Advancing greenhouse gas emission factors for municipal wastewater treatment plants in China

Methane and nitrous oxide emissions from municipal wastewater treatment plants in China: A plant-level and technology-specific study

Urban wastewater treatment plants are considered important greenhouse gas resources with massive emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) during operation. Based on the emission factor approach of pollutant reduction, the 2014 emission inventory of greenhouse gases (CO2, CH4, and N2O) from urban wastewater treatment plants in China was established. In addition, the temporal and spatial distribution and influencing factors of greenhouse gas emissions were analyzed in this study. The results showed that total emissions of greenhouse gas from urban wastewater treatment plants in China was 7348.60 Gg (CO2-eq) in 2014, which included CO2, CH4, and N2O emissions of 6054.57 Gg, 27.47 Gg (769.08 Gg, CO2-eq), and 1.98 Gg (524.95 Gg, CO2-eq), respectively. The difference in greenhouse gas emissions among provinces was significant:high emissions appeared in the eastern areas of China, low emissions were observed in the northwest, and hardly any emissions were found in Xizang. From 2005 to 2014, annual greenhouse gas emissions from urban sewage treatment plants in China increased by 229.4%, and the rates of CO2, CH4, and N2O increased by 217.9%, 217.9%, and 520.3%, respectively. The regional economic development level and number of wastewater treatment plants were correlated the most with the emissions of greenhouse gasses, and the per-capita protein supply was closely related with the N2O emission.

The traditional upscaling approach to greenhouse gas (GHG) emission estimates of inland waters is imprecise, but more precise methods based on environmental drivers are a longstanding challenge. Mexico lacks GHG emission estimates for its inland waters, and only sparse but scientifically validated information is available. This study provides the first GHG emission estimates from Mexican inland waters using 4275 GHG flux measurements from 26 distinctive waterbodies and one local and another global surface area dataset (INEGI and HydroLAKES). GHG emission factors were calculated and subsequently upscaled to estimate total national GHG emissions from the inland waters and compare to other emission measures based on mean global emission factors or size-productivity weighted (SPW) models. Mean (standard error) annual fluxes from all inland waters were 2.2 (5.3) kg CO2 m−2 yr−1, 0.6 (1.14) kg CH4 m−2 yr−1, and 1.0 × 10−3 (6.0 × 10−4) kg N2O m−2 yr−1. Estimates for natural waterbodies are annual average release rates between 74 (87) and 139 (163.23) Tg CO2eq while artificial waterbodies reach between 32 (2) and 21 (21) Tg CO2eq according to INEGI and HydroLAKES datasets, respectively. Considerable uncertainty was determined in the calculated mean emission factor, mostly for anthropogenic emissions. Waterbody area and chlorophyll a concentration were used as proxies to model CO2 and CH4 fluxes through regression analysis. According to SPW and IPCC models, computed mean annual CH4 emission factors were close to our estimates and exhibited a strong influence from eutrophication. In a likely scenario of increased eutrophication in Mexico, an increase in total net emissions from inland waters could be expected.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

Wastewater treatment facilities are major systems for managing swine manure in Korea. These facilities primarily use physical, chemical, and biological processes to remove harmful substances from manure and convert them into compost, liquid fertilizer or biogas. The 2050 carbon neutrality scenario in Korea aims to increase the proportion of manure purification treatment from 13% to 25% by 2030. As manure treatment facilities expand, it is crucial to quantify and monitor their greenhouse gas (GHG) emissions such as methane (CH₄) and nitrous oxide (N₂O) gas emissions. This study aimed to measure the GHG emissions from a swine wastewater treatment plant to develop a country-specific emissions factor for each treatment stage to determine the national GHG inventory. The facility evaluated in this study had tanks for sedimentation, manure retention, denitrification, and aeration (nitrification) and treats 121 tonnes of swine manure from approximately 24,335 pigs. Quantification of the total GHG emissions from the facility was conducted for 24h once per a month, using a CH₄/N<sub>2</sub>O Analyzer. The total emission factors from this facility for CH₄ and N<sub>2</sub>O were estimated 0.59 kg CH₄/head/year and 0.004 kg N₂O/head/year. Also, field-measured data showed 417 tCO<sub>2</sub>-eq/year, whereas 2019 IPCC Tier 2 factors estimated 1,238 tCO<sub>2</sub>-eq/year- a 66% overestimate. The results revealed that the initial treatment stages (sedimentation and manure retention tanks) were the primary emission hotspots. This significant overestimation highlights the critical need for adopting refined specific emission factors based on direct field measurements. In conclusion, it is crucial to ensure that sedimentation and manure retention tanks are gastight to reduce the GHG emissions from a facility. Likewise, direct stage-resolved monitoring is essential to prevent overestimating GHG emissions. Therefore, this study serves as a foundation for the development of effective carbon reduction strategies in manure treatment processes.

For many developing countries, the land use sector, particularly agriculture and forestry, represents a large proportion of their greenhouse gas (GHG) emissions, making this sector a priority for GHG mitigation activities. Previous global surveys (e.g., IPCC 2000) as well as the most recent IPCC assessment report clearly indicate that the greatest technical potential for carbon sequestration and reductions of non-CO2 GHG emissions from the land use sector is in developing countries. Estimates that consider economic feasibility suggest that agriculture and forestry together provide among the greatest opportunities for short-term and low-cost mitigation measures across all sectors of the global economy1 (IPCC 2007). In addition, it is widely recognized that the ecosystem changes entailed by most mitigation practices, i.e., building soil organic matter, reducing losses and tightening nutrient cycles, more efficient production systems and preserving native vegetation, are well aligned with goals of increasing food security and rural development as well as buffering land use systems against climate change (Lal 2004). Hence, there is growing interest in jump-starting the capacity for broad-based engagement in agriculturally-based GHG mitigation projects in developing countries.

Municipal wastewater treatment plants (MWWTPs) are considered significant artificial sources of greenhouse gas (GHG) emissions, in the forms of methane (CH4) and nitrous oxide (N2O), during their normal operations. In this study, we used an emission factor method to determine the spatial and temporal distribution characteristics of GHG emissions from MWWTPs in China during the period 2005–2014; influencing factors and uncertainties were also analyzed. Our results show that total GHG emissions from Chinese MWWTPs increased from 326.54 to 1294.03 Gg CO2‐eq between 2005 and 2014, and that regional distribution was extremely variable. During this decade, the proportion of CH4 in the total GHG emissions decreased from 74.1 to 59.4% while that of N2O increased from 25.9 to 40.6%. The observed increase in N2O was probably due to the enhancement of wastewater discharge standards for nitrogen discharge, resulting in lower waterborne but higher atmospheric levels of nitrogen oxides. Our comparison of GHG emissions from wastewater discharging directly to the aquatic environment with that treated at MWWTPs in 2014 indicate that the latter disposal method resulted in an 18‐fold drop in GHG emissions. Regional economic development level and wastewater treatment capacity were the factors most closely related to GHG emissions from MWWTPs; the per‐capita protein supply was closely related to N2O emissions.

Spatial heterogeneity of factors affecting GHG emission intensity in urban water supply and wastewater treatment systems in China

Estimations of GHG emissions from drained peatlands: Accountability in the trans-border Neman River basin.

Diversity in reservoir surface morphology and climate limits ability to compare and upscale estimates of greenhouse gas emissions

Global economic development has highlighted the issue of climate change, which is one of the most important environmental issues plaguing human beings. It is widely agreed that excessive greenhouse gas (GHG) emissions are important factors contributing to global warming. Many countries have formulated corresponding GHG emission reduction plans to deal with climate change issues. An important GHG emission source is released from sewage-sludge treatment systems. However, there has not been a comprehensive quantitative GHG emissions evaluation system in the case of sewage-sludge treatment systems, due to multiple emission sources, complex processes, and different standards. In previous studies, the Guidelines for National Greenhouse Gas Inventories (Intergovernmental Panel on Climate Change, IPCC, 2006) and Chinese Greenhouse Gas Inventory (National Center for Climate Change Strategy and International Cooperation, NCSC, 2005) were widely applied to estimate GHG emissions from sewage-sludge treatment. However, IPCC does not consider CO2 emissions from sewage treatment, and NCSC does not consider CO2 emissions from the sewage treatment and N2O emissions from sludge treatment. Therefore, the following have been conducted in this study: (1) A GHG estimation model basing on Life Cycle Thinking (LCT) was constructed, and the research objects were CH4, N2O, and CO2 that were produced by the sewage-sludge treatment system. The estimation model of CO2 and N2O, which were ignored in the IPCC report, were analyzed and discussed. The models of the GHG emission estimation were summarized and improved in the urban sewage-sludge treatment system under the different sewage-sludge treatment process scenarios. (2) The GHG emission load of major urban sewage-sludge treatment processes was analyzed, and the level and key links of environmental impacts generated by different processes were identified. This helps to understand and compare the environmental impacts of different treatment processes and provides suggestions for the sustainable development of wastewater treatment processes. (3) The GHG emission characteristics of nine scenarios of different sewage-sludge treatment processes were analyzed, and the environmental impacts caused by energy consumption and chemicals consumption were studied. Consequently, the sewage-sludge treatment process under low carbonization and low environment impact were proposed.

Globally, agriculture is directly responsible for 14% of annual greenhouse gas(GHG) emissions and induces an additional 17% through land use change, mostlyin developing countries (Vermeulen et al 2012). Agricultural intensification andexpansion in these regions is expected to catalyze the most significant relativeincreases in agricultural GHG emissions over the next decade (Smith et al 2008,Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa andAsia are predominately managed by smallholders, with 80% of land holdingssmaller than ten hectares (FAO 2012). One can therefore posit that smallholderfarming significantly impacts the GHG balance of these regions today and willcontinue to do so in the near future.However, our understanding of the effect smallholder farming has on theEarth’s climate system is remarkably limited. Data quantifying existing andreduced GHG emissions and removals of smallholder production systems areavailable for only a handful of crops, livestock, and agroecosystems (Herrero et al2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteenstudies of nitrous oxide emissions from soils have taken place in sub-SaharanAfrica, leaving the rate of emissions virtually undocumented. Due to a scarcity ofdata on GHG sources and sinks, most developing countries currently quantifyagricultural emissions and reductions using IPCC Tier 1 emissions factors.However, current Tier 1 emissions factors are either calibrated to data primarilyderived from developed countries, where agricultural production conditions aredissimilar to that in which the majority of smallholders operate, or from data thatare sparse or of mixed quality in developing countries (IPCC 2006). For the mostpart, there are insufficient emissions data characterizing smallholder agricultureto evaluate the level of accuracy or inaccuracy of current emissions estimates.Consequentially, there is no reliable information on the agricultural GHG budgetsfor developing economies. This dearth of information constrains the capacity totransition to low-carbon agricultural development, opportunities for smallholdersto capitalize on carbon markets, and the negotiating position of developingcountries in global climate policy discourse.Concerns over the poor state of information, in terms of data availability andrepresentation, have fueled appeals for new approaches to quantifying GHGemissions and removals from smallholder agriculture, for both existing conditionsand mitigation interventions (Berry and Ryan 2013, Olander et al 2013).Considering the dependence of quantification approaches on data and the currentdata deficit for smallholder systems, it is clear that in situ measurements must bea core part of initial and future strategies to improve GHG inventories and

The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO₂ and CH₄ emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 10³ t CO₂e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO₂e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO₂, of CH₄ and their equivalence in CO₂e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.

Rank-order concordance among conflicting emissions estimates for informing flight choice

Since about the 1990s China has achieved remarkable progress in urban sanitation. The country has built very extensive infrastructure for wastewater treatment, with 94.5% treatment coverage in urban areas and legally mandated nation-wide full nutrient removal implemented. However, municipal wastewater treatment plants (WWTPs) in China are still confronted with issues rooted in the unique sewage characteristics. This study compares energy recovery, cost of nutrient removal and sludge production between Chinese municipal WWTPs and those in countries with longer wastewater treatment traditions, and highlights the cause-effect relationships between Chinese sewage characteristics – high inorganic suspended solids (ISS) loads, and low COD and C/N ratio, and municipal WWTP process performance in China. Integrated design and operation guidelines for municipal WWTPs are imperative in relation to the unique sewage characteristics in China. Cost-effective measures and solutions are proposed in the paper, and the potential benefits of improving the sustainability of municipal WWTPs in China are estimated.