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Accurate prediction of Reference Evapotranspiration (ET0) is essential for determining crop water requirements, significantly contributing to the effective management and sustainable planning of the world’s scarce water resources. Many statistical and Machine Learning (ML) models have been proposed for predicting ET0, but no universal algorithm has been found to be the best performance for all the diverse conditions. Therefore, this study introduces a novel hybrid modeling approach that integrates the Linear Mixed Effect (LME) model with Nonlinear Autoregressive Neural Network (NANN) and Support Vector Machine (SVM) ML models. By combining the strengths of LME for capturing linear patterns and NANN and SVM for modeling nonlinear residuals, this approach aims to minimize prediction errors and enhance accuracy under diverse conditions. For this purpose, climatic data from 1980 to 2021 from five meteorological stations located in Pakistan were utilized. Moreover, panel (i.e., Levin Lin Chu (LLC), Im, Pesaran, and Shin (IPS)) and Moving Block Bootstrap (MBB) unit root tests were applied to investigate the stationarity in the dataset. Correlation analysis showed that temperature had the highest correlation with ET0among climatic variables. Both hybrid models and the LME model were evaluated across five meteorological stations in Pakistan. Results from the Statistical assessment indicated that although both proposed models exhibited commendable performance in the prediction of ET0, the hybrid model LME-SVM exhibited a comparative advantage over the hybrid model LME-NANN. The Taylor diagram confirmed a strong positive correlation between observed (estimated by modified Hargreaves-Samani equation) and predicted ET0values for both hybrid models.

[Display omitted] Dry reforming of methane (DRM) is one of the most innovative techniques for reducing critical greenhouse gases (such as CO 2 and CH 4 , among others) by converting them into beneficial products such as syngas, which have a variety of industrial applications. Despite the substantial improvements in the DRM process, scaling up DRM still faces considerable challenges in establishing an acceptable and effective catalytic system. There have been numerous review studies on the development of catalysts for DRM that concentrate on transition, noble metal-based catalysts, and the influence of process parameters. Meanwhile, the mutually reinforcing correlations between catalytic properties and DRM activity still require further research. It is important to have a meticulous understanding of the relationship between catalytic performance and physicochemical properties in order to design a catalytic system that is both highly efficient and commercially viable. Subsequently, this review emphasizes the cooperative relationships between catalytic DRM activity and catalytic characteristics like porosity, surface area, metal-support interaction, metal dispersion, oxygen vacancies, metal particle size, reducibility, and chemical composition acidity/basicity. The techno-feasibility assessments, including the characteristics of the technology, the costs of building the plant, the costs and revenues generated, and any non-economic factors involved, are also attained through a technical, economic analysis of DRM. It also discussed the challenges and opportunities for increasing syngas production through DRM. This review will be useful for academic and professional researchers and environmentalists looking for strategies to lower CO 2 and CH 4 emissions. [ABSTRACT FROM AUTHOR]

[Display omitted] Various photocatalysts have been developed for photocatalytic water splitting—one of the most important processes that produces dihydrogen as clean energy for fuel cells. The successful achievements for this application are based mainly on transition metal oxides and some metal sulfides/nitrides. Recently, metal–organic frameworks (MOFs), a class of hybrid functional materials comprising organic backbone tethered infinitively in limitless way by metal-oxide clusters, both of which can be customized accurately at the molecular level for targeted applications, have been able to photocatalytically degrade water. Apart from representing an array of intrinsic structural and physicochemical characteristics, MOFs are well susceptible for various post-synthetic modifications to address specific challenges. Despite years of research in this field and a good number of seminal studies, further efforts should be geared toward the improvement of light absorption and stability of MOFs, which are the principal challenges that should be overcome. This review includes the most recent research that has been committed to MOFs materials for photocatalytic water-splitting applications. It also encompasses a variety of synthetic techniques and post-modifications that have been employed to increase the performance of MOFs. In addition, a brief discussion of the techno-feasibility analysis of water splitting has been offered, extending the conversation to include current challenges and future direction recommendations. The recent advancements of using MOF photocatalysts for water splitting are further described in a way that benchmark achievements and limitations are considered so that the readers can imagine the big picture in this field and pay considerable attention to future solutions. [ABSTRACT FROM AUTHOR]

[Display omitted] Catalytic hydrogenation is one of the most innovative techniques for reducing atmospheric carbon dioxide (CO 2) by converting it into beneficial products such as methanol (CH 3 OH). CH 3 OH is an alternative fuel that offers a practical, effective, and efficient solution to the energy storage problem. Despite the significant advances in the CO 2 hydrogenation process, developing an appropriate and efficient catalytic system remains a significant obstacle and challenge. Many review papers on catalyst development for CO 2 hydrogenation have been published, focusing on the influence of transition, noble metal-based catalysts, and process parameter. However, present knowledge of the mutually reinforcing correlations between catalytic properties and CO 2 hydrogenation activity has to be enhanced. It is very important to have a comprehensive understanding of the relationship between catalytic performance and physicochemical properties in order to create a catalytic system that is both highly efficient and economically viable for commercialization. Therefore, the focus of this review is on the synergistic interactions between catalytic CO 2 hydrogenation activity and catalytic properties such as porosity, surface area, metal-support interaction, metal dispersion, oxygen vacancies, metal particle size, reducibility, and chemical composition acidity/basicity. Furthermore, this review examined and compared the most up-to-date findings on the hydrogenation of CO 2 to CH 3 OH using various heterogeneous catalysts. It also discussed the challenges and prospects for improving CH 3 OH production by CO 2 hydrogenation. Researchers and environmentalists in academia and industry who are interested in finding ways to reduce CO 2 emissions will find this overview to be a valuable resource. [ABSTRACT FROM AUTHOR]

The recent advancements in carbon dioxide reduction reaction (CO2RR) have been reviewed, with a particular emphasis on the innovative design and development of electrocatalysts for CO2reduction. Additionally, the review aims to enhance understanding of the impact of transition metals morphology on CO2RR.

Bread wheat (Triticum aestivumL.) is a staple food of millions across the globe. However, wheat productivity is very low due to inappropriate sowing time, conventional tillage system, and imbalanced fertilization in semiarid regions. Thus, the objective of the study was to assess the impact of different zinc (Zn) application rates on yield and Zn contents of wheat grown under two tillage systems at three sowing times in the semiarid region. The current field experiment consisted of four (04) zinc (Zn) application rates (control 0 kg ha−1(Zn1), 5 kg ha−1(Zn2), 10 kg ha−1(Zn3), and 15 kg ha−1(Zn4)); factorially combined with two tillage systems (zero tillage (ZT), conventional tillage (CT)) and three sowing dates (SD1= 15th November (optimal), SD2= 05th December (late), SD3= 25th December (very late)). The study was conducted during the crop season of 2019–2020 and 2020–2021. The experimental results clearly demonstrated that Zn fertilization and sowing dates significantly affected the growth, yield and grain Zn content of wheat. However, different tillage systems did not significantly affect any parameter during both years of field trial. Among sowing dates, wheat crop sown on 15th November produced highest biological yield (6378 to 6676 kg ha−1) and grain yield (4663 to 4854 kg ha−1) and zinc grain content (30.8 to 32.3 mg kg−1) in comparison to wheat crop sown on 5th December, and 25th December during both years, respectively. Furthermore, zinc application at 10 kg ha−1produced higher biological yield (6771 to 7088 kg ha−1), grain yield (4951 to 5153 kg ha−1), and zinc grain content (34.2 to 35.7 mg kg−1) during both the years, respectively. Zinc application at 10 kg ha−1in wheat crop sown on 15th November seems to be a viable option to achieve higher grain yield with high grain Zn contents under semi-arid region.

Green hydrogen production from saltwater electrolysis could be aided by the use of alkaline water electrolyzers, high-temperature water electrolyzers, anion exchange membranes, proton exchange membranes, and anion exchange membranes.

In recent decades, the substantial rise in atmospheric carbon dioxide (CO2) levels has raised significant environmental concerns, such as global warming and ocean acidification. As a result, reducing carbon emissions and the interest in carbon neutrality have emerged as vital global goals to protect the environment and society. The hydrogenation of CO2to produce BTX (benzene, toluene, xylene), particularly p-xylene (PX), provides a sustainable pathway for CO2transformation toward valuable chemical feedstocks. This review presents an overview of the thermodynamics and reaction mechanisms involved in CO2hydrogenation to BTX and PX, revealing the key factors influencing product selectivity. The effects of catalyst modification methods, including silylation, silicalite encapsulation, core-shell structures, and metal modification, on the selectivity and activity of catalyst are discussed. Factors such as zeolite morphology, catalyst size, contact time, mesoporosity, acidity, and surface alkylation/methylation are analyzed for their effects on BTX and PX selectivity. Lastly, the paper outlines the current challenges and future perspectives in advancing CO2hydrogenation towards the production of BTX and PX, highlighting opportunities for further research and technological advancements in this area.

AbstractDrought is one of the most common climatic or meteorological hazards and has spatiotemporal characteristics that substantially impact the livelihoods and economy worldwide. Therefore, there is a need for efficient procedures that accurately identify spatiotemporal variability. Moreover, it is crucial to continuously assess and monitor spatiotemporal drought occurrence in a certain region to prevent unfavourable impacts. For this purpose, the current study develops a new procedure for spatiotemporal analysis of the region: The Spatio-Temporal Weighted Joint Agglomerative Drought Index (STWJADI). The STWJADI is mainly based on a weighting scheme known as the Spatio-Temporal Two-Stage Standardized Weighting Scheme (STTSSWS). In the first stage of the STTSSWS, the steady-state probabilities are computed for several stations (Astor, Bunji, Chilas, Gupis, Skardu, and Gilgit) of the Northern region of Pakistan at a 1-month time scale (scale-1) to allocate weights for various drought classes. Moreover, in the second stage of the STTSSWS, the weights are allocated based on spatiotemporal weighting characteristics to acquire new weights for the numerous drought classes in the designated region. Further, the spatiotemporal weights attained from STTSSWS are utilized to compute the STWJADI. The outcomes of the STWJADI provide efficient and comprehensive information for drought characterization in the selected region.

AbstractDrought is probably the most multifaceted environmental disaster that results from a precipitation deficiency. It perhaps has directly and indirectly potential effects on people's lives, the economy, and other environmental resources worldwide. However, for reducing the potential negative impacts of drought, the understanding and information about seasonal drought frequency and persistence are crucial for drought early warning and mitigations policies. Therefore, the current research examines the selected stations' seasonal meteorological drought frequency and persistence. For this purpose, ordinal outcomes of the current research are modelled under the set of cumulative Logit Models (CLM). The estimation of the CLM is made from the logit link function. Further, the Brant Test (BT) is used to check the parallel line assumptions. The BT substantiates that the odds ratios are the same across the several drought classes. Thereby the POM is a ubiquitous choice for the current analysis. Therefore, the Proportional Odds Model (POM) is utilized to compute the odds and Probability of Drought Persistence (PDP) in varying seasons (March, April, May (Spring); June, July, August (Summer); September, October, November (Autumn); December, January, February (Winter). Further, Standardized Precipitation Index (SPI) for a certain time scale (i.e. three-month time scale SPI-3) is mainly utilized in POM. Amid SPI and various seasons, the relationship is found significant at a 5% significance level in various stations, including Murree, Rawalpindi, Sialkot, Sargodha, Faisalabad, Bahawalnagar, Bahawalpur, Mianwali, Jhelum Multan, Khanpur, and Lahore. The potential of the current research is substantiated by twelve meteorological stations in a certain province of Punjab, Pakistan. The current research outcomes provide the direction to dynamically identify the spatial interseasonal propagation of meteorological drought. Moreover, the obtained results can be helpful in making useful policies for the early warning system, drought risk assessment, and management, and formulating the drought-reducing plans.

Earthquakes have brought fear to humans due to their unpredictable nature and vast capacity for ruination.The spatial point process plays an essential role in investigating spatial statistics and has become an active research area. This study investigates the behavior of earthquake occurrences using the Strauss point model. The intensity and spatial association among these seismic events are examined using distance-based G-function and model fitting. Dataset used in this study is the earthquakes that occurred in various parts of Khyber Pakhtunkhwa (KP), Pakistan. Results reveal that the intensity plot exhibits the clustered pattern of earthquake events. Distance-based G-function indicates that the distance from one massive earthquake (magnitude greater than 5) to another large earthquake is 1500 m, demonstrating that the large earthquakes occurred close to each other. The estimated parameters of the models suggest that the intensity of the events is inversely proportional to the distance. The intensity of the earthquake events also increases as the depth decreases. The point pattern modeling results show that the Strauss point model is competent to capture the pattern of the earthquake locations depending upon the interaction between earthquakes of different magnitude. Results disclosed that an additional exploratory variable (depth of earthquake) in the model offers performance gain as compared to the single exploratory variable. Strauss model comprehensively examines the dataset.

Citrus chilling injury causes 25% postharvest losses by shortening its storage life and lowers its market value in developed countries. In citrus fruits, chilling injury is characterized by increased moisture loss and disease susceptibility. The present study was conducted with the objective to evaluate the effects of pre-storage heat treatments (HTS: control, 5, 10, 15 and 20 min), heat treatment methods (HTM: wet heat treatment [WHT], vapor heat treatment [VHT]), and exposure temperatures (ET: chilled [2.5 °C], non-chilled [10 °C]) on citrus fruits (cv. Blood Red having sour orange rootstock) during 2015–2016 at the University of Agriculture, Peshawar, Pakistan. Sweet orange fruits and fruit discs of 10 mm diameter were simultaneously treated with WHT and VHT. The heat treated fruits and fruit discs were separated into two lots. One lot was held at non-chilling temperature (10 ± 1 °C), while the other lot was exposed to chilling temperature (2.5 ± 1 °C) for 75 days and packed in fiber board packages. The fruits and fruit discs after 75 days storage at both storage temperatures (10 ± 1 °C and 2.5 ± 1 °C) were incubated at ambient temperature for 7 days (simulated marketing time) and analyzed for different chilling injury parameters. Heat treatment periods (HTS), exposure temperature (ET) and their interactions (HTS × ET) showed significant (p ≤ 0.05) differences for the majority of the traits. The interaction of HTS × HTM was significant (p ≤ 0.05) for weight loss and surface pitting. However, heat treatment methods (HTM) and interaction of ET × HTM × HTS was significant for surface pitting and weight loss, respectively. The non-chilled (10 ± 1 °C) fruits revealed lower weight loss (6.80%), surface pitting (4.40%), disease incidence (7.07%), disease expansion (3.97 mm), ion leakage (15.77%) while the ascorbic acid increased (32.38 mg 100 g−1). However, chilling (2.5 °C) triggered the increase in weight loss (8.32%), surface pitting (16.13%), disease incidence (14.59%), lesion diameter (10.37 mm) and ion leakage (27.96%) in sweet orange. Heat treatments with WHT and VHT for 5–10 min resulted in reduced weight loss (6.07%), surface pitting (3.33%), disease incidence (2.00%), disease expansion (5.00 mm) and ion leakage (20.23%) in sweet oranges. Heat treatments with WHT and VHT for 15–20 min, accelerated the fruit senescence by increasing the weight loss (8.98%), surface pitting (15.67%), disease incidence (15.33%), disease expansion (8.33 mm) and ion leakage (23.62%). In comparison to heat treatment methods, highest weight loss (7.57%), surface pitting (11.53%), ion leakage (21.94%) and disease incidence (10.86%) were recorded in VHT, whereas WHT treatments had lowest weight loss (7.55%), surface pitting (9.00%), ions leakage (21.80%) and disease incidence (10.79%). Therefore, modest WHT pre-storage heat treatment (5–10 min) is recommended for storage of sweet orange.

AbstractDrought monitoring is a complicated issue as it requires multiple meteorological variables to monitor and anticipate drought accurately. Therefore, developing a method that enables researchers, data scientists, and planners to comprehend drought mitigation policies more accurately is essential. In this research, based on the concepts behind the calculation of the Standardized Precipitation Evapotranspiration Index (SPEI), a new drought index is proposed for regional drought monitoring: the Modified Standardized Precipitation Evapotranspiration Index (MSPEI). The potential of the proposed index is based on the estimation of Reference Evapotranspiration (ETo). Therefore, the Modified Hargreaves-Samani (MHS) equation based on fuzzy logic calibration is used to estimate ETo. The proposed index is validated on ten meteorological stations in Pakistan at a one-month time scale. Afterward, based on the Pearson correlation, the performance of the proposed index is compared with the commonly used drought index (SPEI). Results showed a significant correlation (r > 0.7) between the quantitative values of MSPEI and SPEI for all ten stations. Moreover, a modified Tjostheims coefficient is used to estimate and test the spatial correlation between SPEI and MSPEI for different drought classes. According to our findings, the association between the SW, ND, ED, EW, MW, and SD patterns of MSPEI and SPI is 0.74, 0.834, 0.673, 0.592, 0.393, and 0.434, respectively. Meanwhile, considering the significance of future drought trend detection, this research is further extended to detect the future trend of MSPEI by using the Hurst index. In accordance with the results, Bahawalnagar, Sialkot, Lahore, Kotli, and Gilgit all have HI values greater than 0.5 (0.63, 0.58, 0.56, 0.55, and 0.53, respectively). In contrast, Muzaffarabad, Skardu, and Jhelum have HI values 0.47, 0.45 and 0.38, respectively; however, HI values of 0.5 are observed at Dera Ismail Khan (DIK) and Islamabad. Therefore, this research provides a basis for developing and enhancing drought hazard characterization, encouraging researchers and policymakers to monitor and forecast regional droughts using a more accurate drought index.

•Impregnation of zinc and silver nanoparticles on cotton fiber.•The cellulose-nanoparticle composite material is further assessed for removal of metal ions from aqueous medium.•The significant efficiency of synthesized material to remove metal ions can be exploited in wastewater filtration process.

Reference evapotranspiration (ETo) is a key element of the hydrological cycle that integrates atmosphere demand and surface environment conditions. In this study, we aimed to determine the trend of ETousing nonparametric methods – Mann-Kendall and Sen's slope estimator, whereas ETois estimated using globally accepted Food and Agriculture Organization (FAO) Penman-Montieth FAO-56 (PM) method. We used ten years data (2000–2009) of 11 weather stations located in Khyber Pakhtunkhwa Province, Pakistan. We identified a decreasing trend in ETorate has been recorded mostly in winter, pre-monsoon and spring with an upward trend during autumn and red-summer. Further, the statistical analysis shows a significant decreasing trend in EToduring winter. In summary, we provide a systematic approach that may be used in agro meteorological modelling and irrigation development.

Metallic nanoparticles (NPs) are of immense significance due to supra properties especially for contaminant remediation from water. However, NPs release in ecosystem is also threatening. To overcome this prospect, iron oxide (Fe2O3) NPs were composited with cellulose fibers (FeCt) by simple co-precipitation method, and used for removal of mercury, chromium, cobalt, lead, and nickel ions from synthetic wastewater. The batch experiments determined that adsorption kinetics follow Langmuir isotherm model and best fit in pseudo second order reaction. FeCt composite material exhibited enhanced metal adsorption capacity as compared with cellulose polymer. The competitive capacity of all five heavy metals onto the adsorbents followed the adsorption order as Ni2+> Hg2+> Pb2+> Cr3+> Co2+that is also related to the nature and strength of electrostatic interaction among metal ions. The statistical inference (Hotelling’s t2statistics and t test) supports adsorption kinetics. The synthesized NPs composite material can be efficiently used for treatment of wastewater in domestic and industrial water filtration plants.

Elevation has major impact on the climate change. Interpolation of elevation at any location in Pakistan may be useful for predicting environmental parameters such as precipitation, temperature, humidity and wind speed. The locations with low elevations are more effecting global warming as compared with locations at high elevation. Present study interpolates the amount of elevation at unobserved locations using: 1) model-based ordinary kriging; 2) model-based Bayesian kriging with constant trend; 3) model-based Bayesian kriging with varying trend; 4) spatial artificial neural network. Prediction maps of elevation for complete domain are estimated along with prediction standard deviation. The results of suggested methods are compared with means of leave one take others cross validation method. It is observed from cross validation method that model-based Bayesian kriging with constant trend performs better than the other methods of predicting the amount of elevation in Pakistan.

Abstract: Spatial sampling design is concerned with the optimal allocation of samples to spatial coordinates in order to improve in a well-defined sense the estimation and prediction of spatial random fields. Unfortunately, objective functions in spatial sampling design seem to be so complicated so far that most often stochastic search algorithms are used to get these design criteria optimized. Our intention is to show that the minimization of the average kriging variance design criterion shows a mathematically tractable structure when considering the random field as a linear regression model with infinitely many random coefficients. Either the Karhunen–Loeve expansion or the polar spectral representation of the random field may be used to get such a favourable representation. Well-known convex experimental design theory may be applied then to this high dimensional cosine-sine-Bessel surface harmonics random coefficients regression model to calculate spatial sampling designs. We study a monitoring network for rainfall during the monsoon in Pakistan and consider both the optimal deletion and subsequent addition of monitoring stations from/to this network. Only deterministic optimization algorithms and no stochastic search algorithms are used for the task of network optimization. As external drift variables determining the rainfall trend wind, humidity and elevation are considered. [Copyright &y& Elsevier]

In this study, monthly average meteorological data and spatial coordinates are supposed to influence the climate variation. The geographical coordinates are transformed by using the Lambert projection method and are combined with the meteorological data. The complete data is standardised with zero mean and unit variance to remove the effect of different measurement scales. Various clustering methods are used to aggregate data, which are close in space and have similar climate. In our case, medoids clustering algorithm provides good separation and aggregation properties and if Lambert coordinates are used.

AbstractObjectives:The aim of this study was to determine the extent of knowledge that family members of intellectually disabled individuals had in relation to their siblings' psychiatric illness. All siblings lived in residential intellectually disabled housing.Method:We evaluated on three occasions (before providing information about their sibling's psychiatric illness and treatment, and one and six months after providing this information) the families' understanding of their siblings' psychiatric illness.Results:Initially families had a poor knowledge of their siblings' psychiatric illness and treatment, but this improved markedly after information was provided by a medical practitioner. This was a short-term effect, as at six months, families understanding had disimproved although not to pre-consultation levels.Conclusions:The provision of information to family members pertaining to their siblings' psychiatric diagnosis and treatment should be incorporated into the routine treatment of intellectually disabled individuals with a psychiatric illness.

In this paper, we generalize the concept of strong and reciprocal convexity. Some basic properties and results will be presented for the new class of strongly reciprocally p-convex functions. Furthermore, we will discuss the Hermite–Hadamard-type, Jensen-type, and Fejér-type inequalities for the strongly reciprocally p-convex functions.