Fence line Monitoring of Petroleum Refining Plants in Korea: UV-DOAS Analysis of SO2, NO2, and Benzene Emissions for Comprehensive Management

Fence line Monitoring of Petroleum Refining Plants in Korea: UV-DOAS Analysis of SO2, NO2, and Benzene Emissions for Comprehensive Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fence line Monitoring of Petroleum Refining Plants in Korea: UV-DOAS Analysis of SO2, NO2, and Benzene Emissions for Comprehensive Management Cheonwoong Kang, Dongwoo Kim, Jeonghun Kim, Sunghwan Cho, Jungung Yu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4453005/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Increasing public interest in air pollutants driven by climate change and high concentrations of particulate matter (PM) has highlighted the importance of managing pollutants such as SO 2 , NO 2 , and benzene. These compounds are known precursors of secondary PM that affect the climate and human health, with benzene being particularly carcinogenic. Benzene is a volatile organic compound (VOC) that can persist in the atmosphere for approximately 9.4 days, potentially traveling long distances and contributing to the generation of secondary organic aerosols (SOA) through photochemical reactions, leading to elevated fine dust concentrations. To comprehensively manage emissions, leaks, and point sources, a fence line monitoring study was conducted at petroleum refining industrial complexes in Daesan-eup, Chungcheongnam-do, and Yeosu-si, Jeollanam-do in Korea. UV-DOAS technology was used to confirm measurement reliability and monitor emissions. High SO 2 concentrations emitted in a specific direction were identified at the Yeosu-si, and further investigation identified the flare stack of a suspected plant as the emission source. Fence line monitoring was effective in identifying and addressing unknown high-concentration emission sources and devising measures to manage fugitive emissions and leaks. fence line monitoring UV-DOAS SO2 NO2 benzene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights - Basic research to prepare Korea’s fence line monitoring regulation - Using air environment policies (such as tracking and managing main emission sources) without dividing points and fugitive emission sources - UV-DOAS is suitable for measuring SO 2 , NO 2 , and benzene in the atmosphere 1. INTRODUCTION The Korean Ministry of Environment reports that 70% of Korea's PM 2.5 is generated secondarily through photochemical reactions in the atmosphere. They are establishing multifaceted policies to manage the emissions of various gases, such as NO X , SO X , NH 3 , and volatile organic compounds (VOCs), which are precursors that generate secondary particulate matter (PM). PM 2.5 has a small aerodynamic diameter of under 2.5 ㎛; therefore, it can penetrate deep into the human lungs and cause adverse health effects such as cardiovascular disease, respiratory disease, and cancer [ 1 – 4 ]. PM 2.5 is directly emitted in various environments and conditions, such as biomass combustion, coal combustion, vehicle emissions, soil dust, and industrial emissions [ 5 – 7 ]. Pollutants emitted from pollution sources are produced secondarily in the atmosphere through photochemical reactions. They can be divided into primary and secondary aerosols [ 8 ]. Secondary aerosols include secondary organic aerosols (SOAs) and secondary inorganic aerosols (SIAs). Secondary organic aerosols are produced by photochemical reactions of volatile organic compounds (VOCs) [ 9 , 10 ]. Secondary inorganic aerosols mainly consist of sulfate, nitrate, and ammonium, which are produced by the photochemical reactions of gaseous pollutants such as nitrogen oxides (NO X ) and sulfur dioxide (SO 2 ) in the atmosphere [ 11 , 12 ]. Petroleum refining plants have various emission facilities, including processing, combustion, and fugitive emissions. The pollutants emitted from these facilities negatively impact human health and must be managed. In the Korea, various pollutant emission facilities are measured by attaching automatic environmental measurement equipment (TMS, tele monitoring system) or are managed by periodic sampling and measurement. However, it is difficult to measure and manage pollutants in fugitive emission facilities; therefore, they are managed by applying a leak detection and repair (LDAR) program. The LDAR program regularly manages facilities that may leak or emit fugitive materials. Fence line monitoring regulations have been introduced in the United States to accurately measure and manage benzene. Fence line monitoring is a regulation that comprehensively manages pollutants emitted from a plant by measuring at the plant boundary, rather than measuring and managing various emission sources individually [ 13 ]. Benzene is a volatile organic compound that is toxic by itself [ 14 ], with a maximum lifetime of 9.4 days, which is much longer than that of m-xylene. Benzene can travel long distances and has a sufficiently long lifetime for photochemical reactions. In this study, SO 2 , NO 2 , and benzene (which are PM 2.5 precursors and pollutants emitted from various emission sources) were monitored using an open path UV-DOAS analyzer near a petroleum refining plant, and the results were analyzed using wind direction and wind speed data. We intend to explore the possibility of utilization in terms of air environment policies, such as tracking and managing the main emission sources without dividing the points and fugitive emission sources. 2. MATERIALS AND METHODS The measurements were conducted in two regions. Before installation, calibration was performed in the laboratory to confirm the reliability of the UV-DOAS. In order to determine the results of calibration, it was checked whether R 2 ≥ 0.98 (which is the standard for linearity test results among the internal quality control regulations in the Korean Air Pollution Measurement Network operation guidelines) was satisfied. Afterwards, the UV-DOAS was installed next to the Dokgot-ri Air Quality Monitoring Station located in Daesan-eup, Chungcheongnam-do, Korea to check whether UV-DOAS on-site measurements were possible. SO 2 , NO 2 , and benzene were measured for 19 days from March 13 to March 31, 2021. The results were compared with the Dokgot-ri measurement station data to confirm the reliability of the UV-DOAS measurement values and determine whether on-site measurements were possible. Lastly, a UV-DOAS was installed on one side of the site boundary of a plant approximately 950 m in length located in the petroleum refining industrial complex in Yeosu-si, Jeollanam-do, Korea, and SO 2 , NO 2 , and benzene were measured from November 29, 2021, to January 19, 2022. To understand the behavioral characteristics of the pollutants, UV-DOAS measurements and meteorological observation data were analyzed. 2.1. UV-DOAS analyzer UV-DOAS analyzers can measure various chemical species in an optical path free of obstacles, allowing light to pass through. Currently, Korea’s air pollution monitoring network measures pollutants in the air by sampling and analyzing samples at points. The UV-DOAS analyzer had a larger spatial measurement range and good measurement time resolution, enabling real-time measurement compared with the measurement methods of the air pollution monitoring network and the UV-DOAS measurement method. Therefore, it is advantageous to identify trends in the concentration of the target substance. The UV-DOAS used in this study was an AR500 model commercially developed by a Swedish manufacturer, OPSIS. The equipment consists of a pair of emitters (EM150) and receivers (RE150) to form an optical path and is composed of an optical fiber cable that transmits to the analysis unit with little wavelength loss. The light source was equipped with a xenon lamp with a power of 150 W and it could stably irradiate in the 200–400 nm range, which is the UV wavelength necessary for measuring pollutants. The light emitted from the light source reaches the light receiver and finally the analyzer, which is used to measure pollutant concentration in the optical path. The analysis department performed spectroscopy using the grating method. The concentrations of the pollutants were measured using the absorbance wavelength. DOAS technology for analyzing pollutants follows the Lambert–Beer law and uses the principle of measuring the absorption luminosity of chemical species present as pollutants in the air that react with the wavelength of light. 2.2. Selecting validation measurement data The UV-DOAS could adjust the measurement time for each measurement item. It has the advantage of performing real-time measurements because the measurement time interval is short. Additionally, it indicates a monitoring value called the light level, which is a numerical value that represents the luminance of the light received when transmitting light from the emitter to the receiver (0–100%). The light level was used to determine whether the emitter and receiver were in the correct line. If the light level is low, sufficient light for analysis does not reach the analyzer, which can adversely affect the correct measurement values. Data where the light level was less than 20% of the 30-minute average were classified as unreliable and excluded from the 24-hour average value. Additionally, forty-eight 30-minute average data items were generated per day, and a day was completely excluded from the measurement if there were more than 24 pieces of data that were less than 20% of the 30-minute average light level data. Consequently, data from 14 of 19 days measured in Daesan-eup, Chungcheongnam-do, and data from 52 of 60 days measured in Yeosu-si, Jeollanam-do were used. 2.3. UV-DOAS calibration method Before installing the UV-DOAS in the field, calibration was performed using a standard gas to confirm the reliability of the measurement data. The equipment for calibrating the UV-DOAS analyzer consisted of a light source, optical receiver, optical gas cell, calibration bench on which the optical gas cell could be placed, analysis unit, optical fiber cable, and standard gas. Light sources and optical receivers installed and measured in the field have large reflectors because they need to send light far away, whereas those used for calibration do not need to send light far away; therefore, the reflectors are small. However, the xenon lamp used as the light source and the material of the reflector was the same; therefore, there was no change in the wavelength required for analysis. The measurement times and distances were identical to those used in the actual field. The measurement time was set to 30 s for SO 2 and NO 2 and 60 s for benzene, and the measurement distance was set to 870 m. The laboratory where the calibration was performed had a constant temperature of 21°C. Humidity was not measured because the standard gas was a dry gas diluted with N 2 . All the measurement data were expressed to the first decimal place and a calibration procedure was performed after performing a reference calibration to check whether the analysis unit was operating normally. The calibration parameters used were SO 2 , NO 2 , and benzene, and 99.999% N 2 gas was used as the zero point. The concentrations of SO 2 and NO 2 were 192 ppm and 188 ppm, respectively, as certified by Korea, but the concentration of benzene was 50 ppm, which was not officially certified, so it was replaced with the gas manufacturer’s report. The concentration analyzed in the analysis section varied depending on the standard gas concentration and the length of the optical gas cell. The standard gas had one concentration for each item, but the optical gas cell had three lengths: 100, 250, and 350 mm. Linearity was confirmed by varying the analyzed concentrations. 2.4. Verifying UV-DOAS field applicability In Korea, statistics on chemical emissions by region and company are publicly available. In Korea, oil refineries, chemical plants, and steel mills are located in Chungcheongnam-do and Jeollanam-do, where various types of air pollutants (such as SO 2 , NO 2 , and benzene) are emitted. As of 2021, approximately 51 tons of benzene will be emitted annually in Chungcheongnam-do and 31 tons per year in Jeollanam-do. The UV-DOAS was installed immediately next to the Dokgot-ri measuring station in Daesan-eup, Chungcheongnam-do, and the UV-DOAS measurement data were compared with those of the Dokgot-ri measuring station (Fig. 1 (a)). The Dokgot-ri measurement station is operated by the Ministry of the Environment of Korea and the measured data are disclosed to the public. In this experiment, the Dokgot-ri measurement station measurement data were considered reliable. All Dokgot-ri measurement station data were converted to 24-h average data. Using the converted 24 h average data, we compared the differences and trends in the measured data and verified whether the UV-DOAS can measure SO 2 , NO 2 , and benzene in the atmosphere in the field. 2.5. Fence line monitoring of petroleum refining plant A UV-DOAS was installed at the site boundary between two different petroleum refining plants located in Yeosu-si, Jeollanam-do, and the SO 2 , NO 2 and benzene concentrations were measured (Fig. 1 (b)). The two petroleum refining plants are operated by different companies and have different processes; therefore, it was expected that the characteristics of the pollutants emitted into the air would differ. Plant A included crude-oil refining, aromatic production facilities, and flare stacks, whereas plant B included PVC production and NCC processes. The measured concentration, wind direction, and wind speed data were used to analyze and create a pollution map. Wind direction and wind speed data were used from the Korea Meteorological Administration Disaster Prevention and Weather Observation (AWS, Automatic Weather Observation System) Yeosu Industrial Complex branch (34.84946, 127.71496). We confirmed the direction of pollutants that emitted high concentrations using pollutant concentration data based on wind direction and wind speed. This study measured plants A and B (which are expected to have different air pollutant emission characteristics) to determine which plant contributed the most. Subsequently, SkyDOAS (a mobile pollutant measurement system attached to a vehicle that uses the solar spectrum to measure sulfur dioxide emissions in Korea [ 15 ]) was employed to track the main emission sources in detail. Using SkyDOAS, measurements were obtained while driving inside a plant that contributed to high-concentration emissions, and the main source was tracked. 3. RESULTS AND DISCUSSION 3.1. Verification of UV-DOAS analyzer reliability A calibration experiment was performed using standard gas to verify the reliability of the UV-DOAS analyzer, and the possibility of field application was confirmed by installing the UV-DOAS near the Dokgot-ri measuring station in Daesan-eup, Chungcheongnam-do, Korea. A linearity of R 2 = 0.99 was confirmed for SO 2 , NO 2 , and benzene using the UV-DOAS calibration experiment. This satisfies the linearity test result standard of R 2 ≥ 0.98 among the internal quality control regulations in the Korea Air Pollution Monitoring Network operation guidelines. This result confirms the reliability of the UV-DOAS data. After confirming the device performance of the UV-DOAS using a standard gas, the UV-DOAS was installed next to the Dokgot-ri measurement station and compared and analyzed with the Dokgot-ri station measurement data. The R 2 values of SO 2 , NO 2 , and benzene were 0.82, 0.84, and 0.71, respectively, confirming similar trends. However, the Dokgot-ri measuring station measurement method is a point measurement method that collects samples at one point and analyzes only the collected samples, whereas the UV-DOAS measurement method forms an optical path similar to a line and measures pollutants moving across the optical path. Differences may have occurred because this was a line-measurement method. However, the difference was small, and the trends were similar, confirming the reliability of the measurement data and the field applicability of the UV-DOAS. 3.2. Petroleum refining plant fence line monitoring results After confirming the reliability of the UV-DOAS, it was installed and measured on one side of the site boundary of a petroleum refining plant in Yeosu, Jeollanam-do, Korea. Wind direction and speed data were used to analyze the measurement results and identify the main emission sources of specific pollutants. Disaster prevention weather observation data approximately 2 km from the measurement point were used for wind direction and wind speed data. During the monitoring period, the main wind direction was west northwest (WNW), the average wind speed was 4.39 m/s, and the temperature ranged from − 4.5°C to 10.9°C in winter. High case of SO 2 , high concentrations were observed intermittently; however, the trends of NO 2 and benzene were different from those of SO 2 . emission sources may differ (Fig. 3 ). Additionally, NO 2 and benzene fluctuated less than SO 2 and were less affected by wind direction. This could have led to the possibility that NO 2 and benzene emission sources were scattered throughout the area. Therefore, wind direction and wind speed data were used to identify the emission sources of SO 2 , NO 2 , and benzene and to confirm the pollutant behavior characteristics. High concentrations of pollutants were generally observed in the direction of plant A, but mean concentrations of pollutants were also observed in the direction of plant B in the case of NO 2 and benzene (Fig. 4 ). However, extreme emissions were emitted from plant A in the case of SO 2 (Fig. 5 ). The SO 2 concentration was higher than usual for seven days: December 5, December 6, December 8, December 9, December 10, December 24, 2021, and January 5, 2022. On five out of seven days, high concentrations were observed when the main wind was northeasterly; however, on two days (December 10, 2021, December 24, 2021), high concentrations were observed when the main wind was northwesterly (sea breeze). However, it was necessary to examine the SO 2 concentration in detail for the wind direction because the main wind speed was considered. We confirmed that high concentrations were monitored when the wind was blowing east from plant A when we checked the SO 2 data on December 10 and 24, 2021 in detail (Fig. 6 ). Meanwhile, it was difficult to identify specific emission sources for NO 2 and benzene and it was difficult to confirm the correlation with wind direction. There is a possibility that emission sources are scattered or that fugitive emissions occur. As the measurement point is an industrial complex where petroleum refining plants are located, there were some differences in the measured concentrations between weekdays and weekends during the measurement period. The concentrations of all the measured compounds tended to decrease on weekends rather than on weekdays (Table 1 ). The weekend concentration of SO 2 was 3.37 ppb, which was 28% lower than that of the weekday concentration (4.67 ppb). The NO 2 concentration decreased by 32% to 14.47 ppb on weekdays and 9.84 ppb on weekends. The benzene concentration decreased by 16% to 2.47 ppb on weekdays and 2.07 ppb on weekends. The wind rose on weekdays and weekends during the measurement period (Fig. 7 ). The wind direction and speed were similar on weekdays and weekends, which appears to be owing to the operation rate of the emission source and the reduction in traffic volume, rather than the effect of the wind direction and speed. Table 1 Measurement at Yeosu-si test bed during the weekday and weekend. Compounds Weekday average (A) Weekend average (B) Increase/Decrease rate ((B-A)/A) SO 2 4.67 ppb 3.37 ppb -28% NO 2 14.47 ppb 9.84 ppb -32% Benzene 2.47 ppb 2.07 ppb -16% The UV-DOAS measurement results were compared with those from the Wolnae-dong Atmospheric Environment Monitoring Station to determine the emission characteristics of SO 2 ,. SO 2 and NO 2 were measured at the Wolnae-dong Atmospheric Environment Monitoring Station, approximately 3.7 km away from the UV-DOAS. Despite being 3.7 km away from each other, there was little difference in the measured data for NO 2 and a similar trend was observed. However, the concentration of SO 2 did not exhibit a similar trend. We confirmed that SO 2 had an emission source near the UV-DOAS by comparing the pollution map results with the Wolnae-dong Air Quality Monitoring Station measurement data. Therefore, efforts are required to manage it. 3.3. SO 2 measurement inside plant A using SkyDOAS Plant A was expected to emit high concentrations of SO 2 . A high peak of SO 2 was confirmed near the flare stack using SkyDOAS (Fig. 8 ). Gas containing sulfur is emitted from the flare stack without being incinerated or processed. Currently, there are insufficient measures for managing the pollutants emitted from flare stacks. Therefore, a system that can simultaneously manage point and fugitive emission sources (such as fence line) is required. 4. CONCLUSIONS This study obtained basic data on the fence line monitoring method, which is a new management method at the plant or company site level, rather than on the existing air pollutant emission facility management method in Korea. The UV-DOAS (the optical remote sensing device used in this study) is useful for monitoring real-time data on air pollutants emitted from point or fugitive emission sources. It was more economical because no manpower was required for sample collection and analysis. It can also be measured over a long period of time, and it is easy to check the behavioral characteristics of air pollutants emitted from emission sources and track major emission sources using measured concentration, wind direction, and wind speed data. Based on the monitoring results of UV-DOAS, it is possible to identify major emission sources that were previously unknown by utilizing measurement instruments such as SkyDOAS, which can measure the vicinity of the plant process. An integrated concentration profile was obtained by driving downwind from the source and intersecting the plume with the measurement path. This can help in planning specific means and methods for managing sources. This method is expected to be used in future air pollutant reduction evaluations. Fence line monitoring is a method of measuring in the immediate vicinity of a plant, and the measured concentration varies depending on the distance between the emission source and the measurement point, making it difficult to manage by applying existing Korean air quality standards. SO 2 and NO 2 are maintained at low concentrations in the atmosphere through continuous emission control and the development of reduction facilities; however, it is more difficult to meet air quality standards for benzene. In this study, 2.8 ppb benzene was observed, which is higher than the Korean benzene air quality standard of approximately 1.5 ppb (the Korean benzene air quality standard is an annual average, but in this study, it was an average of 52 days). Therefore, long-term monitoring is necessary, but it is expected to be monitored at a higher concentration than the air quality standard. Therefore, separate standards are required for fence line monitoring of benzene. Standards that consider human risks and long-term measurement results must be established to introduce fence line monitoring regulations. Declarations Author Contributions The manuscript was written with contributions from all authors. All the authors approved the final version of the manuscript. ACKNOWLEDGMENT This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment(MOE) of the Republic of Korea ((NIER-2022-01-01-132). References Grigg, J., Particulate matter exposure in children: Relevance to chronic obstructive pulmonary disease, Proceedings of the American Thoracic Society, 6(7), 564–569(2009). Liu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y., & Zhang, Q., Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China, Environmental Pollution, 222, 10–22(2017). Valavanidis, A., Fiotakis, K., & Vlachogianni, T., Airborne particulate matter and human health: Toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms, Journal of Environmental Science & Health Part C Environmental Carcinogenesis & Ecotoxicology Reviews, 26, 339–362(2008). Wu, S., Deng, F., Niu, J., Huang, Q., Liu, Y., & Guo, X., Association of heart rate variability in taxi drivers with marked changes in particulate air pollution in Beijing in 2008, Environmental Health Perspectives, 118(1), 87–91(2010). Kim, S., Kim, T. Y., Yi, S. M., & Heo, J., Source apportionment of PM2.5, using positive matrix factorization (PMF) at a rural site in Korea, Journal of Environmental Management, 214, 325–334(2018). Liu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y., & Zhang, Q., Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China, Environmental Pollution, 222, 10–22(2017). Zong, Z., Wang, X., Tian, C., Chen, Y., Fu, S., Qu, L., Ji, L., Li, J., & Zhang, G., PMF and PSCF based source apportionment of PM2.5 at a regional background site in north China, Atmospheric Research, 203, 207–215(2018). Zhang, Y., Lang, J., Cheng, S., Li, S., Zhou, Y., Chen, D., Zhang, H., & Wang, H., Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn, Science of the Total Environment, 630, 72–82(2018). Rodrigo, S. A., Raul, G. E. M. S., & Manuel, A. L. G., Estimations of primary and secondary organic carbon formation in PM2.5 aerosols of Santiago city. Chile, Atmospheric Environment, 43(13), 2125–2131(2009). Song, C., He, J., Wu, L., Jin, T., Chen, X., Li, R., Ren, P., Zhang, L., & Mao, H., Health burden attributable to ambient PM2.5 in China. Environmental Pollution, 223, 575–586(2017). Tian, M., Liu, Y., Yang, F., Zhang, L., Peng, C., Chen, Y., Shi, G., Wang, H., Luo, B., Jiang, C., Li, B., Takeda, N., & Koizumi, K., Increasing importance of nitrate formation for heavy aerosol pollution in two megacities in Sichuan basin, southwest China, Environmental Pollution, 250, 898–905(2019). Wang, X., Gemayel, R., Hayeck, N., Perrier, S., Charbonnel, N., Xu, C., Chen, H., Zhu, C., Zhang, L., Wang, L., Nizkorodov, S. A., Wang, X., Wang, Z., Wang, T., Mellouki, A., Riva, M., Chen, J., & George, C., Atmospheric photosensitization: A new pathway for sulfate formation, Environmental Science & Technology, 54(6), 3114–3120(2020). US Code of Federal Regulations (CFR) Title 40, Pt. 63, Subpart CC, Appendix, Table 11. Rana, S. and Y. Verma., Biochemical toxicity of benzene, Journal of Environmental Biology, 26(2), 157–168(2005). S. Cho, C. Kang, J. Yoo and J. Kim., Evaluation of Sulfur Oxide Emissions at Integrated Steel Mill in using Optical Remote Sensing (SkyDOAS), Korean Society for Urban Environment, 22(4), 295–303(2022). Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revisions Needed 08 Jun, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 23 May, 2024 First submitted to journal 20 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Jeon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACgwMHEoCUDQ+DBFSAoBbDBrCWNBK0GEOowwzEazFjPPDwwccd52XMpZsfMPyoYTA2byCgxYbhQLLhzDO3eSznHDNg7DnGYCZzgLCWNGnetts8BjcSDBh4GxhsJAg6jOFA+m/etnNALekfGP8So8UYaAszb9sBoJYcA2agLWYEtQADOVlyZlsySEvBYZljEsYEtRjcOJP44WObnT3QYRsfvqmxMZxBSAuDxJkEOPsAPHbwAv72A0SoGgWjYBSMghENACcyP9hRHQkpAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1653-6339","institution":"Inha University","correspondingAuthor":true,"prefix":"","firstName":"Ki-Joon","middleName":"","lastName":"Jeon","suffix":""}],"badges":[],"createdAt":"2024-05-21 07:42:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4453005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4453005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57836782,"identity":"80f2ea95-ee5f-4eb3-8eca-0bd7271d5955","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343795,"visible":true,"origin":"","legend":"\u003cp\u003eTwo measurement areas of UV-DOAS; (a) Daesan-eup test bed location and (b) Yeosu-si test bed location.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/d7e799bf38fb8da4745708e9.png"},{"id":57836786,"identity":"d13706b2-aa71-4bf1-8db7-4096ee535413","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128466,"visible":true,"origin":"","legend":"\u003cp\u003eUV-DOAS and Dokgot-ri measuring station daily average data of (a) SO\u003csub\u003e2\u003c/sub\u003e, (b) NO\u003csub\u003e2\u003c/sub\u003e, and (c) benzene and R\u003csup\u003e2\u003c/sup\u003e result, the correlation coefficient between UV-DOAS and Dokgot-ri measurement station data of (d) SO\u003csub\u003e2\u003c/sub\u003e, (e) NO\u003csub\u003e2\u003c/sub\u003e, and (f) benzene.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/4116a7dcf59aa7ecac8e2ea9.png"},{"id":57836785,"identity":"3961f669-ae07-4c46-a654-d60f625d7a4c","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158598,"visible":true,"origin":"","legend":"\u003cp\u003eThirty-minutes average results measured at Yeosu-si test bed for (a) SO\u003csub\u003e2\u003c/sub\u003e, (b) NO\u003csub\u003e2\u003c/sub\u003e, and (c) benzene.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/2ea7f4ecd30294094f6a4808.png"},{"id":57838618,"identity":"ea995b3b-a7c2-4c93-b2fd-4af74ee03d2f","added_by":"auto","created_at":"2024-06-06 09:23:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222488,"visible":true,"origin":"","legend":"\u003cp\u003ePollution map using Yeosu-si measurement results for (a) SO\u003csub\u003e2\u003c/sub\u003e, (b) NO\u003csub\u003e2\u003c/sub\u003e, and (c) benzene.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/1e95bdd3a7fea2353376db65.png"},{"id":57836784,"identity":"df84c394-b1a6-42fe-ae6f-cfdda8a74f88","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127764,"visible":true,"origin":"","legend":"\u003cp\u003eDaily wind direction (arrow direction), wind speed (arrow length), and concentration (color) of (a) SO\u003csub\u003e2\u003c/sub\u003e, (b) NO\u003csub\u003e2\u003c/sub\u003e, and (c) benzene.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/e37319fa61a3e2390a968267.png"},{"id":57837617,"identity":"f9add8e6-0f29-4bcd-bf3d-150afcc0c674","added_by":"auto","created_at":"2024-06-06 09:15:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":141465,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e concentration results according to wind direction on a day when the main wind is northwest and SO\u003csub\u003e2\u003c/sub\u003e is high on (a) Dec 10 and (b) Dec 24 in 2021.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/c2c39b529bafa9300fd48356.png"},{"id":57836783,"identity":"12de215d-ee23-4eb5-8d5d-6961eae88127","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":96959,"visible":true,"origin":"","legend":"\u003cp\u003eYeosu-si test bed wind rose based on a (a) weekday average and (b) weekend average\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/2d1de6ef5692937ff93b3523.png"},{"id":57836789,"identity":"e02e8028-6efa-4731-b3d3-42046447e79b","added_by":"auto","created_at":"2024-06-06 09:07:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":698174,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e peak measured with SkyDOAS (blue) and flare stack location in plant A.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/a84d3d4b239224f3d37b44c3.png"},{"id":57839152,"identity":"8c47ec04-7e23-4db3-a70a-dacd0569550b","added_by":"auto","created_at":"2024-06-06 09:31:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2339927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4453005/v1/57e3c6fa-4b73-45e7-9eb2-11c77aa39aae.pdf"}],"financialInterests":"","formattedTitle":"Fence line Monitoring of Petroleum Refining Plants in Korea: UV-DOAS Analysis of SO2, NO2, and Benzene Emissions for Comprehensive Management","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eBasic research to prepare Korea\u0026rsquo;s fence line monitoring regulation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eUsing air environment policies (such as tracking and managing main emission sources) without dividing points and fugitive emission sources\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eUV-DOAS is suitable for measuring SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene in the atmosphere\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe Korean Ministry of Environment reports that 70% of Korea's PM\u003csub\u003e2.5\u003c/sub\u003e is generated secondarily through photochemical reactions in the atmosphere. They are establishing multifaceted policies to manage the emissions of various gases, such as NO\u003csub\u003eX\u003c/sub\u003e, SO\u003csub\u003eX\u003c/sub\u003e, NH\u003csub\u003e3\u003c/sub\u003e, and volatile organic compounds (VOCs), which are precursors that generate secondary particulate matter (PM). PM\u003csub\u003e2.5\u003c/sub\u003e has a small aerodynamic diameter of under 2.5 ㎛; therefore, it can penetrate deep into the human lungs and cause adverse health effects such as cardiovascular disease, respiratory disease, and cancer [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PM\u003csub\u003e2.5\u003c/sub\u003e is directly emitted in various environments and conditions, such as biomass combustion, coal combustion, vehicle emissions, soil dust, and industrial emissions [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pollutants emitted from pollution sources are produced secondarily in the atmosphere through photochemical reactions. They can be divided into primary and secondary aerosols [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Secondary aerosols include secondary organic aerosols (SOAs) and secondary inorganic aerosols (SIAs). Secondary organic aerosols are produced by photochemical reactions of volatile organic compounds (VOCs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Secondary inorganic aerosols mainly consist of sulfate, nitrate, and ammonium, which are produced by the photochemical reactions of gaseous pollutants such as nitrogen oxides (NO\u003csub\u003eX\u003c/sub\u003e) and sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) in the atmosphere [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePetroleum refining plants have various emission facilities, including processing, combustion, and fugitive emissions. The pollutants emitted from these facilities negatively impact human health and must be managed. In the Korea, various pollutant emission facilities are measured by attaching automatic environmental measurement equipment (TMS, tele monitoring system) or are managed by periodic sampling and measurement. However, it is difficult to measure and manage pollutants in fugitive emission facilities; therefore, they are managed by applying a leak detection and repair (LDAR) program. The LDAR program regularly manages facilities that may leak or emit fugitive materials. Fence line monitoring regulations have been introduced in the United States to accurately measure and manage benzene. Fence line monitoring is a regulation that comprehensively manages pollutants emitted from a plant by measuring at the plant boundary, rather than measuring and managing various emission sources individually [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Benzene is a volatile organic compound that is toxic by itself [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with a maximum lifetime of 9.4 days, which is much longer than that of m-xylene. Benzene can travel long distances and has a sufficiently long lifetime for photochemical reactions.\u003c/p\u003e \u003cp\u003eIn this study, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene (which are PM\u003csub\u003e2.5\u003c/sub\u003e precursors and pollutants emitted from various emission sources) were monitored using an open path UV-DOAS analyzer near a petroleum refining plant, and the results were analyzed using wind direction and wind speed data. We intend to explore the possibility of utilization in terms of air environment policies, such as tracking and managing the main emission sources without dividing the points and fugitive emission sources.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003eThe measurements were conducted in two regions. Before installation, calibration was performed in the laboratory to confirm the reliability of the UV-DOAS. In order to determine the results of calibration, it was checked whether R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.98 (which is the standard for linearity test results among the internal quality control regulations in the Korean Air Pollution Measurement Network operation guidelines) was satisfied. Afterwards, the UV-DOAS was installed next to the Dokgot-ri Air Quality Monitoring Station located in Daesan-eup, Chungcheongnam-do, Korea to check whether UV-DOAS on-site measurements were possible. SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene were measured for 19 days from March 13 to March 31, 2021. The results were compared with the Dokgot-ri measurement station data to confirm the reliability of the UV-DOAS measurement values and determine whether on-site measurements were possible. Lastly, a UV-DOAS was installed on one side of the site boundary of a plant approximately 950 m in length located in the petroleum refining industrial complex in Yeosu-si, Jeollanam-do, Korea, and SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene were measured from November 29, 2021, to January 19, 2022. To understand the behavioral characteristics of the pollutants, UV-DOAS measurements and meteorological observation data were analyzed.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. UV-DOAS analyzer\u003c/h2\u003e \u003cp\u003eUV-DOAS analyzers can measure various chemical species in an optical path free of obstacles, allowing light to pass through. Currently, Korea\u0026rsquo;s air pollution monitoring network measures pollutants in the air by sampling and analyzing samples at points. The UV-DOAS analyzer had a larger spatial measurement range and good measurement time resolution, enabling real-time measurement compared with the measurement methods of the air pollution monitoring network and the UV-DOAS measurement method. Therefore, it is advantageous to identify trends in the concentration of the target substance.\u003c/p\u003e \u003cp\u003eThe UV-DOAS used in this study was an AR500 model commercially developed by a Swedish manufacturer, OPSIS. The equipment consists of a pair of emitters (EM150) and receivers (RE150) to form an optical path and is composed of an optical fiber cable that transmits to the analysis unit with little wavelength loss. The light source was equipped with a xenon lamp with a power of 150 W and it could stably irradiate in the 200\u0026ndash;400 nm range, which is the UV wavelength necessary for measuring pollutants. The light emitted from the light source reaches the light receiver and finally the analyzer, which is used to measure pollutant concentration in the optical path. The analysis department performed spectroscopy using the grating method. The concentrations of the pollutants were measured using the absorbance wavelength. DOAS technology for analyzing pollutants follows the Lambert\u0026ndash;Beer law and uses the principle of measuring the absorption luminosity of chemical species present as pollutants in the air that react with the wavelength of light.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Selecting validation measurement data\u003c/h2\u003e \u003cp\u003eThe UV-DOAS could adjust the measurement time for each measurement item. It has the advantage of performing real-time measurements because the measurement time interval is short. Additionally, it indicates a monitoring value called the light level, which is a numerical value that represents the luminance of the light received when transmitting light from the emitter to the receiver (0\u0026ndash;100%). The light level was used to determine whether the emitter and receiver were in the correct line. If the light level is low, sufficient light for analysis does not reach the analyzer, which can adversely affect the correct measurement values. Data where the light level was less than 20% of the 30-minute average were classified as unreliable and excluded from the 24-hour average value. Additionally, forty-eight 30-minute average data items were generated per day, and a day was completely excluded from the measurement if there were more than 24 pieces of data that were less than 20% of the 30-minute average light level data. Consequently, data from 14 of 19 days measured in Daesan-eup, Chungcheongnam-do, and data from 52 of 60 days measured in Yeosu-si, Jeollanam-do were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. UV-DOAS calibration method\u003c/h2\u003e \u003cp\u003eBefore installing the UV-DOAS in the field, calibration was performed using a standard gas to confirm the reliability of the measurement data. The equipment for calibrating the UV-DOAS analyzer consisted of a light source, optical receiver, optical gas cell, calibration bench on which the optical gas cell could be placed, analysis unit, optical fiber cable, and standard gas. Light sources and optical receivers installed and measured in the field have large reflectors because they need to send light far away, whereas those used for calibration do not need to send light far away; therefore, the reflectors are small. However, the xenon lamp used as the light source and the material of the reflector was the same; therefore, there was no change in the wavelength required for analysis. The measurement times and distances were identical to those used in the actual field. The measurement time was set to 30 s for SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e and 60 s for benzene, and the measurement distance was set to 870 m.\u003c/p\u003e \u003cp\u003eThe laboratory where the calibration was performed had a constant temperature of 21\u0026deg;C. Humidity was not measured because the standard gas was a dry gas diluted with N\u003csub\u003e2\u003c/sub\u003e. All the measurement data were expressed to the first decimal place and a calibration procedure was performed after performing a reference calibration to check whether the analysis unit was operating normally.\u003c/p\u003e \u003cp\u003eThe calibration parameters used were SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene, and 99.999% N\u003csub\u003e2\u003c/sub\u003e gas was used as the zero point. The concentrations of SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e were 192 ppm and 188 ppm, respectively, as certified by Korea, but the concentration of benzene was 50 ppm, which was not officially certified, so it was replaced with the gas manufacturer\u0026rsquo;s report. The concentration analyzed in the analysis section varied depending on the standard gas concentration and the length of the optical gas cell. The standard gas had one concentration for each item, but the optical gas cell had three lengths: 100, 250, and 350 mm. Linearity was confirmed by varying the analyzed concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Verifying UV-DOAS field applicability\u003c/h2\u003e \u003cp\u003eIn Korea, statistics on chemical emissions by region and company are publicly available. In Korea, oil refineries, chemical plants, and steel mills are located in Chungcheongnam-do and Jeollanam-do, where various types of air pollutants (such as SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene) are emitted. As of 2021, approximately 51 tons of benzene will be emitted annually in Chungcheongnam-do and 31 tons per year in Jeollanam-do. The UV-DOAS was installed immediately next to the Dokgot-ri measuring station in Daesan-eup, Chungcheongnam-do, and the UV-DOAS measurement data were compared with those of the Dokgot-ri measuring station (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a)). The Dokgot-ri measurement station is operated by the Ministry of the Environment of Korea and the measured data are disclosed to the public. In this experiment, the Dokgot-ri measurement station measurement data were considered reliable. All Dokgot-ri measurement station data were converted to 24-h average data. Using the converted 24 h average data, we compared the differences and trends in the measured data and verified whether the UV-DOAS can measure SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene in the atmosphere in the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Fence line monitoring of petroleum refining plant\u003c/h2\u003e \u003cp\u003eA UV-DOAS was installed at the site boundary between two different petroleum refining plants located in Yeosu-si, Jeollanam-do, and the SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and benzene concentrations were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b)). The two petroleum refining plants are operated by different companies and have different processes; therefore, it was expected that the characteristics of the pollutants emitted into the air would differ. Plant A included crude-oil refining, aromatic production facilities, and flare stacks, whereas plant B included PVC production and NCC processes.\u003c/p\u003e \u003cp\u003eThe measured concentration, wind direction, and wind speed data were used to analyze and create a pollution map. Wind direction and wind speed data were used from the Korea Meteorological Administration Disaster Prevention and Weather Observation (AWS, Automatic Weather Observation System) Yeosu Industrial Complex branch (34.84946, 127.71496). We confirmed the direction of pollutants that emitted high concentrations using pollutant concentration data based on wind direction and wind speed. This study measured plants A and B (which are expected to have different air pollutant emission characteristics) to determine which plant contributed the most. Subsequently, SkyDOAS (a mobile pollutant measurement system attached to a vehicle that uses the solar spectrum to measure sulfur dioxide emissions in Korea [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) was employed to track the main emission sources in detail. Using SkyDOAS, measurements were obtained while driving inside a plant that contributed to high-concentration emissions, and the main source was tracked.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Verification of UV-DOAS analyzer reliability\u003c/h2\u003e\n\u003cp\u003eA calibration experiment was performed using standard gas to verify the reliability of the UV-DOAS analyzer, and the possibility of field application was confirmed by installing the UV-DOAS near the Dokgot-ri measuring station in Daesan-eup, Chungcheongnam-do, Korea. A linearity of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99 was confirmed for SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene using the UV-DOAS calibration experiment. This satisfies the linearity test result standard of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.98 among the internal quality control regulations in the Korea Air Pollution Monitoring Network operation guidelines. This result confirms the reliability of the UV-DOAS data.\u003c/p\u003e\n\u003cp\u003eAfter confirming the device performance of the UV-DOAS using a standard gas, the UV-DOAS was installed next to the Dokgot-ri measurement station and compared and analyzed with the Dokgot-ri station measurement data. The R\u003csup\u003e2\u003c/sup\u003e values of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene were 0.82, 0.84, and 0.71, respectively, confirming similar trends. However, the Dokgot-ri measuring station measurement method is a point measurement method that collects samples at one point and analyzes only the collected samples, whereas the UV-DOAS measurement method forms an optical path similar to a line and measures pollutants moving across the optical path. Differences may have occurred because this was a line-measurement method. However, the difference was small, and the trends were similar, confirming the reliability of the measurement data and the field applicability of the UV-DOAS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Petroleum refining plant fence line monitoring results\u003c/h2\u003e\n\u003cp\u003eAfter confirming the reliability of the UV-DOAS, it was installed and measured on one side of the site boundary of a petroleum refining plant in Yeosu, Jeollanam-do, Korea. Wind direction and speed data were used to analyze the measurement results and identify the main emission sources of specific pollutants. Disaster prevention weather observation data approximately 2 km from the measurement point were used for wind direction and wind speed data. During the monitoring period, the main wind direction was west northwest (WNW), the average wind speed was 4.39 m/s, and the temperature ranged from \u0026minus;\u0026thinsp;4.5\u0026deg;C to 10.9\u0026deg;C in winter.\u003c/p\u003e\n\u003cp\u003eHigh case of SO\u003csub\u003e2\u003c/sub\u003e, high concentrations were observed intermittently; however, the trends of NO\u003csub\u003e2\u003c/sub\u003e and benzene were different from those of SO\u003csub\u003e2\u003c/sub\u003e. emission sources may differ (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, NO\u003csub\u003e2\u003c/sub\u003e and benzene fluctuated less than SO\u003csub\u003e2\u003c/sub\u003e and were less affected by wind direction. This could have led to the possibility that NO\u003csub\u003e2\u003c/sub\u003e and benzene emission sources were scattered throughout the area. Therefore, wind direction and wind speed data were used to identify the emission sources of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene and to confirm the pollutant behavior characteristics. High concentrations of pollutants were generally observed in the direction of plant A, but mean concentrations of pollutants were also observed in the direction of plant B in the case of NO\u003csub\u003e2\u003c/sub\u003e and benzene (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, extreme emissions were emitted from plant A in the case of SO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The SO\u003csub\u003e2\u003c/sub\u003e concentration was higher than usual for seven days: December 5, December 6, December 8, December 9, December 10, December 24, 2021, and January 5, 2022. On five out of seven days, high concentrations were observed when the main wind was northeasterly; however, on two days (December 10, 2021, December 24, 2021), high concentrations were observed when the main wind was northwesterly (sea breeze). However, it was necessary to examine the SO\u003csub\u003e2\u003c/sub\u003e concentration in detail for the wind direction because the main wind speed was considered. We confirmed that high concentrations were monitored when the wind was blowing east from plant A when we checked the SO\u003csub\u003e2\u003c/sub\u003e data on December 10 and 24, 2021 in detail (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Meanwhile, it was difficult to identify specific emission sources for NO\u003csub\u003e2\u003c/sub\u003e and benzene and it was difficult to confirm the correlation with wind direction. There is a possibility that emission sources are scattered or that fugitive emissions occur.\u003c/p\u003e\n\u003cp\u003eAs the measurement point is an industrial complex where petroleum refining plants are located, there were some differences in the measured concentrations between weekdays and weekends during the measurement period. The concentrations of all the measured compounds tended to decrease on weekends rather than on weekdays (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The weekend concentration of SO\u003csub\u003e2\u003c/sub\u003e was 3.37 ppb, which was 28% lower than that of the weekday concentration (4.67 ppb). The NO\u003csub\u003e2\u003c/sub\u003e concentration decreased by 32% to 14.47 ppb on weekdays and 9.84 ppb on weekends. The benzene concentration decreased by 16% to 2.47 ppb on weekdays and 2.07 ppb on weekends. The wind rose on weekdays and weekends during the measurement period (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The wind direction and speed were similar on weekdays and weekends, which appears to be owing to the operation rate of the emission source and the reduction in traffic volume, rather than the effect of the wind direction and speed.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement at\u003c/strong\u003e Yeosu-si test bed during the weekday and weekend.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCompounds\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeekday average\u003c/p\u003e\n\u003cp\u003e(A)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeekend average\u003c/p\u003e\n\u003cp\u003e(B)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIncrease/Decrease rate\u003c/p\u003e\n\u003cp\u003e((B-A)/A)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.67 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.37 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-28%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.47 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.84 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-32%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenzene\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.47 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.07 ppb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-16%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe UV-DOAS measurement results were compared with those from the Wolnae-dong Atmospheric Environment Monitoring Station to determine the emission characteristics of SO\u003csub\u003e2\u003c/sub\u003e,. SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e were measured at the Wolnae-dong Atmospheric Environment Monitoring Station, approximately 3.7 km away from the UV-DOAS. Despite being 3.7 km away from each other, there was little difference in the measured data for NO\u003csub\u003e2\u003c/sub\u003e and a similar trend was observed. However, the concentration of SO\u003csub\u003e2\u003c/sub\u003e did not exhibit a similar trend. We confirmed that SO\u003csub\u003e2\u003c/sub\u003e had an emission source near the UV-DOAS by comparing the pollution map results with the Wolnae-dong Air Quality Monitoring Station measurement data. Therefore, efforts are required to manage it.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. SO\u003csub\u003e2\u003c/sub\u003e measurement inside plant A using SkyDOAS\u003c/h2\u003e\n\u003cp\u003ePlant A was expected to emit high concentrations of SO\u003csub\u003e2\u003c/sub\u003e. A high peak of SO\u003csub\u003e2\u003c/sub\u003e was confirmed near the flare stack using SkyDOAS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Gas containing sulfur is emitted from the flare stack without being incinerated or processed. Currently, there are insufficient measures for managing the pollutants emitted from flare stacks. Therefore, a system that can simultaneously manage point and fugitive emission sources (such as fence line) is required.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eThis study obtained basic data on the fence line monitoring method, which is a new management method at the plant or company site level, rather than on the existing air pollutant emission facility management method in Korea. The UV-DOAS (the optical remote sensing device used in this study) is useful for monitoring real-time data on air pollutants emitted from point or fugitive emission sources. It was more economical because no manpower was required for sample collection and analysis. It can also be measured over a long period of time, and it is easy to check the behavioral characteristics of air pollutants emitted from emission sources and track major emission sources using measured concentration, wind direction, and wind speed data. Based on the monitoring results of UV-DOAS, it is possible to identify major emission sources that were previously unknown by utilizing measurement instruments such as SkyDOAS, which can measure the vicinity of the plant process. An integrated concentration profile was obtained by driving downwind from the source and intersecting the plume with the measurement path. This can help in planning specific means and methods for managing sources. This method is expected to be used in future air pollutant reduction evaluations.\u003c/p\u003e \u003cp\u003eFence line monitoring is a method of measuring in the immediate vicinity of a plant, and the measured concentration varies depending on the distance between the emission source and the measurement point, making it difficult to manage by applying existing Korean air quality standards. SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e are maintained at low concentrations in the atmosphere through continuous emission control and the development of reduction facilities; however, it is more difficult to meet air quality standards for benzene. In this study, 2.8 ppb benzene was observed, which is higher than the Korean benzene air quality standard of approximately 1.5 ppb (the Korean benzene air quality standard is an annual average, but in this study, it was an average of 52 days). Therefore, long-term monitoring is necessary, but it is expected to be monitored at a higher concentration than the air quality standard. Therefore, separate standards are required for fence line monitoring of benzene. Standards that consider human risks and long-term measurement results must be established to introduce fence line monitoring regulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eThe manuscript was written with contributions from all authors. All the authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENT\u003c/h2\u003e \u003cp\u003eThis work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment(MOE) of the Republic of Korea ((NIER-2022-01-01-132).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrigg, J., Particulate matter exposure in children: Relevance to chronic obstructive pulmonary disease, Proceedings of the American Thoracic Society, 6(7), 564\u0026ndash;569(2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y., \u0026amp; Zhang, Q., Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China, Environmental Pollution, 222, 10\u0026ndash;22(2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValavanidis, A., Fiotakis, K., \u0026amp; Vlachogianni, T., Airborne particulate matter and human health: Toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms, Journal of Environmental Science \u0026amp; Health Part C Environmental Carcinogenesis \u0026amp; Ecotoxicology Reviews, 26, 339\u0026ndash;362(2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, S., Deng, F., Niu, J., Huang, Q., Liu, Y., \u0026amp; Guo, X., Association of heart rate variability in taxi drivers with marked changes in particulate air pollution in Beijing in 2008, Environmental Health Perspectives, 118(1), 87\u0026ndash;91(2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, S., Kim, T. Y., Yi, S. M., \u0026amp; Heo, J., Source apportionment of PM2.5, using positive matrix factorization (PMF) at a rural site in Korea, Journal of Environmental Management, 214, 325\u0026ndash;334(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y., \u0026amp; Zhang, Q., Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China, Environmental Pollution, 222, 10\u0026ndash;22(2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZong, Z., Wang, X., Tian, C., Chen, Y., Fu, S., Qu, L., Ji, L., Li, J., \u0026amp; Zhang, G., PMF and PSCF based source apportionment of PM2.5 at a regional background site in north China, Atmospheric Research, 203, 207\u0026ndash;215(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y., Lang, J., Cheng, S., Li, S., Zhou, Y., Chen, D., Zhang, H., \u0026amp; Wang, H., Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn, Science of the Total Environment, 630, 72\u0026ndash;82(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigo, S. A., Raul, G. E. M. S., \u0026amp; Manuel, A. L. G., Estimations of primary and secondary organic carbon formation in PM2.5 aerosols of Santiago city. Chile, Atmospheric Environment, 43(13), 2125\u0026ndash;2131(2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, C., He, J., Wu, L., Jin, T., Chen, X., Li, R., Ren, P., Zhang, L., \u0026amp; Mao, H., Health burden attributable to ambient PM2.5 in China. Environmental Pollution, 223, 575\u0026ndash;586(2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, M., Liu, Y., Yang, F., Zhang, L., Peng, C., Chen, Y., Shi, G., Wang, H., Luo, B., Jiang, C., Li, B., Takeda, N., \u0026amp; Koizumi, K., Increasing importance of nitrate formation for heavy aerosol pollution in two megacities in Sichuan basin, southwest China, Environmental Pollution, 250, 898\u0026ndash;905(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Gemayel, R., Hayeck, N., Perrier, S., Charbonnel, N., Xu, C., Chen, H., Zhu, C., Zhang, L., Wang, L., Nizkorodov, S. A., Wang, X., Wang, Z., Wang, T., Mellouki, A., Riva, M., Chen, J., \u0026amp; George, C., Atmospheric photosensitization: A new pathway for sulfate formation, Environmental Science \u0026amp; Technology, 54(6), 3114\u0026ndash;3120(2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS Code of Federal Regulations (CFR) Title 40, Pt. 63, Subpart CC, Appendix, Table 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana, S. and Y. Verma., Biochemical toxicity of benzene, Journal of Environmental Biology, 26(2), 157\u0026ndash;168(2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Cho, C. Kang, J. Yoo and J. Kim., Evaluation of Sulfur Oxide Emissions at Integrated Steel Mill in using Optical Remote Sensing (SkyDOAS), Korean Society for Urban Environment, 22(4), 295\u0026ndash;303(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"korean-journal-of-chemical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kjce","sideBox":"Learn more about [Korean Journal of Chemical Engineering](http://link.springer.com/journal/11814)","snPcode":"11814","submissionUrl":"https://www.editorialmanager.com/kjce/default2.aspx","title":"Korean Journal of Chemical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Subscription","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fence line monitoring, UV-DOAS, SO2, NO2, benzene","lastPublishedDoi":"10.21203/rs.3.rs-4453005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4453005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIncreasing public interest in air pollutants driven by climate change and high concentrations of particulate matter (PM) has highlighted the importance of managing pollutants such as SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and benzene. These compounds are known precursors of secondary PM that affect the climate and human health, with benzene being particularly carcinogenic. Benzene is a volatile organic compound (VOC) that can persist in the atmosphere for approximately 9.4 days, potentially traveling long distances and contributing to the generation of secondary organic aerosols (SOA) through photochemical reactions, leading to elevated fine dust concentrations. To comprehensively manage emissions, leaks, and point sources, a fence line monitoring study was conducted at petroleum refining industrial complexes in Daesan-eup, Chungcheongnam-do, and Yeosu-si, Jeollanam-do in Korea. UV-DOAS technology was used to confirm measurement reliability and monitor emissions. High SO\u003csub\u003e2\u003c/sub\u003e concentrations emitted in a specific direction were identified at the Yeosu-si, and further investigation identified the flare stack of a suspected plant as the emission source. Fence line monitoring was effective in identifying and addressing unknown high-concentration emission sources and devising measures to manage fugitive emissions and leaks.\u003c/p\u003e","manuscriptTitle":"Fence line Monitoring of Petroleum Refining Plants in Korea: UV-DOAS Analysis of SO2, NO2, and Benzene Emissions for Comprehensive Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 09:07:08","doi":"10.21203/rs.3.rs-4453005/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2024-06-08T09:17:01+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-24T15:01:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T14:53:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T07:34:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Korean Journal of Chemical Engineering","date":"2024-05-21T03:42:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"korean-journal-of-chemical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kjce","sideBox":"Learn more about [Korean Journal of Chemical Engineering](http://link.springer.com/journal/11814)","snPcode":"11814","submissionUrl":"https://www.editorialmanager.com/kjce/default2.aspx","title":"Korean Journal of Chemical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Subscription","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bb7365a1-3a92-4437-b896-17d4dc12adeb","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-06T01:16:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 09:07:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4453005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4453005","identity":"rs-4453005","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}