Variable Emission Research Articles

Methane Emission of Italian Mediterranean Buffaloes Measured Using a Laser Detector During a Lactation Cycle

In Italy, the number of farmed dairy buffaloes rose up to approximately 436,000 heads in 2023 (+22% in the last 15 years), a fourfold increase compared to the 1980s, due to the growing market interest in mozzarella cheese. The increased demand for mozzarella cheese, in turn, requires higher production, which can result in increased methane emission from the sector. Therefore, it is necessary to establish mitigation and selection schemes for low-emission strategies. The current study aimed to highlight sources of variation in methane emission from lactating Italian Mediterranean buffaloes measured using a laser methane detector in order to identify practical and methodological aspects to consider when designing experiments focused on methane emission evaluation. Methane (CH4), exhaled from 60 cows, was recorded twice a day during milking, over two weeks per month for at least three months throughout a whole lactation cycle. The animal (individual), days in milk, parity, month, operator, milking entry order, and milking session effects were significant for methane emission (p < 0.0001). Our results showed that laser methane detector may be used as a rapid tool for methane emission studies and highlighted which factors can account for individual measures. This instrument is easy to use, fast, versatile, and not too expensive. These characteristics make it suitable for large-scale herd screening and monitoring.

Cold Storage Effects on Ethylene Emission, Co₂ Accumulation, and TSS Variation in Golden Delicious Apples at 3°C

The study investigates the ethylene production and respiratory metabolism of Golden Delicious apples stored at 3°C for 13 weeks. Apples were all picked at a maturity stage corresponding to the commercial harvest date and stored at 3°C with a relative humidity of 85±5 %. The results showed that TSS increased at the beginning of the storage while the ethylene release increased slowly under the 3⁰C cold storage. The results of this study show that after 7 weeks of storage, the ethylene release rate peaked at 35.5 μL∙kg-1∙h-1, followed by 14.9 μL∙kg-1∙h-1 after 11 weeks. Then it progressively reduced and remained at a low level. Fruit respiration rate decreased during cold storage at 3⁰C and gradually rose after storage. The goal is to develop strategies to extend the shelf life of apples while reducing post-harvest food waste. By reducing food waste, we reduce the ecological footprint of apple cultivation.

Distinct N-cycling microbial communities contribute to microtopographic variation in soil N2O emissions from denitrification

Distinct N-cycling microbial communities contribute to microtopographic variation in soil N2O emissions from denitrification

Spatiotemporal variations of dust emission in Qaidam Basin during the past 40 years

Spatiotemporal variations of dust emission in Qaidam Basin during the past 40 years

Investigating explosive events in a 3D quiet-Sun model: Transition region and coronal response

Transition region explosive events are characterized by the non-Gaussian profiles of the emission lines that form at transition region temperatures, and they are believed to be manifestations of small-scale reconnection events in the transition region. Traditionally, the enhanced emission at the line wings is interpreted as bi-directional outflows generated by the reconnection of oppositely directed magnetic fields. We investigate whether the 2D picture also holds in a more realistic setup of a 3D radiation magnetohydrodynamic (MHD) quiet-Sun model. We also compare the thermal responses in the transition region and corona of different events. We took a 3D self-consistent quiet-Sun model extending from the upper convection zone to the lower corona calculated using the MURaM code. We first synthesized the Si iv line profiles from the model and then located the profiles which show signatures of bi-directional flows. These tend to appear along network lanes, and most do not reach coronal temperatures. We isolated two hot events (around 1 MK) and one cool event (order of 0.1 MK) and examined the magnetic field evolution in and around these selected events. Furthermore, we investigated why some explosive events reach coronal temperatures, while most remain cool. We also examined the emission of these events as seen in the 174 AA passband of the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter and all coronal passbands of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). The field lines around two events reconnect at small angles (i.e., they undergo component reconnection). The third case is associated with the relaxation of a highly twisted flux rope. All three events reveal signatures in the synthesized EUI 174 AA images. The intensity variations in two events are dominated by variations of the coronal emissions, while the cool component seen in the respective channel contributes significantly to the intensity variation in one case. In comparison, one hot event is embedded in regions with higher magnetic field strength and heating rates while the densities are comparable, and the other hot event is heated to coronal temperatures mainly because of the low density. Small-scale heating events seen in the extreme-ultraviolet (EUV) channels of AIA or EUI might be hot or cool. Our results imply that the major difference between the events in which coronal counterparts dominate or not is the amount of converted magnetic energy and/or density in and around the reconnection region.

First Investigation of Long-Term Methane Emissions from Wastewater Treatment Using Satellite Remote Sensing

Wastewater treatment (WWT) contributes 2–9% of global greenhouse gas (GHG) emissions. The noticeable uncertainty in emissions estimation is due in large part to the lack of measurement data. Several methods have recently been developed for monitoring fugitive GHG emissions from WWT. However, limited by the short duration of the monitoring, only “snapshot” data can be obtained, necessitating extrapolation of the limited data for estimating annual emissions. Extrapolation introduces substantial errors, as it fails to account for the spatial and temporal variations of fugitive emissions. This research evaluated the feasibility of studying the long-term CH4 emissions from WWT by analyzing high spatial resolution Sentinel-2 data. Satellite images of a WWT plant in Calgary, Canada, taken between 2019 and 2023, were processed to retrieve CH4 column concentration distributions. Digital image processing techniques were developed and used for extracting the time- and space-varying features of CH4 emissions, which revealed daily, monthly, seasonal, and annual variations. Emission hotspots were also identified and corroborated with ground-based measurements. Despite limitations due to atmospheric scattering, cloud cover, and sensor resolution, which affect precise ground-level concentration assessments, the findings reveal the dynamic nature of fugitive GHG emissions from WWT, indicating the need for continuous monitoring. The results also show the potential of utilizing satellite images for cost-effectively evaluating fugitive CH4 emissions.

A conceptual model explaining spatial variation in soil nitrous oxide emissions in agricultural fields

Soil emissions of nitrous oxide contribute substantially to global warming from agriculture. Spatiotemporal variation in nitrous oxide emissions within agricultural fields leads to uncertainty in the benefits of climate-smart agricultural practices. Here, we present a conceptual model explaining spatial variation in temporal patterns of soil nitrous oxide emissions developed from high spatial resolution measurements of soil nitrous oxide emissions, gross nitrous oxide fluxes, and soil physicochemical properties in two maize fields in Illinois, USA. In sub-field locations with consistently low nitrous oxide emissions, soil nitrate and dissolved organic carbon constrained nitrous oxide production irrespective of changes in soil moisture. In sub-field locations where high emissions occurred episodically, soil nitrate and dissolved organic carbon availability were higher, and increases in soil moisture stimulated nitrous oxide production. These findings form the ‘cannon model’ which conceptualizes how sub-field scale variation in soil nitrate and dissolved organic carbon determines where increases in soil moisture can trigger high soil nitrous oxide emissions within agricultural fields.

Nitrogen isotopes reveal high NOx emissions from arid agricultural soils in the Salton Sea Air Basin

Air quality management commonly aims to mitigate nitrogen oxide (NOx) emissions from combustion, reducing ozone (O3) and particulate matter (PM) pollution. Despite such ongoing efforts, regulations have recently proven ineffective in rural areas like the Salton Sea Air Basin of Southern California, which routinely violates O3 and PM air quality standards. With over $2 billion in annual agricultural sales and low population density, air quality in the region is likely influenced by the year-round farming activity. We conducted a source apportionment of NOx (an important precursor to both O3 and PM) using nitrogen stable isotopes of ambient NO2, which revealed a significant contribution from soil-emitted NOx to the regional budget. The soil source strength was estimated based on the mean δ15N-NOx from each emission category in the California Air Resources Board’s NOx inventory. Our annual average soil emission estimate for the air basin was 11.4 ± 4 tons/d, representing ~ 30% of the extant NOx inventory, 10× larger than the state’s inventory for soil emissions. Unconstrained environmental factors such as nutrient availability, soil moisture, and temperature have a first-order impact on soil NOx production in this agriculturally intensive region, with fertilization and irrigation practices likely driving most of the emissions variability. Without spatially and temporally accurate data on fertilizer application rates and irrigation schedules, it is difficult to determine the direct impacts that these variations have on our observations. Nevertheless, comparative analysis with previous studies indicates that soil NOx emissions in the Imperial Valley are likely underrepresented in current inventories, highlighting the need for more detailed and localized observational data to constrain the sizeable and variable emissions from these arid, agricultural soils.

Effects of Straw and Green Manure Addition on Crop Yield, Soil Properties and CH4 Emissions: A Meta-Analysis

The incorporation of organic amendments is widely acknowledged for its capacity to enhance soil fertility and boost crop productivity. However, whether the addition of organic amendments can improve soil quality and crop production, simultaneously causing methane emissions in paddy fields, deserves further investigation. In this meta-analysis, the effects of different organic amendments on soil nutrient levels, rice yield and CH4 emissions were evaluated in paddy fields based on 328 observations from 77 field trial studies. Our results revealed that the addition of organic amendments significantly increased soil organic carbon (9.47%), microbial biomass carbon (21.13%), microbial biomass nitrogen (28.91%), urease (25.07%) and β-glucosidase (24.41%). Moreover, straw addition significantly increased the CH4 emissions by 152.68% and rice yield by 7.16%; green manure addition significantly increased CH4 emissions by 71.62% and rice yield by 10.09%, respectively. Although both increased the CH4 emissions, green manure had the ability to improve the availability of N, which could improve rice uptake. The regression results showed that the variation in crop yield, soil nutrients and CH4 emissions are influenced through the types and quality of organic amendments. Overall, this study suggests that organic amendments are beneficial in maintaining soil quality and improving rice yield, whereas it also increased the CH4 emissions. These meta-analysis results may provide some references for optimizing organic amendments incorporated into the soil to sustain soil fertility and crop production while mitigating soil constraints and methane emissions.

Colorimetric Thermography by a Long-Infrared Dual-Band Metalens.

Infrared (IR) radiation thermography is extensively utilized in diverse fields due to its non-contact capability. Nevertheless, its effectiveness is often compromised by the significant emissivity variations among different objects, limiting its application to specific setups or focused object types. Colorimetric thermography is introduced as an alternative emissivity-independent method of radiation thermometry. This technique involves measuring radiance across two or more spectral bands and calculating the object's temperature based on the signal ratio, thereby mitigating emissivity effects under certain conditions. However, this method has the trade-off of necessitating bulky optical systems, complex filter imaging configurations, and sensor structures. To meet the requirements of IR thermography for compact structure, lightweight design, and customizability, a dual-band metalens is developed for the IR colorimetric thermography. The central wavelengths targeted are 9.5 and 12.5µm. The dual-band IR imaging by the fabricated dual-band metalens is demonstrated, and the colorimetric thermography of low-emissivity objects is performed without presetting emissivity values. This approach significantly eliminates measurement errors associated with emissivity by an average of 50.16% across a temperature range of 60-180°C. This innovation paves the way for dynamic and multi-target thermography using compact IR systems in complex environments.

Electromagnetic side-channel attack risk assessment on a practical quantum-key-distribution receiver based on multi-class classification

While quantum key distribution (QKD) is a theoretically secure way of growing quantum-safe encryption keys, many practical implementations are challenged due to various open attack vectors, resulting in many variations of QKD protocols. Side channels are one such vector that allows a passive or active eavesdropper to obtain QKD information leaked through practical devices. This paper assesses the feasibility and implications of extracting the raw secret key from far-field radiated emissions from the single-photon avalanche diodes used in a BB84 QKD quad-detector receiver. Enhancement of the attack was also demonstrated through the use of deep-learning model to distinguish radiated emissions due to the four polarized encoding states. To evaluate the severity of such side-channel attack, multi-class classification based on raw-data and pre-processed data is implemented and assessed. Results show that classifiers based on both raw-data and pre-processed features can discern variations of the electromagnetic emissions caused by specific orientations of the detectors within the receiver with an accuracy higher than 90%. This research proposes machine learning models as a technique to assess EM information leakage risk of QKD and highlights the feasibility of side-channel attacks in the far-field region, further emphasizing the need to utilise mechanisms to avoid electromagnetic radiation information leaks and measurement-device-independent QKD protocols.

The Mechanisms Driving Ammonia Emission Variations in Alkaline Soil Under Different Irrigation and Fertilization Management Practices in Arid Regions

The Mechanisms Driving Ammonia Emission Variations in Alkaline Soil Under Different Irrigation and Fertilization Management Practices in Arid Regions

Variable emission factors of CH4 and N2O from WWTPs: A model-based analysis on available data

Climate change and carbon emissions are increasingly becoming a global concern, and thus wastewater treatment plants (WWTPs) are also receiving extensive attention due to direct greenhouse gas (GHG) emissions of methane (CH4) and nitrous oxide (N2O). Although there have been many emission factors (EFs) of CH4 and N2O in literature, they are changeful due to different processes and boundaries, which limits their values for reference and comparison. With this study, in situ monitored CH4 and N2O data reported in literature were retrieved for recalculating their EFs. The average EFs are found to be 0.0011 g CH4/g BOD5-influent, and 0.0017 g N2O-N/g TNinfluent, based on the secondary treatment. Subsequently, the data were analyzed using multivariate linear regression and neural network. The results indicate that BOD5 is the first factor affecting the EF of CH4, revealing a negative correlation and that TN is the second factor affecting the EF of CH4, but having a positive correlation. On the other hand, the neural network is a powerfully predictive and generalizable tool for EFN2O. BOD5 is negatively correlated with EFN2O, and EFN2O reaches to its maximum value at TN = 35 mg/L. Overall, the direct GHG emission intensity is the lowest in the AAO and AO processes, or with the BOD5/TN ratio between 2.5 and 4.9. Medium-sized WWTPs and the Oceania region exhibit the highest GHG emission intensity. With this study, an approximate approach is established to estimate the EFs of CH4 and N2O, which can facilitate to account the carbon footprint of WWTPs and also to aid in optimizing their operational schemes.

Diurnal variations in greenhouse gas diffusive emissions from a river covered with submerged macrophytes: environmental and biotic driving factors

Diurnal variations in greenhouse gas diffusive emissions from a river covered with submerged macrophytes: environmental and biotic driving factors

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

Assessing the Spatial Distribution of Carbon Emissions and Influencing Factors in the Yellow River Basin

The spatial distribution and trend of carbon emissions in the Yellow River Basin—an important ecological barrier and economic belt in China—directly affect the stability of the ecosystem and the sustainable development of the regional economy. Based on the data for carbon emissions in China’s counties from 1997 to 2017, this paper utilizes standard deviation ellipses, Theil index nested decomposition, and geographic detector models to make a comprehensive description of the spatial and temporal distribution and dynamic evolution characteristics of carbon emissions in the Yellow River Basin. Factors influencing carbon emissions are also analyzed from multiple dimensions. According to the findings, (1) carbon emissions at the county level show a clear upward trend without reaching a peak, exhibiting a spatial distribution of higher emissions in the east and lower in the west and higher in the south and lower in the north, with the mid-lower reaches being the center. The junction of the Shandong, Shaanxi, and Gansu provinces further exhibits a significant expansion, forming two core areas of carbon emissions. (2) Carbon emissions at the county level in the Yellow River Basin are influenced by both economic and geographic factors, exhibiting a significant high carbon spillover effect and a low carbon lock-in effect. The gravity center of the distribution has shifted towards the mid-lower reaches, with the upper reaches displaying dispersion tendencies. (3) Intra-regional disparities are the main source of the overall spatial differences in carbon emissions, with the largest disparities being observed in the upper reaches, followed by the middle reaches, and the smallest disparities being observed in the lower reaches. Further analysis shows that the level of economic development is the primary factor influencing the spatial variation of carbon emissions, and the combined effects of population size and industrial agglomeration are the key drivers of the annual growth in carbon emissions.

The uniqueness of pollutant emission and fuel consumption test results for road vehicles tested on a chassis dynamometer

The article considers the issues of non-repeatability of exhaust emission and fuel consumption test results for road vehicles tested on a chassis dynamometer. Empirical results of passenger car tests on a chassis dynamometer in the WLTC (Worldwide Harmonized Light Vehicles Test Cycle) test were used. Tests were conducted in four repeated sets to assess the repeatability of the test results. The road emission of hydrocarbons, non-methane hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter and carbon dioxide, the road number of particulate matter and operational fuel consumption were determined. The coefficient of variation of the values measured in the four tests was determined, taken as a measure of the repeatability of the test results. The coefficient of variation of the road emission of carbon dioxide and operational fuel consumption is the lowest. No significant differences were found in the values of the measured exhaust emission.

The Impact of Firework Ban Relaxation on Variations in SO2 Emissions in China During the 2023 Chinese New Year

During the 2023 Chinese New Year (CNY), many city governments temporarily relaxed firework restrictions, leading to increased sulfur dioxide (SO2) emissions from the combustion of sulfur-containing fireworks. This study employed the four-dimensional variational (4DVar) assimilation system to examine variations in SO2 emissions in China by assimilating hourly ground-based observations. Two experiments were conducted during CNY in 2022 and 2023 to quantify the variations in SO2 emissions. On CNY’s Eve in 2023, following the relaxation of the firework ban, SO2 emissions surged by 8.22 Gg nationwide compared to the previous day with significant increases in the Energy Golden Triangle (2.037 Gg), the North China Plain (1.709 Gg), and northeast China (0.945 Gg). Emissions peaked on CNY’s Eve and rapidly declined in the following two days but remained elevated compared to the pre-CNY period, indicating lingering effects of firework burning. Compared to the forecasts using the prior emissions, the optimized emissions markedly improved the model forecasts of SO2 during the 2023 CNY period, with an increase in the correlation coefficient (R) from 0.13 to 0.64 and a reduction in the root mean square error (RMSE) by 49.2%, demonstrating the effectiveness of the optimized emissions. These findings will be useful for local governments in formulating strategies for firework burning during CNY.

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

Water CO2 Emission Monitoring in a Romanian Peri-Urban Wetland to Enhance GHG Reporting

This study emphasises the complexity of carbon dioxide (CO2) emission dynamics by conducting a wetland case study along the Dambovita River. Our evaluation highlights the importance of considering spatial variability, meteorological parameters and water quality parameters. The variations in CO2 emissions have been monitored using two complementary methods: a closed static chamber and a closed dynamic chamber. The closed dynamic chamber method has the highest level of confidence. The statistical results of correlations facilitated the validation of the closed static chamber method and its independent use in wetland ecosystems. Also, our findings revealed distinct patterns in emissions across locations that are influenced by parameters such as pH, redox potential (ORP), chlorophyll, dissolved oxygen concentration (DO), and temperature for the water–atmosphere interface. These results contribute to the understanding of the carbon cycle in wetlands and contribute to the improvement of greenhouse gas (GHG) reporting by obtaining data with a high level of confidence, regarding the role of wetland ecosystems in the carbon cycle.