Updated national emission inventory and comparison with the Emissions Database for Global Atmospheric Research (EDGAR): case of Lebanon

AbstractPhysically based computational modeling is an effective tool for estimating and predicting the spatial distribution of pollutant concentrations in complex environments. A detailed and up-to-date emission inventory is one of the most important components of atmospheric modeling and a prerequisite for achieving high model performance. Lebanon lacks an accurate inventory of anthropogenic emission fluxes. In the absence of a clear emission standard and standardized activity datasets in Lebanon, this work serves to fill this gap by presenting the first national effort to develop a national emission inventory by exhaustively quantifying detailed multisector, multi-species pollutant emissions in Lebanon for atmospheric pollutants that are internationally monitored and regulated as relevant to air quality. Following the classification of the Emissions Database for Global Atmospheric Research (EDGAR), we present the methodology followed for each subsector based on its characteristics and types of fuels consumed. The estimated emissions encompass gaseous species (CO, NOx, SO2), and particulate matter (PM2.5 and PM10). We compare totals per sector obtained from the newly developed national inventory with the international EDGAR inventory and previously published emission inventories for the country for base year 2010 presenting current discrepancies and analyzing their causes. The observed discrepancies highlight the fact that emission inventories, especially for data-scarce settings, are highly sensitive to the activity data and their underlying assumptions, and to the methodology used to estimate the emissions.

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A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): An application of the Community Emissions Data System (CEDS)

10.5194/essd-2020-103 ◽

2020 ◽

Author(s):

Erin E. McDuffie ◽

Steven J. Smith ◽

Patrick O'Rourke ◽

Kushal Tibrewal ◽

Chandra Venkataraman ◽

...

Keyword(s):

Emission Inventory ◽

Data System ◽

Atmospheric Pollutants ◽

Emission Inventories ◽

Anthropogenic Emission ◽

Fuel Type ◽

Road Transportation ◽

Activity Data ◽

Global Emission ◽

Global Emissions

Abstract. Global anthropogenic emission inventories remain vital for understanding the fate and transport of atmospheric pollution, as well as the resulting impacts on the environment, human health, and society. Rapid changes in today’s society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel-type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx, CO, SO2, NH3, NMVOCs, BC, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture, energy generation, industrial processes, transportation (on-road and non-road), residential, commercial, and other sectors (RCO), waste, solvent use, and international-shipping) and four fuel categories (total coal, solid biofuel, and the sum of liquid fuels and natural gas combustion, plus remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes global gridded (0.5°×0.5°) emission fluxes with monthly time resolution for each compound, sector, and fuel-type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default calibration procedure, and modifications to the final procedures for emissions gridding and aggregation to retain sector and fuel-specific information. Relative to the previous CEDS data released for CMIP6 (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to-date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel-types across multiple source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors, as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), as well as emissions from waste. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture serving as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories, including uncertainties in the underlying activity data and sector- and region-specific emission factors. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS. The CEDSGBD-MAPS emission inventory dataset (both annual country-total and global gridded files) is publicly available and registered under: https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020).

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

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

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.

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

10.5194/acp-2017-127 ◽

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.

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Emerging Concerns on Air Pollutants from Chemistry Laboratories of University: Ozone Formation Potential, Emission Characteristics, and Emission Inventory in Beijing, China

10.21203/rs.3.rs-449206/v1 ◽

2021 ◽

Author(s):

Chenlong Wang ◽

Xiaoxi Zhang ◽

Kun Wang ◽

Jiajia Gao ◽

Penglai Zuo ◽

...

Keyword(s):

Air Pollutants ◽

Emission Inventory ◽

Raw Materials ◽

Air Pollutant ◽

Atmospheric Pollutants ◽

Pollutant Emissions ◽

Ozone Formation ◽

Formation Potential ◽

Ozone Formation Potential ◽

Materials Used

Abstract Chemical laboratories of Universities are an important source of air pollutant emissions in urban area, but their detailed emission factors have rarely been investigated. This study determined the concentration level and chemical composition spectrum of air pollutants from 21 typical chemical laboratories of universities in Beijing. Based on quantitative analysis using a GC-MS/FTIR/FID system, the emission intensity of each laboratory area was estimated, the ozone formation potential (OFP) was calculated, and the emission inventory of atmospheric pollutants in chemical laboratories of universities in Beijing was estimated. According to the results, the atmospheric pollutants discharged by the laboratories could be characterized by wide species distribution and low concentrations of single components. The average concentrations of atmospheric pollutants from the three outlets were 20.6 ± 8.9 µmol/mol (mean ± S.D.), 26.5 ± 4.8 µmol/mol, and 14.7 ± 5.8 µmol/mol. VOC emission was significantly affected using organic solvents. Pollutant emissions from the laboratories exhibited strong periodicity, and the raw materials used in the experiments were the main factor affecting the final pollutant concentration. The emission intensities of atmospheric pollutants from the three outlets were 35.06 ± 38.08 g/(m2·d), 22.83 ± 18.88 g/(m2·d) and 24.03 ± 28.78 g/(m2·d), and their TOFP were 27.8 ± 39.1 µmol/mol, 22.0 ± 21.2 µmol/mol, and 14.5 ± 28.9 µmol/mol. The total emission of atmospheric pollutants from university chemical laboratories in Beijing in 2019 was estimated at approximately 2630.8 ± 2710.3 t, including 675.8 ± 610.6 t of inorganic gaseous pollutants and 1932.0 ± 2081.2 t of VOCs, with Haidian District as the largest contributor.

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Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling

Earth System Science Data ◽

10.5194/essd-13-4191-2021 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4191-4206 ◽

Cited By ~ 3

Author(s):

Thierno Doumbia ◽

Claire Granier ◽

Nellie Elguindi ◽

Idir Bouarar ◽

Sabine Darras ◽

...

Keyword(s):

South America ◽

Eastern China ◽

Atmospheric Modeling ◽

Air Pollutant ◽

Control Measures ◽

The Other ◽

Atmospheric Pollutants ◽

Pollutant Emissions ◽

Adjustment Factors ◽

The Impact

Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world's countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to current global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1×0.1 latitude–longitude degree resolution on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs are provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first 6 months of 2020. Maximum decreases in the total emissions are found in February in eastern China, with an average reduction of 20 %–30 % in NOx, NMVOCs (non-methane volatile organic compounds) and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20 %–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30 %–50 %) in South America. In India and African regions, NOx and NMVOC emissions are reduced on average by 15 %–30 %. For the other species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC (black carbon) are estimated. As discussed in the paper, reductions vary highly across regions and sectors due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid-19 adjustmeNt Factors fOR eMissions) (https://doi.org/10.25326/88; Doumbia et al., 2020). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/, last access: 23 August 2021).

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Gridded Emissions of Air Pollutants for the period 1970–2012 within EDGAR v4.3.2

10.5194/essd-2018-31 ◽

2018 ◽

Cited By ~ 3

Author(s):

Monica Crippa ◽

Diego Guizzardi ◽

Marilena Muntean ◽

Edwin Schaaf ◽

Frank Dentener ◽

...

Keyword(s):

Time Series ◽

Air Pollutants ◽

Emission Inventory ◽

Ghg Emissions ◽

Emission Factors ◽

Air Pollutant ◽

Pollutant Emissions ◽

Activity Data ◽

Time Period ◽

Over Time

Abstract. The new version v4.3.2 of the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) compiles gaseous and particulate air pollutant emissions, making use of the same anthropogenic sectors, time period (1970–2012) and international activity data as used for estimating GHG emissions as described in a companion paper (Janssens-Maenhout et al., 2017). All human activities, except large scale biomass burning and land use, land-use change and forestry, are included in the emissions calculation. The bottom-up compilation methodology of sector-specific emissions was applied consistently for all world countries, providing methodological transparency and comparability between countries. In addition to the activity data used to estimate GHG emissions, air pollutant emissions are determined by the process technology and end-of-pipe emission reduction abatements. Region-specific emission factors and abatement measures were selected from recent scientific available literature and reports. Compared to previous versions of EDGAR, the EDGAR v4.3.2 dataset covers all gaseous and particulate air pollutants, has extended time series (1970–2012) and has been evaluated with QC/QA procedures both for the emission time series (e.g. PM mass balance, gap-filling for missing data, split-up of countries over time, etc.) and gridmaps (full coverage of the world, complete mapping of EDGAR emissions with sector-specific proxies, etc.). This publication focuses on the gaseous air pollutants of CO, NOx, SO2, total NMVOC and NH3 and on the aerosols PM10, PM2.5, BC and OC. Considering the 1970–2012 time period, global emissions of SO2 increased from 99 to 103 Tg, CO from 441 to 562 Tg, NOx from 68 to 122 Tg, NMVOC from 119 to 170 Tg, NH3 from 25 to 59 Tg, PM10 from 37 to 65 Tg, PM2.5 from 24 to 41 Tg, BC from 2.7 to 4.5 Tg and OC from 9 to 11 Tg. We present the country-specific emission totals and analyse the larger emitting countries (including the European Union), to provide insights on major sector contributions. In addition, per capita and per GDP emissions and implied emission factors – the apparent emissions per unit of production or energy consumption are presented. We find that the implied EFs are higher for low income countries compared to high income countries, but in both cases decreasing from 1970 to 2012. The comparison with other global inventories, such as HTAP v2.2 and CEDS, reveals insights on the uncertainties as well as the impact of data revisions (e.g. activity data, emission factors, etc.). As an additional metric we analyse the emission ratios of some pollutants to CO2 (e.g. CO/CO2, NOx/CO2, NOx/CO and SO2/CO2) by sector, region and time to identify any decoupling of air pollutant emissions from energy production activities and to demonstrate the potential of such ratios to compare to satellite derived emission data. Gridded emissions are also made available for the 1970–2012 historic time series, disaggregated for 26 anthropogenic sectors using updated spatial proxies. The analysis of the evolution of hot spots over time allowed us to identify areas with growing emissions and where emissions should be constrained to improve global air quality (e.g. China, India, Middle East and some Southern American countries are often characterized by high emitting areas which are changing rapidly compared to Europe or USA where stable or decreasing emissions are evaluated). Sector-and component specific contributions to gridcell emissions may help the modelling and satellite communities to disaggregate atmospheric column amounts and concentrations into main emitting sectors. This work addresses not only the emission inventory and modelling communities, but also aims to broaden the usefulness information available in a global emission inventory such as EDGAR to also include the measurement community. Data are publicly available online through the EDGAR website http://edgar.jrc.ec.europa.eu/overview.php?v=432_AP&SECURE=123 and registered under DOI: https://data.europa.eu/doi/10.2904/JRC_DATASET_EDGAR.

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-13103-2017 ◽

2017 ◽

Vol 17 (21) ◽

pp. 13103-13118 ◽

Cited By ~ 15

Author(s):

Jianlin Hu ◽

Xun Li ◽

Lin Huang ◽

Qi Ying ◽

Qiang Zhang ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Emission Inventory ◽

Health Effect ◽

Model Performance ◽

Performance Criteria ◽

Ensemble Prediction ◽

Daily Maximum ◽

Observation Data ◽

Emission Inventories

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.

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African anthropogenic emissions inventory for gases and particles from 1990 to 2015

Earth System Science Data ◽

10.5194/essd-13-3691-2021 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3691-3705

Author(s):

Sekou Keita ◽

Catherine Liousse ◽

Eric-Michel Assamoi ◽

Thierno Doumbia ◽

Evelyne Touré N'Datchoh ◽

...

Keyword(s):

Power Plant ◽

Emission Inventory ◽

Pollutant Emissions ◽

Eastern Africa ◽

Pollutant Emission ◽

Ancillary Data ◽

Gaseous Species ◽

Carbonaceous Particles ◽

Emissions Inventory ◽

Western Africa

Abstract. There are very few African regional inventories providing biofuel and fossil fuel emissions. Within the framework of the DACCIWA project, we have developed an African regional anthropogenic emission inventory including the main African polluting sources (wood and charcoal burning, charcoal making, trucks, cars, buses and two-wheeled vehicles, open waste burning, and flaring). To this end, a database on fuel consumption and emission factors specific to Africa was established using the most recent measurements. New spatial proxies (road network, power plant geographical coordinates) were used to convert national emissions into gridded inventories at a 0.1∘ × 0.1∘ spatial resolution. This inventory includes carbonaceous particles (black and organic carbon) and gaseous species (CO, NOx, SO2 and NMVOCs) for the period 1990–2015 with a yearly temporal resolution. We show that all pollutant emissions are globally increasing in Africa during the period 1990–2015 with a growth rate of 95 %, 86 %, 113 %, 112 %, 97 % and 130 % for BC, OC, NOx, CO, SO2 and NMVOCs, respectively. We also show that Western Africa is the highest emitting region of BC, OC, CO and NMVOCs, followed by Eastern Africa, largely due to domestic fire and traffic activities, while Southern Africa and Northern Africa are the highest emitting regions of SO2 and NOx due to industrial and power plant sources. Emissions from this inventory are compared to other regional and global inventories, and the emissions uncertainties are quantified by a Monte Carlo simulation. Finally, this inventory highlights key pollutant emission sectors in which mitigation scenarios should focus on. The DACCIWA inventory (https://doi.org/10.25326/56, Keita et al., 2020) including the annual gridded emission inventory for Africa for the period 1990–2015 is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: 19 July 2021). For review purposes, ECCAD has set up an anonymous repository where subsets of the DACCIWA data can be accessed directly through https://www7.obs-mip.fr/eccad/essd-surf-emis-dacciwa/ (last access: 19 July 2021).

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African Anthropogenic Emissions Inventory for gases and particles from 1990 to 2015

10.5194/essd-2020-328 ◽

2020 ◽

Author(s):

Sekou Keita ◽

Catherine Liousse ◽

Eric-Michel Assamoi ◽

Thierno Doumbia ◽

N’Datchoh Evelyne Touré ◽

...

Keyword(s):

Power Plant ◽

Emission Inventory ◽

Pollutant Emissions ◽

Pollutant Emission ◽

Ancillary Data ◽

Gaseous Species ◽

Carbonaceous Particles ◽

Emissions Inventory ◽

Set Up ◽

Plant Sources

Abstract. There are very few African regional inventories providing biofuel and fossil fuel emissions. Within the framework of the DACCIWA project, we have developed an African regional anthropogenic emission inventory including the main African polluting sources (wood and charcoal burning, charcoal making, truck, car, buses and two wheels vehicles, open waste burning and flaring). To this end, a database on fuel consumption and emission factors specific to Africa was established, using the most recent measurements. New spatial proxies (road network, power plant geographical coordinates) were used to convert national emissions into gridded inventories at a 0.1° × 0.1° spatial resolution. This inventory includes carbonaceous particles (black and organic carbon) and gaseous species (CO, NOx, SO2 and NMVOC) for the period 1990–2015 with a yearly temporal resolution. We show that all pollutant emissions are globally increasing in Africa during the period 1990–2015 with a growth rate of 95 %, 86 %, 113 %, 112 %, 97 %, and 130 % for BC, OC, NOx, CO, SO2 and NMVOC, respectively. We also show that West Africa is the highest emitting region of BC, OC, CO and NMVOC, followed by East Africa, largely due to domestic fire and traffic activities, while Southern Africa and Northern Africa are the highest emitting regions of SO2 and NOx due to industrial and power plant sources. Emissions from this inventory are compared to other regional and global inventories and its uncertainties are quantified by a Monte Carlo simulation. Finally, this inventory highlights key pollutant emission sectors in which mitigation scenarios should focus on. The DACCIWA inventory (https://doi.org/10.25326/56, Keita et al., 2017) including the annual gridded emission inventory for Africa for the period 1990–2015 are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/). For review purposes, ECCAD has set up an anonymous repository where subsets of the DACCIWA data can be accessed directly https://www7.obs-mip.fr/eccad/essd-surf-emis-dacciwa/.

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A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS)

Earth System Science Data ◽

10.5194/essd-12-3413-2020 ◽

2020 ◽

Vol 12 (4) ◽

pp. 3413-3442

Author(s):

Erin E. McDuffie ◽

Steven J. Smith ◽

Patrick O'Rourke ◽

Kushal Tibrewal ◽

Chandra Venkataraman ◽

...

Keyword(s):

Emission Inventory ◽

Data System ◽

Atmospheric Pollutants ◽

Emission Inventories ◽

Anthropogenic Emission ◽

Fuel Type ◽

Road Transportation ◽

International Shipping ◽

Global Emission ◽

Global Emissions

Abstract. Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx; CO; SO2; NH3; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5∘ × 0.5∘) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS (last access: 1 December 2020). The CEDSGBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020c).

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