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Karimi, Keyvan Shayesteh, Nabiollah Sepehri, Zohreh Mozafari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5671502/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In water chlorination for removing pathogens, trihalomethanes (THMs) become among the significant carcinogenic by-products of drinking water chlorination. The conventional measurement means for these compounds is a GC device with an ECD detector (GC-ECD) or GC/MS. A GC-ECD or a GC/MS is utilized for analyzing trihalomethanes at the microgram per liter scale. Due to international sanctions, purchasing an ECD detector is not easy or cost-effective. This article introduces a new concentration method using the Head Space technique in a GC-FID device for measuring the concentration of trihalomethanes. In this method, four compounds, chloroform (CHCl3), di-bromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and bromoform (CHBr3), are plotted on a 5-point calibration chart after being measured on a microgram per liter scale. This method, designed at laboratory, can be used to measure concentrations and analyze data in the laboratories of water and wastewater, environment, petroleum and petrochemistry, etc. High accuracy of the method (µg/L) is the main feature of this method. Here, the design method and advantages of this method are presented with diagrams and tables. Measurement accuracy Concentration method Water distribution network Head Space technique Electron Capture Detector (GC-ECD) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction THMs, compounds resulted from water chlorination, include trihalomethane compounds Chloroform (CHCl3), dibromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and tribromoform (CHBr3). These 4 compounds are indicated in the text and diagram with the numbers 1 to 4 and with the abbreviations and scientific symbols chloroform, dibromochloromethane (DBCM), bromodichloromethane (BDCM) and bromoform, respectively. The usual method for testing THMs in water distribution networks and tanks is with a GC device and an ECD detector. Due to the presence of radioactive nickel-63 in the ECD detector and other issues, it is difficult to import it into the country. In this article, a new method for measuring these 4 trihalomethane compounds on the microgram/liter scale is introduced. First, a brief history of the study of trihalomethanes in the water distribution network of Iran is presented, and in the next section, the importance of current methods for measuring trihalomethanes in country's laboratories is explained. Then, while introducing the weaknesses of current methods, the proposed method is introduced. Finally, the results and discussions are presented by presenting the figures and tables, and then a general conclusion is given at the end of the article. 2. History of Trihalomethane Concentrations in the Iranian Water Distribution Networks Regarding the health and human risks of trihalomethanes, the US Environmental Protection Agency issued regulations in 1979 to control trihalomethanes in drinking water. Where the maximum permissible level for total trihalomethanes, as the annual average level in drinking water, was set at 100 µg/l. Since 1998 this level has been reduced to 80 µg/l (US Environmental Protection Agency, 2006). According to the EPA standard, the maximum contamination level for trihalomethanes is 0.1 (USEPA, 2018). The current methods available to determine THMs in water generally rely on gas chromatography with electron capture or mass spectrometry (Alves de Oliveira et al., 2024; Mohammadpour, 2024). In Iran, various researchers in this field have also used a variety of modern devices to measure very low levels of organic matter in various solutions and liquids. In a study the liquid-liquid extraction method was used to investigate the quality of raw water and effluent from treatment plants in the major cities of Ahvaz, Bandar Abbas, Shiraz, Isfahan, Mashhad, and Tehran in Iran. In the study of the total amount of trihalomethanes by GC-ECD, it was found that the concentration of trihalomethanes in the two cities of Ahvaz and Bandar Abbas in the hot regions of Iran is higher than the permissible limit (Torabi, 1994). A study by (Babaei et al, 2013) was conducted on the concentration of total trihalomethanes in the drinking water distribution network of Ahvaz city in southwest Iran using a GC-ECD device. The concentration of trihalomethanes in 5 cases at the end points of the treatment plant, and in the summer season, was higher than the permissible limit (80 µg/l) of the Iranian standard and the World Health Organization guidelines. So that the average monthly concentrations in the summer season were 6 and 4 times higher than in the spring and fall seasons respectively. In a study to investigate the effect of location and time of sampling in a survey of THM concentrations in new and old water distribution networks (WDNs) of the city of Maragheh in the northwest of Iran, a data comparison was made using GC-ECD, focusing on seasonal changes in THM levels. The average concentrations of chloroform, BDCM, DBCM and bromoform were measured as 28.44 ± 25.18, 66.12 ± 19.5, 16.3 ± 89.0 and 302.0 ± 89.0 µg/L, respectively. The concentration of chloroform in the measurements was 72% higher than the total THMs. The average concentration of total THMs in summer was 69.89 and winter was 50.97 µg/L. Except for bromoform, the concentrations of other THM species in new WDNs were significantly lower than in old WDNs (Jaffari et al., 2024). The similar investigation was carried out in north Iran for studying THMs and HAA5 in drinking water sources and distribution system in drought season that concentration alterations of these organic materials between warm and cool seasons was very conspicuous (Kalankesh et al., 2021). The formation rate and concentration of THMs are organic raw materials, the amount of residual free chlorine, water temperature, water pH, bromine concentration and chlorine contact time. The peak levels in their calibration curve depend on the amount of compounds, the detector response factor, and the time in the detector (Kaarsholm et al., 2021; Jafari et al., 2024). In all these measurements by GC-ECD, the results showed similar changes with respect to the location and time of sampling for THM compounds. In this research, validation characteristics are depicted and presented in the parameters of recovery, precision/validation, LOD, LOQ, area under the curve and retention time section discussions and results. 3. Detectors used in water analysis laboratories in the country Three types of detectors that are very common in gas chromatography perform based on (1) Thermal Conductivity Detector (TCD), (2) Flame Ionization Detector (FID), and (3) Electron Capture Detector (ECD). TCD works based on the difference in thermal conductivity of gases, and is mainly used to detect mineral gases and components to which FID is not sensitive. FID works based on the electrons produced by burning the sample and is detected by measuring the intensity of the ion current. ECD works on the basis of electrons emitted by a radioactive source. These 3 cases are widely utilized in Iran for the analysis of herbicides, toxins, chemical gases, and concentrations of harmful substances in liquids. More than 55 years have passed since the emergence of the most important GC detectors, ECDs, for the detection of electron-dependent compounds (Lovelock and Lipsky et al., 1960) and pulsed operation later introduced (Maggs et al., 1971). Following that, attempts were made to replace the radioactive source used, mainly Ni-63 radioactive foils to produce thermal electrons by ionization of the carrier gas (Wentworth et al., 1992). Only the pulsed helium discharge has been partially practical (Erik Bunert et al., 2017). Due to various detection limitations, electron absorption detectors have been the most sensitive detectors in GCs. In the electron absorption detector, the ionization of the carrier gas is carried out by a beta emitter, such as Ni-63. With impact ionization, the slowly generated free electrons generate a measurable current. Now, if these electrons are captured by organic matter present in water or wastewater, the current decreases (Erik Bunert et al., 2017; Sandoval-González et al., 2024). Simultaneously with the decrease in the current generated by this free electron current, organic components such as trichloroform (CHCl3), di-bromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and tribromoform (CHBr3) etc. are detected (Eliaz and Gileadi, 2019 ؛Bard et al., 2022). In fact, an inert and completely dry carrier gas transports the sample through the column into the ECD )Singh, 2021; Passos and Saraiva, 2019)، where the electrons are slowly generated by nitrogen or a methane/argon mixture. A certified standard mixture with a low content of organic components is measured to obtain a calibration with the standard mixture. By subsequent measurements and comparison with the standard calibration, the components of the sample, their ratio and consequently their concentration are determined. Among them, the conventional detector relevant to this research is the ECD, but because of many issues, the sample concentration method is conducted via HS-20 headspace and GC-FID at the level of µg/l. ECD having these features has very high sensitivity and very good LOD. It is detected by measuring the ion current intensity. In contrast, FID is a destructive mass detector. It has the advantages of high sensitivity, wide linear range, lower operating conditions, low noise and small size. FID is a common detector for the detection of organic compounds (Nyerges et al., 2023; Mátyási et al., 2023; Wang et al., 2023). With all these interpretations, among these analytical techniques and methods available in laboratories, GC-FID is of great importance in analyses (Ntsomboh-Ntsefong Godswill, 2014; Rasheed et al., 2023), for example water and fatty acids. ECD is not able to detect many analytes. The FID in GC-FID, unlike ECD, can be used to detect a significantly wider range of organic compounds. While the high selectivity of GC-ECD is such that it can be several hundred times more sensitive than GC-FID and detect them at much lower concentrations (PubChSwinley and Coning, 2019; Zhu et al., 2021; Reineccius and Qian, 2024). 4. Research method In this method, concentrations of 5ppb, 10ppb, 20ppb, 50ppb, 100ppb of THM were prepared as a mixture (each one including 4 components) and concentrated by head space and analyzed by GC-FID device. The calibration curve of each compound was drawn and analyzed separately. Quality control was performed according to the 17025 standard and curve control was performed at the midpoints of the calibration. In this method, a Shimadzu GC-2030 and a Shimadzu headspace from Japan were exploited. A Bpx5 column with length 30 m, inner diameter 0.25 mm and layer thickness 0.5 µm were applied. A 1:50 split system was used, with a carrier gas velocity of 1 mm/min. In addition, the detector temperature was set to 250°C. The internal standard CCL4 was used as a basis for comparison. In the analysis, THM standard (THM 10x mix Techlab-France) with an initial concentration of 200 ppm and a volume of 1 ml was used. In FID, the hydrogen flow rate was 30 ml/min and the air flow rate was 50 ml/min. Therefore, in the sample concentration method by headspace and FID detector, THM was analyzed at the level of µg/l, and the calibration curves yielded favorable results. In preparing samples for HTMs using headspace in a GC-FID, 15 cc of diluted samples or standards were placed in a 20 cc vial, leaving 15 cc sample and 15 cc empty space above the vial (for headspace). The vial was placed in one of the slots of the Headspace Autosampler (HS-20). The temperature and time settings for a 35-minute GC cycle in the HS-20 were as shown in Table 1 : Table 1 Head Space Temperature and Time Program. Oven temperature 60 Pressure equilibrium time 0.1 Sample line temperature 150 Load time 0.5 Transfer line temperature 150 Load equilibrium time 0.1 Sampling level Needle flush time 5 Multi injective count 1 Injection time 0.1 Pressurize gas pressure 50kpa Pressurize time 0.5 GC cycle time 35 Equilibrium time 5 Time, t, is on the minute and T, temperature, is on the 0 C 5. Discussion and Results The important steps for a laboratory measurement method are method of sampling and sample preparation, instrument analysis conditions and method standardization (Ntsomboh-Ntsefong Godswill, 2014). This section describes the importance of GC method development and validation in water analysis. In this analysis, validation characteristics such as recovery, precision/validation, LOD, LOQ, area under the curve and retention time are presented as important parameters in tables and graphs. Before these conditions, the samples and standard solutions were stored in a refrigerator at 2–4°C and brought to an ambient temperature of 25°C before use. In each of the headspace vials, 15 ml of the sample or standard solution was poured up to the mark line. The upper space of headspace vials was left empty and sealed with a septum for vial cap and placed in the device. A PalinTest chlorimeter (England) was used to determine the residual chlorine level and as such a Hack device to determine the turbidity of the samples. The standard method 6232:1 was used as a template. Various affecting parameters such as temperature, pH, residual chlorine and turbidity are measured the same as the standard methods for water and wastewater in Iranian laboratories. The oven setting conditions are according to the temperature program in Fig. 1 . According to Fig. 1 , the maximum temperature of the BPX-5 column with a length of 30 m, an internal diameter of 0.25 mm and a membrane thickness of 0.50 microns is 340°C, with an equilibrium time of 1 minute at 30°C. The changes in the hold time with respect to temperature for the entire program time of 25 minutes are given in this Fig. 1 . At a hold time of 1 minute, the peak resolution no longer changes, meaning that after 24 minutes at 220°C, the figure becomes a flat line. The headspace setting conditions are according to the computer program in Fig. 2 . In Fig. 2 , the parameters are displayed in two rows that depend on time and temperature. At 60°C, the sample line temperature, transfer line temperature, and gas pressure are given. At 50 kPa, the sample line and transfer line temperatures are both 150°C. The equilibration time and pressurization time, pressurization equilibrium time, load time, load equilibrium time, injection time, and needle flush time are shown for a GC cycle time of 35 min. Sampling was performed when equilibration was complete to obtain a regular peak, which is more dependent on the sample size and temperature. In addition, the GC-230-FID device used and the peripheral equipment are given in Fig. 3 below. The FID detector settings are at 220°C. Four main compounds of THMs include trichloroform (CHCl3), dibromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br), and tribromoform (CHBr3). These 4 compounds are indicated in the text and diagrams with the numbers 1 to 4 and with the abbreviations and scientific symbols chloroform, dibromochloromethane (DBCM), bromodichloromethane (BDCM), and bromoform, respectively. Limit of detection (LOD), the minimum concentration at which its signal can be distinguished from the background signal (Kaarsholm et al., 2021) and limit of quantification (LOQ) or minimum measurable concentration were presented in Table 1 . To investigate the limit of detection (LOD) and limit of quantification (LOQ), eight replicates of THM solutions were prepared in micrograms per liter. After using the calibration curve, it was determined that the standard deviation decreases with increasing concentration. Recovery and accuracy were performed with eight replicates for each THM by adding THM to water at a concentration of 20 ppb, and the results are presented in Table 2 . Table 2 Recovery and accuracy with eight replicates for each THM solution Many high-boiling point substances may cause problems in the absorption of the analyte in the injection section or in the initial section of gas chromatography and liquid chromatography columns. This can subsequently cause negative and positive errors in the “retention time” of the analyte (Rahimifard et al., 2010; Mátyási et al., 2023). In Table 3 , the detection limit was calculated with 7 replicates at an accuracy of 5 ppb. According to the Table 1 , the LOD and LOQ of THMs were 4.8 and 14.42 respectively. In Table 4 , the compounds and the retention times (from number 1 to 4) for THMs are given, with the lowest and highest retention times corresponding to chloroform and bromoform, respectively. Table 3 For each compound, the LOD, LOQ and standard deviation against concentration were determined. Table 4 Retention times of THM compounds All analytical techniques of chromatography with different detectors such as (RI, FID, ECD, ELSD, etc.) require long analytical procedures. LOQ for sample must be done by calibration curves (Cristián et al., 2011). For accurate LOQ, calibration of the GC-FID system with calibration standards for each analyte is essential (Godswill et al., 2014). In fact, the good stability of GC-FID facilitates the calibration curve and the linear range improves the accuracy of operation. Figures 4 and 5 illustrate the variations of the calibration curves of bromodichloromethane (BDCM) and dibromochloromethane (DBCM), respectively. Figure 6 also compares the concentrations of 4 compounds of THM in the calibration curve. 6. Conclusion For extraction of samples the Shimadzu HS-20 headspace and for analysis of THMs the GC-2030-FID were applied. In this method, M-501-PAK calibration with a standard length of 1 ml was used for THM mixture with a concentration of 200 ppm. Solutions of 5 ppb, 10 ppb, 20 ppb, 50 ppb and 100 ppb were prepared using the above standard model and diy distilled water. Samples and standard solutions were extracted in a similar manner with the HS-20 headspace device and analyzed by Shimadzu GC-2030 device. After the method was validated, THM was analyzed in the drinking water distribution network of Shiraz city, and at all points, the THM level was found to be lower than the reporting value or LOQ (14.42 ppb, according to Table 2 ). The quality control of these cases was carried out in accord with the 17025 standard and acceptable results were obtained. In addition, by sampling from the water network and the water storage tanks of Shiraz and analyzing by the new innovative method, the calibration curves was checked at the midpoints of the curve. According to Table 1 deviation was observed less than 10% and method was conducted to daily tests at the laboratory. Declarations Conflict of interest On behalf of all the authors, the corresponding author states that there are no conflicts of interest References Babaei, A. Akbar, Attari, L., Ahmadi Moghadam, M., Alawi, Seyed N., Ahmadi Angali, K (2012) Determination of the concentration of trihalomethane (THM) compounds in the water distribution network of Ahvaz during the years 2010-2011. Quarterly Scientific and Research J. of Gentashapir, Vol. 3, No. 4.http://journals.ajums.ac.ir/jentashapir . Bard AJ, Faulkner LR, White HS (2022) Electrochemical methods: fundamentals and applications. Wiley, 90-102. https://books.google.com.mx/books?id=Sct6EAAAQBAJ. Cristián A, Ferretti, Carlos R., Apesteguía, & Isabel di Cosimo J (2011) Development and validation of a gas chromatography method for the simultaneous determination of multicomponents during monoglyceride synthesis by glycerolysis of methyl oleate: application to homogeneous and heterogeneous catalysis. The J. of the Argentine Chemical Society, vol.98; 16-28. https://DOI: 10.12691/plant-2-3-2. Eliaz N, Gileadi E (2019) Physical electrochemistry: fundamentals, techniques, and applications. Wiley, 30-41. https://books.google.com.mx/books?id=I3BuDwAAQBAJ. Erik Bunert, Ansgar T. Kirk, Jens Oermann, and Stefan Zimmermann (2017) Electron capture detector based on a non-radioactive electron source: operating parameters vs. analytical performance. J. of Sens. Sens. Syst., Vol.6; 381–387. https://doi.org/10.5194/jsss-6-381-2017. James, AT, Martin, AJP (1952) Gas-liquid chromatography: the separation and microestimation of volatile fatty acids from formic acid to dodecanoic acid. Biochem J, 50:679. https://DOI: 10.1042/bj0500679. Kleryton Luiz Alves de Oliveira, Guilherme Mateus Bousada, Cristiane Isaac Cerceau, André Fernando de Oliveira, Renata Pereira Lopes M (2024) Efficient THM quantification in drinking water for minimally-equipped water treatment plants labs. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol.321, 15, 124739. https://doi: 10.1016/ .saa.2024.124739. Kalankesh, L. R., Zazouli, M. A., Susanto, H. and Babanezhad, E (2021) Variability of TOC and DBPs (THMs and HAA5) in drinking water sources and distribution system in drought season: the North Iran case study. Environmental Technology (United Kingdom), 42(1), 100–113. https://doi.org/10.10 80/09593330.2019.1621952. Kunhua Wang, Shufang Kang, Feng Li, Xiaojing Wang, Yaqing Xiao, Jun Wang, Huaide Xu (2022) Relationship between fruit density and physicochemical properties and bioactive composition of mulberry at harvest. J. of Food Composition and Analysis. Vol. 106, 104322. https://doi.org/10.1016/j.jfca.2021.104322 Kaarsholm, K. M. S., Kokkoli, A., Keliri, E., Mines, P. D., Antoniou, M. G., Jakobsen, M. H., & Andersen, H. R (2021) Quantification of Hypochlorite in Water Using the Nutritional Food Additive Pyridoxamine. J. of Water (Switzerland), 13(24); Article 3616. https://doi. org/10.3390/w13243616. Lovelock, J. E. and Lipsky, S. R (1960) Electron affinity spectroscopy – a new method for the identification of functional groups in chemical compounds separated by gas chromatography. J. of Am. Chem. Soc.,Vol.82; 431-433. https://doi.org/10.1021 /ja01487a045. Maggs, R. J., Joynes, P. L., Davies, A. J., and Lovelock, J. E (1971) Electron capture detector. New mode of operation, Anal. J. of Chem., Vol.43; 1966-1971. https://doi.org/10.1021 /ac60 308a014. Mátyási, J., Nyerges, G., Balla, J (2023) Increasing Flame Ionization Detector Response by Silylation: The Effective Carbon Number of Carboxylic Acids. Periodica Polytechnica Chemical Engineering, 67(4), pp. 565–572. https://doi.org/10.3311/PPch.22827 . Mátyási et al. (2023) Increasing Flame Ionization Detector Response by Silylation: The Effective Carbon Number of Carboxylic Acids, Period. Polytech. Chem. Eng., 67(4), pp. 565-572. https://doi.org/10.3311/PPch.22827. Nadali A, Rahmani A, Asgari G, Leili M, Norouzi HA, Naghibi A (2019) The Assessment of Trihalomethanes Concentrations in Drinking Water of Hamadan and Tuyserkan Cities, Western Iran and Its Health Risk on the Exposed Population. J. of Res Health Sci., 19(1): e00441. https://pubmed.ncbi.nlm.nih.gov/31133630. Ntsomboh-Ntsefong Godswill, Ngando-Ebongue Georges Frank, Maho-Yalen Josian Edson, Youmbi Emmanuel, Bell Joseph Martin, Ngalle-Bille Hermine, Tabi-Mbi Kingsley, Likeng-Li-Ngue Benoit Constant, and Nsimi-Mva Armand (2014) GC-FID method development and validation parameters for analysis of palm oil (Elaeis guineensis Jacq.) fatty acids composition. J. of Research in Plant Sciences, Vol. 2, No.3; 53-66. doi: 10.12691/plant-2-3-2. Jafari, N., Behnami, A., Ghayurd, F., Soleimani, A., Mohammadi, A., Mojtaba Pourakbar, Abdolahnejad, A (2024) Analysis of THM formation potential in drinking water networks: Effects of network age, health risks, and seasonal variations in northwest of Iran. Elsevier, J. of Heliyon, 10(14). doi:10.1016/j.heliyon.2024.e34563. PubChSwinley, JP, De Coning, P (2023) A practical guide to gas analysis by gas chromatography. Elsevier. eBook ISBN: 9780128188897em, 80-98. https://pubchem.ncbi. nlm. Nih. gov/compound. Passos M, Saraiva ML (2019) Detection in UV-visible spectrophotometry: detectors, detection systems, and detection strategies. Elsevier, Measure: J Int Measure Confederation, 135:896–904. B.V. https://doi.org/10.1016/j.measurement.2018.12.045. Pooja D, Kumar P, Singh P, Patil S (2019) Sensors in water pollutants monitoring: role of material. Springer, Singapore,120-140. https://books.google.com.mx/books?id=cPm4D WAA QBAJ. DOI: 10.1007/978-981-15-0671-0. Rasheed, S., Hashmi, I., Zhou, Q. et al (2023) Central composite rotatable design for optimization of THM extraction and detection through gas chromatography: a case study. Int. J.Environ. Sci.Technol, 20, 1185–1198.https://doi.org/10.1007/s13762-022-04070-6. Reineccius, G.A., Qian, M.C (2024) Gas Chromatography. In: Ismail, B.P., Nielsen, S.S. (eds) Nielsen's Food Analysis. Food Science Text Series. Springer, Cham, https://doi.org/10.1007/978-3-031-50643-7-14. Rahimi Fard, M., Hajipour, R., and Naderi, M (2010) Sample preparation methods for gas chromatography. J. of Iranian Laboratory Science, Vol.30, No.8, pp.44-53. SID https://sid.ir /paper/959542/fa . Sandoval-González, A., López-García, N.A., Figueroa-Hernandez, E., Cárdenas-Mijangos, J (2024) Advances in Analytical Methods for the Monitoring of Chemical Pollutants in Industrial Effluent Water In Shah, M.P. (eds) Microbial Remediation of Hazardous Chemicals from Water & Industrial Wastewater Treatment Plant. Environmental Science and Engineering. Springer, Cham, 20-33, 43-49. https://doi.org/10.1007/978-3-031-62898-6-17. Singh DK, Pradhan M, Materny A (2021) Modern techniques of spectroscopy: basics, instrumentation, and applications. 30-70, 100-110, Springer, Singapore. https://books.google. com .mx/books ?id=1uImEAAAQBAJ. Torabian, A (1998) An evaluation of THMs in drinking water and a method of its removal. Iranian J. of Public Health, 27(1-2):35-42. https://ijph.tums.ac.ir/index.php/ijph/article/view/ 1773. US Environmental Protection Agency (USEPA) (2006) National primary drinking water regulations: stage 2 disinfectants and disinfection by products rule: Final Rule. Federal Register, Rules and Regulations, 71(2):388-493. https://www.govinfo.gov/content/pkg/FR-2006-01-04/pdf/06-3. Wentworth, W. E., D’Sa, E. D., Cai, H., and Stearns, S (1992) Environmental Applications of the Pulsed-Discharge Electron Capture Detector. J. of Chromatogr. Sci., Vol.30; 478–485, https://doi.org/10.1093/chromsci/30.12.478. Zhu Y, Ariga T, Nakano K, Shikamori Y (2021) Trends and advances in inductively coupled plasma tandem quadruple mass spectrometry (ICP-QMS/QMS) with reaction cell. At Spectrosc, 2021, vol.42, 299–309. https://doi.org/10.46770/AS.2021.710. Kleryton Luiz Alves de Oliveira, Guilherme Mateus Bousada, Cristiane Isaac Cerceau, André Fernando de Oliveira, Renata Pereira Lopes Moreira (2024) Efficient THM quantification in drinking water for minimally-equipped water treatment plants labs. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. Vol. 321, 15 November, 124739. https://doi: 10.1016/j.saa.2024.124739. Mohammadpour, A., Emadi, Z. Berizi, E. Kazemi, A (2024) THMs in chlorinated drinking water: Seasonal variations and health risk assessment in southern Iran. Groundwater for Sustainable Development, Vol. 27, 101342. https://doi.org/10.1016/ j.gsd. 2024.101342. 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Karimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACCSA+wGDAwMPHwHwAxJchXgsbA1sCiM9DlBYwYGPgMQDRhLVItp99eOhGgY0Mm9iZz69u1FjwMLAfProBnxZpnnSDwzkGaTxs0rnbrHOOAR3Gk5Z2A58WOYY0BqCWw2AtxjlsQC0SPGb4tfA/g2nJeWac848ILdIScFtymB/nthGhRXIG2BaQX9LMmHP7JHjYCPlF4nwa8+ecPzb2/NLJjz/nfKuT42c/fAyvFmTABo4kNmKVgwDzB1JUj4JRMApGwcgBAH+APbHEKk7PAAAAAElFTkSuQmCC","orcid":"","institution":"University of Mohaghegh Ardabili Faculty of Engineering","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"","lastName":"Karimi","suffix":""},{"id":409057396,"identity":"0c8b9bdc-d4aa-4aed-a615-78b1352acad6","order_by":1,"name":"Keyvan Shayesteh","email":"","orcid":"","institution":"University of Mohaghegh Ardabili Faculty of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Keyvan","middleName":"","lastName":"Shayesteh","suffix":""},{"id":409057397,"identity":"4ddf4e95-3d68-48be-b033-3150bc6da6ed","order_by":2,"name":"Nabiollah Sepehri","email":"","orcid":"","institution":"Water and Wastewater company","correspondingAuthor":false,"prefix":"","firstName":"Nabiollah","middleName":"","lastName":"Sepehri","suffix":""},{"id":409057398,"identity":"465576bb-86f7-430b-9c0b-4a3b194b4daa","order_by":3,"name":"Zohreh Mozafari","email":"","orcid":"","institution":"Water and Wastewater Company","correspondingAuthor":false,"prefix":"","firstName":"Zohreh","middleName":"","lastName":"Mozafari","suffix":""}],"badges":[],"createdAt":"2024-12-18 18:03:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5671502/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5671502/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75310778,"identity":"8c9148be-fef0-4a1c-b4dc-57c855771dc4","added_by":"auto","created_at":"2025-02-03 09:07:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71357,"visible":true,"origin":"","legend":"\u003cp\u003eOven temperature program versus time for THMs.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/92c98b2a16bf655467ab9d2e.jpg"},{"id":75312575,"identity":"cdafa3a4-b008-4058-9e51-c7485d7540b9","added_by":"auto","created_at":"2025-02-03 09:15:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61636,"visible":true,"origin":"","legend":"\u003cp\u003eHeadspace computer program settings for THMs.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/79d4cbab996b6d16824ac454.jpg"},{"id":75310779,"identity":"410d3fe4-6a0d-4ef4-91ac-8356c800bbca","added_by":"auto","created_at":"2025-02-03 09:07:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37549,"visible":true,"origin":"","legend":"\u003cp\u003eThe images of GC-2030-FID with HS-20 headspace. A Shimadzu HS-20 headspace was used to extract the samples, and a GC-2030-FID device from Shimadzu Japan was used to analyze THMs.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/6f86fe6acf337b098a6ea797.jpg"},{"id":75310785,"identity":"d775be9c-d482-4a3d-9c25-61595f9fafcc","added_by":"auto","created_at":"2025-02-03 09:07:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66858,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of bromodichloromethane (BDCM)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/25d38fff6c6d3697ba40d60e.jpg"},{"id":75313060,"identity":"ea64bc95-15bd-488a-9092-7407d70eee4c","added_by":"auto","created_at":"2025-02-03 09:23:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92164,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of dibromochloromethane (DBCM)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/1238e99e77569dcf362be08f.jpg"},{"id":75310782,"identity":"0a321e16-9a4f-4883-8953-5c2de9a9672b","added_by":"auto","created_at":"2025-02-03 09:07:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":101657,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between peaks and calibration curves in 4 trihalomethane compounds\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/b8ab533868a5594dab4c6b70.jpg"},{"id":90119803,"identity":"651fd38c-d309-4fad-9046-e1f55b94720f","added_by":"auto","created_at":"2025-08-28 17:17:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5671502/v1/1996972c-f077-4a80-86b6-2e68ebf2c1df.pdf"}],"financialInterests":"","formattedTitle":"A new method for analyzing trihalomethanes using a GC-FID device","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTHMs, compounds resulted from water chlorination, include trihalomethane compounds Chloroform (CHCl3), dibromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and tribromoform (CHBr3). These 4 compounds are indicated in the text and diagram with the numbers 1 to 4 and with the abbreviations and scientific symbols chloroform, dibromochloromethane (DBCM), bromodichloromethane (BDCM) and bromoform, respectively. The usual method for testing THMs in water distribution networks and tanks is with a GC device and an ECD detector. Due to the presence of radioactive nickel-63 in the ECD detector and other issues, it is difficult to import it into the country. In this article, a new method for measuring these 4 trihalomethane compounds on the microgram/liter scale is introduced. First, a brief history of the study of trihalomethanes in the water distribution network of Iran is presented, and in the next section, the importance of current methods for measuring trihalomethanes in country's laboratories is explained. Then, while introducing the weaknesses of current methods, the proposed method is introduced. Finally, the results and discussions are presented by presenting the figures and tables, and then a general conclusion is given at the end of the article.\u003c/p\u003e"},{"header":"2. History of Trihalomethane Concentrations in the Iranian Water Distribution Networks","content":"\u003cp\u003eRegarding the health and human risks of trihalomethanes, the US Environmental Protection Agency issued regulations in 1979 to control trihalomethanes in drinking water. Where the maximum permissible level for total trihalomethanes, as the annual average level in drinking water, was set at 100 \u0026micro;g/l. Since 1998 this level has been reduced to 80 \u0026micro;g/l (US Environmental Protection Agency, 2006). According to the EPA standard, the maximum contamination level for trihalomethanes is 0.1 (USEPA, 2018). The current methods available to determine THMs in water generally rely on gas chromatography with electron capture or mass spectrometry (Alves de Oliveira et al., 2024; Mohammadpour, 2024). In Iran, various researchers in this field have also used a variety of modern devices to measure very low levels of organic matter in various solutions and liquids.\u003c/p\u003e \u003cp\u003eIn a study the liquid-liquid extraction method was used to investigate the quality of raw water and effluent from treatment plants in the major cities of Ahvaz, Bandar Abbas, Shiraz, Isfahan, Mashhad, and Tehran in Iran. In the study of the total amount of trihalomethanes by GC-ECD, it was found that the concentration of trihalomethanes in the two cities of Ahvaz and Bandar Abbas in the hot regions of Iran is higher than the permissible limit (Torabi, 1994). A study by (Babaei et al, 2013) was conducted on the concentration of total trihalomethanes in the drinking water distribution network of Ahvaz city in southwest Iran using a GC-ECD device. The concentration of trihalomethanes in 5 cases at the end points of the treatment plant, and in the summer season, was higher than the permissible limit (80 \u0026micro;g/l) of the Iranian standard and the World Health Organization guidelines. So that the average monthly concentrations in the summer season were 6 and 4 times higher than in the spring and fall seasons respectively.\u003c/p\u003e \u003cp\u003eIn a study to investigate the effect of location and time of sampling in a survey of THM concentrations in new and old water distribution networks (WDNs) of the city of Maragheh in the northwest of Iran, a data comparison was made using GC-ECD, focusing on seasonal changes in THM levels. The average concentrations of chloroform, BDCM, DBCM and bromoform were measured as 28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;25.18, 66.12\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5, 16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;89.0 and 302.0\u0026thinsp;\u0026plusmn;\u0026thinsp;89.0 \u0026micro;g/L, respectively. The concentration of chloroform in the measurements was 72% higher than the total THMs. The average concentration of total THMs in summer was 69.89 and winter was 50.97 \u0026micro;g/L. Except for bromoform, the concentrations of other THM species in new WDNs were significantly lower than in old WDNs (Jaffari et al., 2024). The similar investigation was carried out in north Iran for studying THMs and HAA5 in drinking water sources and distribution system in drought season that concentration alterations of these organic materials between warm and cool seasons was very conspicuous (Kalankesh et al., 2021).\u003c/p\u003e \u003cp\u003eThe formation rate and concentration of THMs are organic raw materials, the amount of residual free chlorine, water temperature, water pH, bromine concentration and chlorine contact time. The peak levels in their calibration curve depend on the amount of compounds, the detector response factor, and the time in the detector (Kaarsholm et al., 2021; Jafari et al., 2024). In all these measurements by GC-ECD, the results showed similar changes with respect to the location and time of sampling for THM compounds. In this research, validation characteristics are depicted and presented in the parameters of recovery, precision/validation, LOD, LOQ, area under the curve and retention time section discussions and results.\u003c/p\u003e"},{"header":"3. Detectors used in water analysis laboratories in the country","content":"\u003cp\u003eThree types of detectors that are very common in gas chromatography perform based on (1) Thermal Conductivity Detector (TCD), (2) Flame Ionization Detector (FID), and (3) Electron Capture Detector (ECD). TCD works based on the difference in thermal conductivity of gases, and is mainly used to detect mineral gases and components to which FID is not sensitive. FID works based on the electrons produced by burning the sample and is detected by measuring the intensity of the ion current. ECD works on the basis of electrons emitted by a radioactive source. These 3 cases are widely utilized in Iran for the analysis of herbicides, toxins, chemical gases, and concentrations of harmful substances in liquids.\u003c/p\u003e \u003cp\u003eMore than 55 years have passed since the emergence of the most important GC detectors, ECDs, for the detection of electron-dependent compounds (Lovelock and Lipsky et al., 1960) and pulsed operation later introduced (Maggs et al., 1971). Following that, attempts were made to replace the radioactive source used, mainly Ni-63 radioactive foils to produce thermal electrons by ionization of the carrier gas (Wentworth et al., 1992). Only the pulsed helium discharge has been partially practical (Erik Bunert et al., 2017). Due to various detection limitations, electron absorption detectors have been the most sensitive detectors in GCs. In the electron absorption detector, the ionization of the carrier gas is carried out by a beta emitter, such as Ni-63. With impact ionization, the slowly generated free electrons generate a measurable current. Now, if these electrons are captured by organic matter present in water or wastewater, the current decreases (Erik Bunert et al., 2017; Sandoval-Gonz\u0026aacute;lez et al., 2024). Simultaneously with the decrease in the current generated by this free electron current, organic components such as trichloroform (CHCl3), di-bromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and tribromoform (CHBr3) etc. are detected (Eliaz and Gileadi, 2019 ؛Bard et al., 2022). In fact, an inert and completely dry carrier gas transports the sample through the column into the ECD )Singh, 2021; Passos and Saraiva, 2019)، where the electrons are slowly generated by nitrogen or a methane/argon mixture. A certified standard mixture with a low content of organic components is measured to obtain a calibration with the standard mixture. By subsequent measurements and comparison with the standard calibration, the components of the sample, their ratio and consequently their concentration are determined. Among them, the conventional detector relevant to this research is the ECD, but because of many issues, the sample concentration method is conducted via HS-20 headspace and GC-FID at the level of \u0026micro;g/l.\u003c/p\u003e \u003cp\u003eECD having these features has very high sensitivity and very good LOD. It is detected by measuring the ion current intensity. In contrast, FID is a destructive mass detector. It has the advantages of high sensitivity, wide linear range, lower operating conditions, low noise and small size. FID is a common detector for the detection of organic compounds (Nyerges et al., 2023; M\u0026aacute;ty\u0026aacute;si et al., 2023; Wang et al., 2023). With all these interpretations, among these analytical techniques and methods available in laboratories, GC-FID is of great importance in analyses (Ntsomboh-Ntsefong Godswill, 2014; Rasheed et al., 2023), for example water and fatty acids.\u003c/p\u003e \u003cp\u003eECD is not able to detect many analytes. The FID in GC-FID, unlike ECD, can be used to detect a significantly wider range of organic compounds. While the high selectivity of GC-ECD is such that it can be several hundred times more sensitive than GC-FID and detect them at much lower concentrations (PubChSwinley and Coning, 2019; Zhu et al., 2021; Reineccius and Qian, 2024).\u003c/p\u003e"},{"header":"4. Research method","content":"\u003cp\u003eIn this method, concentrations of 5ppb, 10ppb, 20ppb, 50ppb, 100ppb of THM were prepared as a mixture (each one including 4 components) and concentrated by head space and analyzed by GC-FID device. The calibration curve of each compound was drawn and analyzed separately. Quality control was performed according to the 17025 standard and curve control was performed at the midpoints of the calibration. In this method, a Shimadzu GC-2030 and a Shimadzu headspace from Japan were exploited. A Bpx5 column with length 30 m, inner diameter 0.25 mm and layer thickness 0.5 \u0026micro;m were applied. A 1:50 split system was used, with a carrier gas velocity of 1 mm/min. In addition, the detector temperature was set to 250\u0026deg;C. The internal standard CCL4 was used as a basis for comparison. In the analysis, THM standard (THM 10x mix Techlab-France) with an initial concentration of 200 ppm and a volume of 1 ml was used. In FID, the hydrogen flow rate was 30 ml/min and the air flow rate was 50 ml/min.\u003c/p\u003e \u003cp\u003eTherefore, in the sample concentration method by headspace and FID detector, THM was analyzed at the level of \u0026micro;g/l, and the calibration curves yielded favorable results.\u003c/p\u003e \u003cp\u003eIn preparing samples for HTMs using headspace in a GC-FID, 15 cc of diluted samples or standards were placed in a 20 cc vial, leaving 15 cc sample and 15 cc empty space above the vial (for headspace). The vial was placed in one of the slots of the Headspace Autosampler (HS-20). The temperature and time settings for a 35-minute GC cycle in the HS-20 were as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHead Space Temperature and Time Program.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOven temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePressure equilibrium time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample line temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoad time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfer line temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoad equilibrium time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeedle flush time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti injective count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInjection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressurize gas pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50kpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePressurize time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC cycle time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquilibrium time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTime, t, is on the minute and T, temperature, is on the \u003csup\u003e0\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Discussion and Results","content":"\u003cp\u003eThe important steps for a laboratory measurement method are method of sampling and sample preparation, instrument analysis conditions and method standardization (Ntsomboh-Ntsefong Godswill, 2014). This section describes the importance of GC method development and validation in water analysis. In this analysis, validation characteristics such as recovery, precision/validation, LOD, LOQ, area under the curve and retention time are presented as important parameters in tables and graphs.\u003c/p\u003e \u003cp\u003eBefore these conditions, the samples and standard solutions were stored in a refrigerator at 2\u0026ndash;4\u0026deg;C and brought to an ambient temperature of 25\u0026deg;C before use. In each of the headspace vials, 15 ml of the sample or standard solution was poured up to the mark line. The upper space of headspace vials was left empty and sealed with a septum for vial cap and placed in the device.\u003c/p\u003e \u003cp\u003eA PalinTest chlorimeter (England) was used to determine the residual chlorine level and as such a Hack device to determine the turbidity of the samples. The standard method 6232:1 was used as a template. Various affecting parameters such as temperature, pH, residual chlorine and turbidity are measured the same as the standard methods for water and wastewater in Iranian laboratories. The oven setting conditions are according to the temperature program in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the maximum temperature of the BPX-5 column with a length of 30 m, an internal diameter of 0.25 mm and a membrane thickness of 0.50 microns is 340\u0026deg;C, with an equilibrium time of 1 minute at 30\u0026deg;C. The changes in the hold time with respect to temperature for the entire program time of 25 minutes are given in this Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At a hold time of 1 minute, the peak resolution no longer changes, meaning that after 24 minutes at 220\u0026deg;C, the figure becomes a flat line. The headspace setting conditions are according to the computer program in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the parameters are displayed in two rows that depend on time and temperature. At 60\u0026deg;C, the sample line temperature, transfer line temperature, and gas pressure are given. At 50 kPa, the sample line and transfer line temperatures are both 150\u0026deg;C. The equilibration time and pressurization time, pressurization equilibrium time, load time, load equilibrium time, injection time, and needle flush time are shown for a GC cycle time of 35 min. Sampling was performed when equilibration was complete to obtain a regular peak, which is more dependent on the sample size and temperature. In addition, the GC-230-FID device used and the peripheral equipment are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below. The FID detector settings are at 220\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFour main compounds of THMs include trichloroform (CHCl3), dibromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br), and tribromoform (CHBr3). These 4 compounds are indicated in the text and diagrams with the numbers 1 to 4 and with the abbreviations and scientific symbols chloroform, dibromochloromethane (DBCM), bromodichloromethane (BDCM), and bromoform, respectively.\u003c/p\u003e \u003cp\u003eLimit of detection (LOD), the minimum concentration at which its signal can be distinguished from the background signal (Kaarsholm et al., 2021) and limit of quantification (LOQ) or minimum measurable concentration were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To investigate the limit of detection (LOD) and limit of quantification (LOQ), eight replicates of THM solutions were prepared in micrograms per liter. After using the calibration curve, it was determined that the standard deviation decreases with increasing concentration. Recovery and accuracy were performed with eight replicates for each THM by adding THM to water at a concentration of 20 ppb, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable 2 Recovery and accuracy with eight replicates for each THM solution\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"545\" height=\"173\"\u003e\u003c/p\u003e \u003cp\u003eMany high-boiling point substances may cause problems in the absorption of the analyte in the injection section or in the initial section of gas chromatography and liquid chromatography columns. This can subsequently cause negative and positive errors in the \u0026ldquo;retention time\u0026rdquo; of the analyte (Rahimifard et al., 2010; M\u0026aacute;ty\u0026aacute;si et al., 2023). In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the detection limit was calculated with 7 replicates at an accuracy of 5 ppb. According to the Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the LOD and LOQ of THMs were 4.8 and 14.42 respectively. In Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the compounds and the retention times (from number 1 to 4) for THMs are given, with the lowest and highest retention times corresponding to chloroform and bromoform, respectively.\u003c/p\u003e \u003cp\u003eTable 3 For each compound, the LOD, LOQ and standard deviation against concentration were determined.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"536\" height=\"113\"\u003e\u003c/p\u003e \u003cp\u003eTable 4 Retention times of THM compounds\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"397\" height=\"119\"\u003e\u003c/p\u003e\u003cp\u003eAll analytical techniques of chromatography with different detectors such as (RI, FID, ECD, ELSD, etc.) require long analytical procedures. LOQ for sample must be done by calibration curves (Cristi\u0026aacute;n et al., 2011). For accurate LOQ, calibration of the GC-FID system with calibration standards for each analyte is essential (Godswill et al., 2014). In fact, the good stability of GC-FID facilitates the calibration curve and the linear range improves the accuracy of operation. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate the variations of the calibration curves of bromodichloromethane (BDCM) and dibromochloromethane (DBCM), respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e also compares the concentrations of 4 compounds of THM in the calibration curve.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eFor extraction of samples the Shimadzu HS-20 headspace and for analysis of THMs the GC-2030-FID were applied. In this method, M-501-PAK calibration with a standard length of 1 ml was used for THM mixture with a concentration of 200 ppm. Solutions of 5 ppb, 10 ppb, 20 ppb, 50 ppb and 100 ppb were prepared using the above standard model and diy distilled water. Samples and standard solutions were extracted in a similar manner with the HS-20 headspace device and analyzed by Shimadzu GC-2030 device. After the method was validated, THM was analyzed in the drinking water distribution network of Shiraz city, and at all points, the THM level was found to be lower than the reporting value or LOQ (14.42 ppb, according to Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The quality control of these cases was carried out in accord with the 17025 standard and acceptable results were obtained. In addition, by sampling from the water network and the water storage tanks of Shiraz and analyzing by the new innovative method, the calibration curves was checked at the midpoints of the curve. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e deviation was observed less than 10% and method was conducted to daily tests at the laboratory.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eOn behalf of all the authors, the corresponding author states that there are no conflicts of interest\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBabaei, A. Akbar, Attari, L., Ahmadi Moghadam, M., Alawi, Seyed N., Ahmadi Angali, K (2012) Determination of the concentration of trihalomethane (THM) compounds in the water distribution network of Ahvaz during the years 2010-2011. Quarterly Scientific and Research J. of Gentashapir, Vol. 3, No. 4.http://journals.ajums.ac.ir/jentashapir\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eBard AJ, Faulkner LR, White HS (2022) Electrochemical methods: fundamentals and applications. Wiley, 90-102. https://books.google.com.mx/books?id=Sct6EAAAQBAJ.\u003c/li\u003e\n\u003cli\u003eCristi\u0026aacute;n A, Ferretti, Carlos R., Apestegu\u0026iacute;a, \u0026amp; Isabel di Cosimo J (2011) Development and validation of a gas chromatography method for the simultaneous determination of multicomponents during monoglyceride synthesis by glycerolysis of methyl oleate: application to homogeneous and heterogeneous catalysis. The J. of the Argentine Chemical Society, vol.98; 16-28. \u003cstrong\u003ehttps://DOI:\u003c/strong\u003e 10.12691/plant-2-3-2.\u003c/li\u003e\n\u003cli\u003eEliaz N, Gileadi E (2019) Physical electrochemistry: fundamentals, techniques, and applications. Wiley, 30-41. https://books.google.com.mx/books?id=I3BuDwAAQBAJ.\u003c/li\u003e\n\u003cli\u003eErik Bunert, Ansgar T. Kirk, Jens Oermann, and Stefan Zimmermann (2017) Electron capture detector based on a non-radioactive electron source: operating parameters vs. analytical performance. J. of Sens. Sens. Syst., Vol.6; 381\u0026ndash;387. https://doi.org/10.5194/jsss-6-381-2017.\u003c/li\u003e\n\u003cli\u003eJames, AT, Martin, AJP (1952) Gas-liquid chromatography: the separation and microestimation of volatile fatty acids from formic acid to dodecanoic acid. Biochem J, 50:679. https://DOI: 10.1042/bj0500679.\u003c/li\u003e\n\u003cli\u003eKleryton Luiz Alves de Oliveira, Guilherme Mateus Bousada, Cristiane Isaac Cerceau, Andr\u0026eacute; Fernando de Oliveira, Renata Pereira Lopes M (2024) \u003cspan dir=\"RTL\"\u003e \u003c/span\u003eEfficient THM quantification in drinking water for minimally-equipped water treatment plants labs. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol.321, 15, 124739. https://doi: 10.1016/ .saa.2024.124739.\u003c/li\u003e\n\u003cli\u003eKalankesh, L. R., Zazouli, M. A., Susanto, H. and Babanezhad, E (2021) Variability of TOC and DBPs (THMs and HAA5) in drinking water sources and distribution system in drought season: the North Iran case study. Environmental Technology (United Kingdom), 42(1), 100\u0026ndash;113. https://doi.org/10.10 80/09593330.2019.1621952.\u003c/li\u003e\n\u003cli\u003eKunhua Wang, Shufang Kang, Feng Li, Xiaojing Wang, Yaqing Xiao, Jun Wang, Huaide Xu (2022) Relationship between fruit density and physicochemical properties and bioactive composition of mulberry at harvest. J. of Food Composition and Analysis. Vol. 106, 104322. https://doi.org/10.1016/j.jfca.2021.104322\u003c/li\u003e\n\u003cli\u003eKaarsholm, K. M. S., Kokkoli, A., Keliri, E., Mines, P. D., Antoniou, M. G., Jakobsen, M. H., \u0026amp; Andersen, H. R (2021) Quantification of Hypochlorite in Water Using the Nutritional Food Additive Pyridoxamine. J. of Water (Switzerland), 13(24); Article 3616. https://doi. org/10.3390/w13243616.\u003c/li\u003e\n\u003cli\u003eLovelock, J. E. and Lipsky, S. R (1960) Electron affinity spectroscopy \u0026ndash; a new method for the identification of functional groups in chemical compounds separated by gas chromatography. J. of Am. Chem. Soc.,Vol.82; 431-433. https://doi.org/10.1021 /ja01487a045.\u003c/li\u003e\n\u003cli\u003eMaggs, R. J., Joynes, P. L., Davies, A. J., and Lovelock, J. E (1971) Electron capture detector. New mode of operation, Anal. J. of Chem., Vol.43; 1966-1971. https://doi.org/10.1021 /ac60 308a014. \u003c/li\u003e\n\u003cli\u003eM\u0026aacute;ty\u0026aacute;si, J., Nyerges, G., Balla, J (2023) Increasing Flame Ionization Detector Response by Silylation: The Effective Carbon Number of Carboxylic Acids. Periodica Polytechnica Chemical Engineering, 67(4), pp. 565\u0026ndash;572. https://doi.org/10.3311/PPch.22827\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eM\u0026aacute;ty\u0026aacute;si et al. (2023) Increasing Flame Ionization Detector Response by Silylation: The Effective Carbon Number of Carboxylic Acids, Period. Polytech. Chem. Eng., 67(4), pp. 565-572. https://doi.org/10.3311/PPch.22827.\u003c/li\u003e\n\u003cli\u003eNadali A, Rahmani A, Asgari G, Leili M, Norouzi HA, Naghibi A (2019) The Assessment of Trihalomethanes Concentrations in Drinking Water of Hamadan and Tuyserkan Cities, Western Iran and Its Health Risk on the Exposed Population. J. of Res Health Sci., 19(1): e00441. https://pubmed.ncbi.nlm.nih.gov/31133630.\u003c/li\u003e\n\u003cli\u003eNtsomboh-Ntsefong Godswill, Ngando-Ebongue Georges Frank, Maho-Yalen Josian Edson, Youmbi Emmanuel, Bell Joseph Martin, Ngalle-Bille Hermine, Tabi-Mbi Kingsley, Likeng-Li-Ngue Benoit Constant, and Nsimi-Mva Armand (2014) GC-FID method development and validation parameters for analysis of palm oil (Elaeis guineensis Jacq.) fatty acids composition. J. of Research in Plant Sciences, Vol. 2, No.3; 53-66. doi: 10.12691/plant-2-3-2. \u003c/li\u003e\n\u003cli\u003eJafari, N., Behnami, A., Ghayurd, F., Soleimani, A., Mohammadi, A., Mojtaba Pourakbar, Abdolahnejad, A (2024) Analysis of THM formation potential in drinking water networks: Effects of network age, health risks, and seasonal variations in northwest of Iran. Elsevier, J. of Heliyon, 10(14). doi:10.1016/j.heliyon.2024.e34563.\u003c/li\u003e\n\u003cli\u003ePubChSwinley, JP, De Coning, P (2023) A practical guide to gas analysis by gas chromatography. Elsevier. eBook ISBN: 9780128188897em, 80-98. https://pubchem.ncbi. nlm. Nih. gov/compound.\u003c/li\u003e\n\u003cli\u003ePassos M, Saraiva ML (2019) Detection in UV-visible spectrophotometry: detectors, detection systems, and detection strategies. Elsevier, Measure: J Int Measure Confederation, 135:896\u0026ndash;904. B.V. https://doi.org/10.1016/j.measurement.2018.12.045.\u003c/li\u003e\n\u003cli\u003ePooja D, Kumar P, Singh P, Patil S (2019) Sensors in water pollutants monitoring: role of material. Springer, Singapore,120-140. https://books.google.com.mx/books?id=cPm4D WAA QBAJ. DOI: 10.1007/978-981-15-0671-0.\u003c/li\u003e\n\u003cli\u003eRasheed, S., Hashmi, I., Zhou, Q. et al (2023) Central composite rotatable design for optimization of THM extraction and detection through gas chromatography: a case study. Int. J.Environ. Sci.Technol, 20, 1185\u0026ndash;1198.https://doi.org/10.1007/s13762-022-04070-6.\u003c/li\u003e\n\u003cli\u003eReineccius, G.A., Qian, M.C (2024) Gas Chromatography. In: Ismail, B.P., Nielsen, S.S. (eds) Nielsen\u0026apos;s Food Analysis. Food Science Text Series. Springer, Cham, https://doi.org/10.1007/978-3-031-50643-7-14.\u003c/li\u003e\n\u003cli\u003eRahimi Fard, M., Hajipour, R., and Naderi, M (2010) Sample preparation methods for gas chromatography. J. of Iranian Laboratory Science, Vol.30, No.8, pp.44-53. SID https://sid.ir /paper/959542/fa\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSandoval-Gonz\u0026aacute;lez, A., L\u0026oacute;pez-Garc\u0026iacute;a, N.A., Figueroa-Hernandez, E., C\u0026aacute;rdenas-Mijangos, J (2024) Advances in Analytical Methods for the Monitoring of Chemical Pollutants in Industrial Effluent Water In Shah, M.P. (eds) Microbial Remediation of Hazardous Chemicals from Water \u0026amp; Industrial Wastewater Treatment Plant. Environmental Science and Engineering. Springer, Cham, 20-33, 43-49. https://doi.org/10.1007/978-3-031-62898-6-17.\u003c/li\u003e\n\u003cli\u003eSingh DK, Pradhan M, Materny A (2021) Modern techniques of spectroscopy: basics, instrumentation, and applications. 30-70, 100-110, Springer, Singapore. https://books.google. com .mx/books ?id=1uImEAAAQBAJ.\u003c/li\u003e\n\u003cli\u003eTorabian, A (1998) An\u003cem\u003e \u003c/em\u003eevaluation of THMs in drinking water and a method of its removal. Iranian J. of Public Health, 27(1-2):35-42. https://ijph.tums.ac.ir/index.php/ijph/article/view/ 1773.\u003c/li\u003e\n\u003cli\u003eUS Environmental Protection Agency (USEPA) (2006) National primary drinking water regulations: stage 2 disinfectants and disinfection by products rule: Final Rule. Federal Register, Rules and Regulations, 71(2):388-493. https://www.govinfo.gov/content/pkg/FR-2006-01-04/pdf/06-3.\u003c/li\u003e\n\u003cli\u003eWentworth, W. E., D\u0026rsquo;Sa, E. D., Cai, H., and Stearns, S (1992) Environmental Applications of the Pulsed-Discharge Electron Capture Detector. J. of Chromatogr. Sci., Vol.30; 478\u0026ndash;485, https://doi.org/10.1093/chromsci/30.12.478.\u003c/li\u003e\n\u003cli\u003eZhu Y, Ariga T, Nakano K, Shikamori Y (2021) Trends and advances in inductively coupled plasma tandem quadruple mass spectrometry (ICP-QMS/QMS) with reaction cell. At Spectrosc, 2021, vol.42, 299\u0026ndash;309. https://doi.org/10.46770/AS.2021.710.\u003c/li\u003e\n\u003cli\u003eKleryton Luiz Alves de Oliveira, Guilherme Mateus Bousada, Cristiane Isaac Cerceau, Andr\u0026eacute; Fernando de Oliveira, Renata Pereira Lopes Moreira (2024) Efficient THM quantification in drinking water for minimally-equipped water treatment plants labs. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. Vol. 321, 15 November, 124739. https://doi: 10.1016/j.saa.2024.124739. \u003c/li\u003e\n\u003cli\u003eMohammadpour, A., Emadi, Z. Berizi, E. Kazemi, A (2024)\u003csup\u003e \u003c/sup\u003eTHMs in chlorinated drinking water: Seasonal variations and health risk assessment in southern Iran. Groundwater for Sustainable Development, Vol. 27, 101342. https://doi.org/10.1016/ j.gsd. 2024.101342.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"
[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Measurement accuracy, Concentration method, Water distribution network, Head Space technique, Electron Capture Detector (GC-ECD)","lastPublishedDoi":"10.21203/rs.3.rs-5671502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5671502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn water chlorination for removing pathogens, trihalomethanes (THMs) become among the significant carcinogenic by-products of drinking water chlorination. The conventional measurement means for these compounds is a GC device with an ECD detector (GC-ECD) or GC/MS. A GC-ECD or a GC/MS is utilized for analyzing trihalomethanes at the microgram per liter scale. Due to international sanctions, purchasing an ECD detector is not easy or cost-effective. This article introduces a new concentration method using the Head Space technique in a GC-FID device for measuring the concentration of trihalomethanes. In this method, four compounds, chloroform (CHCl3), di-bromochloromethane (CHClBr2), bromodichloromethane (CHCl2Br) and bromoform (CHBr3), are plotted on a 5-point calibration chart after being measured on a microgram per liter scale. This method, designed at laboratory, can be used to measure concentrations and analyze data in the laboratories of water and wastewater, environment, petroleum and petrochemistry, etc. High accuracy of the method (\u0026micro;g/L) is the main feature of this method. Here, the design method and advantages of this method are presented with diagrams and tables.\u003c/p\u003e","manuscriptTitle":"A new method for analyzing trihalomethanes using a GC-FID device","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:07:01","doi":"10.21203/rs.3.rs-5671502/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"
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