[High Resolution Emission Inventory of Greenhouse Gas and Its Characteristics in Guangdong，China].

Based on the typical city survey data and statistics of Guangdong Province， a 2018-based 3 km×3 km gridded greenhouse gas emissions inventory was developed for Guangdong Province using the combination of top-down and bottom-up emission factor methods. The inventory covered the CO2， CH4， and N2O emissions from energy， industrial processes， agriculture， land use change and forest， waste management， and indirect sources. The results showed that estimates for CO2， CH4， and N2O in Guangdong Province for the year 2018 were 8.5×108， 1.9×106， and 1.1×105 t， respectively， and 8.5×108， 4.0×107， and 3.4×107 t by equivalent carbon dioxide， totaling 9.2×108 t. CO2 was the main greenhouse gas in Guangdong Province， accounting for 92.0% of the total emissions. Energy and indirect sources were the main emission sources， accounting for 77.9% and 7.6%， respectively， totaling 85.5%. Spatial distributions illustrated that most grids were greenhouse gas emissions， whereas some others were greenhouse gas sinks； the greenhouse gas emissions were distributed mainly in the Pearl River Delta region and had certain characteristics of distribution along the road network and channels. The greenhouse gas grids of high emission were mainly the locations of high energy-consuming enterprises such as large power plants， steel mills， and cement plants.

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.

Characterizing the particle number emissions of light-duty gasoline vehicles under different engine technologies and driving conditions.

Vehicle particle number (PN) emissions have attracted increasing public attention due to their severe influence on human health. In this study, we selected 35 light-duty gasoline vehicles (LDGVs) with gasoline direct injection (GDI) and multi-port fuel injection (MPFI) engines to elucidate the main factors influencing PN emissions. Via real driving emission (RDE) and chassis dynamometer tests, we quantified the impact of engine technology, emission standards, engine-start conditions and engine load on vehicle PN emissions. The RDE test results indicated that GDI vehicles generated higher PN emissions than those of MPFI vehicles under hot-running conditions. MPFI vehicle PN emissions were greatly affected by rapidly changing driving conditions, especially vehicles equipped with automatic start-stop systems. In regard to China 6 GDI vehicles equipped with a gasoline particle filter (GPF), their PN emissions were usually low, and peak PN emissions could mainly be attributed to GPF regeneration. Engine manufacturers should optimize GPF regeneration conditions to further reduce particulate emissions. Furthermore, the analysis results of PN emissions for different road types indicated that PN emissions were related to vehicle driving conditions. The vehicle specific power (VSP) could be used as an important explanatory variable to characterize the PN emission rate when distinguishing different engine technologies and emission standards. A real-world LDGV VSP-based PN emission rate was suggested based on the RDE test dataset. The VSP-based emission rate could be considered to more accurately quantify vehicle PN emissions and support the formulation of urban vehicle particle emission control policies.

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.

A newly integrated dataset of volatile organic compounds (VOCs) source profiles and implications for the future development of VOCs profiles in China.

Volatile organic compounds (VOCs) source profiles can be used for a number of purposes, such as creating speciated air pollutant emission inventories and providing inputs to receptor and air quality models. In this study, we first collected and schematically evaluated more than 500 Chinese domestic source profiles from literature and field measurements, and then established a most up-to-date dataset of VOCs source profiles in China by integrating 363 selective VOCs profiles into 101 sector-based source profiles. The profile dataset covers eight major source categories and contains 447 VOCs species including non-methane hydrocarbons (NMHCs) species and oxygenated VOCs (OVOCs) species. The results shown that (1) VOCs composition characteristics exhibit variations for most Level-II source sectors and Level-III sub-sectors even under the same Level-I source category; (2) OVOCs, which were significantly missing in previous profiles, account for more than 95% in cooking and 20- 40% in non-road mobile, biomass burning and solvent use sources; (3) aromatics account for 20%-40% in most emission sources except cooking source, alkenes and alkynes account for ~20% in combustion sources (stationary combustion, mobile source and biomass burning), alkanes are abundant in gasoline-related emission sources(on-road mobile source and fuel oil storage and transportation); (4) missing OVOCs species could bring 30%-50% to ozone formation potentials in most emission sources; and (5) there are considerable differences in VOCs chemical groups and individual species for most emission sources between this dataset and the widely used U.S. SPECIATE database, indicating the importance of developing domestic VOCs source profiles. The dataset developed in this study can help support reactive VOCs species-based ozone control strategy and provide domestic profile data for source apportionment and air quality modeling in China and other countries or regions with similar emission source characteristics.

An updated model-ready emission inventory for Guangdong Province by incorporating big data and mapping onto multiple chemical mechanisms.

An accurate characterization of spatial-temporal emission patterns and speciation of volatile organic compounds (VOCs) for multiple chemical mechanisms is important to improving the air quality ensemble modeling. In this study, we developed a 2017-based high-resolution (3 km × 3 km) model-ready emission inventory for Guangdong Province (GD) by updating estimation methods, emission factors, activity data, and allocation profiles. In particular, a full-localized speciation profile dataset mapped to five chemical mechanisms was developed to promote the determination of VOC speciation, and two dynamic approaches based on big data were used to improve the estimation of ship emissions and open fire biomass burning (OFBB). Compared with previous emissions, more VOC emissions were classified as oxygenated volatile organic compound (OVOC) species, and their contributions to the total ozone formation potential (OFP) in the Pearl River Delta (PRD) region increased by 17%. Formaldehyde became the largest OFP species in GD, accounting for 11.6% of the total OFP, indicating that the model-ready emission inventory developed in this study is more reactive. The high spatial-temporal variability of ship sources and OFBB, which were previously underestimated, was also captured by using big data. Ship emissions during typhoon days and holidays decreased by 23-55%. 95% of OFBB emissions were concentrated in 9% of the GD area and 31% of the days in 2017, demonstrating their strong spatial-temporal variability. In addition, this study revealed that GD emissions have changed rapidly in recent years due to the leap-forward control measures implemented, and thus, they needed to be updated regularly. All of these updates led to a 5-17% decrease in the emission uncertainty for most pollutants. The results of this study provide a reference for how to reduce uncertainties in developing model-ready emission inventories.

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.

[Emission Characteristics of Light-Duty Gasoline Vehicle Exhaust Based on Acceleration Simulation Mode].

In this study, 127 light-duty gasoline cars and 10 light-duty gasoline trucks with different emission standards were selected to explore the influences of different conditions and vehicle parameters on the emission characteristics of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and methane (CH4) using a portable emission measurement system based on a chassis dynamometer under acceleration simulation mode. The results showed that the gaseous pollutants of light-duty gasoline vehicles displayed a relatively lower emission rate under the idle condition, which accounted for only 22.9% and 25.8% of the emission rate at the accelerated condition and constant speed condition, respectively. The pollutant emission characteristics were closely related to the working conditions. The emission rates of CO2 and NOx in the accelerated condition were less than those at the constant speed condition, while the emission rates of CO, HC, and CH4 in the accelerated condition were higher than those at the constant speed condition. In the constant low-speed condition, the emission factors of CO2, CO, NOx, HC, and CH4 were 383.20, 2.98, 1.60, 0.14, and 0.03 g·km-1 for light-duty gasoline cars, respectively, and 360.66, 2.64, 1.61, 0.0055, and 0.0027 g·km-1 for light-duty gasoline trucks, respectively. Tighter emission standards have caused significant reductions in emissions. The emission factors of CO, NOx, HC, and CH4 could be decreased by 87.5%, 97.3%, 97.9%, and 86.4%, respectively, from China Ⅰ to China Ⅴ. A non-linear relationship was found between the age, odometer, vehicle weight, and vehicular emissions. In addition, the engine displacement was positively correlated with vehicular emissions.

Anthropogenic atmospheric toxic metals emission inventory and its spatial characteristics in Guangdong province, China.

Atmospheric toxic metals (TMs) may cause adverse effects on the environment and human health due to their bioavailability and toxicity. High-resolution TMs emission inventory is important input data for assessing human exposure risks, especially synergistic toxicity of multiple toxic metals. By using the latest city- and enterprise-level environment statistical data, an emission inventory of five TMs (Hg, As, Pb, Cd, Cr) in Guangdong province for the year of 2014 was developed using a bottom-up approach. The total emissions of Hg, As, Pb, Cd and Cr in Guangdong were estimated as 17.70, 32.59, 411.34, 13.13, and 84.16 t, respectively. Major emission sources for each TM were different. Hg emissions were dominated by coal combustion (33%), fluorescent lamp (18%) and cement (17%). 78% of Hg emissions were in the form of Hg0, 19% of Hg2+, and only 3% of Hgp due to strict particulate matter control policies. Coal combustion (48%), nonferrous metal smelting (25%) and iron and steel industry (24%) were the major sources of As. Pb emissions primarily came from battery production (42%), iron and steel industry (21%) and gasoline combustion (17%). Cd and Cr emissions were dominated by nonferrous metal smelting (71%) and iron and steel industry (82%), respectively. Most of these TMs were emitted in the non-Pearl River Delta region, where the newly-built iron and steel industry, nonferrous metal smelting and cement production factories were intense. The uncertainties in the five TM emissions were high, due much to high uncertainties in TM emission factors and limited activity data. Thus, to improve the accuracy of these estimates, we recommend more field tests of TM emissions, especially for the industrial process sector. This study provides scientific support for formulating robust TMs control policies to alleviate the high risk of TMs exposure in Guangdong.

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.

Recent developments of anthropogenic air pollutant emission inventories in Guangdong province, China.

Emission inventory (EI) requires continuous updating to improve its quality and reduce its uncertainty. In this study, recent developments on source classification, emission methods, emission factors and spatial-temporal surrogates in the Guangdong regional anthropogenic emission inventory are presented. The developments include: ~40 additional emission sources in a re-classified source classification system, >50 improved spatial and temporal surrogates, 85% of local/domestic emission factors used, and updated estimation methods of on-road mobile, marine, and solvent use sources. The developments were updated to the recent 2012-based high resolution emission inventories, and their results were compared with previous 2006- and 2010-based emission inventories. The results indicated: (1) The total SO2, NOx, CO, PM10, PM2.5, BC, OC, VOCs and NH3 emissions in 2012 were 777.0kt, 1532.2kt, 7305.4kt, 1176.4kt, 480.9kt, 54.2kt, 79.9kt, 1255.1kt and 584.1kt, respectively, for Guangdong province, with higher emission densities observed in the central PRD region. (2) No great changes on source structures were found among three years, but their contributions varied. (3) SO2, PM10 and PM2.5 emissions showed downward trends, likely a result of strict control measures on power plant and industrial combustion sources. (4) NOx emission exhibited relatively stable levels in 2010 and 2012, but contributions from industrial, on-road and non-road mobile sources increased. (5) VOCs emissions showed an upward trend, mainly resulting from dramatically increased light-duty passenger car population and solvent use. (6) Spatial and temporal allocations were updated with constant improvements of spatial and temporal surrogates. (7) Uncertainty ranges of emission estimates were reduced, indicating that the 2012-based PRD regional EI are more reliable. The work shown in this study can be a reference example for other regions to continuously update their emission inventories.

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.

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.