Environmental Monitoring Techniques Research Articles

Analysis of Sulphur Dioxide Concentration in Central University of Jammu (Rahya Suchani) Campus

One major air pollutant that has a negative impact on both the environment and human health is sulfur dioxide (SO2). An analysis of the sulphur dioxide levels on the Central University of Jammu (Rahya Suchani) campus is presented in this abstract. The study's objectives are to measure the amount of SO2 pollution, locate probable sources, and analyze the effects of the pollution on the campus environment and the residents' health. In order to gather information on SO2 concentrations at different points on the university campus, the research makes use of quantitative measurements and environmental monitoring techniques. Data is gathered using specialized equipment that can measure SO2 in ambient air precisely. The study aims to investigate the temporal and spatial fluctuations in SO2 concentration, accounting for variables such as local pollution sources, automobile traffic, industrial processes, and weather patterns. The distribution patterns and locations of elevated SO2 pollution on campus are visualized by statistical analysis and spatial mapping approaches. The goal of this study's findings is to increase university stakeholders' knowledge of the level of SO2 pollution on the Central University of Jammu campus. Through the identification of pollutant sources and patterns, the study offers significant insights for the implementation of mitigation techniques and the promotion of a healthier campus environment.

Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments.

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal-organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent "fingerprints", which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.

Eu3+-Doped Anionic Zinc-Based Organic Framework Ratio Fluorescence Sensing Platform: Supersensitive Visual Identification of Prescription Drugs.

Advanced techniques for both environmental and biological prescription drug monitoring are of ongoing interest. In this work, a fluorescent sensor based on an Eu3+-doped anionic zinc-based metal-organic framework (Eu3+@Zn-MOF) was constructed for rapid visual analysis of the prescription drug molecule demecycline (DEM), achieving both high sensitivity and selectivity. The ligand 2-amino-[1,1'-biphenyl]-4,4'-dicarboxylic acid (bpdc-NH2) not only provides stable cyan fluorescence (467 nm) for the framework through intramolecular charge transfer of bpdc-NH2 infinitesimal disturbanced by Zn2+ but also chelates Eu3+, resulting in red (617 nm) fluorescence. Through the synergy of photoinduced electron transfer and the antenna effect, a bidirectional response to DEM is achieved, enabling concentration quantification. The Eu3+@Zn-MOF platform exhibits a wide linear range (0.25-2.5 μM) to DEM and a detection limit (LOD) of 10.9 nM. Further, we integrated the DEM sensing platform into a paper-based system and utilized a smartphone for the visual detection of DEM in water samples and milk products, demonstrating the potential for large-scale, low-cost utilization of the technology.

A piezoelectric-electromagnetic hybrid energy harvester for low-frequency wave motion and self-sensing wave environment monitoring

Techniques for harvesting energy and environmental monitoring are crucial for the development and application of wave energy. Here, we propose a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) for low-frequency wave motion and self-sensing wave environment monitoring. The PEHEH consists of an electromagnetic energy harvester (EMH) and two distinct types of piezoelectric energy harvesters (PEH-1 and PEH-2). The combination of piezoelectric power generation and electromagnetic power generation can effectively harvest low-frequency wave energy. Using the L-shaped piezoelectric cantilever can make it produce large deformation under low-frequency excitation to obtain better power output. The movement of the spherical magnet in the channel tube changes the magnetic flux in the coil to generate electricity. The PEHEH is designed to float on the surface of the water to harvest wave energy in multiple directions. According to the experimental findings, the PEHEH can obtain a power output of 32.58 mW with the optimal combination of parameters. The PEHEH can supply power to the LEDs and enable them to emit light properly. Meanwhile, using the PEH-1 and PEH-2 as the power supply part and the EMH as the self-sensing part, the PEHEH can realize the monitoring of wave amplitude in the actual wave environment. The above results verified that PEHEH has the functions of self-powering and self-sensing. This research provides a novel technical solution for the development and utilization of wave energy.

PSXIV-16 Evaluation of Water Intake Equations with an Emphasis on Physiological Status and Evaluation of Current Relevance Behind Environmental Parameters

Abstract Water requirements for beef cattle are often computed according to body weight (BW) and environmental variables. Inherent variation in environmental conditions within geographical locations, in addition to a cascade of physiological changes triggered as consequence of such variability are somewhat fomented by multicollinearity amongst these variables. This conundrum generates instability of current predictive models in reaching optimization equilibrium inflating its overall predictability and robustness. The goals herein involved the identification of physiologically relevant parameters and the generation of robust, data-driven, individualistic models through high frequency data loggers. Further, we evaluate re-parametrized and current proposed equations (e.g.: Hicks et al., 1988 and Arias and Mader, 2011) for the prediction of water intake for beef cattle at different life stages and physiological conditions. The dataset utilized consisted of more than 30,000 observations, including longitudinal data on lactating animals (n = 23), grass finished steers (n = 12), grain finished steers (n = 21), and grain finished implanted steers (n = 9), steers backgrounded on a low (n = 20) or moderate (n = 20) plane of nutrition bulls under negative (n = 13), maintenance (n = 13) and positive energy balance (n = 13). Individual monitoring systems allowed for the daily collection of BW, water and feed intake, and ingestive behavior. Additionally, an on-site data-logging weather station (1-min intervals) collected relative humidity, temperature, wind speed, wind direction, solar radiation, and precipitation. All reported statistics were generated on out-of-bag randomly split portions of the dataset. For identification of relevant predictive parameters of water intake, several feature selection and penalization techniques were examined. When comparing our equations to current, the errors generated for water intake predictions using physiological classification and on-site environmental variables, explained 50% more of the variation in predicting water intake (r2 = 0.38 vs r2 = 0.22-0.27). When evaluating the errors of prediction (root mean squared error, RMSE and mean absolute error, MAE), significant decreases of up to three-fold were observed on the equations generated herein (RMSE = 9.05 vs 28.8 to 35.31, MAE = 7.04 vs 26.9 to 33.9). Accurate monitoring of individual parameters such as feed intake were better predictors of water intake than environmental parameters when evaluating re-parametrization of models. Further, inclusion of a physiological stage (categorical variable) was more significant than any of the other environmental predictors. Overall, the results discussed highlight the importance of appropriately accounting for the water requirements of beef cattle and how far-off current assumptions are. Further, it raises concern on current equations that use environmental monitoring equations and techniques that attempt to describe nutrient requirements of the animals, and potentially brings the approach to requirements rather than environment.

Microbial Community Colonization Process Unveiled through eDNA-PFU Technology in Mesocosm Ecosystems.

Microbial communities are essential components of aquatic ecosystems and are widely employed for the detection, protection, and restoration of water ecosystems. The polyurethane foam unit (PFU) method, an effective and widely used environmental monitoring technique, has been improved with the eDNA-PFU method, offering efficiency, rapidity, and standardization advantages. This research aimed to explore the colonization process of microbial communities within PFUs using eDNA-PFU technology. To achieve this, we conducted ten-day monitoring and sequencing of microbial communities within PFUs in a stable and controlled artificial aquatic ecosystem, comparing them with water environmental samples (eDNA samples). Results showed 1065 genera in eDNA-PFU and 1059 in eDNA, with eDNA-PFU detecting 99.95% of eDNA-identified species. Additionally, the diversity indices of bacteria and eukaryotes in both methods showed similar trends over time in the colonization process; however, relative abundance differed. We further analyzed the colonization dynamics of microbes in eDNA-PFU and identified four clusters with varying colonization speeds. Notably, we found differences in colonization rates between bacteria and eukaryotes. Furthermore, the Molecular Ecological Networks (MEN) showed that the network in eDNA-PFU was more modular, forming a unique microbial community differentiated from the aquatic environment. In conclusion, this study, using eDNA-PFU, comprehensively explored microbial colonization and interrelationships in a controlled mesocosm system, providing foundational data and reference standards for its application in aquatic ecosystem monitoring and beyond.

Drawbacks of Traditional Environmental Monitoring Systems

Traditional methods for evaluating water quality have a number of drawbacks. They need expensive, specialized equipment as well as knowledgeable employees first. Second, data loss may result from human error. Thirdly, because people rather than algorithms will be analyzing the obtained data, these schemes cannot foresee future patterns. Additionally, changes in the characteristics of water may result from the sample transit process. Therefore, it is challenging to consistently check water quality using outdated monitoring techniques. The disadvantages of traditional environmental monitoring techniques have been covered in this study.

An enzymatic activity regulation-based clusterzyme sensor array for high-throughput identification of heavy metal ions

The accurate identification and sensitive quantification of heavy metal ions are of great significance, considering that pose a serious threat to environment and human health. Most array-based sensing platforms, to date, utilize nanozymes as sensing elements, but few studies have explored the application of the peroxidase-like activity of clusterzymes in identification of multiple analytes. Herein, for the first time, we developed a clusterzyme sensor array utilizing gold nanoclusters (AuNCs) as sensing elements for five heavy metal ions identification including Hg2+, Pb2+, Cu2+, Cd2+ and Co2+. The heavy metal ions can differentially regulate the peroxidase-like activity of AuNCs, and that can be converted into colorimetric signals with 3,3′,5,5′-tetramethylbenzidine (TMB) as the chromogenic substrate. Subsequently, the generated composite responses can be interpreted by combining pattern recognition algorithms. The developed clusterzyme sensor array can identify five heavy metal ions at concentrations as low as 0.5 μM and their multi-component mixtures. Especially, we demonstrated the successful identification of multiple heavy metal ions in tap water and traditional Chinese medicine, with an accuracy of 100% in blind test. This study provided a simple and effective method for identification and quantification of heavy metal ions, rendering a promising technique for environmental monitoring and drug safety assurance.

Quartz-enhanced photoacoustic spectroscopy (QEPAS) and Beat Frequency-QEPAS techniques for air pollutants detection: A comparison in terms of sensitivity and acquisition time

In this work, a comparison between Quartz Enhanced Photoacoustic Spectroscopy (QEPAS) and Beat Frequency-QEPAS (BF-QEPAS) techniques for environmental monitoring of pollutants is reported. A spectrophone composed of a T-shaped Quartz Tuning Fork (QTF) coupled with resonator tubes was employed as a detection module. An interband cascade laser has been used as an exciting source, allowing the targeting of two NO absorption features, located at 1900.07 cm−1 and 1900.52 cm−1, and a water vapor absorption feature, located at 1901.76 cm−1. Minimum detection limits of 90 ppb and 180 ppb were achieved with QEPAS and BF-QEPAS techniques, respectively, for NO detection. The capability to detect multiple components in the same gas mixture using BF-QEPAS was also demonstrated.

Quartz tuning fork based photoacoustic spectroscopy and sensing

Quartz crystal tuning forks (QTFs) resonators are key components for timing and frequency measurements, due to their high stability, high-quality factors and low power consumptions. Thanks to their piezoelectric properties, QTFs are employed as sensitive element in many fields and systems such as atomic force microscopy, near-field and microwave microscopy, and mass/viscosity sensor. Since 2002, QTFs are also widely used as a sharply resonant acoustic transducer to detect weak photoacoustic excitation for Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS). Among most sensitive optical techniques, QEPAS was demonstrated as the leading-edge technology for addressing these application requirements, providing also modularity, ruggedness, portability and allowing the use of extremely small volumes. QEPAS technique does not require an optical detector, it is wavelength independent, it is immune to environmental noise and can operate in a wide range of temperature and pressure. These factors, together with its proven reliability and ruggedness, represent the main distinct advantages with respect to other laser-based techniques for environmental monitoring and in situ detection. Starting from the basic physical principles governing the QTF physics, I will review the main results achieved by exploiting custom QTFs for QEPAS sensing and as photodetector in LITES setup, with a main focus on real-world applications.

Development of a New Method to Estimate the Water Purification Efficiency of Bulk-Supported Nanosorbents under Realistic Conditions

The direct use of nanosorbents for water purification is limited due to their aggregation and the lack of techniques for their recovery from natural waters. To overcome these problems, the affixation of nanomaterials onto bulk, non-mobile supports has been proposed. However, a method to simulate the efficiency of these sorbents under realistic conditions is still not available. To address this need, this work describes a method for evaluating the sorption efficiency of nanosorbent materials incorporated on bulk supports under non-equilibrium conditions. The method combines the principles of passive sampling, an environmental monitoring technique that is based on passive diffusion of dissolved contaminants from water to a sorbent, with batch sorption experiments that measure sorption under equilibrium conditions, to determine the parameters associated with water purification. These parameters are the maximum sorption capacity of the sorbent and the sampling rate, which is the volume of contaminated water treated per unit of time. From these variables, the deployment time of the sorbent until reaching saturation is proposed as an alternative indicator of sorbent efficiency. As proof-of-principle, the removal of oxyanions from a Zr-metal-organic framework (MOR−1) immobilized on cotton textiles was investigated. The results show that the sorption capacity under passive diffusion uptake conditions, is approximately 20 mg/g for As(VI) and 36 mg/g Se(IV), which is 10 to 30 times lower compared to that determined in batch sorption studies, indicating that conventional equilibrium sorption overestimates the efficiency of the sorbents under realistic conditions. The application of the method to a worst-case scenario, involving the severe contamination of freshwaters with arsenate species, is also demonstrated.

Carbon nanotube substrates enhance SARS-CoV-2 spike protein ion yields in matrix-assisted laser desorption–ionization mass spectrometry

Nanostructured surfaces enhance ion yields in matrix-assisted laser desorption–ionization mass spectrometry (MALDI-MS). The spike protein complex, S1, is one fingerprint signature of Sars-CoV-2 with a mass of 75 kDa. Here, we show that MALDI-MS yields of Sars-CoV-2 spike protein ions in the 100 kDa range are enhanced 50-fold when the matrix–analyte solution is placed on substrates that are coated with a dense forest of multi-walled carbon nanotubes, compared to yields from uncoated substrates. Nanostructured substrates can support the development of mass spectrometry techniques for sensitive pathogen detection and environmental monitoring.

Evaluation of environmental and biological monitoring methods for toluene exposure assessment in paint industry

The aim of this study was to assess the exposure to Toluene in paint industry and to evaluate the environmental and biological monitoring techniques for the assessment of occupational exposure to this aromatic hydrocarbon. In this study, personal active and passive air sampling for toluene measurements, blood and urine sampling respectively for B-Tol and HA or U-Tol analyses for eight workers from two paint and thinner production factories were collected during four successive working days. Correlations were analyzed between biological indicators and environmental toluene exposure levels.The concentration of Toluene measured in air samples ranged from 0.2 to 414.0 ppm (mean = 59.8 ppm), with high variability of atmospheric levels between activities and between days. No significant difference was found between airborne toluene concentrations measured by the two sampling methods. The correlation between air concentrations sampled by the diffusive sampling method and the biomarkers was the best for HA (r = 0.902, p < 0.01), followed by B-Tol (r = 0.820; p < 0.01), o-Cr (r = 0.691; p < 0.01) and U-Tol (r = 0.607; p < 0.05). The correlation was better between air concentrations and urinary metabolites HA and o-Cr for exposure levels higher than 50 ppm (r = 0.931; p < 0.01), and lower than 300 ppm (r = 0.827; p < 0.01), respectively.According to our results, workers in the studied industries are highly exposed to Toluene. Given the high correlation found between toluene concentrations in samples taken on dosimeters and those actively sampled on charcoal tubes, it may be assumed that both sampling methods are valuable. Despite the influencing factors, HA was found to be a reliable biological indicator for the monitoring of occupational exposure to toluene for high exposure levels. However, B-Tol seems to be an interesting alternative, since it is more specific and showed the best correlations with airborne toluene levels.

Impact of Smoke Plumes Transport on Air Quality in Sydney during Extensive Bushfires (2019) in New South Wales, Australia Using Remote Sensing and Ground Data

Smoke aerosol dispersion and transport have a significant impact on air quality levels and can be examined by environmental monitoring and modelling techniques. The purpose of this study is to determine the characteristics of the smoke aerosols and the level of air quality during November and December 2019 under the influence of extensive bushfires in the Sydney area, New South Wales (NSW), Australia. To achieve this goal, air quality and meteorological data were analysed in combination with remote sensing satellite measurements. Meteorological and air quality data were obtained from the Bureau of Meteorology (BOM) and Environmental Protection Agency monitoring sites in NSW. In Richmond the daily maximum average hourly concentration of particulate matter (PM10) was 848.9 μg/m3 at 07:00 UTC on 26 November 2019 and 785 μg/m3 at 07:00 UTC on 12 December 2019. On 10 December 2019, the highest PM10 recorded in the Sydney region was 961.5 μg/m3 in St Marys at 01:00 UTC, while the highest PM2.5 concentration was 714.6 μg/m3 in Oakdale in southwest Sydney at 18:00 UTC. These values all decreased again to the standard level (&lt;50 μg/m3) in a few days. The potential sources of smoke aerosols originated from bushfires to the northwest of Sydney (Blue Mountains) as well as from southwest and northwest NSW. The smoke plumes were revealed by the combined AOD values from Aqua and Terra sensors on the MODIS satellite. In each case, the smoke travelled towards the east coast of Australia and out over the Pacific Ocean. The NAAPS model displays the existence of smoke at ground level, while the CALIPSO satellite data showed that the plumes extended 14 km up into the stratosphere layer. Backward trajectories obtained from the HYSPLIT model agree well with the movement of smoke plumes observed in the MODIS satellite images.

Volatile Organic Compound Sampling through Rotor Unmanned Aerial Vehicle Technique for Environmental Monitoring

To improve the capacity to probe volatile chemical substances in the atmosphere, we designed an unmanned aerial vehicle system for volatile organic compound (VOC) monitoring and sampling. This environmental monitoring unmanned aerial vehicle (EMUAV) platform was equipped with a photoionization detector for continuous VOC monitoring and searching in a pollution air mass. Furthermore, a multifunction airborne microVOC sampler was loaded for sampling. An airbag and absorption tube were applied to collect air samples for further analyzing in the laboratory by GC-FID/MS or TD-GC/MS. By comparing the aerial samples derived from the microVOC sampler with the samples collected at a similar height to a building roof for chemical compositions, the sampling conditions, such as the sampling port location and sampling method, were optimized to ensure the representativeness of the air samples. The results of the sample comparison experiment showed that both the airbag method and the adsorption method could recover 70–130% for most VOC species. Through the aerial measurements, the advantages of this EMUAV system were demonstrated. Therefore, the developed EMUAV system would have immeasurable potential in the field of environment monitoring.

A Review on Carbon Dots: Synthesis, Characterization and Its Application in Optical Sensor for Environmental Monitoring.

The development of carbon dots (CDs), either using green or chemical precursors, has inevitably led to their wide range application, from bioimaging to optoelectronic devices. The reported precursors and properties of these CDs have opened new opportunities for the future development of high-quality CDs and applications. Green precursors were classified into fruits, vegetables, flowers, leaves, seeds, stem, crop residues, fungi/bacteria species, and waste products, while the chemical precursors were classified into acid reagents and non-acid reagents. This paper quickly reviews ten years of the synthesis of CDs using green and chemical precursors. The application of CDs as sensing materials in optical sensor techniques for environmental monitoring, including the detection of heavy metal ions, phenol, pesticides, and nitroaromatic explosives, was also discussed in this review. This profound review will offer knowledge for the upcoming community of researchers interested in synthesizing high-quality CDs for various applications.

Gas-phase CH-Overtone band spectra of methyl acetate and ethyl acetate via incoherent broad-band cavity-enhanced absorption spectroscopy

Gas-phase overtone bands of methyl acetate and ethyl acetate were detected for up to v = 6–0 by incoherent broad-band cavity-enhanced absorption spectroscopy. The signal-to-noise ratios of v = 4–0 were achieved to be 40–50 by the 100 s integration time, suggesting an application possibility of this spectroscopic technique for environmental monitoring. Profiles of the observed overtone bands were analyzed in detail with the aid of theoretical calculations and their remarkable peaks were assigned to the progressions starting from the symmetric CH-stretching bands. The harmonic frequencies, the anharmonicities, and the dissociation energies were derived.

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution.

Mathematical modeling in behavior responses: The tendency-prediction based on a persistence model on real-time data

Research on biological responses to water quality monitoring and toxicity prediction has increased in recognition in recent years. A great deal of biological response data has been produced through online monitoring systems. However, comprehensive information on biological response is missing. Consequently, biological responses cannot be used to monitor water quality. We can resolve this problem through mathematical models. We used the persistent model on behavioral strength data and derived a mathematic model. The threshold points for safe, acclimatization, adjustment and toxicity stages were calculated precisely. We obtained normal (Bn) and persistence (Bp) responses on behavior data when the environmental conditions of fish changed. Transfer of behavior strength from safe mode (Ms) to acclimatization mode (Mac) and Mac to adjustment stage (Maj) resulted rapidly; this indicates that fish sensed the environment changes. Serials of Maj stages were also visualized; hence, we determined upwards and downwards trends for each Maj. The toxicity stage (Mt) has occurred after the extended Maj stage, indicating that the toxic effect of the stressors overwhelmed the adaptive capability of the fish. We derived the threshold point for each stage precisely on the fish behavior strength dataset. Thus, the proposed mathematical model is an efficient model for determining the behavioral changes when toxicants enter an organism's body. Although the proposed mathematical model is derived using specific factors such as fishes, pollutants, and concentrations, it can be applicable for other biological models based on the normal and persistent responses for toxicity prediction. This study strengthens the available alternative environmental monitoring techniques towards water quality monitoring.

Environmental monitoring and assessment: a case in Salalah, Oman.

Multiple companies are operating in the Salalah region of Oman but found out that no concrete practices of environmental monitoring and assessment are in place, even though industries are operating in compliance with local and international regulations. There is no baseline data available to compare for further research. Initial research was started by Sohar University in Oman for monitoring and assessment, but no conclusive outcome was discovered. The objective of this research on the Salalah LPG project is to show the way forward of creating baseline data for further study when undertaken. This research will provide the best available technique (BAT) for stiff control, environmental monitoring and assessment which can help industries to remain in compliance with local regulator EA (Environmental Authority) and international regulations IFC (International Finance Corporation) and produce with sustainability. The raw data collected from 2018 to 2020 are used in research. These data are general aspect generation from the project, dust (PM10) monitoring, noise monitoring, emissions and GHG generation from the fossil fuel (diesel) consumed on weekly and monthly basis and summarized yearly. These raw data were processed by calculation, presentation in tubular format or in curve (graph) to analyse pollutant generation. This research also gives insight into what to do if pollutants cross the regulator's parameters. Apart from regulatory compliance research paper also suggest some improvement recommendation for the betterment of company and ecosystem such as reduction in targeted total emission, GHG emission by using grid power in place of running diesel generators.