Gas Emission Factors Research Articles

A Review of Cradle-to-Gate Greenhouse Gas Emission Factors for Canada’s Harvested Wood Products

Abstract With the previous decade’s (2010 through 2019) greenhouse gas emissions remaining the highest on record, focus on emissions mitigation efforts is paramount. Harvested wood products (HWPs) can store carbon for various timespans depending on the product and its end uses. Life cycle inventories (LCIs) are the base for life cycle analyses (LCAs), as they represent a comprehensive catalogue of the raw data essential to complete an LCA. However, most LCI documentation is in the form of case studies of different types of HWPs, with varying LCI results that reflect varied system boundaries, case-specific conditions, and assumptions. Our goal was to conduct a systematic literature review to evaluate, analyze, and synthesize previously reported Canadian HWP data and to initiate a Canadian database based on reported cradle-to-gate HWP emission factors. HWPs were categorized as lumber, traditional structural panels, mass timber, nonstructural panels, and wood pellets. Based on our analysis, we found that softwood lumber produced the lowest cradle-to-gate emission factor (61.99 kg of CO2 equivalent [CO2eq] per m3 HWP) while I-joists produced the highest (218.55 kg of CO2eq per m3 HWP). Resource extraction emissions accounted for most of the overall emissions for softwood lumber, oriented strand board, cross-laminated timber, and glue-laminated timber. Meanwhile, manufacturing accounted for most of the emissions for plywood, I-joists, cellulosic fiberboard, particleboard, and wood pellets. Substantial gaps exist in published LCI data and, when possible, publishing detailed LCI data is encouraged to support additional HWP life cycle analyses.

Bi-Objective Inventory Policy with Comprehensive Environmental Factors Formulation and Service Level Constraints

As firms and consumers engage with environmental issues, decisions for inventory control need to entail this perspective of sustainability. Most green inventory models employ methods such as carbon caps or taxes for dealing with environmental sustainability. This problem can be more generally tackled via an explicit estimation of the environmental drivers of maintaining inventory in a warehouse, paired with the economic perspective within a transparent multi-objective optimization framework. With this goal, this paper builds on a detailed estimation of environmental and cost factors for a continuous-review inventory policy. The bi-objective problem is tackled by keeping the objective functions separate. In particular, the modeling of greenhouse gas emission or cost performance factors for the inventory encompasses factors that can depend on the decision variables, taking into account aspects such as warehouse location, building characterization, energy usage, and transport requirements. The effects of the emission drivers on the multi-objective optimization decisions are analyzed, considering that the problem can be constrained by multiple service level measures. Stockout response can be multifaceted and different service level measures capture different aspects of inventory shortages, affecting the resulting efficient solutions differently. The results highlight the impact of aspects such as warehouse location and supply capacity on solutions for the multi-objective inventory problem. Managerial decisions are thus influenced by warehousing and supply attributes via a traceable link to specific cost and emission determinants.

Criteria, Greenhouse Gas, and Hazardous Air Pollutant Emissions Factors from Residential Cordwood and Pellet Stoves Using an Integrated Duty Cycle Test Protocol.

Air pollution from residential wood heating (RWH) presents challenges at the intersection of climate and public health. With a revised National Ambient Air Quality Standard (NAAQS, at 9 μg/m3) for particulate matter (PM) in the United States (U.S.), the Environmental Protection Agency (EPA) will likely classify new non-attainment areas due primarily to emissions from RWH. Agencies will use emissions factors (EFs) to develop attainment strategies. Many will rely on EPA modeling platforms based on data from the National Emissions Inventory (NEI). The NEI uses RWH EFs based on data from mid-1990's in-situ studies and a speciation profile from a 2001 study of fireplace emissions. The NEI does not include greenhouse gas (GHG) emissions for this sector, which plays a key role when assessing climate reduction strategies for the buildings sector. Here, we tested seven wood stoves to determine EFs, representing various vintages and control technologies, using a novel test method that reflects in-use operational settings called the Integrated Duty Cycle. The study measured multiple pollutants concurrently: criteria pollutants (particulate matter [PM], CO, and NOx), nonmethane total hydrocarbons (NMTHCs), GHGs, black carbon (eBC), brown carbon (BrC), and multiple hazardous air pollutants (HAPs). We found no significant difference in PM EFs between uncertified and non-catalytic stove technologies. RWH EF results from this study exceeded 2020 NEI RWH EFs for NMTHC and multiple HAPs. Applying our study's EFs to the 2020 NEI suggests that RWH, compared to all other sources, ranks as the 2nd largest source category of formaldehyde; the 3rd largest of benzene, 1,3-butadiene, and acrolein; and the 4th largest of Pb emissions. RWH also emits more methane compared to natural gas or oil residential heating, raising questions about substitution of wood as a climate neutral heating fuel. However, compared to uncertified stoves, pellet stove EFs (except toxic metals) were significantly lower (p < 0.01). In summary, RWH appears to be an underestimated source of PM (non-catalytic technology), methane, NMTHC, toxic metals, and other HAPs, which has important implications for climate and public health policy in the U.S. and globally.

Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass‐based biofuel production

Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass‐based biofuel production

A new IPCA-CPSO-BP Model for Predicting Gas Emission in Underground Coal Mines

Gas has become the primary factor restricting the safe and efficient production of coal mines. Gas emission prediction model is very important for mine gas emission prediction and gas disaster prevention. The improved principal component analysis (IPCA) was used to reduce the dimension of 13 influencing factors of gas emission, and the CPSO-BP neural network prediction model was built with MATLAB software to predict the absolute gas emission of Zhongtai Mining.The results show that the cumulative contribution rate of principal components is not less than 95%, and the number of principal components after improvement is reduced from the original 6 to 3, which effectively solves the problem of excessive principal components and data redundancy caused by the correlation difference between various influencing factors and gas emission amount. At the same time, the optimized particle swarm optimization algorithm alleviates the trouble that the particle swarm optimization algorithm is prone to fall into the local optimal solution. By coupling the improved particle swarm optimization algorithm with BP neural network, the problem of BP neural network's over-dependence on the initial value of weights and thresholds is solved, and the optimal initial weights and thresholds are provided for BP neural network, improving the accuracy of model prediction results. The average relative error of the original BP neural network is 1.6638%, the normalized mean square error is 0.3155, and the regression correlation index is 0.8157. The IPCA-CPSO-BP prediction model decreased to 0.5749%, 0.1143 and 0.9758, respectively. IPCA improves the data dimensionality reduction ability of principal component analysis, IPCA-CPSO-BP enhances the stability of particle swarm optimization algorithm, and significantly improves the prediction accuracy of BP model. The prediction trend is highly consistent with the actual value, which verifies the reliability of the model and provides strong data support for mine gas prevention and control.

Framework for a savanna burning emissions abatement methodology applicable to fire-prone miombo woodlands in southern Africa

Background and aims To assess development of a robust emissions accounting framework for expansive miombo woodland savannas covering ~2 million km2 of southern Africa that typically are burnt under relatively severe late dry season (LDS) conditions. Methods A detailed site-based study of fuel accumulation, combustion and greenhouse gas (GHG) emission factor parameters under early dry season (EDS) and LDS conditions along a central rainfall-productivity and associated miombo vegetation structural and floristics gradient, from lower rainfallsites in northern Botswana to higher rainfall sites in northern Zambia. Key results Assembled field data inform core components of the proposed emissions reduction framework: fuel and combustion conditions sampled across the vegetation/productivity gradient can be represented by three defined Vegetation Fuel Types (VFTs); fuel accumulation, combustion and emissions parameters are presented for these. Applying this framework for an illustrative case, GHG emissions (t CO2-e) from EDS fires were one-third to half those of LDS fires per unit area in eligible miombo VFTs. Conclusions Our accounting framework supports undertaking EDS fire management to significantly reduce emissions and, realistically, burnt extent at landscape scales. We consider application of presented data to development of formal emissions abatement accounting methods, linkages with potential complementary woody biomass and soil organic carbon sequestration approaches, and necessary caveats concerning implementation issues.

Estimation of emission factors of ammonia and greenhouse gases generated from composting manure of Korean beef

Estimation of emission factors of ammonia and greenhouse gases generated from composting manure of Korean beef

Refining greenhouse gas emission factors for Indonesian peatlands and mangroves to meet ambitious climate targets

For countries' emission-reduction efforts under the Paris Agreement to be effective, baseline emission/removals levels and reporting must be as transparent and accurate as possible. For Indonesia, which holds among the largest area of tropical peatlands and mangrove forest in the world, it is particularly important for these high-carbon ecosystems to produce high-accuracy greenhouse gas inventory and to improve national forest reference emissions level/forest reference level. Here, we highlight the opportunity for refining greenhouse gas emission factors (EF) of peatlands and mangroves and describe scientific challenges to support climate policy processes in Indonesia, where 55 to 59% of national emission reduction targets by 2030 depend on mitigation in Forestry and Other Land Use. Based on the stock-difference and flux change approaches, we examine higher-tier EF for drained and rewetted peatland, peatland fires, mangrove conversions, and mangrove on peatland to improve future greenhouse gas flux reporting in Indonesia. We suggest that these refinements will be essential to support Indonesia in achieving Forest and Other Land Use net sink by 2030 and net zero emissions targets by 2060 or earlier.

Greenhouse gas emissions of sewage sludge land application in urban green space: A field experiment in a Bermuda grassland

Sewage sludge land application is recognized as a strategy for recycling resource and replenishing soil nutrients. However, the subsequent greenhouse gas emissions following this practice are not yet fully understood, and the lack of quantitative research and field experiments monitoring these emissions hampers the establishment of reliable emission factors. This study investigated the greenhouse gas emission characteristics of sewage sludge land application through a field experiment that monitoring soil greenhouse gas fluxes. Seven nitrogen input treatments were implemented in a typical Bermuda grassland in China, with D and C representing the amendment of digested and composted sludge, respectively, at the nitrogen input rate of 0, 100, 200, and 300 kg N ha−1. Soil CH4, CO2, and N2O fluxes were measured throughout the entire experimental period, and soil samples from different treatments at various growth stages were analyzed. The results revealed that sewage sludge land application significantly increased soil N2O and CO2 emissions while slightly reducing soil CH4 uptake. The increased CO2 emissions were biogenic and carbon-neutral, mainly due to enhanced plant root respiration. The N2O emissions were the primary greenhouse gas emissions of sewage sludge land application, which were mainly concentrated in two 50-day periods following base and topdressing fertilization, respectively. N2O emissions following base fertilization by rotary tillage were substantially lower than those following topdressing fertilization. A logarithmic response relationship between N input rates and increased soil N2O emissions was observed, suggesting lower N2O emissions from sewage sludge land application compared to conventional N fertilizers at the same N input level. Future field experiments and meta-analysis are necessary to develop reliable greenhouse gas emission factors for sewage sludge land application.

Evaluation of the point sampling method and inter-comparison of remote emission sensing systems for screening real-world car emissions

Emissions from internal combustion vehicles are currently not properly monitored throughout their life cycle. Remote emission sensing (RES) is a technology that can measure emissions under real driving conditions without contact. Current light extinction based RES systems are capable of providing emission factors for various gases, but lack accuracy for particulate matter (PM). Point Sampling (PS) is an extraction-based RES technique that can measure gases as well as various particle metrics such as black carbon or particle number. In this work, we evaluated the performance of a recently developed PS system and the state-of-the-art light extinction based remote sensing devices EDAR (HEAT) and ORSD (OPUS RSE) during co-location measurements. Validation measurements with portable emission measurement systems and emissions screening of several thousand cars in three European cities provide detailed insights into system's performance. Meteorological evaluations showed that the PS capture rate is strongly influenced by wind, but no other weather influences were found. Both light extinction based systems are unable to measure during rain. We found that all three systems tested were capable of screening NOx emissions from pre-Euro 6 diesel cars. Measurement results show the ability of the PS system to quantify high and low PM emitters equally well. The open-path RES systems (EDAR, ORSD) are capable of estimating PM emissions from pre-Euro 5 diesel cars. However, deficiencies of open-path RES systems are evident in the quantification of PM emissions from newer engine technologies (diesel Euro 5 and beyond) and from petrol cars. The PS system has a 2 to 5 times lower capture rate than open-path RES systems, but the PS measurement results are more accurate (more than 5 times for PM and more than 1.35 times for NOx). The good accuracy of individual measurements makes PS a powerful tool for reliable high emitter identification.

Synergies from off-gas analysis and mass balances for wastewater treatment — Some personal reflections on our experiences

Looking back at over a decade of research by herself and her group, the author advocates the added value of gas phase measurements and the application of mass balances, as well as the synergetic benefits obtained when combining both. The increased application of off-gas measurements for greenhouse gas emission monitoring offers a great opportunity to look at other components in the gas phase, particularly oxygen. Mass balances should not be strictly reserved for modellers but also prove useful while conducting lab experiments and studying full-scale measurement data. Combining off-gas measurements with mass balances may serve not only to quantify greenhouse gas emission factors and aeration efficiency but also to follow dynamic concentration profiles of dissolved components without dedicated sensors and/or to calculate other unmeasured variables. Mass-balance-based data reconciliation allows for obtaining reliable and accurate data, and even more when combined with off-gas analysis.

Procedures to combine estimators of greenhouse gases emission factors

BackgroundThis article describes a new procedure to estimate the mean and variance of greenhouse gases (GHG) emission factors based on different, possibly conflicting, estimates for these emission factors. The procedure uses common information such as mean and standard deviation usually reported in IPCC (Intergovernmental Panel on Climate Change) database and other references in the literature that estimate emission factors. Essentially, it is a procedure in the class of meta-analysis, based on the computation of Sa2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{a}^{2}$$\\end{document}, a new estimator for the variance of the emission factor.ResultsWe discuss the quality of this estimator in terms of its probability distribution and show that it is unbiased. The resulting confidence interval for the mean emission factor is tighter than those that would have resulted from using other estimators such as pooled variance and thus, the new procedure improves the accuracy in estimating GHG emissions.The application of the procedure is illustrated in a case study involving the estimation of methane emissions from rice cultivation.ConclusionsThe estimation of emission factors using Sa2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{a}^{2}$$\\end{document} was demonstrated to be more accurate because it is not biased and more precise than alternative methods.

Détermination des facteurs d’émission de GES à travers l’ACV dans les unités d’enfouissement, de compostage et de recyclage du CTVD de Ouagadougou

This study is a contribution to the quantification of greenhouse gas emissions in the waste sector in developing countries, especially through the determination of specific emission factors. The purpose is to determine the greenhouse gas emission factors of the various waste treatment units at the waste treatment and recovery centre in Ouagadougou (Burkina Faso) using the Life Cycle Analysis method. The emission factors were evaluated and then reduced to an equivalent CO2 emission per tonne of waste. The results show that landfill has the highest emission factors, with values ranging from 751.19 to 1,818.6 kg CO2 eq/tonne of waste for direct emissions, depending on whether or not sequestered carbon is taken into account, and from 7.24 to 1.31 kg CO2 eq/tonne for indirect emissions. Composting and plastic recovery units have negative EFs over their entire life cycle (-69.69 and -1736.65 Kg CO2 eq/tonne), thus contributing to net greenhouse gas savings. This study is the first of its kind to provide data that can be used for national accounting of greenhouse gas emissions in the waste sector

Development of Emission Factors from Indonesian Coal-Fired Power Plant Using Continuous Emission Monitoring Data

Continuous Emission Monitoring System (CEMS) is generally used for monitoring compliance with emission standards set by the government regulations and has not been optimally used for other additional purposes yet. If operated CEMS can produce reliable and accurate data, they can develop further specific data such as emission factors. These emission factors can be used for estimating pollutant emission loads from coal combustion activity in Coal-Fired Power Plants (CFPPs) without conducting direct source measurements. In this study, hourly 1 yr CEMS data from several units of CFPPs were processed to develop specific emission factors for principal air pollutants (SO2, NOx, particulates) and greenhouse gases (represented by CO2). Emission factors were determined by dividing the emission load of each pollutant by the amount of combusted coal during 1 yr. The results showed that emission factor ratings for this study could not be classified as A ratings due to the limited number of investigated CEMS facilities. According to the variability of the derived emission factor values, SO2 and CO2 emission factors can be rated as B or above average (with fewer variability values). In comparison, NOx and particulate emission factors can be placed as C or average (with more variability values).

Vehicle greenhouse gas emission factors based on multi-dimensional Bin interval mode

The emission factors of greenhouse gases from motor vehicles are of significant importance for low-carbon and environmentally friendly initiatives. This paper focuses on greenhouse gas emission factors of heavy-duty vehicles. The multi-dimensional Bin interval mode are proposed, which considers multiple parameters such as vehicle speed, acceleration, torque, and rotational speed. Based on this, the emission characteristics of greenhouse gases from heavy-duty vehicles. are investigated under different parameter conditions. The results indicate that as the VSPA value or the engine torque increases, the CO2 emission factor also increases accordingly. The CO2 emission factor is lower at medium to high rotational speeds, while it tends to increase at low rotational speeds. The research findings provide support for monitoring and evaluating greenhouse gas emissions and offer scientific guidance for formulating specific carbon reduction policies, emission targets, and plans.

Gasification Burner Performance Test Using Bio-Coal Fuel

Fuel for high-energy solid fuel combustion systems must meet abundant availability, affordability, and environmental friendliness criteria, particularly having low greenhouse gas emissions. One type of fuel that meets these criteria is bio-coal. Gasification burners employ staged combustion techniques that enhance combustion efficiency and reduce the formation of harmful substances, making them more environmentally friendly. This article discusses the performance testing of gasification burners using bio-coal as fuel, with variable primary air flow rates of 25, 30, 35, 40, and 50 m3/hour and biomass percentages in the bio-coal of 25%, 50%, and 75% by weight. The parameters observed are burner capacity, temperature, and CO2 gas emission factor. The experimental results show that increasing the air flow rate from 25 to 35 m3/hour significantly increases burner capacity and temperature, while further increasing the primary air flow rate to 50 m3/hour results in a gradual increase in burner capacity. The experiments also indicate that increasing the biomass percentage in the bio-coal slightly increases burner capacity and temperature due to the similar calorific values of palm pellet and coal used. The gasification burner technology developed by BBP Tekmira has proven to operate effectively within a capacity range of 8 to 20 kg/hour for bio-coal fuel with biomass percentages ranging from 25% to 75% by weight, producing burner temperatures between 805 and 933 degrees Celsius. Using bio-coal for gasification burners has also been shown to reduce greenhouse gas emissions when the biomass percentage in bio-coal exceeds 64%.

Emission inventory of inorganic trace gases from solid residential fuels over the National Capital Territory of India.

In developing nations, solid residential fuels are the major sources of primary energy for various domestic activities. To date, the emission inventory of inorganic trace gases over National Capital Territory (NCT) was prepared using either default or country-specific emission factors. In this paper, we report (for the first time) the spatial variation of emission factors (EFs) of inorganic trace gases (SO2, NO, NO2, CO, CO2, and CH4) from the residential fuels used in slums and rural areas of NCT determined using dilution chamber in the laboratory. 147 residential fuel samples, including fuelwood, dung cake, crop residues, coal, etc., were collected at 149 NCT locations out of 675 slum clusters and 146 rural villages. The range of EF(s) of SO2 (0.02 ± 0.01 to 0.04 ± 0.01gkg-1), CH4 (0.10 to 0.34gkg-1), NO2 (0.01 to 0.02gkg-1) is lower than the CO (3.55 ± 1.72 to 6.07 ± 1.53gkg-1) and CO2 (0 to 129.45 ± 46.94gkg-1). The north and north west districts of NCT are emission hotspots for CH4, NO, and NO2 emissions, whereas, the southern and northern areas of NCT are for CO2. These citywide emission inventories (0.05° × 0.05°) of inorganic trace gases are prepared using laboratory-determined EFs and available consumption data determined by recent survey information. Among solid residential fuels, fuel wood, and dung cake are two major contributors to inorganic trace gases in NCT.

Household energy-related carbon footprint in residential neighbourhoods in high-latitude cities: A case of Edmonton in Canada

Understanding all the possible greenhouse gas (GHG) emission sources and factors is key to achieving net zero emissions by 2050, as targeted by the Paris Agreement, and thereby, averting the severe consequences of climate change. In this study, we explored household energy-related GHG (HEGHG) emissions in 265 residential neighbourhoods in a high-latitude city that experiences opposite temperature extremes. Our results revealed that the average annual per capita GHG emissions from dwellings in the city are 45% higher than those from personal transportation, highlighting the urgent need for greater emphasis on reducing GHG emissions from dwellings in high-latitude cities. We found that per capita floor area is positively correlated with GHG emissions. The findings also indicate that per capita HEGHG emissions significantly increase with increasing neighbourhood income levels (r = 0.8). An increase in the percentage of households struggling to afford homeownership is also associated with a decrease in HEGHG emissions (r = -0.3). These findings can help facilitate the focus on areas of high-latitude cities where the mitigation measures may have the most impact in reducing carbon footprint.

Gaseous, particulate matter, carbonaceous compound, water-soluble ion, and trace metal emissions measured from 2019 peatland fires in Palangka Raya, Central Kalimantan

This study aimed to develop field emission factors of gaseous pollutants, PM2.5, OC/EC, water-soluble ions, and trace elements and to estimate the total gas and particulate emissions from peatland fires. This study was conducted in Palangka Raya, Central Kalimantan, in September 2019 during the main fire episode. Gas samples were collected using the LGR-ICOS analyzer at a flow rate of 0.5 L/min. In comparison, particulate pollutant samples were collected using two Airmetrics Mini Volume Portable Air Samplers at a flow rate of 5 L/min with Quartz fiber and PTFE filters. For further analysis, quartz fiber filters were used for carbon aerosols and water-soluble inorganic ions, while PTFE filters were used for trace element analysis. The gaseous emission factors determined in this study for CO2, CO, and CH4 were 1589.5 ± 41.8 g/kg, 236.5 ± 25.7 g/kg, and 5.4 ± 1.3 g/kg, respectively. Meanwhile, the emission factors for PM2.5, OC, EC, water-soluble inorganic ions, and elements were 27.8 ± 41.8 g/kg, 18.1 ± 8.9 g/kg, 0.25 ± 0.06 g/kg, 1.7 ± 0.7 g/kg, and 62.5 ± 32.3 mg/kg, respectively. With peat burned density of 0.11 g/cm3, a burned depth of 37 cm, and a total area burned of 317,749 ha, peatland fires in Central Kalimantan in 2019 had emitted CO2, CO, PM2.5, total carbon aerosol, water-soluble ions, and elements about 34.9 ± 0.9 Tg, 5.3 ± 0.6 Tg, 0.6 ± 0.3 Tg, 0.4 ± 0.2 Tg, 36.7 ± 15.9 Gg, and 1.4 ± 0.7 Gg, respectively.

From certificate to physics - Paths to net-zero compatible buildings

The role of heat pumps in a decarbonised (thermal) energy system is pivotal. This technology is often in competition with other technologies such as district heating or boilers. When comparing different heating technologies in respect to their CO2 footprint and their costs, the calculation method of future greenhouse gas emission factors of the different energy carriers and energy prices is decisive. Emission factors need to be based on technical and physical fundamentals as (international) green certificates are not a satisfactory proxy for physical emissions, especially for the case of electricity. Currently the Swiss electricity generation system is mostly decarbonised with its high share of hydropower and nuclear energy. However, Switzerland, located in the heart of Europe, has the highest relative shares of cross border capacities, which are relevant for energy trading. In a still fossil dominated European power system, the high volatility of wholesale power prices is entailing a high (temporal) volatility of power related emissions. Thus, the overall carbon footprint of consumed electricity changes significantly over time. Heating demand also shows a dynamic behaviour. It is therefore likely, that a CO2 footprint based on annual averages in heating demand and emission factors could lead to significant errors. The aim of the research was to draw a comparison of the resulting annual CO2 emissions applying different time step aggregation levels (yearly, monthly, typical days) with the results calculated based on a complete year in hourly resolution. The aim was to determine the relative error made through aggregation.