Continuous Monitoring Of Emissions Research Articles

Influences of shallow groundwater depth on N2O diffusion along the soil profile of summer maize fields in North China Plain

The emissions of nitrous oxide (N2O) from agricultural fields are a significant contribution to global warming. Understanding the mechanisms of N2O emissions from agricultural fields is essential for the development of N2O emission mitigation strategies. Currently, there are extensive studies on N2O emissions on the surface of agricultural soils, while studies on N2O fluxes at the interface between the saturated and unsaturated zones (ISU) are limited. Uncertainties exist regarding N2O emissions from the soil-shallow groundwater systems in agricultural fields. In this study, a three-year lysimeter experiment (2019–2020, 2022) was conducted to simulate the soil-shallow groundwater systems under four controlled shallow groundwater depth (SGD) (i.e., SGD = 40, 70, 110, and 150 cm) conditions in North China Plain (NCP). Weekly continuous monitoring of N2O emissions from soil surface, N2O concentration in the shallow groundwater and the upper 10 cm of pores at the ISU, and nitrogen cycling-related parameters in the soil and groundwater was conducted. The results showed that soil surface N2O emissions increased with decreased shallow groundwater depth, and the highest emissions of 96.44 kg ha−1 and 104.32 kg ha−1 were observed at G2 (SGD = 40 cm) in 2020 and 2022. During the observation period of one maize growing season, shallow groundwater acted as a sink for the unsaturated zone when the groundwater depth was 40 cm, 70 cm, and 110 cm. However, when SGD was 150 cm, shallow groundwater became a source for the unsaturated zone. After fertilization, the groundwater in all treatment plots behaved as a sink for the unsaturated zone, and the diffusion intensity decreased with increasing SGD. The results would provide a theoretical basis for cropland water management to reduce N2O emissions.

Monitoring underwater volcano degassing using fiber-optic sensing

Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change.

Long-term continuous monitoring of methane emissions at an oil and gas facility using a multi-open-path laser dispersion spectrometer

A method for methane emissions monitoring at industrial facility level was developed based on a high precision multi-open-path laser dispersion spectrometer combined with Bayesian analysis algorithms using Monte Carlo Markov Chain (MCMC) inference. From the methane path-averaged concentrations spatially distributed over the facility under study, together with the wind vector, the analysis allows detection, localization and quantification of fugitive methane emissions. This paper describes the very first long term (3 months), continuous (24 h/7 days) deployment of this monitoring system at an operational gas processing and distribution facility. The continuous monitoring system, made of the combination of the open-path high-precision (<10 ppb) methane concentration analyser and the data analysis method, was evaluated with controlled releases of methane of about 5 kg/h for short periods of time (30–60 min). Quantification was successful, with actual emission rates lying well within the quoted uncertainty ranges. Source localisation was found to lack accuracy, with biases of 30–50 m in the direction of the line of sight of the spectrometer, due to the short duration of the controlled releases, the limited wind vector diversity, and complications from air flows around buildings not accounted for by the transport model. Using longer-term data from the deployment, the MCMC algorithm led to the identification of unexpected low intensity persistent sources (<1 kg/h) at the site. Localisation of persistent sources was mostly successful at equipment level (within ~20 m) as confirmed by a subsequent survey with an optical gas imaging (OGI) camera. Quantification of these individual sources was challenging owing to their low intensity, but a consistent estimate of the total methane emission from the facility could be derived using two different inference approaches. These results represent a stepping stone in the development of continuous monitoring systems for methane emissions, pivotal in driving greenhouse gas reduction from industrial facilities. The demonstrated continuous monitoring system gives promising performance in early detection of unexpected emissions and quantification of potentially time-varying emissions from an entire facility.

A novel infrastructure for the continuous monitoring of soil CO2 emissions: a case study at the alto Tiberina near fault observatory in Italy

Static and dynamic stress, along with earthquakes, can trigger the emission and migration of crustal fluids, as frequently observed on the surface and within the upper crust of tectonically active areas such as the northern Apennines of Italy. To investigate the origin of these fluids and their interconnection with the seismogenic process, we complemented The Alto Tiberina Near Fault Observatory (TABOO-NFO), a multidisciplinary monitoring infrastructure composed of a dense array of seismic, geodetic, strain, and radon sensors, with a proper geochemical network grounded on four soil CO2 flux monitoring stations and weather sensors, placed near the main vents of the superficial manifestations. The TABOO-NFO is a state-of-the-art monitoring infrastructure, which allows for studying various geophysical parameters connected to the deformation processes active along a crustal fault system dominated by the Alto Tiberina fault (ATF), which is a 60 km long normal fault dipping at a low angle (&amp;lt;15°–20°). The region is favourable for conducting geochemical studies, as it is characterised by the presence of over-pressurised fluids trapped at certain depths and superficial manifestations associated with the emission of large quantities of fluids. After describing the theoretical framework and the technological aspects based on which we developed the geochemical monitoring network, we described the data recorded in the first months. Over the studied period, the results showed that soil CO2 flux was primarily influenced by environmental parameters, and that the selected sites received a regular supply of deep-origin CO2.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

Abstract. According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Estimating vehicular emission factors and vehicle-induced turbulence: Application of an air quality sensor array for continuous multipoint monitoring in a tunnel

In this study, air quality monitoring nodes (equipped with pollutants (NO, NO2, CO, and PM2.5), temperature, and humidity sensors) were deployed at seven locations inside a road tunnel for continuous multipoint monitoring of traffic emissions. At the same time, in-tunnel turbulence was measured with a 3D-sonic anemometer in the middle of the tunnel to estimate vehicle-induced turbulence (VIT). The emission factors (EFs) of all pollutants estimated from conventional two-point measurements varied significantly among arbitrarily chosen pairs of measurement locations (with a range of 53–193% compared to the mean value). These findings suggest the need for multipoint monitoring for more generalizable EF estimates. Our results indicate that installing a sensor array in a tunnel could enable long-term continuous multipoint monitoring of traffic-related pollutants with limited resources. The EF estimates obtained in this study were higher (∼7 times) than those calculated from an emission inventory based on actual traffic data, particularly for CO and wintertime NOX. One possible explanation for this inconsistency is an underestimation of high emitters in the emission inventory. We also estimated VIT as a function of the total traffic flow rate and the fraction of heavy-duty diesel vehicles in a fleet. We expect our results to be used as input data for local and regional air quality models, which can improve the model's performance. We also expect that long-term continuous monitoring of air pollutants in tunnels with multiple sensor nodes will aid evaluations of the effectiveness of air pollution reduction policies by tracking changes in traffic emissions.

Residue‐ and nitrogen‐induced carbon mineralization varies with soil fertility status

AbstractBy increasing the input of corn (Zea mays L.) residues, synthetic nitrogen (N) fertilization is often assumed to enhance soil storage of organic carbon (C), which could be especially beneficial for improving the fertility of depleted soils. To ascertain whether such a strategy can be effective, C mineralization was compared for two soils with different indigenous N contents by conducting a 60‐day laboratory incubation experiment that involved continuous monitoring of CO2 emissions with periodic sampling for atmospheric δ13C analysis and for determination of soil microbial biomass and cellulolytic enzyme activities. The addition of exogenous N had a stimulatory effect on cumulative CO2 production that was greater for the low than high N supplying soil and more prominent in the first than in the second month of incubation. During residue decomposition, microbial activities were maximized by incubating the low N soil with exogenous N, whereas cellulolytic enzyme activities were greater for the high N soil. Although intensive N fertilization can substantially increase the productivity of low‐fertility soils, the additional residue inputs thereby generated are more effective for promoting C mineralization than sequestration.

СУДОВАЯ СИСТЕМА ОЧИСТКИ ОТРАБОТАВШИХ ГАЗОВ ДВИГАТЕЛЕЙ ОТ ОКСИДОВ УГЛЕРОДА, СЕРЫ И АЗОТА

To clean the exhaust gases of engines from carbon, nitrogen and sulfur oxides, a method and device are proposed,&#x0D; the principle of which is based on the fact that the exhaust gases pass through an absorber, where they&#x0D; are cleaned of nitrogen and sulfur oxides, and then through adsorption sections filled with granules of slag&#x0D; pumice and are purified from carbon oxides as a result of their adsorption. Carbon dioxide, interacting with&#x0D; water particles in the pores of the granules, forms carbonic acid. Then the diluted carbonic acid condensate&#x0D; after cleaning in the separator from solid impurities is discharged overboard. The composition of a shipboard&#x0D; system for continuous laser monitoring and control of emissions of carbon, nitrogen and sulfur oxides contained&#x0D; in the exhaust gases of engines by adjusting their adjustment parameters, as well as using a device for complex&#x0D; exhaust gas purification, is proposed. An algorithm for solving the problem of fulfilling the requirements of&#x0D; regulation 20 "Achievable EEDI" of Appendix VI of the MARPOL 73/78 Convention on carbon oxide emissions&#x0D; has been developed. The implementation of the proposed algorithm is possible using a system for cleaning&#x0D; combustion products from harmful oxides

Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Researchers are searching for ways to better quantify methane emissions from natural gas infrastructure. Current indirect quantification techniques (IQTs) allow for more frequent or continuous measurements with fewer personnel resources than direct methods but lack accuracy and repeatability. Two IQTs are Other Test Method (OTM) 33A and Eddy Covariance (EC). We examined a novel approach to improve the accuracy of single sensor IQT whereby the results from both OTM and EC were combined with two machine learning (ML) models, a random forest (RF) and a neural network (NN). Then, models were enhanced with feature reduction and hyper-parameter tuning and compared to traditional quantification methods. The NN and RF improved upon the default OTM by an average of 44% and 78%, respectively. When compared to traditional OTM estimates with low Data Quality Indicators (DQIs), RF and NN models reduced 1σ errors from ±66% to ±13% and ±34%, respectively. Models also reduced the standard deviation of estimates with 93% and 85% of estimates falling within ±50% of the known release rate. This approach can be deployed with single sensor systems at well sites to improve confidence in reported emissions, reducing the number of anomalous overestimates that would trigger unnecessary site evaluations. Additional improvements could be realized by expanding training datasets with more methane release rates. Further, deployment of such models in a variety of situations could enhance their ability help close the gap between bottom-up inventory and top-down studies by enabling continuous monitoring of temporal emissions that could identify with improved confidence, atypically higher emissions. Accurate remote single sensor systems are key in developing an improved understanding of methane emissions to enable industry to identify and reduce methane emissions.

Spatial–temporal pattern, driving mechanism and optimization policies for embodied carbon emissions transfers in multi-regional tourism: Case study of provinces in China

Tourism is responsible for more than 10% of the global gross domestic product, the growth rate of China's tourism industry is one of the highest in the world. With the continuous prosperity of domestic tourism in China, tourism in China creates large amounts of embodied carbon emissions between regions according to its immense industrial infrastructure and multi-regional mobility. In the context of global climate change and China's “dual carbon target”, the continuous monitoring and analysis of carbon emissions associated with tourism in China are becoming more pressing. This study applied the emissions embodied in the bilateral trade (EEBT) model and structure decomposition analysis (SDA) to analyze the spatial–temporal pattern and driving mechanism of tourism embodied carbon transfers (CETT) between 30 provinces in China. It has been found that the CETT of 30 provinces in China between 1997 and 2012 increased from 33.652 Mt to 164.345 Mt, three times more than previously directly estimated, more provinces in China have become a “net destination” of CETT. Tourism risks becoming an “accelerator” of regional carbon emissions, because of the trend of carbon emissions growing faster than economic growth. Based on carbon-intensity analysis, the main driving forces of net CETT were explored: low-carbon technology of tourism products and services, tourist origin structure, and tourist market scale. Technology driving force and structure driving force reduced net CETT outflows by 64.98 Mt and 646.30 Mt, respectively, 1997 to 2012, while the scale driving force increased by 714.28 Mt. This study puts forward specific implements on the regulation and control of CETT in China's from tourism route, supply chain, and government regime by combining quantitative and qualitative methods. This study proposed a research framework of “measure-intensity-driver-regulation” for CETT and connected with the forefront of global climate change research, in order to effectively guide the low-carbon transformation and development of tourism with new tools and methods, and provide a reference for low-carbon green development in the post-pandemic era for the world.

Multi-Faceted Analysis of Airborne Noise Impact in the Port of Split (I)

This multi-faceted study deals with the analysis of the impact of noise emissions from the cargo terminals in the port of Split, especially in view of the proximity to inhabited areas and the growing number of registered issues and concerns due to its particular location. Three objectives are pursued: the identification of noise sources in the port area, an overview of strategic noise maps and simulations of noise propagation from ships at berth, and the calculation of external costs of noise pollution. In the first, preliminary part of the research project, by conducting a monitoring campaign and analyzing the data on strategic noise maps of the studied area, road and rail traffic were estimated as the main noise sources causing excessive noise emissions for all assessment periods: day (Lday), evening (Levening), night (Lnight), and day-evening-night (Lden) period. Industrial resources, including ports, were identified as having marginal noise emission levels. The calculation of the total external noise costs results in a damage value of €190,166/year, considering the number of affected inhabitants and the assumed noise levels. As an added value of the study, the simulation results of two scenarios have determined the noise propagation of a ship at berth and highlighted the zone of excessive noise under certain conditions. The results of this study should encourage the relevant institutions to strengthen noise management plans and introduce effective and continuous monitoring of noise emissions in critical areas.

Influences of Aqueous Nanofluid Emulsion on Diesel Engine Performance, Combustion, and Emission: IoT (Emission Monitoring System)

Pollution is the primary ecological threat to nature and it also raises the temperature of the Earth. The primary cause of air pollution is exhaust emissions from the combustion chamber. This study aims to focus on controlling emissions using an aqueous nanoemulsion method and to analyze positive vibrations for the reduction of pollutants in diesel engines. In this study 3% NWED (nano-based water emulsion diesel), 7% NWED, and 12% NWED emulsion blended diesel were mixed with Tween-20 as a solvent. In addition, this work was accomplished with a multiuser remote control system for the continuous monitoring of emissions with IoT technology applied to diesel engines. Experimental results have been measured with an IoT kit and five gas analyzers and reported that emission of oxides of nitrogen and hydrocarbons can be reduced by using a 7% NWED blend when compared with diesel. In addition, the experimental result also shows that the brake-specific fuel consumption is reduced with an improvement in thermal efficiency at 7% NWED when compared with other blends.

Интеграция предиктивно-аналитических моделей с IoT-платформой цифрового экомониторинга

The research focuses on the development of applied software systems for automated environmental monitoring. The task of developing and integrating applied software, in particular calculation and analytical models based on machine learning (ML) methods, with an IoT platform of digital eco-monitoring for industrial enterprises is considered. Such a platform is used to create software and hardware systems of CEMS – Continuous Emissions Monitoring System class, designed for continuous monitoring of pollutant emissions into the atmospheric air at production facilities. Use of ML tools integrated with the platform allows to expand significantly the functionality of the existing CEMS, in particular to quickly build new SaaS services for forecasting the dynamics of pollution distribution. Given the high requirements for industrial systems, there is a need to create a specialized software product – an analytical server that implements the management of connected predictive analytical ML models with the required level of service quality, including automatic initialization of new analytical scripts as classes, isolation of individual components, automatic recovery after failures, data security and safety. The paper proposes a scheme of functional and algorithmic interaction between the IoT platform of digital eco- monitoring and the analytical server. The proposed implementation of the analytical server has a hierarchical structure, at the top of which is an application capable of accepting high-level REST requests to initialize calculations in real time. This approach minimizes the impact of one analytical script (class) on another, as well as extending the functionality of the platform in "hot" mode, that is, without stopping or reloading. Results demonstrating automatic initialization and connection of basic ML models for predicting pollutant concentrations are presented.

Variability of Marine Methane Bubble Emissions on the Clayoquot Slope, Offshore Vancouver Island, Between 2017 and 2021

Seabed methane gas emissions occur worldwide at cold seeps located along most continental margins. Fluxes of methane gas released from the seabed in the form of bubbles can be extremely variable even over short time intervals. Some factors controlling the variability are still poorly understood. Here, we report on the results of continuous long-term sonar monitoring of bubble emissions at a depth of 1,260 m on the Clayoquot Slope, northern Cascadia margin. With a total monitoring duration of 4 years and a sampling period of 1 h, this is by far the longest high temporal resolution monitoring of seabed methane gas release ever conducted. Our results provide evidence that the diurnal and semi-diurnal tides influence the timing of the onset and cessation of bubble emissions. However, gas emissions within the monitoring area are active more than 84% of the time, indicating that tides alone are not sufficient to make venting pause. We hypothesize that the gas fluxes are transient but generally sufficiently high to maintain ebullition independently of the tidally-induced bottom pressure variations. Results also show that the tides do not seem to modulate the vigor of active gas emissions.

Single photon Lidar gas imagers for practical and widespread continuous methane monitoring

The accurate and comprehensive identification and quantification of greenhouse gas (GHG) emissions is an essential part of the management and mitigation of climate change. We are developing a novel remote gas imaging sensor for the detection, visualisation, and quantification of methane emissions. The sensor uses a new technique we call Tunable Diode Lidar (TDLidar) which combines aspects of Tunable Diode Laser Absorption Spectroscopy (TDLAS) with Differential Absorption Lidar (DIAL) and Time Correlated Single Photon Counting (TCSPC) to enable remote spectroscopy and ranging with low power semiconductor diode lasers. Our first TDLidar methane sensors use diode lasers with wavelengths around the CH4 absorption line at 1.6509 μm and Peltier-cooled Single Photon Avalanche Diode (SPAD) detectors in a Random Modulation Continuous Wave (RM-CW) Lidar system. Here we characterise our TDLidar methane sensor performance with calibrated gas cells and controlled gas release trials and we demonstrate quantification of leak rates as low as 0.012 g/s and detection at distances over 90 m. The accuracy, speed, and practicality of the sensor, combined with an expectation of low-cost in volume, offers the potential that these sensors can be effectively applied for widespread continuous and autonomous monitoring of industrial methane emissions.

Short-Term Effect of Nitrogen Fertilization on Carbon Mineralization during Corn Residue Decomposition in Soil

The effect of N fertilization on residue decomposition has been studied extensively; however, contrasting results reflect differences in residue quality, the form of N applied, and the type of soil studied. A 60 d laboratory incubation experiment was conducted to ascertain the effect of synthetic N addition on the decomposition of two corn (Zea mays L.) stover mixtures differing in C:N ratio by continuous monitoring of CO2 emissions and periodic measurement of microbial biomass and enzyme activities involved in C and N cycling. Cumulative CO2 production was greater for the high than low N residue treatment, and was significantly increased by the addition of exogenous N. The latter effect was prominent during the first month of incubation, whereas N-treated soils produced less CO2 in the second month, as would be expected due to more rapid substrate depletion from microbial C utilization previously enhanced by greater N availability. The stimulatory effect of exogenous N was verified with respect to active biomass, microbial biomass C and N, and cellulase and protease activities, all of which were significantly correlated with cumulative CO2 production. Intensive N fertilization in modern corn production increases the input of residues but is not conducive to soil C sequestration.

Unwanted mainstream nitritation-denitritation causing massive N2O emissions in a continuous activated sludge process.

Nitrous oxide emissions can contribute significantly to the carbon footprint of municipal wastewater treatment plants even though emissions from conventional nitrogen removal processes are assumed to be moderate. An increased risk for high emissions can occur in connection with process disturbances and nitrite (NO2-) accumulation. This work describes the findings at a large municipal wastewater treatment plant where the levels of NO2- in the activated sludge process effluent were spontaneously and strongly increased on several activated sludge lines which was suspected to be due to shortcut nitrogen removal that stabilized for several months. The high NO2- levels were linked to a dramatic increase in nitrous oxide (N2O) emissions. As much as over 20% of the daily influent nitrogen load was emitted as N2O. These observations indicate that highly increased NO2- levels can occur in conventional activated sludge processes and result in high nitrous oxide emissions. They also raise questions concerning the risk of increased greenhouse gas (GHG) emissions of the nitritation-denitritation processes - although the uncontrolled nature of the event described here must be taken into consideration - and underline the importance of continuous monitoring and control of N2O emissions.

Environmental efficiency of gas purification and ash collection in Russian mining and metallurgical industry

The problem of reducing emissions of sulfur dioxide from the flue gases of coal-fired TPPs is highly relevant for domestic energy in connection with the annual increase in coal production. This article analyzes statistical data in the field of coal mining, describes the current state in the field of gas purification and ash collection in the mining and metallurgical industry of the Russian Federation. To regulate pollutant emissions by stationary sources in Russia, a system of maximum permissible and temporarily agreed emissions (MPE and ENV) was introduced. The country takes part in international programs aimed at protecting the environment from the harmful effects of human activity. Despite the general dynamics of reducing air pollution in the Russian Federation, emissions from mining and metallurgical enterprises only increase. This is due to deficiencies in the technical condition and operation of dust and gas cleaning equipment at electric power enterprises. The main ones are the moral and physical deterioration of the equipment fleet, the need for reconstruction and modernization of devices. The average degree of flue gas cleaning from ash at Russian TPPs is 95.5 %, which is noticeably less than abroad. Efficiency of the gas treatment plants used does not meet modern environmental requirements. The flue gases at our TPPs are not cleaned from sulfur and nitrogen oxides; there are practically no devices for continuous monitoring of TPP emissions. The article provides the rationale for the transition of enterprises to the best available technologies (BAT) in the field of gas purification. The use of a gas conditioning system allows 30 – 40 % reduction in the amount of funds spent on the construction of electrostatic filters. In recent years, in connection with the advent of new technologies for burning coal, filters for the purification of hot gases made of ceramic materials have been created. We have analyzed the state of gas purification and ash collection in the mining and metallurgical industry of the Russian Federation and its impact on the atmosphere. An assessment of the current state of gas purification equipment at the enterprises was carried out.

УСТАНОВКА АВТОМАТИЧЕСКИХ СИСТЕМ НЕПРЕРЫВНОГО КОНТРОЛЯ ВЫБРОСОВ ЗАГРЯЗНЯЮЩИХ ВЕЩЕСТВ

The relevance of the introduction of an automatic system for continuous monitoring and accounting of emissions (ASNK and HC) is considered, taking into account the reliability of decisions.

A Study on Characteristic Emission Factors of Exhaust Gas from Diesel Locomotives.

Use of diesel locomotives in transport is gradually decreasing due to electrification and the introduction of high-speed electric rail. However, in Korea, up to 30% of the transportation of passengers and cargo still uses diesel locomotives and diesel vehicles. Many studies have shown that exhaust gas from diesel locomotives poses a threat to human health. This study examined the characteristics of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons in diesel locomotive engine exhaust. Emission concentrations were evaluated and compared with the existing regulations. In the case of PM and NOx, emission concentrations increased as engine output increased. High concentrations of CO were detected at engine start and acceleration, while hydrocarbons showed weakly increased concentrations regardless of engine power. Based on fuel consumption and engine power, the emission patterns of PM and gaseous substances observed in this study were slightly higher than the U.S. Environmental Protection Agency Tier standard and the Korean emission standard. Continuous monitoring and management of emissions from diesel locomotives are required to comply with emission standards. The findings of this study revealed that emission factors varied based on fuel consumption, engine power, and actual driving patterns. For the first time, a portable emission measurement system (PEMS), normally used to measure exhaust gas from diesel vehicles, was used to measure exhaust gas from diesel locomotives, and the data acquired were compared with previous results. This study is meaningful as the first example of measuring the exhaust gas concentration by connecting a PEMS to a diesel locomotive, and in the future, a study to measure driving characteristics and exhaust gas using a PEMS should be conducted.