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Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR(τα)) of the T-Gr nanofluids were increased significantly.

The impact of different climate scenarios, drought, and water level management on the outflow water quality of peatlands has been investigated. A mesocosm experiment has been conducted within climate control chambers to simulate current (2016–2019 real-time) and future representative concentration pathway (RCP) climate scenarios (RCP 2.6, 4.5 and 8.5) for the peatland mesocosms. To assess the efficiency of a management strategy for improving peatland water quality, water level adjustment was applied to half of the system at the same time for each climate scenario. Furthermore, the mesocosm experienced the 2018 European drought during the simulation years, and the corresponding impact was analyzed. The results of this study indicated that the effect of water level but not climate change on the water quality of peatland mesocosms was significant. The effect of water level management was the largest for ammonium (NH4-N) and five-day biochemical oxygen demand (BOD5), and the smallest for total phosphorus (TP). Drought had a strong impact on chemical variables, increasing their concentration and deteriorating the water quality of peatland outflow. The results highlight the need for water level management in stabilizing nutrient levels in peatland outflows, especially for the warmer climate scenarios to mitigate the negative consequences of global warming.

Capillary suction time (CST) and specific resistance to filtration (SRF) are widely accepted measurements to test for sludge dewaterability properties. Previous investigations indicated that CST and SRF are strongly inter-related for most relevant variables. Only few investigations have shown no correlation between CST and SRF. This study aims to assess the goodness of CST and SRF relationship for different process variables relevant for coagulation flocculation sludge. Findings suggested that CST and SRF demonstrate different trends and are not related well for different rapid mixing velocities, materials and temperatures. The lack of overall relationship can be explained by the use of different procedures and equipment at variable boundary conditions in practice. SRF is more temperature-dependent than CST. The use of alum sulphate (alum) and ferric chloride (ferric) resulted in the lowest CST values, followed by Moringa oleifera. This result contradicted the SRF findings for which alum produced the lowest SRF values followed by M. oleifera and ferric.

In hot climates, achieving a good indoor environmental quality (IEQ) in existing buildings is important especially with climate change challenges as future heat waves will increase in frequency, duration, and intensity. In educational buildings, there is much more focus on the IEQ parameters and the interactions among them that need to be in line with the continuously changing learning environment. This study assesses the IEQ parameters (represented by noise, temperature and humidity) at three selected campus areas (lecture rooms of an administrative department building (LR), main hall of a management department building (MH) and a central library building (CL)) at the Al-Najaf Technical Institute (NTI), Al-Najaf City, Iraq, for the period from May to December 2019. A statistical analysis using a multi-linear regression model was performed to determine the relationship between the selected IEQ parameters and explain the noise level behavior as a function of the temperature and relative humidity. The research indicated that the noise levels and temperature values exceeded the maximum standard limits in all buildings reflecting the displeasing sound and heating quality within the studied areas, while the readings for relative humidity within each building environment complied with standards. Moreover, for both LR and MH buildings (R2 ≥ 0.8, significance F ≤ 0.01), the noise values were satisfactorily modeled by temperature and relative humidity highlighting the interactions between temperature, humidity and noise under consistent conditions. However, the results for the CL building (R2 = 0.6, significance F = 0.1) showed no relationship between the IEQ parameters, highlighting the fact that this building is exposed to unsteady conditions (an irregular number of people using this building during the daytime) resulting in a high variation of data measurements. The current results demonstrate that detailed modeling can be helpful to predict IEQ parameters depending on other known parameters in buildings. The results of the predictive model aligned with the directly measured data. Therefore, its performance is equally effective, but with a significant reduction in cost and time consumed.

Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate. Data quality control and homogenization tests for climate data are critical. Thus, this study aims to evaluate the quality and homogeneity level of precipitation time series in arid and semi-arid climatic zones and specify the breakpoint in the datasets. The proposed methodology has been used to conduct arid and semi-arid representative case studies comprising 40 annual precipitation series for Iraq between 1979 and 2014. A Box-Cox transformation has been used to adjust the non-normally distributed datasets. Outliers have been censored by truncating extreme values. The results of the outliers indicate that they may be caused more by climate variability than by human-induced reasons. Homogeneity adjustments have been developed by applying these four homogeneity tests: Pettitt’s test, the Standard Normal Homogeneity method, Buishand’s test, and von Neumann’s check. Approximately 40% of the series (i.e., 16 stations out of 40) were homogeneous. Each homogeneity test was evaluated separately, and non-homogeneous stations were identified. Then, the series was classified into three groups that were assigned the labels “useful”, “doubtful”, and “suspect”. The results indicated that twenty-one stations were associated with the class ‘suspect’, three of the stations belonged to the class ‘doubtful’, and sixteen locations were within the class ‘useful’. Furthermore, the data analysis indicated no influence of the outliers on the results of the homogeneity tests. Accordingly, the study recommends further research on homogeneity tests that can be applied without considering outlier tests for similar case studies.

Hydropower is one of the significant renewable energy resources. It is regularly requested at peak time steps to meet the load requirements of power systems resources allocation. Therefore, modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems can be a vital task for energy investment. Deriving optimal unknown decision parameters of these reservoir systems is a nonlinear, nonconvex, and complex optimization problem. Herein, a novel optimization algorithm, called an accelerated version of gradient-based optimization (AGBO), is developed to solve a complex multi-reservoir hydropower system. This advised technique uses an efficient adaptive control parameters mechanism to stabilize the global and local search; utilizing an enhanced local escaping operator (ELEO) to extend the chances of getting away from local optima; expanding the exploitation search by applying the sequential quadratic programming (SQP) technique. At first, the developed AGBO algorithm is employed to solve the optimal operation of a complex 10-reservoir hydropower system. Secondly, the possibility of the AGBO algorithm within the global optimization problems is illustrated by numerical tests of 23 mathematical benchmark functions. Optimal results show that the proposed AGBO can approach to 0.9999% of the optimal global solution. As a result, the advised method is the most superior one compared to the other advanced optimization algorithms for maximizing the load demands in hydropower system. In conclusion, this offers a productive tool to solve the complex hydropower multi-reservoir optimization systems.

Ozone is an important air pollutant that causes many challenges for human health, such as lung diseases. The negative impacts of exogenous ozone on cultivated plants have been discussed in several publications, but the positive impacts are less investigated. The current study is an attempt to answer the following question: is there any positive contribution of ozone pretreatment in growing plants under stress? Plants grown in unheated plastic greenhouses suffer from cold stress during the winter when the temperature drops to 5–10 °C. This stress can also be enhanced under saline soil conditions in arid regions. Treatments involving different ozone application methods (seed priming and seedling foliar application) and cold pretreatment (4 °C for 36 h) were compared with untreated controls of two sweet pepper hybrids (Zidenka and Lirica) in an unheated plastic greenhouse. In general, the Lirica hybrid outperformed the Zidenka hybrid in growth and fruit yield and showed good adaptation to cold stress resulting from ozone treatment through foliar application on seedlings. Moreover, cold treatment was associated with higher values of the studied parameters compared to the control (untreated). A concentration of 20 ppm of ozone in the first season and as 30 ppm of ozone in the second season increased plant leaf area, proline content, and APX activity, all of which improved the plant’s defense mechanisms against cold stress. These benefits contributed to high fruit sets; early, total, and marketable fruit yields; and fruit quality under cold stress. The highest yield (16.52 kg m−2) was attained with 30 ppm ozone applied as a seedling spray, compared with 10.07 kg m−2 in the control. Therefore, the foliar application of ozone up to 30 ppm can be recommended for sweet pepper hybrids of Lirica under unheated plastic greenhouse conditions. Further investigations are needed to study the interaction of combined ozone and cold stress, as well as various levels of soil salinity.

Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on node features or pipe connections individually. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (LocDiffs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (DemDiffs) between areas are 0.13, 0.27, and 0.29, respectively. The LocDiff and DemDiff of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method.

Urban areas are mostly heterogeneous due to settlements and vegetation including forests, water bodies and many other land use and land cover (LULC) classes. Due to the overwhelming population pressure, urbanization, industrial works and transportation systems, urban areas have been suffering from a deficiency of green spaces, which leads to an increase in the variation of temperature in urban areas. This study investigates the conceptual framework design towards urban green space (UGS) and thermal variability over Kolkata and Howrah city using advanced remote sensing (RS) and geospatial methods. The low green space is located in the highly built-up area, which is influenced by thermal variations. Therefore, the heat stress index showed a high area located within the central, north, northwestern and some parts of the southern areas. The vegetated areas decreased by 8.62% during the ten years studied and the other land uses increased by 11.23%. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) showed significant changes with R2 values between 0.48 (2010) and 0.23 (2020), respectively. The correlation among the LST and the normalized difference built-up index (NDBI) showed a notable level of change with R2 values between 0.38 (2010) and 0.61 (2020), respectively. The results are expected to contribute significantly towards urban development and planning, policymaking and support for key stakeholders responsible for the sustainable urban planning procedures and processes.

Non-steroidal anti-inflammatory drugs (NSAIDs) (concentration

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.

Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.

For companies, notably in the realms of energy and power supply, the essential requirement for highly efficient thermal transport solutions has become a serious concern. Current research highlighted the use of metallic oxides and carbon-based nanofluids as heat transfer fluids. This work examined two carbon forms (PEG@GNPs & PEG@TGr) and two types of metallic oxides (Al2O3 & SiO2) in a square heated pipe in the mass fraction of 0.1 wt.%. Laboratory conditions were as follows: 6401 ≤ Re ≤ 11,907 and wall heat flux = 11,205 W/m2. The effective thermal–physical and heat transfer properties were assessed for fully developed turbulent fluid flow at 20–60 °C. The thermal and hydraulic performances of nanofluids were rated in terms of pumping power, performance index (PI), and performance evaluation criteria (PEC). The heat transfer coefficients of the nanofluids improved the most: PEG@GNPs = 44.4%, PEG@TGr = 41.2%, Al2O3 = 22.5%, and SiO2 = 24%. Meanwhile, the highest augmentation in the Nu of the nanofluids was as follows: PEG@GNPs = 35%, PEG@TGr = 30.1%, Al2O3 = 20.6%, and SiO2 = 21.9%. The pressure loss and friction factor increased the highest, by 20.8–23.7% and 3.57–3.85%, respectively. In the end, the general performance of nanofluids has shown that they would be a good alternative to the traditional working fluids in heat transfer requests.

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.

Particularly in the Nordic region, water excess and shortage (drought) are becoming more frequent phenomena that challenge the development of agriculture and crop production. Identification of appropriate water management strategies is essential (i) to ensure sustainable water resources management for crop production and the functioning of healthy ecosystems; and (ii) to improve resilience to hydrological extremes. Integrated hydrological models offer that potential through understanding and forecasting of hydrological systems under anthropogenic and climatic influences, and providing information for improved decision-making. This study aims to develop a decision support instrument based on integrated hydrological modelling to identify appropriate management solutions and improve field- and catchment-scale water management in Nordic agriculture. The study area is Tyrnävä catchment, located in the northern part of Finland near Oulu city. Initially, the available hydro-climatological and hydrogeological data of the Tyrnävä catchment are characterized in detail. Then the hydrogeological parameters of the model are identified based on existing hydrogeological, climatic and remotely sensed data and their spatial, temporal and vertical variability. Next, a regional integrated surface-subsurface hydrological model is set up using HydroGeosphere. After successful calibration and validation using observed groundwater level, river discharge and soil moisture data, the model will be used in implementing and evaluating different management strategies (e.g., different irrigation options during droughts and controlled drainage management) for the future and their influence on the surface and groundwater systems. Uncertainty arising from different sources will be quantified using the Integrated Bayesian Multi-model Uncertainty Estimation Framework with the support of a supercomputer to improve the reliability and accuracy of the decision support instrument. Additionally, stakeholders’ involvement through local workshops is ensured throughout the modelling study, from the beginning to obtain reliable and useful decision support. Finally, based on these results, informed decisions regarding the appropriate water management can be made, which is important for sustainable water resources management for crop production and the functioning of healthy ecosystems particularly in Nordic agriculture.

Mathematical models are useful for predicting the reactions of watercourses such as rivers due to the entry of contaminants. Some of these models are able to simulate the effects of present and future loadings as well as aid managers and officials in making decisions even if the data are sparse. In other words, river water quality preservation requires more investment in wastewater treatment and/or the installation of collection and control systems; it may also limit activity expansion in the river basin. The conservation of watersheds such as the Maroon and Jarahi basin, which provide water for drinking and for industrial and agricultural use, is socio-economically vital. Therefore, the first stage of managing the conservation of water resources is understanding their qualitative changes. For this purpose, the QUAL2KW mathematical model was utilized to simulate the river water quality in this example region. According to the reported values of water quality parameters and pollutants at monitoring stations, it was established that the river is at a critical condition in terms of biochemical oxygen demand (BOD) pollution due to the discharge of urban and industrial wastewater, as well as high electrical conductivity (EC) due to the drainage of agricultural lands. Based on the statistics calculated during the validation step, the authors concluded that the QUAL2KW water quality model is reliable in the simulation of qualitative parameters and the pollution data of the study area; namely the Maroon and Jarahi river basin located in the south-west of Iran. This will help stakeholders to better manage watersheds with sparse data. This region has been suffering from climate change which has led to droughts and the construction of several dams to retain water. For the second and third stations, the NASH (named after the mathematician John Forbes Nash) values were 0.96 and 0.92, respectively, indicating a relatively high model accuracy. The evaluation using the root mean square errors (RMSE) and NASH showed that the quality of water at the second station was better than the other two stations based on the coefficient of determination R2. Since there were three drains at station number 3, the wastewater entering the Maroon River had a higher level of contamination.

Global warming is by far the most significant issue caused by climate change. Over the past few decades, heat stress has intensified into a serious issue that has a negative impact on crop production. Hence, it is crucial to modify cultivation systems to cope with this kind of stress, particularly in arid dry regions. In comparison to open-field cultivation, tomato production under protected cultivation techniques in walk-in tunnels that are suited for different farmers’ financial abilities was evaluated during the late summer season. The studied tunnels included a shaded net tunnel with natural ventilation, net tunnel with a fogging system and plastic tunnel with evaporative cooling (wet pad and fans). For the operation of fogging and evaporative cooling systems, solar energy was used as a sustainable, eco-friendly energy source. The results indicated that the solar energy system successfully operated the studied cooling systems. All studied protective cultivation techniques mitigated heat stress on tomato plant and improved the microclimate under walk-in tunnels. Moreover, evaporative cooling and fogging systems significantly increased plant leaf area, cell membrane efficiency and the contents of chlorophyll, relative water and proline compared to the net tunnel with natural ventilation. Furthermore, a marked reduction in physiological disorders was noticed. Improved physiological and biochemical parameters and limited physiological diseases led to higher fruit set, marketable fruit yield and total productivity. The percentage of marketable fruit yield increased by around 31.5% with an evaporative cooling system, 28.8% with a fogging system and 17% with a shaded net tunnel with no positive cooling as compared to an open field. However, the plants grown in open-field cultivation without protection significantly deteriorated from heat stress and had a high incidence of physiological disorders. The most incident physiological disorders were blossom-end rot, cracking, internal white tissues, sunscald, puffiness, blotchy ripening, cat face and exserted stigma. It is recommended to use a solar energy system to modify microclimate conditions through fogging or evaporative cooling under walk-in tunnels to ameliorate heat stress on grown tomato in the late summer season for higher fruit yield and fewer physiological disorders.

Investigating the spatiotemporal distribution of climate data and their impact on the allocation of the regional aridity and meteorological drought, particularly in semi-arid and arid climate, it is critical to evaluate the climate variability effect and propose sufficient adaptation strategies. The coefficient of variation, precipitation concentration index and anomaly index were used to evaluate the climate variability, while the Mann-Kendall and Sen’s slope were applied for trend analysis, together with homogeneity tests. The aridity was evaluated using the alpha form of the reconnaissance drought index (Mohammed & Scholz, Water Resour Manag 31(1):531–538, 2017c), whereas drought episodes were predicted by applying three of the commonly used meteorological drought indices, which are the standardised reconnaissance drought index, standardized precipitation index and standardized precipitation evapotranspiration index. The Upper Zab River Basin (UZRB), which is located in the northern part of Iraq and covers a high range of climate variability, has been considered as an illustrative basin for arid and semi-arid climatic conditions. There were general increasing trends in average temperature and potential evapotranspiration and decreasing trends in precipitation from the upstream to the downstream of the UZRB. The long-term analysis of climate data indicates that the number of dry years has temporally risen and the basin has experienced succeeding years of drought, particularly after 1994/1995. There was a potential link between drought, aridity and climate variability. Pettitt’s, SNHT, Buishand’s and von Neumann’s homogeneity test results demonstrated that there is an evident alteration in the mean of the drought and aridity between the pre- and post-alteration point (1994).

The exploitation and management of groundwater in an integrated manner is gaining global interest. Rapid population growth is frequently linked to climate change. In order to meet the growing demand for public water supply and irrigation, especially in arid and semi-arid climate regions, groundwater is used excessively. This paper considers the Erbil province of the Iraqi Kurdistan Region as a representative case study for semi-arid climate areas where current practices of groundwater resource utilization lack a solid regulatory framework and where monitoring systems are often absent. The role of climate change in the assessment of aquifers is assessed. Long-term average recharge and extraction rates in relation to groundwater storage have been evaluated with the aim to avoid adverse long-term impacts on groundwater resources. A groundwater balance method has been used to quantify the storage of groundwater within aquifers. Results revealed that there is a considerable imbalance between the input (groundwater recharge) to the Erbil province aquifers and corresponding output (groundwater withdrawn). The reduction of losses in water use, increases in irrigation efficiency, raising of public good water-use practices, and the establishment of a regulatory framework to appropriately manage groundwater resources are outlined.

Although remarkable progress has been achieved in reducing hunger and poverty and improving people’s health in the past couple of decades, humanity still faces considerable socio-economic and sustainability challenges. Ensuring sustainable access to safe and sufficient water, improved sanitation facilities, clean energy sources and healthy food is a necessary requirement for ending hunger and poverty, advancing health and achieving all the goals of the sustainable development agenda. Socio-economic variables are determinant factors of water, energy and food accessibility. Ecological region, income and education are measures used in this study, which aims to examine a pairwise comparison of water and energy sources, sanitation facilities and food accessibilities in six sampling communities and tracking progress towards achieving the sustainable development goals (SDG) at a local scale using nine specific SDG indicators. A study involving questionnaire administration covering 1785 households across the three ecological regions of Katsina state was performed. Two communities representing urban and rural households from each of the ecological regions were selected. An analysis of variance was used to test the equality of resource accessibility across the investigated communities followed up by a post hoc analysis to identify significant mean groups. The results showed that the overall access level to safely manage water and sanitation facilities were 16.5% and 28.1%, respectively. Access indices of 1.83 and 1.24 for electricity and cooking fuel, respectively, were calculated in the study area. The study revealed that location, education and income are the key drivers of water, energy and food access and choice.

This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through “cross-impact matrix multiplication applied to classification” (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., “unavailability of proper personal protective equipment (PPE)” and “lack of proper communication”) emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., “anxious about isolation/quarantine,” “subject to violent crimes,” “feeling frustrated and powerless,” and “exhausting shifts/hours without clear end”) are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor “subject to violent crimes” is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.

Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.

Businesses in the present era are dealing with a complex and unprecedented brew of social, environmental, and technological trends. This requires sophisticated, sustainability-based management. Yet organizations are often reluctant to place sustainability core to their business strategies with the mistaken belief that the costs associated with environmental investments outweigh the benefits. The Global Climate Risk Index has placed Pakistan on 5th position in the list of nations, most susceptible to climate change in its recent report. Pakistan lost the lives of 9,989 people, incurred economic losses of $ 3.8 billion, and faced 152 shocking climates between 1999 and 2018. Based on this information, it is established that Pakistan's susceptibility to climate change is growing unprecedentedly and industrial pollution is one of the biggest contributors in this respect. The country needs to take emergency measures to address this issue. With this background, the present study aims to investigate the impact of environmental sustainability on environmental and economic performance with the mediating effect of environmental innovation in the manufacturing sector of Pakistan. The results show that environmental sustainability is a significant predictor of environmental and economic performance and environmental innovation mediates this relationship. The findings of the present study provide better insights to policymakers to address the environmental degradation, resulting from industrial pollution

The study is aimed to analyze the determinants of the effectiveness of SOPs in mass gatherings for containing COVID-19. The overall design of the study involves a literature review, data collection by field survey, structural modeling, and analysis. The study is built on the experts' opinion of a focus group (representing people who recently participated in and are responsible for mass gatherings). The study uses the discussion of the literature review to identify the determinants, interpretive structural modeling (ISM) for developing and analyzing a structural model, and Matrice d'Impacts Croises Multiplication Appliquée a un Classement (MICMAC) for corroboration of results of the ISM/classification of determinants. From the literature review, a list of determinants is generated and verified by a panel of experts. The results of the ISM revealed that the determinants “legal environment of the country,” “practicability of SOPs,” “perceived benefit of adapting SOPs,” and “possibilities of avoiding gathering” occupied the top of the model, therefore, they are less critical determinants, whereas “nature of gathering” occupied the bottom of the model, and is thus the most critical determinant. The remaining determinants form the middle of the model, and are therefore moderately severe. The results of MICMAC show that the determinant “perceived benefit of adapting SOPs” is dependent, “nature of gathering” is independent, and all others are linked. The results of MICMAC implicitly substantiate the findings of the ISM. The overall results of the study show that “nature of gathering” is the key determinant. This research does not require a priori theory since it is a theory-building study that uses an inductive approach. It is based on real data and it is useful for local authorities, organizers, participants (attendees/visitors) of mass gatherings, health officials/regulators, researchers, and the community at large. This study has fundamental importance for planning and preparing for such events while ensuring the minimum risk of COVID-19 transmission.

Customers have become very sensitive regarding the innovative evaluation of services. Due to competition in the hospitality industry, it is a challenge for hotel marketers to understand customers’ behavior. There is scant research in the hotel industry of Pakistan and especially on boutique hotels. This research seeks to measure the relationship between substantive, communicative elements of the sustainable servicescape and behavioral intentions (word of mouth) in a boutique hotel setting. However, the mediating effect of the overall perceived image is examined between these constructs. Responses of boutique hotel visitors were collected from Lahore, Islamabad, Faisalabad, and Murree. Data were analyzed by using structural equation modeling (SEM). Results display that both substantive and communicative servicescape elements positively affect the perceived image of customers, which has a positive influence on behavioral intentions such as word of mouth (WOM). Theoretical and practical implications are also discussed.

Remediation of contaminated soil and sediment is important for improving the eco-environmental quality. Electro-kinetic remediation (EKR) is an environmentally friendly technology to migrate and remove pollutants from the soil and sediment matrix. This paper analyses the mechanism and performance of EKR of heavy metals, organic pollutants, and compound pollutants. Moreover, the effect of optimizing individual EKR through soil and sediment pre-treatment (adding acid/oxidant/co-solvent/surfactant, stirring, heating, etc.), electrode optimization (exchange electrode, anode approximation, electrode matrix, etc.), and applying multi-technology combination (electro-kinetic permeable reaction barrier/Fenton/ion, exchange membrane/ultrasonic/electrolyte enhancement, etc.) was evaluated. Factors including incomplete separation of pollutants, variation in physico-chemical properties and microstructure of soil/sediment, and difficulties in in situ practice have restrained the field application of EKR. To solve the above technical challenge, an integrated EKR technology based on pollutant in situ separation, followed by separated contaminant treatment, and subsequent valuable elements recovery is proposed.

Since Pakistan is vulnerable to changing climatic conditions, the country needs emergency measures at every level to mitigate their effect. Many studies have addressed Corporate Social Responsibility (CSR) at the macro or institutional level, but its effect at the micro-level is largely ignored in the contemporary literature. The present study aims at filling this gap by highlighting the role of micro-level CSR on Environmental Performance (ENP) and how it is influenced by employees’ pro-environmental (PEB) behavior and gender. This study has been carried out in the context of deteriorating environmental conditions, scarce CSR activities and a male dominant society. Data were collected through a self-administered questionnaire targeting the manufacturing and the service sectors of Pakistan and analyzed through the structural equation modeling technique, using AMOS and SPSS software. The results of this study confirm that micro-level CSR initiatives directly (β = 0.39⁎⁎) and indirectly (β = 0.031⁎⁎) influence the environmental performance of an organization by means of employees’ PEB. A conditional indirect gender effect on this interaction was also observed, with women (β =0.17⁎⁎) exerting a stronger effect than men (β = 0.031⁎⁎). These findings may be helpful for policymakers involved in environmental management: It is possible to reduce the environmental footprint of Pakistan's organizations by encouraging employee's PEB and particularly by actively involving women which are systematically set apart from decision making in Pakistan.

Water purification is one of the most essential services provided by wetlands. A lot of concerns regarding wetlands subjected to climate change relate to their susceptibility to hydrological change and the increase in temperature as a result of global warming. A warmer condition may accelerate the rate of decomposition and release of nutrients, which can be exported downstream and cause serious ecological challenges; e.g., eutrophication and acidification. The aim of this study is to investigate the effect of climate change on water quality in peatland and constructed wetland ecosystems subject to water level management. For this purpose, the authors simulated the current climate scenario base on the database from Malmö station (Scania, Sweden) for 2016 and 2017 as well as the future climate scenarios for the last 30 years of the century based on the Representative Concentration Pathway (RCP) and different regional climate models (RCM) for a region wider than Scania County. For future climate change, the authors simulated low (RCP 2.6), moderate (RCP 4.5) and extreme (RCP 8.5) climate scenarios. All simulations were conducted within climate chambers for experimental peatland and constructed wetland mesocosms. Our results demonstrate that the effect of climate scenario is significantly different for peatlands and constructed wetlands (interactive effect) for the combined chemical variables. The warmest climate scenario RCP 8.5 is linked to a higher water purification function for constructed wetlands, but to a lower water purification function and a subsequent deterioration of peatland water qualities, even if subjected to water level management. The explanation for the different response of constructed wetlands and peatlands to climate change could be due to the fact that the substrate in the constructed wetland mesocosms and peatlands was different in terms of the organic matter quality and quantity. The utilization of nutrients by the plants and microbial community readily exceed the mineralization under a limited nutrient content (as we had in constructed wetland) when the temperature rises. However, concerning the extreme scenario RCP 8.5, the peatlands have shown a tendency to have reverse processes.

Social sustainability is the much emphasized organizational phenomenon in Western literature, however, in emerging economies, its importance has only been realized in the recent past. Social sustainability is the amiability of the relationship between employees and the organizations on a relatively permanent basis. Social sustainability is the key determinant of organizational sustainability and organizational effectiveness. As healthcare organizations are labor-intensive, the role of social sustainability in hospitals is more crucial. The purpose of the present study is to understand the role of work safety in improving social sustainability in public sector hospitals. To this effect, we collected data from 431 healthcare professionals of a large public sector tertiary and teaching hospital in the city of Lahore Pakistan and analyzed the data using structural equation modeling (SEM). The results uncovered certain important facts, which were not expected per se. Job design, coworkers’ behavior towards work safety, and supervisors’ role in ensuring work safety are the key factors that influence social sustainability. However, surprisingly, in the eyes of employees, management practices and safety programs/policies do not contribute to the work safety of the hospital under study. Keeping in view the findings, we suggest that management must participate in work safety affairs directly and formulate indigenous policies and programs according to local needs. Job analysis is needed to redesign job structures to meet workplace safety requirements. Formal and informal training will be beneficial to make workers and supervisors more aware, more sensitive, and more responsible regarding work safety.

Drought might shift the role of wetlands from carbon sinks to carbon sources. Higher temperatures and more rain can maintain the role of wetlands as carbon sinks. There might be a critical water level at which a wetland can preserve its services. A new framework for conducting wetland mesocosm experiments has been proposed. The proposed experiment allows for testing of dynamic process-based model results.

The aim of this article is to determine how human interventions in upstream countries coupled with drought events are affecting the flow regime of downstream countries using the Nile River basin for illustrative purposes. This has been addressed by assessing climate change in the study area through analyses of precipitation data obtained from the Global Precipitation Climatology Centre (GPCC) and detecting if there is a trend, and subsequently calculating drought events in the main basins impacting on the downstream flow. Then river discharge data were analysed using different hydraulic indices at key stations in the downstream country and measuring the alterations occurring in the flow. The degree of alteration is a function of the number of civil engineering projects being in operation and classified by time windows; pre-alteration is between 1900 and 1925, while the alteration period is between 1933 and 2012. The alteration period was classified into three periods based on the degree of alteration. The findings revealed that there are changes in the river flow regime caused by both changes in the rainfall pattern in addition to the regulation in the upstream countries. There is a direct relationship between the interventions in the upstream countries and changes in the flow regime especially when coupled with drought events. By increasing the water usage upstream, there is an increase in the alteration of the flow downstream. The years between 2000 and 2012 were linked to the highest alterations between the modified years.

Two controlled experiments were operated to evaluate the impact of pH on the treatment efficiency of azo dyes within artificial textile wastewater using ponds as a polishing step. The objectives were to assess the (1) inflow water quality; (2) the presence of Lemna minor and the algae Oedogonium spp. on the dye removal and the outflow water quality; and (3) suitability of applying artificial textile wastewater and the impact of this wastewater and the dye on plants. Findings indicate that the shallow ponds planted with L. minor and/or algae treated the dye Basic Red 46 (BR46) significantly (p 0.05) impact of pH. The potential of L. minor and algae for removal of BR46 was 31% and 25%, respectively. The removal of BR46 was around 89% for ponds planted with L. minor and algae, 58% for algae ponds and 33% for control ponds. Wetlands with L. minor are successful and economic in the complete degradation of BR46 at concentrations of 10 mg L−1 for any pH value between 6 and 9. The removal of ammonium-nitrogen (NH4-N), nitrate-nitrogen (NO3-N) and ortho-phosphate-phosphorus (PO4-P) was higher in planted than in unplanted ponds. The mean outflow values of chemical oxygen demand, NH4-N, NO3-N, suspended solids and total dissolved solids were within the limits for safe discharge. The artificial wastewater constituents after dilution (1 part of artificial wastewater to 24 parts of raw water) were suitable to use within the treatment system containing L. minor.

This study investigates and challenges the capability of standard and hybrid soft computing models of fuzzy c-means clustering adaptive neuro-fuzzy inference system (ANFIS), wavenet and artificial neural networks (MLPNN and RBFNN) to estimate the spillway aerator air demand in dams. For the learning process, four different meta-heuristic optimization methods (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA) and biogeography-based optimization (BBO)) are considered as alternatives to the classical optimization algorithms of the data-driven models. In addition to the data-driven models, the multiple linear regressions and some empirical relations are used to evaluate the performance of the models. Evaluation of the models is assessed with five different statistical parameters as well as the diagnostic tool of the Taylor’s diagram. Analysis of the models’ outcome reveals that the ANFIS-GA has the best performance associated with a standard root mean square error of 0.309 and a coefficient of determination (R 2 ) of 0.93.

Sustainable management of transboundary rivers, especially in water-scarce regions is increasingly becoming more challenging due to the collective adverse impact of upstream development and climate change on transboundary scale. Two major tributaries of the Tigris River, the Diyala (Sīrvān) and the Lesser Zab River basins, are shared between Iraq and Iran. They were adopted as representative basins for a large number of transboundary rivers, where short-sighted perspectives are currently ruling their management policies. Climate change is anticipated to lead to greater frequency and intensity of droughts, and higher tension is likely to emerge. The authors developed a technical support framework (TSF) that helps decision-making to alleviate the combined negative impact of climate change at basin scale and upstream human-induced impairments. The TSF encompasses six key measures: (1) cross-sectoral trade-offs; (2) improved water-use efficiency and reduction of losses; (3) development of a sound groundwater planning policy; (4) cutbacks in demand while maintaining environmental flow in the river; (5) inter- and intra-basin water transfer systems; and (6) quantification of current supply-demand gaps and sizing a future gaps. The framework supports the sustainable management of water resources in both Diyala and Lesser Zab basins as well as others in common river catchments. The recommendation is that a lower riparian country should put in place measures such as increasing water-use efficiency, rehabilitation of damaged and deteriorated irrigation facilities, and inter- and intra-basin water transfer arrangements to reduce the gap between growing water demands and corresponding supplies.

The authors conducted a socio-technical cost–benefit analysis (CBA) in Alexandra Township (Alex for short) by combining three water loss control investment strategies: (i) pipeline and infrastructure upgrades, (ii) repair and maintenance, and (iii) socio-domestic retrofitting capital. The researchers performed the CBA using sensitivity analysis methodologies such as marginal cost of capital (MCC), weighted average cost of capital (WACC), coefficient of variance (CV), the net present value (NPV) ratio, and cumulative and total cost methods. The findings for socio-domestic retrofitting capital investment showed that at an average investment cost of USD 5735 per household, consumption was reduced from 1369.4 m3/year to 301.99 m3/year, whereas a projected water savings average of 521.05 m3/household/year was achieved. The results show that the cumulative cost of water losses equaled USD 43.9 million per year, and that, if the water loss trend continues beyond the year 2026, almost 100% of the system input volume (SIV) will be non-revenue water (NRW) in the water balance. Using the MCC method, the integrated results for the socio-technical strategy showed that the economic level of leakage (ELL) was achieved at a WACC of 16.2, a CV of 0.66, and an NPV ratio or net capital risk of 0.246. This study demonstrates that the socio-technical approach is a viable alternative for water loss control and may be adopted in various parts of the world.

Urgent water and food security challenges, particularly in continental and boreal regions, need to be addressed by initiatives such as the Horizon 2020-funded project WATer retention and nutrient recycling in soils and streams for improved AGRIcultural production (WATERAGRI). A new methodological framework for the sustainable management of various solutions resilient to climate change has been developed. The results indicate that the effect of the climate scenario is significantly different for peatlands and constructed wetlands. The findings also highlight that remote-sensing-based yield prediction models developed from vegetation indices have the potential to provide quantitative and timely information on crops for large regions or even at the local farm scale. Verification of remotely sensed data is one of the prerequisites for the proper utilization and understanding of data. Research shows that current serious game applications fall short due to challenges such as not clarifying the decision problem, the lack of use of decision quality indicators and limited use of gaming. Overall, WATERAGRI solutions improve water and food security by adapting agriculture to climate change, recycling nutrients and providing educational tools to the farming community. Farmers in small agricultural catchments benefit directly from WATERAGRI, but over the long-term, the general public does as well.

This Special Issue is concerned with treatment wetlands, which are artificial (or constructed) wetlands usually treating municipal or industrial wastewater, greywater and storm water runoff [...]

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment.

Indexación Scopus Achieving consumer loyalty will remain a prime objective of every business in the current volatile, uncertain, complex and ambiguous environment (VUCA), which demands some out-of-thebox thinking on the part of policymakers to sustain the competitive advantage. Sustainability has been emerging as a “new normal” for all industries of the present era, which forces all sectors to incorporate it into their business operations. There have been studies on the relationship between sustainability and consumer loyalty, but these studies are inconclusive, further establishing the importance of more research in this area. In this frame of reference, the present research objective is to test the dimensional impact of corporate social responsibility (CSR) activities (economic, environmental, social) on banking consumers’ loyalty in the context of Pakistan. The data were collected from different bank account holders, which were then analyzed through the structural equation modeling (SEM) technique in AMOS. The results revealed that all three dimensions of CSR positively relate to consumer loyalty. The banking industry policymakers can benefit from the findings of the present study to understand sustainability from the lens of marketing to drive a higher level of consumer loyalty. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. https://www.mdpi.com/2071-1050/13/7/3828

While prior studies have largely addressed corporate social responsibility (CSR) at a macro or institutional level, its importance at the micro or individual level is to date underexplored, especially in the context of developing economies. Further, it is not clear from the studies in the extant literature how the role of females is more important in the context of environmental management as compared to males. Similarly, micro-level CSR (MCSR) is emerging as a “new organizational value”, and the organizations that acknowledge this “new organizational value” and incorporate it into their business operations are likely to achieve sustainability objectives far better as compared to their counterparts. The present study investigates the impact of MCSR on employees’ pro-environmental behavior with the moderating effect of gender in the healthcare sector of Pakistan. The data were collected from five large hospitals in the city of Lahore through a self-administered questionnaire and analyzed using structural equation modeling (SEM) in AMOS software. A total of 533 out of 800 responses were received, which were used for data analysis of the present study. The results revealed that MCSR positively influences employee’s pro-environmental behavior, and gender moderates this relationship but the moderating effect of females is stronger as compared to males. The findings of the present study would help policymakers understand the importance of MCSR as a “new organizational value” to influence employees’ pro-environmental behavior with a special focus to promote the proactive role of females at workplaces.

Climate change and drought episode impact integrated with anthropogenic pressure have become an increasing concern for water resource managers, particularly in arid and semi-arid climatic zones. This chapter presents a comprehensive methodology to predict the prospective impact of such changes at a basin scale. The Lower Zab River Basin, northern Iraq, has been selected as a representative case study. The methodology has been achieved through estimation of drought severity and climate change impact during the human intervention periods to separate the influence of climatic abnormality and measure the hydrologic deviations as a result of streamflow regulation configurations. The Indicators of Hydrologic Alteration (IHA) method has been applied to quantify the hydrological alterations of numerous hydrological characteristics. The Hydrologiska Byrans Vattenbalansavdelning (The Water Balance Department of the Hydrological Bureau) hydrologic model was used to define the boundary conditions for the reservoir capacity yield model, which was applied to derive the reservoir capacity-yield-reliability relationships, comprising daily reservoir inflow from the basin with the size of 14,924 km2 into a reservoir with the capacity of 6.80 Gm3. Owing to the future precipitation reduction and potential evapotranspiration increase during the worst case scenario (−40% precipitation and +30% potential evapotranspiration), substantial reductions in the streamflow of between −56% and −58% are anticipated for the dry and wet seasons, respectively. Model simulations recommend that the reservoir reliability would generally decrease due to a decline in reservoir inflow. The study outcomes assist water resource managers and policymakers responsible for mitigating the effects of climate change.

Organizational sustainability is the reality which is considered essential for the success of all kind of organizations. Researchers and academicians struggle to conceptualize this phenomenon while practitioners endeavor to achieve it on the ground. Much has been done in resource-affluent countries of the world, whereas in the least developed countries, social sustainability is now becoming a new normal. The purpose of this research was to understand the social perspective of organizational sustainability and the roles of electronic performance appraisal and transformational leadership in shaping it. Data were collected from the healthcare sector through the administration of close-ended questionnaires to a randomly selected sample, 320 out of 400 questionnaires with an acceptable degree of accuracy were selected for statistical treatment. Inferential statistics were applied using structural equation modeling (SEM). Results confirmed the existence of definite contributions of the electronic performance appraisal system and transformational leadership towards organizational sustainability. However, our findings surprisingly ruled out the moderating role of transformational leadership on the cause and effect relationship between electronic performance appraisal and organizational social sustainability. In the face of a scarcity of related literature in the given context and due to the partial novelty of the model, the findings of this study add significantly to the existing bank of literature in the field. It will also be handy for the guidance of practitioners who are engaged in sustainability management.

Stress factors such as climate change and drought may switch the role of temperate peatlands from carbon dioxide (CO2 ) sinks to sources, leading to positive feedback to global climate change. Water level management has been regarded as an important climate change mitigation strategy as it can sustain the natural net CO2 sink function of a peatland. Little is known about how resilient peatlands are in the face of future climate change scenarios, as well as how effectively water level management can sustain the CO2 sink function to mitigate global warming. The authors assess the effect of climate change on CO2 exchange of south Swedish temperate peatlands, which were either unmanaged or subject to water level regulation. Climate chamber simulations were conducted using experimental peatland mesocosms exposed to current and future representative concentration pathway (RCP) climate scenarios (RCP 2.6, 4.5 and 8.5). The results showed that all managed and unmanaged systems under future climate scenarios could serve as CO2 sinks throughout the experimental period. However, the 2018 extreme drought caused the unmanaged mesocosms under the RCP 4.5 and RCP 8.5 switch from a net CO2 sink to a source during summer. Surprisingly, the unmanaged mesocosms under RCP 2.6 benefited from the warmer climate, and served as the best sink among the other unmanaged systems. Water level management had the greatest effect on the CO2 sink function under RCP 8.5 and RCP 4.5, which improved their CO2 sink capability up to six and two times, respectively. Under the current climate scenario, water level management had a negative effect on the CO2 sink function, and it had almost no effect under RCP 2.6. Therefore, the researchers conclude that water level management is necessary for RCP 8.5, beneficial for RCP 4.5 and unimportant for RCP 2.6 and the current climate.