• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.139-152
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• 2018

The urban model inter-comparison study (UMICS) was conducted in order to improve the performance of air quality models (AQMs) for simulating fine particulate matter ($PM_{2.5}$) in the Greater Tokyo Area of Japan. UMICS consists of three phases: the first phase focusing on elemental carbon (UMICS1), the second phase focusing on sulfate, nitrate and ammonium (UMICS2), and the third phase focusing on organic aerosol (OA) (UMICS 3). In UMICS2/3, all the participating AQMs were the Community Multiscale Air Quality modeling system (CMAQ) with different configurations, and they similarly overestimated $PM_{2.5}$ nitrate concentration and underestimated $PM_{2.5}$ OA concentration. Various sensitivity analyses on CMAQ configurations, emissions and boundary concentrations, and meteorological fields were conducted in order to seek pathways for improvement of $PM_{2.5}$ simulation. The sensitivity analyses revealed that $PM_{2.5}$ nitrate concentration was highly sensitive to emissions of ammonia ($NH_3$) and dry deposition of nitric acid ($HNO_3$) and $NH_3$, and $PM_{2.5}$ OA concentration was highly sensitive to emissions of condensable organic compounds (COC). It was found that $PM_{2.5}$ simulation was substantially improved by using modified monthly profile of $NH_3$ emissions, larger dry deposition velocities of $HNO_3$ and $NH_3$, and additionally estimated COC emissions. Moreover, variability in $PM_{2.5}$ simulation was estimated from the results of all the sensitivity analyses. The variabilities on CMAQ configurations, chemical inputs (emissions and boundary concentrations), and meteorological fields were 6.1-6.5, 9.7-10.9, and 10.3-12.3%, respectively.

• Journal of Environmental Science International
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• v.19 no.7
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• pp.829-841
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• 2010

The estimation of a biogenic volatile organic compound (BVOC, especially isoprene) and the influence of isoprene emissions on ozone concentrations in the Greater Busan Area (GBA) were carried out based on a numerical modeling approach during a high ozone episode. The BVOC emissions were estimated using a biogenic emission information system (BEIS v3.14) with vegetation data provided by the forest geographical information system (FGIS), land use data provided by the environmental geographical information system (EGIS), and meteorological data simulated by the MM5. Ozone simulation was performed by two sets of simulation scenarios: (1) without (CASE1) and (2) with isoprene emissions (CASE2). The isoprene emission (82 ton $day^{-1}$) in the GBA was estimated to be the most dominant BVOC followed by methanol (56) and carbon monoxide (28). Largest impacts of isoprene emissions on the ozone concentrations (CASE2-CASE1) were predicted to be about 4 ppb in inland locations where a high isoprene was emitted and to be about 2 ppb in the downwind and/or convergence regions of wind due to both the photochemical reaction of ozone precursors (e.g., high isoprene emissions) and meteorological conditions (e.g., local transport).

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.2
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• pp.150-160
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• 2014

Greenhouse gas (GHG) and Air Pollution (AP) emission inventories have been constructed and estimated independently up-to-date in Seoul. It causes difficulty in GHG and AP integrated management due to a difference in emission inventories. In this study, we constructed GHG and AP integrated emission inventories for direct and indirect sources in Seoul during the year 2010 in Energy activities for estimating GHG and AP emissions were derived from IPCC guideline, guidelines for local government greenhouse inventories, air pollutants calculation manual, and Indirect Emission Factors (IEF) reported by Korea Power Exchange. The annual GHG emission was estimated as 50,530,566 $tonCO_{2eq}$, of which 54.8% resulted from direct sources and the remaining 45.2% from indirect sources. Among direct sources, transportation sector emitted the largest GHG, accounting for 47.3% of the total emission from direct sources. As with indirect sources, purchased electricity sector only emitted 98.6% of the total emission from indirect sources. The annual AP emission was estimated as 283,701 tonAP, of which 85.9% was contributed by the combined AP emissions of transportation and fugitive sectors. Estimation of individual air pollutant showed that the largest source were transportation sector for CO, $NO_x$, TSP, $PM_{10}$ and NH3, non-energy sector for $SO_x$, and fugitive sector for VOCs. This study found some limitations in estimating GHG and AP integrated emissions, such as nonconforming emission inventories between GHG and AP, and no indirect AP emission factor of purchased electricity, and so on. Those should be further studied and improved for more effective GHG and AP integrated management.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.72-82
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• 2008

Benzene is a very harmful and toxic compound known as human carcinogen by all routes of exposure. Owing to the risky feature of benzene, several countries such as Japan, UK and EU have established the ambient air quality standard and protect from that risk of it. Korea also has designated it as one of the criteria air pollutants and established the concentration limit ($5\;{\mu}g/m^3$) in the air and is going to apply the standard from 2010. Benzene is emitted from various sources such as combustion plants, production processes, waste treatment facilities and also automobiles. Mobile source is known as one of the major emission sources of benzene. In this study, we estimated the domestic emissions of benzene from mobile source and compared the results with those of advanced countries. Mobile source was divided into 2 categories, Le., on-road source and non-road source. The total emissions of benzene from mobile source were estimated as 3,106 tons/yr and 1,612 tons/yr was emitted from on-road source and 1,494 tons/yr was from non-road source. Emission ratio of benzene from on-road source showed that 80.0% was from passenger cars, 10.1% was from taxis, 7.2% was from light-duty vehicles, 2.5% was from heavy-duty vehicles and 0.2% was from buses. In the case of non-road source, the distribution showed that 66.3% was from construction machineries, 14.5% was from locomotives, 11.7% was from ships, 7.1% was from agriculture equipments and 0.5% was from aircrafts. The cold-start emissions were estimated as 942 tons/yr and this value was almost 1.5 times greater than that for hot engine emissions (608 tons/yr). In addition, the fuel-based distribution was 65.9%, 31.1% and 2.8% from gasoline, LPG and diesel vehicles, respectively. The emission ratio from mobile source occupied 65% and 30% of total benzene emissions in USA and UK, respectively. In case of Korea, the emission ratio of benzene from mobile source occupied 29% (15% from on-road source, 14% from non-road source) which showed similar value with UK.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.190-202
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• 2012

Emissions of air pollutants and greenhouse gases (GHGs) by aircraft at the Gimhae International Airport (GIA) were investigated using the Emissions and Dispersion Modeling System (EDMS) version 5.1.3. The number of Landing and Take-Off (LTO) at the GIA for aircraft B737 was dominant, accounting for more than 60% of the total LTOs. For air pollutant emissions, CO was the most dominant pollutant by aircraft, followed by $NO_x$, VOCs, $SO_x$, etc. The emissions of CO, $NO_x$, and VOCs in 2009 (and 2010) at the GIA were 974 (968), 447 (433), 118 (122) ton/yr, respectively. The emissions of GHGs such as $CO_2$, $CH_4$, and $N_2O$ in 2009 (and 2010) were 110,795 (111,114), -0.157 (-0.151), and 1,989 (1,998) ton/yr, respectively. The negative number in $CH_4$ emission represents the consumption of atmospheric $CH_4$ in the engine. In addition, the emissions of most air pollutants (except for $PM_{10}$) and GHGs were estimated to be high in Taxi-Out and Climb-Out modes.

• Asian Journal of Business Environment
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• v.10 no.4
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• pp.21-27
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• 2020

Purpose: The present study examines the effects of tourism on carbon dioxide emissions for selected South Asian economies over the time from 1995 to 2016. Research design, data and methodology: The present study is an annual time series analysis of tourism and CO2 emissions. The data is taken from World Development Indicators, an official data bank of World Bank. The study sample covers four South Asian countries, namely Bangladesh, India, Pakistan, Sri Lanka and Nepal. The empirical analysis is conducted by employing Pedroni panel cointegration, Fully Modified OLS, and Dynamic OLS approaches of estimation. Results: Tourism significantly increases environmental degradation in selected South Asian economies. The empirical estimated results indicate, that 1 % increase in tourism related activities leads to 0.16 % increase in CO2 emissions. In addition energy consumption and GDP are also causing an upsurge in CO2 emissions in the selected panel of South Asian economies. As the empirical results indicate that 1% increase in GDP stimulates carbon dioxide emissions by 0.23%. Conclusion: In order to protect the environment, the study emphasizes that sustainable tourism practices need to be promoted in the selected South Asian countries. Policy implication and provided and discussed.

• Journal of Environmental Science International
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• v.23 no.11
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• pp.1881-1888
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• 2014

Greenhouse gas (GHG) emissions from dye wastewater treatment processes were estimated by analysing their mass and energy balances, which were then used as baseline information for environmental assessment. The total GHG emissions from dye wastewater treatment plants were divided into direct emissions from the treatment processes and indirect ones from electricity usage. The amounts of $CO_2$, $CH_4$ and $N_2O$ emissions were calculated according to the Intergovernmental Panel on Clime Change (IPCC) guideline for the GHG target management system. For 3 years between 2011 and 2013, direct and indirect emissions were on average 8,742.7 and 7,892.0 Ton.$CO_2eq/year$, respectively, with the former exhibiting 52.6 %. Also, compared to 2012, in 2013, the eco-efficiency indicator by the GHG emissions was found to be more than 1, suggesting that environmental quality was effectively improved.

• Asian Journal of Atmospheric Environment
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• v.7 no.1
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• pp.25-37
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• 2013

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.4
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• pp.460-471
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• 2011

A port has been regarded as a significant contributor to air pollution in the surrounding areas. Port-related air pollutants are released from not only marine vessels, but also various land-side sources at ports, which include cargo handling equipment, vehicles, locomotives, and fugitive dust sources by port activities such as bulk handling and vehicle movements. However, most studies in Korea have only focused on vessel emissions and there is a lack of information on the emissions from other sources at port. In this study, in order to establish the port-related emission inventory and evaluate the relative contribution of these sources to air emissions from the Port of Incheon, the emissions from land-side sources were estimated and the CAPSS (Clean Air Policy Support System) data for vessel emissions were used. In particular, the detailed information and activity data for the cargo handling equipment source were collected and the emission factors and emissions by equipment types were calculated using U.S. EPA methodologies. Total HC, CO, $NO_x$, $PM_{10}$, and $SO_2$ emissions from port-related sources including the vessel in 2007 were calculated as 229 ton/year, 638 ton/year, 4,861 ton/year, 307 ton/year, and 3,995 ton/year, respectively. It was found that the vessel was the largest contributor to air pollutant emissions from the port, the cargo handling equipment was responsible for about from 8% to 13% of HC, CO, and $NO_x$ emissions and the resuspended road dust contributed about 39% for $PM_{10}$ emissions. The results of this study will be used to establish the management and reduction strategies of air pollution in the port.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.1
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• pp.61-70
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• 2014

South Korea is under pressure to reduce $CO_2$ emissions because of expanding request for reducing $CO_2$ emissions. In many industry sectors, $CO_2$ emissions were analyzed to reduce $CO_2$ emissions. But little effort in researching agricultural sector has been undertaken because it is recognized as environmentally friendly industry. The object of this research is to estimate $CO_2$ emissions of crops and analysis on correlation between $CO_2$ emissions and production, acreage of crops. In this study, environmental input-output analysis was used to estimate $CO_2$ emissions of agricultural sector and spearman correlation coefficient was used to analysis on correlation between $CO_2$ emissions and statics like production and acreage. As a result, rice, barley, pulses and horticultural specialities had correlation with acreage and correlation coefficients of these crops were 0.800~0.933. Regression equations about $CO_2$ emissions and acreage of rice, barley, pulses and horticultural specialities was made and $R^2$ of these equations were 0.615~0.929. Using equations, $CO_2$ emissions of rice, barley, pulses and horticultural specialities can be estimated with acreage.