Pollutant Emissions Research Articles

AI-based data mining approach to control the environmental impact of conventional energy technologies

Environmental pollution remains one of the foremost existential threats to human well-being, despite the concerted efforts and implementation of various programmes aimed at fostering cleaner air. The contemporary global economic and energy landscape, characterised by multifaceted challenges, has undeniably hindered the efficacy of efforts to kerb air pollutant emissions. Solid fuels persist as primary sources of energy production in numerous countries, serving both the residential and industrial sectors. However, combustion of such fuels, particularly within domestic heating units (DHUs), engenders the release of a diverse array of organic compounds characterised by intricate structures and potent mutagenic and environmentally hazardous properties.However, the combustion process, if properly regulated, can be carried out in an environmentally sustainable manner. The intricate interplay of myriad factors that influence the composition and quality of chimney flue gases underscores the complexity inherent in controlling the combustion process. Artificial intelligence (AI) has emerged as a versatile tool with applications that span various domains, including environmental monitoring systems. In this study, we posit the utilisation of artificial neural networks (ANNs) as a sophisticated data mining technique to control the emission of flue gases contingent on the specific boiler and fuel utilised. Feed forward predictive models with back propagation were utilized for AI-based data mining aiming at the prediction of the concentration of flue gas components. The highest coefficients of model fit goodness were obtained for CO2, NOx and SO2 with R2 equal to 0.99, 0.98 and 0.99, respectively. The study demonstrated the feasibility and effectiveness of using AI-based data mining to predict emissions from conventional energy technologies. By leveraging the predictive capabilities of ANNs, it is possible to significantly reduce the environmental impact of solid fuel combustion, contributing to cleaner air and improved public health.

Diesel Engine Turbocharger Monitoring by Processing Accelerometric Signals through Empirical Mode Decomposition and Independent Component Analysis

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine’s range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring.

Impacts of current and climate induced changes in atmospheric stagnation on Indian surface PM2.5 pollution

Severe PM2.5 pollution threatens public health in India. Atmospheric stagnation traps emitted pollutants, worsening their health impacts. Global warming is anticipated to alter future stagnation patterns, impacting the effectiveness of air quality policies. Here, we develop a region-specific index that characterizes meteorological conditions driving stagnation and associated PM2.5 increases. Applying this index to an ensemble of climate models and global warming scenarios, we find that future stagnation changes result from both global CO2-driven circulation changes and local aerosol-driven meteorological responses. By 2100, we project an increase in winter stagnation in the Indo-Gangetic Plain (IGP) of 7 ± 3 days that leads to an increase in PM2.5 of ~7 ug/m3 in a high-warming and high-aerosol scenario. However, annual stagnation occurrences decrease across most of India. Thus, stringent air quality regulations in the IGP during winters will be critical to reduce surface PM2.5 concentrations as climate warms. Such regulations will directly improve air quality while reducing future stagnation occurrences, providing additional air quality benefits.

Advancements in Synthetic Biology for Enhancing Cyanobacterial Capabilities in Sustainable Plastic Production: A Green Horizon Perspective

This comprehensive review investigates the potential of cyanobacteria, particularly nitrogen-fixing strains, in addressing global challenges pertaining to plastic pollution and carbon emissions. By analyzing the distinctive characteristics of cyanobacteria, including their minimal growth requirements, high photosynthetic efficiency, and rapid growth rates, this study elucidates their crucial role in transforming carbon sequestration, biofuel generation, and biodegradable plastic production. The investigation emphasizes cyanobacteria’s efficiency in photosynthesis, positioning them as optimal candidates for cost-effective bioplastic production with minimized land usage. Furthermore, the study explores their unconventional yet promising utilization in biodiesel production, mitigating environmental concerns such as sulfur emissions and the presence of aromatic hydrocarbons. The resulting biodiesel exhibits significant combustion potential, establishing cyanobacteria as a viable option for sustainable biofuel production. Through a comprehensive assessment of both achievements and challenges encountered during the commercialization process, this review offers valuable insights into the diverse contributions of cyanobacteria. Its objective is to provide guidance to researchers, policymakers, and industries interested in harnessing bio-inspired approaches for structural and sustainable applications, thereby advancing global efforts towards environmentally conscious plastic and biofuel production.

Interaction between liquid droplets and membrane surfaces

Multi-stage flue gas cleaning systems are used to reduce the pollutant emissions from fuel combustion. A variety of filters have been developed to capture soot particles in flue gases. Such filters are usually based on membranes. Another popular way of reducing the anthropogenic emissions in flue gases is by adding agents or water to fuels. Not much information is available on the impact of vapor produced by the combustion of such fuels on membrane permeability characteristics. Membrane permeability to gas flow should be studied with due consideration of membrane wettability and droplet agglomeration on it. This research focuses on identifying the main patterns of the interaction between liquid droplets and membranes of various types. Using the experimental findings, we have identified two droplet-membrane interaction regimes–spreading and breakup–as well as the interaction phases when each of these two regimes is observed. We have also established that the maximum spreading diameter remains relatively insensitive to the surface wettability at low Weber numbers. The analysis of the experimental findings has shown that an air pocket is formed under a droplet if its impact velocity exceeds 2.5 m/s. The impact with an air pocket triggers droplet breakup and reduces the maximum spreading diameter. This process is a decisive factor in favor of using membranes in flue gas cleaning systems. The findings of this paper can be used to extend the operating life of filtration membranes used in thermal power plants.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Investigation of the Coupling Between the Dynamics of Vortical Structures and Flame Stability in Bluff-Body Premixed Combustion Using Extended Spectral Proper Orthogonal Decomposition

Abstract Large eddy simulation data of a bluff-body stabilized flame are analyzed using spectral proper orthogonal decomposition (SPOD) to investigate: (i) the role of flame-vortex interactions in the dominant flow dynamics, and (ii) how the proper choice of the cross-spectral density (CSD) defining SPOD can assist in identifying the underlying dynamics. Bluff-body flame holders aim to achieve stable flames under lean premixed conditions to minimize pollutant emissions. The recirculation region induced by the body promotes the mixing of hot combustion products with unburnt gases, preventing the global blowoff. However, the coupling between the shear layers and flame-induced vorticity sources can result in large flow structures that either contribute to increased flame stability or exhibit features typical of the early stages of flame blowout. SPOD is a data-driven technique remarkably powerful in extracting low-dimensional models. For each frequency, it computes a basis of orthogonal modes that maximizes the content of a predefined CSD in the leading modes. By choosing physically-relevant variables to construct the CSD, different physics can be explored, which is used here to investigate the coupled dynamics between the flame-induced baroclinic torque, vortical structures, and the temperature field. The results show that the vorticity and temperature fields exhibit low-dimensional dynamics characterized by a narrowband frequency and its harmonics; these dynamics are varicose oscillations of the flame region, governed by the baroclinic torque. Sinuous oscillations typical of wake instability for non-reactive flows are also present, suggesting a competition between them.

Experimental study on combustion and emissions of an ammonia/diesel dual-fuel engine using split-injection strategy

Blending high-reactivity fuels is recognized as an effective way to improve poor combustion performance of ammonia (NH3). In this work, pressure trajectories, flame evolutions, and emission characteristics of ammonia/n-dodecane dual fuels were studied in an optical engine, addressing split-injection strategy. Synchronization measurement of in-cylinder pressure and high-speed photography was performed for combustion evolutions, and FT-IR spectroscopy was adopted for nitrogen-based emissions. Results demonstrate that pre-injection timing and its energy ratio play a significant role in improving engine thermal efficiency and pollution emissions. Specifically, compared to the single-injection strategy, split-injection strategy leads to a reduced peak in-cylinder pressure, optimized combustion phasing, and shortened combustion duration, achieving an increase in the indicated mean effective pressure by 15.8 %. An early pre-injection timing with an energy ratio of 40 %∼60 % yields superior thermal efficiency compared to single injection at the same injection timing. For the delayed pre-injection timing, increasing pre-injection energy ratio is required to achieve optimal engine performance. Combustion visualizations show that the improved flame development under split-injection conditions is attributed to high-reactivity fuel stratification and enhanced premixing processes. Regarding nitrogen-based emissions, unburned NH3 and N2O emissions are insignificantly reduced, and NOx emissions correlate with the sensitivity of flame development to in-cylinder temperature. On the premise of ensuring stable combustion, all nitrogen-based emissions are reduced at a pre-injection energy ratio of 40 %, and an energy ratio of 60 % reduces N2O and unburned NH3 emissions while increasing NO and NO2 emissions. The current work can provide useful insights into the optimization and control of ammonia/diesel dual-fuel engines in terms of combustion and emissions.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013–2020

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013–2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

An inter-provincial coordinate model under Renewable Portfolio Standards policy based on tradable green certificate options trading

The current territorial management model under Renewable Portfolio Standards in China cannot fully develop renewable energy due to the mismatch between high energy-consumption regions and renewable energy-rich regions. Therefore, we proposed an inter-provincial coordinate model based on tradable green certificate options trading (CMTOT), which includes four components: (1) A dual-objective optimization model is established to minimize the RPS target realization cost and maximize the pollutant emission reduction based on Blacke-Scholes option pricing model. (2) The identification of buyer and seller provinces of tradable green certificate (TGC) options based on the above model. (3) An optimization model in buyers' or sellers' cooperation alliance is established. (4) The cost allocation models by Shapley value and Nucleolus are established to ensure the fairness in different scenarios. A case study of Tianjin, Shandong, Shanxi and Inner Mongolia shows that the CMTOT can effectively reduce the RPS target realization cost by 16.79 % and increase the pollutant emission reduction by 4.32 %. A sensitivity analysis confirms the robustness of the CMTOT. The results show that the CMTOT can both fully develop renewable energy and effectively reduce pollutant emission. Therefore, we proposed some policy recommendations to support the implementation of the CMTOT.

Combustion Control of Ship’s Oil-Fired Boilers Based on Prediction of Flame Images

This study proposes and validates a novel combustion control system for oil-fired boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs and applies internal model control (IMC)-based proportional-integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC filter constant tuning through experimentation is essential for achieving effective control performance. Experimental results showed that optimal control performance was achieved when the filter constant λ was set to 1.5. In this scenario, the peak overshoot Mp was reduced to 0.19245, and the Integral of Squared Error (ISE) was minimized to 10.1159, ensuring a stable response with minimal oscillation and maintaining a fast response speed. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

Adsorption recovery of chlorinated volatile organic compounds on coffee ground-based activated carbon of tunable porosity

Chlorinated volatile organic compounds (CVOCs) play an indispensable role in industrial production, but meanwhile pose serious threats to human health. As a response to the urgent and challenging requirements of reducing pollution and carbon emissions, in this work, the critically favorable ultra-micropores and hierarchical porous structure are constructed onto the low-cost coffee ground-based activated carbon (AC) for the CVOCs adsorption recovery. To regulate the processes of carbonization and activation, the formation of the porosity is elaborated in terms of elements, functional groups and graphited structure. As expected, abundant ultra-micropores (<1 nm) are particularly apt to the adsorption of 1,2-dichloroethane (1,2-DCE) of small size and also contribute positively to the adsorption of chlorobenzene (CB). Moreover, the sample S400-4 possesses a large surface area (1643 m2/g), pore volume (0.89 cm3/g) and hierarchical porous structures that greatly benefit the adsorption of both 1,2-DCE (2.11 mmol/g) and CB (4.15 mmol/g). The advantage is ascribed to the facilitated transfer of CVOCs through the hierarchical porous structures and the higher affinity of CB for the π-π interaction. S400-4 presents a good performance in the recycle tests and with the interference of co-existing components alike, which indicates the engineering application potential of coffee ground-based AC. Hopefully, this work sheds light on the structure-function relationship between biomass-based AC and CVOCs adsorption, and contributes to the targeted development of suitable CVOCs adsorption materials and recovery technologies.

Assessment of combustion development and pollutant emissions of a spark ignition engine fueled by ammonia and ammonia-hydrogen blends

Ammonia is a promising carbon-free fuel for internal combustion engines, but its combustion properties are very poor if compared with conventional fuels. Thus, hydrogen enrichment can be the proper solution in order to overtake the issues related to neat ammonia fueling. In this paper, the authors use a 3D numerical model coupled with a chemical kinetic approach to assess the operation of a light-duty spark-ignition engine fueled by neat ammonia and ammonia-hydrogen blends (up to 15% of hydrogen by volume). The proposed model reproduces the analyzed experimental operating points with a good accuracy. As example, considering the in-cylinder pressure peak, the difference between the calculated maximum pressure and the mean measured one ranges between 1.6% and 6.8% for the examined cases. The 3D approach enables an in-depth analysis of flame development that also allows to detect the effect of hydrogen enrichment on both combustion development and emissions. Unburned ammonia trends are captured, reproducing the measured decrease of ammonia emissions for increasing hydrogen enrichment. The emissions of NOx are also well reproduced. Simulations also highlighted that dissociation of ammonia produces H2, which is detected in the combustion chamber also if neat ammonia is burned and, at least according to the chemical mechanism used, not all the produced hydrogen oxidizes. Only very small quantities of intermediates related to ammonia combustion are calculated at exhaust valve opening, decreasing for an increasing hydrogen content in the fuel mixture.

Combustion and co-combustion of biochar: Combustion performance and pollutant emissions

Biomass is a renewable and clean energy source, offering the potential to reduce fossil fuel consumption and greenhouse gas emissions when it is used as a biofuel. However, the disadvantages of raw biomass such as poor grindability, high moisture content, and low calorific value hinder its widespread industrial combustion. Thermochemical conversion techniques are widely used to convert biomass to biochar, which serves not only as an adsorbent and soil amendment but also as solid fuels alternative to fossil energy. The upgraded biochar exhibits better physicochemical properties and improved combustion performance. This paper focuses on the properties and combustion behavior of biochar as a solid fuel, reviewing common production techniques of biochar and their effects on yields and properties of biochar. It also examines the combustion characteristics and kinetics of biochar in single- and co-combustion scenarios with other fuels, such as coal, sludge, and petroleum coke, analyzing the influencing factors of combustion behavior and kinetic parameters. This study further investigates the emission characteristics during the combustion of biochar, including gaseous pollutants (such as CO, NOx, SOx, and HCl), ash-related issues, and particulate matter (PM) emissions. Additionally, a new fuel type named “bioslurry”, which is developed with biochar as a main material, is introduced. Furthermore, this paper discusses the techno-economic and environmental aspects of biochar application, exploring the feasibility of its industrial production and utilization.

Characterization of Waste Biomass Fuel Prepared from Coffee and Tea Production: Its Properties, Combustion, and Emissions

In order to reduce global warming, new energy fuels that use waste biomass to replace traditional coal are rapidly developing. The main purpose of this study is to investigate the feasibility behavior of different biomass materials such as spent coffee grounds (SCGs) and spent tea grounds (STGs) as fuel during combustion and their impact on the environment. This study involves using fuel shaping and co-firing methods to increase the fuel calorific value and reduce the emissions of pollutants, such as NOX and SO2, and greenhouse gas CO2. The produced gas content was analyzed using the HORIBA (PG-250) laboratory combustion apparatus. The results indicate that, among the measured formed particles, SCG:STG = 8:2, 6:4, and 4:6 had the lowest post-combustion pollutant gas emissions. Compared to using only waste coffee grounds as fuel, the NOx emissions were reduced from 166 ppm to 102 ppm, the CO emissions were reduced from 22 ppm to 12 ppm, and the CO2 emissions were reduced from 629 ppm to 323 ppm. In addition, the emission of SO2, the main component of acid rain, was reduced by 20 times compared to the combustion of traditional fuels. The SO2 emission of five different proportions of biomass fuels was 5 ppm, which is much lower than that of traditional coal fuels. Therefore, SCG and STG mixed fuels can replace coal as fuel while reducing harmful gasses.

Can intelligent manufacturing drive green development in China's pharmaceutical industry? -- Evidence from listed enterprises

The pharmaceutical industry's historical extensive pollution emissions have caused severe environmental issues. In the context of the evolving intelligent age, it is urgent to investigate the potential of intelligent manufacturing in improving the environmental performance of the pharmaceutical industry. Our study employs panel data from 110 publicly listed pharmaceutical enterprises covering the period from 2012 to 2020 to assess the impact of intelligent manufacturing on the environmental performance of the pharmaceutical sector. The results reveal a substantial positive effect of intelligent manufacturing on the environmental performance of these enterprises. Additionally, we identify three primary mechanisms for this enhancement: supply chain optimization, the emergence of technological innovations, and structural optimization. A notable observation is that enterprises not certified in environmental management systems or operating within highly competitive markets exhibit a greater potential for enhancing their environmental performance. Drawing upon these insights, our study proposes detailed policy recommendations to leverage intelligent manufacturing for the sustainable progression of the pharmaceutical industry.

Linking industrial emissions and dietary exposure to human burdens of polychlorinated naphthalenes

Relationships between toxic pollutant emissions during industrial processes and toxic pollutant dietary intakes and adverse health burdens have not yet been quantitatively clarified. Polychlorinated naphthalenes (PCNs) are typical industrial pollutants that are carcinogenic and of increasing concern. In this study, we established an interpretable machine learning model for quantifying the contributions of industrial emissions and dietary intakes of PCNs to health effects. We used the SHapley Additive exPlanations model to achieve individualized interpretability, enabling us to evaluate the specific contributions of individual feature values towards PCNs concentration levels. A strong relationship between PCN dietary intake and body burden was found using a robust large-scale PCN diet survey database for China containing the results of the analyses of 17,280 dietary samples and 4480 breast milk samples. Industrial emissions and dietary intake contributed 12 % and 52 %, respectively, of the PCN burden in breast milk. The model quantified the contributions of food consumption and industrial emissions to PCN exposure, which will be useful for performing accurate health risk assessments and developing reduction strategies of PCNs.

Multi-objective robust optimization design framework for low-pollution emission burners

The optimal design of low-pollution emission burners plays an important role in controlling pollutant emissions of industrial equipment, and is crucial for the sustainable development of the national economy and environmental protection. However, many uncertain factors challenge the optimal design of low-pollution emission burners. The Latin hypercube sampling (LHS) method was used to obtain sampling data representing the distribution of the uncertain variable. The training dataset was obtained using the turbulent combustion coupling model. A high-precision sparse polynomial chaos expansion (PCE) model was constructed by the degree-adaptive scheme and least angle regression (LAR) algorithm. Furthermore, the Legendre polynomial is introduced to establish a continuous robust optimization model. The model is carried out by the non-dominated sorting genetic algorithm II (NSGA-II). The results show that the excess air coefficient of 1.227 is optimal. Compared with the excess air coefficient of 1.20 under the discrete robust optimization, the optimal coefficient can further reduce pollutant emissions and bring strong robustness to the ethylene cracking furnace. It has also been proven that the continuous robust optimization scheme improves the optimization granularity. Compared with discrete robust optimization, this method reduces the number of samples by 66.7 %.

Energy production from landfill gas, emissions and pollution indicators–Opportunities and barriers to implementing circular economy

The goal of this research was to test the air emissions resulting from landfill gas combustion in a 250-kW cogeneration unit, in the context of pollutant emission due to the produced electricity. The test results mainly revealed emissions of carbon dioxide, sulfur dioxide, nitrogen oxides, carbon oxides, and dust. The volume of emissions of individual pollutants was compared with the emission values reported from the emission balancing database. The highest emission burdens concerned the production of one MWh of electricity for carbon dioxide 579.80 kgCO2∙MWh−1, carbon monoxide 2.57 kgCO2∙MWh−1, and nitrogen oxides 1.77 kgNOx∙MWh−1. It is worth noting that small cogeneration installations are not covered by emission standards and so they are not equipped with a device for reducing gaseous and particulate pollution. The tests revealed a difference between the actual emissions from the tested installation and the values reported to the national database. The innovation of this study is that it demonstrates the real emission burden of renewable energy in the context of sustainable development. The results can be used as input material for discussion to apply gaseous and particulate pollution reduction systems for cogeneration installations powered by landfill gas, including various types of biogases.

U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations.

Oil and natural gas (O&G) production and processing activities have changed markedly across the U.S. over the past several years. However, the impacts of these changes on air pollution and greenhouse gas emissions are not clear. In this study, we examine U.S. ethane (C2H6) emissions, which are primarily from O&G activities, during years 2015-2020. We use C2H6 observations made by the NOAA Global Monitoring Laboratory and partner organizations from towers and aircraft and estimate emissions from these observations by using an inverse model. We find that U.S. C2H6 emissions (4.43 ± 0.2 Tg·yr-1) are approximately three times those estimated by the EPA's 2017 National Emissions Inventory (NEI) platform (1.54 Tg·yr-1) and exhibit a very different seasonal cycle. We also find that changes in U.S. C2H6 emissions are decoupled from reported changes in production; emissions increased 6.3 ± 7.6% (0.25 ± 0.31 Tg) between 2015 and 2020 while reported C2H6 production increased by a much larger amount (78%). Our results also suggest an apparent correlation between C2H6 emissions and C2H6 spot prices, where prices could be a proxy for pressure on the infrastructure across the supply chain. Overall, these results provide insight into how U.S. C2H6 emissions are changing over time.