scholarly journals Development of the CREATE Inventory in Support of Integrated Climate and Air Quality Modeling for Asia

2020 ◽  
Vol 12 (19) ◽  
pp. 7930 ◽  
Author(s):  
Jung-Hun Woo ◽  
Younha Kim ◽  
Hyeon-Kook Kim ◽  
Ki-Chul Choi ◽  
Jeong-Hee Eum ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Biomass Burning ◽  
Environmental Protection Agency ◽  
Emission Inventory ◽  
Emission Inventories ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Transport Experiment ◽  
Natural Emissions

A bottom-up emissions inventory is one of the most important data sets needed to understand air quality (AQ) and climate change (CC). Several emission inventories have been developed for Asia, including Transport and Chemical Evolution over the Pacific (TRACE-P), Regional Emission Inventory in Asia (REAS), and Inter-Continental Chemical Transport Experiment (INTEX) and, while these have been used successfully for many international studies, they have limitations including restricted amounts of information on pollutant types and low levels of transparency with respect to the polluting sectors or fuel types involved. To address these shortcomings, we developed: (1) a base-year, bottom-up anthropogenic emissions inventory for Asia, using the most current parameters and international frameworks (i.e., the Greenhouse gas—Air pollution INteractions and Synergies (GAINS) model); and (2) a base-year, natural emissions inventory for biogenic and biomass burning. For (1), we focused mainly on China, South Korea, and Japan; however, we also covered emission inventories for other regions in Asia using data covering recent energy/industry statistics, emission factors, and control technology penetration. The emissions inventory (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE)) covers 54 fuel classes, 201 subsectors, and 13 pollutants, namely SO2, NOx, CO, non-methane volatile organic compounds (NMVOC), NH3, OC, BC, PM10, PM2.5, CO2, CH4, N2O, and Hg. For the base-year natural emissions inventory, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and BlueSky-Asia frameworks were used to estimate biogenic and biomass burning emissions, respectively. Since the CREATE emission inventory was designed/developed using international climate change/air quality (CC/AQ) assessment frameworks, such as GAINS, and has been fully connected with the most comprehensive emissions modeling systems—such as the US Environmental Protection Agency (EPA) Chemical Manufacturing Area Source (CMAS) system—it can be used to support various climate and AQ integrated modeling studies, both now and in the future.

Evaluation of Air Pollutant Emission Inventories in East Asia

2020 ◽  
Author(s):  
Younha Kim ◽  
Jung-hun Woo ◽  
Youjung Jang ◽  
Minwoo Park ◽  
Bomi Kim ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
East Asia ◽  
Emission Inventory ◽  
Air Pollutant ◽  
Emission Inventories ◽  
Top Down ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Quality Study

<p>Concentration of air pollutants such as tropospheric ozone and aerosols are mainly affected by meteorological variables and emissions. East Asia has large amount of anthropogenic and natural air pollutant emissions and has been putting lots of efforts to improve air quality. In order to seek effective ways to mitigate future air pollution, it is essential to understand the current emissions and their impacts on air quality. Emission inventory is one of the key datasets required to understand air quality and find ways to improve it. Amounts and spatial-temporal distributions of emissions are, however, not easy to estimate due to their complicate nature, therefore introduce significant uncertainties.</p><p>In this study, we had developed an updated version of our Asian emissions inventory, named NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) in support of climate-air quality study. We first inter-compare multiple bottom-up inventories to understand discrepancies among the dataset(sectoral, spatial). We then inter-compare those bottom-up emissions to the satellite-based top-down emission estimates to understand uncertainties of the databases. The bottom-up emission inventories used for this study are: CREATE, MEIC(Multiresolution Emission Inventory for China), REAS (Regional Emission inventory in ASia), and ECLIPSE(Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). The satellite-derived top-down emission inventory had been acquired from the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm data from the GlobEmissions website.</p><p>The analysis showed that some discrepancies, in terms of emission amounts, sectoral shares and spatial distribution patterns, exist among the datasets. We analyzed further to find out which parameters could affect more on those discrepancies. Co-analysis of top-down and bottom-up emissions inventory help us to evaluate emissions amount and spatial distribution. These analysis are helpful for the development of more consistent and reliable inventories with the aim of reducing the uncertainties in air quality study. More results of evaluation of emissions will be presented on site.     </p><p>Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment(MOE)(2019000160007). This research was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW) (NRF-2017M3D8A1092022).</p>

scholarly journals Bottom-Up Emission Inventory and Its Spatio-Temporal Distribution from Paved Road Dust Based on Field Investigation: A Case Study of Harbin, Northeast China

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Lili Li ◽  
Kun Wang ◽  
Zhijian Sun ◽  
Weiye Wang ◽  
Qingliang Zhao ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Emission Inventory ◽  
Road Dust ◽  
Field Investigation ◽  
Pollutant Emissions ◽  
County Level ◽  
Bottom Up ◽  
Spatial Allocation ◽  
Paved Road

Road dust is one of the primary sources of particulate matter which has implications for air quality, climate and health. With the aim of characterizing the emissions, in this study, a bottom-up approach of county level emission inventory from paved road dust based on field investigation was developed. An inventory of high-resolution paved road dust (PRD) emissions by monthly and spatial allocation at 1 km × 1 km resolution in Harbin in 2016 was compiled using accessible county level, seasonal data and local parameters based on field investigation to increase temporal-spatial resolution. The results demonstrated the total PRD emissions of TSP, PM10, and PM2.5 in Harbin were 270,207 t, 54,597 t, 14,059 t, respectively. The temporal variation trends of pollutant emissions from PRD was consistent with the characteristics of precipitation, with lower emissions in winter and summer, and higher emissions in spring and autumn. The spatial allocation of emissions has a strong association with Harbin’s road network, mainly concentrating in the central urban area compared to the surrounding counties. Through scenario analysis, positive control measures were essential and effective for PRD pollution. The inventory developed in this study reflected the level of fugitive dust on paved road in Harbin, and it could reduce particulate matter pollution with the development of mitigation strategies and could comply with air quality modelling requirements, especially in the frigid region of northeastern China.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of 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 was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) 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 O3-1 h. The study demonstrates that ensemble predictions by 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 effects studies.

scholarly journals Emission Inventory Processing of Biomass Burning from A Global Dataset for Air Quality Modeling

2020 ◽  
Author(s):  
Ernesto Pino-Cortés ◽  
Samuel Carrasco ◽  
Luis A. Díaz-Robles ◽  
Francisco Cubillos ◽  
Fidel Vallejo ◽  
...  
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Emission Inventory ◽  
Sparse Matrix ◽  
Local Level ◽  
Air Quality Modeling ◽  
Simulation Software ◽  
Atmospheric Pollutants ◽  
Fire Emissions ◽  
Quality Modeling

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.

scholarly journals Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns

2008 ◽  
Vol 8 (5) ◽  
pp. 16981-17036 ◽  
Author(s):  
T. Stavrakou ◽  
J.-F. Müller ◽  
I. De Smedt ◽  
M. Van Roozendael ◽  
G. R. van der Werf ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Amazon Basin ◽  
Transport Model ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Chemical Mechanism ◽  
Emission Inventories ◽  
Chemical Transport Model ◽  
Emissions Inventory ◽  
Fire Emissions

Abstract. A new one-decade dataset of formaldehyde (HCHO) columns retrieved from GOME and SCIAMACHY is compared with HCHO columns simulated by an updated version of the IMAGES global chemical transport model. This model version includes an optimized chemical scheme with respect to HCHO production, where the short-term and final HCHO yields from pyrogenically emitted non-methane volatile organic compounds (NMVOCs) are estimated from the Master Chemical Mechanism (MCM) and an explicit speciation profile of pyrogenic emissions. The model is driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, whereas biogenic emissions are provided either by the Global Emissions Inventory Activity (GEIA), or by a newly developed inventory based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) algorithms driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparisons focus on tropical ecosystems, North America and China, which experience strong biogenic and biomass burning NMVOC emissions reflected in the enhanced measured HCHO columns. These comparisons aim at testing the ability of the model to reproduce the observed features of the HCHO distribution on the global scale and at providing a first assessment of the performance of the current emission inventories. The high correlation coefficients (r>0.8) between the observed and simulated columns over most regions indicate a very good consistency between the model, the implemented inventories and the HCHO dataset. The use of the MEGAN-ECMWF inventory improves the model/data agreement in almost all regions, but biases persist over parts of Africa and the Northern Australia. Although neither GFED version is consistent with the data over all regions, a better match is achieved over Indonesia and Southern Africa when GFEDv2 is used, but GFEDv1 succeeds better in getting the correct seasonal patterns and intensities of the fire episodes over the Amazon basin, as reflected by the higher correlations calculated in this region.

scholarly journals The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty

2011 ◽  
Vol 11 (24) ◽  
pp. 12973-13000 ◽  
Author(s):  
S. P. Urbanski ◽  
W. M. Hao ◽  
B. Nordgren
Keyword(s):  
United States ◽  
Biomass Burning ◽  
Emission Factor ◽  
Emission Inventory ◽  
Wildland Fire ◽  
Western United States ◽  
Burned Area ◽  
Emission Inventories ◽  
Fuel Loading ◽  
Global Modeling

Abstract. Biomass burning emission inventories serve as critical input for atmospheric chemical transport models that are used to understand the role of biomass fires in the chemical composition of the atmosphere, air quality, and the climate system. Significant progress has been achieved in the development of regional and global biomass burning emission inventories over the past decade using satellite remote sensing technology for fire detection and burned area mapping. However, agreement among biomass burning emission inventories is frequently poor. Furthermore, the uncertainties of the emission estimates are typically not well characterized, particularly at the spatio-temporal scales pertinent to regional air quality modeling. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires, WF) in the contiguous United States (CONUS). The model combines observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua satellites, meteorological analyses, fuel loading maps, an emission factor database, and fuel condition and fuel consumption models to estimate emissions from WF. WFEI was used to estimate emissions of CO (ECO) and PM2.5 (EPM2.5) for the western United States from 2003–2008. The uncertainties in the inventory estimates of ECO and EPM2.5 (uECO and uEPM2.5, respectively) have been explored across spatial and temporal scales relevant to regional and global modeling applications. In order to evaluate the uncertainty in our emission estimates across multiple scales we used a figure of merit, the half mass uncertainty, ũEX (where X = CO or PM2.5), defined such that for a given aggregation level 50% of total emissions occurred from elements with uEX ũEX. The sensitivity of the WFEI estimates of ECO and EPM2.5 to uncertainties in mapped fuel loading, fuel consumption, burned area and emission factors have also been examined. The estimated annual, domain wide ECO ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual, domain wide EPM2.5 were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual WF emissions were a significant share of total emissions from non-WF sources (agriculture, dust, non-WF fire, fuel combustion, industrial processes, transportation, solvent, and miscellaneous) in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, WF emissions were ~20% of total (WF + non-WF) CO emissions and ~39% of total PM2.5 emissions. During the months with the greatest fire activity, WF accounted for the majority of total CO and PM2.5 emitted across the study region. Uncertainties in annual, domain wide emissions was 28% to 51% for CO and 40% to 65% for PM2.5. Sensitivity of ũECO and ũEPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50% of total ECO with an uncertainty <133% and half of total EPM2.5 with an uncertainty <146%. ũECO and ũEPM2.5 are reduced by more than half at the scale of global modeling applications (Δ x = 100 km, Δ t = 30 day) where 50% of total emissions are estimated with an uncertainty <50% for CO and <64% for PM2.5. Uncertainties in the estimates of burned area drives the emission uncertainties at regional scales. At global scales ũECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 plays the dominant role in ũEPM2.5. Our analysis indicates that the large scale aggregate uncertainties (e.g. the uncertainty in annual CO emitted for CONUS) typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of regional scale modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

scholarly journals Enhanced atmospheric oxidizing capacity in simulating air quality with updated emission inventories for power plants especially for haze periods over East China

2017 ◽  
Author(s):  
Lei Zhang ◽  
Tianliang Zhao ◽  
Sunling Gong ◽  
Shaofei Kong ◽  
Lili Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Emission Inventory ◽  
Environmental Changes ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Atmospheric Environment ◽  
Pollutant Emission ◽  
East China ◽  
Emission Inventories

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 simulations were executed to assess the atmospheric environmental change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that (1) compared to the power emissions of MEIC, PM2.5, PM10, SO2 and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs were higher in the UEIPP, reflecting a large discrepancy in the power emissions over East China; (2) In accordance with the changes of UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC and CO, whose concentrations in atmosphere are highly dependent on emission changes. (3) Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced, reflecting by increased oxidizing agents, e.g. O3 and OH, thus directly strengthened the chemical production from SO2 and NOx to sulfate and nitrate, which offset the reduction of primary PM2.5 emissions especially in the haze days. This study indicated the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with the implications on air quality and environmental changes.

scholarly journals Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

2017 ◽  
Vol 17 (16) ◽  
pp. 10125-10141 ◽  
Author(s):  
Jieying Ding ◽  
Kazuyuki Miyazaki ◽  
Ronald Johannes van der A ◽  
Bas Mijling ◽  
Jun-ichi Kurokawa ◽  
...  
Keyword(s):  
Kalman Filter ◽  
East Asia ◽  
Rural Areas ◽  
Emission Inventory ◽  
Regional Scale ◽  
Policy Support ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Temporal Behaviour

Abstract. We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

scholarly journals Evaluation of nitrogen oxides (NO<sub><i>x</i></sub>) sources and sinks and ozone production in Colombia and surrounding areas

2020 ◽  
Vol 20 (15) ◽  
pp. 9441-9458
Author(s):  
Johannes G. M. Barten ◽  
Laurens N. Ganzeveld ◽  
Auke J. Visser ◽  
Rodrigo Jiménez ◽  
Maarten C. Krol
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Nitrogen Oxides ◽  
Biomass Burning ◽  
Diurnal Cycle ◽  
Ozone Production ◽  
Spatial And Temporal Variability ◽  
Emission Inventories ◽  
Mixing Ratios ◽  
Baseline State

Abstract. In Colombia, industrialization and a shift towards intensified agriculture have led to increased emissions of air pollutants. However, the baseline state of air quality in Colombia is relatively unknown. In this study we aim to assess the baseline state of air quality in Colombia with a focus on the spatial and temporal variability in emissions and atmospheric burden of nitrogen oxides (NOx = NO + NO2) and evaluate surface NOx, ozone (O3) and carbon monoxide (CO) mixing ratios. We quantify the magnitude and spatial distribution of the four major NOx sources (lightning, anthropogenic activities, soil biogenic emissions and biomass burning) by integrating global NOx emission inventories into the mesoscale meteorology and atmospheric chemistry model, namely Weather Research and Forecasting (WRF) coupled with Chemistry (collectively WRF-Chem), at a similar resolution (∼25 km) to the Emission Database for Global Atmospheric Research (EDGAR) anthropogenic emission inventory and the Ozone Monitoring Instrument (OMI) remote sensing observations. The model indicates the largest contribution by lightning emissions (1258 Gg N yr−1), even after already significantly reducing the emissions, followed by anthropogenic (933 Gg N yr−1), soil biogenic (187 Gg N yr−1) and biomass burning emissions (104 Gg N yr−1). The comparison with OMI remote sensing observations indicated a mean bias of tropospheric NO2 columns over the whole domain (WRF-Chem minus OMI) of 0.02 (90 % CI: [−0.43, 0.70]) ×1015 molecules cm−2, which is <5 % of the mean column. However, the simulated NO2 columns are overestimated and underestimated in regions where lightning and biomass burning emissions dominate, respectively. WRF-Chem was unable to capture NOx and CO urban pollutant mixing ratios, neither in timing nor in magnitude. Yet, WRF-Chem was able to simulate the urban diurnal cycle of O3 satisfactorily but with a systematic overestimation of 10 parts per billion (ppb) due to the equally large underestimation of NO mixing ratios and, consequently, titration. This indicates that these city environments are in the NOx-saturated regime with frequent O3 titration. We conducted sensitivity experiments with an online meteorology–chemistry single-column model (SCM) to evaluate how WRF-Chem subgrid-scale-enhanced emissions could explain an improved representation of the observed O3, CO and NOx diurnal cycles. Interestingly, the SCM simulation, showing especially a shallower nocturnal inversion layer, results in a better representation of the observed diurnal cycle of urban pollutant mixing ratios without an enhancement in emissions. This stresses that, besides application of higher-resolution emission inventories and model experiments, the diurnal cycle in boundary layer dynamics (and advection) should be critically evaluated in models such as WRF-Chem to assess urban air quality. Overall, we present a concise method to quantify air quality in regions with limited surface measurements by integrating in situ and remote sensing observations. This study identifies four distinctly different source regions and shows their interannual and seasonal variability during the last 1.5 decades. It serves as a base to assess scenarios of future air quality in Colombia or similar regions with contrasting emission regimes, complex terrain and a limited air quality monitoring network.

scholarly journals The Wildland Fire Emission Inventory: emission estimates and an evaluation of uncertainty

2011 ◽  
Vol 11 (8) ◽  
pp. 23349-23419
Author(s):  
S. P. Urbanski ◽  
W. M. Hao ◽  
B. Nordgren
Keyword(s):  
United States ◽  
Biomass Burning ◽  
Emission Inventory ◽  
Wildland Fire ◽  
Western United States ◽  
Emission Inventories ◽  
Wildland Fires ◽  
Global Modeling ◽  
Fire Emissions ◽  
Pm2.5 Emissions

Abstract. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires) in the contiguous United States (CONUS). WFEI was used to estimate emissions of CO and PM2.5 for the western United States from 2003–2008. The estimated annual CO emitted ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual PM2.5 emitted were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual wildland fire emissions were significant compared to other emission sources in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, fire emissions were ~20 % of total CO emissions and ~39 % of total PM2.5 emissions. During the months with the greatest fire activity, wildland fires accounted for the majority of CO and PM2.5 emitted across the study region. The uncertainty in the inventory estimates of CO and PM2.5 emissions (ECO and EPM2.5, respectively) have been quantified across spatial and temporal scales relevant to regional and global modeling applications. The uncertainty in annual, domain wide emissions was 28 % to 51 % for CO and 40 % to 65 % for PM2.5. Sensitivity of the uncertainty in ECO and EPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50 % of total ECO with an uncertainty <133 % and half of total EPM2.5 with an uncertainty <146 %. The uncertainty in ECO and EPM2.5 is significantly reduced at the scale of global modeling applications (Δx = 100 km, Δt = 30 day). Fifty percent of total emissions are estimated with an uncertainty <50 % for CO and <64 % for PM2.5. Uncertainty in the burned area drives the emission uncertainties at regional scales. At global scales the uncertainty in ECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 drives the EPM2.5 uncertainty. Our uncertainty analysis indicates that the large scale aggregate uncertainties (e.g. annual, CONUS) that are typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

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