Greenhouse Gas Emission and Mitigation in Municipal Wastewater Treatment Plants

Abstract. To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

Net-zero greenhouse gas emission from wastewater treatment: Mechanisms, opportunities and perspectives

China is one of the largest contributors to global greenhouse gas (GHG) emissions, and the livestock sector is a major source of non-CO2 GHG emissions. Mitigation of GHG emissions from the livestock sector is beneficial to the sustainable development of the livestock sector in China. This study investigated the provincial level of GHG emissions from the livestock sector between 2000 and 2020 in China, to determine the driving factors affecting the provincial-level GHG emissions from the livestock sector, based on the logarithmic mean Divisia index (LMDI) model, which took into account of technological progress, livestock structure, economic factor, and agricultural population. Moreover, a gray model GM (1, 1) was used to predict livestock GHG emissions in each province until 2030 in China. The results showed that the GHG of Chinese livestock sector was decreased from 195.1 million tons (MT) CO2e in 2000 to 157.2 MT CO2e in 2020. Henan, Shandong, and Hebei provinces were the main contributors to the reduction in Chinese livestock GHG emissions, with their livestock GHG emissions reduced by 60.1%, 53.5% and 45.5%, respectively, in 2020 as compared to 2000. The reduction in GHG emissions from the Chinese livestock sector can be attributed to two main factors: technological progress and the shrinking of the agricultural laborers. In contrast, the agricultural economic development model with high input and high emissions showed a negative impact on GHG emission reduction in China’s livestock sector. Furthermore, the different livestock structure in each province led to different GHG reduction effects on the livestock sector. Under the gray model GM (1,1), the GHG emissions of the livestock sector will be reduced by 33.7% in 2030 as compared with 2020 in China, and the efficiency factor will account for 76.6% of the positive effect of GHG reduction in 2030. The eastern coastal region will be the main contributor to the reduction of GHG emissions from the Chinese livestock sector in 2030. Moreover, recommendations (such as upgrading livestock management methods and promoting carbon emission mitigation industries) should be proposed for the environmentally sustainable development of the livestock sector in the future.

Background: Climate change is predicted to be our century's most significant health threat. In 2021, 46 countries committed to environmentally sustainable low carbon health care systems. Of those, 34 were from low- and middle-income countries (LMICs). Currently, health systems are responsible for 4.4% of global greenhouse gas (GHG) emissions, with health systems in high-income countries (HICs) contributing the largest proportion to the sector's GHG emissions. However, future increases are predicted in LMICs in the absence of robust GHG mitigation. This systematic review aims to identify evidence-based GHG mitigation interventions to guide the transformation of health care systems towards net zero, specifically in LMICs. Additionally, potential synergies between interventions that aid adaption to climate change and mitigate GHG emissions will be investigated. Methods: This protocol will follow the 'Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist of recommended items to address in a systematic review protocol'. A comprehensive search will be conducted on electronic databases identified as relevant. Search terms were identified to capture all relevant peer-reviewed, primary research published between 1990 and 2022. The risk of bias will be assessed, and the quality of evidence graded. The eventual narrative synthesis will feed into a theory of change framework on GHG mitigation of health care systems in LMICs. Discussion: This systematic review will synthesise the existing evidence around GHG mitigation interventions across all scopes of emissions, including scope 1 (health care operations), scope 2 (energy), and scope 3 (supply chains). It can be used to inform recommendations on how health care systems in LMICs can reduce emissions while prioritising which actions to take to gain the most significant reductions in GHG emissions, considering ease of implementation, scope and cost. Finally, this can catalyse further research in this area which is urgently needed.

Background: Climate change is predicted to be our century's most significant health threat. In 2021, 46 countries committed to environmentally sustainable low carbon health care systems. Of those, 34 were from low- and middle-income countries (LMICs). Currently, health systems are responsible for 4.4% of global greenhouse gas (GHG) emissions, with health systems in high-income countries (HICs) contributing the largest proportion to the sector's GHG emissions. However, future increases are predicted in LMICs in the absence of robust GHG mitigation. This systematic review aims to identify evidence-based GHG mitigation interventions to guide the transformation of health care systems towards net zero, specifically in LMICs. Additionally, potential synergies between interventions that aid adaption to climate change and mitigate GHG emissions will be investigated. Methods: This protocol will follow the 'Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist of recommended items to address in a systematic review protocol'. A comprehensive search will be conducted on electronic databases identified as relevant. Search terms were identified to capture all relevant peer-reviewed, primary research published between 1990 and 2022. The risk of bias will be assessed, and the quality of evidence graded. The eventual narrative synthesis will feed into a theory of change framework on GHG mitigation of health care systems in LMICs. Discussion: This systematic review will synthesise the existing evidence around GHG mitigation interventions across all scopes of emissions, including scope 1 (health care operations), scope 2 (energy), and scope 3 (supply chains). It can be used to inform recommendations on how health care systems in LMICs can reduce emissions while prioritising which actions to take to gain the most significant reductions in GHG emissions, considering ease of implementation, scope and cost. Finally, this can catalyse further research in this area which is urgently needed.

BackgroundFood production accounts for 30% of global greenhouse gas (GHG) emissions. Less environmentally sustainable diets are also often more processed, energy-dense and nutrient-poor. To date, the environmental impact of diets have mostly been based on a limited number of broad food groups.ObjectivesWe link GHG emissions to over 3000 foods, assessing associations between individuals’ GHG emissions, their nutrient requirements and their demographic characteristics. We also identify additional information required in dietary assessment to generate more accurate environmental impact data for individual-level diets.MethodsGHG emissions of individual foods, including process stages prior to retail, were added to the UK Composition Of Foods Integrated Dataset (COFID) composition tables and linked to automated online dietary assessment for 212 adults over three 24-hour periods. Variations in GHG emissions were explored by dietary pattern, demographic characteristics and World Health Organization Recommended Nutrient Intakes (RNIs).ResultsGHG emissions estimates were linked to 98% (n = 3233) of food items. Meat explained 32% of diet-related GHG emissions; 15% from drinks; 14% from dairy; and 8% from cakes, biscuits and confectionery. Non-vegetarian diets had GHG emissions 59% (95% CI 18%, 115%) higher than vegetarian. Men had 41% (20%, 64%) higher GHG emissions than women. Individuals meeting RNIs for saturated fats, carbohydrates and sodium had lower GHG emissions compared to those exceeding the RNI.DiscussionPolicies encouraging sustainable diets should focus on plant-based diets. Substituting tea, coffee and alcohol with more sustainable alternatives, whilst reducing less nutritious sweet snacks, presents further opportunities. Healthier diets had lower GHG emissions, demonstrating consistency between planetary and personal health. Further detail could be gained from incorporating brand, production methods, post-retail emissions, country of origin, and additional environmental impact indicators.

Greenhouse gas emissions trends and drivers insights from the domestic aviation in Thailand

The refining industry is the third-largest source of global greenhouse gas (GHG) emissions from stationary sources, so it is at the forefront of the energy transition and net zero pathways. The dynamics of contributors in this sector such as crucial countries, leading enterprises, and key emission processes are vital to identifying key GHG emitters and supporting targeted emission reduction, yet they are still poorly understood. Here, we established a global sub-refinery GHG emission dataset in a long time series based on life cycle method. Globally, cumulative GHG emissions from refineries reached approximately 34.1 gigatons (Gt) in the period 2000-2021 with an average annual increasing rate of 0.7%, dominated by the United States, EU27&UK, and China. In 2021, the top 20 countries with the largest GHG emissions of oil refining accounted for 83.9% of global emissions from refineries, compared with 79.5% in 2000. Moreover, over the past two decades, 53.9-57.0% of total GHG emissions came from the top 20 oil refining enterprises with the largest GHG emissions in 12 of these 20 countries. Retiring or installing mitigation technologies in the top 20% of refineries with the largest GHG emissions and refineries with GHG emissions of more than 0.1 Gt will reduce the level of GHG emissions by 38.0%-100.0% in these enterprises. Specifically, low-carbon technologies installed on furnaces and boilers as well as steam methane reforming will enable substantial GHG mitigation of more than 54.0% at the refining unit level. Therefore, our results suggest that policies targeting a relatively small number of super-emission contributors could significantly reduce GHG emissions from global oil refining.

Agriculture is responsible for a large share of global greenhouse gas (GHG) emissions, especially for methane and nitrous oxide emissions. Applying a bio-economic whole-farm model, we assessed five GHG mitigation options on their economic suitability to reduce emissions from grassland-based suckler cow farms. Among the assessed options, only compensation by agroforestry systems and the choice of an adequate production system showed the potential to significantly reduce emissions. If an adequate production system is chosen, GHG emissions per kilogram of meat can be reduced by up to 18% – from 21.9 to 18 kg CO2-eq./kg of meat – while total gross margin can be increased by up to 14%. Through the application of an agroforestry system, GHG emissions in all systems can be further reduced to 7.5 kg CO2-eq./kg meat – equating to a reduction of GHG emissions of 48% to 66% – at costs between 0.03 CHF/kg meat and 0.38 CHF/kg meat depending on the production system and the state of the system before the reduction. In contrast, the addition of lipids to the diet or a cover to the slurry tank has neither the potential to reduce GHG emissions significantly nor are they cost-effective enough to be implemented. Nitrification inhibitors can reduce GHG emissions up to 10%, but costs for this reduction are much higher than for agroforestry systems. The application of agroforestry systems to suckler farming in Switzerland therefore seems to be an adequate option to reduce GHG emission significantly for a relatively low price.

Agricultural activities are a substantial contributor to global greenhouse gas (GHG) emissions, accounting for about 58% of the world's anthropogenic non-carbon dioxide GHG emissions and 14% of all anthropogenic GHG emissions, and agriculture is often viewed as a potential source of relatively low-cost emissions reductions. We estimate the costs of GHG mitigation for 36 world agricultural regions for the 2000–2020 period, taking into account net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. For croplands and rice cultivation, we use biophysical, process-based models (DAYCENT and DNDC) to capture the net GHG and yield effects of baseline and mitigation scenarios for different world regions. For the livestock sector, we use information from the literature on key mitigation options and apply the mitigation options to emission baselines compiled by EPA.

Agricultural activities are a substantial contributor to global greenhouse gas (GHG) emissions, accounting for about 58% of the world's anthropogenic non‐carbon dioxide GHG emissions and 14% of all anthropogenic GHG emissions, and agriculture is often viewed as a potential source of relatively low‐cost emissions reductions. We estimate the costs of GHG mitigation for 36 world agricultural regions for the 2000–2020 period, taking into account net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. For croplands and rice cultivation, we use biophysical, process‐based models (DAYCENT and DNDC) to capture the net GHG and yield effects of baseline and mitigation scenarios for different world regions. For the livestock sector, we use information from the literature on key mitigation options and apply the mitigation options to emission baselines compiled by EPA.

The role of ‘indirect’ greenhouse gas emissions in tourism: Assessing the hidden carbon impacts from a holiday package tour

Temporal and spatial variation of greenhouse gas emissions from a limited-controlled landfill site

The transportation sector accounts for 16% of global greenhouse gas (GHG) emissions and is under formidable pressure to decarbonize. With a growing number of countries making commitments to achieve carbon neutrality or "net-zero" emissions within the next few decades, it is imperative for transportation researchers and policymakers to understand the viable pathways towards achieving carbon neutrality for light-duty transport. This chapter discusses the transportation policies and GHG emissions of the three largest markets in the world—the U.S., China, and the European Union. The life cycle GHG emissions of various vehicle technologies are evaluated while highlighting the regional and temporal differences. We then use market penetration and fleet models, developed specifically for each market, to comprehensively assess the light-duty transport energy demand and GHG emissions under various scenarios. The modeling results show that battery electric vehicles (BEVs) will increase in market share, but internal combustion engine vehicles (ICEV) will continue to dominate the passenger vehicle stock in the next 20 years under most scenarios. Improving ICEV efficiency can play a critical role in meeting GHG regulations in the near- and medium-term. BEVs, whose GHG emissions are highly dependent on the source of electricity generation, will play an essential role in the long-term as the electric grid becomes cleaner. In summary, transportation policies should be technology agnostic and consider emissions based on the whole life cycle. Moreover, a holistic approach to reducing transportation GHG emissions is key to achieving global environmental goals.

Machine learning can be a game-changer for a global warming prediction. About 75% global greenhouse gas (GHG) emissions cause by energy sector and this indicate a major concern to global warming community. In this study, non-supervisory machine learning technique has been used to predict the GHG effect relate to net calorific value based on intergovernmental panel on climate change (IPCC) standard. The study focuses on the characteristic of coal that is used in power generation sector and its chemical effluent that obtained from ultimate analysis (dried basis; Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur and Ash) as gas emissions is concerned. The dataset shows, coal from different origin and type produce GHG emissions range approximately between 86.95 and 108.23 k-tonne CO2/TJ with the net calorific value of 19.77 to 27.17 MJ/kg-coal. While, for ultimate analysis, the percentage of Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur and Ash are in the range of [65.05 – 73.3], [1.46 – 5.49], [1.2 – 19.06], [0.3 – 1.20] and [4.82 – 15.96] respectively. In this study, principal component analysis is used to screen the training dataset and feed forward structure from artificial neural networks are used which allows the trained model to determine the GHG emission factor based on the given input data. The network relative errors of year 2017 dataset were used to adjust the weight value and as a result, the networks give r-square of 0.91678, which subsequently the trained networks are simulated for GHG emissions prediction for year 2018 at accuracy of r-square 0.82191. Furthermore, the study also shows, they are significant effect from coal characteristic towards GHS emissions and study proposed an optimal solution to simultaneously maximise power generation (in net calorific value per consumption weight) and reducing GHG value (k-tonne CO2/TJ) of coal plant.