Emission Factor Method Research Articles

Identifying an over tenfold variation in carbon intensities of coal mines in China by multi-scale multi-benchmark accounting

Complete and accurate accounting of greenhouse gas (GHG) emissions from coal mines in China is a critical basis for identifying mitigation potential and making reduction strategies. Previous studies based on the emission factor method and life cycle assessment are limited in accounting scope completeness, data reliability, or result applicability at multiple scales and benchmarks. The objective of this study was to carry out a multi-scale multi-benchmark GHG accounting of Chinese coal mines to build a shared understanding and facilitate discussion on this topic. We established a complete accounting model to cover upstream indirect, direct, and downstream indirect GHG emissions related to coal mining. A total of 1151 operating coal mines were investigated to model site-level carbon emissions based on mass and energy benchmarks and then aggregated to 99 cities, 19 provinces, and the country for accounting. The results showed an over 14-fold variation in mass-based site-level accounting, with the highest uncertainty of less than 15%. This variability performed almost the same at city-level aggregating, whereas narrowed to over twofold at the provincial level. Energy-based accounting presented an over 17-fold variation at the site-level with a similar uncertainty to mass-based accounting. Such variability was narrowed to over sevenfold and approximately threefold in the city- and province-level aggregations, respectively. The national average mass- and energy-based carbon footprint of coal mining were 0.39 kg CO2-eq/kg and 0.0185 kg CO2-eq/MJ, respectively, in which approximately 36% of the total GHG emissions were omitted if mining methane escape was reported only. These results suggest improving the methane utilization rate and optimizing coal production and consumption patterns, which implicates cleaner production and sustainable development strategies for the coal industry in China. The conclusions are helpful to identify individual carbon emission gaps, clarify regional emission responsibilities, coordinate the national carbon neutrality process, and promote a low-carbon coal supply system.

Research on Carbon Emission Characteristics of Rural Buildings Based on LMDI-LEAP Model

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents’ awareness of energy conservation.

An Innovative Method for BTEX Emission Inventory and Development of Mitigation Measures in Developing Countries—A Case Study: Ho Chi Minh City, Vietnam

Benzene, toluene, ethylbenzene, and xylenes (BTEX) are carcinogenic pollutants. However, the average concentration in 1 h of some pollutants belonging to BTEX, such as benzene, in Ho Chi Minh City (HCMC) is higher than the national standard QCVN 06:2009/BTNMT by about ten times. This research is the first to calculate the emission of BTEX for developing countries on a city scale. This paper developed a method to calculate cold emission factors based on hot emission factors for BTEX. Five spreadsheets developed and calculated these cold emission factors for five vehicle categories. A comprehensive emission inventory (EI) for BTEX was conducted in HCMC to determine the cause of BTEX pollution. An innovative methodology with bottom-up and top-down combination was applied to conduct BTEX EI, in which the EMISENS model was utilized to generate the EI for road traffic sources, and the emission factors method was utilized for other emission sources. Among emission reasons, motorcycles contribute the highest to HCMC air pollution, responsible for 93%, 90%, 98.9%, and 91.5% of benzene, toluene, ethylbenzene, and xylene, respectively. Cars contributed 5%, 6%, 0.8%, and 6.5% of benzene, toluene, ethylbenzene, and xylene, respectively. For LDVs, the emission from benzene, toluene, ethylbenzene, and xylene accounted for 1%, 2%, 0.2%, and 1.9%. The major reasons for point sources were metal production, which had 1%, 2%, and 0.1% for benzene, toluene, ethylbenzene, and xylenes emissions. The area source had a minority emission of total BTEX in Ho Chi Minh City. Our findings can be used to invest in the most significant sources to reduce BTEX in HCMC. Our approach can be applied in similar urban areas in BTEX EI. This research also developed nine measures to reduce BTEX in HCMC for human health protection.

Study on the Evolution and Trends of Agricultural Carbon Emission Intensity and Agricultural Economic Development Levels—Evidence from Jiangxi Province

The study of the evolutionary patterns and trends of agricultural carbon emission intensity and agricultural economic development levels plays an important role in promoting the green and low-carbon sustainable development of agriculture. This paper adopts the carbon emission factor method to measure the agricultural carbon emissions in Jiangxi Province from 2001 to 2020, uses the LMDI decomposition method to explore the drivers of carbon emissions, and further analyzes the coupling relationship between agricultural carbon emissions and the agricultural economy using the Tapio decoupling model, based on which a GM (1,1) model is used to forecast the agricultural carbon emissions in Jiangxi Province from 2001 to 2015. According to the research results, agricultural carbon emissions in Jiangxi Province show a trend of “rising and then falling”, with the intensity decreasing; the level of economic development is the main factor that increases carbon emissions, while the efficiency of agricultural production, the size of the labor force, and the structure of agricultural production have positive effects in terms of reducing carbon emissions. How to reduce carbon emissions while promoting agricultural economic development is an issue that remains to be addressed in the future. Further analysis found that the decoupling states of Jiangxi Province from 2001 to 2009 switched between strong decoupling and weak decoupling, with weak decoupling dominating the years 2010–2015 and strong decoupling dominating from 2016 onwards. With the continuous promotion of carbon emission reduction, agricultural carbon emissions in Jiangxi Province will continue to show a decreasing trend over the next five years. Three policy recommendations are put forth in order to advance the effort to reduce agricultural carbon emissions in Jiangxi Province: cultivating high-quality and low-carbon rice varieties, switching to green agricultural production, and coordinating the connection between economic growth and agricultural carbon emissions.

Measurement and Spatial–Temporal Characteristics of Agricultural Carbon Emission in China: An Internal Structural Perspective

The focus of world governance on climate change has been on the industrial and transport sectors, yet the agricultural sector produces a lot of greenhouse gases, and this has always been ignored. This paper focuses on China, one of the world’s largest agricultural countries, and it investigates its agriculture carbon emission from a new perspective of the internal structure of it, which is relatively under-researched. Carbon metrology, the emission factor method and kernel density estimations are used to analyze China’s agricultural carbon emissions structure and its regional differences and its dynamic evolution characteristics. We find that: (1) China’s total amount of agricultural carbon emissions showed a ladder-like upward trend, but the growth rate of it has gradually slowed down; the inter-provincial heterogeneity of the agricultural carbon emissions was obvious. (2) From the standpoint of the grain functional areas, the annual total amount of agricultural carbon emissions and the amount of carbon emissions of each carbon source in the major grain producing areas were significantly higher than those in the major grain sales areas and the production–sales balance areas, and the carbon emission intensity in the major grain producing areas was the lowest overall. (3) In regard to the internal structure, China’s agricultural carbon emissions mainly came from the livestock and poultry, rice planting and agricultural energy sectors; the proportion of carbon emissions that were caused by the agricultural materials, agricultural energy and soil increased in general, and the inter-provincial differences between them expanded, while the inter-provincial differences between livestock and poultry gradually decreased. The proportion of carbon emissions from the six major agricultural carbon sources showed a convergence trend, and their kernel density had a right tail phenomenon. Our research deepens the understanding of China’s agricultural carbon emission structure, contributes to the rational optimization of the agricultural structure, and helps the agriculture sector and the rural areas to reach the carbon peak.

Comprehensive assessment of land use carbon emissions of a coal resource-based city, China

Carbon emissions reduction is crucial for global sustainable development. While few studies pay attention to land use carbon emissions (LUCEs) in coal-based cities. We firstly used the emission factor method proposed by IPCC to estimate the carbon sources and carbon sinks of land use in Huainan, a typical city of China, from 1991 to 2020. Then we used Logarithmic mean Divisia index (LMDI) model and Tapio decoupling elasticity to identify the driving factors of land use carbon emission changes and analyze their intrinsic relationship with economic growth of Huainan. The results revealed that the total LUCEs in Huainan increased significantly, and the carbon sources from land use were always higher than the carbon sinks. It also showed that economic development and built-up land change were the main driving factors of LUCEs growth in Huainan. Moreover, LUCEs had a synergistic relationship with the decoupling elasticity of economic growth in different development stages of the coal-based city. Therefore, we predicted LUCEs in Huainan City from 2020 to 2035 under three scenarios. Finally, we proposed some recommendations to reduce the LUCEs and develop the low-carbon economy. We hope to not only extend the research of land use carbon emissions on coal-based city level, but also promote the low-carbon development of coal-based cities in China.

Calculation and scenario prediction of methane emissions from agricultural activities in China under the background of “carbon peak”

Climate warming has become a difficult problem all over the world. As the most important component of greenhouse gas, methane plays a vital role in the earth’s climate system. CH4MOD model and emission factor method are used in calculating and analyzing the methane emission from agricultural activities in China. The scenario analysis method is used in predicting the methane emission from agricultural activities in China. The results show that: (1) The changing trend of total methane emission from agricultural activities in China from 2001 to 2020 increased firstly, then decreased, and finally tended to steady fluctuation, in which the contribution of livestock intestinal fermentation emission was the largest. (2) From 2001 to 2020, GHGI1 of methane in agricultural activities in China showed a downward trend, while GHGI2 fluctuated. (3) China’s methane emissions from agricultural activities are predicted to increase under different scenarios from 2021 to 2030. To meet China’s goal of reaching a carbon peak by 2030, the strict emission reduction scenario is more suitable for China’s future methane emission development trend than other scenarios.

Carbon emission reduction calculation for the green transformation of traditional hotel design

The green transformation design of traditional hotels aims to reduce the consumption of materials and energy and reduce the emission of harmful substances. Carbon emission, usually used to measure energy consumption, is a representative indicator to evaluate the effect of the green transformation process. This paper adopts the emission factor method to measure the carbon emission reduction in the process of hotels’ green transformation from the whole life cycle of hotels, and the results show that: 1) From the perspective of the hotel life cycle, the construction process is the stage with the most carbon emissions. 2) From the perspective of a single hotel, high-star hotels have larger emissions than low-star hotels; 3) As the number of three and four-star hotels far exceeds the five-star hotels, overall, four-star hotels ranked first in total emission reduction. 4) From the perspective of provinces, the hotel industry emission reduction potential of different provinces varies greatly, among which Guangdong, Zhejiang, and Jiangsu provinces are at the top of the list. The future carbon reduction potential of the Chinese hotel industry is very considerable. The content of this paper enriches the research in the field of carbon emission measurement and also provides a reference for the management agency to designate the carbon peaking and carbon neutrality goals of the hotel industry.

Cost–Benefit Analysis of Synergistic CO2 and NOx Energy-Efficient Technologies for the Road Transport Sector in China

The transportation sector is a major source of greenhouse gases and air pollutants, and it has a crucial effect on the synergistic reduction of NOx and carbon. In order to find the energy-efficient vehicle technologies with the highest net reduction potential and lowest net reduction cost over the life cycle, this study traced the CO2 and NOx emission streams of 33 energy-efficient technologies, hidden in the supply chain during the production phase, through structural path analysis, and measured the emission reductions during the use phase using the emission factor method. Moreover, we applied structural decomposition analysis to quantify the three main drivers, including emission intensity, industrial structure, and final demand, of changes in CO2 and NOx emissions from 11 transport subsectors during 2012–2018. Results indicate that CO2 emissions of the transport sector more than doubled from 2012 to 2018; however, the influence of NOx was less significant. The final demand of the road subsector was the most significant driver contributing to CO2 emission changes, with an increase of 109.27 Mt. The emission intensity of road transportation caused the greatest mitigation effect on NOx emission changes, with a decrease of 1902 Kt. The findings of the scenario analysis demonstrate that the most efficient action of the pure electric technology for passenger cars reduces 20.92 Mt NOx emissions, and the parallel hybrid technology for heavy trucks offers the greatest cost effectiveness with a net abatement of 2577 Mt CO2 over its life cycle. Consequently, the aggressive development of new energy technology has become a prerequisite strategy to synergistically reduce CO2 and NOx emissions.

Evolution of Logistics Industry Carbon Emissions in Heilongjiang Province, China

This paper analysed the spatio-temporal distribution characteristics and impact mechanism of the logistics carbon emissions in China’s Heilongjiang Province. Different levels of logistics carbon emissions during 2000–2020 were calculated using an energy-based emission factor method, and the Tapio decoupling model was used to analyse the decoupling effect between carbon emissions and economics. Then, a hotspot analysis and a spatial autocorrection analysis were used to study the spatial-temporal patterns of logistics carbon emissions. Finally, the influencing factors were analysed by using the logarithmic mean divisia index. Results showed that logistics carbon emissions in Heilongjiang increased from 1.66 million tons in 2000 to 6.21 million tons in 2020. The overall decoupling state was expansion negative decoupling, and it turned into strong decoupling in the last five years. The hotspots of logistics carbon emissions were mainly concentrated in developed cities in the south, and the coldspots were mainly in undeveloped regions in the north. Economic development was the main factor driving the increase of logistics carbon emissions, and logistics development was the main factor inhibiting the growth of logistics carbon emissions. The study results can provide references for formulation region-specific carbon reduction measures, which are beneficial to the low-carbon sustainable development of the logistics industry in this region.

How Do Transportation Influencing Factors Affect Air Pollutants from Vehicles in China? Evidence from Threshold Effect

In recent years, China has promoted a series of legal norms to reduce the environmental impact of air pollutants from vehicles. The three main vehicle emission species (carbon monoxide, hydrocarbons, nitrogen oxides) contribute significantly to air pollution. In this study, the emission factor method was used to estimate air pollutants from vehicles in 31 provinces from 2006 to 2016. The results show a trend of total vehicle carbon monoxide (CO) and hydrocarbons (HC) emissions decreasing with time; the vehicle nitrogen oxides (NOx) emission trend is divided into two stages: an upward trend between 2006 and 2012 and a downward trend after 2012. Based on a panel threshold, a regression method was used to divide the vehicle NOx and CO emissions in China into four emission zones: low emissions, medium emissions, high emissions, and extra-high emissions. Vehicle HC emissions were divided into three emission zones, which corresponded to low emissions, medium emissions, and high emissions. Overall, vehicle pollution emission efficiency and per capita GDP have a significant inhibitory effect on the three main air pollutants from vehicles (NOx, HC, CO). Both passenger and freight turnover have significant roles in promoting the three air pollutants from vehicles (NOx, HC, CO). Road density and road carrying capacity have a significant role in promoting vehicle HC and CO emissions. Increasing truck proportion inhibits vehicle CO emissions and promotes vehicle NOx emissions. The urbanization rate has a positive effect on vehicle HC and CO emissions. Moreover, there is obvious heterogeneity in different emission zones of the three air pollutants from vehicles (NOx, HC, CO).

How does sponge city construction affect carbon emission from integrated urban drainage system?

The integrated urban drainage system has complicated sources of greenhouse gases (GHG) emissions, which is one of contribute to climate change. As a new model of low-carbon ecological city development in China, sponge city has great potential of carbon emission reduction. However, limited by the lack of awareness of the importance of carbon emissions from integrated urban drainage system, few studies have focused on the impact of sponge city construction on the carbon emissions from integrated urban drainage system. This study proposed an accounting framework for carbon emissions from integrated urban drainage system. The emission factor method was used to calculate carbon emissions. A case study was conducted in Xiamen, China, the carbon emission level of integrated urban drainage system and the carbon emission reduction effect of sponge city construction were discussed. The results show that the carbon emission from traditional city drainage system and sponge city drainage system accounts for about 3.4% and 1.7% of the city's carbon emission, respectively. Wastewater treatment and Sludge Treatment and Disposal are the main contributors to the carbon emissions from integrated urban drainage system. Electricity consumption is the main contributors to the indirect carbon emissions. Adjusting the sludge disposal method has the best carbon emission reduction effect, raising the standard of wastewater treatment plant tail water will lead to an increase in carbon emissions. Compared with the traditional city drainage system, the sponge city drainage system can reduce carbon emissions from integrated urban drainage system by an average of 49%, and the corresponding economic benefit was approximately 91–297 CNY/104 m3 wastewater. This study plays a positive role in understanding the carbon emission level of integrated urban drainage system and planning the low-carbon development of the city in the future.

Application of grey model in influencing factors analysis and trend prediction of carbon emission in Shanxi Province.

In recent years, global warming has attracted extensive attention. The main cause of global warming is the emission of greenhouse gases, known as carbon emissions. Therefore, it is of great significance to explore the influencing factors of carbon emissions and accurately predict carbon emissions for reducing carbon emissions and slowing down climate warming. This paper takes the carbon emissions of Shanxi Province in China as the research object. Firstly, the emission factor method is used to calculate the carbon emissions, and then the grey correlation model is used to screen out the factors that have a greater impact on carbon emissions (per capita GDP, urbanization rate, resident population, energy consumption, expenditure on R&D projects). Then, an improved grey multi-variable convolution integral model (AGMC(1, N)) is used to accurately predict carbon emissions. The results show that the application of the AGMC(1,N) model to carbon emission prediction has a good prediction effect. In addition, the carbon emissions of Shanxi Province will increase with the growth rate of per capita GDP, energy consumption, resident population, and expenditure on R&D projects, while the carbon emissions will gradually decrease with the increase of urbanization level. The prediction results provide the direction for carbon emission reduction in Shanxi Province. At the same time, theAGMC(1,N) model can also be applied to the prediction of carbon emissions in other provinces or other fields.

Possible Scenarios for Reduction of Carbon Dioxide Emissions in Serbia by Generating Electricity from Natural Gas

The main purpose of this paper is to develop possible scenarios for reducing carbon dioxide (CO2) emissions in Serbia by switching from coal-fired to natural gas-fired electricity generation by the end of 2050. Accordingly, the challenges are to establish scenarios and identify measures that are best suited to all Western Balkan countries. In particular, this paper proposes a number of energy mix scenarios that offer some options for reducing CO2 emissions while maintaining an economic way of consumption. Projections of these reduction scenarios are obtained by combining the Kaya identity with the emission factor method. A simplified methodology based on sensitivity analysis is used as a source of data to estimate the nonstatistical uncertainty limits for the projections of the reduction scenarios. The sensitivity analysis is carried out using historical data from Serbia for the period from 1990 to 2019. In addition, a direct verification of the proposed methodology is performed based on historical data for 2020. The developed scenarios 1 and 2 show that CO2 emissions could be reduced by 10.94% and up to 74.44% from baseline in 1990, respectively. The obtained results are also contrasted with the data available for some other countries with similar experiences. Finally, it is found that the developed scenarios are achievable only at significantly decreased levels of coal-fired electricity generation.

Agricultural Ammonia Emission Inventory and Its Distribution in Xining City

Based on the field research activity level, the emission inventory of agricultural ammonia in Xining City in 2018 was established using the emission factor method. The emission characteristics of agricultural ammonia in Xining City were analyzed, a spatial grid distribution of 3 km×3 km was carried out by ArcGIS, and an uncertainty analysis of the ammonia emission inventory of livestock and poultry breeding and nitrogen fertilizer application was carried out via Monte Carlo simulation. The results showed that the total amount of ammonia emissions from agricultural sources in Xining City in 2018 was 4644.58 t. Among them, the total annual ammonia emissions from nitrogen fertilizer application and livestock and poultry farming were 1664.84 t and 2979.75 t, respectively, accounting for 35.84% and 64.16% of the total ammonia emissions from agricultural sources in Xining City. The spatial distribution results showed that the agricultural source ammonia emissions of Xining City mainly came from Datong County, Huangyuan County, and Huangzhong County, which accounted for 40.10%, 30.66%, and 28.05% of the total agricultural source ammonia emissions of Xining City, respectively. From the perspective of monthly distribution, no monthly difference in ammonia emissions was found in livestock and poultry farming. The ammonia emissions from nitrogen fertilizer application mainly existed in June (799.96 t) and July (768.48 t), accounting for 48.05% and 46.16% of the annual emissions. Monte Carlo simulation results showed that under a 95% confidence interval, the uncertainties of ammonia emissions from nitrogen fertilizer application agricultural source ammonia were low, ranging from -24.38% to 26.71%.

Vehicle emissions of primary air pollutants from 2009 to 2019 and projection for the 14th Five-Year Plan period in Beijing, China

Over the past decade, the emission standards and fuel standards in Beijing have been upgraded twice, and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles. Through the formulation and implementation of these policies, the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), and fine particulate matter (PM2.5) in 2019 were 147.9, 25.3, 43.4, and 0.91 kton in Beijing, respectively. The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control. In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing, different vehicle pollution control scenarios were established, and emissions reductions were projected. The results show that the emissions of four air pollutants (CO, VOCs, NOx, and PM2.5) from vehicles in Beijing decreased by an average of 68% in 2019, compared to their levels in 2009. The contribution of NOx emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019, which indicated that clean and energy-saving diesel vehicle fleets should be further improved. Electric vehicle adoption could be an important measure to reduce pollutant emissions. With the further upgrading of vehicle structure and the adoption of electric vehicles, it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period.

Intelligent Measurement and Monitoring of Carbon Emissions for 5G Shared Smart Logistics

With the upgrading of logistics demand and the innovation of modern information technology, the smart logistics platform integrates advanced concepts, technologies, and management methods, maximizes the integration of logistics resources and circulation channels, and effectively improves the efficiency of logistics transactions, but its energy consumption problem is particularly prominent. The study of intelligent measurement and monitoring of carbon emissions in smart logistics is of great value to reduce energy consumption, reduce carbon emissions in buildings, and improve the environment. In this paper, by comparing and analyzing the accounting standards of carbon emissions and their calculation methods, the carbon emission factor method is selected as the method to study the carbon emissions of the smart logistics process in this paper. The working principle of each key storage technology in the smart logistics process is analyzed to find out the equipment factors affecting the carbon emission of each storage technology in the smart logistics process, and the carbon emission calculation model of each key storage technology is established separately by using the carbon emission factor method. Meanwhile, according to the development history of energy consumption assessment, the assessment process of different stages from logistics storage energy consumption assessment to smart logistics energy consumption assessment is analyzed, and based on this, a carbon emission energy consumption assessment framework based on 5G shared smart logistics is constructed. This paper applies the supply chain idea to define the smart logistics supply chain, constructs a conceptual model of the smart logistics supply chain considering carbon emissions, and at the same time combines the characteristics of the smart logistics supply chain to analyze the correlation between the carbon emissions of the smart logistics supply chain and the related social, environmental, and economic systems.

Estimation of Nitrous Oxide Emission from River System Based on Water Discharge and Dissolved Nitrous Oxide Concentration

Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (N2O) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine N2O emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine N2O dissolved concentrations ρ(N2O), N2O fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine N2O emission flux to estimate annual N2O emission quantity for the entire river system. The results indicated that observed riverine ρ(N2O) (0.03-2.14 μg·L-1) and the N2O fluxes[1.32-82.79 μg·(m2·h)-1] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of ρ(N2O) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the N2O emission fluxes were mainly affected by river water discharges and ρ(N2O). A multiple regression model that incorporates variables of river water discharge and ρ(N2O) could explain 90% of the variability in riverine N2O emission fluxes and has high accuracy. The model estimated N2O emission quantity from the entire Yonganxi River system of 3.67 t·a-1, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate N2O emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of N2O emission for the entire river system and provides a reference method for estimating riverine N2O emission with more accuracy.

Carbon Emission Reduction of Tunnel Construction Machinery System Based on Self-Organizing Map-Global Particle Swarm Optimization With Multiple Weight Varying Models

The impact of greenhouse gas emissions from construction activities on the environment is becoming more and more obvious. It is imperative to adopt corresponding techniques and management measures to restrain carbon emissions from construction industry, especially construction of infrastructures such as transportation tunnels. Through the Life Cycle Assessment theory and carbon emission factor method, we established a carbon emission model which can be applied to quantify the carbon emission of the mechanized construction system of tunnels constructed by drill-and-blast method. This model exhibits considerable non-convexity after incorporating various constraint conditions, and thus its task of finding the optimums becomes an NP-hard problem. Then, we propose a self-organizing map-global particle swarm optimization algorithm which incorporates multiple weight varying strategies and self-organizing mapping networks for adaptive adjustment of particle trajectories to improve the ability of searching optimums. The feasibility and advantages of the proposed algorithm were verified through a series of experiments, and finally it is combined with a quantitative model and applied to the optimization of the construction machinery unit combination for the F4 section of the Wushaoling Tunnel, and quite good optimization results are achieved, optimized configuration reduced the cycle duration from 660 minutes to 432 minutes while the total emission of carbon equivalent was reduced by 88.4% compared to original machinery system configuration, which offers reference and inspiration to other researches of carbon emission reduction in the construction of tunnels or other infrastructures.

Analysis of Carbon Emission Energy Inventory from Refrigerant Production and Recycling Carbon Compensation

At present, the massive emissions of carbon dioxide and nitrogen oxides and other greenhouse gases caused by human activities have caused more and more serious negative effects on global climate change. In order to cope with global warming and achieve sustainable development, achieve “carbon neutrality” as soon as possible. In the refrigeration industry, it is necessary to reduce greenhouse gas emissions related to refrigerants, including the production, use, and recycling of refrigerants. This paper has carried out the calculation of greenhouse gas emissions during the refrigerant preparation process, and compared and analyzed the emission reductions of refrigerant recycling and reuse; the research based on the energy consumption of the refrigerant production process uses the greenhouse gas emission inventory analysis method to Taking refrigerant R134a as an example, the carbon emission accounting boundary of the production process is set, the emission source is determined, the emission is calculated based on the emission factor method, and the emission inventory is established; the carbon offset effect of the recycling and reuse of the refrigerant is analyzed. The research results show that if the entire refrigerant industry fully recycles waste refrigerants, it can reduce carbon emissions by about 29.7% compared to just producing new refrigerants.