Continuous Emission Monitoring Research Articles

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Plants' emission behaviors under dual control of pollutant concentration and quantity

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO2) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China.

Development of a sampling assembly for PM2.5 CEMS

For an extractive type particulate matter continuous emission monitoring system (PM CEMS), the sampling assembly acts as an important interface between the exhaust gas and sensing unit. It serves to transport the aerosol sample and pre-treat it to meet the working conditions required by the sensing unit. To obtain representative samples, the sampling assembly should be designed to minimize the loss of particles. In this study, a conditioner based on a dilution approach was proposed and experimentally validated for use with pipelines for PM CEMS targeting particles less than 2.5 μm in diameter (PM2.5). The results showed that for 3.625 μm particles, the transport efficiency through the conditioner was 98.30% at a dilution ratio of 10. Meanwhile, the conditioner could reduce the sample air temperature to below 50 °C without condensation at flue gas temperatures up to 150 °C and water contents below 10%. Finally, a sampling assembly consisting of the conditioner, a 90° bend sampling probe, and a straight tube was designed to obtain transport efficiency higher than 95%, a stated requirement in this study for particles with a diameter less than 3.625 μm. Overall, this study contributed to designing and verifying a sampling assembly for PM CEMS and provided new strategies for improving monitoring data quality using PM CEMS.

An improved hourly-resolved atmospheric NOx emission inventory of industrial sources based on Continuous Emission Monitoring System data: Case of Jiangsu Province, China

Current emission inventories with low spatial and temporal resolutions, slow updates, and great uncertainty can no longer meet the new demands for the precise prevention and control of air pollution. This study considered industrial sources of Jiangsu province in 2018 as the research object and divided key industrial sources into 16 processes. Based on the continuous emission monitoring system (CEMS) data of key enterprises, millions of hourly scale monitoring data from 17842-point source enterprises at 169289 emission outlets were collected. An hourly scale high-resolution industrial source NOx emission inventory was constructed and compared with existing inventories. The total NOx emissions of power plants, industrial boilers, ferrous metal manufacturing, non-metallic mineral manufacturing, and chemical manufacturing industries in Jiangsu Province in 2018 were 55, 27, 64, 28, and 3 Gg, respectively. The total emissions from industrial sources were high in summer, reaching a peak of 17 Gg in July, and low in winter, reaching 11 Gg in February. The emission factors of the power plants, coking, and cement industries decreased by 15.95%, 29.03%, and 51.61%, respectively. Hourly scales showed that the power plants had the most considerable fluctuation in 24 h emissions at 5.94%, with high emissions occurring in the afternoon and at night. However, the 24 h emissions of ferrous metal manufacturing fluctuated slightly, at only 3.22%, and the high emissions mostly occurred at night. The WRF-Chem model was used for simulation validation. The NO2 simulation results based on this study's inventory were significantly better than those based on the Multi-resolution Emission Inventory for China (MEIC), with normalized mean biases (NMBs) of −7.1% and −10.7% for January and July, respectively, as opposed to 30.8% and 14.4% in the MEIC. The hourly scale high-precision emission inventory established in this study is significant for formulating real-time differentiated precise prevention and control policies and improving the accuracy of air quality models.

Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China

Abstract. This work presents a new model-free inversion estimation framework (MFIEF) using daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 columns and observed fluxes from the continuous emission monitoring system (CEMS) to quantify 3 years of daily scale emissions of NOx at 0.05∘×0.05∘ over Shanxi Province, a major world-wide energy-producing and energy-consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.86, 1.03, and 1.05 Tg yr−1 respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 53 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (63–508 km) and lifetime of NOx (7.1–18.1 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx/NO2, on a pixel-to-pixel basis, is demonstrated to correlate with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx/NO2 ratio, while coking, industrial boilers, and aluminum oxide factories show a relatively lower ratio. Variance maximization is applied to daily TROPOMI NO2 columns, which facilitates identification of three orthogonal and statistically significant modes of variability, and successfully attributes them both spatially and temporally to (a) this work's computed emissions, (b) remotely sensed TROPOMI ultraviolet aerosol index (UVAI), and (c) computed transport based on TROPOMI NO2.

Emission characteristics of condensable particulate matter (CPM) from FCC flue gas

Particulate matter (PM) as a major air pollutant, generally includes filterable particulate matter (FPM) and condensable particulate matter (CPM). CPM has gradually attracted widespread attention recently, due to its increasing proportion in total PM emissions. Fluid catalytic cracking (FCC) units, the main emission source in refineries, mostly use wet flue gas desulfurization (WFGD), which will produce a large amount of CPM. However, CPM emission and composition of FCC units are actually unclear. In this work, we aimed to understand the emission characteristics of CPM in FCC flue gas and provide some potential control strategies. Here, the stack tests of three typical FCC units were conducted to monitor FPM and CPM, and the field monitoring FPM results are higher than the concentration provided by Continuous Emission Monitoring System (CEMS). The emission of CPM is at a high-level concentration from 28.88 to 86.17 mg/Nm3, divided into inorganic fraction and organic fraction. The inorganic fraction is mainly composed in CPM, where water-soluble ions including SO42−, Na+, NH4+, NO3−, CN−, Cl−, and F−, are the major contributors. Moreover, a variety of organic compounds are detected as qualitative analysis of organic fraction in CPM, which can be roughly classified into alkanes, esters, aromatics, and others. Finally, on the basis of the understanding of the characteristics of CPM, we have proposed two strategies for CPM control. This work is expected to advance CPM emission regulation and control in FCC units.

공장 대기오염 모니터링 응용의 규모 확대를 위한 컨테이너화된 접근법

The deterioration of air quality stems from air pollutants emitted by industrial sectors. Government agencies in many countries regulate the total amount of emissions released during production, necessitating a continuous monitoring system to measure specific air pollutants. However, the existing systems are tailored to a specific plant, making it difficult to incorporate sophisticated components like streaming data processing engines. To address this issue, we propose a containerized approach that bundles rich components for continuous emission monitoring and enables rapid deployments in a new plant. We demonstrate the effectiveness of our approach using datasets obtained from real-world factory production.

Pilot experimental study on pollutant emission characteristics from co-combustion of coal and spent cathode carbon block

Spent cathode carbon block (SCCB) is a hazardous waste produced by the electrolytic aluminum industry. Collaborative treatment of the SCCB in pulverized coal boilers is a potentially valuable technology. Pilot experiments were carried out on a 240 t/h pulverized coal boiler and its supporting flue gas treatment measures as a function of the mixing ratio of SCCB to verify the feasibility of coal collaborative disposal of SCCB. The thermal conversion characteristics of fluoride after mixing SCCB with coal were studied. A continuous emission monitoring system was used to monitor the impact on SO2, NOx and particulate matter in real time. The effectiveness of existing flue gas control measures was evaluated, and optimization suggestions of the flue gas control measures were postulated. The content of heavy metals (Hg, Cd, Ni, Pb, As, Se) in solid waste from co-combustion and the physical phase of solid waste were investigated. In addition, the transformation of heavy metals such as Hg, Cd, Ni, Pb, Cr, As and fluoride in defluorination wastewater during flue gas treatment were analyzed. The results show that it is feasible to use a pulverized coal boiler to dispose SCCB. The existing flue gas treatment measures proved to be appropriate, and desulfurization wastewater can achieve the discharge standards. Defluorination gypsum, fly ash and slag are not hazardous waste. When the mixing ratio of SCCB is increased, the existing neutralization and flocculation precipitation processes of the desulfurization wastewater are affected. It is suggested to add Al2O3 for the defluorination process before the desulfurization tower.

Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO2 and NOx emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

High Spatial Resolution Emission Inventory of Air Pollutants and Carbon in China’s Independent Coking Industry

China is the largest producer and exporter of coke globally, which means that it is very important to understand the characteristics of air pollutants and carbon emissions from China’s independent coking industry. This study was the first to establish a bottom-up inventory of the air pollutants and carbon emissions of China’s independent coking industry during 2001–2018 based on continuous emission monitoring system online monitoring data and unit-based corporate information. Based on the developed emission inventory, four scenarios were established to analyze potential emissions reduction of air pollutants and carbon dioxide (CO2) in future. The emissions of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC) decreased by 62.11%, 63.41%, 72.85%, 63.41% and 63.41%, respectively. CO2, carbon monoxide (CO), volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions increased by 355.51%, 355.51%, 355.51% and 99.74%, respectively. In 2018, PM10, PM2.5, SO2, NOx, BC, OC, CO, VOCs and CO2 emissions were, respectively. 45.20, 16.91, 63.84, 117.71, 5.07, 5.92, 554.91, 1026.58 Gg, and 176.88 Tg. Shanxi province made the greatest contributions to the total emissions of air pollutants and CO2 from this industry by 25.01%. The emission source that contributed most to PM2.5 (SO2 and NOX) emissions was coke pushing (quenching and the coke oven chimney respectively) in 2018. Under the ULE scenario (2018–2035), PM2.5 and SO2 emissions will reduce by more than 30%. Under the PCP scenario, PM2.5 and SO2 emissions will reduce by more than 55%. Under the CBP scenario, CO2 emissions will peak at 197.99 Tg in 2025 and decrease to 70% of the peak in 2035. The results showed the emission characteristics of air pollutants and CO2, future emission with several scenarios and cooperative reduction potential in China’s independent coking industry, which provides scientific support for the development of pollution control strategies.

A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement.

Evaluation of the nitrogen oxide emission inventory with TROPOMI observations

To compensate for the emissions missed or underestimated in the national bottom-up emission inventories, we apply the high spatial resolution satellite data from TROPOspheric Monitoring Instrument (TROPOMI) to estimate the top-down nitrogen oxide (NOx) emissions in regional scales. The NOx chemical lifetime is derived based on ground-based measurements of ozone photolysis rate, ultra-violet (UV) index, and temperature. For five designated regions of western Taiwan, the derived lifetime is about 1–2 h in summer and 2–4 h in winter. The retrieved 2021 annual emissions for regions near two major pollution sources, Taichung thermal power plant and Mailiao Industrial Zone, are comparable with the emission from the Continuous Emission Monitoring System (CEMS), with a difference of 6% and −12%, respectively. After validating the data and methods, the NOx emissions for five regions of western Taiwan are derived and applied to evaluate the bottom-up inventories. For northern and southern Taiwan, the top-down emissions agree well with emission inventories. The top-down emissions are 12%, 23%, and 16% higher than emission inventories for north-central, central, and south-central Taiwan, respectively. This indicates that the bottom-up inventories are underestimated from north-central to south-central Taiwan, which may be associated with the uncertainties from traffic sources. Given the various complex pollution sources, deriving NOx emissions from space allows us to acquire a better understanding of emissions on urban scales and improve the bottom-up emission inventories.

Developed compositional source profile and estimated emissions of condensable particulate matter from coal-fired power plants: A case study of Yantai, China

The emission and environmental impact of condensable particulate matter (CPM) from coal-fired power plants (CFPPs) are of increasing concern worldwide. Many studies on the characteristics of CPM emission have been conducted in China, but its source profile remains unclear, and its emission inventory remains high uncertainty. In this work, the latest measurements reported in the latest 33 studies for CPM inorganic and organic species emitted from CFPPs in China were summarized, and then a compositional source profile of CPM for CFPPs was developed for the first time in China, which involved 10 inorganic species and 71 organic species. In addition, the CPM emission inventory of CFPPs in Yantai of China was developed based on surveyed activity data, continuous emission monitoring system (CEMS), and the latest measurement data. The results show that: (1) Inorganic species accounted for 77.64 % of CPM emitted from CFPPs in Yantai, among which SO42− had the highest content, accounting for 23.74 % of CPM, followed by Cl−, accounting for 11.95 %; (2) Organic matter accounted for 22.36 % of CPM, among which alkanes accounted for the largest proportion of organic fraction (72.7 %); (3) Emission concentration method (EC) and CEMS-based emission ratio method (ERFPM,CEMS) were recommended to estimate CPM emissions for CFPPs; (4) The estimated CPM emission inventories of Yantai CFPPs in 2020 by the EC method and the ERFPM,CEMS method were 1231 tons and 929 tons, respectively, with uncertainties of −34 % ∼ 33 % and −27 % ∼ 57 %, respectively; (5) CPM emissions were mainly distributed in the northern coastal areas of Yantai. This developed CPM source profile and emission inventory can provide basic data for assessing the impacts of CPM on air quality and health. In addition, this study can provide an important methodology for developing CPM emission inventories and CPM emission source profiles for stationary combustion sources in other regions.

Regional Transport of PM2.5 from Coal-Fired Power Plants in the Fenwei Plain, China.

The Fenwei Plain (FWP) remains one of the worst PM2.5-polluted regions in China, although its air quality has improved in recent years. To evaluate the regional transport characteristics of PM2.5 emitted by coal-fired power plants in the FWP in wintertime, the primary PM2.5, SO2, and NOx emissions from coal-fired power plants with large units (≥300 MW) in 11 cities of the area in January 2019 were collected based on the Continuous Emission Monitoring System (CEMS). The spatial distribution and source contribution of primary and secondary PM2.5 concentrations were investigated using the Weather Research and Forecast (WRF) model and the California Puff (CALPUFF) model. The results showed that secondary PM2.5 was transported over a larger range than primary PM2.5 and that secondary nitrate was the main component of the total PM2.5 concentration, accounting for more than 70%. High concentrations of primary, secondary, and total PM2.5 mainly occurred in the Shaanxi region of the FWP, especially in Xianyang, where the PM2.5 concentrations were the highest among the 11 cities, even though its pollutant emissions were at moderate levels. The PM2.5 concentrations in Sanmenxia and Yuncheng primarily came from regional transport, accounting for 64% and 68%, respectively, while those in other cities were dominated by local emissions, accounting for more than 63%. The results may help to understand the regional transport characteristics of pollutants emitted from elevated point sources over a complex terrain.

Assessing Detection Efficiencies for Continuous Methane Emission Monitoring Systems at Oil and Gas Production Sites

Continuous monitoring systems, consisting of multiple fixed sensors, are increasingly being deployed at oil and gas production sites to detect methane emissions. While these monitoring systems operate continuously, their efficiency in detecting emissions will depend on meteorological conditions, sensor detection limits, the number of sensors deployed, and sensor placement strategies. This work demonstrates an approach to assess the effectiveness of continuous sensor networks in detecting infinite-duration and fixed-duration emission events. The case studies examine a single idealized source and a group of nine different sources at varying heights and locations on a single pad. Using site-specific meteorological data and dispersion modeling, the emission detection performance is characterized. For these case studies, infinite-duration emission events are detected within 1 h to multiple days, depending on the number of sensors deployed. The percentage of fixed-duration emission events that are detected ranged from less than 10% to more than 90%, depending on the number of sources, emission release height, emission event duration, and the number of sensors deployed. While these results are specific to these case studies, the analysis framework described in this work can be broadly applied in the evaluation of continuous emission monitoring network designs.

A Multitask Learning Model for the Prediction of NOx Emissions in Municipal Solid Waste Incineration Processes

Selective non-catalytic reduction (SNCR) is a commercially available technology that can effectively reduce the nitrogen oxides ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {NO}}_{x}$ </tex-math></inline-formula> ) emissions in municipal solid waste incineration (MSWI) processes. Real-time measurement of NOx emissions is important to improve the denitration efficiency of SNCR. However, there are commonly time delays to obtain the measurement results from the traditional continuous emission monitoring system (CEMS). In this study, a prediction model based on multitask learning (MTL) is proposed for real-time measurement of NOx emissions. First, time delays are analyzed by using the maximum average cross-correlation function (MACCF) method, and sequences are adjusted according to the time delays, thereby recovering the original data pattern. Second, input variables are selected using a maximal information coefficient (MIC) method to reduce redundant information. Then, an MTL model is constructed for the prediction of NOx emissions. By using the MTL mechanism to share information of related prediction tasks, the potential correlations of industrial time-series data are mined, therefore improving the generalization performance of the model. Also, a self-organizing radial basis function neural network is designed as the module of the MTL model to further improve the prediction accuracy of the model. A strategy for incrementally constructing the MTL model is developed to guarantee the computational efficiency of the model. Finally, the established model is tested using a benchmark dataset and the real industrial data from an MSWI plant. The experimental results show that the proposed MTL model outperforms other state-of-the-art models on computational efficiency and prediction accuracy, demonstrating the potentiality of the MTL model in real-time measurement of NOx emissions in MSWI processes.

The Air Quality Monitoring Tool Based on Internet of Things to Monitor Pollution Emissions Continuously

As a result of pollutant emissions, air pollution occurs which causes various problems such as global climate change, environmental degradation, and diseases due to reduced air quality such as respiratory infections, asthma, and even lung cancer. Polluted air cannot be insulated; therefore, a tool is needed to monitor air quality. So far, in Indonesia the tool for monitoring air quality in the form of an ISPU (Indeks Standar Pencemaran Udara = Air Pollution Standard Index) monitoring station submitted the data daily or not continuously. Whereas air pollution creating from emissions needs to be carried out continuously, so that in 2021, the Minister of Environment and Forestry issued a regulation on the Information System for Continuous Emission Monitoring System (CEMS). Monitoring using CEMS is deemed necessary because correct, accurate, and continuous emission data and information need to be carried out to find out emissions in an integrated manner. This research was conducted to develop CEMS based on internet of things. Pollutant sensors in the form of methane (CH₄), butane (C₄H₁₀), carbon monoxide (CO), particulate (PM_2.5), and ammonia (NH₃) are assembled on the Arduino Uno microcontroller. MQ-4 sensors are used for measuring methane (CH₄), MQ-2 sensors for butane (C₄H₁₀), MQ-7 sensors for carbon monoxide (CO), GP2Y1010AU0F sensors for particles (PM_2.5), and MQ-137 sensors for ammonia (NH₃). Data from the sensor is transmitted to the Liquid Crystal Display (LCD) via the Arduino Uno microcontroller, where it is shown on smartphones as numbers. This web-based air quality monitoring program should continuously track air pollution emissions. The main finding shows that the concentration of particulate matter has the highest concentration (above the emission quality standard) among other sensors, namely 52.29 µg/m³ on June 15, 2022 in South Tangerang, Indonesia. Based on these results, air quality monitoring for (PM_2.5) should be checked periodically.

Gradient boosting decision tree in the prediction of NO[formula omitted] emission of waste incineration

This paper investigates the real-time prediction of nitrogen oxides (NOx) emission by using around 17000 samples involved in a collection of three-day real data from a waste incineration power plant. To disclose the relationship between the ammonia (NH3) ejection and NOx emission, we choose the NOx reduction from inlet to outlet rather than the NOx concentration monitored by continuous emission monitoring system (CEMS). A hybrid procedure is developed to select appropriate features from the large and unsynchronized data, with which we establish a model based on the gradient boosting decision tree (GBDT) for the prediction. Computational experiments demonstrate that, with root mean square error (RMSE) values being 1.851 and 3.593 for training and test data, respectively, GBDT outperforms its two popular counterparts, supporting vector regression (SVR) and long short-term memory (LSTM). Shapley additive explanations (SHAP) is also conducted for analysis.

Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data

The shipping industry is one of the strongest anthropogenic emitters of NOx—a substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the NO2 plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote-sensing-based global emission monitoring system, an automated procedure for the estimation of NO2 emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data, as well as the absence of the ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of NO2 plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to more than a 20% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step towards the development of an automated procedure for global ship emission monitoring using remote sensing data.

The Effect of a Hybrid Pretreatment Device for CEMS on the Simultaneous Removal of PM2.5 and Water Vapor

Stationary emission sources still account for a significant portion of total air pollution emissions. Continuous emission monitoring systems (CEMS) have been used to estimate the emissions of stack pollutants. A large amount of moisture and other interfering factors in the sample discharged from a stack result in the loss of target gases due to artifact formation or gas absorption, thereby reducing measurement accuracy. Therefore, a pretreatment process is essential. Among various pretreatment technologies available, a cyclone with a rapid cooling unit is a special one that can be applied to remove particles and water vapor at the same time in CEMS. This study aimed at the simultaneous removal of water vapor and particles by operating a hybrid pretreatment device at low temperatures such as −5, −15, and −25 °C. When using the hybrid cyclone under the conditions of high temperature (180 °C), humidity (150 g/m3), PM2.5 (1 mg/m3), and SO2 (105.2 ppm) concentrations, the reduction rates of water vapor and PM2.5 concentration and the recovery rates of SO2 concentration were 82.2, 80.2, and 96.4%, respectively. These data suggested that the hybrid cyclone could be used as a pretreatment device for CEMS.