Emission Load Research Articles

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region

The Gaussian-based dispersion model American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is being used to predict concentration for air quality management in several countries. A study was conducted for an industrial area, Chembur of Mumbai city in India, to assess the agreement of observed surface meteorology and weather research and forecasting (WRF) output through AERMOD with ground-level NOx and PM10 concentrations. The model was run with both meteorology and emission inventory. When results were compared, it was observed that the air quality predictions were better with the use of WRF output data for a model run than with the observed meteorological data. This study showed that the onsite meteorological data can be generated by WRF which saves resources and time, and it could be a good option in low-middle income countries (LIMC) where meteorological stations are not available. Also, this study quantifies the source contribution in the ambient air quality for the region. NOx and PM10 emission loads were always observed to be high from the industries but NOx concentration was high from vehicular sources and PM10 concentration was high from industrial sources in ambient concentration. This methodology can help the regulatory authorities to develop control strategies for air quality management in LIMC.

The impact of combining data sets of fluorescence excitation - emission matrices of dissolved organic matter from various aquatic sources on the information retrieved by PARAFAC modeling

Despite that fluorescence spectroscopy coupled with Parallel Factor Analysis (PARAFAC) has been widely used in the investigation of Fluorescent Dissolved Organic Matter (FDOM) in aquatic systems, the proper performance of PARAFAC analysis on datasets originating from various sources is not to be taken for granted. In this study, we examine the impact of the co-analysis of datasets from various natural water systems located in the same geographical region in the Eastern Mediterranean Sea. For this purpose three datasets were formed representative of open sea waters (SW), rivers and streams (RV) and lagoons (LG). The Excitation Emission Matrices (EEMs) derived from fluorescence analysis were subjected to individual PARAFAC analysis per dataset as well as combined analyses i.e.: SWRV, SWLG, RVLG, ALL (SW-RV-LG). We evaluated the reliability of the components that were validated in the combined models through the investigation of model’s residuals and components correlation. We also assessed the similarity of the common identified components among models in regards of: (a) spectral position, by calculating the Tucker congruence coefficient (TCC) of the excitation and emission loadings of the PARAFAC components, and (b) fluorescence intensity, through regression analysis of Fmax, among models. Our analysis showed that for natural waters of various sources within the same geographical region, combined PARAFAC modeling can have both negative as well as positive impact. In the case of the combined SWLG and RVLG models, the PARAFAC analysis was able to resolve the fulvic component that was initially observed only in the LG dataset and thus a new component for SW and RV datasets was resolved. The fulvic-like component was actually identified for the first time in the open sea using the combined datasets. Moreover in the combined SWRV analysis tyrosine-like component was resolved which was found initially only in the RV dataset. Contrary, tyrosine-like component was lost in the combined RVLG dataset. We also show that the resolution of extra components in a combined analysis is not always a good fit for the dataset and the model should be assessed in terms of residuals prior acceptance. Finally, our study proposes that the similarity of the common components between combined and individual models is largely dependent on the similarity between the components of the individual models and that the estimation of the Fmax of a component is probably less affected by data diversity compared to the estimation of its spectral position.

Development of Vulnerability Framework for Assessing the Air Pollution-Related Health Impacts in Four Districts of Delhi, India

Air pollution and its deleterious public health risks are a major concern across the world. It is one of the leading health risk factors causing high mortality and disease burden in many cities. Epidemiological risk assessment studies have generated considerable evidence of disease burden due to air pollution. Such evidence proves useful as inputs for framing various air quality management and public health protection policies. Implementation of these policies also necessitates a critical insight from a perspective of differential population vulnerability to air pollution. Hence, the present study proposes a methodological approach for developing an integrated vulnerability assessment framework that focuses on multidimensional aspects adhering to vulnerability in nexus with health risk characterization. The designed framework has been applied in a pilot case study undertaken in four districts of Delhi, India. Weightage has been multiplied by a normalized score for each indicator to calculate the vulnerability score. The score reflects relative vulnerability level based on the components – emission load, exposure, land use–land cover features, sensitivity and coping capacity. Findings of the study have shown maximum score for East Delhi and minimum score for New Delhi. Hence the study throws light on the factors that are contributing towards the vulnerability of human health to air pollution.

Deep decarbonization in Northeastern North America: The value of electricity market integration and hydropower

In several countries, electricity systems are under strong decarbonization pressure. In particular, the Canadian provinces of Quebec and Ontario as well as the states of the northeastern United States have committed to cut their greenhouse emissions by more than 70% (with respect to emission levels of 1990). Increased collaboration and integration between jurisdictions could decrease such decarbonization costs, especially when important hydropower resources are available.Using a capacity expansion and dispatch model of the Northeastern North American electricity sector, we analyze the impact of emission reduction targets, load levels and availability of power technologies in a range of scenarios, in order to assess the benefits of regional cooperation. Our results show that for deep decarbonization, the electricity system costs can be significantly reduced through integration, especially by adding more interconnection capacity. These costs savings and benefits are however not evenly allocated between jurisdictions, creating potentially difficult collaboration incentives.

A memetic algorithm based on two_Arch2 for multi-depot heterogeneous-vehicle capacitated arc routing problem

With the rapid growth in the number of motor vehicles, traffic pollution has become an increasingly serious problem, due to high carbon emission and low load utilization rate. It is reasonable to plan vehicle driving routes on urban roads to deliver services, (i.e., to tackle the capacitated arc routing problem (CARP)). Previous studies on CARP have typically considered at most two objectives simultaneously; however, two objectives are not sufficient to solve the pollution problem. Consequently, a many-objective optimization model of multi-depot heterogeneous-vehicle CARP is constructed in this study that considers four objective functions: total cost, makespan, carbon emission and load utilization rate. Furthermore, based on the ideas of two-archive and information entropy, we propose a memetic algorithm based on Two_Arch2 (MATA) to tackle the constructed model. The experimental results show that MATA effectively optimizes the many-objective model and solves the problem.

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

ANALYSIS OF REDUCING CO2 EMISSIONS USING SPIRULINA MICROALGAE

Greenhouse Gases (GHG) consists of various types of gases that are produced either naturally from the environment or from the activities of living things, some examples of the dominant GHGs are water vapor, carbon dioxide (CO2), methane (CH4), nitrogen oxides (NOx) and Sulfur Oxide (SOx), the largest contributors to GHG emissions are in the Energy sector, amounting to 175.62 million tons of CO2. Microalgae are the most primitive plants, can grow in low water quality with the availability of adequate nutrients and sunlight. The amount of CO2 that can be absorbed by 1 kg of dry spirulina is 1.83 kg of CO2. In addition, Spirulina Platensis can tolerate gas content of SOx, NOx and CO2 whose concentrations are <12%. This study aims to determine the process of utilizing CO2 gas emissions from power plant for the cultivation of Spirulina Platensis microalgae at PT. Indonesia Power UPJP Perak Grati. Based on the research results, the average emission load value generated from power plant, especially HRSG 1.1, is 10,403.31 tons CO2/ month on average. The temperature factor has a significant correlation with the growth of microalgae cells with an inverse correlation. Based on the tests carried out to determine the relationship between changes in the flow rate of CO2 in microalgae cultivation ponds to the growth of microalgae cells, it was found that the addition of CO2 in the cultivation pond with a flow rate of 1 L/ minute had a greater effect than other treatments. The amount of CO2 absorption by microalgae installations with a flow rate variation of 1 liter CO2/ minute is able to absorb 0.2766 tons of CO2/ month, or is only capable of <1% of the average emission load of HRSG 1.1 per month.

Study on Acoustic Emission Characteristics of Low-Temperature Asphalt Concrete Cracking Damage.

In this study, asphalt concrete specimens were subjected to a semicircle bending test at −10 °C to simulate the process of the development of cracks in asphalt concrete at low temperature. The acoustic emission parameters were collected during the test, the variation characteristics of acoustic emission parameters were analyzed, and the peakedness value was introduced to evaluate the damage of asphalt concrete. The dynamic evolution of fracture development was analyzed by periods with acoustic emission source location. The results indicate that the damage of asphalt mixtures shows an obvious brittle characteristic at low temperature, acoustic emission signals mainly originate from the crack damage caused by tensile stress, and the strength and number of signals can reflect the degree of crack development. Based on acoustic emission parameters and load curves, the cracking damage of asphalt concrete at low temperature in this study can be divided into three periods: a calm period, a stable development period, and a rapid fracture period. The crack point occurred and propagated upward rapidly in the rapid fracture period. During this period, acoustic emission parameters such as ringing count, acoustic emission energy, and amplitude increased suddenly; furthermore, the peakedness value reached its peak in this period and corresponded well with the low-temperature damage of asphalt concrete. Acoustic emission source location technology can track position of crack points and the propagation path of cracks, reflecting the dynamic evolution process of asphalt concrete crack damage at low temperature.

Parametric design of turbocharger rotor system under exhaust emission loads via surrogate model

The common failure modes of a turbocharger rotor are bearing wear, impeller-casing rub events and shaft cracks due to high thermal currents. A reliable design of such rotors is therefore essential in order to avoid the vibration-based failure incidents during operation. The dynamic response gives vital information about the rotor condition as well as it helps in the inverse modelling to predict the optimum parameters. Present work deals with dynamic analysis and optimum design of a turbocharger rotor system subjected to ideal exhaust gas emission loads. The exhaust emission loads at the turbine in radial direction are modelled as Muszynska’s nonlinear seal forces, while the axial gas emission loads are treated as periodic blade passing excitations. The critical vibration characteristics of the rotor system running at uniform speed are analysed using the finite element model by considering nonlinear floating ring bearing forces and are initially validated with an experimental work performed on a turbocharger test-rig. Furthermore, the effects of gas load as well as floating ring bearing parameters on the critical frequencies and amplitudes are studied in detail. The effective input–output relationships are established using counter-propagation neural network (CPNN) learning scheme. Surrogate model using an improved Cuckoo search optimization in-conjunction with trained CPNN model in function evaluations is employed to predict the optimum values of the bearing parameters and relative emission load data. The relative output frequency response with optimized parameters is presented and effectiveness of the approach over the finite element based function evaluation scheme is illustrated.

Emission and Mass Load of Artificial Sweeteners from a Pig Farm to Its Surrounding Environment: Contribution of Airborne Pathway and Biomonitoring Potential.

An investigation was conducted by determining artificial sweeteners (ASs) in 80 samples from various environmental matrices, including dry deposition, rainfall, soil, leaf, and bark samples around a pig farm in Tianjin, China. Saccharin, cyclamate, and acesulfame were predominant in dry deposition and rainfall samples. Spatially, the distribution of ASs showed a consistent trend of farm center > downwind sites > upwind sites > reference site. The annual total mass loads of saccharin (70%), cyclamate (25%), and acesulfame (5%) via dry deposition and precipitation within a 5 km radius of the pig farm were estimated at 3.9 and 6.2 kg in the average-case and worst-case scenarios, respectively, accounting for 12-18% of the overall emission, indicating that pig farms are a significant source of ASs to the atmosphere and to the vicinal environment via dry and wet deposition. The distribution trends of ASs in tree bark and leaves were similar and tree bark performed better in passively biomonitoring the AS contamination. Overall, pig farms were predicted to release 65-114, 22-38, 2.0-3.5, and 0.6-1.1 tons by feed application in China, Europe, Latin America, and North America, respectively, to the vicinal environment via dry deposition and precipitation.

The Potential to Reduce Pollutant Emissions from Individual Household Sector by Involving Citizens as Project Stockholders

Atmospheric pollutants come from many sources, mainly from industry and transport as well as from households. Each sector is characterized by different specificity that concerns both the volume of emissions and the type of pollutants emitted. Each of them has also different potential to reduce the load of emissions. The purpose of this article is to show the role and potential of individual households in the fight to improve air quality. The article also aims at showing that by raising public awareness and changing habits of individual users of central heating installations, it is possible to reduce the amount of pollutants emitted from this sector. An important part of the work is a summary of a survey conducted among 150 residents of Wroclaw. The study entitled Awareness of residents of the city of Wroclaw about the sources of atmospheric pollution contained 27 questions regarding general knowledge and air quality in Wroclaw.

Emission and ecological risk of pharmaceuticals and personal care products affected by tourism in Sanya City, China.

In recent years, concern around the impact of pharmaceuticals and personal care products (PPCPs) on the environment has grown. In order to investigate the influence of tourists on emissions and ecological risk of PPCPs, the concentrations of thirty PPCPs were measured in influent and effluent from the four largest wastewater treatment plants, as well as surface river water at six sites in a famous tourist city (Sanya City, China). Substantial increasing trends on PPCPs levels were observed from low to high tourist season (ng/L to µg/L, or µg/L to mg/L). Caffeine (CAF) was dominant with concentrations as high as 185µg/L. Emission load per capita was estimated to explore the contribution of different populations. Tourist migrant population might be a dominant contributor, as they were mostly elderly people who took long-term medication. The predicted no-effect concentration was derived using the species sensitivity distribution method to calculate the ecological risk quotient (RQ) of the dominant PPCPs. Additionally, RQs of seven dominant PPCPs in rivers were > 1, indicating high chronic ecological risk for freshwater ecosystems. The results of this study will assist in raising the awareness and improving management of emerging pollutants in less industrialized regions.

Implications of Denmark’s Water Price Reform for Riverine and Coastal Surface Water Quality

Article 9 of the EU’s Water Framework Directive suggests that Member States should provide “adequate incentives” for efficient use of water resources. Although the Directive is mainly about protecting the ecological quality of water bodies, control of quantity serves as an “ancillary element” in delivering on the objectives. Despite their financial difficulties, Member States have been slow to bring their policies on water pricing up to the wording and 2010 deadline of the Directive’s article 9.
 This Article explores the significance of water pricing reform for the ecological quality objectives for surface waters and, as a stepping stone in this analysis, for water resource efficiency. It does so with a catchment-based analysis of implications from water pricing reform introduced in the early 1990s in Denmark. Household water use is found to have been 50% higher per capita before the reform, which introduced full-costpricing and a water supply tax.
 Good data availability for the catchment allows the analysis to demonstrate estimates for the improvements in water flows as well as for a specific water quality parameter. Despite the significant reduction in water demand, the main river is affected only at the margin. For smaller streams and brooks, however, there are more notable impacts for water quality and with potential benefits for rare species dependent on clean waters. A small reduction in emission loadings to coastal waters has comparatively high economic value. The Article finds that water pricing has an important role to play for future management.

Analytical study of carbon dioxide equivalent emission from agricultural drain surfaces — a case study from Egypt*

AbstractMethane emission is usually converted into equivalent carbon dioxide (CO2e) in order to compare it with other sectoral emissions. Estimation of methane from ‘agricultural drain surfaces’ is not accounted for in most of the derivation methods. This research paper dealt with the estimation of CO2e emission from ‘surfaces of the agricultural drains network in Egypt’ and then estimated the projected future CO2e emission loads trend for the period 2046–2065. CO2e emission was estimated using general circulation models for climate change under expected temperature variation in the coming decades. Annual average total CO2e emission loads from the BHD surface and its surrounding areas to the atmosphere were about 3790 t eq. CO2e yr‾¹ for the period 2046–2065. The ‘additional’ total emission of CO2e from the agriculture drainage network surface in relation to total emissions from the agricultural sector in Egypt was about 13.8%. It also accounted for about 1.4% of overall CO2e emission in Egypt. This research paper, furthermore, developed a methane indicative emission map for the Egyptian agricultural drainage network for the period 2046–2065, through which it is possible to estimate CO2e emission loads at any segment on any agricultural drain in Egypt during the period 2046–2065.

Emission Estimation and Fate Simulation of Dichlorvos in the Dongjiang River Watershed

The application of pesticides and their constant inputs have resulted in the universal contamination of the environments within watersheds, and it is essential to assess the level of contamination. Compared with traditional monitoring methods, models coupled with GIS show more obvious advantages. However, the difficult access to application scenarios and emission data of pesticides hinder the modeling and assessment of the pesticides used in watersheds. We established an emission estimation method of pesticides, which was based on the planting scenarios and pesticide application standards of different crops in administered regions and by situational analysis and mathematical deduction, the usage of each sub-basin was able to be derived. Then, by using the aforementioned results as source input data, a semi-distributed watershed hydrological, SWAT (soil and water assessment tool) model, was used to simulate the fate of dichlorvos in the Dongjiang River watershed as an example. The verification results show that the comparisons of magnitude between the simulation and the observations were almost within 1, indicating that the SWAT model, which is based on a source input estimation, was able to assess the environmental fate of pesticides in watershed effectively. Also, the modeling results indicated that the annual emission load of the dichlorvos in the watershed accounted for 3.72% of the usage. Moreover, the losses due to degradation and other reactions within the channels reach 2.35%. Most reaches of the Dongjiang River watershed have mass concentrations over 0.1 μg·L-1, demonstrating that certain safety concerns exist.

Estimasi Emisi Kendaraan Bermotor dan Tampakan Lumut Kerak untuk Memprediksi Paparan Traffic-Related Air Pollution pada Sekolah Dasar di Kota Kendari

Increasing the number of vehicles in Kendari could has the potential to increase TRAP exposure in elementary schools located on the side of the highway. The purpose of this study was to describe the emission load of vehicles, the appearance of lichens and to predict exposure to TRAP in Sekolah Dasar Negeri (SDN) on the side of the highway in Kendari City. This study uses a quantitative approach, survey methods with sampling techniques in three different locations. The results showed that the emission burden of vehicles at SDN 6 Kendari and SDN 25 Kendari was higher, the percentage of lichens present was lower and the appearance was duller than SDN 92 Kendari. This condition can be predicted as an initial indication of a decline in air quality at that location due to periodic exposure to TRAP.

The development of software for calculating green open space adequacy to absorb CO2 in Bangkalan city

Bangkalan Regency, located at the west of Madura Island covering the area 1,260.14 Km2, has developed tremendously in some recent years, particularly in the post-development of Suramadu Bridge. To anticipate detrimental effects on the environment due to Bangkalan Regency improvement, the researchers developed software for calculating total CO2 emissions of vehicles that could be absorbed by Green Open Space (GOS) in this city. The software was developed through Hypertext Preprocessor (PHP) based on the results of empirical study upon emission load and total trees calculation required for absorbing emission residue of vehicles. The software yielded from this research was successfully developed as indicated by the appropriateness of results between manual calculation and software one.

Carrying capacity of air quality analysis as part of air pollution control strategy in Kendal Industrial Estate, Kendal Regency, Central Java

The development of the industrial sector in Indonesia produces benefits and carries risks, one of which is air pollution. Industrial estates play an important role as economic growth sources with their emissions should have cared about carrying air quality capacity in their area. The projected carrying capacity of air quality is predicted using a simple box model with the emission factor using IPPS. Historical ambient air quality measurements in this area were also collected. Based on the calculation, results show the constructed industries emission load of SO2, NO2, CO, VOC, and TSP 109 tons/year, 128 tons/year, 42 tons/year, 1263 tons/year, and 2008 tons/year. To comply with the ambient air quality standard, the maximum number of industries that can be accommodated in those areas is 190 industries. The critical parameter for violating ambient air quality standard is TSP. Thus, air pollution control strategy should be focused on reducing TSP emission load.

Research on low-carbon power planning with gas turbine units based on carbon transactions

In the context of low carbon economy, introducing carbon trading and developing low-carbon energy generation is an important means to realize low-carbon development of the power system. Because gas power generation has the advantages of high efficiency, low carbon emission and strong peak load capability, the gas generator unit is added to the planning plan and a low carbon power planning model based on carbon trading is established. The goal of the model is to minimize the cost of the system integration. The cost includes investment operation cost and carbon transaction cost. And the natural gas supply constraints and carbon trading constraints are increased in the constraint condition. Finally, the discrete bacterial colony chemotaxis algorithm is adopted to solve this model. Through the model comparison and sensitivity analysis, it is concluded that the addition of gas turbine unit and carbon trading mechanism can optimize the power supply structure, promote the construction of low carbon unit. and realize the conclusion of low carbon emission reduction of power system. And the results verify the effectiveness of the proposed power planning model.