Estimation of GHG Emissions from Water Reclamation Plants in Beijing.

BackgroundSocietal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain. Here, we examine GHG emissions for the whole production chain, with the estimated cost of carbon included as an extra cost on traits in the breeding objective of the production system.MethodsWe examined an example beef production system where economic merit was measured from weaning to slaughter. The estimated cost of the carbon dioxide equivalent (CO2-e) associated with feed intake change is included in the economic values calculated for the breeding objective traits and comes in addition to the cost of the feed associated with trait change. GHG emission effects on the production system are accumulated over the breeding objective traits, and the reduction in GHG emissions is evaluated, for different carbon prices, both for the individual animal and the production system.ResultsMultiple-trait selection in beef cattle can reduce total GHG and GHG emissions per unit of product while increasing economic performance if the cost of feed in the breeding objective is high. When carbon price was $10, $20, $30 and $40/ton CO2-e, selection decreased total GHG emissions by 1.1, 1.6, 2.1 and 2.6% per generation, respectively. When the cost of feed for the breeding objective was low, selection reduced total GHG emissions only if carbon price was high (~ $80/ton CO2-e). Ignoring the costs of GHG emissions when feed cost was low substantially increased emissions (e.g. 4.4% per generation or ~ 8.8% in 10 years).ConclusionsThe ability to reduce GHG emissions in beef cattle depends on the cost of feed in the breeding objective of the production system. Multiple-trait selection will reduce emissions, while improving economic performance, if the cost of feed in the breeding objective is high. If it is low, greater growth will be favoured, leading to an increase in GHG emissions that may be undesirable.

The aim of this study was to estimate the total greenhouse gas (GHG) emissions generated from whole life cycle stages of a sewer pipeline system and suggest the strategies to mitigate GHG emissions from the system. The process-based life cycle assessment (LCA) with a city-scale inventory database of a sewer pipeline system was conducted. The GHG emissions (direct, indirect, and embodied) generated from a sewer pipeline system in Daejeon Metropolitan City (DMC), South Korea, were estimated for a case study. The potential improvement actions which can mitigate GHG emissions were evaluated through a scenario analysis based on a sensitivity analysis. The amount of GHG emissions varied with the size (150, 300, 450, 700, and 900 mm) and materials (polyvinyl chloride (PVC), polyethylene (PE), concrete, and cast iron) of the pipeline. Pipes with smaller diameter emitted less GHG, and the concrete pipe generated lower amount of GHG than pipes made from other materials. The case study demonstrated that the operation (OP) stage (3.67 × 104 t CO2eq year−1, 64.9%) is the most significant for total GHG emissions (5.65 × 104 t CO2eq year−1) because a huge amount of CH4 (3.51 × 104 t CO2eq year−1) can be generated at the stage due to biofilm reaction in the inner surface of pipeline. Mitigation of CH4 emissions by reducing hydraulic retention time (HRT), optimizing surface area-to-volume (A/V) ratio of pipes, and lowering biofilm reaction during the OP stage could be effective ways to reduce total GHG emissions from the sewer pipeline system. For the rehabilitation of sewer pipeline system in DMC, the use of small diameter pipe, combination of pipe materials, and periodic maintenance activities are suggested as suitable strategies that could mitigate GHG emissions. This study demonstrated the usability and appropriateness of the process-based LCA providing effective GHG mitigation strategies at a city-scale sewer pipeline system. The results obtained from this study could be applied to the development of comprehensive models which can precisely estimate all GHG emissions generated from sewer pipeline and other urban environmental systems.

The increasing global demand for vegetable oils has resulted in a significant increase in the area under oil palm in the tropics during the last couple of decades, and this is projected to increase further. The Roundtable on Sustainable Palm Oil discourages the conversion of peatlands to oil palm and rubber plantations. However, our understanding of the effects on soil organic carbon (SOC) stocks and associated greenhouse gas (GHG) emissions of land use conversion is incomplete, especially for mineral soils under primary forests, secondary forests, rubber and other perennial plantations in the tropics. In this review we synthesised information on SOC stocks and GHG emissions from tropical mineral soils under forest, oil palm and rubber plantations and other agroecosystems across the tropical regions. We found that the largest SOC losses occurred after land use conversion from primary forest to oil palm and rubber plantations. Secondary forest and pasture lands showed lower SOC losses as well as total GHG (CO2, N2O and CH4) emissions when converted to oil palm and rubber plantations. However, due to the limited data available on all three GHG emissions, there remains high uncertainty in GHG emissions estimates, and regional GHG accounting is more reliable. We recommend long-term monitoring of oil palm and other perennial plantations established on tropical mineral soils on different soil types and regions on SOC stock changes and total GHG emissions and evaluate appropriate management practices to optimise production and sustainable economic returns, and minimise environmental impact.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

In this study, a comparative analysis was presented to detect the quota of urban and rural areas from total greenhouse gas (GHG) emissions in 26 selected countries of the Middle East and Central Asia (MECA) during 1994–2014. For this purpose, 18 independent variables such as land area, population characteristics, energy use and consumption, gross domestic product (GDP), CO2 emissions, etc., were considered in addition to one dependent variable of total GHG emissions. Statistical modeling to investigate GHG emissions was constructed comprising the quantitative procedures of the correlation test and clustering analysis, which can be considered as the fundamental basis of each econometric analysis. The GHG emissions from the urban (rural) sector of total countries in 2014 were obtained as 3313.4 (1135.6) Mt of CO2 equivalents, which is about 74.5% (25.5%) of the total GHG emissions (4449.1 Mt of CO2 equivalents) in the MECA region. The correlation test between GHG emissions and urban indicators revealed the significant records (R from 0.745 to 0.981) compared with rural indicators (R from 0.337 to 0.890). Based on the clustering analysis of the countries, Cluster A, comprised of three countries of Iran, Saudi Arabia, and Turkey, was categorized as countries with very high contributing to the total GHG emissions in the MECA region (~ 43.3%). The quotas of emissions from urban and rural sectors in the Cluster A were estimated as 83.1% and 16.9% from the total GHG emissions in 2014 (1921.3 Mt of CO2), while the same quotas were predicted as 73.1% and 26.9% from the total GHG emissions in 2030 (1921.3 Mt of CO2). This study carried out comprehensive research on the GHG emissions from the urban and rural areas in a crucial region of the world, which is faced with the rising growth of population, urbanization, globalization, high-energy use, and fuel consumption.

Whole-farm systems modelling of greenhouse gas emissions from pastoral suckler beef cow production systems

Abstract. Conferences, meetings and congresses are an important part of today's economic and scientific world. But the environmental impact, especially from greenhouse gas emissions associated with travel, can be extensive. Anthropogenic greenhouse gas (GHG) emissions account for the warming of the atmosphere and oceans. This study draws on the need to quantify and reduce greenhouse gas emissions associated with travel activities and aims to give suggestions for organizers and participants on possible ways to reduce greenhouse gas emissions, demonstrated on the example of the European Geography Association (EGEA) Annual Congress 2013 in Wasilkow, Poland. The lack of a comprehensive methodology for the estimation of greenhouse gas emissions from travel led to an outline of a methodology that uses geographic information systems (GIS) to calculate travel distances. The calculation of travel distances in GIS is adapted from actual transportation infrastructure, derived from the open-source platform OpenStreetMap. The methodology also aims to assess the possibilities to reduce GHG emissions by choosing different means of transportation and a more central conference location. The results of the participants of the EGEA congress, who shared their travel data for this study, show that the total travel distance adds up to 238 000 km, with average travel distance of 2429 km per participant. The travel activities of the participants in the study result in total GHG emissions of 39 300 kg CO2-eq including both outward and return trip. On average a participant caused GHG emissions of 401 kg CO2-eq. In addition, the analysis of the travel data showed differences in travel behaviour depending on the distance between conference site and point of origin. The findings on travel behaviour have then been used to give an estimation of total greenhouse gas emissions from travel for all participants of the conference, which result in a total amount of 79 711 kg CO2-eq. The potential for reducing greenhouse gas emissions by substituting short flights with train rides and car rides with bus and train rides is limited. Only 6 % of greenhouse gas emissions could be saved by applying these measures. Further considerable savings could only be made by substituting longer flights (32.6 %) or choosing a more central conference location (26.3 %).

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

Wastewater treatment plants (WWTPs) using membrane bioreactor (MBR) technology have been considered a significant source of greenhouse gas (GHG) emissions. This study chose a small-scale wastewater treatment plant using MBR technology to estimate its potential for GHG emissions. The total GHG emissions from this wastewater treatment plant ranged from 2,802 to 11,946kg CO2 -eq/month within the 4-year study period, and they were mainly attributable to electricity consumption (79.94%) followed by chemical usages (17.13%) and on-site GHG emissions (2.93%). The on-site GHG emissions varied monthly, but most of them ranged from 80 to 160kg CO2 -eq/month. The aeration tank was an important operating unit for GHG emissions. Off-site GHG emissions mainly came from carbon dioxide (CO2 ) emissions resulting from electricity consumption. The results of this study provide useful information about the potential of GHG emissions from WWTPs using MBR technology and indicate that WWTPs can be sustainably managed. PRACTITIONER POINTS: Wastewater treatment plants have been considered a source of greenhouse gas emissions. Total greenhouse gas emissions from the wastewater treatment plants using membrane bioreactor were mainly attributable to electricity consumption. On-site greenhouse gas emissions were relatively insignificant in this study.

Wastewater treatment systems contribute significantly to anthropogenic greenhouse gas emissions. The main greenhouse gases emitted during the wastewater treatment processes are methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2). Sequential batch reactor (SBR) is a type of an activated sludge process, and due to its high efficiency, currently, this is the preferred technology for the construction of new wastewater treatment plants (WWTPs). This study presents the estimation of greenhouse gas (GHG) emissions from SBR domestic wastewater treatment plants in Navi Mumbai, Maharashtra. We estimated direct emissions from wastewater treatment processes as well as indirect emissions due to energy usage during the treatment process. A total emission of ~35 kt CO2-eq/year was estimated for six SBR-based WWTPs having combined treatment capacity of 474 MLD. All except one of these plants were well managed. In the case of not so well-managed SBR plants, significant methane production occurs during the treatment process. In the long run, if these plants are not well managed, the emission could increase by three to fourfolds for the same treatment capacity. In either case, major GHG emissions are due to CH4 emission during the treatment process. The contribution of N2O is negligible towards total GHG emissions.

Studies on the sustainability of crop production systems should consider both the carbon (C) footprint and the crop yield. Knowledge is urgently needed to estimate the C cost of maize (Zea mays L.) production in a continuous monoculture or in rotation with a leguminous crop, the popular rotation system in North America. In this study, we used a 19-year field experiment with maize under different levels of synthetic N treatments in a continuous culture or rotation with forage legume (Alfalfa or red clover; Medicago sativa L./Trifolium pratense L.) or soybean (Glycine max L. Merr) to assess the sustainability of maize production systems by estimating total greenhouse gas (GHG) emissions (kg CO2 eq ha−1) and the equivalent C cost of yield or C footprint (kg CO2 eq kg−1 grain). High N application increased both total GHG emissions and the C footprint across all the rotation systems. Compared to continuous maize monoculture (MM), maize following forage (alfalfa or red clover; FM) or grain (soybean; SM) legumes was estimated to generate greater total GHG emissions, however both FM and SM had a lower C footprint across all N levels due to increased productivity. When compared to MM treated with 100 kg N ha−1, maize treated with 100 kg N ha−1, following a forage legume resulted in a 5 % increase in total GHG emissions while reducing the C footprint by 17 %. Similarly, in 18 out of the 19-year period, maize treated with 100 kg N ha−1, following soybean (SM) had a minimal effect on total GHG emissions (1 %), but reduced the C footprint by 8 %. Compared to the conventional MM with the 200 kg N ha−1 treatment, FM with the 100 kg N ha−1 treatment had 40 % lower total GHG emissions and 46 % lower C footprint. Maize with 100 kg N ha−1 following soybean had a 42 % lower total GHG emissions and 41 % lower C footprint than MM treated with 200 kg N ha−1. Clearly, there was a trade-off among total GHG emissions, C footprint and yield, and yield and GHG emissions or C footprint not linearly related. Our data indicate that maize production with 100 kg N ha−1 in rotation with forage or grain legumes can maintain high productivity while reducing GHG emissions and the C footprint when compared to a continuous maize cropping system with 200 kg N ha−1.

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.

Disaggregated greenhouse gas emission inventories from agriculture via a coupled economic-ecosystem model

본 연구에서는 산재되어 있는 부산항 입출항 선박의 개별 활동도(정박 접안 특성) 및 선박제원 정보를 기존 항만운영정보시스템(PORT-MIS) DB에 연계 구축하기 위한 방법론을 제시하고, 연계 구축된 3가지 DB를 이용하여 18개월(2009.01~2010.06) 동안 부산항에 입출항한 선박의 온실가스 배출량을 산정하여 그 결과를 비교 분석하였다. 본 연구에서는 선박의 기본 활동도 변수만을 포함하고 있는 저해상도의 L-PORT-MIS DB에 각 선박의 정박시간 자료를 추가하여 중해상도의 M-PORT-MIS DB를 연계 구축하였으며, 각 선박의 온실가스 배출량에 직접적인 영향을 주는 엔진출력 등과 같은 선박제원 정보를 연계시켜 고해상도의 H-PORT-MIS DB를 구축하였다. 각 활동도 DB를 이용한 선박의 온실가스 배출량 산정결과, 선박 활동도의 해상도가 높아질수록 총 온실가스 배출량은 감소하는 것으로 분석되었다. 구체적으로 저해상도 및 중해상도의 선박 활동도 자료를 이용할 경우에는 과거에 집계화된 정박 및 접안 특성에 의존하여 온실가스 배출량이 과다 산정되는 반면, 고해상도의 선박 활동도 자료를 이용할 경우에는 각 선박의 개별 접안 정박 특성과 엔진출력이 고려되는바 H-PORT-MIS DB를 이용한 선박의 온실가스 배출량은 보다 신뢰성 높은 추정치로 판단된다. 이처럼 부산항을 입출항하는 개별 선박의 특성을 반영하여 온실가스 배출량을 산정했을 경우 그 추정치는 기존 추정치와 매우 달라질 수 있어 실효성 있는 온실가스 저감대책 수립을 위해서는 본 연구에서 제안한 DB의 연계 구축이 시급하다. This study presents the linkage method combining the existing Port Management Information System (PORT-MIS) DB with the scattered vessel activity data sets including the hotelling and maneuvering characteristics and specification information of the vessels arriving and departing from the port of Busan from January 2009 to June 2010. By linking the data sets, this study made three types of vessel activity databases: L-PORT-MIS DB with low-level vessel activities, M-PORT-MIS DB with medium-level vessel activities such as hotelling time, H-PORT-MIS DB with high-level vessel activities such as hotelling time, engine power, etc. The greenhouse gas (GHG) emissions estimation results show that total GHG emissions decreases when the detailed vessel activities are employed. This decrease in the total GHG emissions by the level of vessel activities implies that the GHG emissions from the low and medium level vessel activities are overestimated due to the aggregated hotelling/maneuvering times and speeds resulting from the past vessel specifications. Therefore, the GHG emissions using the H-PORT-MIS DB are more reliable GHG emission estimates in that the vessel specifications and the observed hotelling time of each vessel are employed in the estimation process. Hence, the high-level vessel activity dataset should be constructed to implement more suitable countermeasures for reducing the GHG emissions in the port of Busan.

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N(2) O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO(2) ) and methane (CH(4) ) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N(2) O emissions (a factor of 3) but not in the CH(4) emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N(2) O emissions, large variability (3-6 ton CO(2) e day(-1) ) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4 ton CO(2) e day(-1) ). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH(4) emissions (about 5%), but significantly decreased N(2) O emissions in the water line (by 14%).