Estimated Emission Factors Research Articles

Evaluation of green residues management of selected grape varieties

The paper presents the possibilities of energy management of residues from the cultivation of grapes of the ‘Regent’, ‘Rondo’ and ‘Seyval Blanc’ cultivars. The research was conducted in southeastern Poland in the Sandomierska Upland in 2022. The aim of the research was to demonstrate the influence of grape variety on yield capacity in relation to the extraction of biomass residues in the form of leaves. An attempt was made to identify the variety that is characterised by obtaining the most effective and average parameters, i.e. yield size and quality, leaf mass and surface area, and their impact on energy and fuel parameters. The study analysed the following crop parameters, i.e. number and mass of grapes, number and mass of berries; leaf quality parameters, i.e. mass including petioles and area. An energy assessment in Laboratory in Department of Power Engineering and Transportation was carried out by performing proximate and ultimate analysis and estimating emission factors and volumetric composition of exhaust gas. The study showed that the material with the highest energy potential was characterised by ‘Regent’, while the lowest potential was shown for ‘Rondo’. Grapevines of the ‘Rondo’ cultivar were characterised by the highest obtained biomass among the evaluated varieties. The research showed that the most effective in practical cultivation is the use of the Regent variety, which was characterised by the average parameters of the obtained yield and growth values, and the highest fuel energy potential.

Beyond the Average: emissions from electricity and the implications of decarbonization policies in Guangdong

Various policy interventions have been proposed to decarbonize the Chinese economy through the electricity sector. This study is the first to use generation-by-plant data to analyze the emissions from the electricity sector in a Chinese region and evaluate the environmental impacts of various policy interventions. Using regression techniques, the average emission factor, marginal emission factor, and marginal displacement factor, are estimated for different pollutants of Guangdong, the most affluent and populous province in China. Results reveal a decline in average emission factors over the sample period of 2017-2021, owing to the expansion of renewable energy. This study also finds a less pronounced disparity between marginal emission factors and average emission factors compared with that in developed countries, probably due to the prevalence of coal-burning plants and lower renewable energy penetration in Guangdong. Using the estimated emission factors, this study also examines the environmental effect of time-of-use tariffs, indirect emissions of electric vehicles, and carbon displacement effect of wind energy. The conclusions reveals that the time-of-use tariffs moderately reduced CO2 emissions, and the extensive adoption of electric vehicles and wind energy substantially reduces carbon emissions, indicating that decarbonizing the economy needs the coordination of various policies.

Quantifying methane emissions during dry season and estimating emission factors in alpacas grazing Andean grasslands

This study aimed to (1) measure methane emissions from alpacas grazing in Andean grasslands during the dry season and compare them with those of sheep under similar conditions, and (2) estimate the methane conversion and emission factor for alpacas grazing in Andean grasslands, considering methodology, animal class, and season. The comparison involved nine alpacas and nine sheep, with measurements taken for dry matter intake, diet composition, and enteric methane emissions measured with the sulfur hexafluoride technique. Alpacas selected a more digestible diet than sheep (0.62 vs. 0.50 g/g of dry matter), even with limited feed availability. Methane emissions were similar between the two species. For the second objective, the study used observations from the first objective and mean treatments from a literature review. The developed model predicted methane conversion factors, considering animal class and measurement methodology, with no significant influence observed for season. The predicted methane conversion factors (% gross energy in feed converted to methane) for alpacas grazing in Andean grasslands were 7.71 for juveniles (i.e., tuis) alpacas and 8.71 for adults, corresponding to emission factors of 6.49 and 9.77 g per alpaca and day, respectively. These results emphasize the importance of considering animal class, measurement methods, and regional variations when estimating methane emission factors for alpacas.

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

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.

Characterization and apportionment of carbonaceous aerosol emission factors from light-duty and heavy-duty vehicle fleets in Maharashtra, India

This study aims to investigate the characteristics of carbonaceous aerosols and estimate emission factor (EF) based on roadway tunnel measurements, from two distinct vehicular fleets: an all light-duty vehicle (LDV) fleet, and a mixed fleet of 80% LDV and 20% heavy-duty vehicle (HDV). Carbonaceous content (organic carbon: OC and elemental carbon: EC) in total fine particles (PM2.5) accounted for 41% ± 6.8% in LDV fleet and 48% ± 7.2% in mixed fleet. While higher volatile OC dominated in the LDV fleet emissions, in mixed fleet, lower volatile OC and EC emissions dominated due to the presence of higher HDV and super-emitter (SE) fractions which led to significantly higher optically active absorbing aerosols. Reconstructed HDV fleet EF was higher than LDV fleet by 36 times (PM2.5), 19 times (OC) and 51 times (EC). Our findings emphasize the significance of implementing vehicle inspection and maintenance programs, coupled with decarbonization of HDVs to mitigate on-road vehicular emissions in India.

Non-methane hydrocarbons from integrated semiconductor manufacturing processes: Assessments of chemical footprints, emissions factors, and treatment efficiency

Non-methane hydrocarbons (NMHCs) are harmful chemicals produced from organic solvent use in industrial sectors. Wafer fabrication process from semiconductor fabs utilizes many organic solvents that could intensify NMHCs emissions. This study comprehensively assessed the Total NMHCs (TNMHCs) emissions from 141 exhaust pipes of 59 semiconductor fabs in Taiwan with regards to their concentrations, emission factors, treatment efficiency, and total chemical footprint (ChF) for human toxicity. Throughout three semi-processes of wafer fabrication, the raw emitted NMHCs concentrations were in the ranges of 3–131.25 ppm, 8–288 ppm, and 23–567 ppm for wafer cleaning (WC), photoresist stripping (PS), and photolithography (PL). The average untreated TNMHCs emissions rate was 0.2 ± 0.08 kg/h < 1.72 ± 0.09 kg/h < 2.55 ± 0.08 kg/h in the order of WC < PS < PL, whereas the average treated emissions rates were in the ranges of 0.09–0.10 kg/h. The estimated emission factors for wafer fabrication process were 0.30 ± 0.04 kg/kg, 0.07 ± 0.02 kg/piece, and 1.37 ± 0.11 kg/m2. Further assessment has found that zeolite rotary concentrator and regenerative thermal oxidations (ZRC + RTO) possess the highest overall performances for abating TNMHCs emission with efficiency ranges of 92.31–99.56% (n = 66). ZRC + RTO could produce outlet TNMHCs within the ranges of 2.94–5.56 ppm, which were lower than the upcoming revised emission standard of <14 ppm. ChF analysis of the estimated NMHCs species showed that PL had the largest untreated ChF, followed by PS, and WC of which the ChF were 15.72, 7.05, and 6.79 cases. The speciated ChF was dominated by acetone, methanol, and isopropyl alcohol from three semi-processes. The ChF analysis in this study could potentially become a supporting instrument for industrial sectors to determine the major contributing processes and chemicals with adverse impacts to human health.

Data-driven method for estimating emission factors of multiple pollutants from excavators based on portable emission measurement system and online driving characteristic identification

This study aims to explore the factors that influence the emission characteristics of multiple pollutants from non-road mobile machinery (NRMM) under real-world conditions and to establish a data-driven method for calculating accurate emission factors. This research focused on NRMM excavators meeting the third-stage emission standards and identified the actual work characteristics of 108 excavators in different scenarios based on a self-developed testing system for 368,000 h. Additionally, a portable emission testing system (PEMS) was used to study the instantaneous emission characteristics under different driving styles and modes for 10 EC210 excavators with the largest engineering construction inventory. The results showed that the average time proportions of idling, working, and moving modes for excavators were 21 %, 66 %, and 13 %, respectively. The results also revealed that the instantaneous emission rates of multiple pollutants varied significantly under different driving styles and modes. Driving style affected the hydraulic pump power change rate through hydraulic pilot pressure, and engine load surge caused turbocharger response delay and in-cylinder combustion deterioration, which affected pollutant emissions. Driving mode affected the emission characteristics of idling, high-speed idling, moving, and working modes of excavators through the external characteristics corresponding to the engine speed gear set. The data-driven method for calculating emission factors differed from the traditional method for most indicators to varying degrees. In terms of fuel-based emission factors (EFfs), except for the EFfNOx indicator, which was 7.859 % higher than the traditional method, the other three indicators were significantly lower than the traditional method. In terms of power-based emission factors (EFps), except for EFpPM and EFpPN, the other two indicators were much higher than the traditional method. EFpCO and EFpNOx were 7.93 % and 20.332 % higher than the traditional method, respectively. It is recommended to use the data-driven method based on the actual driving data distribution to provide scientific support for accurately establishing the emission inventory.

Assessment of carbon dioxide emission factors from power generation in Burkina Faso

Power generation is the second largest source of greenhouse gases (GHGs), particularly carbon dioxide (CO2), in Burkina Faso's energy sector. When preparing the National Communications on Climate Change, Burkina Faso uses the default emission factors of the Intergovernmental Panel on Climate Change (IPCC) to estimate emissions from power generation. This study presents an assessment of CO2 emission factors from power generation for 2018 in Burkina Faso and an assessment of the contribution of renewable energy to the reduction of CO2 emissions. The national electricity company of Burkina Faso was chosen as the scope for this study. The estimation of emission factors for combustion is based on an analysis of fuels which are characterised in terms of molecular composition, density and water content. For CO2, the emission factor is 76 903 kg/TJ for the combustion of Heavy Fuel Oil (HFO) and 73 525 kg/TJ for that of Distillate Diesel Oil (DDO). Using these emission factors, the CO2 emissions attributable to power generation in 2018 were estimated at almost 580 Gg. The CO2 emission factor for thermal power generation was estimated at 0.663 kg/kWh and that of the electricity generation mix at 0.569 kg/kWh. Finally, the use of 14.25% renewable energy in electricity generation avoided 16.7% of CO2 emissions in 2018. The emission factor of electricity production decreases with the increase in the share of renewable energy in the energy mix.

Study on morphological composition of waste and the estimation of greenhouse gases from waste landfilling from the Republic of Moldova

The paper summarizes the research results on the development country country-specific emissions factors for the Greenhouse Gas Inventory (GHGI) for Source Category “5A Solid Waste Disposal on Land”, namely the degradable organic carbon (DOC), fraction of degradable organic carbon which decomposes (DOCf) and fraction of methane. The factors are determined based on the results of the survey on the composition of municipal solid waste, which was investigated during one year in the Republic of Moldova: from spring 2023 to 2024. The obtained morphological composition and results refer to three seasons: spring, summer and autumn and offer the opportunity to observe the variations among the seasons. After finalizing the survey on morphological composition, the final results will be used for estimation of country specific emission factors, which will allow to estimate accurately the emissions of methane from landfilling of waste in the Republic of Moldova, necessary for the development of a policy framework and tracking the progress in achievement of NDC. It will also improve the Intergovernmental Panel for Climate Change (IPCC) methodologies and Emission Factors for assessing the GHG emissions originating from the waste sector.

High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China

Accurate assessment of coal mine methane (CMM) emissions is a prerequisite for defining baselines and assessing the effectiveness of mitigation measures. Such an endeavor is jeopardized, however, by large uncertainties in current CMM estimates. Here, we assimilated atmospheric methane column concentrations observed by the TROPOMI space borne instrument in a high-resolution regional inversion to estimate CMM emissions in Shanxi, a province representing 15% of the global coal production. The emissions are estimated to be 8.5± 0.6 and 8.6± 0.6 Tg CH4 yr-1 in 2019 and 2020, respectively, close to upper bound of current bottom-up estimates. Data from more than a thousand of individual mines indicate that our estimated emission factors increase significantly with coal mining depth at prefecture level, suggesting that ongoing deeper mining will increase CMM emission intensity. Our results show robustness of estimating CMM emissions utilizing TROPOMI images and highlight potential of monitoring methane leakages and emissions from satellites.

The role of stormwater infrastructure in regional methane emissions.

Stormwater infrastructure has been recently indicated as a potential hotspot for methane (CH4) emissions. Although local assessments based on direct CH4 measurements are increasingly available, there is currently no standardized approach for evaluating CH4 emissions from different types of stormwater infrastructure, including permanently impounded or fast-draining structures in Urban Drainage Systems (UDS). Therefore, a comparative analysis with wastewater infrastructure systems, such as wastewater treatment plants (WWTPs), is not yet possible. Here, we present a conceptual framework for the first-order quantification and upscaling of CH4 emissions from stormwater infrastructure at local and national scales. We combined in-situ and ex-situ measurements of CH4 emissions with purposely acquired data from selected stormwater facilities to provide initial estimates of CH4 emissions and emission factors for stormwater infrastructure in Germany. The results show that while stormwater infrastructure might emit comparable amounts of CH4 per area as natural and anthropogenically impacted inland waters, it may exhibit higher mean emission factors (up to 7 times) than conventional WWTPs, indicating less efficiency in limiting CH4 emissions than WWTPs. This is particularly true for permanently impounded facilities, which showed substantially higher mean surface CH4 emissions (up to 632mg m-2 d-1) than fast-draining infrastructure (0.5-1.28mg m-2 d-1). Permanently impounded sedimentation basins for stormwater management alone may reach up to 60% of the total CH4 emissions originating from WWTPs in Germany. These results are in conflict with the ongoing trend towards increasing implementation of impounded stormwater infrastructure systems, highlighting the urgent need for more extensive assessments of their impact on CH4 dynamics.

Emission factors of ozone-depleting chloromethanes during production processes based on field measurements surrounding a typical chloromethane plant in China

Chloromethanes, including mono, di, tri, and tetrachloromethane, can contribute to stratospheric ozone depletion and hinder ozone layer recovery. Leakage from industrial chloromethane production plants is a major anthropogenic source of chloromethanes; however, accurate estimations of the emission factors are limited. In this study, a plant-scale inversion system was established using field measurements, dispersion simulation, and inversion algorithms to quantify the emission factors of chloromethanes from chloromethane production plants. The results show that, with the exception of the emission factors (kg/kg respective chloromethane produced) of trichloromethane (0.02 ± 0.006), those of dichloromethane (0.04 ± 0.02) and tetrachloromethane (0.03 ± 0.02) were comparable to those provided by the Intergovernmental Panel on Climate Change (0.04 kg/kg fluorinated gas produced). Compared with previous records of emission factors of chloromethanes, this study provided the latest and, to the best of our knowledge, the first field-measurement based estimates of emission factors. Subsequently, using these latest estimates, the emissions of monochloromethane (56 ± 21 Gg/yr), dichloromethane (43 ± 18 Gg/yr), trichloromethane (9.6 ± 3.9 Gg/yr), and tetrachloromethane (2.2 ± 1.6 Gg/yr) from chloromethane production plants in China from 2020 were estimated. Considering the detrimental effects of chloromethanes for ozone layer recovery, the method developed in this study can provide valuable insights for studying emission factors of chloromethanes from other sources and other ozone-depleting substances to accurately assess their impact on the ozone layer.

Ammonia Emission Estimation of Biogas Production Facilities in South Korea: Consideration of the Emission Factor Development

This study identified the need to develop ammonia (NH3) emission factors for biogas production facilities in Korea and examined the base unit that should be considered when developing emission factors. The analysis showed that the ammonia concentration of three biogas production facilities ranged from approximately 0.04 to 8 ppm. The NH3 emission factors were found to be 0.005 kg NH3 ton-waste and 0.150 kg NH3/10−3 Nm3-biogas. The estimated emission factors were also used to calculate the total emissions, which were found to be small. The uncertainty of the emission factors ranged from approximately −5% to +8% for the waste-based emission factors and approximately −5% to +7% for the biogas-based emission factors. Although the uncertainty of the emission factor differences is not large, it is low compared to the international emission factor uncertainty (maximum 191%, minimum −40.7%). Considering the development of waste-based and biogas-based emission factors, there is not much difference in terms of uncertainty of emission factors, so it is judged that it will be easier to develop emission factors based on biogas production in terms of securing and managing overall data.

Methane emissions from rice paddies globally: A quantitative statistical review of controlling variables and modelling of emission factors

Greenhouse gas (GHG) modelling tools or the Intergovernmental Panel on Climate Change (IPCC) inventory methods are often used to identify suitable mitigation strategies for GHG emissions from rice, since measuring them in field is challenging and costly. Here we report an up-to-date quantitative review on methane (CH4) emission from rice paddies using information obtained from peer-review articles. Statistical analysis was conducted on the factors controlling CH4 emissions and a generalised additive model (GAM) was developed to estimate emission factors (EFs). Results showed that emissions were strongly linked to water regime, soil texture and organic amendment practices. Fields that were rainfed during the dry season or saturated emitted 70% and 56% that of continuously flooded fields, while applying straw off-season instead of within-season could decrease emissions by 48%. An independent dataset was used to evaluate the new model performance against existing models with the new model showing R2 values of 0.47 (n: 169), compared to 0.01–0.09 (n: 169) for the existing models. New baseline EFs was estimated at global, regional, and Country scale with result showing that using different pre-season water management when calculating baseline EFs at country level is vital in order to reflect the variation between tropical and temperate rice regions accurately. Our findings shows that the new model is more sensitive in capturing differences in management practices between tropical and temperate rice, and their impacts on CH4 emissions with baseline EF calculations accounting for these differences providing sound mitigation strategies.

On-road remote sensing of vehicles in Dublin: Measurement and emission factor estimation

On-road remote sensing of vehicles in Dublin: Measurement and emission factor estimation

An approach of localizing MOVES to estimate emission factors of trucks

Freight has become one of the major contributors to air pollution. This research proposes a method to systematically estimate truck vehicle emissions at the road segment level through localizing MOVES, a widely-used vehicle emission estimation model. We first design a protocol of converting percentage values of rotating speed and torque of engine to second-by-second vehicle speed to accommodate the differences between driving cycles adopted in local emission standards and those used in MOVES. In order to identify the best model year for estimating emissions under different local emission standards, we propose an approach of comparing emission outcomes rather than emission factors, considering the differences in unit used between MOVES and emission standards. To calculate road segment level emission factors, we weight original factors by integrating vehicle fleet information which contains the shares of vehicles under different emission standards and at different ages. We apply the approach to a major freight corridor area in Shanghai and calculate emission factors by air pollutant, average speed of road sections, and road type. Dynamic emissions of each road section per hour are calculated to reflect the spatial distribution of truck emissions. The research outcomes may help local departments, especially in developing countries, better estimate freight vehicle emissions and make policies correspondingly to control their impacts on public health.

Pilot Study on Carbon Footprint of Small-Scale Beef Cattle Production at South-West Region of Bangladesh

The study was conducted to investigate feed basket composition, greenhouse gas (GHG) emission factors and carbon footprint of beef cattle production at Khulna division, Bangladesh. Data were collected on herd structure, farm feedstuff, manure management and farm operation of 231 beef cattle farms by interviewing farmers and filling up open questionnaires. The feed basket of 75 beef cattle of different genotypes in 24 farms was studied by keeping a record of individual feedstuff intake for three months. Results indicated that the feed basket of a 321 (±131) kg beef cattle, irrespective of genotype, was consisted of rice straw, Napier grass, maize fodder, jumbo grass and concentrate mixture at 3.61 (±1.60), 3.25 (±4.66), 0.21 (±1.34), 0.07 (±0.58), 1.73 (±3.08) and 3.69 (±1.40) kg/d, respectively. The digestibility of diet, volatile solids in manure and nitrogen excretion rate were estimated as 65 (±3.89) %, 8.3 (±2.01) and 0.29 (±0.07) kg/d/1000 kg LW, respectively. The emission factors of enteric CH4, manure CH4 were 46 (±18), and 9.06 (±10.86) kg CH4/yr/animal. The direct and indirect N2O emission factors due to volatilization and leaching were 2.96 (±0.01), 0.06 (±0.07) and 0.017 (±0.019) kg N2O/yr/animal, respectively. Based on the results of emission factors, including the carbon footprint of farm feedstuff (0.33 (±0.33) kg CO2e/yr/t fresh) and farm operation emission factor (131 (±109) kg CO2e/yr/cattle), the carbon footprint of beef cattle production was estimated as 7.68 (±0.98) kg CO2e/kg LW of beef cattle. Estimation of GHG emission factors for all sources in each livestock enterprise, and then accounting for carbon footprint may help realize the environmental cost of production and undertake appropriate mitigation measures. Bang. J. Livs. Res. Vol. 29 (1&amp;2), 2022: P. 34-52

Estimation of greenhouse gas emission factors for the wetland, peatland, and cropland

In Mongolia, greenhouse gas (GHG) and emission factors (EFs) from land-use categories including cropland, wetland, and peatland, have not been estimated. The main purpose of this study was to estimate GHG EF based on field measurement data for the wetland, peatland, and cropland using the IPPC guideline’s Volume-2 method. The determination of EFs for the land-use categories is crucial as it contributes to the national GHG inventory. Study sites were selected at the wetland, peatland, and cropland in (1) Khurkh river valley, and (2) near Ulaanbaatar city. Fieldwork was conducted during the spring and summer seasons of 2020-2021 and CO2 concentrations at study sites were measured using EGM-4 equipment with replicates (n=87). At the study sites, soil samples were collected from four different depths including 0-5 cm, 5-10 cm, 10-15 cm, and 15-30 cm. As the results, estimated EFs (± standard error) of the CO2 and CH4 were 357.2 g [CO2] m-2 yr-1 (±125.7) and 18.8 g [CH4] m-2 yr-1 (±6.6) in the peatland, 193.4 g [CO2] m-2 yr-1 (±53.8) and 10.2 g [CH4] m-2 yr-1 (±2.8) in the wetland, and 118.0 g [CO2] m-2 yr-1 (±29.4) and 6.2 g [CH4] m-2 yr-1 (±1.5) in the cropland. As these EFs measurements were determined by direct-field measurement method with seasonal and daily replicates within a year, they can be used as a “default value” for land-use categories in Mongolia.&#x0D; Чийг намаг, хүлэрт намаг болон тариалангийн талбайд хүлэмжийн хийн ялгарлын коэффициентыг тооцох нь&#x0D; ХУРААНГУЙ&#x0D; Монгол улсад газар ашиглалтын төрлүүд болох газар тариалан, чийг намаг, хүлэрт намагтай газраас ялгарч буй хүлэмжийн хий болон ялгарлын коэффициентыг тооцсон судалгаа одоог хүртэл хийгдээгүй байдаг. Энэхүү судалгаа нь IPCC-ын гарын авлагын Volume-2 арга зүйгээр газар ашиглалтийн төрлүүд дээрх хүлэмжийн ялгарлын коэффициентыг хээрийн шууд хэмжилтийн аргад суурилан тооцох зорилготой. Газар ашиглалтын төрлүүдэд ялгарлын коэффициентыг тодорхойлох нь үндэсний хэмжээний хүлэмжийн хийн тооллогод бодит хувь нэмэр оруулах ач холбогдолтой юм. Судалгаанд Хэнтий аймгийн (1) Хурхын голын хөндий, (2) Улаанбаатар хотын ойролцоох чийг намаг, хүлэрт намаг, тариалангийн талбайд судалгааны цэгүүдийг сонгосон. Судалгааг 2020-2021 оны хавар, зуны улиралд хийсэн бөгөөд СО2 ялгарлыг EGM-4 багаж ашиглан тодорхой давталттай (n=87) хэмжилт хийсэн. Сонгосон цэгүүдэд хөрсний дээжийг 0-5 см, 5-10 см, 10-15 см, 15-30 см гүнээс тус тус цуглуулсан. Судалгааны үр дүнгээр хүлэрт намгийн хүлэмжийн ялгарлын коэффициент нь нүүрстөрөгчийн давхар исэл, метан хийн хувьд 357.2 гр [CO2] м-2 жил-1 (±125.7), 18.8 гр [CH4] м-2 жил-1 (±6.6), чийг намгархаг газарт 193.4 гр [CO2] м-2 жил-1 (±53.8), 10.2 гр [CH4] м-2 жил-1 (±2.8), харин тариалангийн талбайд 118.0 гр [CO2] м-2 жил-1 (±29.4), 6.2 гр [CH4] м-2 жил-1 (±1.5) тус тус байсан. Энэхүү хэмжилтүүд нь улирлын болон цагийн давтамжтайгаар нэг жилийн хугацаанд шууд хэмжилтийн аргаар тодорхойлсон учир Монгол орны газар ашиглалтын төрлүүд дээр лавлах утга (default value) болон ашиглах бүрэн боломжтой юм.&#x0D; Түлхүүр үгс: Тариалангийн талбай, Хүлэмжийн хий, Хүлэрт намаг, Чийг намаг, Ялгарлын коэффициент

Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model.

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.