Identification Of VOCs Research Articles

A novel microwave pretreated hot air drying (PMt-HD) process for improving drying efficiency and drying quality of Z. bungeanum

The traditional drying method of Fresh Z. bungeanum has the problems of long drying time and serious loss of nutrients, which seriously restricts its efficient and high-value utilization. This study utilized a self-developed penetrating microwave infrared hot air rolling bed dryer (PMIHRBD) to optimize the drying process and compare microwave hot air drying, microwave pretreated hot-air drying (PMt-HD), and freeze drying with hot air drying (HD). The results showed that the drying time of PM10-HD was 48% shorter than that of HD, and the contents of TFC, TPC and TAC were increased by 17.6%, 23.7% and 20.7%, the yield of essential oil was increased by 59.6%, and the antioxidant activity was significantly enhanced. According to the identification of VOCs, hydrocarbons, esters and alcohols played a major role in the formation of Z. bungeanum odor, and HD samples had similar characteristics to PM10-HD samples. For flavor, PM10-HD showed a higher five-flavor richness than other drying processes. In summary, PM10-HD provided a fast and efficient drying process for harvesting high quality Z. bungeanum.

Real‐time mildew detection and gradation in simulated containerized soybeans: Insights from GC‐IMS analysis of mVOCs and VOCs

AbstractIn the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real‐time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography‐ion migration spectroscopy (GC‐IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3‐methyl‐2‐butenal, etc. Therefore, the combination of GC‐IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation.

Sawtooth wave temperature modulation measurement method for recognizing five kinds of VOCs based on ZnO gas sensor

The dynamic measurement of temperature modulation can improve selectivity of semiconductor gas sensor. However, most of dynamic measurements only have one or two characteristic peaks, which makes great difficulties to subsequent gas identification. To solve this problem, this paper increased the characteristic peaks of dynamic response of ZnO sensor by adjusting the period of sawtooth wave, and expanded the difference of dynamic responses of different gases, so as to realize the efficient detection and identification of different VOCs. The experiments find that when the period of sawtooth wave is 90 s, the dynamic responses of the sensor to the five gases of ethanol, toluene, 2-butanone, acetone and ethyl ether are better, with many characteristic peaks. Combined with support vector machine, the recognition rate reaches 100 %, and it is difficult to misjudge the type and concentration of gas, which improves the selectivity.

Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning

Accurate and selective detection of target gas/volatile organic compounds (VOCs) is of utmost importance. The chemiresistive gas sensors have been a desirable candidate due to their compact footprint and ease of fabrication, but they show poor selectivity. This work presents a combination of nanomaterials-based chemiresistive gas sensors with machine learning (ML) techniques to achieve sensitive, selective, and quantified detection of tested VOCs. The sensor array consists of four separate sensing layers over interdigitated electrodes-based platform. The sensing materials were comprised of silver, gold, palladium, and platinum nanoparticles decorated on tin oxide hollow-sphere structures which were successfully synthesized through chemical routes and characterized accordingly. Surface decoration of different metal nanoparticles has produced sensitive and diverse sensing patterns among the tested VOCs. The sensing mechanism and related gas sensing kinetics were then analyzed to explain high sensitivity and diverse sensing phenomena. The subsequent incorporation of ML models has resulted in qualitative and quantitative detection of VOCs. A comparative analysis was carried out among different types of applied features and ML models with reasoning. Particularly, a deep neural network (DNN) model with time series (TS) response sequence as input information, delivered the best performance. The DNN_TS model presented an average classification accuracy of 98.33 %, in conjunction with excellent concentration prediction. The DNN_TS model showed a very fast prediction time of 2.74 µs with adaptive learning while utilizing minimum computing resources, which favors the real-time sensing capability. The reported results promote the development of an autonomous, smart, and selective gas sensor system for real-time applications.

Novel combined waveform temperature modulation method of NiO-In2O3 based gas sensor for measuring and identifying VOC gases

The extremely widely applied metal oxide semiconductor (MOS) gas sensors suffer from underdeveloped selectivity, and dynamic measurement methods are expected to solve this issue. In this paper, the In 2 O 3 and NiO-In 2 O 3 gas sensors were prepared, and then their common gas sensing performances were measured. The response and response time of the NiO-In 2 O 3 gas sensor were more excellent, and thus it was chosen for dynamic measurement. We perform periodic temperature modulated dynamic measurement of six common VOCs. By comparing and analyzing the response curves under common heating waveforms, a combined waveform of rectangular wave and triangular wave is customized as the heating waveform. We can obtain response curves with rich features and high amplitude, which is beneficial to the qualitative and quantitative identification of VOCs. The gas types are first identified employing a support vector machine (SVM) with 100% accuracy with a stepwise identification strategy. Subsequently, the data set is processed applying principal component analysis (PCA) and the principal components with a cumulative contribution above 90% are selected for concentration prediction. Polynomial fitting is used for quantitative analysis of VOCs with a relative error of regarding 5%. The results are indicative that the combination of rectangular-triangular wave temperature modulation and stepwise identification scheme can effectively solve the issue of underdeveloped selectivity of gas sensors. • The sensing properties of In 2 O 3 and NiO-In 2 O 3 composites to VOCs were compared and NiO-In 2 O 3 was more excellent. • A combined waveform of rectangular and triangular was customized as the heating waveform. • The gas types were identified employing a support vector machine (SVM) with 100% accuracy. • Concentration prediction was performed applying a combined algorithm with a relative error of regarding 5%.

Single-Atom Tailoring of Two-Dimensional Atomic Crystals Enables Highly Efficient Detection and Pattern Recognition of Chemical Vapors

Low-dimensional semiconductor materials, such as single-walled carbon nanotubes, two-dimensional (2D) atomic crystals, and organic frameworks, have been widely adapted as ideal platforms to construct various chemo/biosensors with satisfying sensitivity. However, the general drawbacks in chemiresistive devices, including high operation temperatures, low response to low-polarity molecules, and poor selectivity, have limited their real-world applications. In this study, 2D materials (graphene, MoS2, and WSe2) were systematically functionalized with series of monodispersed single atomic sites (Pt, Co, and Ru) through a facile approach to construct single-atom sensors (SASs) for the detection of VOCs at room temperature. The structural and catalytic characteristics of SAs successfully translated into enhanced gas-sensing performance, with a 1-2 orders of magnitude increase in relative response to ethanol (@5 ppm) and acetone (@20 ppm) vapors (in all M-2D SASs as compared to pristine substrates), high selectivity to VOCs against relative humidity (M-WSe2 SASs), and fast response/recovery time (11/58 s for Pt-Graphene and 22/48 s for Pt-MoS2 to 50 ppm ethanol, 9/57 s for Pt-Graphene and 15/75 s for Pt-MoS2 to 200 ppm acetone) that are several times faster than the pristine 2D materials. Density functional theory (DFT) calculations revealed the signaling mechanism in SASs, and the data were further trained to build machine learning (ML) models for predicting the adsorption energies and sensing performance using the features of adsorption heights, metal charge, and charge transfer between the adsorbed VOCs and SASs sites. Finally, the rich combination of the metal single atoms and 2D atomic crystal supports were converted to cross-sensitive SA sensor array that allows for detection and identification of different VOCs.

The Identification, Periodic Variation and Spreading Risk of VOCs Released from a Pharmaceutical Factory

The Identification, Periodic Variation and Spreading Risk of VOCs Released from a Pharmaceutical Factory

Source Identification of VOCs and their Environmental Health Risk in a Petrochemical Industrial Area

Ambient VOCs in the vicinity of a petrochemical industrial area were analyzed for their health impact and potential emission sources. Comprehensive measurements of VOCs were conducted based on U.S. EPA TO-15. Potential carcinogenic and non-carcinogenic inhalation risks were evaluated by comparing the measured concentrations with the inhalation unit risk (IUR) and reference concentration (RfC). The results indicated that a high carcinogenic risk occurred from 1,2 dibromoethane and benzene, while non-carcinogenic risks were attributed to 1,3 butadiene, 1,1,2 trichloroethane, and 3-chloropropene. The Positive Matrix Factorization (PMF) Version 5.0 was further utilized to estimate the contribution of specific sources to the VOC mixing ratio. The results revealed that the average VOC concentration in the community area was dominated by aromatic hydrocarbons, with toluene having the highest concentration. Vehicle exhaust was evaluated as the most contributing emission source of the VOC mixing ratio, followed by industrial processes. Specific VOC ratios were also applied to identify VOC sources. The T/B ratio was within the range 3.54–5.15, confirming that vehicle emissions were the main source of pollutants during the entire investigated period in the community area. As for the industrial area, the average VOC concentration was dominated by alkenes. Industrial processes and the petrochemical industry were the major sources of VOCs. The health risk assessment in the industrial area indicated that acrolein had the highest risk for non-carcinogens. 1,2-dichloroethane and 1,3-butadiene showed high potential as carcinogens.

Identification of key volatile organic compounds in aircraft cabins and associated inhalation health risks

The identification of key VOCs during flights is important in creating a satisfactory aircraft cabin environment. Two VOC databases for the building indoor environment (from 251 occupied residences) and the aircraft cabin environment (from 56 commercial flights) were compared, to determine the common compounds (detection rate (DR) > 70%) in the two environments and the characteristic VOCs (only those with high DR during flights) in aircraft cabins. Possible VOC emission sources in flights were also discussed. As TVOC is usually viewed as a general indicator of air quality, the prediction of TVOC concentration was carried out using BP neural network algorithm, and the average error between the predicted and measured values was 55.35 μg/m3 (R2 = 0.80). Meanwhile, the VOCs’ inhalation cancer/non-cancer risks to crew members and passengers were calculated on the basis of detection rates, exposure concentrations, and health risk assessments. Six compounds (i.e., formaldehyde, benzene, tetrachloroethylene, trichloromethane, 1,2-dichloroethane, and naphthalene) were proposed as the key VOCs in the existing aircraft cabin environment, presenting a risk to crew members that is higher than the US EPA proposed acceptable level (evaluated mean value > 1E-06). The estimated lifetime excess cancer/non-cancer risks for passengers were all below the assessment criteria. Based on a summary of various VOC limits in five built environments, hierarchical design of VOC concentration limits is recommended for the aircraft environment.

The Identification, Periodic Variation and Health Risk Assessment of Vocs Released from Pharmaceutical Factory

The Identification, Periodic Variation and Health Risk Assessment of Vocs Released from Pharmaceutical Factory

Design of MEMS Cantilever Sensors for Identification of VOCs Using IntelliSuite

Abstract In this paper design of VOC sensor using MEMS is presented. A rectangular MEMS cantilever with dimensions 225µmx80µmx2µm is designed and simulated. The deflection property was studied analytically as well as with Intellisuite software. It was observed that the analytical and simulation results differ by 5%. Later in the micro cantilever was tested experimentally on Omnicant with Acetone as an analyte. It was observed that the experimental and simulation results were correlating with the analytical results.

The choice between flowers of closely related plant species by generalist pollinator: identification of relevant VOCs

False blister beetles of the genus Oedemera (Coleoptera: Oedemeridae) are known as generalist pollinators of many plant species, but their mutualistic interactions are still insufficiently explored. The aim of the study was to identify the floral compounds emitted by sympatric and simultaneously co-flowering strawberry species that could serve for Oedemera beetles as olfactory cues of suitable flowers. In the test locality, flowers of Fragaria vesca and Fragaria viridis were visited by Oedemera lurida and Oedemera virescens beetles. O. lurida preferred flowers of F. viridis to those of F. vesca, whereas O. virescens tended to prefer F. vesca to F. viridis. GC-EAD analysis revealed that O. lurida perceived three compounds of F. vesca and four compounds of F. viridis flowers. Among these, nonanal, 2-phenylacetaldehyde and an unidentified compound (UC1) were common to both strawberries, while the amount of one compound (1,4-dimethoxybenzene) required to elicit an EAG response was sufficient in F. viridis, but not in F. vesca. Our results suggest that O. lurida are capable of choosing between flowers of the two strawberries based on VOCs’ with 1,4-dimethoxybenzene as a relevant key component. O. virescens responded to eight compounds present in F. vesca flowers. The flowers contained three compounds EAG-active towards O. lurida and O. virescens (nonanal, 2-phenylacetaldehyde, UC1) and five compounds EAG-active towards O. virescens only (acetic acid, decanal, (E)-non-2-enal, linalool, and UC2). In F. vesca and F. viridis flower extracts as well as headspaces, the identified compounds differed qualitatively only. (E)-Non-2-enal and acetic acid were recorded for the first time in Fragaria. Local specialization of Oedemera beetles in flower visiting was proved and relevant VOCs were identified.

Identification and quantification of VOCs by proton transfer reaction time of flight mass spectrometry: An experimental workflow for the optimization of specificity, sensitivity, and accuracy.

Proton transfer reaction time of flight mass spectrometry (PTR‐ToF‐MS) is a direct injection MS technique, allowing for the sensitive and real‐time detection, identification, and quantification of volatile organic compounds. When aiming to employ PTR‐ToF‐MS for targeted volatile organic compound analysis, some methodological questions must be addressed, such as the need to correctly identify product ions, or evaluating the quantitation accuracy. This work proposes a workflow for PTR‐ToF‐MS method development, addressing the main issues affecting the reliable identification and quantification of target compounds. We determined the fragmentation patterns of 13 selected compounds (aldehydes, fatty acids, phenols). Experiments were conducted under breath‐relevant conditions (100% humid air), and within an extended range of reduced electric field values (E/N = 48–144 Td), obtained by changing drift tube voltage. Reactivity was inspected using H3O+, NO+, and O2 + as primary ions. The results show that a relatively low (<90 Td) E/N often permits to reduce fragmentation enhancing sensitivity and identification capabilities, particularly in the case of aldehydes using NO+, where a 4‐fold increase in sensitivity is obtained by means of drift voltage reduction. We developed a novel calibration methodology, relying on diffusion tubes used as gravimetric standards. For each of the tested compounds, it was possible to define suitable conditions whereby experimental error, defined as difference between gravimetric measurements and calculated concentrations, was 8% or lower.

Source Identification of VOCs In METU Campus Through Factor Analysis

In this study, 55 ozone precursor VOCs, which are routinely measured in PAMS (Photochemical Assessment Monitoring Station) were measured in a suburban station located at Middle East Technical University, Environmental Engineering Department in, Ankara. Daily air samples were collected in evacuated canisters between January – December, 2014. Collected samples were analyzed with GC-FID system and concentrations of 51 VOCs were determined. Mean VOC concentrations ranged between 0.048±0.061 µg m-3 (cis-2-penten) and 10±13 µg m-3 (toluene). Average benzene concentration was 1.49 ± 1.74 μg m-3. Factor Analysis (FA) was applied to determine the major sources of VOCs that contribute to the measured concentrations in the university campus. FA application revealed nine factors that can be grouped under four major components, including (1) transportation: gasoline vehicle exhaust emissions, evaporative losses from gasoline vehicles, gasoline evaporation in gas stations and diesel emissions, (2) industrial emissions: industrial evaporation and industrial application, (3) solvent emissions: surface coatings and solvent use and (4) asphalt application.

On the importance of accurate quantification of individual volatile metabolites in exhaled breath

It is argued that shortcomings of certain approaches to breath analysis research based on superficial interpretation of non-quantitative data are inadvertently inhibiting the progression of non-invasive breath analysis into clinical practice. The objective of this perspective is to suggest more clinically profitable approaches to breath research. Thus, following a discourse on the challenges and expectations in breath research, a brief indication is given of the analytical techniques currently used for the analysis of very humid exhaled breath. The seminal work that has been carried out using GC-MS revealed that exhaled breath comprises large numbers of trace volatile organic compounds, VOCs. Unfortunately, analysis of these valuable GC-MS data is mostly performed using chemometrics to distinguish the VOC content of breath samples collected from patients and healthy controls, and reliable quantification of the VOCs is rarely deemed necessary. This limited approach ignores the requirements of clinically acceptable biomarkers and misses the opportunity to identify relationships between the concentrations of individual VOCs and certain related physiological or metabolic parameters. Therefore, a plea is made for more effort to be directed towards the positive identification and accurate quantification of individual VOCs in exhaled breath, which are more physiologically meaningful as best exemplified by the quantification of breath nitric oxide, NO. Support for the value of individual VOC quantification is illustrated by the SIFT-MS studies of breath hydrogen cyanide, HCN, a biomarker of Pseudomonas aeruginosa infection, breath acetic acid as an indicator of airways acidification in cystic fibrosis patients, and n-pentane as a breath biomarker of inflammation in idiopathic bowel disease patients. These single VOCs could be used as non-invasive monitors of the efficacy of therapeutic intervention. The increase of breath methanol following the ingestion of a known amount of the sweetener aspartame impressively shows that accurate breath analysis is a reliable indicator of blood concentrations. However, using individual VOCs for specific disease diagnosis does have its problems and it is, perhaps, more appropriate to see their concentrations as proxy markers of general underlying physiological change. We dedicate this perspective to Lars Gustafsson for his seminal work on breath research and especially for his pioneering work on nitric oxide measurements in exhaled breath in asthma, which best shows the utility and value of the quantification of individual breath biomarkers on which this perspective focuses.

Evaluation of Stable Isotope 13C6-glucose on Volatile Organic Compounds in Different Stages of Mediterranean Fruit Fly (Medfly) Ceratitis Capitata (Diptera: Tephritidae)

The Mediterranean fruit fly (Medfly) Ceratitis Capitata (Diptera: Tephidae), as most of the Tephritidae species, is a pest of great economic importance around the world. For Integrated Pest Management (IPM) and biological pest management purposes, this research will investigate characterization of the broadest possible range of volatile organic compounds and the possible changing trends of volatile biological emissions during development stages of insect. During the last years, many types of research have been done to understand chemical communications between pest-pest interactions and about the insect responses to specific volatile organic compounds. An early comparison of the VOCs emitted from larvae, pupae and adult was performed. Our research focuses on the comparison of volatile compounds emitted from a different stage of Medfly using stable isotope 13C6-glucose. Gas Chromatography (GC) technique coupled with Flame Ionization Detection (FID) and gas chromatography with mass spectrometry (GC-MS) for identification of VOCs was employed. Head Space-Solid Phase Micro Extraction HSSPME method with Three-phase fiber 50/30 µm divinylbenzene/carboxen/ polydimethylsiloxane (DVB/CAR/PDMS) was used. The results showed that there are different chemicals emitted in a different stage of Medfly (Larvae, pupae, and adults M/F) especially in the adult stage. GC-MS detected 27 compounds from larvae, 23 compounds from pupae and 29 compounds from adults. These different VOCs emitted in different stages of Medfly were clearly displayed, and a broad range of emitted volatile compounds was successfully described. The characterization of release patterns could be useful tool for the selection of compounds and for further investigated in biological studies to understand of the key semi-chemicals involved in medfly behaviour.

Analysis of trace contaminants in hot gas streams using time-weighted average solid-phase microextraction: Pilot-scale validation

A new method was developed for collecting, identifying and quantifying contaminants in hot process gas streams using time-weighted average (TWA) passive sampling with retracted solid-phase microextraction (SPME) and gas chromatography. The previous lab scale proof-of-concept with benzene was expanded to include the remaining major tar compounds of interest in syngas: toluene, styrene, indene, and naphthalene. The new method was tested on high T (⩾100°C) process gas from a pilot-scale fluidized bed gasifier feeding switchgrass and compared side-by-side with conventional impingers-based method. Fourteen additional compounds were identified, representing 40–60% improvement over the conventional method’s detection capacity. Differences between the two methods were 1–20% and as much as 40–100% depending on the sampling location. Compared to the inconsistent conventional method, the SPME-TWA offered a simplified, solvent-free approach capable of drastically reducing sampling and sample preparation time and improving analytical reliability. The improved sensitivity of the new method enabled identification and quantification of VOCs beyond the capability of the conventional approaches, reaching concentrations in the ppb range (low mg/m3). RSDs associated with the TWA-SPME were <10%, with most lab-based trials yielding <2%. Calibrations were performed down to the lowest expected values of tar concentrations in ppb ranges (low mg/Nm3, with successful measurement of tar concentrations at times >4000ppm (up to 10g/Nm3). The new method can be a valid alternative to the conventional method for light tar quantification under certain conditions. The opportunity also exists to exploit TWA-SPME for process gas streams analysis e.g., pyrolysis vapors and combustion exhaust.

Source Identification of VOCs in a Petrochemical Complex by Applying Open-Path Fourier Transform Infrared Spectrometry

The petrochemical complex examined in this work includes a great variety of facilities and factories that emit various odorants. A stationary open-path Fourier transform infrared (OPFTIR) system can be used for routine VOC and odor monitoring. However, when odor episodes occur, only multiple mobile OPFTIR systems are able to identify the odorant sources effectively and efficiently. In this study, N, N-dimethyl formamide (DMF) was found to be the most commonly detected odorant emitted from the investigated petrochemical complex by routine monitoring of a stationary OPFTIR system. Then the sequential deployments of a pair of mobile OPFTIR systems were carried out both upwind and downwind of the different sections of the focal area. The pollution rose plots derived from the data obtained by the pair of mobile OPFTIR systems identified the DMF sources. By conducting correlation analyses on the data obtained from the mobile OPFTIR situated at the downwind location of the DMF emission sources, we found that besides DMF, the dry PU synthetic leather process of plant B was also the major odorant source of 2-butanone, ethyl acetate and isopropanol. The source identification measure developed in this study can be used to clarify possible odorant sources not only for petrochemical industrial complexes but also for other areas associated with various emission sources.

Polydiacetylene paper-based colorimetric sensor array for vapor phase detection and identification of volatile organic compounds

Detection and identification of VOCs in their vapor phase is essential for safety and quality assessment. In this work, a novel platform of a paper-based polydiacetylene (PDA) colorimetric sensor array is prepared from eight diacetylene monomers, six of which are amphiphilic and the other two are bolaamphiphilic. To fabricate the sensors, monomers are coated onto a filter paper surface using the drop-casting technique and converted to PDAs by UV irradiation. The PDA sensors show solvent induced irreversible color transition upon exposure to VOC vapors. When combined into a sensing array, the color change pattern as measured by RGB values and statistically analyzed by principal component analysis (PCA) is capable of distinguishing 18 distinct VOCs in the vapor phase. The PCA score and loading plots also allow the reduction of the sensing elements in the array from eight to three PDAs that are capable of classifying 18 VOCs. Utilizing an array containing only two PDAs, various types of automotive fuels including gasoline, gasohol and diesel are successfully classified.

VOC emissions from urban municipal solid waste dump sites

Municipal solid waste management in India is becoming important in view of the fact thatincreasing amount of solid waste generated in most cities is now being recognized as a major publichealth problem. Poor management of solid waste leads to problems, which transcend traditionalenvironmental boundaries and contribute to air, waters and soil pollution,The percentage of India's population living in cities and urban areas has doubled to 28.8% by 200 Ifrom 14% at the time of Independence, showing the rapid pace of urbanization. The progressivelyimproved standards of living and the wasteful consumer attitudes have resulted in increase ofquantities of municipal wastes to be handled.The present system of solid waste management in India, like any other developing country, isfraught with many inadequacies. Illegal dumping is a major problem that raises significant concernswith regard to safety, property values, and quality of life in our communities. Poor collection ordisposal practices are the problem. Since most cities in India still lack properly engineered landfillsfor safe disposal, waste here is mostly disposed in open dumps, which causes major environmentalhavoc.Apart from other environmental hazards from open dumping of municipal solid waste, one of themajor problems that rise are the uncontrolled emissions of VOCs or Volatile organic Compounds.VOCs are well known to be hazardous to human health and are potent carcinogens,The present paper deals with qualitative and quantitative identification of VOCs from a twomunicipal waste dump site in Mumbai. Air at dump sites was sampled and analyzed on GC/MS inaccordance with USEPA TO-17 compendium method for analysis of toxic compounds. As many as20 VOCs were qualitatively identified and some VOC's were quantified, Concentration of Benzenewas observed in the order of 0.6ppm, Some of the VOCs identified were Hazardous Air Pollutantsaccording to USEPA clean air act amendment of 1990,