Uncertainties In Emission Inventories Research Articles

Effects of environmental and housing system factors on ammonia and greenhouse gas emissions from cattle barns: A meta-analysis of a global data collation

This study provides a meta-analysis on the relationships between cattle barn CH4, NH3 and N2O emission rates and their key drivers (i.e., housing type, floor type, environmental conditions). Understanding these relationships is essential to reduce uncertainties in emission inventories and suggest targeted mitigation measures. The total number of daily emission rates included in the analysis was 139 for CH4, 293 for NH3 and 100 for N2O emissions. Emission rates in the database showed a large variation with 45–803.5 g/LU d−1 for CH4, 0.036–146.7 gN LU−1 d−1 for NH3, and 0.002–18 gN LU−1 d−1 for N2O emissions. Despite the high emission variability, significant effects were identified·NH3 showed positive correlation with air temperature; NH3 emissions differed between housing types but not between floor types·NH3 emissions from tied stalls were lower than the ones from cubicle housing regardless of the floor type. Additionally, NH3 emissions from loose housings were lower than the ones from cubicle housing·NH3 and N2O emission rates from temperate wet zones were lower than the ones from temperate dry zones. CH4 emission rates were affected by environmental factors only and not by housing and floor type, showing negative correlation with air temperature and humidity. The factors investigated can be suggested as ancillary variables and descriptors when cattle barn emissions are measured, in order to make best use of emission data. Country-specific data of these key drivers can be included into national inventories to adapt them to different agroecosystems and support targeted policies.

Pollutant emissions from biomass burning: A review on emission characteristics, environmental impacts, and research perspectives

Biomass is one most abundant resource on the earth providing important energies in support of socioeconomic development in many areas. Burning of biomass fuels comprises to nearly 10% of the total energy from anthropogenic combustion processes; however, as the burning is usually incomplete, this process yields products of incomplete combustion posing consequently significant impacts on air quality, human health, and climate change. Here, we analyzed spatiotemporal characteristics in intentional and unintentional biomass burning from different sectors, discussed impacts of biomass burning emissions on indoor and outdoor air quality, and consequent influences on human health. The global total consumption amount of biomass including both natural and anthropogenic sources was approximately 7900 Tg in 2019, with significantly large regional and sectorial discrepancies among regions. Globally, anthropogenic biomass burning amounts increased gradually, but notably in some developing countries like China residential consumption of biomass fuels, as one large sector of biomass use, decreased over time. Uncommercial biomass consumption needs to be accurately quantified. There are relatively rich datasets of pollutant emission factors from biomass burning, including laboratory and field tests, but still large variations exit and contribute substantially to the uncertainty in emission inventory. Global primary PM2.5, black carbon and organic carbon emissions from biomass burning were about 51, 4.6, and 29 Tg, respectively, contributing to nearly 70%, 55%, and 90% of the total emission from all sources, and emissions from the residential sector and open fires are major sources. Brown carbon emissions from biomass burning attracts growing interests but available studies adopted different methodologies challenging the comparability of those results. Biomass burning emissions polluted not only ambient air but more severely indoor air quality, adversely affecting human health. Future studies that should be emphasized and promoted are suggested.

Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2

Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

Evaluation of the CAMS reanalysis for atmospheric black carbon and carbon monoxide over the north China plain

Black carbon (BC) and carbon monoxide (CO) at different model levels from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis were comprehensively evaluated against observations performed simultaneously on both surface and mountain sites in winter and summer in the North China Plain for the first time. CAMS could capture the seasonal difference in BC and CO emission on both sites but showed significant and persistent biases. Biases were high on the surface site and low on the mountain site for both seasons, implying the uncertainties in emission inventories used in the CAMS reanalysis which may have more influence near source. Biases were reduced and the correlation coefficient of CAMS BC with observed BC increased when two datasets were compared on a daily basis, which suggests daily or longer time averaged CAMS BC could be more suitable for trend analysis. Although CAMS could generally reproduce the distinct diurnal variation of BC and CO on both sites, the inaccurate representation of the daily evolution of planetary boundary layer (PBL) in model may bring more uncertainties to the concentration biases on surface from midnight to early morning. BC hydrophilic ratio from CAMS displayed large biases compared to observations with no seasonal difference on both sites, which was probably resulted from the initial emission state of BC hygroscopicity for all source types in model. Uncertainties in the removal processes and the simplified aging processes in model could further induce uncertainty in modelling BC hydrophilic ratio in the CAMS. These results could not only be referenced for the improvement on CAMS reanalysis but also facilitate model or trend analysis of BC and CO pollution by utilizing the CAMS reanalysis product from both short- and long-term perspectives, which will be beneficial to both the mitigation and policy-making on primary emissions in China.

Drivers of 2013–2020 ozone trends in the Sichuan Basin, China: Impacts of meteorology and precursor emission changes

The Sichuan Basin (SCB) of China is known for excessive ozone (O3) pollution owing to high anthropogenic emissions combined with terrain-induced poor ventilation and weak wind fields against the surrounding mountains. While O3 pollution has emerged as a prominent concern in southwestern China yet variations in O3 levels during 2013–2020 are still unclear and the dominant factor in explaining the long-term O3 trend throughout the SCB remains elusive due to uncertainties in emission inventory and variability associated with meteorological conditions. Here, we use extensive basin-wide ambient measurements to examine the spatial pattern and trend of O3 and leverage OMI and TROPOMI satellites in conjunction with MEIC emission inventory to track emission changes. Sensitivity simulations are conducted by using WRF-CMAQ model to investigate the impacts of meteorological variability and emission changes on O3 changes over 2013–2020. O3 concentrations exhibit obvious interannual increases during 2013–2019 and a slight decrease in 2020. Both decreases in the MEIC emission inventory (−2.9% yr−1) and OMI NO2 column density (−3.1% yr−1) reflects the declining trend in NOx emissions over 2013–2020, while anthropogenic VOCs were not adequately regulated during 2013–2017, which explained the majority of deteriorated O3 pollution from 2013 to 2017. Furthermore, attribution analysis based on CMAQ simulations indicate that the unexpected aggravated O3 levels in 2019 is not only modulated by disproportional reductions in VOCs and NOx emissions, but also associated with unfavorable meteorological conditions featured by profound heatwaves and frequent stagnant conditions. In 2020, the abnormal meteorological conditions in May leads to substantial increase of O3 by 26.8 μg m−3 as compared to May 2019, while the considerable enhancement was fully offset by low O3 levels over the whole period which attributes to substantial emission reductions. This study reveals the long-term trend of O3 levels and precursor emissions and highlights the effects of meteorological variability and emission changes on O3 pollution over the SCB, with strong implications for designing effective O3 control measures.

VOCs Emission Inventory and Uncertainty Analysis of Industry in Qingdao Based on Latin Hypercube Sampling and Monte Carlo Method

In recent years, fine particulate matter(PM2.5) and ozone(O3) have become the main air pollutants in cities in China. Volatile organic compounds(VOCs) are one of the important precursors of PM2.5, O3, and secondary organic aerosols. The establishment of VOCs emission inventory is therefore of great significance for controlling the amount of PM2.5 and O3. To date, the coefficient method has been used, which has error transmission of activity level, parameter and model, leading to the uncertainty of emission inventory. Multivariate uncertainty quantitative analysis of VOCs emission inventory provides an accurate alternative which has not been reported in China. The bottom-up method is adopted to collect the activity level of each enterprise. The variables of pollution control measures are obtained from surveys conducted with enterprises. The VOCs emission inventory of Qingdao from industrial source is established using an optimized coefficient method. The uncertainty of the VOCs inventory on the impact of univariate and multivariate variables is simulated by combining the Monte Carlo method(MC) with Latin hypercube sampling method(LHS). The results show that the total VOCs emissions were 44700 tons from industrial sources in 2019(unoptimized coefficient method:31100 tons).The rubber and plastic industries, metal products, and oil/coal/other fuel processing contributed more VOCs, which accounted for 40.26% of the total emissions. The uncertainty of multivariate simulation is higher than that of single variable. The uncertainty from process(-9.72%-230.51%) and solvent using source(-14.14%-122.77%) is significantly higher than uncertainty from combustion source(-15.62%-36.41%). The main sectors affecting the uncertainty of the VOCs inventory include:the chemical, papermaking, and textile industries(emission factors); metal, automobile manufacturing, and chemical industries(removal rate, facility operating rate); industries of petroleum processing and ferrous metal smelting(too few samples). VOCs emissions are mainly distributed in the east of the West Coast New district, north of Dazhu Mountain, south of Jimo district, north of Chengyang district, northeast of Jiaozhou district, built-up area of Pingdu district, and southeast of Laixi district.

Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

Application of machine learning algorithms to improve numerical simulation prediction of PM2.5 and chemical components

PM2.5 and its toxic chemical components are critical air pollutants that have been associated with respiratory diseases and human mortality. Therefore, the accurate prediction of PM2.5 and its chemical components is necessary to formulate emission reduction measures. Although chemical transport models can provide continuous temporal and spatial estimates of pollutants, the uncertainties in emission inventories, meteorological processes, and chemical mechanisms reduce the accuracy of modeling results. In particular, the discrepancy between simulated PM2.5 components and ground monitoring data remain large, with varying degrees of over and underestimation. To improve the accuracy of numerical simulation forecasts, we applied three typical machine learning algorithms to calibrate deviations. For this, we employed major meteorological parameters along with simulated and observed pollutant concentrations as inputs. The results showed that random forest and support vector regression (SVR) models presented much better predictive performance (R = 0.71–0.81 and 0.76–0.82, respectively) compared with multiple linear regression (MLR, R = 0.41–0.61). Moreover, their root mean square error and mean absolute error were 17–76% and 33–79% lower than those of MLR, respectively. The SVR model presented the most accurate prediction of PM2.5 components. The predicted proportions of PM2.5 components reflected the variations in pollution sources, which can be used to analyze the causes of pollution and thereby support the air quality management. More accurate prediction of PM2.5 components can promote exposure assessments and provide a basis for health studies.

Anthropogenic emission inventory of multiple air pollutants and their spatiotemporal variations in 2017 for the Shandong Province, China

Shandong is the most populous and highly industrialized province in eastern China, and the resultant poor air quality is a cause for widespread concern. This study combines bottom–up and top–down approaches to develop a high-resolution anthropogenic emission inventory of air pollutants for 2017. The inventory was developed based on updated emission factors and detailed activity data. The emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter with aerodynamic diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively), carbon monoxide (CO), volatile organic compounds (VOCs), and ammonia (NH3) were estimated to be 1387.8, 2488.6, 5281.7, 3193.0, 9250.7, 2254.7, and 1210.6 kt, respectively. Power plants were the largest contributors of SO2 and NOx emissions accounting for 43.7% and 41.9% of the total emissions, respectively. CO emissions mainly originated from industrial processes (40.1%), mobile sources (24.8%), and fossil fuel burning (21.2%). The major sources of PM10 and PM2.5 emissions were industrial processes and fugitive dust, contributing 83.0% and 86.9% of their total emissions, respectively. Industrial processes (60.0%) contributed the largest VOC emissions, followed by mobile sources (16.8%) and solvent use (14.5%). Livestock and N-fertilizers were major emitters of NH3, accounting for 69.9% and 21.2% of the total emissions, respectively. Emissions were spatially allocated to grid cells with a resolution of 0.05 ° × 0.05 ° based on spatial surrogates, using Geographic Information System (GIS). Heavy pollutant emissions were mainly concentrated in the central and eastern areas of Shandong, while high NH3–emissions occurred in the western region. Most pollutant emissions from industrial sectors occurred in June and July, while low emissions were recorded between January and February. Range uncertainties in emission inventory were quantified using Monte Carlo simulations. Our inventory provides effective information to understand local pollutant emission characteristics, perform air quality simulations, and formulate pollution control measures.

Characterization, seasonal variation, source apportionment and health risk assessment of black carbon over an urban region of East India

Black carbon (BC) is produced due to the incomplete combustion of fossil fuels and rapidly accelerates the globe. The study of the source apportionment exploration of BC is inaccurate due to lacking observational limitations and uncertainties in emission inventories. In the present work, we measured BC mass concentrations using a 7-wavelength Aethalometer (AE-33). In this study, BC mass concentration was ranging from 2 to 13.43 μgm−3 and was minimum during monsoon (3.23 ± 0.38 μgm-3) and maximum in the winter season (11.68 ± 2.55μgm−3). The annual mean BC mass concentration and PM2.5 were reported as 6.60 ± 3.50 μgm−3 and 119.07 ± 43.44 μgm-3, respectively. In overall study, the BC aerosol was contributing 6.92% (winter), 5.09% (summer), 3.79% (monsoon) and 5.25% (post-monsoon) to the total mass concentration of PM2.5. Backward Trajectories have shown that the airborne particulate matter comes from different directions in different seasons. Correlation analysis has found a strong relationship between BC and PM2.5 (r = 0.89, r2 = 0.80). However, an inverse relationship was noticed between BC and temperature(r = −0.92), wind speed (r = −0.61), precipitation (r = −0.60) and humidity (r = −0.52) during the observational period. Apart from describing BC's atmospheric impacts, the health risk assessment of BC during various seasons over Jamshedpur city is also illustrated in detail.

The impacts of fossil fuel emission uncertainties and accounting for 3-D chemical CO2 production on inverse natural carbon flux estimates from satellite and in situ data

Atmospheric carbon dioxide (CO2) inversions for estimating natural carbon fluxes typically do not allow for adjustment of fossil fuel CO2 emissions, despite significant uncertainties in emission inventories and inadequacies in the specification of international bunker emissions in inversions. Also, most inversions place CO2 release from fossil fuel combustion and biospheric sources entirely at the surface. However, a non-negligible portion of the emissions actually occurs in the form of reduced carbon species, which are eventually oxidized to CO2 downwind. Omission of this ‘chemical pump’ can result in a significant redistribution of the inferred total carbon fluxes among regions. We assess the impacts of different prescriptions of fossil fuel emissions and accounting for the chemical pump on flux estimation, with a novel aspect of conducting both satellite CO2 observation-based and surface in situ-based inversions. We apply 3-D carbon monoxide (CO) loss rates archived from a state-of-the-art GEOS chemistry and climate model simulation in a forward transport model run to simulate the distribution of CO2 originating from oxidation of carbon species. We also subtract amounts from the prior surface CO2 fluxes that are actually emitted in the form of fossil and biospheric CO, methane, and non-methane volatile organic compounds (VOCs). We find that the posterior large-scale fluxes are generally insensitive to the finer-scale spatial differences between the ODIAC and CDIAC fossil fuel CO2 gridded datasets and assumptions about international bunker emissions. However, accounting for 3-D chemical CO2 production and the surface correction shifts the global carbon sink, e.g. from land to ocean and from the tropics to the north, with a magnitude and even direction that depend on assumptions about the surface correction. A GOSAT satellite-based inversion is more sensitive to the chemical pump than one using in situ observations, exhibiting substantial flux impacts of 0.28, 0.53, and −0.47 Pg C yr−1 over tropical land, global land, and oceans, due to differences in the horizontal and vertical sampling of the two observation types. Overall, the biases from neglecting the chemical pump appear to be minor relative to the flux estimate uncertainties and the differences between the in situ and GOSAT inversions, but their relative importance will grow in the future as observational coverage further increases and satellite retrieval biases decrease.

Research on the Emission Inventory and Pollution Characteristics of Air Pollutants from Ships in Beijing-Tianjin-Hebei Area

As the key areas of China’s air pollution control, Beijing-Tianjin-Hebei region are the busiest sailing area for ships. Therefore, the impact of air pollution caused by ships cannot be ignored. The bottom-up “dynamic method” was used in this study and the ships’ navigation process was divided into three processes: docking, entering and exiting port and cruising, and the emission of air pollutants from ships was calculated respectively, and the calculation method of air pollutants emission inventory for ships with high temporal and spatial resolution was established. Based on the ships’ basic information data from maritime database of China Classification Society and Lloyd’s Register, the Automatic Identification System data and the emission factor data for ships, the emission inventory for ships in 2014 in Beijing-Tianjin-Hebei area was calculated. In addition, the study also analyzed emission share rate of air pollutants from ships and the uncertainty of emission inventory.

Uncertainty Analysis on Process Organized Emission Inventory in Petrochemical Enterprises of Hainan Province

The study qualitatively and quantitatively evaluated the uncertainty of the emission inventory of SO2, NOx, particulate matter (PM) and non-methane volatile organic compounds (NMVOCs) based on the measurement method in the process of organized sources in eight typical petroleum refining enterprises in Hainan Province in 2017. According to the TRACE-P inventory grading, the activity level data uncertainty was between I and II, and the emission factor rating was roughly C-level. The qualitative assessment of the emission inventory is “good”. The Monte Carlo simulation model was used to quantitatively evaluate the uncertainty. The results show that the uncertainties in emission inventory of SO2, NOx, PM and NMVOCs are ±2.9%, ±6.3%, ±7.6% and ±13.7%, respectively, with the highest uncertainty in NMVOCs emissions. The study on emission inventory uncertainty will help decision makers to scientifically formulate air pollutant control strategies for the accessibility of pollutant emission reduction targets of petrochemical enterprises and guide the improvement of emission inventory and data collection.

Speciated PM Composition and Gas and Particle Emission Factors for Diesel Construction Machinery in China

ABSTRACT On-board emission measurements were performed on nine diesel construction machines operated under real-world conditions in China. The emission factors (EFs) for NOx, CO, the total hydrocarbons, PM, and the particle number concentration were determined under actual operating conditions, i.e., during idling, moving, and working. To investigate the chemical composition of PM from diesel machinery, organic carbon (OC), containing particulate organic matter (POM); elemental carbon (EC); water-soluble ions (WSIs); and elements were also analyzed herein. OC was the most abundant component (contributing 39.3 ± 11% of the mass), followed by EC (37.7 ± 13%). POM species, including n-alkanes, hopanes, polycyclic aromatic hydrocarbons (PAHs), and n-fatty acids, contributed approximately 2.4–6.4% of the total PM mass. Compounds with 3 or 4 aromatic rings, including pyrene, phenanthrene, and fluoranthene, dominated the particulate PAHs, accounting for 62% of the total mass. These results are consistent with those of previous studies and can help to minimize uncertainties in emission inventories and source apportionment for non-road machinery.

Evaluation of EU air quality standards through modeling and the FAIRMODE benchmarking methodology

We evaluate air quality modeling over the East Mediterranean using the benchmarking methodology developed in the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE). FAIRMODE aims to provide a harmonized approach of model evaluation for regulatory purposes. We test the methodology by assessing the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) against ground-based air quality observations over Cyprus, a member state of the European Union. Two nested domains are used (at 50- and 10-km horizontal grid spacing) with the comparison performed over the innermost domain. We consider performance indicators reflecting regulations for air quality standards (maximum daily 8-hourly mean ozone, hourly nitrogen dioxide, and daily fine particulate matter concentrations). The WRF-Chem model is found to satisfy the proposed performance objectives regarding ozone and NO2, though it underestimates the latter in urban areas possibly due to uncertainties in emission inventories. Fine particulate matter is well represented by the model, except on days with strong influence from natural sources, highlighting the necessity for fine-tuning dust mobilization and transport in the region. The objectives are fulfilled even though discrepancies exist between model and observations. Our results indicate the need for more stringent performance criteria at relatively low concentrations. Overall, we find that the methodology provides in-depth information and relevant statistical metrics to guide air quality and model assessments for monitoring compliance with the EU Air Quality Directives and other guidelines to limit the impact of air pollution on human health and ecosystems.

Assessment of Regional Mercury Deposition and Emission Outflow in Mainland China

AbstractMainland China is the largest emission region of anthropogenic mercury (Hg) in the world. There have been concerns regarding the emission outflow and chemical transport budget of Hg in the region. Earlier assessments of Hg chemical transport were based on relatively outdated emission data. Recent estimates for anthropogenic (Wu et al., 2016, https://doi.org/10.1021/acs.est.6b04308) and natural Hg emissions (X. Wang, Lin, et al., 2016, https://doi.org/10.5194/acp‐16‐11125‐2016) show substantial differences from earlier emission inventories. In this study, we applied the updated Hg emission estimates to reassess the regional transport budget using Community Multiscale Air Quality‐Hg v5.1. Our results show that the variation of simulated concentration, deposition, and associated emission outflow of Hg are primarily influenced by the spatial‐temporal variation of Hg emissions, monsoon shifts, and particulate matter pollution in China. Total Hg deposition in Mainland China is estimated to be 422 Mg/year, and approximately two thirds of the deposition is contributed by domestic emissions. The net Hg transport budget from Mainland China is 511 Mg/year, contributing to 10% of Hg deposition in other regions of the world. This reassessment points to a ~25% reduction in total annual outflow compared to the previous estimate by Lin et al. (2010, https://doi.org/10.5194/acp‐10‐1853‐2010). Such reduction is mainly caused by changes in Hg emission quantity, speciation, and spatial/temporal distributions. More modeling studies focusing on reducing uncertainties of emission inventories and Hg atmospheric chemistry are needed for continuous assessment of Hg emission outflow in this emission‐intensive region.

Updated emission inventories of power plants in simulating air quality during haze periods over East China

Abstract. Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu Province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting model with Chemistry (WRF-Chem), two simulation experiments were executed to assess the atmospheric environment change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that power plant emissions of PM2.5, PM10, SO2, and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs (non-methane volatile organic compounds) were higher in UEIPP relative to those in MEIC, reflecting a large discrepancy in the power plant emissions over East China. In accordance with the changes in UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC, and CO. Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced. This was reflected by increased oxidizing agents, e.g., O3 and OH, thus directly strengthening the chemical production from SO2 and NOx to sulfate and nitrate, respectively, which offset the reduction of primary PM2.5 emissions especially on haze days. This study indicates the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with implications on air quality and environmental changes.

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Evaluation of summertime surface ozone in Kanto area of Japan using a semi-regional model and observation

Surface ozone is an air pollutant and harmful to human life. The spatial distribution of the air pollution has been estimated by chemical transport models, but still there are large uncertainties depending on detailed condition of the region. In this study, we extended Goto et al. (2015a) for implementing a chemical transport model to simulate short-lived gases such as ozone over Kanto area (around Tokyo in Japan) for August 2010. Comparison of simulation results with observed data indicated that the model had ability to capture observed ozone diurnal cycles over the target region with high correlation coefficients (0.69–0.81). The simulation result showed a vital role of meteorological conditions in the model performance. The correlation coefficients were much higher (0.78–0.87) and biases were lower (<35%) when the meteorological conditions were stable. In contrast, dominance of local pressure system and an associated complex wind field were main reasons for overestimated surface ozone concentrations in the unstable weather conditions.This study helped achieve a better understanding of the chemistry transport model performance under unstable meteorological conditions in the Kanto area. Maximal association between meteorological factors and surface ozone distribution was revealed. In addition, uncertainty of emission inventories of ozone precursors especially the underestimate NOx level certainly contributed to high level surface ozone during nighttime in this study.

A new conceptual model for quantifying transboundary contribution of atmospheric pollutants in the East Asian Pacific rim region

Transboundary transport of air pollution is a serious environmental concern as pollutant affects both human health and the environment. Many numerical approaches have been utilized to quantify the amounts of pollutants transported to receptor regions, based on emission inventories from possible source regions. However, sparse temporal–spatial observational data and uncertainty in emission inventories might make the transboundary transport contribution difficult to estimate. This study presents a conceptual quantitative approach that uses transport pathway classification in combination with curve fitting models to simulate an air pollutant concentration baseline for pollution background concentrations. This approach is used to investigate the transboundary transport contribution of atmospheric pollutants to a metropolitan area in the East Asian Pacific rim region. Trajectory analysis categorized pollution sources for the study area into three regions: East Asia, Southeast Asia, and Taiwan cities. The occurrence frequency and transboundary contribution results suggest the predominant source region is the East Asian continent. This study also presents an application to evaluate heavy pollution cases for health concerns. This new baseline construction model provides a useful tool for the study of the contribution of transboundary pollution delivered to receptors, especially for areas deficient in emission inventories and regulatory monitoring data for harmful air pollutants.