Hourly Basis Research Articles

Light-induced alteration in pigment signature and growth in a tropical surf-zone phytoplankton assemblage (southwestern Bay of Bengal, India)

Abstract We present here the first study documenting the variability in growth and pigment signature with fluctuating light in a tropical surf-zone (coastal Bay of Bengal) phytoplankton assemblage. In response to an 8.5 times light cut-off for 3 days, 50% of the particulate organic matter (POC)-based growth rate (μPOC d−1) was maintained. Chlorophyll a (Chla) and light-harvesting pigment (LHP) contents exhibited a reverse trend without increasing POC contents, suggesting that the Chla-based growth rate can be biased under fluctuating light. Certain commonly occurring diatoms like Skeletonema, Leptocylindrus and Chaetoceros exhibited high light preference, whereas another diatom Thalassiosira proliferated at low light. Phytoplankton pigment signature was also monitored on an hourly basis over a day and the lowest LHP contents were associated with the highest photon flux densities, whereas the highest values were noticed during morning and evening hours. The ratio between photo-protective to LHP remained < 0.5, indicating that LHP contents were always greater than photo-protective pigment values and the cells were not severely light stressed. Iron (Fe) supplementation did not alter POC accumulation; photo-protective to LHP ratios also indicated Fe adequacy in this coastal water and the observed variability in the pigment signature was entirely attributed to variable light.

The Significance of Urban Rail Transit Systems in Mitigating Air Pollution Effects: The Case of China

Air pollution is a global problem and the transportation industry is one of its major causes, yet the impact of transportation infrastructure on air quality is little understood. It is vital to know about evaluating the effects of transportation infrastructure on air quality. The urban road has a role in defining air pollution and automobiles significantly contribute to the air quality in urban areas. The issues are a result of the automobile’s impact on air quality. China put substantial investments in its metropolitan subway networks. The information on air quality on an hourly basis, daily metrological data, and demographic profiles of various cities with significant subways from the year 2013 to the year 2014 used. This study examines the effect of substantial investment on urban air quality. The Discontinuity Based Ordinary Least Squares (DB-OLS) on data for further analysis with results are incorporated in the study. The researchers witnessed a 14% reduction in air pollution during the daytime in those not rushed cities. Researchers discovered that the impacts are less severe in urban areas with a lower population density and a higher income. The results will be less dangerous if more subways affect the air quality.

PV–battery-hydrogen plant: Cutting green hydrogen costs through multi-market positioning

Integration of renewable energy sources as one of the pillars of the power system decarbonization efforts is making a significant progress. However, large shares of renewables require additional flexibility to keep the system stable. Battery storage was identified as one of the solutions to restore the grid balance in short timeframes, from day-ahead to real time. Currently, the research community is trying to find an adequate technology for longer duration energy storage. Hydrogen, as an energy carrier, appears as a good choice for such task. Apart from hydrogen energy storage potential, it can also be used to implement power-to-gas technology able to mitigate renewable energy curtailment through the process of electrolysis. The produced hydrogen gas can be either used to partially decarbonize the natural gas grids or simply sold as hydrogen fuel.The main novelty of this paper is the creation of a mathematical model of a renewable power plant coupled with a battery storage and a hydrogen facility for trading in three day-ahead energy markets, i.e. electricity, natural gas and hydrogen, plus in the power balancing market subject to the imbalance settlement mechanism. This approach enables a long-term profitability analysis of different renewable, battery and hydrogen architectures (hydrogen energy storage, power-to-gas and their combination) and their participation in different markets.The results indicate that the battery energy storage provides balancing services to the transmission system operator almost exclusively, while it never provides balancing for its own imbalance needs, since this option is less financially attractive. The electrolyzer and the fuel cell operate at least one third of the year, depending on the observed case, and often provide a reserve. When considering the hydrogen market, the electrolyzer operates almost the entire year due to lucrative hydrogen prices. Both the battery storage and the hydrogen tank perform arbitrage in the day-ahead market, where the battery optimizes its operation on an hourly basis (short-term) and the hydrogen tank on a daily basis (medium- to long-term).

A Multi-Model Multi-Scale Approach to Estimate the Impact of the 2007 Large-Scale Forest Fires in Peloponnese, Greece

The hydrological impact of large-scale forest fires in a large basin is investigated on both a daily and an hourly basis. A basin of 877 km2 was chosen, with 37% of its area having been burnt in the summer of 2007. Five models are employed, namely SWAT (semi-distributed), GR4J, GR5J, and GR6J (lumped) for the daily time step, and HEC-HMS (semi-distributed) for the hourly time step. As SWAT and HEC-HMS implement the SCS-CN method, the change in the Curve Number (CN) from pre-fire to post-fire conditions is estimated along with the post-fire trend of CN for both time steps. Regarding the daily time step, a 20% post-fire increase in CN proved necessary for the accurate streamflow prediction, whereas ignoring this led to an underestimation of 22% on average. On an hourly time basis, CN was 95 for burnt areas after the fire, with a mildly decreasing trend after the third year and still above 90 until the fifth year. When neglecting this, peak flow is seriously underestimated (35–70%). The post-fire trend lines of CN for the two-time steps showed statistically equal slopes. Finally, GR models accurately predicted runoff while constraining one model parameter, which proved useful for the realistic prediction of other variables.

Influence of frost and local stiffness variations on the strain mode shapes of a steel arch bridge

Natural frequencies are probably the most widely used modal characteristics in vibration-based monitoring. However, they can be highly influenced by temperature and this influence can completely mask the effect of even severe damage. This translates into a necessity for time-consuming data-normalization techniques to remove the influence of temperature and identify damage. Displacement mode shapes of homogeneous material structures are less influenced by temperature, but obtaining them in a dense grid, which is required for damage localization, is cumbersome due to the large number of sensors needed. Strain mode shapes on the other hand can be insensitive to temperature variations, while obtaining them in a dense grid is possible when fiber-optic sensors such as fiber-Bragg gratings (FBG) are used. This work presents the results of the continuous monitoring of a steel railway bridge for a period of almost two years, where modal data were collected for a wide temperature range. The bridge is instrumented with eighty FBG strain sensors, multiplexed in four fibers. The natural frequencies and strain mode shapes of ten modes have been automatically identified from operational strain time histories, on an hourly basis. A clear influence of temperature on the natural frequency of most modes is identified, especially during frost periods. On the contrary, the strain mode shapes are mostly insensitive to temperature changes and only these of some higher-order modes are slightly and uniformly influenced when frost occurs. This behavior is confirmed also by a finite element model (FE) of the bridge. Furthermore, the FE model is used to investigate the influence of local stiffness changes on the modal characteristics. A clear and local change of the modal strain amplitude is observed at the location of the reduced stiffness, especially when information from all modes is combined in a sensitive damage index.

Regression model-based hourly aggregated electricity demand prediction

The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is crucial for energy and demand management. In this study, demand and its categorical features data for three years are segregated into four seasons and then fed to an efficient Machine Learning Categorical Boosting (ML CatBoost) Regressor model to predict the next year’s demand. It uses a new gradient boosting algorithm that handles categorical features adeptly. Also, this model uses a new scheme for estimating leaf values while choosing tree structure which reduces overfitting. Further, hourly electricity demand data from the Electricity Reliability Council of Texas (ERCOT western) is used as the benchmark data to evaluate the CatBoost model. Moreover, five other ML models were developed, analyzed, and tested for the same ERCOT data for predicting the hourly aggregated electricity demand. The suggested model is compared with the long short-term memory neural network (LSTM-NN) and five other ML models in terms of performance evaluation matrices. Here, one additional performance evaluation parameter, the Coefficient of variation Root Mean Squared (CV-RMSE) is evaluated in addition to the benchmark paper’s parameters. In addition, the importance of accurate prediction in the electric grid for clean energy is discussed.

Technical/economic feasibility of the adequacy of a fully renewable power system: The case study of the Sardinia island

The paper presents a technical/economic feasibility study of the adequacy of a 100% renewable power system. The study relies on a hybrid nodal/zonal linear DCOPF formulation, developed by the authors in order to study years-long time frames of power system operation on an hourly basis. The formulation sizes and operates a 100% renewable generation mix amongst the most widely used sources, such as pumped storage hydro, photovoltaic (PV), wind and biomass plants, also including electrochemical batteries (BESSs) as a further storage option. An existing medium scale power system, namely the 400/230/150 kV Sardinian transmission system, is the realistic case study. Technical/economic feasibility is investigated by means of the proposed formulation for different scenarios and operational insights are also provided.

Air Pollution Trend and Variation during a Mega Festival of Firecrackers (Diwali) in Context to COVID-19 Pandemic

The present study was conducted in Lucknow city to assess the impact of firecracker burning during Diwali, from 2 November 2021–6 November 2021 including the pre and post-Diwali days. The concentrations of PM10, PM2.5, SO2, NO2, CO, O3, benzene and toluene, were monitored from the Central Pollution Control Board site on an hourly basis. The Air Quality Index was also recorded for PM10, PM2.5, SO2 and NO2. A questionnaire survey was done with 51 doctors to know the reported complaints post-Diwali. On Diwali night the PM2.5 value reached 262 µg m−3 around 22:00 hours and the maximum value (900 µg m−3) was obtained on 5 November, reported from the Central School monitoring station. From Gomti Nagar highest PM2.5 value obtained on Diwali day was 538 µg m−3 at 23:00 hours reaching 519 µg m−3 post-Diwali. Areas belonging to the old part of the city witnessed higher variations as PM2.5 crossed 900 µg m−3, in Lalbagh and Talkatora areas. The multivariate analysis showed that on Diwali night there was an increase of 204, 386, 344 and 341 in the PM2.5 concentration reported from Gomtinagar, Central School, Talkatora and Lalbagh stations, showing that firecracker burning resulted in a significant increase in air pollution. The Toluene/Benzene ratio was mostly more than 1 indicating that toluene and benzene may be emitted from other sources as well including the mobile sources. Around 50–75% rise was seen in the number of patients post-Diwali. 57.1% of the reported cases had respiratory issues, followed by allergic reactions. The data obtained from Lalbagh, Talkatora and Central School showed that although the values remained high, a decreasing trend was seen in the AQI compared to previous years which is a good sign and may be attributed to public awareness and the ongoing pandemic making people conscious.

Coordinated control scheme for EV charging and volt/var devices scheduling to regulate voltages of active distribution networks

This paper proposes a three-stage model predictive control (MPC)-based centralized coordinated approach to schedule charging of electric vehicles (EV) and volt/var (VV) devices. The approach aims at maintaining bus voltage magnitudes and state-of-charge (SOC) of EV battery within desired limits with minimal usage of control resources and cost of electricity consumption. The first stage determines the optimal operating points of traditional discrete control devices on an hourly basis. The second stage dispatches the optimal set-points of power electronics interfaced fast devices [photovoltaic (PV) and EV inverters] every one minute. The third stage schedules charging/discharging of EV half-hourly with respect to the real-time electricity price. Furthermore, several rules are formulated to adjust the local volt/var curve of PV and EV inverters according to the voltage magnitudes. The combined central and local control approach ensures that EVs attain the desired SOC at the time of their departure from the charging station without violating the voltage limits. The proposed control approach is tested in a 33-bus distribution network and 38-bus distribution network with different operating conditions. Simulation results depict that the performance of the proposed control approach is better than uncoordinated charging in terms of reduced voltage deviations, energy loss, and control resources utilization. • A three-stage model predictive control (MPC)-based centralized coordinated approach as upper-level control. • Rules are formulated to adjust the local volt/var curve of PV and EV inverters. • Aims at maintaining bus voltage magnitudes and reach desired state-of-charge of EV battery. • Objectives are minimal usage of control resources and cost of electricity consumption. • Coordinated EV charging along with the operation of other VRD (smart inverters, OLTC).

Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions

The global agenda to increase the renewable energy share has driven many countries and entities to harness solar energy from solar photovoltaic (PV) systems. However, the power generation of PV systems is strongly affected by climate conditions. Therefore, the main objective of this study is to analyze and predict the power generation of different PV technologies under arid desert climate conditions on an hourly basis. Two areas have been considered as case studies: Adrar in Algeria and Alice Springs in Australia. A total of nine physical models and input parameter combinations from six different power plants have been used and tested for the suitability of the proposed models for predicting the power yield of PV power plants depending on solar irradiance and other meteorological variables. Then, an ensemble learning technique is applied to improve the performance capabilities of the best-fit input combinations. The results reveal that the global irradiance, ambient air temperature, and relative humidity combination are the most related to the PV power output of all technologies under all-sky conditions and provide effective and efficient performance with the proposed ensemble learning, with an estimated accuracy of over 99%.

Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources

In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.

Nutrient fluxes in the Bons Sinais Estuary (Mozambique) – sources and sinks

ABSTRACT Estuaries are the main gateway of nutrients into marine coastal waters. Human activities and biodegradation processes in coastal freshwater and mangrove swamps are the major sources of nutrients. This study examines the sources, sinks and flux rates of nitrogen (N), phosphorus (P) and silicon (Si) through the interfaces: river estuary, mangrove estuary and ocean estuary. Measurements of the concentrations of nutrients and water velocity were made near the surface on an hourly basis, and then integrated over the flood and ebb flows over the cross-sectional area and a budget estimated over the tidal cycle, at the three interfaces. The results indicated that freshwater swamps, along the river basin, and mangrove swamps were the major source of nitrate in the estuary; the urban sewage effluent was the major source of phosphate; major sources of silicate were the freshwater swamps and coastal ocean waters. Mangrove swamp showed a tendency to retain phosphate, confirming the hypothesis that mangroves are effective sewage filters and pollutant retainers. The river exported nitrate into the estuary at a rate of 59 tons per tidal cycle, and then the estuary as a whole exported nitrate into the coastal waters at a rate of 68 tons per tidal cycle. The present study contributes to the explanation of the observed high coastal productivity of the Sofala Bank and to the development of prognostic and predictive models to quantify and predict the nutrient export to – and productivity of – coastal waters.

Climate Dataset for South Africa by the Agricultural Research Council

Long-term, reliable, continuous and real-time weather and climatic data are essential for efficient management and sustainable use of natural resources. This paper describes the weather station network (WSN) of the Agricultural Research Council (ARC) of South Africa, including information on instrumentation, data retrieval and processing protocols, calibration and maintenance protocols, as well as applications of the collected data. To this end, the WSN of the ARC consists of over 600 automatic weather stations that are distributed across the country to cover a wide range of agro-climatic zones. At each weather station, air temperature, rainfall, relative humidity, solar irradiance, wind speed and direction are monitored and archived on an hourly basis. The main objective of this WSN is to archive climate information for South Africa as well as supply the agricultural community with weather data to support decision-making.

Water demand profile before and during COVID-19 pandemic in a Brazilian social housing complex

The COVID-19 pandemic has changed the way resources are consumed around the world. The relationship between the pandemic and water consumption has important implications for the management of water use and must be evaluated in depth. The main goal of this research paper is to establish a comparison between pre-pandemic and pandemic water consumption profiles for 14 social-housing buildings located in Joinville, Southern Brazil. Telemetry data from each apartment were collected on an hourly basis before and during the COVID-19 pandemic. The analysis was based on descriptive statistics on the hourly and daily water consumption in addition to its profile plots. The best probability distribution fitting was also determined. To assess the differences in water consumption due to de pandemic, the Wilcoxon-Mann-Whitney test was employed and a Generalized Linear Model with mixed effects was fitted to the data. The Lognormal distribution was shown to be the most appropriate to model the water consumption data. Due to the COVID-19 pandemic, the two daily peak consumption periods changed from 12 h to 15 h and from 19 h to 21 h. The COVID-19 pandemic also impacted daily water consumption, leading to a small, yet significant, increase in demand in the first quarter of the pandemic period.

Who is adjusting to whom?: Differences in elephant diel activity in wildlife corridors across different human-modified landscapes

The global impact of increased human activities has consequences on the conservation of wildlife. Understanding how wildlife adapts to increased human pressures with urban expansion and agricultural areas is fundamental to future conservation plans of any species. However, there is a belief that large wild free-ranging carnivores and ungulates, cannot coexist with people, limited studies have looked at wildlife movements through differing human-dominated landscapes at finer spatial scales, in Africa. This information is vital as the human population is only going to increase and the wildlife protected areas decrease. We used remote-sensor camera traps to identify the movement patterns of African elephant (Loxodonta africana) through six wildlife corridors in Botswana. The wildlife corridors were located in two different human-dominated landscapes (agricultural/urban), with varying degrees of human impact. While we found that elephants use corridors in both landscapes, they use the urban corridors both diurnally and nocturnally in contrast to agricultural corridors which were only nocturnal. Our results provide evidence for temporal partitioning of corridor use by elephants. We identified that seasonality and landscape were important factors in determining the presence of elephants in the corridors. Our findings demonstrate that elephant diel patterns of use of the wildlife corridor differs based on the surrounding human land-uses on an hourly basis and daily basis, revealing potential adaptation and risk avoidance behaviour.

Particle size distribution and particulate matter concentrations during synoptic and convective dust events in West Texas

Abstract. Dust events are an important and complex constituent of the atmospheric system that can impact Earth's climate, the environment, and human health. The frequency of dust events in the Southern High Plains of West Texas has increased over the past 2 decades, yet their impact on air quality in this region is still unclear. This is due to the fact that there is only one air quality monitoring station that measures only PM2.5 concentrations (particulate matter with an aerodynamic diameter < 2.5 µm), and there is no information on other PM sizes or the particle size distribution. The Aerosol Research Observation Station (AEROS) unit provides insight into the local variation in particle concentration during different dust events and allows for a better understanding of the impact of dust events on air quality. As this area is prone to dust events, we were wondering if dust events generated by different meteorological causes (synoptic vs. convective) would present similar particle concentrations or particle size distributions. Thus, in this project, three different dust events were measured by AEROS and compared. Each dust event originated from a different direction and lasted a different duration. One of the dust events was synoptic (10 April 2019) and two were convective (5 and 21 June 2019). Measurements of particle mass and number concentration, size distribution, and meteorological conditions for each dust event were compared. The synoptic dust event (on 10 April) was longer (12 h) and had stronger wind speed conditions (up to 22.1 m s−1), whereas the two respective convective dust events on 5 and 21 June lasted only 20 and 30 min and had lower wind speeds (up to 16.5 and 13.4 m s−1). Observation of PM based on daily and hourly values showed an impact on air quality, yet measurements based on daily and hourly values underestimate the impact of the convective dust events. Observations based on a shorter timescale (10 min) reveal the true impact of the two convective dust events. A comparison of the particle size distribution showed that all three dust events presented an increase in particles in the 0.3–10 µm size range. Comparisons of the particle concentration for particles > 5 and > 10 µm show very high values during the dust events. Some particle sizes even increase in concentration by ∼ 2 orders of magnitude compared with the time before the dust event. This leads us to speculate that the impact of convective dust events on air quality in this region is underestimated with the current (hourly basis) method.

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

The gas grid as a vector for regional decarbonisation - a techno economic case study for biomethane injection and natural gas heavy goods vehicles

This paper presents a novel method for incorporating the seasonal variations in gas demand into an assessment of the economic viability of a biomethane production and injection facility. A simulation of a gas distribution (Dx) network was built to investigate the impact of limits imposed by the gas network operator on the quantity of biomethane that can be injected. The results calculated the grid’s capacity to accept biomethane on an hourly basis over the course of a year. Scenarios of maximum, minimum and no demand at a Dx-connected compressed natural gas (CNG) filling station were computed for the 3 potential locations being investigated for the biomethane production and injection facility. This data was then used to determine a range of possible plant sizes for each potential facility location and CNG demand scenario. Next, a spatially explicit geographical information systems (GIS) model was created to map the distribution of feedstock suitable for biomethane production in the surrounding area and determine transportation distances. These two submodels fed into a techno-economic model that calculates the net present value (NPV) and levelised cost of energy (LCOE) for each configuration. Notably, the profitability of the plant was seen to increase proportionally with an increase in demand at the CNG filling station. Location 2 was determined to be the most economically viable site for the biomethane production and injection facility. The most economically competitive configurations resulted in an LCOE of 81.63 €/MWh, 83.59 €/MWh, and 83.73 €/MWh, with corresponding NPVs of M€ 9.56, M€ 3.98 and M€ 0.94, for maximum, minimum and no demand at the CNG filling station respectively. The most competitive configurations are achieved at a plant size of 115 GWh/a for the maximum CNG demand, 82.2 GWh/a for the minimum CNG demand, and 81.8 GWh/a for no CNG demand replacing 40%, 34%, and 35% of annual natural gas demand in the Dx network respectively.

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.

A Review on Cyberloafing: The Effects of Social Platforms on Work Performance

Cyberloafing is a collection of work-related behaviours in which an employee engages in electronically-mediated activities, notably through the use of the internet, that are unrelated to their job duties. The development of communication technology has become a blessing in disguise for employees’ work performance, and over the years, organisations have tried to improve work-related activities that would have an impact on their employees’ work performance and also deal with how they cope with the usability of the internet. This paper aims to review and analyse cyberloafing with regards to the use of social platforms on work performance in previous studies conducted in Saudi Arabia. The findings demonstrate that increasing educational activities on an hourly basis has a favourable impact on work performance, whereas reducing time spent on social media networks increases time spent on work-related activities and hence increases work performance. Employers should create a balance between work and leisure time for their employees to ensure more productivity as technology makes people spend more time on their devices. Hence, work-leisure policies will be crucial in improving work performance and maintaining discipline during working hours.