Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal mine Velenje

Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal mine Velenje | 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 Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal mine Velenje Gregor Uranjek, Milena Horvat, Radmila Milačič, Janez Rošer, Jože Kotnik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2279834/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Oct, 2023 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 7 You are reading this latest preprint version Abstract Underground coal extraction at Coal Mine Velenje occasionally gives rise to odour complaints from local residents. This manuscript describes a robust quantification of odorous emissions of mine sources and a model-based analysis aimed to establish a better understanding of the sources, concentrations, dispersion, and possible control of odorous compounds during coal extraction process. Major odour sources during underground mining are released volatile sulphur compounds from coal seam, that have characteristic malodours at extremely low concentrations at µg/m 3 levels. Analysis of 1028 gas samples taken over a six-year period (2008-2013) reveal that dimethyl sulphide ((CH 3 ) 2 S) is the major odour active compound present in the mine, being detected on 679 occasions throughout the mine. While hydrogen sulphide (H 2 S) and sulphur dioxide (SO 2 ) were detected 5 and 26 times. Analysis of gas samples has shown that main DMS sources in the mine are coal extraction locations at longwall faces and development headings and that DMS is releasing during transport from main coal transport system. The dispersion simulations of odour sources in the mine have shown that the concentrations of DMS at median levels can represents relatively modest odour nuisance. While at peak levels the concentration of DMS remained sufficiently high to create an odour problem both in the mine and on the surface. Overall, dispersion simulations have shown that ventilation regulation on its own is not sufficient as an odour abatement measure. coal mine dimethyl sulphide odour coal gases mine ventilation dispersion modelling Figures Figure 1 Figure 2 Figure 3 Introduction Coal (lignite) excavation at Coal Mine Velenje [1] (CMV) occasionally emits unpleasant odours, which can affect a miner’s attention and hence safety awareness. Furthermore, fugitive odour emissions have a negative effect on the quality of life for local communities, which has become an increasing source of complaints. For these reasons, CMV has been carrying out research into techniques of controlling its odour emissions. This requires an understanding of the specific mining processes, identifying and quantifying the sources of odour and the odour active compounds responsible (Brattoli et al, 2014). Experience (distinctive smell to mine operatives) and historical gas concentration measurements have shown that the main odours at the mine are due to volatile sulphur compounds [2] (VSC). These compounds have extremely low odour detection thresholds (µg/m 3 range), which are many times lower than their toxic threshold limit value [3] (TLV) in mg/m 3 range (Rosenkranz and Cunningham, 2003). CMV is one of the largest modern deep mines in Europe. It mines the largest Slovenian lignite deposit, which is one of the thickest known coal seams in the world. The seam is bowl shaped, 8.5 km in length and 1.5-2.5 km wide with an average depth of 300 m (200-600 m deep) and extends almost under the entire Šaleška Valley (Si et al, 2015). The seam is on average 60 m thick with a maximum thickness of 165.8 m (Brezigar 1987; Markič, 2009; Markič and Sachsenhofer, 2010). Over the past 147 years of operation, the mine has produced more than 252 million tonnes of coal with future plans to extract another 103 million tonnes. The main VSC at CMV is dimethyl sulphide [4] (DMS), while hydrogen sulphide (H 2 S) and sulphur dioxide (SO 2 ) are less significant. The odour thresholds of detected VSC’s are: SO 2 2.32 mg/m 3 , H 2 S°0.58 µg/m 3 and DMS 7.6 µg/m 3 (Nagata, 2003). DMS has a distinctive offensive smell similar to that of decaying cabbage and has negative hedonic characteristics (Qiao et al., 2011). At CMV, the presence of DMS was first noted in late 1980’s as an unpleasant odour when the access roadways for the upper NW part of the coal seam were being developed. Later, in the 1990’s, DMS was once again encountered when CO gas sensors sounded without any visible indication of a significant oxidation process, which was later found to be due to the cross-sensitivity of the CO sensors to DMS. Currently, coal production is increasingly centred in this area and odour complaints are expected to increase in the future. The origin of the DMS in the lignite seam is yet to be fully understood, but it is believed that it originates from the early stages of coal formation during the decay of organic matter (Kozinc, 2005). Dimethyl sulphide is produced during the anaerobic microbial decomposition of methoxylated aromatic compounds present in the freshwater sediments (Finster et al., 1990) such as the lignite-bearing Pliocene sediments of the early Velenje basin (Markič, 2009; Markič and Sachsenhofer, 2010). Therefore, it is believed that the DMS is retained during the rapid accumulation and burial of plant material and subsequent coal formation only to be released during coal extraction. There exists an extensive body of literature concerning odour theory, including odour perception (Powers and Corzangeno, 2004a; Powers et al., 2004a), odour parameters (St. Croix Sensory, Inc., 2003; Powers and Corzangeno, 2004b; Powers et al., 2004b; Nicolai and Pohl, 2005), analytical methods (IPPC, 2002; DEFRA, 2010; Gebicki et al., 2016; Conti et al, 2020), sensory – olfactometry (IPPC, 2002; McGinley and McGinley, 2003; St. Croix Sensory, Inc., 2003; Nicolai and Pohl, 2005; Gebicki et al., 2016; Conti et al, 2020), sensory – electronic nose (Karakaya et al, 2020; Kim et al. 2022) , sampling and emission rate determination of odour sources (Parcsi, 2005; Trabue et al., 2008; Hudson, 2009; Boeker et al. 2010; Juarez-Galan et al., 2010; Parcsi et al., 2010; Gebicki et al., 2016; Bylinski et al. 2019), monitoring methods (Benzo et al., 2010; Kost and Richter, 2010), and atmospheric dispersion modelling (Boeker et al., 2000Freeman et al., 2000; Scire et al. 2000; Xing, 2006; Li, 2009; Conti et al, 2020). Most studies, excluding those on odour theory, relate to industries that have well defined odour nuisance issues e.g., the agricultural and livestock industry (Parcsi, 2005; Xing, 2006; Koziel et al., 2010), waste water treatment plants (Freeman et al., 2000), municipal waste sites, recycling facilities, transfer stations, composting facilities (Fischer et al., 1999, McKendry et al.,2002; Gebicki et al., 2016), and industrial plants such as paper pulp mills, petroleum refineries, food processing, leather manufacturing, smelting of non-ferrous ores, steel mills, the manufacture of certain abrasives, paint manufacture, rendering, sulphur dioxide scrubbing, starch manufacturing and (Kenneth et al, 2004), tobacco factory (Zagustina et al., 2010), biogas production (Vanek et al., 2015), etc. On the other hand, little information is available concerning fugitive emissions of odorous gasses from mining activities in general. In addition, addressing potential odour issues as part of making an impact assessment is relatively new in planning coal mining projects. Odour dispersion modelling has been performed as part of an air quality impact assessment for a new ventilation shaft at the Illiwarra coal mine, NSW, Australia (Kellaghan, 2010). For modelling purposes, the mine used cumulative odour measurements taken in the mine’s ventilation air from existing ventilation shafts. Gas compositional analysis revealed that the volatile organic compounds [5] (VOC) were mostly below the limits of detection and did not pose an odour issue. Similarly, the Tasman Underground Mine (NSW, Australia), when seeking consent to extend their underground mine operations, also performed an air quality assessment on account that the development of the mine could potentially produce odorous emissions from the existing and proposed ventilation shafts (Kellaghan, 2012). No odour impact, based on either the levels of odorants in the ventilation shafts or in the actual coal seam, was detected. The Wilpinjong Coal Mine (NSW, Australia) analysed ambient air quality as a response to complaints by local residents (Cox and Isley, 2014). All of the active odour compounds were below the human odour detection threshold and it is not clear from the study which odorants were responsible for the complaints. The Kanmantoo Copper Mine (SA, Australia) has performed a study for environment protection and rehabilitation while (PEPR, 2016) seeking consent for extending the life of open-pit mine for excavation and production of copper-gold concentrate. Odour monitoring results has confirmed the predicted odour dispersion model and showed that odour is not anticipated to result in negative impacts from the mining operations. The study of environmental impact of gold mines in Oman and the pollution impact by heavy metals (Abdul-Wahab and Marikar,2012) also, included odour measurements from water from nearby well used for irrigation. The odour from the water was not detected. Surprisingly, none of these studies included DMS in their analyses despite its low odour threshold. The literature research since 2011 point out, that there are almost no published studies exist of DMS emissions from mining activities. Only published work referring DMS to mining activities are from oil sand mine operations in Fort McKay area in Alberta (Canada) and from those performed by CMV. In the air quality investigation of 30 km radius around Fort McKay of fife year monitoring period was performed due to the countless environmental complaints regarding oil sand mining activity in the area (AER, 2016). During monitoring period between 2010 and 2014 were 172 complaints and 165 were related to odours. Between 47 priority odorant candidates was also DMS. The investigation focused on six oil sand mines and one in situ facility. Selected were 16 continuous monitoring point and various others sampling points. Ambient air from sampling canisters of 1-hour and 24-hour samples were analysed for 60 volatile organic compounds and 20 reduced sulphur compounds. The DMS presence was analysed in 126 samples and was never detected (detection limit was 2.58 µg/m 3 ). The only published studies referring to DMS emissions from coal mining activities are those performed on coal from Velenje Coal Mine of gaseous sulphur emissions (COS, CS 2 and DMS) from coal stockpiles (Kozinc et al., 2004; Kozinc, 2005) and a study on the levels of DMS in the return airway of a longwall face (Zapušek and Marcel, 1998), which were briefly summarized by Zhang (2013) in an IEA Clean Coal Centre report. The estimated daily emissions of COS and CS 2 for the whole stockpile in the sampling period were 20 g of CS 2 and 70 g of COS (gas concentrations were in µg/m 3 range). The DMS concentrations fell to less than 2.58 mg/m 3 within a few days as it is only released from freshly loaded coal. The measured DMS concentration levels in the mine air, which was sampled once a week for 15 consecutive weeks in the return airway of a longwall face during coal production, ranged from 55.47 to 128.48 mg/m 3 . During a series of in-situ coal desorption tests made in 1998 and 1999 (5 boreholes and 10 desorption tests (Erico Ltd, 1998&1999)), DMS was detected in all but one sample (max. concentration 516 mg/m 3 ), while H 2 S was <1.42 mg/m 3 . In the industrial or agricultural processes such as pulp and paper manufacturing, oil or petroleum refining, food decay, composting, landfilling, fish processing, sewage and wastewater treatment, leather manufacturing, paint, rendering plants, sulphur dioxide scrubbing, and starch manufacturing plants; DMS is a typical gaseous odour pollutant. The removal or degradation of DMS odorant before exhausting into the atmosphere is of great significance to improve the local air quality. DMS has the unique capability of enhancing and intensifying other odours. Due to this property, it is used in warning odorants and odour masking agents (Kenneth et al., 2004). DMS is also a substantial contributor of the aroma to some food items, such as beer (Stafisso et al., 2011), red wines (Lytra et al., 2014), truffles (Feng et al., 2019), many vegetables and fruits (tomatoes, sweetcorn, grapes, asparagus, and brassicas), honey (Schäfer et al., 2009 and McGorrin, 2011), chewing gum (Kenneth et al., 2004), cheddar cheese (Qian and Burbank, 2007), etc. One of most significant discharges of DMS and other reduces sulphur compounds (H 2 S, CH 3 SH, (CH 3 ) 2 S 2 ) can be from Kraft pulp mills (Kenneth et al, 2004). There are many sources in the mill. Some sources emit a small gas volume with high concentrations (blow heat recovery; turpentine recovery vent; evaporator hotwell vent; and foul condensate storage tank), while others have large volumes with low concentrations (brown stock washer filtrate, tanks, and hood; weak and strong black liquor storage tanks; knotter hood; black liquor oxidation vent; and contaminated condensate tanks). Typical DMS concentrations of high volume source is 0.52 mg/m 3 and for low volume source is 38,700 mg/m 3 . Ambient air samples were collected at several locations in the community around a major Canadian pulp and paper plant over a period of several months, before and after major process changes (Catalan et al, 2007). In spring of 2006 they permanently closed one of two Kraft pulp mills on site and the shutting down of a chemical recovery boiler and associated black liquor oxidation systems. DMS was found to be the most abundant reduced sulphur compound in ambient air before the changes with an average concentration of 3.84 µg/m 3 . After the changes, the average concentrations of DMS decreased by 70 %. At landfill sites over 300 trace compounds have been identified in landfill gas. Unpleasant odours are usually associated with the sulphur containing compounds, primarily mercaptans and sulphides. The vast range of trace compounds measured in landfill gas reflects both the anaerobic decomposition processes taking place in the waste mass and the wide range of chemicals introduced via the industrial and commercial waste streams (McKendry et al.,2002). DMS is common odorant in landfill gas typically found in concertation range between 0.02 mg/m 3 and 135 mg/m 3 . The study of sulphur source from livestock production in Denmark exposes H 2 S as major sulphur source. Finisher pig production is estimated to be the largest source of atmospheric sulphur in Denmark (Feilberg et al., 2017). The only other sulphur compounds measured consistently in the ppb range are methanethiol and DMS, but these only constitute about 2–5% of H 2 S. Measurement campaigns were carried out over 6 year-period from 2009 to 2015 on fife pig production facilities. The measured concentrations DMS were between 4.39 µg/m 3 and 10.58 µg/m 3 . The study in 2018, of identification of odour sources in two biogas plants in Poland showed DMS concentrations up to 1.26 mg/m 3 (Wisniewska et al., 2019). Physical-chemical and biological techniques are now available for removing odours from air streams including: biofilters, biotrickling filters, membrane bioreactors, wet scrubbing, adsorption, and chemisorption, and more recently, methods based on photo-dissociation, electron beam irradiation, corona discharge decomposition and catalytic and ozone oxidation (Qiao et al., 2011). However, DMS is one of the least biodegradable compounds among the odorous sulphur containing gaseous pollutants; consequently, it always needs improved systems out of the conventional biological setups. Traditional physical-chemical approaches to DMS removal mainly include wet scrubbing, adsorption, and chemisorption (Kenneth et al., 2004). One of the largest available odour control systems designed to serve, for example, a water treatment plant, consists of a series of either bio-filters or chemical scrubber units with capacities of up to 69.4 m 3 /s (ASK Piearcey Ltd, 2014). In underground coal mining, cumulative air flowrates are extreme and, at CMV, are between 340 m 3 /s and 420 m 3 /s. Clearly, existing systems could not possibly handle the large volumes of exhaust gases emitted from a coal mine and either new or upgraded solutions must be developed. In addition, odorous mine gas emissions depend on many factors, including the natural characteristics of the coal, presence of odour active compounds in the coal seam, production and ventilation design, and coal production intensity. For these reasons, it is a challenge to predict their actual concentration. The objectives of this study were to recognize and estimate the main odour sources in the mine and to construct ventilation model of CMV to perform model-based odour analysis. This was achieved by taking into consideration the characteristics of mine ventilation, mine gateway system (airways), and estimated odorous emissions of mine sources in order to establish a better understanding of the sources, dispersion, and ventilation based control options to reduce the presence of odour active compounds released during the coal extraction process. [1] CMV - Coal mine Velenje (https://www.rlv.si) [2] VSC - volatile sulphur compounds [3] TLV - threshold limit value [4] DMS – dimethyl sulphide (CH 3 ) 2 S [5] VOC - volatile organic compounds Materials And Methods Analysis of odorous gas emissions As the first step in this study, the long-term monthly monitoring data of gases concentrations in the mine atmosphere were analysed in order to determine the main odorants in the mine, their source, and to estimate emissions. Monthly chemical analysis of mine gasses are carried out to control gas concentrations in mine air and includes the following gases: CH 4 , CO 2 , DMS, H 2 S, SO2, O2, CO, H 2 , NO, NO 2 and N 2 . The N 2 content of the mine gas is the difference between the sum of the monitored gases and from 100% (Erico Ltd, 2008–2013). The concentration of CH 4 , CO 2 , DMS and H 2 S were determined using gas chromatography. The test method PM 3.01 was used for CH 4 , CO 2 and DMS while the test method PM 3.02 was used to determine H 2 S (Erico Ltd, 2008–2013). The concentrations of O 2 , CO, H 2 , NO, NO 2 , SO 2 were determined using a gas meter with a built-in electrochemical sensor (Echo d.o.o., Slovenia). Oxygen and CO concentrations were determined using the PM 3.03 test method, while the remainder were determined using the PM 3.04 test method. All three methods were developed by Erico – since 2017 is named Eurofins Erico (Erico Ltd, 2008–2013) and are granted by an accreditation body (i.e., Slovenian Accreditation). These mine air samples were collected in Tedlar® sampling gas bags and analysed in the laboratory (Erico Ltd, 2008–2013). The monitoring sites were selected systematically in such a way that all coal production activities can be controlled. Air samples were collected in the return airflows of all main work sites in the mine an in all main returns of mine ventilation. Samples were not collected simultaneous at specific monthly monitoring campaign. Odour modelling in the mine and simulation of odorous gas emissions The exhaust main ventilation at the VCM is provided by two main fans, and a series of smaller auxiliary fans for ventilation of development sections or dead-end headings. The main fans are located at the Pesje and Šoštanj ventilation stations (Fig. 1 ). Each fan draws air up from the mine from five surface air intakes (situation as of 2012, Fig. 1 ). The main fan located at the Šoštanj station is a Turmag GVhv 31-1800 with nominal power 1800kW (auxiliary fan: the same type), while at the Pesje station is installed a TLT-GAF 34 − 31 with nominal power 800kW (auxiliary fan: Turmag GLH-28-660 with nominal power 600 kW), (Salobir, 2009a ). The Šoštanj station provides approximately two-thirds of the required airflow rate, with the Pesje station providing the remaining one-third. The whole mine consists of 50–60 km gateways and facilities. The purpose of odour modelling in the mine was to simulate dispersion of odorous gas emissions from its potential sources and to the surface under variable mine ventilation conditions and the characteristic concentrations of odour compounds. The odour modelling in the mine is based on mine ventilation model of VCM designed in Ventsim Visual™ software (Ventsim™). The software allows 3D graphical representation, simulated paths and concentrations of smoke, dust, diesel particles or gas for planning of emergency situations, short and long-term planning of ventilation, and the simulation of gas and aerosol concentrations (Ventsim™, 2013). The software can also be adopted for odour dispersion simulations, when odour concentration of the source is known or when odour is presenting single odorous compound, respectively. It is possible to simulate odour concentration because odour units – OU/m 3 are the number of dilutions of the odorous air to the odour threshold. The calculation of the dilution factors for olfactometry is based on the ratio of total volumetric flow divided by the odorous sample flow (McGinley, 2000; Brattoli et al., 2011 ; Bylinski et al 2017 ): $$\text{Z}=\frac{{\text{V}}_{\text{d}}+{\text{V}}_{\text{o}}}{{\text{V}}_{\text{o}}}$$ 1 where V d is the volumetric flow rate of odour-free diluted air and V o is the volumetric flow rate of the odorous air sample and Z is the dilution factor. The software treats every component in the mine air in the same manner. The concentrations of gaseous compounds are diluted according to the dilution ratios of the return airways and their dilution at airways junctions. For each studied component, also decay mechanism can be modelled. Designed ventilation/odour model is based on ventilation and mine data from October 2012 using monthly ventilation parameters for determination of air flowrate airways and represents the situation of mine ventilation in 57.85 km of underground facilities. Ventilation parameters are used each month to create a ventilation map of the VCM. The adjusted air flowrate of each airway were then determined by considering Kirchoff’s first and second law theorems (McPherson, 1993 ). The mine ventilation map is a visual representation of the situation in the underground gateways (airways) and facilities with respect to adjusted airflows, direction of airflow and locations of ventilation regulators: doors, barriers, boreholes, and shafts, auxiliary ventilators, and locations of gas sensors. The total resistances of the airways in the model are summarized from the “Zračenje” software developed in-house (Žibert, 2006 ). Figure 1 , besides the mine gateway system, shows the locations of the main air intakes (service shaft NOP, ventilation shaft Šoštanj II, service shaft Škale, the main coal transport drift and Hrastovec drift, the main air exits - returns (ventilation stations Šoštanj and Pesje), the longwall faces (K-130/B and K-65/A) and the development headings: 4, 6, 7, 8, 11 and 13. The deepest part of the mine is approximately 500 m deep. First, were used the whole data set to create a 3D model that defined every airway according to length, profile (round profiles were used for shafts, boreholes, ducts, and modified profiles for specific types of gateways used in CMV), cross-section, airway type, and total resistance. Other data included the average surface temperatures, both dry (10°C) and wet (7°C), together with temperature and atmospheric pressure (985 mBar). The network air density was calculated from the average monthly temperatures and pressures (1.20 kg/m 3 ). In the model fixed air flows were used for the upcast ventilation shafts (ventilation station stations), in the airways of the auxiliary ventilators and in the ventilation boreholes (all together 24 fixed flows). The model does not consider natural ventilation and compressible flows, which only have a significant effect when simulating mines deeper than 500m (Ventsim™, 2013). All the other software settings were left as default. In the second step, the air flowrate and directions of airways were modelled, iteratively adjusted for their total resistances and different ventilation regulation measures. To control the adjusted total resistances, were considered pressure drops on the main fans, which were 3460 Pa at the Šoštanj and 2040 Pa at the Pesje ventilation stations. The general regulation of mine ventilation is possible by positioning the angles of main fans blades setup at each ventilation station. Main fan at Šoštanj has adjustable blades between angles − 10° and + 10°, while at Pesje the main fan has blades that can be adjusted between 20° and + 2°. The main fans characteristic curves (Salobir, 2009b ) are customized in the Ventsim™ for the air flowrate simulation at: Šoštanj: -10°, -8°, -6°, -4°, -2°, 0°, + 2°, + 4°, + 6°, + 8° and + 10° and for Pesje: -20°, -18°, -16°, -14°, -12°, -10°, -8°, -6°, -4°, -2°, 0° and + 2°. The setup of fan’s blades in October 2012 were + 1° (Šoštanj) and − 11° (Pesje). Verification of the model was based on the differences between the modelled and calculated air flowrate and between modelled and measured depressions of the main fans. The accuracy of the model, estimated on the basis of calculated and modelled airflows quantities in 226 airways, is ± 0.07 m 3 /s. The modelled values of the main fan depressions were 3459.7 Pa at Šoštanj and 2040.4 Pa at Pesje stations. Results And Discussion Analysis of odorous gas emissions and estimation of odorous sources Over a six-year period (2008/1-2013/12) 1,028 point measurements were taken. The monitoring sites were selected systematically so that the return airways of all production and development worksites and all main return airways were included. Under normal working conditions at the mine, H 2 S and SO 2 were rarely detected and levels of SO 2 throughout the mine exceeded the limit of detection on only 26 occasions (24 x 2.67 mg/m 3 , 1 x 5.34 mg/m 3 and 1 x 10.68 mg/m 3 ), while H 2 S was only detected 5 times (2.41–13.63 mg/m 3 ). The limit of detection of SO 2 was 2.67 mg/m 3 and of H 2 S was 1.42 mg/m 3 . The DMS were detected in 679 out of 1,028 samples in levels of DMS between 2.58 mg/m 3 and > 129 mg/m 3 (levels above 129 mg/m 3 were recorded as > 129 mg/m 3 ). The analysis results confirmed DMS as the major odorant with main sources at the longwall faces (coal extraction working sites), the main coal transport system (system of rubber belt conveyors that is transporting coal directly to the surface), and the development headings (gateway building working sites). The analysis results of characteristic DMS concentrations and flowrates of DMS sources in return airways of main odour sources are presented in Table 1 . Table 1 Characteristic concentrations and mass flows of DMS in the returns of mine odour sources. Odour Source Longwall faces Main coal transport Development headings DMS concentration [mg/m 3 ] Number of samples 163 72 398 MAX 50.0 34.0 50.0 MEAN 20.4 3.1 9.1 MIN 0.0 0.0 0.0 Std 21.4 5.9 13.4 Percentile 97.5% 50.0 17.0 50.0 Percentile 75% 50.0 3.6 11.8 Percentile 50% 12.0 0.0 2.9 Percentile 25% 0.0 0.0 0.0 Percentile 2.5% 0.0 0.0 0.0 Average airflow rate [m 3 /s] 35.4 71.8 7.3 Median DMS source [mg/s] 1080 0 54 Peak DMS source [mg/s] 4498 3106 930 The result shows great variations of DMS concentrations for each confirmed odour source and also that DMS was present at sources between 44% and 71% of the time. In the returns airways of longwall faces the DMS was not detected in 47 out of 163 samples and in the return airway of the main coal transport system, the DMS was not detected in 40 times out of 72 measurements. In the development headings, DMS was not detected in 136 out of 398 samples. For estimation of characteristic emissions of DMS sources were taken into consideration median DMS concentration at average air flowrates and for estimation of DMS sources peak emissions were taken into consideration percentile 97.5% DMS concentrations at average air flowrates. Longwall faces and development headings as odour sources are regarding ventilation and dispersion relatively simple if not considering the variability of production intensity and amount of DMS presence in the coal. While the main coal transport system with six successive conveyers of total length 2.6 km and six intakes of fresh air at junctions and connection to the surface (Fig. 1 ), and six leakage connections with main return airways is an odour source with very complex dispersion. All six leakages are dispersed to the Ventilation station Pesje. Detected DMS in return airway of main coal transport in Preloge pit shows that is being released during transport and that also all return airways – leakages in Pesje pit must be considered. For the main coal transport source in the model was considered that DMS is releasing at constant release rate from the constant mass flow of coal through the whole length. The source in the model was divided to each individual part of main coal transport airways accordingly to their lengths. Each individual part represented in the model as a partial source of DMS. The simulation results showed that 34.5% of main coal transport source was dispersed to the Ventilation station Pesje and 65.5% was dispersed to the Ventilation station Šoštanj which was detected with monthly measurements. Considering simulation results the whole main coal transport DMS emission rate at peak concentrations is 4,815.9 mg/s and is potentially the biggest DMS source at peak concentrations. The division of whole source in the model at leakages from L1 to L6 was 3.5%, 1.8%, 6.1%, 6.7%, 6.7% and 9.7%. Locations of leakages are marked on Fig. 2 (bottom figure) as a green circle. Analysis of monthly DMS concentrations shows that DMS is released by desorption processes as the coal is being transported from the mine similar to desorption from the coal in the stockpiles (Kozinc 2005 and Zhang, 2013 ) and VCM coal samples from boreholes in the coal seam (Erico Ltd, 1998 &1999). Simulations of DMS and odour concentrations The odour model is based on DMS emissions. The model is considered inert since it assumes that all the sources of DMS do not decay over time. It is known that DMS in the atmosphere does decay through reactions with photochemically produced hydroxyl (OH·) and nitrate (NO 3 ) radicals, ozone (O 3 ) and nitrogen dioxide (NO 2 ) (Kenneth et al, 2004 and Chen and Jang, 2012). A typical atmospheric half-life for DMS in the environment is from several hours to 3.5 days. In addition, photochemical oxidation, although important on the surface, is not considered relevant in the underground mine. At DMS study in the return airways of a longwall faces in CMV (Zapušek and Marcel, 1998), also the DMS stability was tested. A Tedlar gas sampling bag was filled with synthetic air (20% oxygen and 80% nitrogen) and with DMS standard with concentration of 51.3 mg/m 3 and analysed every day by the gas chromatograph. During analysis gas standard in gas sampling bag were stored in dark place to avoid the UV induced photodecomposition. The test results showed that DMS in Tedlar gas sampling bags is stable for at least 4 days. During the study of effects of process changes on concentrations of individual malodorous sulphur compounds in ambient air near a Kraft pulp plant in Thunder Bay, Ontario, Canada (Catalan et al, 2007 ; Catalan et al, 2009 ) also the stability of reduced sulphur compounds in the Teflon sampling bags was assessed. A gas mixture also containing 3.87 µg/m 3 of DMS was introduced in a clean Teflon bag and then periodically withdrawing aliquots which were analysed to monitor the changes in concentration over time. Any change from the initial concentration was due to decomposition of compounds in the gas phase or adsorption on the bag walls. The concentration of DMS was found to remain constant for more than 3.5 hours. Similar results were obtained when the initial concentrations were doubled. Simulations of traveling (spread) times of DMS from the sources to the surface at operational air flowrate as of October 2021 showed that traveling times from longwall faces were between 556 s and 750 s, from development headings were between 750 s and 1,836 s and from main coal transport were between 521 s and 1,923 s. The longest traveling time 7,409 s or 2.1 hours was from main coal transport at simulation were the main fans blades at station Šoštanj was set on -10° (minimum air flowrate) and at station Pesje was set on + 2°. The extreme traveling time is due to the changed airflow directions in some airways and dispersion through station Pesje instead station Šoštanj as normal. Based on the cases described above and modelled traveling times, any decay mechanisms of DMS were not considered in the model. The identified odour sources were the longwall faces K.-130/B and K.-65/A, road building faces 4. 6, 7, 8, 11 and 13 and the main coal transport system. In the model sources are represented as point sources for each source, except at main coal transport, where the whole source in the model represents 7 point sources at return airway of main coal transport and at 6 leakages (Fig. 2 , bottom figure). For the study of DMS and odour dispersion analysis were considered 8 simulation scenarios. Four for DMS and four for odour dispersion. Simulations tested dispersion of characteristic DMS and odour concentrations at median and peak levels and all characteristic concentrations at the operational air flowrate as of October 2012 and at maximum possible air flowrate due to the main fans characteristics (station Šoštanj at + 10° and station Pesje + 2°). The monitoring locations from 1 to 50 and Ventilation stations Šoštanj (VSS) and Pesje (VSP) in the model (Fig. 2 ) were systematically selected to follow all dilutions in return airways from the sources and to the surface. The simulations results are presented in Table 2 and for simulation results of peak DMS concentrations at operational airflows in Fig. 2 . The simulation results reveal a high odour concentration despite the low DMS concentrations also at median sources because of the low odour detection threshold of DMS (1 mg/m 3 = 129 OU/m 3 , (Nagata, 2003 )). Odour concentration from median sources at ventilations stations means that the odorous emissions at Ventilation station Šoštanj must be diluted to the odour detection threshold for additional 672 times by the atmosphere and at Ventilation station Pesje for additional 1,253 times. The odorous emissions from Šoštanj were 177,145 OU.m 3 /s and from Pesje 149,370 OU.m 3 /s. While the emissions from Šoštanj are higher, the concentrations are lower due to the higher dilution rates of sources due to the 2.2 times higher airflow rate than in Pesje. For the longwall face k.-130/B in Preloge pit the dilution rate to the surface was 9.3 and for the longwall face k.-65/A in Pesje pit was 3.2. Dilution ratios for development heading in Preloge pit were between 28.7 and 75.3 and in Pesje pit only development heading num. 7 were dispersed to Pesje with dilution ratio 15.2. At median sources main coal transport was not recognized as DMS/odour source as more the 50% DMS was not detected. The dilution ratio at peak sources were 3.7 in Šoštanj and between 29.8 and 91,7 in Pesje. In comparison, the odour emission modelling of newly planned ventilation shaft at the Illiwarra coal mine, NSW, Australia (Kellaghan, 2010 ) showed that at an odour source equivalent to 219,500 OU.m 3 /s predicted that odour concentrations 3 OU/m3 would exceed only 1% of the time, what is in accordance with local odour regulations. In Slovenia there is no odour regulations. If only level of odour emissions of CMV are compared, without taking into consideration atmospheric conditions and vicinity of settlements, at the median odour sources no odour complaints are expected. On the other hand, at peak sources what is considered as “worst case scenario”, the odorous emissions from Šoštanj with 1,607,932 OU.m 3 /s and from Pesje with 928,558 OU.m 3 /s are likely to lead to odour complaints. If considered scenario with only one longwall face at peak levels the emission rate would be 580.242 UO.m 3 /s. Figure 3 present a graphical visualization of main results of characteristic DMS mine sources estimation, and characteristic odour emissions on the surface. Simulations of regulation of the main fans at peak levels to provide maximum air flowrate resulted in an overall additional reduction of concentrations for 15.4% on average. The ventilation reduction potential of concentrations regarding operational air flowrate as of October 2012 in Preloge pit was 10.6% and in Pesje pit was 22.9%. At peak sources, the average concentrations were 9.3 times higher than at median sources at monitored locations and total emissions of peak sources were 7.8 times higher than of median sources. Table 2 The simulation results at the monitored locations. Monitored location Operational airflows Maximum airflows Odour/DMS Reduction [m 3 /s] Median concentrations Peak concentrations [m 3 /s] Median concentrations Peak concentrations [mg/m 3 ] [OU/m 3 ] [mg/m 3 ] [OU/m 3 ] [mg/m 3 ] [OU/m 3 ] [mg/m 3 ] [OU/m 3 ] [%] 1 35.0 31.3 4,044 130.7 16,885 39.3 27.9 3,604 116.3 15,024 10.9 2 37.3 29.4 3,798 122.6 15,838 41.9 26.1 3,372 109.0 14,081 11.2 3 39.0 28.1 3,630 117.4 15,166 44.7 24.5 3,165 102.2 13,203 12.9 4 50.4 22.0 2,842 94.7 12,234 56.7 19.5 2,519 83.8 10,826 11.4 5 61.8 18.4 2,377 85.6 11,058 69.5 16.4 2,119 75.7 9,779 11.2 6 92.2 13.2 1,705 72.2 9,327 103.6 11.7 1,511 64.1 8,281 11.3 7 57.5 13.0 1,679 71.0 9,172 64.4 11.5 1,486 62.9 8,126 11.5 8 76.5 10.5 1,356 65.7 8,488 86.4 9.2 1,189 57.9 7,480 12.1 9 77.5 10.3 1,331 64.8 8,371 87.4 9.1 1,176 57.2 7,389 11.7 10 36.6 9.6 1,240 60.4 7,803 41.7 8.5 1,098 53.2 6,873 11.7 11 91.3 10.1 1,305 59.1 7,635 102.9 8.9 1,150 52.1 6,731 11.9 12 39.6 7.5 969 47.0 6,072 44.8 6.7 866 42.1 5,439 10.5 13 100.8 9.7 1,253 57.0 7,364 112.8 8.6 1,111 50.5 6,524 11.4 14 86.1 4.6 594 41.2 5,322 96.3 4.2 543 37.4 4,832 9.0 15 75.7 4.4 568 39.8 5,142 85.4 4.0 517 36.1 4,664 9.2 16 114.2 9.1 1,176 55.0 7,105 127.1 8.1 1,046 48.9 6,317 11.0 17 149.5 2.2 284 41.8 5,400 166.0 2.1 271 37.6 4,857 7.3 18 127.5 8.4 1,085 53.6 6,924 141.7 7.5 969 47.7 6,162 10.9 19 136.1 2.2 284 41.8 5,400 151.3 2.1 271 37.6 4,857 7.3 20 71.1 0.0 0 44.4 5,736 77.4 0.0 0 40.7 5,258 8.3 21 73.8 0.0 0 42.8 5,529 80.5 0.0 0 39.2 5,064 8.4 22 11.6 4.7 607 81.1 10,477 13.1 4.2 543 72.2 9,327 10.8 23 12.6 2.0 258 34.1 4,405 13.7 1.8 233 31.5 4,069 8.8 24 14.0 3.9 504 67.4 8,707 16.2 3.4 439 58.3 7,532 13.2 25 19.0 2.9 375 49.8 6,433 22.0 2.5 323 43.0 5,555 13.7 26 25.0 2.2 284 37.7 4,870 27.6 2.0 258 34.1 4,405 9.3 27 6.9 1.7 220 28.9 3,733 7.8 1.5 194 26.3 3,398 10.4 28 15.1 1.9 245 33.3 4,302 17.4 1.7 220 30.0 3,876 10.2 29 9.1 1.5 194 26.5 3,423 10.7 1.3 168 22.7 2,933 13.8 30 21.1 4.0 517 68.8 8,888 23.4 3.6 465 62.7 8,100 9.4 31 30.2 3.2 413 55.9 7,222 34.1 2.9 375 50.2 6,485 9.8 32 33.5 2.9 375 50.4 6,511 37.8 2.6 336 45.2 5,839 10.3 33 40.1 1.4 181 23.6 3,049 53.3 1.0 129 17.7 2,287 26.8 34 37.7 15.9 2,054 84.1 10,865 50.4 11.9 1,537 63.0 8,139 25.1 35 46.9 24.5 3,165 116.3 15,024 59.6 19.3 2,493 91.7 11,846 21.2 36 49.2 23.4 3,023 111.9 14,456 62.6 18.4 2,377 88.1 11,381 21.3 37 57.6 20.0 2,584 95.6 12,350 73.6 15.7 2,028 74.9 9,676 21.6 38 60.6 19.0 2,455 90.9 11,743 80.4 14.4 1,860 68.7 8,875 24.3 39 63.3 18.2 2,351 87.1 11,252 83.8 13.8 1,783 65.8 8,500 24.3 40 20.7 3.2 413 15.5 2,002 26.6 2.5 323 11.7 1,511 23.2 41 62.3 17.4 2,248 83.4 10,774 81.8 13.3 1,718 63.7 8,229 23.6 42 25.2 2.7 349 31.4 4,056 32.8 2.0 258 24.0 3,100 24.7 43 27.2 2.5 323 29.1 3,759 35.6 1.8 233 22.1 2,855 26.0 44 30.1 15.3 1,977 88.5 11,433 38.4 11.8 1,524 68.1 8,798 23.0 45 57.3 9.2 1,189 60.3 7,790 74.0 7.0 904 46.0 5,943 23.8 46 59.9 8.8 1,137 62.6 8,087 77.4 6.7 869 47.8 6,175 23.6 47 39.5 15.8 2,041 82.6 10,671 51.9 12.3 1,589 63.9 8,255 22.4 48 45.2 13.9 1,796 72.3 9,340 57.6 11.1 1,434 57.6 7,441 20.2 49 56.3 11.1 1,434 58.0 7,493 72.0 8.9 1,150 46.1 5,955 20.2 50 62.9 8.4 1,085 62.2 8,035 81.4 6.4 827 47.5 6,136 23.7 VSP 119.2 9.7 1,253 60.3 7,790 153.3 7.5 969 46.9 6,059 22.5 VSS 263.7 5.2 672 47.2 6,098 293.0 4.7 607 42.5 5,490 9.8 AVERAGE 10.0 1,293 63.6 8212 8.4 1,080 53.6 6,922 15.4 The characteristic levels of DMS (Table 1 ) shows great variations. At peak concentrations longwall face source is 4.2 times higher than at median concentrations and at development heading source is 17.2 times higher. Main coal transport is not considered as DMS source at median levels and at peak levels is potentially the biggest source in the mine. The results indicates that DMS is not released only with extraction at longwall faces and development heading but is also releasing from the coal while is being transported to the surface. It is likely to be adsorbed on the lignite structure or trapped in the coal matrix, similarly as CO 2 (Zavšek, 2004 ). From an adsorption/desorption study (Markič, 2009 ) of gases from different lithotypes of Velenje lignite it was observed that the different lithotypes have significantly contrasting desorption properties related to differences in porosity. The specific surface area of pores in homogenous fine detrital lignite is more than 180 m 2 /g and 35 m 2 /g for xylite. Released DMS amount from sources greatly varies due to natural characteristics of the coal, presence of DMS in coal, production and ventilation design, and coal production intensity. The odour concentrations estimation of mine air are based on the DMS concentrations and its odour detection threshold. Simulation results shows odour concentrations at ventilations stations between 672 OU/m 3 and 7,790 OU/m 3 . So far, rare separate point odour concentrations measurements at CMV were conducted. The monitored odour concentration levels were similar as modelled levels. Four separate odour concentrations measurements at ventilation stations (NLHEF, 2017) in 2016 gave odour concentrations between 850 OU/m 3 and 4,500 OU/m 3 and on 26.11.2007 three measurements (NIPH, 2008) showed odour concentrations between 3,900 OU/m 3 and 8,400 OU/m 3 . Conclusions There is almost no information concerning the fugitive emissions of DMS and odours in general from underground coal mining activities. In this paper was addressed this by describing and quantifying a dispersion of odours gases released from sources of CMV by focusing on analysis of gases monthly measurements in the mine and simulations of characteristics emissions of odorous compounds with mine ventilation model constructed in Ventsim™. This research identified DMS as a major odorant in the VCM released from longwall faces, development headings and main coal transport during coal extraction process. Its very low odour threshold means that it can create an odour nuisance even at trace levels. The dispersion simulations of odour sources in the mine show that median emissions represent relatively modest odour nuisance. While during peak emissions in the exit airways odour is potentially high to be disturbing and on the surface at the ventilation stations would be subject to odour complaints from the local residents. Simulating to additionally reduce odour levels with increasing air flowrate with the regulation of main fans showed that is not an effective measure for mitigating odorous emissions, while measures by reducing coal production would impose severe economic penalties. Since DMS is not regularly monitored in mines and levels are significantly varying due to its content and distribution in the coal, releasing mechanisms, mine ventilation design and varying production intensity during the coal extraction process, the future work will focus on real-time monitoring of DMS levels and study of its correlations to coal extractions process to better understand and more accurately estimate odorous emissions from specific work phases of coal extractions process. The DMS content in the seam is related to the petrographic heterogeneity of the coal, future research will involve investigating the coal desorption characteristics of DMS from coal. In addition, to effectively address the odour issue at the VCM, especially in relation to fugitive odour emissions at the surface and for the design of technical measures for odour control, monitoring, and dispersion modelling of odour sources on the surface are necessary from hereafter. However, underground coal mines are not widely recognized as an odour nuisance, and the development of technical abatement solutions to control odour from coalmining operations, especially given the large volume of ventilated air produced by the mine, will need more recognition of the problem and more support for its solving. Declarations Acknowledgements This study was conducted in the framework of the project ″Young researchers from Industry 2010″ and its operation was partly financed by the European Union, European Social Fund. The authors are grateful to Velenje Coal Mine, which provided the necessary assets and data, and especially to Mine ventilation team of Velenje Coal Mine for technical support and for necessary mine ventilation parameters. The authors are also grateful to prof. Sevket Durucan and prof. Anna Korre from Imperial College London for all suggestions and recommendations. Funding: This study was conducted in the framework of the project ″Young researchers from Industry 2010″ and its operation was partly financed by the European Union, European Social Fund. Competing Interests: Authors declare no financial or non-financial interests that are directly or indirectly related to this work. Author Contributions Statement: Writing - original draft preparation, Gregor Uranjek, Milena Horvat, Radmila Milačič, Janez Rošer and Jože Kotnik; writing - review and editing, Gregor Uranjek, Milena Horvat, Radmila Milačič, Janez Rošer and Jože Kotnik; visualization, Gregor Uranjek.; supervision, Milena Horvat, Radmila Milačič, Janez Rošer and Jože Kotnik. All authors have read and agreed to the published version of the manuscript. Data availability statement: The supplement data that support the findings of this study are available from Coal mine Velenje (Premogovnik Velenje d.o.o.; https://www.rlv.si/) but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Coal mine Velenje. References Abdul-Wahab, S. & Marikar, F. (2012). The environmental impact of gold mines: pollution by heavy metals. 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Si, G., Jamnikar, S., Lazar, J., Shi, J.Q., Durucan, S., Korre, A., & Zavšek, S. (2015). Monitoring and modelling of gas dynamics in multi-level longwall top coal caving of ultra-thick coal seams, Part I: Borehole measurements and a conceptual model for gas emission zones. International Journal of Coal Geology . 144, 98-110. Stafisso, A., Marconi, O., Perretti, G., & Fantozzi, P. (2011). Determination of dimethyl sulphide in brewery samples by headspace gas chromatography mass spectrometry (HS – GC/ MS). Italian Journal of Food Science , 23(1). Pp. 19–27. St. Croix Sensory. Inc. (2003). A detailed assessment of the science and technology of odour measurement. St. Croix Sensory. Inc. Trabue, S., Scoggin, K., Mitloehner, F., Li, H., Burns, R., & Xin, H. (2008). Field sampling method for quantifying volatile sulfur compounds from animal feeding operations. Atmospheric Environment , 42 3332–3341. Ventsim Visual™. (2013). User Guide v3.2. Ventsim software . Xing, Y. (2006). Evaluation of commercial air dispersion models for livestock odour dispersion simulation. Department of Agricultural and Bioresource Engineering. University of Saskatchewan . Saskatoon (Canada). (Master of Science Thesis). 138 pp. Zapušek, A., & Marsel, J. (1998). Determination of dimethyl sulphide in coal mine atmosphere by gas chromatography. 22nd International Symposium on Chromatography . Roma. September 13-18, 1998. 283 pp. Zavšek, S. (2004). Model for research of structural and petrographically changes of the Velenje lignite depending on various stress states and presence of gases. University of Ljubljana, Faculty of Natural Sciences and Engineering. (PhD thesis) Zhang, X. (2013). Gaseous emissions from coal stockpiles. IEA Clean Coal Centre . ISBN 978-92-9029-533-4. 29 pp. Vanek, M., Mitterpach, J., & Zacharova, A. (2015). Odour control in biogas plant – case study. 15th International Multidisciplinary Scientific GeoConference SGEM . Book4. pp. 353-360. Wiśniewska, M., Kulig, A. & Lelicińska-Serafin, K. (2019). Comparative analysis of preliminary identification and characteristic of odour sources in biogas plants processing municipal waste in Poland. SN Applied Sciences . 1, 550. https://doi.org/10.1007/s42452-019-0534-0. Zagustina, N. A., Krikunova, N. I., Kulikova, A. K., Misharina, T. A., Romanov, M. E., Ruzhitsky, A. O., Terenina, M. B., Veprizky, A. A., Zhukov, V. G., & Popov, V. O. (2010). Composition of air emission from a tobacco factory and development of the biocatalyst for odour control. Journal of Chemical Technology and Biotechnology . 85. pp. 320 - 327. Žibert, Z. (2006). Determination of ventilation parameters according to barometric method. 8th Mining and Geotechnology Scientific Conference at "40th jump over the leather" . University of Ljubljana, Faculty of Natural Sciences and Engineering. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Oct, 2023 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Major revision 23 Jun, 2023 Reviews received at journal 14 Jun, 2023 Reviewers agreed at journal 12 Jun, 2023 Reviewers invited by journal 11 Jan, 2023 Editor assigned by journal 04 Jan, 2023 Submission checks completed at journal 04 Jan, 2023 First submitted to journal 16 Nov, 2022 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2279834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":165152155,"identity":"5519b1e3-dadb-45d0-b270-58c224adc1eb","order_by":0,"name":"Gregor Uranjek","email":"","orcid":"","institution":"Jožef Stefan International Postgraduate School Ljubljana","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gregor","middleName":"","lastName":"Uranjek","suffix":""},{"id":165152156,"identity":"0d9c0d76-9b82-4404-b6d4-31804dd4a435","order_by":1,"name":"Milena Horvat","email":"","orcid":"","institution":"Jožef Stefan International Postgraduate School Ljubljana","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Milena","middleName":"","lastName":"Horvat","suffix":""},{"id":165152157,"identity":"44ad5999-16cb-4abc-9106-241f4df06712","order_by":2,"name":"Radmila Milačič","email":"","orcid":"","institution":"Jožef Stefan International Postgraduate School Ljubljana","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Radmila","middleName":"","lastName":"Milačič","suffix":""},{"id":165152158,"identity":"9b00e401-9e6b-43ea-accd-810e59c645d9","order_by":3,"name":"Janez Rošer","email":"","orcid":"","institution":"University of Ljubljana, Faculty of Natural Sciences and Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Janez","middleName":"","lastName":"Rošer","suffix":""},{"id":165152159,"identity":"0132de59-9ede-43e7-b757-71bb54b646fa","order_by":4,"name":"Jože Kotnik","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie2QsQrCMBCGrxTSJeAaQewrpJMIfZhIobOjUykUzFJw9TF8hEAgU8G1Y32DutXNCyLidB0F80GOC+Tj7g9AIPCDRLWvBk/MokFx38xUBCqxnKW88AowJmY9jrW+DdDl1SZh5WFYVZBqarHWFBL6Umwb5nrFLUhLKWflBIxWSJscUTEgyfjnnZ7eyl5xXKwhlYIB9F5hDhTHEeRirYuF6srlxbJCYBZOZsnwx8bR5Qt5ddn90Vbr9GQIpcaiPjOBEzMA0u/rRAqBQCDwhzwBP0Q/L7VjrBIAAAAASUVORK5CYII=","orcid":"","institution":"Jožef Stefan International Postgraduate School Ljubljana","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jože","middleName":"","lastName":"Kotnik","suffix":""}],"badges":[],"createdAt":"2022-11-16 10:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2279834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2279834/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-023-11755-z","type":"published","date":"2023-10-04T15:01:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31313385,"identity":"389b8512-13ac-4a10-b33d-39336091936f","added_by":"auto","created_at":"2023-01-09 15:49:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211438,"visible":true,"origin":"","legend":"\u003cp\u003eCMV plan modelled in VentsimTM as of October 2012 with adjusted airflows (m3/s). The lines represents the actual gateways in the mine and the colours of the gateways (legend in upper-left corner) shows the different types of odour sources and the purpose of gateways regarding the mine ventilation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2279834/v1/198c21c21d96a6e509ef77c7.png"},{"id":31312471,"identity":"205719a5-9310-4591-acf2-65d123eb4629","added_by":"auto","created_at":"2023-01-09 15:41:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":346814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe monitoring location of simulations in Preloge pit (upper) and Pesje pit (bottom) with the simulated DMS concentrations at peak DMS sources. In the middle is presented enlargement of longwall panel k.-130/B area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2279834/v1/50c1f11f04132a0ad419601a.png"},{"id":31312470,"identity":"32bbb8fa-3699-424f-9643-6a36566b764b","added_by":"auto","created_at":"2023-01-09 15:41:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical visualization of main results of assessment of DMS odorous emissions during coal extraction process in CMV.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2279834/v1/7bba3e7e885ffa5673ce9a31.png"},{"id":44301748,"identity":"03c6b9ad-b0dc-432c-8de3-75ab6968c62d","added_by":"auto","created_at":"2023-10-09 15:07:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2279834/v1/afbfb180-cca4-448f-9800-3fbea634f683.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal mine Velenje","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoal (lignite) excavation at Coal Mine Velenje\u003csup\u003e[1]\u003c/sup\u003e (CMV) occasionally emits unpleasant odours, which can affect a miner\u0026rsquo;s attention and hence safety awareness. Furthermore, fugitive odour emissions have a negative effect on the quality of life for local communities, which has become an increasing source of complaints. For these reasons, CMV has been carrying out research into techniques of controlling its odour emissions. This requires an understanding of the specific mining processes, identifying and quantifying the sources of odour and the odour active compounds responsible (Brattoli et al, 2014). Experience (distinctive smell to mine operatives) and historical gas concentration measurements have shown that the main odours at the mine are due to volatile sulphur compounds\u003csup\u003e[2]\u003c/sup\u003e (VSC). These compounds have extremely low odour detection thresholds (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e range), which are many times lower than their toxic threshold limit value\u003csup\u003e[3]\u003c/sup\u003e (TLV) in mg/m\u003csup\u003e3\u003c/sup\u003e range (Rosenkranz and Cunningham, 2003).\u003c/p\u003e\n\u003cp\u003eCMV is one of the largest modern deep mines in Europe. It mines the largest Slovenian lignite deposit, which is one of the thickest known coal seams in the world. The seam is bowl shaped, 8.5 km in length and 1.5-2.5 km wide with an average depth of 300 m (200-600 m deep) and extends almost under the entire \u0026Scaron;ale\u0026scaron;ka Valley (Si et al, 2015). The seam is on average 60 m thick with a maximum thickness of 165.8 m (Brezigar 1987; Markič, 2009; Markič and Sachsenhofer, 2010). Over the past 147 years of operation, the mine has produced more than 252 million tonnes of coal with future plans to extract another 103 million tonnes.\u003c/p\u003e\n\u003cp\u003eThe main VSC at CMV is dimethyl sulphide\u003csup\u003e[4]\u003c/sup\u003e (DMS), while hydrogen sulphide (H\u003csub\u003e2\u003c/sub\u003eS) and sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) are less significant. The odour thresholds of detected VSC\u0026rsquo;s are: SO\u003csub\u003e2\u003c/sub\u003e 2.32 mg/m\u003csup\u003e3\u003c/sup\u003e, H\u003csub\u003e2\u003c/sub\u003eS\u0026deg;0.58 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and DMS 7.6 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (Nagata, 2003).\u003c/p\u003e\n\u003cp\u003eDMS has a distinctive offensive smell similar to that of decaying cabbage and has negative hedonic characteristics (Qiao et al., 2011). At CMV, the presence of DMS was first noted in late 1980\u0026rsquo;s as an unpleasant odour when the access roadways for the upper NW part of the coal seam were being developed. Later, in the 1990\u0026rsquo;s, DMS was once again encountered when CO gas sensors sounded without any visible indication of a significant oxidation process, which was later found to be due to the cross-sensitivity of the CO sensors to DMS. Currently, coal production is increasingly centred in this area and odour complaints are expected to increase in the future. The origin of the DMS in the lignite seam is yet to be fully understood, but it is believed that it originates from the early stages of coal formation during the decay of organic matter (Kozinc, 2005). Dimethyl sulphide is produced during the anaerobic microbial decomposition of methoxylated aromatic compounds present in the freshwater sediments (Finster et al., 1990) such as the lignite-bearing Pliocene sediments of the early Velenje basin (Markič, 2009; Markič and Sachsenhofer, 2010). Therefore, it is believed that the DMS is retained during the rapid accumulation and burial of plant material and subsequent coal formation only to be released during coal extraction.\u003c/p\u003e\n\u003cp\u003eThere exists an extensive body of literature concerning odour theory, including odour perception (Powers and Corzangeno, 2004a; Powers et al., 2004a), odour parameters (St. Croix Sensory, Inc., 2003; Powers and Corzangeno, 2004b; Powers et al., 2004b; Nicolai and Pohl, 2005), analytical methods (IPPC, 2002; DEFRA, 2010; Gebicki et al., 2016; Conti et al, 2020), sensory \u0026ndash; olfactometry (IPPC, 2002; McGinley and McGinley, 2003; St. Croix Sensory, Inc., 2003; Nicolai and Pohl, 2005; Gebicki et al., 2016; Conti et al, 2020), sensory \u0026ndash; electronic nose (Karakaya et al, 2020; Kim et al. 2022) , sampling and emission rate determination of odour sources (Parcsi, 2005; Trabue et al., 2008; Hudson, 2009; Boeker et al. 2010; Juarez-Galan et al., 2010; Parcsi et al., 2010; Gebicki et al., 2016; Bylinski et al. 2019), monitoring methods (Benzo et al., 2010; Kost and Richter, 2010), and atmospheric dispersion modelling (Boeker et al., 2000Freeman et al., 2000; Scire et al. 2000; Xing, 2006; Li, 2009; Conti et al, 2020). Most studies, excluding those on odour theory, relate to industries that have well defined odour nuisance issues e.g., the agricultural and livestock industry (Parcsi, 2005; Xing, 2006; Koziel et al., 2010), waste water treatment plants (Freeman et al., 2000), municipal waste sites, recycling facilities, transfer stations, composting facilities (Fischer et al., 1999, McKendry et al.,2002; Gebicki et al., 2016), and industrial plants such as paper pulp mills, petroleum refineries, food processing, leather manufacturing, smelting of non-ferrous ores, steel mills, the manufacture of certain abrasives, paint manufacture, rendering, sulphur dioxide scrubbing, starch manufacturing and (Kenneth et al, 2004), tobacco factory (Zagustina et al., 2010), biogas production (Vanek et al., 2015), etc.\u003c/p\u003e\n\u003cp\u003eOn the other hand, little information is available concerning fugitive emissions of odorous gasses from mining activities in general. In addition, addressing potential odour issues as part of making an impact assessment is relatively new in planning coal mining projects. Odour dispersion modelling has been performed as part of an air quality impact assessment for a new ventilation shaft at the Illiwarra coal mine, NSW, Australia (Kellaghan, 2010). For modelling purposes, the mine used cumulative odour measurements taken in the mine\u0026rsquo;s ventilation air from existing ventilation shafts. Gas compositional analysis revealed that the volatile organic compounds\u003csup\u003e[5]\u003c/sup\u003e (VOC) were mostly below the limits of detection and did not pose an odour issue. Similarly, the Tasman Underground Mine (NSW, Australia), when seeking consent to extend their underground mine operations, also performed an air quality assessment on account that the development of the mine could potentially produce odorous emissions from the existing and proposed ventilation shafts (Kellaghan, 2012). No odour impact, based on either the levels of odorants in the ventilation shafts or in the actual coal seam, was detected. The Wilpinjong Coal Mine (NSW, Australia) analysed ambient air quality as a response to complaints by local residents (Cox and Isley, 2014). All of the active odour compounds were below the human odour detection threshold and it is not clear from the study which odorants were responsible for the complaints. The Kanmantoo Copper Mine (SA, Australia) has performed a study for environment protection and rehabilitation while (PEPR, 2016) seeking consent for extending the life of open-pit mine for excavation and production of copper-gold concentrate. Odour monitoring results has confirmed the predicted odour dispersion model and showed that odour is not anticipated to result in negative impacts from the mining operations. The study of environmental impact of gold mines in Oman and the pollution impact by heavy metals (Abdul-Wahab and Marikar,2012) also, included odour measurements from water from nearby well used for irrigation. The odour from the water was not detected. Surprisingly, none of these studies included DMS in their analyses despite its low odour threshold.\u003c/p\u003e\n\u003cp\u003eThe literature research since 2011 point out, that there are almost no published studies exist of DMS emissions from mining activities. Only published work referring DMS to mining activities are from oil sand mine operations in Fort McKay area in Alberta (Canada) and from those performed by CMV. In the air quality investigation of 30 km radius around Fort McKay of fife year monitoring period was performed due to the countless environmental complaints regarding oil sand mining activity in the area (AER, 2016). During monitoring period between 2010 and 2014 were 172 complaints and 165 were related to odours. Between 47 priority odorant candidates was also DMS. The investigation focused on six oil sand mines and one in situ facility. Selected were 16 continuous monitoring point and various others sampling points. Ambient air from sampling canisters of 1-hour and 24-hour samples were analysed for 60 volatile organic compounds and 20 reduced sulphur compounds. The DMS presence was analysed in 126 samples and was never detected (detection limit was 2.58 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eThe only published studies referring to DMS emissions from coal mining activities are those performed on coal from Velenje Coal Mine of gaseous sulphur emissions (COS, CS\u003csub\u003e2\u003c/sub\u003e and DMS) from coal stockpiles (Kozinc et al., 2004; Kozinc, 2005) and a study on the levels of DMS in the return airway of a longwall face (Zapu\u0026scaron;ek and Marcel, 1998), which were briefly summarized by Zhang (2013) in an IEA Clean Coal Centre report. The estimated daily emissions of COS and CS\u003csub\u003e2\u003c/sub\u003e for the whole stockpile in the sampling period were 20 g of CS\u003csub\u003e2\u003c/sub\u003e and 70 g of COS (gas concentrations were in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e range). The DMS concentrations fell to less than 2.58 mg/m\u003csup\u003e3\u003c/sup\u003e within a few days as it is only released from freshly loaded coal. The measured DMS concentration levels in the mine air, which was sampled once a week for 15 consecutive weeks in the return airway of a longwall face during coal production, ranged from 55.47 to 128.48 mg/m\u003csup\u003e3\u003c/sup\u003e. During a series of in-situ coal desorption tests made in 1998 and 1999 (5 boreholes and 10 desorption tests (Erico Ltd, 1998\u0026amp;1999)), DMS was detected in all but one sample (max. concentration 516 mg/m\u003csup\u003e3\u003c/sup\u003e), while H\u003csub\u003e2\u003c/sub\u003eS was \u0026lt;1.42 mg/m\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the industrial or agricultural processes such as pulp and paper manufacturing, oil or petroleum refining, food decay, composting, landfilling, fish processing, sewage and wastewater treatment, leather manufacturing, paint, rendering plants, sulphur dioxide scrubbing, and starch manufacturing plants; DMS is a typical gaseous odour pollutant. The removal or degradation of DMS odorant before exhausting into the atmosphere is of great significance to improve the local air quality. DMS has the unique capability of enhancing and intensifying other odours. Due to this property, it is used in warning odorants and odour masking agents (Kenneth et al., 2004).\u003c/p\u003e\n\u003cp\u003eDMS is also a substantial contributor of the aroma to some food items, such as beer (Stafisso et al., 2011), red wines (Lytra et al., 2014), truffles (Feng et al., 2019), many vegetables and fruits (tomatoes, sweetcorn, grapes, asparagus, and brassicas), honey (Sch\u0026auml;fer et al., 2009 and McGorrin, 2011), chewing gum (Kenneth et al., 2004), cheddar cheese (Qian and Burbank, 2007), etc.\u003c/p\u003e\n\u003cp\u003eOne of most significant discharges of DMS and other reduces sulphur compounds (H\u003csub\u003e2\u003c/sub\u003eS, CH\u003csub\u003e3\u003c/sub\u003eSH, (CH\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003e) can be from Kraft pulp mills (Kenneth et al, 2004). There are many sources in the mill. Some sources emit a small gas volume with high concentrations (blow heat recovery; turpentine recovery vent; evaporator hotwell vent; and foul condensate storage tank), while others have large volumes with low concentrations (brown stock washer filtrate, tanks, and hood; weak and strong black liquor storage tanks; knotter hood; black liquor oxidation vent; and contaminated condensate tanks). Typical DMS concentrations of high volume source is 0.52 mg/m\u003csup\u003e3\u003c/sup\u003e and for low volume source is 38,700 mg/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAmbient air samples were collected at several locations in the community around a major Canadian pulp and paper plant over a period of several months, before and after major process changes (Catalan et al, 2007). In spring of 2006 they permanently closed one of two Kraft pulp mills on site and the shutting down of a chemical recovery boiler and associated black liquor oxidation systems. DMS was found to be the most abundant reduced sulphur compound in ambient air before the changes with an average concentration of 3.84 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. After the changes, the average concentrations of DMS decreased by 70 %.\u003c/p\u003e\n\u003cp\u003eAt landfill sites over 300 trace compounds have been identified in landfill gas. Unpleasant odours are usually associated with the sulphur containing compounds, primarily mercaptans and sulphides. The vast range of trace compounds measured in landfill gas reflects both the anaerobic decomposition processes taking place in the waste mass and the wide range of chemicals introduced via the industrial and commercial waste streams (McKendry et al.,2002). DMS is common odorant in landfill gas typically found in concertation range between 0.02 mg/m\u003csup\u003e3\u003c/sup\u003e and 135 mg/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study of sulphur source from livestock production in Denmark exposes H\u003csub\u003e2\u003c/sub\u003eS as major sulphur source. Finisher pig production is estimated to be the largest source of atmospheric sulphur in Denmark (Feilberg et al., 2017). The only other sulphur compounds measured consistently in the ppb range are methanethiol and DMS, but these only constitute about 2\u0026ndash;5% of H\u003csub\u003e2\u003c/sub\u003eS. Measurement campaigns were carried out over 6 year-period from 2009 to 2015 on fife pig production facilities. The measured concentrations DMS were between 4.39 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 10.58 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study in 2018, of identification of odour sources in two biogas plants in Poland showed DMS concentrations up to 1.26 mg/m\u003csup\u003e3\u003c/sup\u003e (Wisniewska et al., 2019).\u003c/p\u003e\n\u003cp\u003ePhysical-chemical and biological techniques are now available for removing odours from air streams including: biofilters, biotrickling filters, membrane bioreactors, wet scrubbing, adsorption, and chemisorption, and more recently, methods based on photo-dissociation, electron beam irradiation, corona discharge decomposition and catalytic and ozone oxidation (Qiao et al., 2011). However, DMS is one of the least biodegradable compounds among the odorous sulphur containing gaseous pollutants; consequently, it always needs improved systems out of the conventional biological setups. Traditional physical-chemical approaches to DMS removal mainly include wet scrubbing, adsorption, and chemisorption (Kenneth et al., 2004).\u003c/p\u003e\n\u003cp\u003eOne of the largest available odour control systems designed to serve, for example, a water treatment plant, consists of a series of either bio-filters or chemical scrubber units with capacities of up to 69.4 m\u003csup\u003e3\u003c/sup\u003e/s (ASK Piearcey Ltd, 2014). In underground coal mining, cumulative air flowrates are extreme and, at CMV, are between 340 m\u003csup\u003e3\u003c/sup\u003e/s and 420 m\u003csup\u003e3\u003c/sup\u003e/s. Clearly, existing systems could not possibly handle the large volumes of exhaust gases emitted from a coal mine and either new or upgraded solutions must be developed. In addition, odorous mine gas emissions depend on many factors, including the natural characteristics of the coal, presence of odour active compounds in the coal seam, production and ventilation design, and coal production intensity. For these reasons, it is a challenge to predict their actual concentration.\u003c/p\u003e\n\u003cp\u003eThe objectives of this study were to recognize and estimate the main odour sources in the mine and to construct ventilation model of CMV to perform model-based odour analysis.\u003c/p\u003e\n\u003cp\u003eThis was achieved by taking into consideration the characteristics of mine ventilation, mine gateway system (airways), and estimated odorous emissions of mine sources in order to establish a better understanding of the sources, dispersion, and ventilation based control options to reduce the presence of odour active compounds released during the coal extraction process.\u003c/p\u003e\n\u003cp\u003e[1] CMV - Coal mine Velenje (https://www.rlv.si)\u003c/p\u003e\n\u003cp\u003e[2] VSC - volatile sulphur compounds\u003c/p\u003e\n\u003cp\u003e[3] TLV - threshold limit value\u003c/p\u003e\n\u003cp\u003e[4] DMS \u0026ndash; dimethyl sulphide (CH\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eS\u003c/p\u003e\n\u003cp\u003e[5] VOC - volatile organic compounds\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eAnalysis of odorous gas emissions\u003c/h2\u003e \u003cp\u003eAs the first step in this study, the long-term monthly monitoring data of gases concentrations in the mine atmosphere were analysed in order to determine the main odorants in the mine, their source, and to estimate emissions.\u003c/p\u003e \u003cp\u003eMonthly chemical analysis of mine gasses are carried out to control gas concentrations in mine air and includes the following gases: CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, DMS, H\u003csub\u003e2\u003c/sub\u003eS, SO2, O2, CO, H\u003csub\u003e2\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe N\u003csub\u003e2\u003c/sub\u003e content of the mine gas is the difference between the sum of the monitored gases and from 100% (Erico Ltd, 2008\u0026ndash;2013). The concentration of CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, DMS and H\u003csub\u003e2\u003c/sub\u003eS were determined using gas chromatography. The test method PM 3.01 was used for CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and DMS while the test method PM 3.02 was used to determine H\u003csub\u003e2\u003c/sub\u003eS (Erico Ltd, 2008\u0026ndash;2013). The concentrations of O\u003csub\u003e2\u003c/sub\u003e, CO, H\u003csub\u003e2\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e were determined using a gas meter with a built-in electrochemical sensor (Echo d.o.o., Slovenia). Oxygen and CO concentrations were determined using the PM 3.03 test method, while the remainder were determined using the PM 3.04 test method. All three methods were developed by Erico \u0026ndash; since 2017 is named Eurofins Erico (Erico Ltd, 2008\u0026ndash;2013) and are granted by an accreditation body (i.e., Slovenian Accreditation).\u003c/p\u003e \u003cp\u003eThese mine air samples were collected in Tedlar\u0026reg; sampling gas bags and analysed in the laboratory (Erico Ltd, 2008\u0026ndash;2013). The monitoring sites were selected systematically in such a way that all coal production activities can be controlled. Air samples were collected in the return airflows of all main work sites in the mine an in all main returns of mine ventilation. Samples were not collected simultaneous at specific monthly monitoring campaign.\u003c/p\u003e \u003ch2\u003eOdour modelling in the mine and simulation of odorous gas emissions\u003c/h2\u003e \u003cp\u003eThe exhaust main ventilation at the VCM is provided by two main fans, and a series of smaller auxiliary fans for ventilation of development sections or dead-end headings. The main fans are located at the Pesje and Šoštanj ventilation stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each fan draws air up from the mine from five surface air intakes (situation as of 2012, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main fan located at the Šoštanj station is a Turmag GVhv 31-1800 with nominal power 1800kW (auxiliary fan: the same type), while at the Pesje station is installed a TLT-GAF 34\u0026thinsp;\u0026minus;\u0026thinsp;31 with nominal power 800kW (auxiliary fan: Turmag GLH-28-660 with nominal power 600 kW), (Salobir, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e). The Šoštanj station provides approximately two-thirds of the required airflow rate, with the Pesje station providing the remaining one-third. The whole mine consists of 50\u0026ndash;60 km gateways and facilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe purpose of odour modelling in the mine was to simulate dispersion of odorous gas emissions from its potential sources and to the surface under variable mine ventilation conditions and the characteristic concentrations of odour compounds.\u003c/p\u003e \u003cp\u003eThe odour modelling in the mine is based on mine ventilation model of VCM designed in Ventsim Visual\u0026trade; software (Ventsim\u0026trade;). The software allows 3D graphical representation, simulated paths and concentrations of smoke, dust, diesel particles or gas for planning of emergency situations, short and long-term planning of ventilation, and the simulation of gas and aerosol concentrations (Ventsim\u0026trade;, 2013). The software can also be adopted for odour dispersion simulations, when odour concentration of the source is known or when odour is presenting single odorous compound, respectively. It is possible to simulate odour concentration because odour units \u0026ndash; OU/m\u003csup\u003e3\u003c/sup\u003e are the number of dilutions of the odorous air to the odour threshold. The calculation of the dilution factors for olfactometry is based on the ratio of total volumetric flow divided by the odorous sample flow (McGinley, 2000; Brattoli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bylinski et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{Z}=\\frac{{\\text{V}}_{\\text{d}}+{\\text{V}}_{\\text{o}}}{{\\text{V}}_{\\text{o}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere V\u003csub\u003ed\u003c/sub\u003e is the volumetric flow rate of odour-free diluted air and V\u003csub\u003eo\u003c/sub\u003e is the volumetric flow rate of the odorous air sample and Z is the dilution factor.\u003c/p\u003e \u003cp\u003eThe software treats every component in the mine air in the same manner. The concentrations of gaseous compounds are diluted according to the dilution ratios of the return airways and their dilution at airways junctions. For each studied component, also decay mechanism can be modelled.\u003c/p\u003e \u003cp\u003eDesigned ventilation/odour model is based on ventilation and mine data from October 2012 using monthly ventilation parameters for determination of air flowrate airways and represents the situation of mine ventilation in 57.85 km of underground facilities. Ventilation parameters are used each month to create a ventilation map of the VCM. The adjusted air flowrate of each airway were then determined by considering Kirchoff\u0026rsquo;s first and second law theorems (McPherson, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The mine ventilation map is a visual representation of the situation in the underground gateways (airways) and facilities with respect to adjusted airflows, direction of airflow and locations of ventilation regulators: doors, barriers, boreholes, and shafts, auxiliary ventilators, and locations of gas sensors. The total resistances of the airways in the model are summarized from the \u0026ldquo;Zračenje\u0026rdquo; software developed in-house (Žibert, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, besides the mine gateway system, shows the locations of the main air intakes (service shaft NOP, ventilation shaft Šoštanj II, service shaft Škale, the main coal transport drift and Hrastovec drift, the main air exits - returns (ventilation stations Šoštanj and Pesje), the longwall faces (K-130/B and K-65/A) and the development headings: 4, 6, 7, 8, 11 and 13.\u003c/p\u003e \u003cp\u003eThe deepest part of the mine is approximately 500 m deep. First, were used the whole data set to create a 3D model that defined every airway according to length, profile (round profiles were used for shafts, boreholes, ducts, and modified profiles for specific types of gateways used in CMV), cross-section, airway type, and total resistance. Other data included the average surface temperatures, both dry (10\u0026deg;C) and wet (7\u0026deg;C), together with temperature and atmospheric pressure (985 mBar). The network air density was calculated from the average monthly temperatures and pressures (1.20 kg/m\u003csup\u003e3\u003c/sup\u003e). In the model fixed air flows were used for the upcast ventilation shafts (ventilation station stations), in the airways of the auxiliary ventilators and in the ventilation boreholes (all together 24 fixed flows). The model does not consider natural ventilation and compressible flows, which only have a significant effect when simulating mines deeper than 500m (Ventsim\u0026trade;, 2013). All the other software settings were left as default.\u003c/p\u003e \u003cp\u003eIn the second step, the air flowrate and directions of airways were modelled, iteratively adjusted for their total resistances and different ventilation regulation measures. To control the adjusted total resistances, were considered pressure drops on the main fans, which were 3460 Pa at the Šoštanj and 2040 Pa at the Pesje ventilation stations. The general regulation of mine ventilation is possible by positioning the angles of main fans blades setup at each ventilation station. Main fan at Šoštanj has adjustable blades between angles \u0026minus;\u0026thinsp;10\u0026deg; and +\u0026thinsp;10\u0026deg;, while at Pesje the main fan has blades that can be adjusted between 20\u0026deg; and +\u0026thinsp;2\u0026deg;. The main fans characteristic curves (Salobir, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e) are customized in the Ventsim\u0026trade; for the air flowrate simulation at: Šoštanj: -10\u0026deg;, -8\u0026deg;, -6\u0026deg;, -4\u0026deg;, -2\u0026deg;, 0\u0026deg;, +\u0026thinsp;2\u0026deg;, +\u0026thinsp;4\u0026deg;, +\u0026thinsp;6\u0026deg;, +\u0026thinsp;8\u0026deg; and +\u0026thinsp;10\u0026deg; and for Pesje: -20\u0026deg;, -18\u0026deg;, -16\u0026deg;, -14\u0026deg;, -12\u0026deg;, -10\u0026deg;, -8\u0026deg;, -6\u0026deg;, -4\u0026deg;, -2\u0026deg;, 0\u0026deg; and +\u0026thinsp;2\u0026deg;. The setup of fan\u0026rsquo;s blades in October 2012 were +\u0026thinsp;1\u0026deg; (Šoštanj) and \u0026minus;\u0026thinsp;11\u0026deg; (Pesje). Verification of the model was based on the differences between the modelled and calculated air flowrate and between modelled and measured depressions of the main fans. The accuracy of the model, estimated on the basis of calculated and modelled airflows quantities in 226 airways, is \u0026plusmn;\u0026thinsp;0.07 m\u003csup\u003e3\u003c/sup\u003e/s. The modelled values of the main fan depressions were 3459.7 Pa at Šoštanj and 2040.4 Pa at Pesje stations.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003ch2\u003eAnalysis of odorous gas emissions and estimation of odorous sources\u003c/h2\u003e\n\u003cp\u003eOver a six-year period (2008/1-2013/12) 1,028 point measurements were taken. The monitoring sites were selected systematically so that the return airways of all production and development worksites and all main return airways were included. Under normal working conditions at the mine, H\u003csub\u003e2\u003c/sub\u003eS and SO\u003csub\u003e2\u003c/sub\u003e were rarely detected and levels of SO\u003csub\u003e2\u003c/sub\u003e throughout the mine exceeded the limit of detection on only 26 occasions (24 x 2.67 mg/m\u003csup\u003e3\u003c/sup\u003e, 1 x 5.34 mg/m\u003csup\u003e3\u003c/sup\u003e and 1 x 10.68 mg/m\u003csup\u003e3\u003c/sup\u003e), while H\u003csub\u003e2\u003c/sub\u003eS was only detected 5 times (2.41\u0026ndash;13.63 mg/m\u003csup\u003e3\u003c/sup\u003e). The limit of detection of SO\u003csub\u003e2\u003c/sub\u003e was 2.67 mg/m\u003csup\u003e3\u003c/sup\u003e and of H\u003csub\u003e2\u003c/sub\u003eS was 1.42 mg/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe DMS were detected in 679 out of 1,028 samples in levels of DMS between 2.58 mg/m\u003csup\u003e3\u003c/sup\u003e and \u0026gt;\u0026thinsp;129 mg/m\u003csup\u003e3\u003c/sup\u003e (levels above 129 mg/m\u003csup\u003e3\u003c/sup\u003e were recorded as \u0026gt;\u0026thinsp;129 mg/m\u003csup\u003e3\u003c/sup\u003e). The analysis results confirmed DMS as the major odorant with main sources at the longwall faces (coal extraction working sites), the main coal transport system (system of rubber belt conveyors that is transporting coal directly to the surface), and the development headings (gateway building working sites). The analysis results of characteristic DMS concentrations and flowrates of DMS sources in return airways of main odour sources are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristic concentrations and mass flows of DMS in the returns of mine odour sources.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 40.0394%;\"\u003e\n \u003cp\u003eOdour Source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003eLongwall faces\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003eMain coal transport\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003eDevelopment headings\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\" rowspan=\"10\" style=\"width: 23.8166%;\"\u003e\n \u003cp\u003eDMS concentration [mg/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003eNumber of samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003eMEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003eStd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003ePercentile 97.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003ePercentile 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003ePercentile 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003ePercentile 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 16.3379%;\"\u003e\n \u003cp\u003ePercentile 2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 40.0394%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage airflow rate [m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/s]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 40.0394%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian DMS source [mg/s]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1080\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 40.0394%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak DMS source [mg/s]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.3465%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4498\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.7186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 17.3734%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e930\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe result shows great variations of DMS concentrations for each confirmed odour source and also that DMS was present at sources between 44% and 71% of the time. In the returns airways of longwall faces the DMS was not detected in 47 out of 163 samples and in the return airway of the main coal transport system, the DMS was not detected in 40 times out of 72 measurements. In the development headings, DMS was not detected in 136 out of 398 samples. For estimation of characteristic emissions of DMS sources were taken into consideration median DMS concentration at average air flowrates and for estimation of DMS sources peak emissions were taken into consideration percentile 97.5% DMS concentrations at average air flowrates.\u003c/p\u003e\n\u003cp\u003eLongwall faces and development headings as odour sources are regarding ventilation and dispersion relatively simple if not considering the variability of production intensity and amount of DMS presence in the coal. While the main coal transport system with six successive conveyers of total length 2.6 km and six intakes of fresh air at junctions and connection to the surface (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and six leakage connections with main return airways is an odour source with very complex dispersion. All six leakages are dispersed to the Ventilation station Pesje. Detected DMS in return airway of main coal transport in Preloge pit shows that is being released during transport and that also all return airways \u0026ndash; leakages in Pesje pit must be considered.\u003c/p\u003e\n\u003cp\u003eFor the main coal transport source in the model was considered that DMS is releasing at constant release rate from the constant mass flow of coal through the whole length. The source in the model was divided to each individual part of main coal transport airways accordingly to their lengths. Each individual part represented in the model as a partial source of DMS. The simulation results showed that 34.5% of main coal transport source was dispersed to the Ventilation station Pesje and 65.5% was dispersed to the Ventilation station \u0026Scaron;o\u0026scaron;tanj which was detected with monthly measurements. Considering simulation results the whole main coal transport DMS emission rate at peak concentrations is 4,815.9 mg/s and is potentially the biggest DMS source at peak concentrations. The division of whole source in the model at leakages from L1 to L6 was 3.5%, 1.8%, 6.1%, 6.7%, 6.7% and 9.7%. Locations of leakages are marked on Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (bottom figure) as a green circle.\u003c/p\u003e\n\u003cp\u003eAnalysis of monthly DMS concentrations shows that DMS is released by desorption processes as the coal is being transported from the mine similar to desorption from the coal in the stockpiles (Kozinc \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e and Zhang, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and VCM coal samples from boreholes in the coal seam (Erico Ltd, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e\u0026amp;1999).\u003c/p\u003e\n\u003ch2\u003eSimulations of DMS and odour concentrations\u003c/h2\u003e\n\u003cp\u003eThe odour model is based on DMS emissions. The model is considered inert since it assumes that all the sources of DMS do not decay over time.\u003c/p\u003e\n\u003cp\u003eIt is known that DMS in the atmosphere does decay through reactions with photochemically produced hydroxyl (OH\u0026middot;) and nitrate (NO\u003csub\u003e3\u003c/sub\u003e) radicals, ozone (O\u003csub\u003e3\u003c/sub\u003e) and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) (Kenneth et al, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e and Chen and Jang, 2012). A typical atmospheric half-life for DMS in the environment is from several hours to 3.5 days.\u003c/p\u003e\n\u003cp\u003eIn addition, photochemical oxidation, although important on the surface, is not considered relevant in the underground mine.\u003c/p\u003e\n\u003cp\u003eAt DMS study in the return airways of a longwall faces in CMV (Zapu\u0026scaron;ek and Marcel, 1998), also the DMS stability was tested. A Tedlar gas sampling bag was filled with synthetic air (20% oxygen and 80% nitrogen) and with DMS standard with concentration of 51.3 mg/m\u003csup\u003e3\u003c/sup\u003e and analysed every day by the gas chromatograph. During analysis gas standard in gas sampling bag were stored in dark place to avoid the UV induced photodecomposition. The test results showed that DMS in Tedlar gas sampling bags is stable for at least 4 days.\u003c/p\u003e\n\u003cp\u003eDuring the study of effects of process changes on concentrations of individual malodorous sulphur compounds in ambient air near a Kraft pulp plant in Thunder Bay, Ontario, Canada (Catalan et al, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Catalan et al, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) also the stability of reduced sulphur compounds in the Teflon sampling bags was assessed.\u003c/p\u003e\n\u003cp\u003eA gas mixture also containing 3.87 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of DMS was introduced in a clean Teflon bag and then periodically withdrawing aliquots which were analysed to monitor the changes in concentration over time. Any change from the initial concentration was due to decomposition of compounds in the gas phase or adsorption on the bag walls. The concentration of DMS was found to remain constant for more than 3.5 hours. Similar results were obtained when the initial concentrations were doubled.\u003c/p\u003e\n\u003cp\u003eSimulations of traveling (spread) times of DMS from the sources to the surface at operational air flowrate as of October 2021 showed that traveling times from longwall faces were between 556 s and 750 s, from development headings were between 750 s and 1,836 s and from main coal transport were between 521 s and 1,923 s. The longest traveling time 7,409 s or 2.1 hours was from main coal transport at simulation were the main fans blades at station \u0026Scaron;o\u0026scaron;tanj was set on -10\u0026deg; (minimum air flowrate) and at station Pesje was set on +\u0026thinsp;2\u0026deg;. The extreme traveling time is due to the changed airflow directions in some airways and dispersion through station Pesje instead station \u0026Scaron;o\u0026scaron;tanj as normal.\u003c/p\u003e\n\u003cp\u003eBased on the cases described above and modelled traveling times, any decay mechanisms of DMS were not considered in the model. The identified odour sources were the longwall faces K.-130/B and K.-65/A, road building faces 4. 6, 7, 8, 11 and 13 and the main coal transport system. In the model sources are represented as point sources for each source, except at main coal transport, where the whole source in the model represents 7 point sources at return airway of main coal transport and at 6 leakages (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, bottom figure).\u003c/p\u003e\n\u003cp\u003eFor the study of DMS and odour dispersion analysis were considered 8 simulation scenarios. Four for DMS and four for odour dispersion. Simulations tested dispersion of characteristic DMS and odour concentrations at median and peak levels and all characteristic concentrations at the operational air flowrate as of October 2012 and at maximum possible air flowrate due to the main fans characteristics (station \u0026Scaron;o\u0026scaron;tanj at +\u0026thinsp;10\u0026deg; and station Pesje\u0026thinsp;+\u0026thinsp;2\u0026deg;). The monitoring locations from 1 to 50 and Ventilation stations \u0026Scaron;o\u0026scaron;tanj (VSS) and Pesje (VSP) in the model (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) were systematically selected to follow all dilutions in return airways from the sources and to the surface. The simulations results are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and for simulation results of peak DMS concentrations at operational airflows in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe simulation results reveal a high odour concentration despite the low DMS concentrations also at median sources because of the low odour detection threshold of DMS (1 mg/m\u003csup\u003e3\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;129 OU/m\u003csup\u003e3\u003c/sup\u003e, (Nagata, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e)). Odour concentration from median sources at ventilations stations means that the odorous emissions at Ventilation station \u0026Scaron;o\u0026scaron;tanj must be diluted to the odour detection threshold for additional 672 times by the atmosphere and at Ventilation station Pesje for additional 1,253 times. The odorous emissions from \u0026Scaron;o\u0026scaron;tanj were 177,145 OU.m\u003csup\u003e3\u003c/sup\u003e/s and from Pesje 149,370 OU.m\u003csup\u003e3\u003c/sup\u003e/s. While the emissions from \u0026Scaron;o\u0026scaron;tanj are higher, the concentrations are lower due to the higher dilution rates of sources due to the 2.2 times higher airflow rate than in Pesje. For the longwall face k.-130/B in Preloge pit the dilution rate to the surface was 9.3 and for the longwall face k.-65/A in Pesje pit was 3.2. Dilution ratios for development heading in Preloge pit were between 28.7 and 75.3 and in Pesje pit only development heading num. 7 were dispersed to Pesje with dilution ratio 15.2. At median sources main coal transport was not recognized as DMS/odour source as more the 50% DMS was not detected. The dilution ratio at peak sources were 3.7 in \u0026Scaron;o\u0026scaron;tanj and between 29.8 and 91,7 in Pesje.\u003c/p\u003e\n\u003cp\u003eIn comparison, the odour emission modelling of newly planned ventilation shaft at the Illiwarra coal mine, NSW, Australia (Kellaghan, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) showed that at an odour source equivalent to 219,500 OU.m\u003csup\u003e3\u003c/sup\u003e/s predicted that odour concentrations 3 OU/m3 would exceed only 1% of the time, what is in accordance with local odour regulations. In Slovenia there is no odour regulations. If only level of odour emissions of CMV are compared, without taking into consideration atmospheric conditions and vicinity of settlements, at the median odour sources no odour complaints are expected. On the other hand, at peak sources what is considered as \u0026ldquo;worst case scenario\u0026rdquo;, the odorous emissions from \u0026Scaron;o\u0026scaron;tanj with 1,607,932 OU.m\u003csup\u003e3\u003c/sup\u003e/s and from Pesje with 928,558 OU.m\u003csup\u003e3\u003c/sup\u003e/s are likely to lead to odour complaints. If considered scenario with only one longwall face at peak levels the emission rate would be 580.242 UO.m\u003csup\u003e3\u003c/sup\u003e/s. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e present a graphical visualization of main results of characteristic DMS mine sources estimation, and characteristic odour emissions on the surface.\u003c/p\u003e\n\u003cp\u003eSimulations of regulation of the main fans at peak levels to provide maximum air flowrate resulted in an overall additional reduction of concentrations for 15.4% on average. The ventilation reduction potential of concentrations regarding operational air flowrate as of October 2012 in Preloge pit was 10.6% and in Pesje pit was 22.9%. At peak sources, the average concentrations were 9.3 times higher than at median sources at monitored locations and total emissions of peak sources were 7.8 times higher than of median sources.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe simulation results at the monitored locations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMonitored location\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eOperational airflows\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eMaximum airflows\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOdour/DMS Reduction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e[m\u003csup\u003e3\u003c/sup\u003e/s]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMedian concentrations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePeak concentrations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e[m\u003csup\u003e3\u003c/sup\u003e/s]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMedian concentrations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePeak concentrations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[mg/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[OU/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[mg/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[OU/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[mg/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[OU/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[mg/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[OU/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[%]\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\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.2\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e263.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e672\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e47.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e293.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e607\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5,490\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVERAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,293\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8212\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,080\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e53.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,922\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristic levels of DMS (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) shows great variations. At peak concentrations longwall face source is 4.2 times higher than at median concentrations and at development heading source is 17.2 times higher. Main coal transport is not considered as DMS source at median levels and at peak levels is potentially the biggest source in the mine. The results indicates that DMS is not released only with extraction at longwall faces and development heading but is also releasing from the coal while is being transported to the surface. It is likely to be adsorbed on the lignite structure or trapped in the coal matrix, similarly as CO\u003csub\u003e2\u003c/sub\u003e (Zav\u0026scaron;ek, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). From an adsorption/desorption study (Markič, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) of gases from different lithotypes of Velenje lignite it was observed that the different lithotypes have significantly contrasting desorption properties related to differences in porosity. The specific surface area of pores in homogenous fine detrital lignite is more than 180 m\u003csup\u003e2\u003c/sup\u003e/g and 35 m\u003csup\u003e2\u003c/sup\u003e/g for xylite. Released DMS amount from sources greatly varies due to natural characteristics of the coal, presence of DMS in coal, production and ventilation design, and coal production intensity.\u003c/p\u003e\n\u003cp\u003eThe odour concentrations estimation of mine air are based on the DMS concentrations and its odour detection threshold. Simulation results shows odour concentrations at ventilations stations between 672 OU/m\u003csup\u003e3\u003c/sup\u003e and 7,790 OU/m\u003csup\u003e3\u003c/sup\u003e. So far, rare separate point odour concentrations measurements at CMV were conducted. The monitored odour concentration levels were similar as modelled levels. Four separate odour concentrations measurements at ventilation stations (NLHEF, 2017) in 2016 gave odour concentrations between 850 OU/m\u003csup\u003e3\u003c/sup\u003e and 4,500 OU/m\u003csup\u003e3\u003c/sup\u003e and on 26.11.2007 three measurements (NIPH, 2008) showed odour concentrations between 3,900 OU/m\u003csup\u003e3\u003c/sup\u003e and 8,400 OU/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is almost no information concerning the fugitive emissions of DMS and odours in general from underground coal mining activities.\u003c/p\u003e \u003cp\u003eIn this paper was addressed this by describing and quantifying a dispersion of odours gases released from sources of CMV by focusing on analysis of gases monthly measurements in the mine and simulations of characteristics emissions of odorous compounds with mine ventilation model constructed in Ventsim\u0026trade;.\u003c/p\u003e \u003cp\u003eThis research identified DMS as a major odorant in the VCM released from longwall faces, development headings and main coal transport during coal extraction process. Its very low odour threshold means that it can create an odour nuisance even at trace levels.\u003c/p\u003e \u003cp\u003eThe dispersion simulations of odour sources in the mine show that median emissions represent relatively modest odour nuisance. While during peak emissions in the exit airways odour is potentially high to be disturbing and on the surface at the ventilation stations would be subject to odour complaints from the local residents. Simulating to additionally reduce odour levels with increasing air flowrate with the regulation of main fans showed that is not an effective measure for mitigating odorous emissions, while measures by reducing coal production would impose severe economic penalties.\u003c/p\u003e \u003cp\u003eSince DMS is not regularly monitored in mines and levels are significantly varying due to its content and distribution in the coal, releasing mechanisms, mine ventilation design and varying production intensity during the coal extraction process, the future work will focus on real-time monitoring of DMS levels and study of its correlations to coal extractions process to better understand and more accurately estimate odorous emissions from specific work phases of coal extractions process.\u003c/p\u003e \u003cp\u003eThe DMS content in the seam is related to the petrographic heterogeneity of the coal, future research will involve investigating the coal desorption characteristics of DMS from coal.\u003c/p\u003e \u003cp\u003eIn addition, to effectively address the odour issue at the VCM, especially in relation to fugitive odour emissions at the surface and for the design of technical measures for odour control, monitoring, and dispersion modelling of odour sources on the surface are necessary from hereafter.\u003c/p\u003e \u003cp\u003eHowever, underground coal mines are not widely recognized as an odour nuisance, and the development of technical abatement solutions to control odour from coalmining operations, especially given the large volume of ventilated air produced by the mine, will need more recognition of the problem and more support for its solving.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conducted in the framework of the project \u0026Prime;Young researchers from Industry 2010\u0026Prime; and its operation was partly financed by the European Union, European Social Fund. The authors are grateful to Velenje Coal Mine, which provided the necessary assets and data, and especially to Mine ventilation team of Velenje Coal Mine for technical support and for necessary mine ventilation parameters. The authors are also grateful to prof. Sevket Durucan and prof. Anna Korre from Imperial College London for all suggestions and recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was conducted in the framework of the project \u0026Prime;Young researchers from Industry 2010\u0026Prime; and its operation was partly financed by the European Union, European Social Fund.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e Authors declare no financial or non-financial interests that are directly or indirectly related to this work.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement:\u003c/strong\u003e Writing - original draft preparation, Gregor Uranjek, Milena Horvat, Radmila Milačič, Janez Ro\u0026scaron;er and Jože Kotnik; writing - review and editing, Gregor Uranjek, Milena Horvat, Radmila Milačič, Janez Ro\u0026scaron;er and Jože Kotnik; visualization, Gregor Uranjek.; supervision, Milena Horvat, Radmila Milačič, Janez Ro\u0026scaron;er and Jože Kotnik. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe supplement data that support the findings of this study are available from Coal mine Velenje (Premogovnik Velenje d.o.o.; https://www.rlv.si/) but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Coal mine Velenje.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdul-Wahab, S. \u0026amp; Marikar, F. (2012). The environmental impact of gold mines: pollution by heavy metals. \u003cem\u003eCentral European Journal of Engineering\u003c/em\u003e. 2(2) pp. 304-313. DOI: 10.2478/s13531-011-0052-3.\u003c/li\u003e\n\u003cli\u003eAlberta Energy Regulator and Alberta Health (AER). (2016). 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University of Ljubljana, Faculty of Natural Sciences and Engineering.\u003c/li\u003e\n\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"coal mine, dimethyl sulphide, odour, coal gases, mine ventilation, dispersion modelling","lastPublishedDoi":"10.21203/rs.3.rs-2279834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2279834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderground coal extraction at Coal Mine Velenje occasionally gives rise to odour complaints from local residents.\u003c/p\u003e\n\u003cp\u003eThis manuscript describes a robust quantification of odorous emissions of mine sources and a model-based analysis aimed to establish a better understanding of the sources, concentrations, dispersion, and possible control of odorous compounds during coal extraction process.\u003c/p\u003e\n\u003cp\u003eMajor odour sources during underground mining are released volatile sulphur compounds from coal seam, that have characteristic malodours at extremely low concentrations at µg/m\u003csup\u003e3\u003c/sup\u003e levels. Analysis of 1028 gas samples taken over a six-year period (2008-2013) reveal that dimethyl sulphide ((CH\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eS) is the major odour active compound present in the mine, being detected on 679 occasions throughout the mine. While hydrogen sulphide (H\u003csub\u003e2\u003c/sub\u003eS) and sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) were detected 5 and 26 times.\u003c/p\u003e\n\u003cp\u003eAnalysis of gas samples has shown that main DMS sources in the mine are coal extraction locations at longwall faces and development headings and that DMS is releasing during transport from main coal transport system.\u003c/p\u003e\n\u003cp\u003eThe dispersion simulations of odour sources in the mine have shown that the concentrations of DMS at median levels can represents relatively modest odour nuisance. While at peak levels the concentration of DMS remained sufficiently high to create an odour problem both in the mine and on the surface. Overall, dispersion simulations have shown that ventilation regulation on its own is not sufficient as an odour abatement measure.\u003c/p\u003e","manuscriptTitle":"Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal mine Velenje","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-09 15:41:37","doi":"10.21203/rs.3.rs-2279834/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-06-23T16:07:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-14T14:05:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67b9bff6-be71-4cb5-bb69-5cd3e58b1365","date":"2023-06-12T07:07:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-01-11T16:27:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-05T04:23:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-01-05T04:23:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2022-11-16T10:03:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"21d0649e-088f-4cbe-b8b6-49e776d42fcc","owner":[],"postedDate":"January 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-09T15:04:44+00:00","versionOfRecord":{"articleIdentity":"rs-2279834","link":"https://doi.org/10.1007/s10661-023-11755-z","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2023-10-04 15:01:20","publishedOnDateReadable":"October 4th, 2023"},"versionCreatedAt":"2023-01-09 15:41:37","video":"","vorDoi":"10.1007/s10661-023-11755-z","vorDoiUrl":"https://doi.org/10.1007/s10661-023-11755-z","workflowStages":[]},"version":"v1","identity":"rs-2279834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2279834","identity":"rs-2279834","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}