Linear modelling of water potential and supply for decentralized Energy-Water-Food systems - case Study St. Rupert Mayer, Zimbabwe

Limited water accessibility threatens the development of communities, especially where agriculture is the main income source.  The implementation of decentralized Energy-Water-Food systems is a promising approach to improve the situation in these communities, creating synergies and improving the profitability of the system. The model urbs optimizes Energy-Water-Food systems to generate the highest revenues, considering the local conditions and sustainability limits. This work improves the hydrogeological part of urbs in order to model the water potential of a given community, establishing interrelations of the water sector with the energy and food sectors, and maximizing the long-term benefits within the sustainability limits. The proposed method was applied to the rural community of St. Rupert Mayer in Zimbabwe. In order to analyse the impact of data uncertainty on the model results, the sensitivity of the main input parameters is analysed. The results indicate that it is important to implement reliable input data for dimensioning the proper system configuration, as otherwise the whole system would not be sustainable.&nbs

Detecting and predicting the impact of land use changes on groundwater quality, a case study in Northern Kelantan, Malaysia

The conversions of forests and grass land to urban and farmland has exerted significant changes on terrestrial ecosystems. However, quantifying how these changes can affect the quality of water resources is still a challenge for hydrologists. Nitrate concentrations can be applied as an indicator to trace the link between land use changes and groundwater quality due to their solubility and easy transport from their source to the groundwater. In this study, 25 year records (from 1989 to 2014) of nitrate concentrations are applied to show the impact of land use changes on the quality of groundwater in Northern Kelantan, Malaysia, where large scale deforestation in recent decades has occurred. The results from the integration of time series analysis and geospatial modelling revealed that nitrate (NO3-N) concentrations significantly increased with approximately 8.1% and 3.89% annually in agricultural and residential wells, respectively, over 25 years. In 1989 only 1% of the total area had a nitrate value greater than 10 mg/L; and this value increased sharply to 48% by 2014. The significant increase in nitrate was only observed in a shallow aquifer with a 3.74% annual nitrate increase. Based on the result of the Autoregressive Integrated Moving Average (ARIMA) model the nitrate contamination is expected to continue to rise by about 2.64% and 3.9% annually until 2030 in agricultural and residential areas. The present study develops techniques for detecting and predicting the impact of land use changes on environmental parameters as an essential step in land and water resource management strategy development

The long-term impacts of anthropogenic and natural processes on groundwater deterioration in a multilayered aquifer

In many regions around the world, there are issues associated with groundwater resources due to human and natural factors. However, the relation between these factors is difficult to determine due to the large number of parameters and complex processes required. In order to understand the relation between land use allocations, the intrinsic factors of the aquifer, climate change data and groundwater chemistry in the multilayered aquifer system in Malaysia's Northern Kelantan Basin, twenty-two years hydrogeochemical data set was used in this research. The groundwater salinisation in the intermediate aquifer, which mainly extends along the coastal line, was revealed through the hydrogeochemical investigation. Even so, there had been no significant trend detected on groundwater salinity from 1989 to 2011. In contrast to salinity, as seen from the nitrate contaminations there had been significantly increasing trends in the shallow aquifer, particularly in the central part of the study area. Additionally, a strong association between high nitrate values and the areas covered with palm oil cultivations and mixed agricultural have been detected by a multiple correspondence analysis (MCA), which implies that the increasing nitrate concentrations are associated with nitrate loading from the application of N-fertilisers. From the process of groundwater salinisation in the intermediate aquifer, could be seen that it has a strong correlation the aquifer lithology, specifically marine sediments which are influenced by the ancient seawater trapped within the sediments

Assessment of the potential contamination risk of nitrate in groundwater using indicator kriging (in Amol-Babol Plain, Iran)

In arid and semi-arid regions such as Amol–Babol Plain in north Iran, groundwater is a major source of drinking water. Excessive usage of fertilizers in agricultural land, domestic sewage and industrial wastewater may result in nitrate contamination. The main objective of this study is to assess the potential contamination risk of nitrate pollution. The groundwater samples were collected from 100 agriculture wells during wet and dry seasons in 2009 and analyzed for nitrate concentration. Indicator kriging (IK) method is applied to create maps indicating the predicted probability of nitrate concentrations in groundwater exceeding the WHO drinking water standard of 10 mg/L-N. Based on the risk probability maps, some areas on the southern side of Babol City and the north and north-western side of Amol City showed a high probability of nitrate contamination. Seasonal maps indicated that the probability of nitrate contamination increased in the wet season, compared to the dry season in the study area, due to increase runoff from irrigated lands. Indicator kriging with local indicator thresholds is shown to be a reliable method to assess uncertainty in the estimation

Groundwater quality assessment using integrated geochemical methods, multivariate statistical analysis, and geostatistical technique in shallow coastal aquifer of Terengganu, Malaysia

Extensive agricultural, residential, and industrial activities have increased demand for water supplies, which can lead to groundwater quality degradation. The integration of geochemical methods, multivariate statistical analysis, and geostatistical approaches were carried out on 169 groundwater samples to elucidate the regional factors and processes that influencing the geochemical composition of groundwater in coastal shallow aquifer of Terengganu, Malaysia. Hydrochemical modelling revealed that the abundance of Ca and Mg was contributed by carbonate and silicate weathering while higher HCO3 and Cl were resulted from reverse ion exchange reaction. Therefore, the dominant hydrogeochemical facies of groundwater was Ca-Mg-HCO3-Cl type. The influence of salinization resulting from seawater mixing to the groundwater was corroborated by Cl/HCO3 ratio, which affected around 50.9% of the groundwater samples slightly or moderately. Spatial mapping using ordinary kriging found that the threat of sea water intrusion is more prominent in the major river confluence especially around Terengganu and Marang River in the northeast and Dungun and Kemaman River confluence in southeast of study area. Moreover, factor analyses concluded that salinization, anthropogenic activities, reverse ion exchange, weathering processes, agricultural impact, and seasonal variations were the factors that regulate 63% of the major ion chemistry in study area. Finally, these findings showed the importance of understanding the hydrochemical characteristics for effective utilization, aquifer protection, and prediction of changes to minimize the effects of salinization and reduce human pollution such as agriculture and urbanization. It is essential steps in order to safeguard the utilization of groundwater resources for future generations

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain, Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area

Hydrogeochemistry and groundwater quality assessment of the multilayered aquifer in Lower Kelantan Basin, Kelantan, Malaysia

Continual expansion of population density, urbanization, agriculture, and industry in most parts of the world has increased the generation of pollution, which contributes to the deterioration of surface water quality. This causes the dependence on groundwater sources for their daily needs to accumulate day by day, which raises concerns about their quality and hydrogeochemistry. This study was carried out to increase understanding of the geological setup and assess the groundwater hydrogeochemical characteristics of the multilayered aquifers in Lower Kelantan Basin. Based on lithological data correlation of exploration wells, the study area can be divided into three main aquifers: shallow, intermediate and deep aquifers. From these three aquifers, 101 groundwater samples were collected and analyzed for various parameters. The results showed that pH values in the shallow, intermediate and deep aquifers were generally acidic to slightly alkaline. The sequences of major cations and anions were Na+ > Ca2+ > Mg2+ > K+ and HCO3− > Cl− > SO42− > CO32−, respectively. In the intermediate aquifer, the influence of ancient seawater was the primary factor that contributed to the elevated values of electrical conductivity (EC), Cl− and total dissolved solids (TDS). The main facies in the shallow aquifer were Ca–HCO3 and Na–HCO3 water types. The water types were dominated by Na–Cl and Na–HCO3 in the intermediate aquifer and by Na–HCO3 in the deep aquifer. The Gibbs diagram reveals that the majority of groundwater samples belonged to the deep aquifer and fell in the rock dominance zone. Shallow aquifer samples mostly fell in the rainfall zone, suggesting that this aquifer is affected by anthropogenic activities. In contrast, the results suggest that the deep aquifer is heavily influenced by natural processes

CO2 Emission Inventory of on road vehicles in Selangor State Inpeninsular Malaysia

The transportation sector has greatly contributed to the socio-economic development with inherent environmental impacts. This study estimated the emission of CO2 from transportation sector, particularly from the use of passenger cars in Selangor Malaysia. The total CO2 emission from the region was calculated based on total fuel consumption (Kg) and Emission Factor of CO2 (gr/kg fuel). Lorries and cars were responsible for the highest CO2 emission and the emissions rate were directly related to the type and amount of fuel used and emission factor of fuel. High amount of CO2 emission was due to increase in vehicles on the road thereby increasing pollution on the environment. GIS is one of the most suitable methods to estimate the total CO2 emission and the split between different vehicle types as it determined by the kilometre covered for each vehicle category

Spatial Assessment of Groundwater Quality Monitoring Wells Using Indicator Kriging and Risk Mapping, Amol-Babol Plain, Iran

The main aim of monitoring wells is to assess the conditions of groundwater quality in the aquifer system. An inappropriate distribution of sampling wells could produce insufficient or redundant data concerning groundwater quality. An optimal selection of representative monitoring well locations can be obtained by considering the natural and anthropogenic potential of pollution sources; the hydrogeological setting; and assessment of any existing data regarding monitoring networks. The main objective of this paper was to develop a new approach to identifying areas with a high risk of nitrate pollution for the Amol-Babol Plain, Iran. The indicator kriging method was applied to identify regions with a high probability of nitrate contamination using data obtained from 147 monitoring wells. The US-EPA DRASTIC method was then used in a GIS environment to assess groundwater vulnerability to nitrate contamination, and combined with data concerning the distribution of sources to produce a risk map. In the study area, around 3% of the total area has a strong probability of exceeding the nitrate threshold and a high–moderate risk of pollution, but is not covered adequately by sampling wells. However, the number of monitoring wells could be reduced in most parts of the study area to minimize redundant data and the cost of monitoring

Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran

Groundwater plays an essential role for human, animal, and plant life as well as an indispensable resource for the economy, especially in arid and semi-arid region. Appropriate monitoring strategies are required to assess the conditions of groundwater quality in the aquifer system, prevention of a potential threat to human health, and measurement of the efficiency of water protection. The main aim of this study is to assess and redesign the information-cost-effective groundwater monitoring network using geostatistical techniques in Amol-Babol Plain, Iran. The integration of multivariate statistical methods with geostatistical interpolation techniques revealed that salinity and total and faecal coliforms as time independent variables and hardness as a time dependent variable influenced the groundwater quality in the study area. The graphical geochemical analyses justified that the groundwater types vary from fresh water type in the west and south sides, to brackish-saline water type in central and eastern sides, and to saline water on the north-eastern area. Hydrogeochemical investigation revealed that evaporation/precipitation and dissolution of carbonate minerals as dominant factors, which control groundwater salinity and hardness in the study area, respectively. Since the agricultural lands cover more than 80% of the plain, the newly devised GIS-Index integration approach was proposed in order to identify the suitability of groundwater for irrigation usage and to determine suitable zones for irrigation activities based on the irrigation water quality index (IWQ) and hydrogeological factors. The index approach shows that more than 90% of the total study area has good to excellent suitability condition for irrigation purpose. Groundwater quality assessment based on the data obtained from arbitrary sampling wells might be presented redundant or shortage of information. Therefore, monitoring network wells should be optimized in information-cost-effective way, based on the current groundwater quality data and vulnerability of aquifer to contamination. DRASTIC model was applied as a vulnerability assessment method based on the physical environmental aquifer parameters for assessing potential risk zone of aquifer to contamination, which showed more than 88% of the total area was classified as low to moderate risk to pollutant. A new optimization approach was proposed for redesign monitoring network wells using optimization algorithm based on the vulnerability of aquifer to contaminations, estimation error of sampling wells, nearest distance between wells, and source of contamination in the study area. Application of mass estimation error revealed that 100 and 74 sampling wells are suitable scenarios for monitoring natural and anthropogenic contaminant, respectively. Combination of the selected scenarios in GIS showed that contaminant mass detection capacity of around 86% can be obtained from 114 sampling wells, instead of 154 initial sampling wells