scholarly journals Greenhouse Gas Pollution Based on Energy use and its Mitigation Potential in the City of Surakarta, Indonesia

2020 ◽  
Vol 52 (1) ◽  
pp. 1
Author(s):  
Prabang Setyono ◽  
Widhi Himawan ◽  
Cynthia Permata Sari ◽  
Totok Gunawan ◽  
Sigit Heru Murti

Considered as a trigger of climate change, greenhouse gas (GHG) is a global environmental issue. The City of Surakarta in Indonesia consists mainly of urban areas with high intensities of anthropogenic fossil energy consumption and, potentially, GHG emission. It is topographically a basin area and most likely prompts a Thermal Inversion, creating a risk of accumulation and entrapment of air pollutants or GHGs at low altitudes. Vegetation has been reported to mitigate the rate of increase in emissions because it acts as a natural carbon sink. This study aimed to mitigate the GHG emissions from energy consumption in Surakarta and formulate recommendations for control. It commenced with calculating the emission factors based on the IPCC formula and determining the key categories using the Level Assessment approach. It also involved computing the vegetation density according to the NDVI values of the interpretation of Sentinel 2A imagery. The estimation results showed that in 2018, the emission loads from the energy consumption in Surakarta reached 1,217,385.05 (tons of CO2e). The key categories of these emissions were electricity consumption, transportation on highways, and the domestic sector, with transportation on highways being the top priority. These loads have exceeded the local carrying capacity because they create an imbalance between emission and natural GHG sequestration by vegetations.

2016 ◽  
Vol 43 (2) ◽  
pp. 140-147 ◽  
Author(s):  
XIAODONG CHEN ◽  
JENNIFER DE LA ROSA ◽  
M. NILS PETERSON ◽  
YING ZHONG ◽  
CHUNTIAN LU

SUMMARYHousehold consumption is a major contributor to global greenhouse gas emissions. Some behaviours (for example energy use and vehicle use) may have far larger impacts than others (for example green consumerism of household products). Here, the driving forces of green consumerism and two domestic energy uses (electricity consumption and vehicle fuel use) are compared. This study found that environmental attitudes predicted green consumerism, but not electricity consumption or vehicle fuel use. Furthermore, green consumerism was correlated with income and individual level demographic factors, while energy consumption was primarily predicted by household size and structural constraints. Because household energy consumption has greater environmental impacts than green consumerism, policies that aim to improve pro-environmental attitudes may not be effective in mitigating greenhouse gas emissions. Policies should rather aim to change structural constraints influencing transportation and household energy decisions and improve the conspicuousness of household energy consumption.


2021 ◽  
Vol 14 (2) ◽  
pp. 1111-1126
Author(s):  
Florian Dietrich ◽  
Jia Chen ◽  
Benno Voggenreiter ◽  
Patrick Aigner ◽  
Nico Nachtigall ◽  
...  

Abstract. In order to mitigate climate change, it is crucial to understand urban greenhouse gas (GHG) emissions precisely, as more than two-thirds of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly. We present the Munich Urban Carbon Column network (MUCCnet), the world's first urban sensor network, which has been permanently measuring GHGs, based on the principle of differential column measurements (DCMs), since summer 2019. These column measurements and column concentration differences are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city, making them a favorable input for an inversion framework and, therefore, a well-suited candidate for the quantification of GHG emissions. However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring column-averaged CO2, CH4 and CO concentrations with a solar-tracking Fourier transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This also includes software that starts and stops the measurements autonomously and can be used independently from the enclosure system. Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO2 since 2015, as well as concentration gradients between sites upwind and downwind of the city. Thanks to the automation, we were also able to continue taking measurements during the COVID-19 lockdown in spring 2020. By correlating the CO2 column concentration gradients to the traffic amount, we demonstrate that our network is capable of detecting variations in urban emissions. The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this approach for long-term greenhouse gas monitoring in urban areas.


2020 ◽  
Vol 81 (6) ◽  
pp. 1283-1295
Author(s):  
Iliana Cardenes ◽  
Jim W. Hall ◽  
Nick Eyre ◽  
Aman Majid ◽  
Simon Jarvis

Abstract Regulations to ensure adequate wastewater treatment are becoming more stringent as the negative effects of different pollutants on human health and the environment are understood. However, treatment of wastewater to remove pollutants is energy intensive, so has added significantly to the operation costs of wastewater treatment plants. Analysis from six of the largest wastewater treatment works in South East England reveals that the energy consumption of these treatment works has doubled in the last five years due to expansions to meet increasingly stringent effluent standards and population growth. This study quantifies the relationship between energy use for wastewater treatment and four measures of pollution in effluents from UK wastewater treatment works (biochemical oxygen demand, ammoniacal nitrogen, chemical oxygen demand and suspended solids). The linear regression results show that indicators of these pollutants in effluents, together with the extension of plants to improve wastewater treatment, can predict over 95% of energy consumption. Secondly, using scenarios, the energy consumption and greenhouse gas emissions of effluent quality standards are estimated. The study finds that tightening effluent standards to increase water quality could result in a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional MTCO2 per year from treating wastewater in large works in the UK.


2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Muhammad Imran ◽  
Orhan Ozcatalbas

AbstractThis study aimed to model energy use, energy efficiency, and greenhouse gas emissions in rain-fed wheat production by using a nonparametric data envelopment analysis (DEA) method. Data were collected through face-to-face interviews with 140 wheat farmers in 4 districts of Antalya Province. The energy inputs (independent variables) were human labor, seeds, chemical fertilizers, herbicides, and diesel fuel, and the energy output was the dependent variable. The results showed that the average energy consumption and the output energy for the studied wheat production system were 21. 07GJ ha−1 and 50. 99 GJ ha−1, respectively, and the total GHG emissions were calculated to be 592.12 kg CO2eq ha−1. Chemical fertilizer has the highest share of energy consumption and total GHG emissions. Based on the results from DEA, the technical efficiency of the farmers was found to be 0.81, while pure technical and scale efficiencies were 0.65 and 0.76, respectively. The results also highlighted that there is a potential opportunity to save approximately 14% (2.93 GJ ha−1) of the total energy consumption and consequently a 17% reduction in GHG emissions by following the optimal amounts of energy consumption while keeping the wheat yield constant. Efficient use of energy and reduction in GHG emissions will lead to resource efficiency and sustainable production, which is the main aim of the green economy.


Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.


2017 ◽  
Vol 30 (1) ◽  
pp. 191-214 ◽  
Author(s):  
Meryl Jagarnath ◽  
Tirusha Thambiran

Because current emissions accounting approaches focus on an entire city, cities are often considered to be large emitters of greenhouse gas (GHG) emissions, with no attention to the variation within them. This makes it more difficult to identify climate change mitigation strategies that can simultaneously reduce emissions and address place-specific development challenges. In response to this gap, a bottom-up emissions inventory study was undertaken to identify high emission zones and development goals for the Durban metropolitan area (eThekwini Municipality). The study is the first attempt at creating a spatially disaggregated emissions inventory for key sectors in Durban. The results indicate that particular groups and economic activities are responsible for more emissions, and socio-spatial development and emission inequalities are found both within the city and within the high emission zone. This is valuable information for the municipality in tailoring mitigation efforts to reduce emissions and address development gaps for low-carbon spatial planning whilst contributing to objectives for social justice.


Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5664
Author(s):  
Wenjing Wei ◽  
Peter B. Samuelsson ◽  
Anders Tilliander ◽  
Rutger Gyllenram ◽  
Pär G. Jönsson

The primary energy consumption and greenhouse gas emissions from nickel smelting products have been assessed through case studies using a process model based on mass and energy balance. The required primary energy for producing nickel metal, nickel oxide, ferronickel, and nickel pig iron is 174 GJ/t alloy (174 GJ/t contained Ni), 369 GJ/t alloy (485 GJ/t contained Ni), 110 GJ/t alloy (309 GJ/t contained Ni), and 60 GJ/t alloy (598 GJ/t contained Ni), respectively. Furthermore, the associated GHG emissions are 14 tCO2-eq/t alloy (14 tCO2-eq/t contained Ni), 30 t CO2-eq/t alloy (40 t CO2-eq/t contained Ni), 6 t CO2-eq/t alloy (18 t CO2-eq/t contained Ni), and 7 t CO2-eq/t alloy (69 t CO2-eq/t contained Ni). A possible carbon emission reduction can be observed by comparing ore type, ore grade, and electricity source, as well as allocation strategy. The suggested process model overcomes the limitation of a conventional life cycle assessment study which considers the process as a ‘black box’ and allows for an identification of further possibilities to implement sustainable nickel production.


2018 ◽  
Author(s):  
Sara Torabi Moghadam ◽  
Silvia Coccolo ◽  
Guglielmina Mutani ◽  
Patrizia Lombardi ◽  
Jean Louis Scartezzini ◽  
...  

The spatial visualization is a very useful tool to help decision-makers in the urban planning process to create future energy transition strategies, implementing energy efficiency and renewable energy technologies in the context of sustainable cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban renovation scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes is proposed, here, to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this paper is to present an innovative solution for the computation and visualization of energy saving at the city scale.The energy demand of cities, as well as the micro-climatic conditions, are calculated by using a simplified 3D model designed as function of the city urban geometrical and physical characteristics. Data are extracted from a GIS database that was used in a previous study. In this paper, we showed how the number of buildings to be simulated can be drastically reduced without affecting the accuracy of the results. This model is then used to evaluate the influence of two set of renovation solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to all cities worldwide with limited amount of information.


2020 ◽  
Author(s):  
Theresa Klausner ◽  
Mariano Mertens ◽  
Heidi Huntrieser ◽  
Michal Galkowski ◽  
Gerrit Kuhlmann ◽  
...  

<p>Urban areas are recognised as a significant source of greenhouse gas emissions (GHG), such as carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>). The total amount of urban GHG emissions, especially for CH<sub>4</sub>, however, is not well quantified. Here we report on airborne in situ measurements using a Picarro G1301-m analyser aboard the DLR Cessna Grand Caravan to study GHG emissions downwind of the German capital city Berlin. In total, five aircraft-based mass balance experiments were conducted in July 2018 within the Urban Climate Under Change [UC]<sup>2</sup> project. The detection and isolation of the Berlin plume was often challenging because of comparatively small GHG signals above variable atmospheric background concentrations. However, on July 20<sup>th</sup> enhancements of up to 4 ppm CO<sub>2</sub> and 21 ppb CH<sub>4</sub> were observed over a horizontal extent of roughly 45 to 65 km downwind of Berlin. These enhanced mixing ratios are clearly distinguishable from the background and can partly be assigned to city emissions. The estimated CO<sub>2</sub> emission flux of 1.39 ± 0.75 t s<sup>-1 </sup>is in agreement with current inventories, while the CH<sub>4</sub> emission flux of 5.20 ± 1.61 kg s<sup>-1</sup> is almost two times larger than the highest reported value in the inventories. We localized the source area with HYSPLIT trajectory calculations and the high resolution numerical model MECO(n) (down to ~1 km), and investigated the contribution from sewage-treatment plants and waste deposition to CH<sub>4</sub>, which are treated differently by the emission inventories. Our work highlights the importance of a) strong CH<sub>4</sub> sources in the surroundings of Berlin and b) a detailed knowledge of GHG inflow mixing ratios to suitably estimate emission rates.</p>


2021 ◽  
Author(s):  
Corinna Peters

This study assesses changes in mobility behaviour in the City of Barcelona due the COVID‐19pandemic and its impact on air pollution and GHG emissions. Urban transport is an important sourceof global greenhouse gas (GHG) emissions. Improving urban mobility patterns is therefore crucial formitigating climate change. This study combines quantitative survey data and official governmentdata with in‐depth interviews with public administration officials of the City. Data illustrates thatBarcelona has experienced an unprecedented reduction in mobility during the lockdown (a 90%drop) and mobility remained at comparatively low levels throughout the year 2020. Most remarkableis the decrease in the use of public transport in 2020 compared to pre‐pandemic levels, whereas roadtraffic has decreased to a lesser extent and cycling surged at times to levels up to 60% higher thanpre‐pandemic levels. These changes in mobility have led to a radical and historic reduction in airpollution, with NO2 and PM10 concentration complying with WHO guidelines in 2020. Reductions inGHG emissions for Barcelona’s transport sector are estimated at almost 250.000 t CO2eq in 2020 (7%of the City’s overall annual emissions). The study derives policy implications aimed at achieving along‐term shift towards climate‐friendlier, low‐emission transport in Barcelona, namely how torecover lost demand in public transport and seize the opportunity that the crisis brings for reform byfurther reducing road traffic and establishing a 'cycling culture' in Barcelona, as already achieved inother European cities.


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