Spatiotemporal analysis of dust patterns over Mesopotamia and their impact on Khuzestan province,Iran

Dust storms are yet quite frequent in various parts of the world, particularly ancient Mesopotamia (approximately corresponding to most parts of Iraq as well as certain surrounding regions toward its north). To add to the ongoing difficulty, monitoring dust patterns has been proven to be a rather difficult endeavor given the absence of reliable ground-based monitoring stations in the corresponding area. Additionally, western provinces of Iran, especially Khuzestan in the southwest of Iran, have been severely affected by dust storms carried by the westerly winds, blown through neighboring countries in ancient Mesopotamia. This study proceeds to employ aerosol optical depth (AOD), extracted from the MODerate-resolution Imaging Spectroradiometer onboard the Terra spacecraft, to assess spatial dust variations between 2001 and 2017 over the Khuzestan province and ancient Mesopotamia. The variations were also correlated with the temporal dust changes in the Khuzestan province. Frequency of occurrence for AOD?>?1 was used to identify and categorize major dust sources in the aforementioned regions. The findings were indicative of an increasing trend in the annual AODs of the Khuzestan province, which eventually led to a significant increase from 2008 toward the end of 2012, but decreased again in the following years. Correspondingly, the entire time period (2001–2017) was further divided into three sub-periods: the first time period spanning from 2001 to 2007, followed by the second from 2008 to 2012 and finally a third time period from 2013 to 2017. Dust source identification was speculative of numerous dust spots in Iraq, Syria, Kuwait and also the southern parts of Khuzestan province which have become more active in recent years. Additionally, a large active dust spot was pinpointed between the northwest Iraq and eastern Syria border which has become extremely active during the second time period, possibly due to a severe drought in the Fertile Crescent.

     

Copper(II) Biosorption on Soda Lignin From Oil Palm Empty Fruit Bunches (EFB)

The adsorption of Cu(II) ions from aqueous solutions by soda lignin as an absorbent using a batch adsorption system is presented in this paper. The soda lignin used in this study was extracted from black liquor derived from oil palm empty fruit bunches (EFB) using 20% v/v sulfuric acid. The effects of varying experimental parameters such as pH value, adsorbent dosage, different concentrations of Cu(II) ions, and agitation period were investigated. The results revealed that the optimum adsorption of Cu(II) onto soda lignin was recorded at a pH of 5.0 at an adsorbent dosage of 0.5 g soda lignin and an agitation period of 40 min. The adsorption capacities and rates of Cu(II) ions onto soda lignin was evaluated. The Langmuir and Freundlich adsorption models were applied to calculate the isotherm constants. It was found that the adsorption isothermal data could be well interpreted by the Freundlich model. The kinetic experimental data properly correlated with the pseudo‐second‐order kinetic model, which implies that chemical sorption is the rate‐limiting step.     

Integrated coastal zone management programme northwest African region case

The development of industrial and tourism activities as well as the increase in the urban population in the northwest African region has generally been highly concentrated in the coastal zone. Due to high rates of population growth, urbanisation is likely to increase dramatically in the coming future. This paper presents a physical and socio-economic environmental analysis of the region and highlights the few efforts that have called for the development of co-ordinated strategies in view of integrated coastal zone management. Experience in the implementation of methodologies for adopting integrated coastal zone management strategies in these countries is discussed.     

Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm

Input variable selection (IVS) is a necessary step in modeling water resources systems. Neglecting this step may lead to unnecessary model complexity and reduced model accuracy. In this paper, we apply the minimum redundancy maximum relevance (MRMR) algorithm to identifying the most relevant set of inputs in modeling a water resources system. We further introduce two modified versions of the MRMR algorithm (α-MRMR and β-MRMR), where α and β are correction factors that are found to increase and decrease as a power-law function, respectively, with the progress of the input selection algorithms and the increase of the number of selected input variables. We apply the proposed algorithms to 22 reservoirs in California to predict daily releases based on a set from a 121 potential input variables. Results indicate that the two proposed algorithms are good measures of model inputs as reflected in enhanced model performance. The α-MRMR and β-MRMR values exhibit strong negative correlation to model performance as depicted in lower root-mean-square-error (RMSE) values.     

Groundwater Quality Modeling with a Small Data Set

Seventeen groundwater quality variables collected during an 8‐year period (2006 to 2013) in Andimeshk, Iran, were used to implement an artificial neural network (NN) with the purpose of constructing a water quality index (WQI). The method leading to the WQI avoids instabilities and overparameterization, two problems common when working with relatively small data sets. The groundwater quality variables used to construct the WQI were selected based on principal component analysis (PCA) by which the number of variables were decreased to six. To fulfill the goals of this study, the performance of three methods (1) bootstrap aggregation with early stopping; (2) noise injection; and (3) ensemble averaging with early stopping was compared. The criteria used for performance analysis was based on mean squared error (MSE) and coefficient of determination (R²) of the test data set and the correlation coefficients between WQI targets and NN predictions. This study confirmed the importance of PCA for variable selection and dimensionality reduction to reduce the risk of overfitting. Ensemble averaging with early stopping proved to be the best performed method. Owing to its high coefficient of determination (R² = 0.80) and correlation coefficient (r=0.91), we recommended ensemble averaging with early stopping as an accurate NN modeling procedure for water quality prediction in similar studies.     

Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting

Natural Resources Research - In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major...     

Factors encouraging sustainability integration into institutions of higher education

In view of the mountainous evidence on destruction of environmental quality and societal well-being as a consequence of rapid economic development, sustainability has gained vast attention from the community and industrial players. Tertiary education is a platform through which sustainability can be inculcated within the society as it imparts knowledge and provides various trainings. There has been extensive research on factors that encourage sustainability integration into Institutions of Higher Education in the last decade. However, majority of the previous publications only discuss one or two factors exclusively and there is no literature that summarizes and discusses such factors in a collective manner. This paper provides an overview of the main factors that encourage sustainability integration into Institutions of Higher Education in the last decade. It aims at providing a one-stop reference for future researchers who need a reference on factors that encourage sustainability integration into Institutions of Higher Education, especially those who are interested in conducting a progressive research in this context. Accordingly, a review of relevant publications from year 2000 and above was conducted and it was found that there are generally eight main factors, which encourage sustainability integration into Institutions of Higher Education, which are: (1) integration into curricula; (2) suitable pedagogy; (3) campus management; (4) research; (5) opportunities provision; (6) availability of social capital; (7) awareness level; and (8) community outreach. There is no indicator on the impact level of these factors, and thus, it is suggested that relevant research can be conducted in future.     

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.