Search

Nitrogen oxides (NOx = NO + NO2), produced in urban areas and industrial facilities (particularly in fossil fuel-fired power plants), are major sources of air pollutants, with implications for human health, leading local and national authorities to estimate their emissions using inventories. In Qatar, these inventories are not systematically updated, while the country is experiencing fast economic growth. Here, we use spaceborne retrievals of nitrogen dioxide (NO2) columns at high spatial resolution from the TROPOMI instrument to estimate NOx emissions in Qatar from 2019 to 2022 with a flux-divergence scheme, according to which emissions are calculated as the sum of a transport term and a sink term representing the three-body reaction comprising NO2 and hydroxyl radical (OH). Our results highlight emissions from gas power plants in the north-east of the country, and from the urban area of the capital Doha. The emissions from cement plants in the west and different industrial facilities in the south-east are under-estimated, due to frequent low-quality measurements of NO2 columns in these areas. Our top-down model estimates a weekly cycle with lower emissions on Fridays compared to the rest of the week, which is consistent with social norms in the country, and an annual cycle with mean emissions of 9.56 kt per month for the four-year period. These monthly emissions differ from CAMS-GLOB-ANT_v5.3 and EDGARv6.1 global inventories, for which the annual cycle is less marked and the average emissions are respectively 1.44 and 1.68 times higher. Our emission estimates are correlated with local electricity generation, and allow to infer a mean NOx emission factor of 0.557 tNOx.GWh−1 for the three gas power plants in the Ras Laffan area.

Public charging stations provide charging services as well as parking for the growing population of electric vehicles (EVs). Effective management of these facilities is becoming crucial, with a significant proportion of drivers remaining parked even after the services are completed. This phenomenon, known as overstay, results in an underutilization of station resources and becomes a barrier to other electric vehicle drivers seeking charging services. To that end, this article presents (i) stochastic modeling of charging stations with overstaying customers, and (ii) a methodology to calculate station capacities with respect to a performance metric probability of loss of load that represents the percentage of unsatisfied demand. The station model is constructed using a two-dimensional Markov chain reflecting interactions among the idle, charging, and overstaying customers. Initially, the generalized small-scale charging station model is studied to investigate the behavior of the station parameters. Then, the general model is extended using the statistical large deviation theory to cover the case of large-scale charging stations. Effective demand, a deterministic quantity, for each charger is calculated, and station capacity is calculated in terms of the above-mentioned performance metric. The case studies demonstrate that calculating effective demand-based capacity leads to substantial savings when provisioning station resources. However, a significant proportion of these savings diminish with increasing rate of overstay customers and durations.

Vehicle mobility analysis is a critical input to make data-driven decisions to deploy electric vehicle (EV) charging infrastructures that are required for mass EV adoption. Traditionally, infrastructure planning is carried out by following manufacturer's specification on EV performance and using low-resolution data from national travel surveys. On the other hand, EV performance significantly degrades in countries like Qatar and other neighbouring Gulf States due to the hot desert climate. Moreover, the lack of public travel surveys require the creation of GPS-based mobility datasets to analyse spatio-temporal EV demand. To that end, this paper presents the first vehicle mobility dataset in the Gulf Cooperation Council (GCC) region by analysing data collected from seven vehicles (six petrol car and one EV) using telematics devices for nine months. The gathered data is processed using machine learning based clustering algorithms to reveal location analysis to examine daily activities, trip patterns, and impacts of weather on fuel efficiency. The results show that the EV driving experience is very risk-adverse due to reduced driving ranges in summer and lack of charging infrastructure. Also, home and workplace charging are well-suited for the recorded population, as the daily trip lengths are within the range of most EV models. We also report significant differences in fuel efficiency between summer and winter driving due to air-conditioning needs. The findings will shed light into GCC region's net-zero transition and decarbonise one of the most carbon-intensive economies of the world.

To support, plug-in electric vehicle (PEV) growth, there is a need to design and operate charging stations without increasing peak system demand. In this chapter, first, an overview of on-going demonstration and testing studies are presented to show the complexity of the actual charging infrastructures and uncertainties related to customer demand. Then, an analytical model for a large-scale charging station with an on-site energy storage unit is introduced. The charging system is modelled by a Markov-modulated Poisson Processes with a twodimensional Markov chain. A Matrix geometric based algorithm is used to solve steady state probability distribution to compute optimal energy storage size. Case studies are presented to show (i) the relationships between energy storage size, grid power and PEV demand and (ii) how on-site storage can reduce peak electricity consumption and the station’s monthly electricity bill.

Effects of Mentha piperita (peppermint) extract and juice on performance and immune parameters were evaluated in laying hens. A total of 252 Babcock laying hens were allocated to seven treatments with four replications of nine hens. The control hens were fed a basal diet without supplementation. Other hens were given diets supplemented with mentha extract (ME) at 50 (50ME), 100 (100ME), and 200 (200ME) mg/kg of feed or with 50 mentha juice (50MJ), 100 (100MJ) and 200 (200MJ) mg/L that was provided in the drinking water. No significant differences were detected among treatments in bodyweight, feed intake, egg mass, egg production, eggshell breaking strength, Haugh unit, and haematological and serological parameters. The ratio of gram feed to gram egg mass (feed conversion) was significantly better in the birds that received 100ME and 200ME compared with the control hens. The yolk colour index was higher in mid trial analysis (28th day). Thus, although the ME supplementation had a positive effect on feed conversion ratio and egg yolk colour at dosage rates up to 200 mg/kg, further research is needed to establish the efficacy of this herbal product and to determine the most appropriate amount to include in diets for laying hens.

Even though Qatar&rsquo, s per capita electricity consumption is one of the highest in the world, little is currently known about behind-the-meter power consumption. The residential sector is the largest consumer of electricity, accounting for approximately 59% of the overall consumption of electricity. As energy subsidies lead to budget deficits and overconsumption of carbon resources, there is a pressing need to examine the residential load profile to better understand consumption patterns and uncover potential solutions for more efficient usage. Residential load profiles are typically influenced by seasonal and socio-economic factors. Furthermore, household load profiles can be used to examine the viability of rooftop photovoltaic (PV) systems. In this study, a total of 10 houses in Qatar were chosen, and their power demand was monitored for over a year using smart energy monitors. This empirical research was conducted to achieve the following goals: (1) creation of the first high-resolution residential load profiles in Qatar and in the Gulf region, (2) analyses of the acquired load profiles and the determining factors that affect energy consumption, and (3) calculation of self-consumption values, analysis of the viability of household rooftop PV systems, and discussing potential use-cases for energy storage systems. Investigation of this topic is particularly important for Qatar as the country is adopting a sizable portion of PV systems (5% by 2021) and promotes sustainable energy options as a part of a national development strategy. Results show that there are significant differences between per-household and per-capita consumption due to factors such as electricity subsidies, household income and size, and air-conditioner type. Moreover, due to high electricity consumption, distributed energy storage units for bill management applications have limited applicability with current pricing tariffs. To the best of authors&rsquo, knowledge, this is the first study conducted in Qatar and in the Gulf region where a growing amount of interest is given to measure and improve building energy performance.

WOS: 000488458006166 …

Acute lymphoblastic leukemia (ALL) is the most common type of cancer in children. The identification of prognostic factors has become essential in design and analysis of modern therapeutic trials. The aim of this study is to present the expression patterns of lamin A/C, lamin B1 and B2 and their prognostic value in childhood acute lymphoblastic leukemias, which are thought to be related with cell proliferation and apoptosis. The study was carried out between January 2008 and March 2014 at the Cukurova University Faculty of Medicine, Division of Pediatric Oncology and sixty-four patients diagnosed with ALL and 35 control cases were included in the study. Real Time-Polymerase Chain Reaction (RT-PCR) method was used to detect lamin A/C, lamin B1 and lamin B2 protein expression at the time of the diagnosis and at the end of the induction treatment. At the time of the diagnosis, lamin B1 protein gene expression was lower in ALL patients when compared to the control group (p= 0.001). When lamin gene expression levels at the time of diagnosis and after induction therapy were compared, the lamin A/C and B2 gene expressions were significantly lower after the administration of induction chemotherapy (both values p= 0.0001). The decrease in lamin B1 gene expression was not statistically significant (p= 0.403). When the lamin expression levels in the control group were compared with the levels after induction chemotheraphy, lamin expressions were found to be significantly lower (all p values 0.0001). In childhood ALL, lamin B1 protein gene expression could be used as a diagnostic marker, while decrease in the lamin A/C and B2 protein gene expressions after induction could be used as a marker for response to therapy. Our study is the first to show in lamin protein gene expression relationship with diagnosis and prognosis in ALL in literature. Comprehensive multicenter studies are needed with a larger cohort of patients to reach more reliable results. © 2019, UHOD - Uluslararasi Hematoloji Onkoloji Dergisi. All rights reserved. TF2013LTP34, ID715 This work was supported by ‘Cukurova University Research Projects Funding Unit’ with Project number TF2013LTP34 (ID715).