Characterization of regional CH 4 emanation and total particulate pollution from the underground coal mines

Characterization of regional CH 4 emanation and total particulate pollution from the underground coal mines | 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 Article Characterization of regional CH 4 emanation and total particulate pollution from the underground coal mines Ayesha Ayub, Sheikh Saeed Ahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2040898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Emission of methane from the underground coalmine is currently a global concern. The study aims to quantify the emission of potent toxic gases along with atmospheric dust in the suburbs of underground coal mines, in the field of Balochistan Pakistan. Related variables selected for quality check included particulate matter (i.e. PM10), CH 4 , O 2 , CO and elemental composition of PM10 (i.e. Cr, Cd, Co, Fe, Cu, Pb, Ni and Mn). A seasonal comparative study was designed. Widely applied GIS tool (i.e.IDW) was incorporated. Strengthening data with correlation matrix analysis apprehended interrelationship among the variables. Air quality variables were found above the safe allowable limits set by various standards (WHO, EPA, NIOSH, U.S National Ambient Air Concentration). No significant seasonal variation was recorded; but the pollutant concentration remained elevated during both seasons. Pearson correlation matrix analysis showed that CH 4 had a strong negative correlation with O 2 . Moreover, air probed inside the underground coalmine showed a deteriorated status. This alarming status is primarily attributed to all the mining activities and secondarily to vehicular emissions, mine fire and poor ventilation system. This study will provide a baseline data for concerned authorities for planning management, pollutant prevention and strategies for environmental monitoring in future. Air quality Heavy metal analysis Pearson correlation coefficient Spatial analysis Inverse distance interpolation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pollutants that are persistent in environment tend to pose a great risk to human wellbeing all around the world. All the natural processes occurring in environment can fabricate a number of pollutants, but contaminants that are generated by anthropogenic activities in very large amounts such as industrial manufacturing, mining operations and agricultural practices is very crucial to evaluate. Significant pathways for transport of these contaminants are via air, soil, water and biota. Contaminants that are transported by atmospheric air may take place directly by transfer through volatilized particles or via particulate matter or aerosol (Csavina et al. 2012 ). Relatively transport of volatile or non-volatile pollutants through other media like biota, soil and water is much slower than air. It has greater potential to cross the global and provincial scales because atmospheric air is not impounded to a substantial extent by potential barriers or topographic limits that might cause hampering in the transport process, as it would have been in case with biota, soil and water. Furthermore, atmospheric air have significant potential to carry pollutants at much faster rate throughout the environment because the air velocity is much more greater compare to the groundwater or surface water and other potential biological vectors in environment (Braune et al. 2005). So, the assessment of air pollution at potential pollution sites is of utmost importance. Air pollution inside the coal mines is basically due to the discharge of fugitive gases along with the production of total particulate matter (i.e PM10, PM2.5) (Masto et al., 2017 ). Coal mines are scattered across the world and is considered as a significant pollution source contributing to release about 25 million tons of methane gas along with other fugitive gases (Singh 2005). Among the gases released may include methane (CH 4) , hydrogen sulphide (H 2 S) and carbon monoxide (CO) (Pandey 2014). Source of these gaseous emissions is mainly because of the coalification process. The coal formation process is known as coalification. Coal seam contain some amount of methane (CH 4 ) along with other gases trapped within its body and is produced during the coalification process (Ju et al. 2016 ). This considerable fraction of potent gas is trapped in the coal body under pressure and the contiguous rock strata. The trapped methane gas is diffused out during the mining operations when the coal seam is cracked along the process (Irving and Tailakov 2016 ). As the mine gets deeper, the amount of methane gas in the coal gets higher. With the proceeding process of the mining operations, methane is released inside the mine air and that eventually finds its way into the outer atmosphere (Lloyd 2002 ). Methane is a greenhouse gas that is considered to be a potent gas as compared to CO 2 . According to recent report methane gas is growing in the atmosphere at a faster rate than CO 2 . Emission of fugitive CH 4 , from coalmines are recorded around the globe, and represents about 8% of world’s total anthropogenic CH 4 emissions. This concentration percentage of CH 4 emission constitutes about 17% input to the overall greenhouse gas emissions. Generally, coal mine CH 4 is a fine description for all emitted CH 4 prior to and after and also during the mining activities. There is a significant variability in the flow rate and composition of various emitted gases during the coal mining procedures. Considerably, in a typical gassy coal mine, CH 4 is emanated through three main streams: (1) Ventilation air (i.e. 0.1 to 1%), (2) Gases leaked from coal mineral before the mining operations (i.e. 60 to 95%), and (3) gases leaked from the operational areas of coal mine, e.g. goafs (i.e. 30 to 95%). Approximately about 64% of the coalmine CH 4 is contributed from the ventilation air methane mostly from a gassy coal mine (Su et al. 2005). In current study air is probed inside as well as outside of coal mines at the selected sites. Another significant pollution contributor around the coal mine is the atmospheric particulates. Contaminants transported by atmospheric dust has become an important concern around the world, as air masses loaded with significant amount of dust particles frequently spread across the intercontinental and continental boundaries and that consequently have adverse environmental problems in downwind depositional regions (Csavina et al. 2012 ). The main source of atmospheric dust pollution in the coal mines are the typical mining operations i.e. blasting, drilling, cutting, loading and unloading of the extracted mineral, exposed pit faces and discarded overburden wastes (Huertas et al. 2012 ). Basically, dust particles source in the coal mines can be categorized as a primary source that actually generate the dust particles but the secondary source which causes the dispersion of these dust particles and moves it from place to place is known as fugitive dust (Singh 2005). So the current study is basically designed to quantify and analyze the gaseous emission and particulate matter in order to appraise the quality of ambient air around the coal mining fields of the selected region. Seasonal studies are also conducted to evaluate a comparative analysis. Geographic information system (GIS), provides a latest data analyzing, modeling and processing methods to assist the academics and decision-makers in order to interpret and provides a better way to visualize statistics to precede more significant and valuable research information. An analyst utilizes different sources of GIS facts and figures for a more depictive and supporting view of complicated situations (Jumaah et al. 2019 ). Research information emerging in the field of quality assessment and monitoring of ambient air has adopted GIS, as a fundamental tool for characterizing and monitoring related problems. Moreover, there is a high demand and awareness of GIS managing or assessing the large spatially referenced data that are being sampled at small scales in the communal and environmental fields (Tian et al. 2019 ). More specifically in the current research, Inverse Distance Weighting (IDW) related to GIS has been used to achieve interpolation with the help of air quality concentration data (Kumar 2016 ). Similarly, GIS interpolation technique for fog mapping has been effectively applied and the research outcomes concluded a more obvious spatial distribution of contaminant in Malaysia (Dominick et al. 2012). Air pollution reduces the surrounding air quality which has profound effect on humans and the surrounding flora and fauna (Chaulya 2004 ). So, the current study is focused to find the significant atmospheric dust fall, mineral content, and their morphological characteristics (Rout et al. 2014 ). Researcher all around the world conducted studies to map the gaseous emission as well as particulate fallout from the coalmines (Campa et al. 2011; Ekbal et al. 2015; Zhang et al. 2014 ; Baris 2013 ). Methods Study Area Pakistan’s largest province Balochistan covers an area of 347190 km 2 . Meteorological data suggest that it is the most driest province of country. It is geographically delimited 32 o , 06' north and 60 o , 52' east (Giri et al. 2017). The province climate is semi-arid continental (Durowoju et al. 2016). It has diversified precipitation pattern, annually records between 200-350 mm. Temperature of the region greatly varies with the elevation between -3º to 38º Celsius from above the sea level. The daily mean, minimum and maximum temperatures probed is 31 C o , 16 o C and 27 C o respectively (Gurdal 2011). Balochistan is the custodian of vast reservoir of natural gas and barite along with many rare minerals. Besides, these resources huge deposits of minerals like magnesite, silica and sulphur are also present. The current research area hosts the huge deposits of coal mineral mainly of bituminous to sub-bituminous and also lignite coal type. Among the vast coalfields of Balochistan, five most functional and largest coal fields were selected as monitoring stations i.e. Chamalang monitoring stations (C1), Duki monitoring stations (D2), Harnai monitoring stations (H3), Khost monitoring stations (K4) and Sharagh monitoring stations (S5) as depicted in fig 1. These coal mining fields are scattered across the four districts of province Balochistan i.e. District Loralai, District Duki, District Harnai and District Ziarat. Air sampling Seasonal sampling was done for a comparative study in the current study. First batch of samples were collected during the summer season from 5 th June till 5 th August 2018, and for the second batch of samples, sampling was conducted during the winter season from 20 th January till 20 th March 2019. It was observed during the field survey, all the coal mining operations were continuously performed for a labor of 24 hours by taking short break intervals for meals. Assessment for the air quality, an inside as well as outside of the underground coal mine was evaluated for different concentrations of emitted gases like CH 4 , CO, H 2 S and O 2 for the comparative study. These gases were probed by Portable Multi 4 Gas Detector (Model CD4) (Jeremy et al. 2018). For the measurement of total particulate matter (i.e. PM10), samples were collected on a pretreated (oven dried) and weighted Teflon filter paper of size 8” × 10” using a portable high-volume air sampler (µ 10 inlet) and the model used was Graseby Andersen/ GMW Model 1200 with average flow rate of 36 acfm. High-volume sampler was installed at each coalmining filed sampling station (i.e. C1, D2, H3, K4 and S5). HVS was operated in a standard shelter following the manual method and successfully collected an 8-hrs sample. HVS was operated during mainly the daytime from 9 am till 5 pm and near the most operational signal points (Antoszczyszyn et al. 2016). Two air samples were collected on monthly basis for about three months at each of the monitoring stations. So, a total of 30 samples were collected per season. Sampling was carried out under the same environmental conditions (Pandey 2014). Measurement of CH 4 , CO, H 2 S and O 2 Portable Multi 4 Gas Detector (Model CD4) was used to probe the potent gases. Device was first calibrated and than a complete procedure was followed as instructed by the manual provided in the device kit. Concentration was first recorded inside the underground coalmine and then compared with the outside gaseous concentration. Data logger software was used to save, display and analyze data as recorded for each coal mining monitoring stations using multi-gas detector during both the seasons. Furthermore, Microsoft Excel data sheets were prepared for correlation matrix analysis (Jeremy et al. 2018). Measurement of Particulate Matter (PM10) PM10 calculation Teflon filter paper was used to collect sample of total particulate matter i.e. PM10. Filter paper was first dried in oven for about 5-6 mints at 100 0 C to remove moisture content. The oven dried filter paper was then carefully weighed and noted before sampling. After the completion of sampling procedure via HVS, filter paper was again carefully weighed. The average flow rate of the operating sampler was also noted manually. The data obtained was followed by a calculation done step by step for PM10 (EPA 2004). Hot acid extraction procedure was carried out for heavy metals concentration in the atmospheric dust collected via HVS (i.e. PM10). First, a strip size of 1” × 8” from 8” × 10” was cut from each of the sampled filter paper using a pizza cutter. About 20 ml acid solution of HCl/HNO 3 was pipette out into pretreated and labeled 150 ml Griffin beaker. Then by using a plastic forcep the strip was gently placed lower in a beaker containing the acid mixture (HCl/HNO 3 ) so that the strip was entirely covered. Beaker was then placed on the hot plate contained in a fume hood. Beaker was roofed with a watch glass for 30 mints. The processed solution after time laps was later on allowed to cool at room temperature. Beaker wall was than rinsed with de-ionized water. Then about 20 ml of reagent water was added to beaker and placed to stand for about 30 minutes. This step allowed the acid to diffuse from the filter into the rinse water. The extraction was then transferred into 50 ml volumetric flask. Beaker walls was again rinsed and added to the flask. Extraction was than diluted with Type I water up to the mark. A Teflon syringe was used to pull-up the sample. A filter disc was placed on syringe and the sample was placed into the sterile 30 ml centrifuge tube. Tube was filled up to 20 ml of filtered digestate. Finally, the extraction was ready for analysis. Atomic absorption spectrophotometer was used for heavy metals analysis (Ehi-Eromosele et al. 2012). Air Quality Analysis Correlation matrix analysis A widely applied Pearson’s Correlation Coefficient was employed for air quality data to know the significant interrelationship among the analyzed variables. Correlation matrix analysis was carried out via XL STAT (2019). Excel sheets were prepared using Microsoft Office 365 ProPlus. Inverse Distance Weighted (IDW) Interpolation During the field sampling in both seasons, each sampling station for air data was located by mean of hand-held portable GPS device i.e. Garmin eTrex GPS. Each coordinate point was than imported to GIS software i.e. ArcMap 10.2 through a point layer. Excel data sheets containing analysed results of pollutant gases, Particulate matter (PM10) and concentration of heavy metals were then transported to ArcMap 10.2. IDW technique was used to delineate the spatial dispensation of selected pollutant gases and heavy metals (Kumar 2016). Results And Discussion Physicochemical Analysis of Air Samples Upon analysis of the collected air samples, table 1 and 2 shows the average value for each of the analyzed air quality parameter for all the selected sampling station during both the seasons. Parameter Chamalang Duki Harnai Sharagh Khost Standards PM10 µg/m3 737.46 360.75 374.73 372.65 368.82 50 µg/m3 (WHO) CH 4 (%) 0.36 0.2 0.16 0.5 1.31 0.1 % (NIOSH) O 2 (%) 20.25 20.3 20.45 20 18.75 21 % (WHO) H 2 S ppm N.D N.D N.D N.D N.D - CO ppm 4 5.5 5.25 4.5 19.5 4.37 (EPA) Cd ppm 0.0067 0.0444 0.0539 0.0256 0.0202 0.001 (U.S National Ambient Air Concentration) Cr ppm 0.0409 0.0304 0.0237 0.0219 0.0829 0.002 (U.S National Ambient Air Concentration) Co ppm 0.4 0.1358 0.0302 0.0473 0.1208 0.001 (U.S National Ambient Air Concentration) Cu ppm 0.046 0.0421 0.0115 0.0881 0.0651 0.01 (U.S National Ambient Air Concentration) Fe ppm 1.4592 2.2676 5.9451 1.9817 3.4014 0.3 (U.S National Ambient Air Concentration) Pb ppm 0.0125 0.001 N.D 0.0015 0.0136 0.02 (U.S National Ambient Air Concentration) Mn ppm 0.0124 0.014 0.0346 0.0462 0.0247 0.001 (U.S National Ambient Air Concentration) Ni ppm 0.1711 0.1656 0.2871 0.1255 0.1159 0.006 (U.S National Ambient Air Concentration) Table 1. Physicochemical analysis of air samples collected during summer season Parameter Chamalang Duki Harnai Sharagh Khost Standards PM10 µg/m3 758.43 366.83 725.67 378.43 365.78 50 µg/m3 (WHO) CH 4 (%) 0.32 0.185 0.185 0.36 1.43 0.1 % (NIOSH) O 2 (%) 20.4 20.25 20.3 19.8 19.35 21 % (WHO) H 2 S ppm N.D N.D N.D N.D N.D - CO ppm 4 5.5 5.5 5.5 19 4.37 (EPA) Cd ppm 0.0189 0.0242 0.0417 0.0525 0.0444 0.001 (U.S National Ambient Air Concentration) Cr ppm 0.0204 0.1403 0.0202 0.1215 0.2303 0.002 (U.S National Ambient Air Concentration) Co ppm 0.317 0.0075 0.1434 0.1132 0.1132 0.001 (U.S National Ambient Air Concentration) Cu ppm 0.0421 0.0575 0.0421 0.046 0.069 0.01 (U.S National Ambient Air Concentration) Fe ppm 9.0014 6.1915 22.2521 7.4831 14.8775 0.3 (U.S National Ambient Air Concentration) Pb ppm 0.0132 0.0109 0.0308 0.0019 0.0121 0.02 (U.S National Ambient Air Concentration) Mn ppm 0.0429 0.0692 0.0791 0.0833 0.08 0.001 (U.S National Ambient Air Concentration) Ni ppm 0.1546 0.1435 0.2208 0.138 0.0994 0.006 (U.S National Ambient Air Concentration) Table 2. Physicochemical analysis of air samples collected during winter season Seasonal Variation of PM10, CH 4 , O 2 and CO Results showed that the average PM10 concentration during both the season was about 737.46 µg/m 3 and 368.73 µg/m 3 respectively. No significant variation for PM10 concentration was recorded at Duki, Khost and Sharagh station. While PM10 sampled at Harnai study site showed elevated PM10 concentration during winter (i.e.737.46 µg/m3) and lower during summer (i.e. 368.73 µg/m3). The overall average result calculated for PM10 is depicted in fig 2a and conclude that the concentration exceeds the safe permissible limit as set by the World Health Organization (WHO) for all the selected coal mining sites. Elevated levels of PM10 concentration around the coal mining area is mainly because of coal mining activities like blasting, drilling, loading, and unloading of overburdens, loading and unloading of excavated coal, exposed pits faces and exhausts from machinery (Pandey et al. 2014). The results for PM10 are in line with several other research studies as conducted by Nayak et al (2018), Gutam et al (2016), Pokorna et al (2016) and Pandey et al (2019). Further analysis of atmospheric dust (PM10) for heavy metals showed a great variation in both seasons throughout the selected sampling stations. All the traces of heavy metals associated with PM 10 were found to cross the safe permissible limits as set by U.S National Ambient Air concentration. WHO has no safe permissible limits set for the trace elements associated with dust (PM 10) in the ambient air. Methane (CH 4 ) gas concentration was higher inside the deep underground coal mines at most of the selected study sites as compare to the outside concentration. However no significant seasonal variation was observed at all study sites. During winter season the highest inside as well as outside CH 4 concentration recorded was about 1.41% and 1.44% at Khost station. Inferring the overall results as shown in fig 2b shows that the CH 4 concentration recorded at all the study sites in both seasons exceeded the permissible limits as declared by Nation Institute for Occupational Safety and Health (NIOSH). Quantification of Oxygen levels with multi-gas detector, inside as well as outside the coalmines as displayed in fig 2c clearly shows that the O 2 level at each sampling site in both seasons was very low based on the required limit set by Environmental Protection Agency (EPA) i.e. 21%. But O 2 level inside coalmines significantly dropped as compared to the outside levels. However, Concentration of O 2 was found to be lower inside coalmines, due to the heavy accumulation of CH 4 and CO consequently causes lowering of the O 2 level. This can be attributed to the poor ventilation system inside the coalmines. Several studies conducted throughout the world tend to support the current results for the pollution assessment and accumulation of such gaseous emission around the coalmines i.e. Bibler et al (1998) and Kirchgessner et al (1993). Carbon monoxide (CO) along with other gases was also detected. The obtained results as illustrated in fig 2d showed the exceeded level of CO and most likely to cross the permissible limit recorded inside as well as outside the underground coalmines at all study locations during both the seasons. While safe level of CO concentration was recorded outside the coalmine at Chamalang monitoring station. The highest level of CO was detected outside the Khost coalmine station during both the seasons i.e. 21 and 22 ppm respectively. Similarly, the highest CO level inside the coalmine during summer and winter was also recorded at Khost i.e. 18 and 16 ppm. During the coal excavation CH 4 and CO is released and accumulated inside coalmine if not properly ventilated. Therefore, readings acquired through gas detector showed a mix variation during both the seasons, but it was clearly observed that the gases concentration probed inside were significantly heavier than outside of coalmines. WHO has no permissible exposure limit for CH 4 but NIOSH maximum recommended safe methane concentration is about 0.1 %. Similarly, EPA has not set a required concentration for O 2 but a safe limit has been set for CO. Moreover, WHO has set a required O 2 concentration i.e. 21%. Linkage Analysis of Matrix Correlation analysis of complex matrix was carried out between the different air quality parameters that included PM10, CH 4 , O 2 , CO and heavy metals associated with PM10 (i.e. Ca, Cd, K, Co, Cu, Cr, Fe, Mn, Pb and Ni) in order to apprehend interrelationship among the variables. Pearson Correlation matrix analysis’s result for both the seasons is shown in Table 3. Color scheme depicted in fig 3 represent a more visual presentation of correlation matrix analysis. Examining the analysis result of correlation pattern between variables revealed an irregular pattern. Strong positive and negative correlations were observed. Among different quantified gases, one of the most substantial gas like methane (CH 4 ) revealed a significant correlation with most of air quality parameters. CH 4 showed a strong negative association with O 2 (-0.84) and Ni (-0.85). With the increasing concentration of CH 4 gas leads to cause a decrease in the O 2 level inside the coalmines as well as outside the coal mine. As CH 4 gas has a binding nature, when methane is released from coal seams during mining activities it can binds with oxygen and thus decreasing the level of oxygen. So, the decrease in O 2 level can cause the decline in the quality of air (Yusuf et al ., 2016). Strong positive association was shown by CH 4 with the Cu i.e. 0.79. Similarly, O 2 showed a strong positive association with Ni (0.87) and strong negative association with Cu (-0.86), Cr (-0.66) and CO (-0.61). CO showed a slight positive response with Cr (0.55) and slight negative response with Ni (-0.51). Slight positive response was also showed by Cr. Correlation between heavy metals extracted from the PM10 samples also illustrated significant result. Cd showed a slight positive correlation with Mn (i.e. 0.44) but a well-built negative association was shown i.e. Pb (-0.63) and Co (-0.52). Similarly, Cr had a significant positive correlation with Co with the value of 0.51 and a negative relationship was observed with Ni (-0.59). Slight negative relation was also observed with Co i.e. -0.40. Whereas Co has a strong association with Pb. Iron (Fe) had a strong positive impact on Mn (i.e. 0.75) and showed a strong association with Ca (i.e. 0.60). Similarly, Ni had a strong negative association with Cu (-0.87), CH4 (-0.85), Cr (-0.59) and CO (-0.51). Moreover, a positive correlation represents the same source i.e. the coal mineral. All these sets have a strong positive association. Thus, represents a same source. While sets with negative correlation are Ni-Cu, Ni-CH 4 , Ni-CO, Ni-Cr, O 2 -Cr, O 2 -Cu, O 2 -CO, Mn-Pb, Co-Pb and Cd-Pb. The negative sets of association of Ni and Pb shows that the source is of anthropogenic source. Many research studies have been conducted around the coalmines for air quality assessment adapting the Pearson’s correlation analysis and support the current results for correlation analysis (Dubey et al. 2012; Bray et al. 2017; Huertas et al. 2012; Tripta et al. 2015). K and Ca had no significant interaction with each of the heavy metals and also with the quantified gases within the study area. Variables CH 4 (%) O 2 (%) CO ppm Cd ppm Ca ppm Cr ppm Co ppm Cu ppm Fe ppm Pb ppm Mn ppm Ni ppm K ppm CH 4 (%) 1 O 2 (%) -0.84 1 CO ppm 0.30 -0.61 1 Cd ppm -0.25 0.11 0.34 1 Ca ppm -0.39 0.38 -0.07 0.09 1 Cr ppm 0.42 -0.66 0.55 0.12 -0.13 1 Co ppm 0.15 0.15 -0.29 -0.52 -0.10 -0.40 1 Cu ppm 0.79 -0.86 0.33 -0.27 -0.19 0.51 -0.29 1 Fe ppm -0.14 0.08 0.34 0.29 0.60 -0.02 -0.02 -0.19 1 Pb ppm 0.25 -0.24 0.17 -0.63 0.20 -0.14 0.59 0.12 0.41 1 Mn ppm 0.10 -0.29 0.34 0.44 0.37 0.24 -0.40 0.26 0.75 0.08 1 Ni ppm -0.85 0.87 -0.51 0.10 0.31 -0.59 0.26 -0.87 -0.06 -0.13 -0.37 1 K ppm 0.31 -0.07 -0.34 0.21 0.15 0.15 0.14 0.06 -0.01 -0.18 0.18 0.01 1 Table 3. Correlation matrix of air quality parameters Spatial analysis of Air Quality parameters Spatial examination of air quality data showed significant results. In current study methane, oxygen and carbon monoxide showed a very alarming distribution patterns throughout the sampling stations. Spatial distribution of Methane, Oxygen and Carbon monoxide Quantification of Methane (CH 4 ) gas inside and outside the underground coal mines at selected coalmining fields during the two seasons i.e. summer and winter is illustrated in fig. 4. Interpreting the spatial distribution of map obtained for CH 4 concentration showed that the highest concentration was observed at Khost and lowest at Duki coalmining sites. CH 4 concentration recorded at all study sites was above the allowable permissible limits as assigned by the NIOSH i.e. 0.1 % during both seasons. Spatial distribution of O 2 is displayed in fig 5. Observing the maps, it is clearly noticeable that the O 2 concentration is significantly very low in the coalmining site of Khost during both seasons. And the overall O 2 concentration recorded at each of study sites showed that the concentration of O 2 is very low according to the required concentration set by WHO that is 21% during summer and winter. Concentration of CO as depicted in fig 6 showed that the CO concentration was higher at Khost mining filed as compare to other coalmining sites during both the seasons. The safe permissible limit set for CO in ambient air according to Environmental protection agency (EPA) is about 4.37 ppm. By comparing the current values recorded at all the study sites showed that the values exceeded the safe permissible limit. No significant result was obtained for hydrogen sulphide (H 2 S). The study sites had no feasible of source of H 2 S gas. Variation pattern obtained on maps for gases like CH 4 and CO showed that during both seasons the concentration level for mentioned gases is maximum at the Khost mining site. Similarly, maps obtained for O 2 level also showed that the levels of O 2 were minimum at Khost during both the seasons. This can be attributed to the poor ventilation system inside the coalmines of Khost study area. Poor ventilation declines the quality of air inside the underground coalmines and is very dangerous for the mine workers. Several studies conducted at different coal mining sites around the world, significantly supports the current results and methodology of representation of obtained results via GIS tool i.e. IDW interpolation technique (Espitia-perez et al. 2018; Jha et al. 2011; Salve et al. 2007; Squizzato et al. 2018). Conclusion Assessment of air quality and monitoring was the chief objective of current study. The monitoring data recorded around the selected coal fields of Balochistan revealed significant spatial and seasonal variations. The concentration level of PM10, CH 4 , CO, O 2 and heavy metals in PM10 samples were alarming. The seasonal variations depend on source of pollutant formation and emission or pollutant distribution mechanisms, these factors are also affected by other meteorological factors like wind speed, precipitation pattern, relative humidity and distance from the source of pollution area. Computation of PM10 concentration concluded that all the study sites are highly polluted, and the values are above the safe permissible limits set by WHO. CH 4 concentration crossed the required limit as set by NIOSH. Higher level of CH 4 can lead to mine fire and creates explosive hazards if not properly ventilated. Level of O 2 was below the required concentration as assigned by EPA. Continuous accumulation of gases causes the drop in O 2 levels. This can lead to suffocation, a life threatening situation for all the underground mine workers. Concentration of CO was found above the permissible limit set by WHO standard. CO is a toxic gas as it is released when coal is oxidized. So, when exposed to as little as 0.1% of CO can cause death within few minutes. Accumulation of these dangerous gases was the result of continuous mining activities without a proper ventilation system. Pearson’s correlation analysis showed significant association between variables of the same origin and that of anthropogenic sources. Interpolation distance weight (IDW) maps generated showed a significant variation of parameter concentration across the five coalmining monitoring stations. Most significant results were generated for Khost coal mine monitoring station followed by Harnai monitoring station. It can be concluded for the air quality assessment that the air in the suburbs of coalmine was deteriorated. The primary air pollution contributors at study site were overall coal mining activities and the secondary contributors were vehicular emissions, mine fire, windblown through overburdens and unpaved roads. These are main overall factors that deteriorate the air quality around the coal mines. Moreover, annually many incidents in the underground coalmines are reported throughout the Balochistan province. Accidents in coalmines caused the death of about hundreds of coal mine workers either in ferocious fire erupted after trapped methane gas exploded inside mine or developed a severe health condition due to lack of training and facilities. The evaluated air quality data enabled an insight for policy makers for the air quality management and ecological conservation. Hence, the study provides a global scenario for air pollution around active underground coalmines. Declarations Acknowledgments The first author most graciously acknowledges Fatima Jinnah Women University, Rawalpindi and Higher Education Commission (HEC) of Pakistan by providing Indigenous fellowship throughout her Ph.D. studies. Author contribution Conceptualization: Ayesha Ayub and Sheikh Saeed Ahmad; Methodology: Ayesha Ayub and Sheikh Saeed Ahmad; formal analysis and investigation: Ayesha Ayub; writing—original draft preparation: Ayesha Ayub; writing—review and editing: Ayesha Ayub and Sheikh Saeed Ahmad. Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Competing interests The authors declare no competing interests. Data availability Data will be made available on reasonable request. Ethics declaration The authors affirm that the study does not involve human or animal subjects. References Hao, Y. et al . How harmful is air pollution to economic development? New evidence from PM2.5 concentrations of Chinese cities. J. Clean Prod 172 ,743–757. (2018) Antoszczyszyn, T. & Michalska, A. The potential risk of environmental contamination by mercury contained in Polish coal mining waste. J. Sustain. 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Environ Res 111 ,1018-1023. https://doi.org/10.1016/j.envres.2011.07.001(2011). Chaulya, S.K. Assessment and management of air quality for an opencast coal mining area. J. Enviro Manag 70 , 1-14. https://doi.org/10.1016/j.jenvman.2003.09.018 (2004). Csavina, J. et al . A review on the importance of metals and metalloids in atmospheric dust and aerosol from mining operations. Sci. Total Environ 433 , 58-73. https://doi.org/10.1016/j.scitotenv.2012.06.013 (2012). Dominick, D. & Aris, A.Z. Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmos Environ 60 ,172-181. https://doi.org/10.1016/j.atmosenv.2012.06.021(2012). Dubey, B., Pal, A.K. & Singh, G. Trace metal composition of airborne particulate matter in the coal mining and non-mining areas of Dhanbad region, Jharkhand, India. Atmos. Pollut. Res 3 , 238-246. https://doi.org/10.5094/APR.2012.026 (2012). Durowoju, O.S., Odiya, J.O. & Ekosse, G.E. Variation of Heavy metals from geothermal spring to surrounding soil and Mangifera Indica- Siloam Village, Limpopo Province. Sustainability 6 , 1-12. https://doi.org/10.3390/su8010060 (2016). Ehi-Eromosele, C.O., Adaramodu, A.A., Anake, W.U., Ajanaku, C.O. & Edobor-Osoh, A. Comparison of three methods of digestion for trace metal analysis in Surface dust collected from an E-waste recycling site. Nat. Sci 10 , 42-47. Accessed from http://www.sciencepub.net/nature (2012). Ekbal, M.A., Gupta, H. Assessment of Ambient Air Quality Index of Coal City Dhanbad for Public Health Information. J.Energy. Res. Environ. Technol 1 , 50-53. Accessed from Krishi Sanskriti (2015). Espitia-perez, L. et al . Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors. Chemosphere 206 , 203-216. https://doi.org/10.1016/j.chemosphere.2018.04.049 (2018). Giri, S., Singh, A.K. & Mahato, M.K. Metal contamination of agriculture soils in the copper mining areas of Singhbhum shear zone in India. J. Ear. Sys. Sci 49 ,1-13. 10.1007/s12040-017-0833-z (2017). Gurdal, G. Abundances and modes of occurrence of trace elements in the Can coals (Miocene). Canakkale-Turkey. Int. J. Coal. Geo 87 ,157-173. https://doi.org/10.1016/j.coal.2011.06.008 (2011). Huertas, J.I., Camacho, D.A., Huertas, M.E. Standardize emissions inventory methodology for open pit mining areas. Environ. Sci. Pollut. Res 1-17. https://doi.org/10.1007/s11356-012-0778-3 (2012). Huertas, J.I., Huertas, M.E., Lzquierdo, S. & Gonzalez, E.D. Air quality impact assessment of multiple open pit coal mines in northern Colombia. J.Environ. Manag 93 ,121-129. https://doi.org/10.1016/j.jenvman.2011.08.007 (2012). Irving, W. & Tailakov, O. CH 4 EMISSIONS: COAL MINING AND HANDLING. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories . 129-144. Accessed from Microsoft Word - 2.7_CH4_Coal_Mining_Handling.doc (iges.or.jp) (2016). Jeremy,W., Christoph, B., Florian, B., Lorke, A. & Bodmer, P. Measuring CO2 and CH4 with a portable gas analyzer: Closed-loop operation, optimization and assessment. PLOSone . 1-16. https://doi.org/10.1371/journal.pone.0193973 (2018). Jha, D.K., Sabesan, M., Das, A.K. & Vinithkumar, N.V. Evaluation of interpolation technique for Air Quality parameters in port Blair, India. Uni. J. Environ. Res. Tech 1 , 301-310. Accessed from (PDF) Evaluation of Interpolation Technique for Air Quality Parameters in Port Blair, India (researchgate.net) (2011) Ju, Y. et al . A new approach to estimate fugitive methane emissions from coal mining in China. Sci. total. Env. 543 , 514-523. https://doi.org/10.1016/j.scitotenv.2015.11.024 (2016). Jumaah, H.J., Ameen, M.H., Kalantar, B., Rizeei, H.M. & Jumaah, S.J. Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomatics, Nat Hazards Risks 10 , 2185-2199. https://doi.org/10.1080/19475705.2019.1683084 (2019). Kirchgessner, D.A., Piccot, S.D. & Winkler, J.D. Estimation of global methane emissions from coal mines. Chemosphere 4 , 453-472. https://doi.org/10.1016/0045-6535(93)90438-B (1993). Kumar, A., Patil, R.S., Dikshit, A.K., Kuma, R. Air Quality Assessment Using Interpolation Technique. Env. Asia 2 , 140-149. DOI:10.14456/ea.2016.18 (2016). Kumar, S. What are the 4 stages of coal formation? Accessed from https://www.quora.com/What-are-the-4-stages-of-coal-formation (2016). Lloyd, P.J. Coal mining and the environment. Energy Research Institute, University of Cape Town . Accessed from Microsoft Word - IBA.environment.doc (uwc.ac.za) (2002). Masto, R.E., George, J., Rout, T.K. & Ram, L.C. Multi element exposure risk from soil and dust in a coal industrial area. J. Geochemical Explo 176 , 100-107. https://doi.org/10.1016/j.gexplo.2015.12.009 (2017). National Institute for Occupational safety and Health (NIOSH). 1985b. Nayak, T. & Chowdhury, I.R. Health damages from air pollution: Evidence from opencast coal mining region of Odisha, India. Eco. Econom. Soci 1 , 43-65. DOI:10.37773/ees.v1i1.9 (2018). Pandey, B. & Agrawal, M. & Singh, S. Assessment of air pollution around coal mining area: Emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis. Atmos. Pollut. Res 1 , 79-86. https://doi.org/10.5094/APR.2014.010 (2014). Pandey, B., Mukherjee, A., Agarawal, M. & Singh, S. Assessment of seasonal and site-specific variation in soil Physical, Chemical and Biological properties around opencast coal mines. Pedosphere 5 , 642-655. DOI:10.1016/S1002-0160(17)60431-4 (2019) . Pokorna, P., Hovorka, J. & Brejcha, J. Impact of mining activities on the air quality in the village nearby a coal strip mine. Earth Environ. Sci 44 , 1-5. https://doi.org/10.1088/1755-1315/44/3/032021(2016). Rout, T.K., Masto, R.E., Padhy, P.K. & George, J. Dust fall and elemental flux in a coal mining area. J. Geochem. Exp 1 -13. https://doi.org/10.1016/j.gexplo.2014.04.003 (2014). Salve, P.R., Satapathy, D.R., Katpatal, Y.B. & Wate, S.R. Assessing spatial occurrence of ground level Ozone around coal mining areas of Chandrapur District, Maharashtra, India. Environ. Monit . Assess 3 , 87-98. https://doi.org/10.1007/s10661-006-9562-5 (2007). Singh, K.P., Malik, A., Sinha, S., Singh, V.K. & Murthy, R.C. Estimation of source of Heavy metal contamination in sediments of Gomti river (India) using principal component analysis. Water Air Soil pollut 166 , 321-341. https://doi.org/10.1007/s11270-005-5268-5 (2005). Squizzato, S., Masiol, M., Rich, D.Q. & Hopke, P.K. PM2.5 and gaseous pollutants in Newyork state during 2005-2016: Spatial variability, temporal trends, and economic influences. Atmos. Environ 183 , 209-224. https://doi.org/10.1016/j.atmosenv.2018.03.045 (2018). Su, S., Beath, A., Guo, H. & Mallett, C. An assessment of mine methane mitigation and utilization technologies. Prog. Energy Combust . Sci 31 , 123-170. https://doi.org/10.1016/j.pecs.2004.11.001(2005). Tripta. & Srivastava, D.N. A statistical approach to a stationary environmental assessment of air quality in a coal mining area of eastern IGP region, India. Ame. Int. J. Res. Sci. Tech. Eng. Math 15 ,141-144. Accessed from (PDF) A Statistical Approach to a Stationary Environmental Assessment of Air Quality in a Coal Mining Area of Eastern IGP Region, India (researchgate.net) (2015). Tian, Y., Yao, X., Chen, L. Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics. Compt. Environ. Urban. System 75 , 35-48 https://doi.org/10.1016/j.compenvurbsys.2019.01.003 (2019). U.S National Ambient Air Concentration. Accessed from Document Display | NEPIS | US EPA (1986). WHO. WHO guideline for Particulate Matter. Accessed from Air Quality Guidelines: Global Update 2005: Particulate Matter, Ozone ... - World Health Organization - Google Books (2005). Yusuf, M., Ibrahim, E., Saleh, E., Ridho, M.R. & Iskandar, I. The relationship between the decline of oxygen and the increase of Methane Gas (CH 4 ) emissions on the environment health of the plant. Int. J. Collab. Res. Intern. Med. Public Health 7, 457- 464. Accessed from IJCRIMPH (iomcworld.org) (2016). Zhang, Y. et al . Air Quality in Lanzhou, a Major Industrial City in China: Characteristics of Air Pollution and Review of Existing Evidence from Air Pollution and Health (2014). Additional Declarations No competing interests reported. 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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-2040898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":135954456,"identity":"5a269548-cf99-4294-82ce-0fd18b3a0c62","order_by":0,"name":"Ayesha Ayub","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYJACZjApwdj+4UMFiMvcQKwW5jbGGWdAXEaitbC3MfO2gVgEtPDPSD78uaDGLnHD7ca2hzPn1UbztwO1/KjYhlOLxI20NOkZx5ITN9w52G7wcdvx3BmHGRsYe87cxm3NjRwzZh425sQNNxIbJGduO5bbANTCzNiGW4v8jfzPn3n+1YO1SPPOOZY7n5AWgxs5DNK8bYdBWtqkeRtqcjcQ0mJ45pmZNG/fceOZdw42G844diB3I1DLQXx+kTue/Pgzz7dq2b7b7Q8ffKipy513/vDBBz8q8HhfIAFMOTZAuIfB5AHc6oGAHyJtD+XW4VU8CkbBKBgFIxMAAANaZ6CobB60AAAAAElFTkSuQmCC","orcid":"","institution":"Fatima Jinnah Women University, The Mall","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Ayub","suffix":""},{"id":135954457,"identity":"9741e13c-cb1c-4910-af26-4433f7c34c0b","order_by":1,"name":"Sheikh Saeed Ahmad","email":"","orcid":"","institution":"Fatima Jinnah Women University, The Mall","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sheikh","middleName":"Saeed","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2022-09-07 09:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2040898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2040898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":26470829,"identity":"59c4c799-cf5d-4a02-9155-56ede9071203","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193676,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/ae2c9d8dcae7199b31a7c48d.png"},{"id":26470831,"identity":"28902141-2d3b-451a-9af5-bc357642a2de","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137476,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation of (a) PM10; (b) CH\u003csub\u003e4\u003c/sub\u003e; (c) O2 and (d) CO\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/2838e7d317d6ede6ed538b7f.png"},{"id":26470828,"identity":"aa6e1b4a-aa38-408b-b85b-4269f4e8d914","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4875,"visible":true,"origin":"","legend":"\u003cp\u003eImage of correlation matrix\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/6f1e23131624804438df7949.png"},{"id":26470832,"identity":"a89980f9-f2d0-4a43-bc93-267a9abe20b4","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":339322,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial dispensation of CH\u003csub\u003e4\u003c/sub\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Summer season \u003cstrong\u003e(b)\u003c/strong\u003e Winter season\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/f8cc96fe13ec93d3a9d05d44.png"},{"id":26470830,"identity":"c6a54ffb-ba6a-434f-8654-d88f639e955b","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":292683,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial dispensation of O\u003csub\u003e2\u003c/sub\u003e \u003cstrong\u003e(a)\u003c/strong\u003eSummer season \u003cstrong\u003e(b)\u003c/strong\u003e Winter season\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/9acb0a20bbf583c2c23c7b08.png"},{"id":26470833,"identity":"67cfb3ed-d42d-4f9b-85b2-c9d22d1a9aa2","added_by":"auto","created_at":"2022-09-14 20:53:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":272263,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial dispensation of CO \u003cstrong\u003e(a)\u003c/strong\u003e Summer season \u003cstrong\u003e(b) \u003c/strong\u003eWinter season\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/ba46a096d9420625c6257aa3.png"},{"id":34622184,"identity":"2bc6f513-f897-4143-8f87-f1d8dd2b371b","added_by":"auto","created_at":"2023-03-22 03:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2040898/v1/4fdfd99e-bb02-4351-815b-17c6b81232db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of regional CH 4 emanation and total particulate pollution from the underground coal mines","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePollutants that are persistent in environment tend to pose a great risk to human wellbeing all around the world. All the natural processes occurring in environment can fabricate a number of pollutants, but contaminants that are generated by anthropogenic activities in very large amounts such as industrial manufacturing, mining operations and agricultural practices is very crucial to evaluate. Significant pathways for transport of these contaminants are via air, soil, water and biota. Contaminants that are transported by atmospheric air may take place directly by transfer through volatilized particles or via particulate matter or aerosol (Csavina et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Relatively transport of volatile or non-volatile pollutants through other media like biota, soil and water is much slower than air. It has greater potential to cross the global and provincial scales because atmospheric air is not impounded to a substantial extent by potential barriers or topographic limits that might cause hampering in the transport process, as it would have been in case with biota, soil and water. Furthermore, atmospheric air have significant potential to carry pollutants at much faster rate throughout the environment because the air velocity is much more greater compare to the groundwater or surface water and other potential biological vectors in environment (Braune et al. 2005). So, the assessment of air pollution at potential pollution sites is of utmost importance. Air pollution inside the coal mines is basically due to the discharge of fugitive gases along with the production of total particulate matter (i.e PM10, PM2.5) (Masto et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Coal mines are scattered across the world and is considered as a significant pollution source contributing to release about 25\u0026nbsp;million tons of methane gas along with other fugitive gases (Singh 2005). Among the gases released may include methane (CH\u003csub\u003e4)\u003c/sub\u003e, hydrogen sulphide (H\u003csub\u003e2\u003c/sub\u003eS) and carbon monoxide (CO) (Pandey 2014).\u003c/p\u003e \u003cp\u003eSource of these gaseous emissions is mainly because of the coalification process. The coal formation process is known as coalification. Coal seam contain some amount of methane (CH\u003csub\u003e4\u003c/sub\u003e) along with other gases trapped within its body and is produced during the coalification process (Ju et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This considerable fraction of potent gas is trapped in the coal body under pressure and the contiguous rock strata. The trapped methane gas is diffused out during the mining operations when the coal seam is cracked along the process (Irving and Tailakov \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As the mine gets deeper, the amount of methane gas in the coal gets higher. With the proceeding process of the mining operations, methane is released inside the mine air and that eventually finds its way into the outer atmosphere (Lloyd \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMethane is a greenhouse gas that is considered to be a potent gas as compared to CO\u003csub\u003e2\u003c/sub\u003e. According to recent report methane gas is growing in the atmosphere at a faster rate than CO\u003csub\u003e2\u003c/sub\u003e. Emission of fugitive CH\u003csub\u003e4\u003c/sub\u003e, from coalmines are recorded around the globe, and represents about 8% of world\u0026rsquo;s total anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emissions. This concentration percentage of CH\u003csub\u003e4\u003c/sub\u003e emission constitutes about 17% input to the overall greenhouse gas emissions. Generally, coal mine CH\u003csub\u003e4\u003c/sub\u003e is a fine description for all emitted CH\u003csub\u003e4\u003c/sub\u003e prior to and after and also during the mining activities. There is a significant variability in the flow rate and composition of various emitted gases during the coal mining procedures. Considerably, in a typical gassy coal mine, CH\u003csub\u003e4\u003c/sub\u003e is emanated through three main streams: (1) Ventilation air (i.e. 0.1 to 1%), (2) Gases leaked from coal mineral before the mining operations (i.e. 60 to 95%), and (3) gases leaked from the operational areas of coal mine, e.g. goafs (i.e. 30 to 95%). Approximately about 64% of the coalmine CH\u003csub\u003e4\u003c/sub\u003e is contributed from the ventilation air methane mostly from a gassy coal mine (Su et al. 2005). In current study air is probed inside as well as outside of coal mines at the selected sites.\u003c/p\u003e \u003cp\u003eAnother significant pollution contributor around the coal mine is the atmospheric particulates. Contaminants transported by atmospheric dust has become an important concern around the world, as air masses loaded with significant amount of dust particles frequently spread across the intercontinental and continental boundaries and that consequently have adverse environmental problems in downwind depositional regions (Csavina et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The main source of atmospheric dust pollution in the coal mines are the typical mining operations i.e. blasting, drilling, cutting, loading and unloading of the extracted mineral, exposed pit faces and discarded overburden wastes (Huertas et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Basically, dust particles source in the coal mines can be categorized as a primary source that actually generate the dust particles but the secondary source which causes the dispersion of these dust particles and moves it from place to place is known as fugitive dust (Singh 2005). So the current study is basically designed to quantify and analyze the gaseous emission and particulate matter in order to appraise the quality of ambient air around the coal mining fields of the selected region. Seasonal studies are also conducted to evaluate a comparative analysis.\u003c/p\u003e \u003cp\u003eGeographic information system (GIS), provides a latest data analyzing, modeling and processing methods to assist the academics and decision-makers in order to interpret and provides a better way to visualize statistics to precede more significant and valuable research information. An analyst utilizes different sources of GIS facts and figures for a more depictive and supporting view of complicated situations (Jumaah et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Research information emerging in the field of quality assessment and monitoring of ambient air has adopted GIS, as a fundamental tool for characterizing and monitoring related problems. Moreover, there is a high demand and awareness of GIS managing or assessing the large spatially referenced data that are being sampled at small scales in the communal and environmental fields (Tian et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). More specifically in the current research, Inverse Distance Weighting (IDW) related to GIS has been used to achieve interpolation with the help of air quality concentration data (Kumar \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, GIS interpolation technique for fog mapping has been effectively applied and the research outcomes concluded a more obvious spatial distribution of contaminant in Malaysia (Dominick et al. 2012). Air pollution reduces the surrounding air quality which has profound effect on humans and the surrounding flora and fauna (Chaulya \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). So, the current study is focused to find the significant atmospheric dust fall, mineral content, and their morphological characteristics (Rout et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Researcher all around the world conducted studies to map the gaseous emission as well as particulate fallout from the coalmines (Campa et al. 2011; Ekbal et al. 2015; Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Baris \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePakistan\u0026rsquo;s largest province Balochistan covers an area of 347190 km\u003csup\u003e2\u003c/sup\u003e. Meteorological data suggest that it is the most driest province of country. It is geographically delimited\u0026nbsp;32\u003csup\u003eo\u003c/sup\u003e, 06\u0026apos; north and 60\u003csup\u003eo\u003c/sup\u003e, 52\u0026apos; east (Giri et al.\u0026nbsp;2017). The province climate is semi-arid continental (Durowoju et al.\u0026nbsp;2016). It has diversified precipitation pattern, annually records between 200-350 mm. Temperature of the region greatly varies with the elevation between -3\u0026ordm; to 38\u0026ordm; Celsius from above the sea level. The daily mean, minimum and maximum temperatures probed is 31 C\u003csup\u003eo\u003c/sup\u003e, 16\u003csup\u003eo\u003c/sup\u003eC and 27 C\u003csup\u003eo\u003c/sup\u003e respectively (Gurdal 2011). Balochistan is the custodian of vast reservoir of natural gas and barite along with many rare minerals. Besides, these resources huge deposits of minerals like magnesite, silica and sulphur are also present. The current research area hosts the huge deposits of coal mineral mainly of bituminous to sub-bituminous and also lignite coal type. Among the vast coalfields of Balochistan, five most functional and largest coal fields were selected as monitoring stations i.e. Chamalang monitoring stations (C1), Duki monitoring stations (D2), Harnai monitoring stations (H3), Khost monitoring stations (K4) and Sharagh monitoring stations (S5) as depicted in fig 1. These coal mining fields are scattered across the four districts of province Balochistan i.e. District Loralai, District Duki, District Harnai and District Ziarat.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAir sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeasonal sampling was done for a comparative study in the current study. First batch of samples were collected during the summer season from 5\u003csup\u003eth\u003c/sup\u003e June till 5\u003csup\u003eth\u003c/sup\u003e August 2018, and for the second batch of samples, sampling was conducted during the winter season from 20\u003csup\u003eth\u003c/sup\u003e January till 20\u003csup\u003eth\u003c/sup\u003e March 2019. It was observed during the field survey, all the coal mining operations were continuously performed for a labor of 24 hours by taking short break intervals for meals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssessment for the air quality, an inside as well as outside of the underground coal mine was evaluated for different concentrations of emitted gases like CH\u003csub\u003e4\u003c/sub\u003e, CO, H\u003csub\u003e2\u003c/sub\u003eS and O\u003csub\u003e2\u003c/sub\u003e for the comparative study. These gases were probed by Portable Multi 4 Gas Detector (Model CD4) (Jeremy et al. 2018). For the measurement of total particulate matter (i.e. PM10), samples were collected on a pretreated (oven dried) and weighted Teflon filter paper of size 8\u0026rdquo; \u0026times; 10\u0026rdquo; using a portable high-volume air sampler (\u0026micro; 10 inlet) and the model used was Graseby Andersen/ GMW Model 1200 with average flow rate of 36 acfm. High-volume sampler was installed at each coalmining filed sampling station (i.e. C1, D2, H3, K4 and S5). HVS was operated in a standard shelter following the manual method and successfully collected an 8-hrs sample. HVS was operated during mainly the daytime from 9 am till 5 pm and near the most operational signal points (Antoszczyszyn et al. 2016). Two air samples were collected on monthly basis for about three months at each of the monitoring stations. So, a total of 30 samples were collected per season. Sampling was carried out under the same environmental conditions (Pandey 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of CH\u003csub\u003e4\u003c/sub\u003e, CO, H\u003csub\u003e2\u003c/sub\u003eS and O\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePortable Multi 4 Gas Detector (Model CD4) was used to probe the potent gases. Device was first calibrated and than a complete procedure was followed as instructed by the manual provided in the device kit. Concentration was first recorded inside the underground coalmine and then compared with the outside gaseous concentration. Data logger software was used to save, display and analyze data as recorded for each coal mining monitoring stations using multi-gas detector during both the seasons. Furthermore, Microsoft Excel data sheets were prepared for correlation matrix analysis (Jeremy et al.\u0026nbsp;2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Particulate Matter (PM10)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePM10 calculation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTeflon filter paper was used to collect sample of total particulate matter i.e. PM10. Filter paper was first dried in oven for about 5-6 mints at 100\u003csup\u003e0\u003c/sup\u003eC to remove moisture content. The oven dried filter paper was then carefully weighed and noted before sampling. After the completion of sampling procedure via HVS, filter paper was again carefully weighed. The average flow rate of the operating sampler was also noted manually. The data obtained was followed by a calculation done step by step for PM10 (EPA 2004). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eHot acid extraction procedure was carried out for heavy metals concentration in the atmospheric dust collected via HVS (i.e. PM10). First, a strip size of 1\u0026rdquo; \u0026times; 8\u0026rdquo; from 8\u0026rdquo; \u0026times; 10\u0026rdquo; was cut from each of the sampled filter paper using a pizza cutter. About 20 ml acid solution of HCl/HNO\u003csub\u003e3\u003c/sub\u003e was pipette out into pretreated and labeled 150 ml Griffin beaker. Then by using a plastic forcep the strip was gently placed lower in a beaker containing the acid mixture (HCl/HNO\u003csub\u003e3\u003c/sub\u003e) so that the strip was entirely covered. Beaker was then placed on the hot plate contained in a fume hood. Beaker was roofed with a watch glass for 30 mints. The processed solution after time laps was later on allowed to cool at room temperature. Beaker wall was than rinsed with de-ionized water. Then about 20 ml of reagent water was added to beaker and placed to stand for about 30 minutes. This step allowed the acid to diffuse from the filter into the rinse water. The extraction was then transferred into 50 ml volumetric flask. Beaker walls was again rinsed and added to the flask. Extraction was than diluted with Type I water up to the mark. A Teflon syringe was used to pull-up the sample. A filter disc was placed on syringe and the sample was placed into the sterile 30 ml centrifuge tube. Tube was filled up to 20 ml of filtered digestate. Finally, the extraction was ready for analysis. Atomic absorption spectrophotometer was used for heavy metals analysis (Ehi-Eromosele et al. 2012). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAir Quality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelation matrix analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA widely applied Pearson\u0026rsquo;s Correlation Coefficient was employed for air quality data to know the significant interrelationship among the analyzed variables. Correlation matrix analysis was carried out via XL STAT (2019). \u0026nbsp;Excel sheets were prepared using Microsoft Office 365 ProPlus.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInverse Distance Weighted (IDW) Interpolation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDuring the field sampling in both seasons, each sampling station for air data was located by mean of hand-held portable GPS device i.e. Garmin eTrex GPS. Each coordinate point was than imported to GIS software i.e. ArcMap 10.2 through a point layer. Excel data sheets containing analysed results of pollutant gases, Particulate matter (PM10) and concentration of heavy metals were then transported to ArcMap 10.2. IDW technique was used to delineate the spatial dispensation of selected pollutant gases and heavy metals (Kumar 2016).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003e\u003cstrong\u003ePhysicochemical Analysis of Air Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon analysis of the collected air samples, table 1 and 2 shows the average value for each of the analyzed air quality parameter for all the selected sampling station during both the seasons.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003eChamalang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003eDuki\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eHarnai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eSharagh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eKhost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003eStandards\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003ePM10 \u0026micro;g/m3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e737.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e360.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e374.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e372.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e368.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50 \u0026micro;g/m3 \u0026nbsp; (WHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.1 % (NIOSH)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e20.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e20.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e21 % (WHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eS ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCO ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e4.37 (EPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCd ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCr ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.002 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCo ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.1358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCu ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.01 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eFe ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e1.4592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e2.2676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e5.9451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e1.9817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e3.4014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.3 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003ePb ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.02 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eMn ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eNi ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.1711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.1656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.2871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.006 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePhysicochemical analysis of air samples\u0026nbsp;collected during summer season\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003eChamalang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003eDuki\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eHarnai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eSharagh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eKhost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003eStandards\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003ePM10 \u0026micro;g/m3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e758.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e366.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e725.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e378.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e365.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50 \u0026micro;g/m3 \u0026nbsp; (WHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.1 % (NIOSH)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e20.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e19.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e21 % (WHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eS ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003eN.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCO ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e4.37 (EPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCd ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCr ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.1403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.2303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.002 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCo ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.1132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eCu ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.01 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eFe ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e9.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e6.1915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e22.2521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e7.4831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e14.8775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.3 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003ePb ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.02 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eMn ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.0692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.001 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.908242612752721%\"\u003e\n \u003cp\u003eNi ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.81959564541213%\"\u003e\n \u003cp\u003e0.1546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.26438569206843%\"\u003e\n \u003cp\u003e0.1435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.2208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.864696734059098%\"\u003e\n \u003cp\u003e0.0994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.41368584758943%\"\u003e\n \u003cp\u003e0.006 (U.S National Ambient Air Concentration)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePhysicochemical analysis of air samples collected during winter season\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeasonal Variation of PM10, CH\u003csub\u003e4\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e and CO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults showed that the average PM10 concentration during both the season was about 737.46 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 368.73 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e respectively. No significant variation for PM10 concentration was recorded at Duki, Khost and Sharagh station. While PM10 sampled at Harnai study site showed elevated PM10 concentration during winter (i.e.737.46 \u0026micro;g/m3) and lower during summer (i.e. 368.73 \u0026micro;g/m3). The overall average result calculated for PM10 is depicted in fig 2a and conclude that the concentration exceeds the safe permissible limit as set by the World Health Organization (WHO) for all the selected coal mining sites. Elevated levels of PM10 concentration around the coal mining area is mainly because of coal mining activities like blasting, drilling, loading, and unloading of overburdens, loading and unloading of excavated coal, exposed pits faces and exhausts from machinery (Pandey et al. 2014). The results for PM10 are in line with several other research studies as conducted by Nayak et al (2018), Gutam et al (2016), Pokorna et al (2016) and Pandey et al (2019). Further analysis of atmospheric dust (PM10) for heavy metals showed a great variation in both seasons throughout the selected sampling stations. All the traces of heavy metals associated with PM 10 were found to cross the safe permissible limits as set by U.S National Ambient Air concentration. WHO has no safe permissible limits set for the trace elements associated with dust (PM 10) in the ambient air.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethane (CH\u003csub\u003e4\u003c/sub\u003e) gas concentration was higher inside the deep underground coal mines at most of the selected study sites as compare to the outside concentration. However no significant seasonal variation was observed at all study sites. During winter season the highest inside as well as outside CH\u003csub\u003e4\u003c/sub\u003e concentration recorded was about 1.41% and 1.44% at Khost station. Inferring the overall results as shown in fig 2b shows that the CH\u003csub\u003e4\u003c/sub\u003e concentration recorded at all the study sites in both seasons exceeded the permissible limits as declared by Nation Institute for Occupational Safety and Health (NIOSH). Quantification of Oxygen levels with multi-gas detector, inside as well as outside the coalmines as displayed in fig 2c clearly shows that the O\u003csub\u003e2\u003c/sub\u003e level at each sampling site in both seasons was very low based on the required limit set by Environmental Protection Agency (EPA) i.e. 21%. But O\u003csub\u003e2\u003c/sub\u003e level inside coalmines significantly dropped as compared to the outside levels. However, Concentration of O\u003csub\u003e2\u003c/sub\u003e was found to be lower inside coalmines, due to the heavy accumulation of CH\u003csub\u003e4\u003c/sub\u003e and CO consequently causes lowering of the O\u003csub\u003e2\u003c/sub\u003e level. This can be attributed to the poor ventilation system inside the coalmines. \u0026nbsp;Several studies conducted throughout the world tend to support the current results for the pollution assessment and accumulation of such gaseous emission around the coalmines i.e. Bibler et al (1998) and Kirchgessner et al (1993).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Carbon monoxide (CO) along with other gases was also detected. The obtained results as illustrated in fig 2d showed the exceeded level of CO and most likely to cross the permissible limit recorded inside as well as outside the underground coalmines at all study locations during both the seasons. While safe level of CO concentration was recorded outside the coalmine at Chamalang monitoring station. The highest level of CO was detected outside the Khost coalmine station during both the seasons i.e. 21 and 22 ppm respectively. Similarly, the highest CO level inside the coalmine during summer and winter was also recorded at Khost i.e. 18 and 16 ppm. During the coal excavation CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eand CO is released and accumulated inside coalmine if not properly ventilated. Therefore, readings acquired through gas detector showed a mix variation during both the seasons, but it was clearly observed that the gases concentration probed inside were significantly heavier than outside of coalmines. WHO has no permissible exposure limit for CH\u003csub\u003e4\u003c/sub\u003e but NIOSH maximum recommended safe methane concentration is about 0.1 %. Similarly, EPA has not set a required concentration for O\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ebut a safe limit has been set for CO. Moreover, WHO has set a required O\u003csub\u003e2\u003c/sub\u003e concentration i.e. 21%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinkage Analysis of Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of complex matrix was carried out between the different air quality parameters that included PM10, CH\u003csub\u003e4\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e, CO and heavy metals associated with PM10 (i.e. Ca, Cd, K, Co, Cu, Cr, Fe, Mn, Pb and Ni) in order to apprehend interrelationship among the variables.\u0026nbsp;Pearson Correlation matrix analysis\u0026rsquo;s result for both the seasons is shown in Table 3. Color scheme depicted in fig 3 represent a more visual presentation of correlation matrix analysis. Examining the analysis result of correlation pattern between variables revealed an irregular pattern. Strong positive and negative correlations were observed. Among different quantified gases, one of the most substantial gas like methane (CH\u003csub\u003e4\u003c/sub\u003e) revealed a significant correlation with most of air quality parameters. CH\u003csub\u003e4\u003c/sub\u003e showed a strong negative association with O\u003csub\u003e2\u003c/sub\u003e (-0.84) and Ni (-0.85). With the increasing concentration of CH\u003csub\u003e4\u003c/sub\u003e gas leads to cause a decrease in the O\u003csub\u003e2\u003c/sub\u003e level inside the coalmines as well as outside the coal mine. As CH\u003csub\u003e4\u003c/sub\u003e gas has a binding nature, when methane is released from coal seams during mining activities it can binds with oxygen and thus decreasing the level of oxygen. So, the decrease in O\u003csub\u003e2\u003c/sub\u003e level can cause the decline in the quality of air (Yusuf \u003cem\u003eet al\u003c/em\u003e., 2016). \u0026nbsp;Strong positive association was shown by CH\u003csub\u003e4\u003c/sub\u003e with the Cu i.e. 0.79. Similarly, O\u003csub\u003e2\u003c/sub\u003e showed a strong positive association with Ni (0.87) and strong negative association with Cu (-0.86), Cr (-0.66) and CO (-0.61). CO showed a slight positive response with Cr (0.55) and slight negative response with Ni (-0.51). Slight positive response was also showed by Cr.\u003c/p\u003e\n\u003cp\u003eCorrelation between heavy metals extracted from the PM10 samples also illustrated significant result. Cd showed a slight positive correlation with Mn (i.e. 0.44) but a well-built negative association was shown i.e. Pb (-0.63) and Co (-0.52). Similarly, Cr had a significant positive correlation with Co with the value of 0.51 and a negative relationship was observed with Ni (-0.59). Slight negative relation was also observed with Co i.e. -0.40. Whereas Co has a strong association with Pb. Iron (Fe) had a strong positive impact on Mn (i.e. 0.75) and showed a strong association with Ca (i.e. 0.60). Similarly, Ni had a strong negative association with Cu (-0.87), CH4 (-0.85), Cr (-0.59) and CO (-0.51). Moreover, a positive correlation represents the same source i.e. the coal mineral. All these sets have a strong positive association. Thus, represents a same source. While sets with negative correlation are Ni-Cu, Ni-CH\u003csub\u003e4\u003c/sub\u003e, Ni-CO, Ni-Cr, O\u003csub\u003e2\u003c/sub\u003e-Cr, O\u003csub\u003e2\u003c/sub\u003e-Cu, O\u003csub\u003e2\u003c/sub\u003e-CO, Mn-Pb, Co-Pb and Cd-Pb. The negative sets of association of Ni and Pb shows that the source is of anthropogenic source. Many research studies have been conducted around the coalmines for air quality assessment adapting the Pearson\u0026rsquo;s correlation analysis and support the current results for correlation analysis (Dubey et al.\u0026nbsp;2012; Bray et al.\u0026nbsp;2017; Huertas et al.\u0026nbsp;2012; Tripta et al.\u0026nbsp;2015). K and Ca had no significant interaction with each of the heavy metals and also with the quantified gases within the study area.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCO ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCd ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCa ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCr ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCo ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eCu ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eFe ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003ePb ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eMn ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eNi ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003eK ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCO ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCd ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCa ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCr ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCo ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eCu ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eFe ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003ePb ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eMn ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eNi ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.693053311793214%\"\u003e\n \u003cp\u003eK ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.946688206785137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\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\u0026nbsp;\u003cstrong\u003eTable 3. Correlation matrix of air quality parameters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial analysis of Air Quality parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Spatial examination of air quality data showed significant results. In current study methane, oxygen and carbon monoxide showed a very alarming distribution patterns throughout the sampling stations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpatial distribution of Methane, Oxygen and Carbon monoxide\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eQuantification of Methane (CH\u003csub\u003e4\u003c/sub\u003e) gas inside and outside the underground coal mines at selected coalmining fields during the two seasons i.e. summer and winter is illustrated in fig. 4. Interpreting the spatial distribution of map obtained for CH\u003csub\u003e4\u003c/sub\u003e concentration showed that the highest concentration was observed at Khost and lowest at Duki coalmining sites. CH\u003csub\u003e4\u003c/sub\u003e concentration recorded at all study sites was above the allowable permissible limits as assigned by the NIOSH i.e. 0.1 % during both seasons. Spatial distribution of O\u003csub\u003e2\u003c/sub\u003e is displayed in fig 5. Observing the maps, it is clearly noticeable that the O\u003csub\u003e2\u003c/sub\u003e concentration is significantly very low in the coalmining site of Khost during both seasons. And the overall O\u003csub\u003e2\u003c/sub\u003e concentration recorded at each of study sites showed that the concentration of O\u003csub\u003e2\u003c/sub\u003e is very low according to the required concentration set by WHO that is 21% during summer and winter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcentration of CO as depicted in fig 6 showed that the CO concentration was higher at Khost mining filed as compare to other coalmining sites during both the seasons. The safe permissible limit set for CO in ambient air according to Environmental protection agency (EPA) is about 4.37 ppm. By comparing the current values recorded at all the study sites showed that the values exceeded the safe permissible limit. No significant result was obtained for hydrogen sulphide (H\u003csub\u003e2\u003c/sub\u003eS). The study sites had no feasible of source of H\u003csub\u003e2\u003c/sub\u003eS gas.\u003c/p\u003e\n\u003cp\u003eVariation pattern obtained on maps for gases like CH\u003csub\u003e4\u003c/sub\u003e and CO showed that during both seasons the concentration level for mentioned gases is maximum at the Khost mining site. Similarly, maps obtained for O\u003csub\u003e2\u003c/sub\u003e level also showed that the levels of O\u003csub\u003e2\u003c/sub\u003e were minimum at Khost during both the seasons. This can be attributed to the poor ventilation system inside the coalmines of Khost study area. Poor ventilation declines the quality of air inside the underground coalmines and is very dangerous for the mine workers. \u0026nbsp; Several studies conducted at different coal mining sites around the world, significantly supports the current results and methodology of representation of obtained results via GIS tool i.e. IDW interpolation technique (Espitia-perez et al. 2018; Jha et al. \u0026nbsp;2011; Salve et al. 2007; Squizzato et al. 2018).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAssessment of air quality and monitoring was the chief objective of current study. The monitoring data recorded around the selected coal fields of Balochistan revealed significant spatial and seasonal variations. The concentration level of PM10, CH\u003csub\u003e4\u003c/sub\u003e, CO, O\u003csub\u003e2\u003c/sub\u003e and heavy metals in PM10 samples were alarming. The seasonal variations depend on source of pollutant formation and emission or pollutant distribution mechanisms, these factors are also affected by other meteorological factors like wind speed, precipitation pattern, relative humidity and distance from the source of pollution area.\u003c/p\u003e \u003cp\u003eComputation of PM10 concentration concluded that all the study sites are highly polluted, and the values are above the safe permissible limits set by WHO. CH\u003csub\u003e4\u003c/sub\u003e concentration crossed the required limit as set by NIOSH. Higher level of CH\u003csub\u003e4\u003c/sub\u003e can lead to mine fire and creates explosive hazards if not properly ventilated. Level of O\u003csub\u003e2\u003c/sub\u003e was below the required concentration as assigned by EPA. Continuous accumulation of gases causes the drop in O\u003csub\u003e2\u003c/sub\u003e levels. This can lead to suffocation, a life threatening situation for all the underground mine workers. Concentration of CO was found above the permissible limit set by WHO standard. CO is a toxic gas as it is released when coal is oxidized. So, when exposed to as little as 0.1% of CO can cause death within few minutes. Accumulation of these dangerous gases was the result of continuous mining activities without a proper ventilation system.\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis showed significant association between variables of the same origin and that of anthropogenic sources. Interpolation distance weight (IDW) maps generated showed a significant variation of parameter concentration across the five coalmining monitoring stations. Most significant results were generated for Khost coal mine monitoring station followed by Harnai monitoring station.\u003c/p\u003e \u003cp\u003eIt can be concluded for the air quality assessment that the air in the suburbs of coalmine was deteriorated. The primary air pollution contributors at study site were overall coal mining activities and the secondary contributors were vehicular emissions, mine fire, windblown through overburdens and unpaved roads. These are main overall factors that deteriorate the air quality around the coal mines. Moreover, annually many incidents in the underground coalmines are reported throughout the Balochistan province. Accidents in coalmines caused the death of about hundreds of coal mine workers either in ferocious fire erupted after trapped methane gas exploded inside mine or developed a severe health condition due to lack of training and facilities. The evaluated air quality data enabled an insight for policy makers for the air quality management and ecological conservation. Hence, the study provides a global scenario for air pollution around active underground coalmines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author most graciously acknowledges Fatima Jinnah Women University, Rawalpindi and Higher Education Commission (HEC) of Pakistan by providing Indigenous fellowship throughout her Ph.D. studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Ayesha Ayub and Sheikh Saeed Ahmad; Methodology: Ayesha Ayub and Sheikh Saeed Ahmad; formal analysis and investigation: Ayesha Ayub; writing\u0026mdash;original draft preparation: Ayesha Ayub; writing\u0026mdash;review and editing: Ayesha Ayub and Sheikh Saeed Ahmad.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that the study does not involve human or animal subjects.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHao, Y. \u003cem\u003eet al\u003c/em\u003e. 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Air Quality in Lanzhou, a Major Industrial City in China: Characteristics of Air Pollution and Review of Existing Evidence from Air Pollution and Health (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air quality, Heavy metal analysis, Pearson correlation coefficient, Spatial analysis, Inverse distance interpolation","lastPublishedDoi":"10.21203/rs.3.rs-2040898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2040898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmission of methane from the underground coalmine is currently a global concern. The study aims to quantify the emission of potent toxic gases along with atmospheric dust in the suburbs of underground coal mines, in the field of Balochistan Pakistan. Related variables selected for quality check included particulate matter (i.e. PM10), CH\u003csub\u003e4\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e, CO and elemental composition of PM10 (i.e. Cr, Cd, Co, Fe, Cu, Pb, Ni and Mn). A seasonal comparative study was designed. Widely applied GIS tool (i.e.IDW) was incorporated. Strengthening data with correlation matrix analysis apprehended interrelationship among the variables. Air quality variables were found above the safe allowable limits set by various standards (WHO, EPA, NIOSH, U.S National Ambient Air Concentration). No significant seasonal variation was recorded; but the pollutant concentration remained elevated during both seasons. Pearson correlation matrix analysis showed that CH\u003csub\u003e4\u003c/sub\u003e had a strong negative correlation with O\u003csub\u003e2\u003c/sub\u003e. Moreover, air probed inside the underground coalmine showed a deteriorated status. This alarming status is primarily attributed to all the mining activities and secondarily to vehicular emissions, mine fire and poor ventilation system. This study will provide a baseline data for concerned authorities for planning management, pollutant prevention and strategies for environmental monitoring in future.\u003c/p\u003e","manuscriptTitle":"Characterization of regional CH 4 emanation and total particulate pollution from the underground coal mines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-14 20:53:10","doi":"10.21203/rs.3.rs-2040898/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c09865d-0863-4801-b2a9-fb71a7bda7d1","owner":[],"postedDate":"September 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-22T03:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-14 20:53:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2040898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2040898","identity":"rs-2040898","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}