Climate change, water quality and water-related challenges : a review with focus on Pakistan

Climate variability is heavily impacting human health all around the globe, in particular, on residents of developing countries. Impacts on surface water and groundwater resources and water-related illnesses are increasing, especially under changing climate scenarios such as diversity in rainfall patterns, increasing temperature, flash floods, severe droughts, heatwaves and heavy precipitation. Emerging water-related diseases such as dengue fever and chikungunya are reappearing and impacting on the life of the deprived; as such, the provision of safe water and health care is in great demand in developing countries to combat the spread of infectious diseases. Government, academia and private water bodies are conducting water quality surveys and providing health care facilities, but there is still a need to improve the present strategies concerning water treatment and management, as well as governance. In this review paper, climate change pattern and risks associated with water-related diseases in developing countries, with particular focus on Pakistan, and novel methods for controlling both waterborne and water-related diseases are discussed. This study is important for public health care, particularly in developing countries, for policy makers, and researchers working in the area of climate change, water quality and risk assessment

Computing air demand using the Takagi–Sugeno model for dam outlets

An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN) and multiple linear regression (MLR) models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash–Sutcliffe efficiency, the correlation coefficient and the Bias

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Mapping atopic dermatitis and anti–IL-22 response signatures to type 2–low severe neutrophilic asthma

Background: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases. Objective: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti–IL-22 (fezakinumab [FZ]) is enriched in severe asthma. Methods: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort. Results: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, TH2, and TH17/TH22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with TH22/IL-22 pathways. Conclusions: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases

Modeling of dissolved oxygen applying stepwise regression and a template-based fuzzy logic system

This paper develops a template-based fuzzy inference system (TFIS) capable of simulating the dissolved oxygen (DO) concentration of an example stream by using daily data (2009–2012). Stepwise regression (SR) analysis and Mallows’ Cp statistic were employed to select the best set of independent parameters for the input vector of the TFIS and the regressive models. The most effective inputs were determined for temperature and specific conductivity (P-value=0.000), whereas flow rate (P-value=0.002) and pH (P-value=0.004) were found to be less effective parameters. The TFIS and SR models undersimulated the magnitude of high DO concentrations (>12.5  mg/L), whereas the low and medium DO concentrations were oversimulated. However, they were closer to the observed data. A comparison of the prediction accuracy of the TFIS and SR methods indicated that the TFIS approach was more accurate in simulating DO concentrations, except for low concentrations of DO (<10  mg/L). The TFIS method was found to be superior to the conventional SR model

Technical and economic evaluation of the deficit irrigation on yield of cotton

A field experiment was carried out over two years to investigate the effects of deficit irrigation applied with surface and subsurface drip irrigation systems on water use efficiency and yield of cotton. The experiments were carried out during 2014-2015 in Kerman Province (Iran) in a split plot based on a randomized complete block design with three replications. Treatments considered three levels of irrigation, based on 100 (L1, full irrigation), 80 (L2) and 60 (L3) percent of crop water demand at each irrigation event, as main plots, as well as two drip irrigation methods, including surface (S1) and subsurface (S2), as subplots. All treatments were assessed in terms of yield, water use efficiency, as well as of economic aspects, including investment preference determination. Two-year comparison showed that yield, water use efficiency, boll weight, number of boll and plant height in subsurface drip irrigation system (S2) were increased 10.8, 11, 7.45, 12.8 and 11.2 percent compared to surface drip irrigation system (S1), respectively. Economic analysis showed that applying 100 percent of crop water requirement in subsurface drip irrigation (L1S2) was superior to the other treatments. Acknowledgement

Assessment of straight and meandering furrow irrigation strategies under different inflow rates

This paper reports the effect of straight furrow (SF) and meandering furrow (MF) irrigation strategies, as well as inflow rate, on infiltration and hydraulic parameters including advance time, recession time, and runoff hydrograph. The performance of SF and MF irrigation in terms of runoff ratio, deep percolation, and application efficiency was evaluated in 6 furrow fields at Shahid Bahonar University of Kerman, Iran. The required data were collected from the farm, consisting of free drainage furrows with length 70 m, top width 0.8 m, depth 0.25 m, and slope 0.2%. The advance and recession times were significantly longer in MF than SF irrigation. The infiltration was estimated by Lewis-Kostiakov equation. The infiltration coefficients were calculated: The values of k were higher and of a were lower in MF furrows than in SF furrows. The average runoff ratio and application efficiency for the SF irrigation events were 50.53% and 49.07%, respectively, while those of the MF irrigation events were 7.04% and 52.94%, respectively. Based on the results, the velocity of water advance in MF irrigation is decreased and, thus, the runoff, erosion losses, mass of fertilizer lost and surface water contamination were reduced. Using a lower inflow rate and appropriate irrigation time leads to better management outcomes in irrigation systems

Hydrodynamic modelling of free water-surface constructed storm water wetlands using a finite volume technique

One of the key factors in designing free water-surface constructed wetlands (FWS CW) is the hydraulic efficiency (λ), which depends primarily on the retention time of the polluted storm water. Increasing the hydraulic retention time (HRT) at various flow levels will increase λ of the overall constructed wetland (CW). The effects of characteristic geometric features that increase HRT were explored through the use of a two-dimensional depth-average hydrodynamic model. This numerical model was developed to solve the equations of continuity and motions on an unstructured triangular mesh using the Galerkin finite volume formulation and equations of the k–ε turbulence model. Eighty-nine diverse forms of artificial FWS CW with 11 different aspect ratios were numerically simulated and subsequently analysed for four scenarios: rectangular CW, modified rectangular CW with rounded edges, different inlet/outlet configurations of CW, and surface and submerged obstructions in front of the inlet part of the CW. Results from the simulations showed that increasing the aspect ratio has a direct influence on the enhancement of λ in all cases. However, the aspect ratio should be at least 9 in order to achieve an appropriate rate for λ in rectangular CW. Modified rounded rectangular CW improved λ by up to 23%, which allowed for the selection of a reduced aspect ratio. Simulation results showed that CW with low aspect ratios benefited from obstructions and optimized inlet/outlet configurations in terms of improved HRT

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R2), root-mean-square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models. </div

Online sequential extreme learning machine in river water quality (turbidity) prediction: a comparative study on different data mining approaches

As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine (OS-ELM) in predicting daily values of turbidity in Brandywine Creek, Pennsylvania, is evaluated. For this purpose, in addition to the developed OS-ELM, several data-driven models, that is, multilayer perceptron neural network (MLPANN), the classification and regression tree (CART), the group method of data handling (GMDH) and the response surface method (RSM) have been applied. The general findings of the study confirm the superiority of the OS-ELM model over the other applied models so that the OS-ELM improved the averaged RMSE of the predicted values 9.1, 11.7, 20.5 and 29.3% over the MLPANN, GMDH, RSM and CART models, respectively.</p