scholarly journals Supplementary material to "Nepal Emission Inventory (NEEMI): a high resolution technology-based bottom-up emissions inventory for Nepal 2001–2016"

2019 ◽  
Author(s):  
Pankaj Sadavarte ◽  
Maheswar Rupakheti ◽  
Prakash V. Bhave ◽  
Kiran Shakya ◽  
Mark G. Lawrence
Keyword(s):  
High Resolution ◽  
Emission Inventory ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Supplementary Material

scholarly journals Spatial Analysis of the Ship Gas Emission Inventory in the Port of Busan Using Bottom-Up Approach Based on AIS Data

2021 ◽  
Vol 9 (12) ◽  
pp. 1457
Author(s):  
Donghan Woo ◽  
Namkyun Im
Keyword(s):  
Spatial Analysis ◽  
Emission Inventory ◽  
Geographical Area ◽  
Automatic Identification ◽  
Identification System ◽  
Gas Emission ◽  
Gas Emissions ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Coastal Residents

Dense hub port-cities have been suffering from ship gas emissions causing atmospheric pollution and a threat to the health of coastal residents. To control ship gas emissions, many regulations have been established internationally. Analyses of ship gas emission inventories are essential to quantify mass and track emission changes over time in a given geographical area. Based on the gas emissions inventory, applicable regulations such as Emission Control Area (ECA) realization and Vessel Speed Reduction (VSR) may be established. The ship gas emission inventory (CO2, CO, NOx, SOx and PM) from the Busan Port (BP), including the North Port (NP) and Gamcheon Dadae-po Port (GDP), which is the biggest port in the Republic of Korea and which is also surrounded by residential, commercial, and industrial areas, were spatially analyzed. To calculate geographical ship gas emissions in real-time, this study introduces a bottom-up methodology using Automatic Identification System (AIS) data. According to the geographical density analysis of the gas emissions inventory, this study highlights that about 35% of the annual ship gas emissions of BP in 2019 were concentrated in the passageway to NP because of high ship speeds when leaving or arriving at the port. To protect the health of coastal residents, ship speed limit regulations along the passageway should be revised based on our spatial analysis results. The spatial analysis of the ship gas emission inventory in BP will be useful basic data for properly evaluating the local gas emission state on newly established or revised environmental regulations for BP.

scholarly journals Nepal Emission Inventory (NEEMI): a high resolution technology-based bottom-up emissions inventory for Nepal 2001–2016

2019 ◽  
Author(s):  
Pankaj Sadavarte ◽  
Maheswar Rupakheti ◽  
Prakash V. Bhave ◽  
Kiran Shakya ◽  
Mark G. Lawrence
Keyword(s):  
Air Pollution ◽  
Energy Consumption ◽  
High Resolution ◽  
Total Energy ◽  
Emission Inventory ◽  
Point Sources ◽  
Detailed Knowledge ◽  
Total Energy Consumption ◽  
Bottom Up ◽  
Open Burning

Abstract. The lack of a comprehensive, up-to-date emission inventory for the Himalayan region is a major challenge in understanding the regional air pollution, including its impacts, mitigation, and the relevant atmospheric processes. This study develops a high resolution (1 km × 1 km) present-day emission inventory for Nepal with a higher-tier approach (detailed) to understanding the current combustion technologies and sectoral energy consumption. We estimate emissions of aerosols, trace gases and greenhouse gases from five energy-use sectors (residential, industry, commercial, agriculture and transport) and an open-burning source (agro-residue) for the period 2001–2016 (with 2011 as the base year), using bottom-up methodologies. Newly-measured country-specific emission factors (EFs) are used for emission estimates. It is estimated that the national total energy consumption in 2011 was 378 PJ with the residential sector being the largest energy consumer (79 %), followed by the industry (11 %) and transport (7 %) sectors. Biomass is the dominant energy source contributing 88 % to national total energy consumption, while the share of fossil fuel is only 12 %. With regards to open burning of the crop waste, it is estimated that 9.3 million tons of agro-waste was burned after harvesting crops in 2011. Nationally, 8.4 Tg CO2, 666 Gg CH4, 2.5 Gg N2O, 72 Gg NOX, 1984 Gg CO, 477 Gg NMVOC, 239 Gg PM2.5, 28 Gg BC, 99 Gg OC and 28 Gg SO2 were emitted from these sources in 2011. The energy consumption was also estimated for each year for the period 2001–2016 which shows an increase by a factor of 1.6 in 2016, while the emissions of various species increased by a factor of 1.2–2.4 with respect to 2001. An assessment of the top polluting technologies shows high emissions from traditional cookstoves using firewood, dungcakes, and agricultural residues, and open burning emissions of wood and residues. In addition, high emissions were also encountered from fixed chimney Bull's Trench kilns for brick production, cement kilns, two-wheeler gasoline vehicles, heavy diesel freight vehicles and kerosene lamps. A GIS-based gridded 1 km × 1 km population density map incorporating land-use and land cover data, settlement points, and topography was used for the spatial distribution of residential emissions. Geospatial locations were assigned to point sources, while activity-based proxies were used for other sources. Emissions were apportioned across different months from brick production, the agriculture sector, diesel generators, and space and water heating, using respective temporal variations of the activities. It was found that April had the maximum PM2.5 emissions, followed by December, January and February. Also, a wide variation in emissions distribution was found, highlighting the pockets of growing urbanization and the detailed knowledge about the emission sources. These emissions will be of value for further studies, especially air quality modelling studies focused on understanding the likely effectiveness of air pollution mitigation measures in Nepal.

scholarly journals Supplementary material to "TNO_CAMS high resolution European emission inventory 2000–2014 for anthropogenic CO<sub>2</sub> and future years following two different pathways"

2017 ◽  
Author(s):  
Hugo A. C. Denier van der Gon ◽  
Jeroen J. P. Kuenen ◽  
Greet Janssens-Maenhout ◽  
Ulrike Döring ◽  
Sander Jonkers ◽  
...  
Keyword(s):  
High Resolution ◽  
Emission Inventory ◽  
Supplementary Material

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&amp;#160; &amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry &amp; 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).&lt;/p&gt;

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.

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