• Journal of Environmental Science International
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• v.30 no.10
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• pp.789-802
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• 2021

This study analyzed the characteristics of high PM_2.5 episodes that meets the concentration criteria of Emergency Reduction Measures Plan (ERMP) in Busan during the 2015-2020, and compared with those in Seoul. As a first step, the CAPSS-2017 emission data was employed to analyze the emission differences between Busan and Seoul, and pointed out that Busan emission included the dominance of ship emissions (37.7%) among total PM_2.5 city emissions, whereas fugitive PM_2.5 emission was the highest in Seoul. These emission characteristics are indicating that the controlling action plan should be uniquely applied to cope with ERMP in each region. We selected extremely high PM_2.5 episode days that meet the criteria of ERMP levels. In Busan, Ulsan, and Gyeongnam region, 15, 16, and 8 days of extremely high PM_2.5 cases were found, respectively, whereas Seoul showed approximately doubling of occurrences with 37 cases. However, the occurrences in summer season indicated big differences between two cities: the proportion of summer-season occurrence was 13-25% in Busan, whereas no single case have occurred in Seoul. This is suggesting the needs of comprehensive summer emission reduction plan with focusing on sulfur reduction to effectively cope with the ERMP levels in summer in the southeastern region, including Busan.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.6
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• pp.555-568
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• 2014

The construction equipment is one of the major sources for hazardous air pollutants in Korea, and the its management has been of great concern recently. The objective of this study was to estimate each contribution of emission of construction equipments according to their production year, electric power consumption and type. To achieve this goal, we developed pollutant emission factors for the machineries manufactured after 2009, which are excluded from the present framework of Korean air pollutants inventory, CAPSS. More than 800 data obtained from emission investigations were utilized for the estimation. Compared with the previous estimation, the scheme used this study was modified to incorporate new emission factors as well as to include the corresponding activity data. Such improvement allow us to gain more detailed emission informations which are better characterized by specifications of construction equipments. The total amount of pollutants emitted from construction equipments in 2011 were estimated as 126.8, 7.0, 58.3, and 17.0 kton for $NO_x$, PM, CO, and VOC, respectively. The estimation results indicate that the increase in the emission of equipments is significantly related to their age and power consumption. The emissions of the older ones manufactured from 1992~1996 were estimated to be the contribution ranged from 23.7% to 26.8%, whereas the newer ones (2009~2011) showed the attributions of 11.3~21.5%. In addition, the results show that the emission of each equipment was increased with the increase in the electric power consumption of engine, probably due to their average output power. Among the nine types of machinery compared, excavators and forklifts were investigated to contribute relatively higher emissions in the level of 39.8~44.0% and 32.0~34.2%, respectively.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.183-193
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• 2009

Air pollution due to vehicle exhaust gas is considered to be a main contributor to the issues of transportation & environment. Furthermore it is raising concern over life quality and public health and is also perceived as a global issue. This research aims at providing helping hands for both central and local governments to set up and promote efficient atmospheric quality improvement policies, with the help of the travel demand forecasting model and GIS. More specifically, it tries to produce the overall emission level with time and space-based high resolution framework. This research, based on bottom-up approach reflecting vehicular traffic characteristics, suggested an improved approach to estimating emission level, by using a traffic model with a total of vehicular mileage revised by surveyed value and atmosphere model. Summing up, using the method proposed, the improvement of the reliability of the emissions inventory from the mobile pollutions sources is expected by the proposed integrated paradigm of transportation and atmosphere modeling approach as a new alternative.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.517-533
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• 2018

We utilize the CAMx (Comprehensive Air Quality Model with eXtensions) system and the PSAT (Particulate Source Apportionment Technology) diagnostic tool to determine the $PM_{2.5}$ concentration and to perform its source apportionment in the southeastern region of South Korea. For a year-long simulation, eight local authorities in the region such as Pohang, Daegu, Gyeongju, Ulsan, Busan-Gimhae, Gosung-Changwon, Hadong, and all remaining areas in Gyeongsangnam-do, are selected as source areas based on the emission rates of $NO_x$, $SO_x$, VOC, and primary PM in CAPSS (Clean Air Policy Support System) 2013 emissions inventory. The CAMx-PSAT simulation shows that Pohang has the highest $PM_{2.5}$ self-contribution rate (25%), followed by Hadong (15%) and Busan-Gimhae (14%). With the exception of Pohang, which has intense fugitive dust emissions, other authorities are strongly affected by emissions from their neighboring areas. This may be measured as much as 1 to 2 times higher than that of the self-contribution rate. Based on these estimations, we conclude that the efficiency of emission reduction measures to mitigate $PM_{2.5}$ concentrations in the southeastern region of South Korea can be maximized when the efforts of local or regional emission controls are combined with those from neighboring regions. A comprehensive control policy planning based on the collaboration between neighboring jurisdictional boundaries is required.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.14-25
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• 2019

From 2019 onwards, more stringent regulations (from Stage 4 to Stage 5) are to be implemented in Europe in order to reduce the air pollutant emissions. In South Korea, the government authorities started to make new regulation to meet the European regulation. As a first step, the air pollutant emissions such as CO, NOx, SOx, TSP, $PM_{10}$, $PM_{2.5}$, VOC, $NH_3$ by agricultural machinery were analyzed based on CAPSS inventory along with the analysis in the general aspect in this study. Three levels of analysis was conducted each in agricultural machinery aspect along with in the general aspect. Per agricultural tractor, all kinds of the air pollutant emissions decreased by 25, 25, 99, 25, 25, 25, 25% for the CO, NOx, SOx, TSP, $PM_{10}$, VOC, $NH_3$ emissions each from the year 2000 to the year 2014. Per combine harvester, all kinds of the air pollutant emissions decreased by 63, 63, 91, 63, 63, 63, 63% for the CO, NOx, SOx, TSP, $PM_{10}$, VOC, $NH_3$ emissions each from the year 2000 to the year 2014.

• Journal of the Korean earth science society
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• v.34 no.3
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• pp.209-223
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• 2013

The purpose of this study was to estimate the contributed concentration of each emission source to $CH_4$ by verifying the simulated concentration of $CH_4$ in the Korean peninsula, and then to compare the $CH_4$ emission used to the $CH_4$ simulation with that of a box model. We simulated the Weather Research Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to estimate the mean concentration of $CH_4$ during the period of April 1 to 22 August 2010 in the Korean peninsula. The $CH_4$ emissions within the model were adopted by the anthropogenic emission inventory of both the EDGAR of the global emissions and the GHG-CAPSS of the green house gases in Korea, and by the global biogenic emission inventory of the MEGAN. These $CH_4$ emission data were validated by comparing the $CH_4$ modeling data with the concentration data measured at two different location, Ulnungdo and Anmyeondo in Korea. The contributed concentration of $CH_4$ estimated from the domestic emission sources in verification of the $CH_4$ modeling at Ulnungdo was represented in about 20%, which originated from $CH_4$ sources such as stock farm products (8%), energy contribution and industrial processes (6%), wastes (5%), and biogenesis and landuse (1%) in the Korean peninsula. In addition, one that transported from China was about 9%, and the background concentration of $CH_4$ was shown in about 70%. Furthermore, the $CH_4$ emission estimated from a box model was similar to that of the WRF-CMAQ model.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.445-457
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• 2017

In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.4
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• pp.377-392
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• 2017

A set of BFM (Brute Force Method) simulations with the CMAQ (Community Multiscale Air Quality) model were conducted in order to estimate self-contributions and conversion rates of PPM (Primary $PM_{2.5}$), $NO_x$, $SO_2$, $NH_3$, and VOC emissions to $PM_{2.5}$ concentrations over the SMA (Seoul Metropolitan Area). CAPSS (Clean Air Policy Support System) 2013 EI (emissions inventory) from the NIER (National Institute of Environmental Research) was used for the base and sensitivity simulations. SCCs (Source Classification Codes) in the EI were utilized to group the emissions into area, mobile, and point source categories. PPM and $PM_{2.5}$ precursor emissions from each source category were reduced by 50%. In turn, air quality was simulated with CMAQ during January, April, July, and October in 2014 for the BFM runs. In this study, seasonal variations of SMA $PM_{2.5}$ self-sensitivities to PPM, $SO_2$, and $NH_3$ emissions can be observed even when the seasonal emission rates are almost identical. For example, when the mobile PPM emissions from the SMA were 634 TPM (Tons Per Month) and 603 TPM in January and July, self-contributions of the emissions to monthly mean $PM_{2.5}$ were $2.7{\mu}g/m^3$ and $1.3{\mu}g/m^3$ for the months, respectively. Similarly, while $NH_3$ emissions from area sources were 4,169 TPM and 3,951 TPM in January and July, the self-contributions to monthly mean $PM_{2.5}$ for the months were $2.0{\mu}g/m^3$ and $4.4{\mu}g/m^3$, respectively. Meanwhile, emission-to-$PM_{2.5}$ conversion rates of precursors vary among source categories. For instance, the annual mean conversion rates of the SMA mobile, area, and point sources were 19.3, 10.8, and $6.6{\mu}g/m^3/10^6TPY$ for $SO_2$ emissions while those rates for PPM emissions were 268.6, 207.7, and 181.5 (${\mu}g/m^3/10^6TPY$), respectively, over the region. The results demonstrate that SMA $PM_{2.5}$ responses to the same amount of reduction in precursor emissions differ for source categories and in time (e.g. seasons), which is important when the cost-benefit analysis is conducted during air quality improvement planning. On the other hand, annual mean $PM_{2.5}$ sensitivities to the SMA $NO_x$ emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive $NO_x$ reductions are required for the SMA to overcome '$NO_x$ disbenefit' under the base condition.