve OBB emission estimations by fusing multiple active fires detected by MODIS, Visible Infrared Imaging Radiometer onboard the Suomi National Polar-orbiting Partnership (VIIRS S-NPP) and Himawari-8. The fusion of multiple active fires can capture the missing small fires and the large fires take place during the non-overpass time of MODIS observations. Also, regional-based fire radiative power (FRP) cycle reconstruction models and OBB emission coefficients were developed to address the large spatial discrepancies of OBB emission estimations across China and to promote the estimate to an hourly resolution. Using the new approach, hourly gridded OBB emissions in China were developed and can be updated with a lag of 1-day, or even near-real-time when real-time multiple active fires are available. OBB emissions in China based on this approach were more than 3 times of those in previous datasets. Evaluations revealed that the spatial distribution of the estimated PM2.5 emissions from this study was more consistent with the ambient PM2.5 concentrations during several episodes than existing datasets. The hourly OBB emissions provide new insight into its spatiotemporal variations, enhance timely and reliable air quality modeling and forecast, and support the formulation of accurate prevention and control policies of OBB.

Variability in real-world emissions and fuel consumption by diesel construction vehicles and policy implications.

Strategically reducing the emission of non-road mobile source especially diesel construction vehicle (DCV) has a large potential in improving air quality and has attracted much scientific and public attention in recent years around the world. In this study, we explored real-world fuel consumption rate and gaseous emissions factors for multiple pollutants of three typical DCVs in China. The sampling campaign considered the operation mode, cumulative operation hour, emission standard stage and engine power. Results show that the accumulated fuel consumption per hour of vehicle weight for working, load-free moving and idling modes was 0.3, 0.2 and 0.1 kg/h·tons, respectively. The fuel-based NOx emission factor exhibited a bimodal distribution at 27 and 41 g/kg. The fuel-based emission factors for volatile organic compounds (VOCs) were in the range of 0.8 to 2.6 g/kg, where alkene and alkane were the dominant components (>80%), i.e., ethylene, acetylene, propylene, and isobutane. We observed that the ratio of toluene and benzene concentration (T/B) (1.4 ± 1.3) differed from other key emission sources and may be used as the specific indicator of DCV emission exhaust. Our estimates suggest that in 2017 the fuel consumption and NOx emissions of DCV emission accounted for 22-28% of non-road mobile sources in China; NOX emissions were 2.7 times higher than those in 2006, and it is forecasted that NOx emissions would reduce by 23% between 2017 and 2025 with the implementation of stage IV and the strict supervision policy. The comprehensive dataset on DCV emissions will either guide the government to establish precise and effective policies to regulate the non-road mobile source or significantly improve our understanding of source apportionment of atmospheric NOx and VOCs, both of which are key precursors of haze and ozone pollution.

Role of export industries on ozone pollution and its precursors in China.

This study seeks to estimate how global supply chain relocates emissions of tropospheric ozone precursors and its impacts in shaping ozone formation. Here we show that goods produced in China for foreign markets lead to an increase of domestic non-methane volatile organic compounds (NMVOCs) emissions by 3.5 million tons in 2013; about 13% of the national total or, equivalent to half of emissions from European Union. Production for export increases concentration of NMVOCs (including some carcinogenic species) and peak ozone levels by 20-30% and 6-15% respectively, in the coastal areas. It contributes to an estimated 16,889 (3,839-30,663, 95% CI) premature deaths annually combining the effects of NMVOCs and ozone, but could be reduced by nearly 40% by closing the technology gap between China and EU. Export demand also alters the emission ratios between NMVOCs and nitrogen oxides and hence the ozone chemistry in the east and south coast.

China CO2 emission accounts 1997-2015.

China is the world's top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China's CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China.

Reconciling discrepancies in the source characterization of VOCs between emission inventories and receptor modeling.

Emission inventory (EI) and receptor model (RM) are two of the three source apportionment (SA) methods recommended by Ministry of Environment of China and used widely to provide independent views on emission source identifications. How to interpret the mixed results they provide, however, were less studied. In this study, a cross-validation study was conducted in one of China's fast-developing and highly populated city cluster- the Pearl River Delta (PRD) region. By utilizing a highly resolved speciated regional EI and a region-wide gridded volatile organic compounds (VOCs) speciation measurement campaign, we elucidated underlying factors for discrepancies between EI and RM and proposed ways for their interpretations with the aim to achieve a scientifically plausible source identification. Results showed that numbers of species, temporal and spatial resolutions used for comparison, photochemical loss of reactive species, potential missing sources in EI and tracers used in RM were important factors contributed to the discrepancies. Ensuring the consensus of species used in EIs and RMs, utilizing a larger spatial coverage and longer time span, addressing the impacts of photochemical losses, and supplementing emissions from missing sources could help reconcile the discrepancies in VOC source characterizations acquired using both approaches. By leveraging the advantages and circumventing the disadvantages in both methods, the EI and RM could play synergistic roles to obtain robust SAs to improve air quality management practices.

Species-specified VOC emissions derived from a gridded study in the Pearl River Delta, China.

This study provides a top-down approach to establish an emission inventory of volatile organic compounds (VOC) based on ambient measurements, by combining the box model and positive matrix factorization (PMF) model. Species-specified VOC emissions, source contributions, and spatial distributions are determined based on regional-scale gridded measurements between September 2008 to December 2009 in the Pearl River Delta (PRD), China. The most prevalent anthropogenic species in the PRD was toluene estimated by the box model to be annual emissions of 167.8 ± 100.5 Gg, followed by m,p-xylene (68.0 ± 45.0 Gg), i-pentane (49.2 ± 40.0 Gg), ethene (47.6 ± 27.6 Gg), n-butane (47.5 ± 40.7 Gg), and benzene (46.8 ± 29.0 Gg). Alkanes such as propane, i-butane, and n-pentane were 2-8 times higher in box model than emission inventories (EI). Species with fewer emissions were highly variable between EI and box model results. Hotspots of VOC emissions were identified in southwestern PRD and port areas, which were not reflected by bottom-up EI. This suggests more research is needed for VOC emissions in the EI, especially for fuel evaporation, industrial operations and marine vessels. The species-specified top-down method can help improve the quality of these emission inventories.

Sector-based VOCs emission factors and source profiles for the surface coating industry in the Pearl River Delta region of China.

Accurate depiction of VOCs emission characteristics is essential for the formulation of VOCs control strategies. As one of the continuous efforts in improving VOCs emission characterization in the Pearl River Delta (PRD) region, this study targeted on surface coating industry, the most important VOCs emission sources in the PRD. Sectors in analysis included shipbuilding coating, wood furniture coating, metal surface coating, plastic surface coating, automobile coating and fabric surface coating. Sector-based field measurement was conducted to characterize VOCs emission factors and source profiles in the PRD. It was found that the raw material-based VOCs emission factors for these six sectors ranged from 0.34 to 0.58kg VOCs per kg of raw materials (kg·kg-1) while the emission factors based on the production yield varied from 0.59kg to 13.72t VOCs for each production manufactured. VOCs emission factors of surface coating industry were therefore preferably calculated based on raw materials with low uncertainties. Source profiles differed greatly among different sectors. Aromatic was the largest group for shipbuilding coating, wood furniture coating, metal surface coating and automobile coating while the oxygenated VOCs (OVOCs) were the most abundant in the plastic and fabric surface coating sectors. The major species of aromatic VOCs in each of these six sectors were similar, mainly toluene and m/p-xylene, while the OVOCs varied among the different sectors. VOCs profiles in the three processes of auto industry, i.e., auto coating, auto drying and auto repairing, also showed large variations. The major species in these sectors in the PRD were similar with other places but the proportions of individual compounds were different. Some special components were also detected in the PRD region. This study highlighted the importance of updating local source profiles in a comprehensive and timely manner.

An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China.

Ship emissions contribute significantly to air pollution and impose health risks to residents along the coastal area. By using the refined data from the Automatic Identification System (AIS), this study developed a highly resolved ship emission inventory for the Pearl River Delta (PRD) region, China, home to three of ten busiest ports in the world. The region-wide SO2, NOX, CO, PM10, PM2.5, and VOC emissions in 2013 were estimated to be 61,484, 103,717, 10,599, 7155, 6605, and 4195t, respectively. Ocean going vessels were the largest contributors of the total emissions, followed by coastal vessels and river vessels. In terms of ship type, container ship was the leading contributor, followed by conventional cargo ship, dry bulk carrier, fishing ship, and oil tanker. These five ship types accounted for >90% of total emissions. The spatial distributions of emissions revealed that the key emission hot spots all concentrated within the newly proposed emission control area (ECA) and ship emissions within ECA covered >80% of total ship emissions in the PRD, highlighting the importance of ECA in emissions reduction in the PRD. The uncertainties of emission estimates of pollutants were quantified, with lower bounds of -24.5% to -21.2% and upper bounds of 28.6% to 33.3% at 95% confidence intervals. The lower uncertainties in this study highlighted the powerfulness of AIS data in improving ship emission estimates. The AIS-based bottom-up methodology can be used for developing and upgrading ship emission inventory and formulating effective control measures on ship emissions in other port regions wherever possible.

Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China.

The increasing ground-ozone (O3) levels, accompanied by decreasing SO2, NO2, PM10 and PM2.5 concentrations benefited from air pollution control measures implemented in recent years, initiated a serious challenge to control Volatile Organic Compound (VOC) emissions in the Pearl River Delta (PRD) region, China. Speciated VOC emission inventory is fundamental for estimating Ozone Formation Potentials (OFPs) to identify key reactive VOC species and sources in order to formulate efficient O3 control strategies. With the use of the latest bulk VOC emission inventory and local source profiles, this study developed the PRD regional speciated Oxygenated Volatile Organic Compound (OVOC) and VOC emission inventories to identify the key emission-based and OFP-based VOC sources and species. Results showed that: (1) Methyl alcohol, acetone and ethyl acetate were the major constituents in the OVOC emissions from industrial solvents, household solvents, architectural paints and biogenic sources; (2) from the emission-based perspective, aromatics, alkanes, OVOCs and alkenes made up 39.2%, 28.2%, 15.9% and 10.9% of anthropogenic VOCs; (3) from the OFP-based perspective, aromatics and alkenes become predominant with contributions of 59.4% and 25.8% respectively; (4) ethene, m/p-xylene, toluene, 1,2,4-trimethyl benzene and other 24 high OFP-contributing species were the key reactive species that contributed to 52% of anthropogenic emissions and up to 80% of OFPs; and (5) industrial solvents, industrial process, gasoline vehicles and motorcycles were major emission sources of these key reactive species. Policy implications for O3 control strategy were discussed. The OFP cap was proposed to regulate VOC control policies in the PRD region due to its flexibility in reducing the overall OFP of VOC emission sources in practice.

[Marine Emission Inventory and Its Temporal and Spatial Characteristics in the City of Shenzhen].

To analyze the characteristic of marine emission in Shenzhen City, activity-based and fuel-based approaches were utilized to develop the marine emission inventory for the year of 2010, using the vessel files from the Lloyd's register of shipping (LR) and vessel track data from the automatic identification system (AIS). The marine emission inventory was temporally (resolution: 1 hour) and spatially (resolution: 1 km x 1 km) allocated based on the vessel track data. Results showed that total emissions of SO2, NO(x), CO, PM10, PM2.5 and VOCs from marine vessels in Shenzhen City were about 13.6 x 10(3), 23.3 x 10(3), 2.2 x 10(3), 1.9 x 10(3), 1.7 x 10(3) and 1. x 10(3) t, respectively. Among various types of marine vessels, emission from container vessels was the highest; for different driving modes, hotelling mode was found with the largest mission. Marine emissions were generally higher in the daytime, with vessel-specific peaks. For spatial distributions, in general, marine emissions were zonally distributed with hot spots in the western port group, Dapeng Bay and the key waterway.