Emission Estimates Research Articles

In-situ online investigation of biogenic volatile organic compounds emissions from tropical rainforests in Hainan, China

Biogenic volatile organic compounds (BVOCs) emitted by tropical plants represent a significant proportion of global emissions, but the in-situ BVOC measurements in tropical rainforests are extremely sparse. Herein, a vehicle-mounted mobile monitoring system was developed for in-situ online investigations of BVOC emissions from thirty representative tree species in the tropical rainforests of Hainan Island, southern China. The results showed that monoterpenes were the primary BVOCs emitted from most broadleaf trees. Isoprene, sabinene, γ-terpinene and β-ocimene were the most abundant BVOCs. Localized canopy-scale emission factors (EFs) exhibited notable discrepancies with the defaults in the Model of Emissions of Gases and Aerosols from Nature (MEGAN), specifically with isoprene EFs being slightly lower than the model, while the EFs for monoterpenes and sesquiterpenes were 1 to 27 times higher than those in MEGAN. The BVOC emission inventory for the predominant mature forest species in Hainan Island in 2023 was further estimated to be 244.43 Gg C·yr−1, with isoprene and monoterpenes accounting for 74 % and 16 %, respectively. Additionally, unimodal monthly variation patterns were revealed, with BVOC emissions peaked in July (30.08 Gg C·yr−1) and bottomed in January (8.84 Gg C·yr−1). This study demonstrates the potential and versatility of the applied mobile platform for in-situ online measurements of plant volatiles. Our findings provide important data support for reducing uncertainties in BVOC emission estimations in tropical rainforests and for evaluating their health benefits in the context of forest therapy.

An Integrated Field Study of Turbulence and Dispersion Variations in Road Microenvironments.

Traffic-related air pollutants (TRAPs) emitted from vehicle tailpipes disperse into nearby microenvironments, posing potential exposure risks. Thus, accurately identifying the emission hotspots of TRAPs is essential for assessing potential exposure risks. We investigated the relationship between turbulent kinetic energy (TKE) and pollutant dispersion (D) through an integrated field measurement. A five-year near-road sampling campaign (5 min based) near a light-duty vehicle-restricted roadway and an on-road sampling campaign (5 s based) on isolated proving grounds were conducted. The D was first calculated based on vehicle emission and pollutant concentrations and then paired with TKE measurements. Here, 198 near-road and 377 on-road measurement pairs were collected. In the near-road measurements, TKE and D showed a positive relationship (R2 ≥ 0.69) with the vehicle flow rate, while they showed similar decay patterns and sensitivity to vehicle types in the on-road measurements. A relationship between TKE and D (TKE-D) was developed through these measurements, demonstrating a robust correlation (R2 ≥ 0.61) and consistent slope values (1.1-1.3). These findings provide field evidence for the positive association between TKE and D, irrespective of the measurement techniques or locations. The TKE-D relationship enables vehicle emission estimation with TKE as the sole input, facilitating the identification of emission hotspots with high spatiotemporal resolution.

Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution

Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations and source emission estimations. In this study, a comprehensive optical flow framework is developed to offer a new perspective on quantitative characterization of interregional transport of atmospheric pollution based on synergistic observations from geostationary and sun-synchronous satellites. In this framework, the high-frequency continuous aerosol observing images are regarded as video in computer vision, and an aerosol dynamic optical flow algorithm is proposed by incorporating aerosol-specific assumptions and constraints, overcoming the limitation that traditional optical flow methods are typically confined to rigid bodies. Results demonstrate that the developed optical flow framework could distinguish the aerosol transport process from other dynamic processes of aerosol development and accurately capture the fast-changing details of transport processes. Moreover, the satellite-based optical flow framework achieves aerosol transport results comparable to those of widely accepted model-based methods, demonstrating the physical interpretation of pixel-based optical flow results and highlighting its effectiveness in quantitative characterization of the atmospheric pollution transport process via the Aerosol Transport Index (ATI). Furthermore, a case analysis of long-term assessments of interregional transport of atmospheric pollution indicates that Beijing acts as a “sink” of atmospheric pollution, and a downward trend could be found from the annually averaged transported aerosol net loadings due to the emission reduction policy. Compared with model-based methods, the satellite-based optical flow framework is directly grounded in observations and does not rely on emission inventories that take years to update. Therefore, it not only helps improve understanding the patterns of atmospheric pollution interregional transport, but also provides a more efficient and economical way to assess the effectiveness of regional joint control policy.

Carbon emission characteristics and carbon reduction analysis of employee travel-taking a research institute as an example

This paper adopts the “baseline scenario method” to construct a comprehensive model for calculating and reducing carbon emissions generated by employee travel, including the accounting of carbon emissions from commuting and business travel, as well as the assessment of green travel for carbon reduction. The study employs methods such as questionnaires and on-site interviews to collect travel data from employees of a research institute in Beijing as a case study. The results show that employees’ commuting methods are diverse, with the subway being the primary mode of travel; however, business travel generates higher carbon emissions, particularly among employees with higher education levels. The research concludes that the model proposed in this paper provides a framework for preliminary carbon emission estimation, but to improve the accuracy of the estimates, more variables and factors need to be considered, and the limitations of the model are pointed out. The research findings have significant implications for policy and institutional practices, suggesting the adoption of more targeted measures to reduce the use of high-carbon-emission travel methods and to encourage the use of green travel options. With the continuous advancement of data collection technologies in the future, it will be possible to further establish a more refined carbon emission accounting model and obtain more accurate and comprehensive travel data, thereby providing solid data support for the development of more effective carbon reduction strategies and policies.

High Resolution (30 m) Burned Area Product Improves the Ability for Carbon Emission Estimation in Africa

AbstractFire significantly contributes to greenhouse gas emissions. The current global burned area (BA) products mainly have coarse native spatial resolution, which leads to underestimation of global BA and carbon emissions from biomass burning. Performances of BA products in Africa from GABAM (30 m), MCD64A1 (500 m), GFED4s (0.25°), FireCCI51 (250 m), and GFED5 (0.25°) were compared. From 2014 to 2020, GFED5 detected the most BA, 1.58 times more than GABAM during the same period. GABAM detected 0.09 Mkm2 more burned area than FireCCI51 on average. From 2014 to 2016, GABAM detected an average of 2.99 Mkm2 of BA in Africa, which was 1.03 times more than GFED4s. From 2014 to 2021, the average African BA derived from GABAM was 2.89 Mkm2, 1.22 times more than MCD64A1. The increase in BA will inevitably lead to an increase in the estimation of carbon emissions from biomass burning. Based on GABAM products and GFED framework, we estimated the average vegetation burning carbon emissions in Africa from 2014 to 2021 to be 1113.25 Tg, which is higher than GFED4s' carbon emissions in the same time period. This shows that the use of high‐resolution (30 m) burned area products to estimate carbon emissions can effectively avoid the underestimation of overall fire carbon emissions.

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

AbstractTidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO2) and methane (CH4) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO2 and CH4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO2 and CH4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (Reco) modules explained, on average, 73% of the variation in CO2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH4 module also explained the majority of variance in CH4 emissions in validation sites (R2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on Reco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH4 emissions, and (d) CH4 responses to non‐periodic changes in salinity.

Representative CO2 emissions pathways for China's provinces toward carbon neutrality under the Paris Agreement's 2 °C target

China has committed to achieving carbon neutrality by 2060. It is essential to develop representative CO2 emissions pathways at the provincial level that align with the national target to facilitate effective policy implementation and scientific research. To address inconsistencies between provincial aggregate emissions and national estimates, this study compares the 2021 CO2 emissions estimates of China's provinces from the bottom‒up emissions factor method and the top‒down atmospheric CO2 concentration inversion method. We find that these methods yield comparable results for the emissions from energy combustion and industrial processes at the provincial level. Based on a review of existing research on CO2 pathways for China's provinces, we propose a set of representative pathways for China's provinces. These pathways align with past practices of allocating national emissions intensity reduction targets to provinces and are consistent with the national Tsinghua‒CMA pathway. The proposed pathways require provinces to sequentially peak their emissions by 2030, followed by rapid emissions reduction. Compared to a reference scenario without the carbon neutrality target, these pathways would incur an estimated cumulative GDP loss of about 1% between 2020 and 2060. However, there are notable regional variations, with some regions in the northwest potentially experiencing higher economic growth due to the availability of high-quality low-carbon resources. We recommend that China enhance provincial-level CO2 emissions accounting by cross-validating bottom‒up and top‒down methods. Additionally, careful consideration should be given to aligning provincial targets with national and global commitments when updating pathways toward carbon neutrality.

Comparison of observation- and inventory-based methane emissions for eight large global emitters

Abstract. Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

Climate change and greenhouse gas emissions from soils under the winter wheat agroecosystem in Ukraine

The issue of global climate change is one of the most important challenges for modern environmental science. In this regard, assessment of greenhouse gas emissions from soils under agroecosystems is an extremely important task. The main purpose of the study was to assess the potential emissions of greenhouse gases (CO2 and N2O) from the soils under the winter wheat agroecosystem during its spring-summer growing season under the climate change in Ukraine. A comprehensive model of greenhouse gas emissions from agroecosystem soils was used as a theoretical basis. The study considered the main agroclimatic regions of Ukraine, such as Polissia, Forest-Steppe, Northern Steppe, and Southern Steppe. The modelling was performed for average long-term conditions of 1981–2020 and under scenario conditions (using the RCP4.5 climate scenario) for the decades of 2021–2030, 2031–2040, and 2041–2050. Estimates of CO2 emissions were obtained for the agroclimatic regions of Ukraine. According to the results, the largest CO2 emissions (0.886-0.940 t C-CO2/ha) will be expected when the average productive moisture supplies in the arable soil layer for the spring-summer period of winter wheat vegetation are 20–40 mm and the air temperature is 12–15 °C. Rise in the level of moisture reserves, averaged for the period, for more than 40 mm and the air temperature of 11–12 °C is accompanied by decrease in the level of CO2 emission (0.645–0.658 t C-CO2/ha). In arid conditions, with a decrease in the period-average moisture supplies in the arable soil layer to less than 20 mm and heightened average air temperatures (14.5–15.2 °C), a decrease in CO2 emissions is expected (0.755–0.770 t C-CO2/ha). The study showed that the highest N2O emission (1.087–1.703 kg N-N2O/ha) is expected at a high level of moistening (Md being equal to 0.474–0.713 relative units and air temperature – 11.3–13.0 °C) with a high intensity of nitrification and denitrification processes. Conditions characterizing the average level of N2O emission (0.657–0.890 kg N-N2O/ha) are formed under the moistening at a level of Md equaling 0.296–0.447 relative units and air temperature of 13.4–14.7 °C. There is a slowdown in denitrification processes and an increase in the share of emissions owing to nitrification. It has been found that the lowest N2O emission (0.508–0.537 kg N–N2O/ha) is expected under conditions of severe and very severe drought (according to the Md criterion), with a heightened temperature averaged at the level of 14.5–15.2 °C during the spring-summer period of winter wheat vegetation.

Automatic Question Answering From Large ESG Reports

ESG reports contain crucial information about the corporations' environmental impact, social responsibilities, and governance. Many compliance audits rely on answering questions based on these reports. Moreover, for tasks such as Scope 3 GHG emissions estimation, a company needs to look at the ESG reports of all its typically numerous suppliers to tabulate its own compliance reports. Manually finding specific information from these large documents is immensely time-consuming. This paper presents the first system that automatically answers questions from an ESG report, using advanced machine learning and natural language processing. The proposed system also locates and highlights a cropped screenshot from the report providing the answer. The authors devise two methods for inferring the textual answer, one based on a transformer model pre-trained for extractive question answering and another using a large language model. The task-agnostic method overcomes the challenge of the lengthiness of ESG reports in a cost-effective manner.

Navigating Climate Change: Alberta’s Carbon Program for Sustainable Agriculture

In 2021, the Government of Alberta launched the carbon program initiative to evaluate environmental practices and greenhouse gas reduction strategies in the agricultural sector. The program has produced various technical reports, policy briefing papers, industry surveys and roundtables. This policy paper consolidates the findings of the research. It presents an analysis of Alberta’s greenhouse gas emission profile, historical trends and the policy framework, as well as an examination of mitigation strategies such as carbon pricing and their effectiveness in Alberta’s agricultural system. The key question addressed in this paper is how Alberta can continue to support its thriving agricultural industry while responding to the federal and global calls to significantly reduce its methane and nitrous oxide emissions and fulfil Canada’s climate commitments. The paper also outlines the obstacles that producers encounter when implementing these strategies, as well as the limitations of the current emission estimation methodology in measuring the impact. To effectively address the challenges of emission mitigation in Alberta’s agriculture sector, a co-ordinated approach at both the federal and provincial levels is crucial. The paper concludes with the following recommendations that outline specific actions to help reduce uncertainties and support producers in implementing best management practices (BMPs) to lower greenhouse gas emissions.

Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission

Abstract. Reducing methane (CH4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20×20 m2 pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100–150 kg h−1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (∼ 20×20 m2) mapped over large swaths (∼ 100×100 km2) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130×400 m2 pixel size over a target area measuring ∼ 200×200 km2), thereby delivering quantitative data on basin-wide methane emissions.

Distribution and control mechanism of pCO2 and water-air CO2 efflux in the Pearl River Estuary.

Estuaries are generally considered to be important sources of atmospheric CO2. However, the differences between estuaries, and inadequate observations of partial pressure of CO2 in estuarine water (pCO2water) hamper global estuarine CO2 budgeting. In this study, the longitudinal distribution of CO2 in the waters of Modaomen (MSE) and Lingdingyang (LSE), two sub-estuaries of the Pearl River Estuary (PRE), and its influencing mechanism are studied. The change in the distribution of pCO2water along the distance from the upstream estuary to the ocean between LSE and MSE was significantly different. pCO2water at the LSE ranges from 238 to 7267 µatm, whereas the MSE ranges from 406 to 3078 µatm. Stronger microbial respiration and relatively long water retention times were the main influences that led to higher pCO2water at LSE than at MSE. Seasonally, the increase of soil CO2 into the water in the upstream basin caused by precipitation is the potential influencing factor that the water pCO2water in the flood season is higher than in the dry season. PRE was a net source of atmospheric CO2 with an average annual water-air flux of 41.2 ± 33.3mmolm-2day-1. Our results suggest that the differences in longitudinal gradients of pCO2water between estuaries in the same region and the effects of different gas transport velocity models on CO2 emission estimates need to be considered in estuarine CO2 emission budgeting.

PSIX-23 Evaluation of number of goats grazing grass-based pasture for estimation of methane emission using GreenFeed system

Abstract The objective of this study was to evaluate the number of goats in pasture grazing on methane (CH4) emissions estimates using the GreenFeed system (GFS). The study involved growing meat goats, approximately 1.2 yr old and body weight (BW) of 23.9 ± 0.63 kg, over seven 2-wk periods. In periods 1, 2, and 3, 24 goats were used (Treatment 1: G-24), while periods 4 and 5 involved 18 goats (Treatment 2: G-18), and periods 6 and 7 used 12 goats (Treatment 3: G-12). The pasture area was divided into three paddocks for grazing, except during gas measurements in a stationary respiration calorimetry system (SCS) room for 2 d, where goats were fed fresh forage from the same pastureland. The GFS was programmed to dispense pellets and measured gas at specific intervals of the day (i.e., at 0700-0800 h, 1300-1400 h, 1900-2000 h, and 0100-0200 h), provided 120 g/d of concentrate pellets to attract goats for gas measurements. Statistical analysis using mixed model procedures of SAS included SCS data from preceding and succeeding periods as covariates for GFS gas data analysis. Daily dry matter (DM) intake was greater (P < 0.001) in G-12 compared with G-24 and G-18, but when expressed g/kg BW, intakes were similar (P = 0.081) across treatments. Conversely, digested DM and gross energy intakes were greater (P < 0.001) in G-24 than in G-18 and G-12 (494, 509, and 577 g/d and SE = 21.3 and 7.91, 8.10, and 9.20 MJ/d and SE = 0.359 for G-24, G-18, and G-12, respectively). Daily CH4 production was unaffected (P = 0.254) by treatment (26.6, 25.2, and 26.1 g/d; SE = 0.819), despite variations in daily GFS visits by goats (3.26, 2.61, and 2.53 visits/d and SE = 0.106 for G-24, G-18, and G-12, respectively; P < 0.001). However, CH4 emissions in g/kg DM intake or digestible DM intake differed among treatments, with G-18 intermediate between G-24 and G-12 (35.6, 33.4, and 32.0 g/kg DM intake, SE = 1.02, P = 0.024; 54.1, 51.2, and 48.9 g/kg digestible DM intake, SE = 1.71, and P < 0.001 for G-24, G-18, and G-12, respectively). Carbon dioxide production, expressed in g/d and g/kg DM intake, was greater in G-12 and G-18 compared with G-24. Heat production ranked greatest in G-12, followed by G-18 and G-24. Regardless of treatment, CH4 emissions in g/d, g/kg DM intake or g/kg digestible DM intake were greater in GFS than in SCS (25.5 and 9.56 g/d, 33.8 and 14.4 g/kg DM intake, and 51.2 and 23.8 g/kg digestible DM intake for GFS and SCS, respectively; P < 0.001). In conclusion, differences in CH4 emissions in g/kg DM intake and digestible DM intake among treatments suggests that number of animals in grazing pasture may influence CH4 emission estimates in goats.

PSLBII-20 Estimation of genetic parameters for methane emissions and residual feed intake in Nellore cattle

Abstract The objective of this study was to obtain (co)variance components, heritability, and genetic correlation estimates for enteric methane emissions, residual feed intake, growth, carcass and reproductive indicator traits. Data from 43 feed efficiency trials conducted over a 13-yr period on a single purebred Nellore herd in Central Brazil, comprising 3,400 intact males and 2,167 females, were analyzed. Trial data along with diet NDF content were used to estimate the methane emission (g CH4/kg carcass gain) for each individual, using an equation developed by Medeiros et al., 2014: CH4 (g/d) = -0.1011 + 0.02062*DMI (kg/d) + 0.001648*NDF (% of DMI). Other data included pedigree information, body weight (BW), carcass traits, and reproductive indicator traits. Continuous traits were analyzed using a linear animal model, and threshold traits were analyzed using a threshold animal model. Two-trait analyses were performed using the restricted maximum likelihood to estimate the variance components, heritabilities and genetic and phenotypic correlations among traits. The estimated heritability of methane emissions was 0.136 ± 0.0314. The genetic correlations of methane emissions with growth and carcass traits were low and not different from zero: BW at 450 d (0.064 ± 0.140), scrotal circumference at 365 d (0.188 ± 0.155), longissimus muscle area (-0.090 ± 0.132), 12th-13th rib fat (-0.122 ± 0.142), rump fat (0.080 ± 0.119), intramuscular fat % (-0.108 ± 0.152), BW at 210 d (-0.077 ± 0.168). By contrast, there were the low genetic correlation of methane emissions with early calving probability (-0.260; -0.504 – 0.026); moderate genetic correlations of methane emissions with age at first calving (0.519 ± 0.202) and DM intake (0.512 ± 0.116); and the high correlation of methane with residual feed intake (RFI; 0.832 ± 0.073). Thus, selection to improve weaning and yearling weights, and carcass traits would not affect methane emissions. Genetic selection to reduce methane emissions is feasible and would also reduce DMI and RFI, as well as female sexual precocity traits. Conversely, selection to improve RFI can be used to identify animals with decreased methane emissions. Medeiros, S. R, L. G. Baioni, A. Berndt, M. C. Freua, T. Z. Albertini, C. Costa Jr., e G. B. Feltrin (2014) Modeling enteric methane emission from beef cattle in Brazil: A proposed equation performed by principal component analyses and mixed modeling multiple regression. Proceedings, Livestock, Climate Change and Food Security Conference, Madrid.

PSXII-14 Fecal near infrared reflectance spectroscopy for indirect prediction of methane emissions from grazing cattle

Abstract Methane (CH4) production from enteric fermentation of ruminants accounts for over a quarter of the greenhouse gas emissions from the agriculture sector in the U.S. Accurate and precise quantification of CH4 is essential to identify strategies for mitigating CH4 emissions. Numerous techniques and methodologies exist for measuring enteric CH4 emissions from ruminants, however, many were designed for specific research purposes, potentially limiting their applicability on commercial farms. Our objective was to evaluate the application of fecal near infrared reflectance spectroscopy (FNIRS) to provide inputs for indirect CH4 prediction calibrations. The Grazingland Animal Nutrition Laboratory (GAN Lab) has produced FNIRS predictions of grazing livestock diet quality since 1995 in conjunction with animal performance (weight/body condition change) generated by the Nutritional Balance Analyzer (Nutbal) Software package. FNIRS is a non-invasive approach that provides a significant opportunity to inform models for grazing cattle CH4 emissions. From a comprehensive U.S. dataset of approximately 30,000 samples, we selected a subset representing beef cattle grazing the gulf coast region of Texas from 2016 to 2019 (n = 85). Inputs of diet crude protein (CP), diet digestible organic matter (DOM), live weight (LW), and estimated live weight change (LWC) were used to predict feed intake (FI) employing a published equation and, from outputs generated by Nutbal, i.e. FI_Eq and FI_Nut, respectively. The FI (kg DM/animal.day) generated by both methods were applied to established prediction equations to generate preliminary estimates of CH4 emissions (CH4 FI_Eq and CH4 FI_Nut, respectively). Differences among estimations were assessed through ANOVA, with significance determined at P < 0.05. The estimated FI was different (P < 0.01) for both methodologies, resulting in different (P < 0.01) predictions of CH4 emissions. The CH4 prediction for CH4 FI_Eq was 0.24 ± 0.03 kg/animal.day, compared to 0.38± 0.06 kg/animal.day for CH4_Nut. A difference was observed between years for the CH4 predictions estimated with CH4 FI_Eq (P < 0.01). The FNIRS/Nutbal technique provided useful inputs to the established CH4 calibrations but will need to be validated against CH4 reference methods. The Gan Lab FNIRS/Nutbal dataset offers an opportunity to not only estimate U.S. grazing ruminant CH4 emissions over the past 30 years, but also predictions of current and future emissions at individual operation to national scales. This technique will enable producers with the knowledge to optimize forage resources management, mitigate CH4 emissions, and enhance the sustainability of grazing animal agriculture.

Biogenic Volatile Organic Compound Emission and Its Response to Land Cover Changes in China During 2001–2020 Using an Improved High‐Precision Vegetation Data Set

AbstractBiogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most.

Data-driven approach to evaluate the impact of hull roughness on main engine load of river-sea ships

Hull roughness increases the ship's resistance during navigation, significantly increasing fuel consumption and emissions. The study proposed the hull fouling factors (HFFs) to evaluate the time-varying impact of hull roughness on main engine load (MEL) for the three bulk carriers. Initially, ships' Automatic Identification System data, operational data, and weather and sea state data were fused to develop predictive models for MEL. Specifically, the Random Forest (RF) model outperformed others in predicting MEL. Then, by adopting a controlled variable experiment, the RF model was utilized to evaluate the impact of hull roughness on MEL as the days since clean (DSC) increased. Hull roughness analysis results showed that the HFFs of ship operated primarily on sea routes showed an increasing trend with the increasing DSCs. In contrast, the HFFs of ships primarily operated on the river-sea routes did not change significantly. The approach revealed significant temporal variations in hull roughness for ships operating in different routes. Besides, the proposed HFFs could capture the impact of sea temperature, salinity, current velocity and ship speed on hull roughness. Overall, the finding provides new insights into the time-varying hull roughness impacts on sea-to-river ships, and supports hull cleaning strategies, energy management and emission estimates.

455 Evaluating the effect of a phenology-based timeline on the interpretation of enteric methane emission results

Abstract The methodology of analyzing methane emissions in cattle grazing native rangeland is imperative for producing reliable and repeatable estimations of total enteric emissions on an individual and herd basis. However, due to seasonal differences in climate from year to year, there are distinct differences in forage biomass availability and nutrient composition on extensive rangelands. Thus, our objective was to evaluate the differences in methane emissions using phenological forage phases compared with specific time periods. Yearling Angus steers (n = 127) were allocated to one of six pastures grazed from June to September (2023) at the South Dakota State University Cottonwood Field Station (Cottonwood, SD). Each pasture was equipped with a GreenFeed (C-Lock Inc., Rapid City, SD) pasture system to measure individual animal CH4 emissions. A subset of CH4 data from two cool-season-dominated pastures were aggregated from the overall study. We used two methods to segment CH4 by either 1) month (June, July, August) or 2) phenology (vegetative, reproductive, and senescence) to derive average CH4 (gּ animalּ -1ּ d-1) for each period. Western wheatgrass (WWG), a cool-season native, was used as a proxy for cool season grass development. On May 28th, 2023, the growing degree days (GDD) reached 632, representing the 3.5 leaf stage. WWG has been documented to reach maturity at approximately 1,100 GDD, which occurred on June 20th, 2023. Nutrient content of the forage (i.e., lignin) was used to approximate the start of senescence on August 11th. These developmental markers created estimates of the vegetative, reproductive, and senescence periods within our emissions data. Differences in enteric emissions for each approach were analyzed using a linear mixed effects model in R, and post-hoc contrast between methods was assessed using the lsmeans package. There were no statistical differences in CH4 by month (P > 0.05), and the means for June, July, and August were 217, 214, and 211 ± 4, respectively. By phenological phases, there was a difference (P < 0.05) between maturity and senescence, with means for vegetative, reproductive, and senescence as 214, 219, and 206 ± 4, respectively. Methane intensity (gּ animal-1ּ d-1) per period was then multiplied by the number of days in each month or phase to estimate total emissions for the entire grazing season (86 d). The monthly approach totaled 18,383 g CH4 per animal, while phenology totaled 18,504 g for the grazing season. This is a difference of 0.654% between the same data segmented by month or by phenology. Therefore, because of the dynamic nature of forage and cattle production, it is likely that accounting for phenology will provide greater clarity for GHG on rangeland systems rather than arbitrary periods of time and will require the use of dynamic models to handle the complexity of enteric emissions of grazing animals.