The devastations and damages caused by climate change are apparent across the globe, specifically in the South Asian region where vulnerabilities to climate change among residents are high and climate change adaptation and mitigation awareness are extremely low. Pakistan's low adaptive capacity due to high poverty rate, limited financial resources and shortage of physical resources, and continual extreme climatic events including varying temperature, continual flooding, melting glaciers, saturation of lakes, earthquakes, hurricanes, storms, avalanches, droughts, scarcity of water, pest diseases, human healthcare issues, and seasonal and lifestyle changes have persistently threatened the ecosystem, biodiversity, human communities, animal habitations, forests, lands, and oceans with a potential to cause further damages in the future. The likely effect of climate change on common residents of Pakistan with comparison to the world and their per capita impact of climate change are terribly high with local animal species such as lions, vultures, dolphins, and tortoise facing extinction regardless of generating and contributing diminutively to global GHG emissions. The findings of the review suggested that GHG emissions cause climate change which has impacted agriculture livestock and forestry, weather trends and patterns, food water and energy security, and society of Pakistan. This review is a sectorial evaluation of climate change mitigation and adaption approaches in Pakistan in the aforementioned sectors and its economic costs which were identified to be between 7 to 14 billion USD per annum. The research suggested that governmental interference is essential for sustainable development of the country through strict accountability of resources and regulation implemented in the past for generating state-of-the-art climate policy.

A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan

Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a ferocious demand for obtaining an accurate prediction of WQIs is of high importance for perception of pollutant patterns in natural streams. Field studies conducted on different rivers indicated that there is no general relationship to yield water quality parameters with a permissible level of accuracy. Over the past decades, several artificial intelligence (AI) models have been employed to predict more precise estimation of WQIs rather than conventional models. In this way, through the current study, multivariate adaptive regression spline (MARS) and least square-support vector machine (LS-SVM), as machine learning methods, were used to predict indices of the five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). To improve the proposed approaches, 200 series of field data, collected from Karoun River southwest of Iran, pertain to the nine independent input parameters, namely electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), orthophosphate ([Formula: see text]), nitrite ([Formula: see text]), nitrate nitrogen ([Formula: see text]), turbidity, and pH. The performances of the LS-SVM and MARS techniques were quantified in both training and testing stages by means of several statistical parameters. Furthermore, the results of the proposed AI models were compared with those obtained using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression equations. Results of the present research work indicated that the proposed artificial intelligence techniques, as machine learning classifiers, were found to be efficient in order to predict water quality parameters.

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India

In the recent years, the use of sewage water for irrigation has attracted the attention of arid and semi-arid countries where the availability of fresh water is poor. Despite the potential use of sewage water in crop irrigation as effective and sustainable strategy, the environmental and human risks behind this use need to be deeply investigated. In this regard, an experiment was carried out under field conditions in Nursery, University College of Agriculture Sargodha, to evaluate the possible health risks of undesirable metals in wheat grains. Wheat variety Sarang was cultivated and irrigated with different combinations of ground (GW) and sewage water (SW). The concentrations of heavy metals (Cr, Cd, Ni, and Pb) and trace elements (Cu, Zn, and Fe) in wheat grains as well as in soil were determined. Moreover, the pollution load index (PLI), accumulation factor (AF), daily intake of metals (DIM), and health risk index (HRI) were calculated. Results showed that the concentration trend of heavy metals was Pb<Cr<Cu<Ni<Cd<Zn<Fe and Cr<Cu<Pb<Cd<Ni<Fe<Zn in soil and wheat, respectively. Among metals, Cd concentration in wheat exceeded the permissible limits regardless water quality, whereas Pb concentration in grain was within the acceptable levels as suggested by World Health Organization, when 100 % of SW was used for irrigation. Similar observation was reported for Cd concentration in the soil when wheat was irrigated with 100 % SW. In comparison to soil, the edible part of wheat presented lower concentration of all studied metals, except for Zn which was much higher compared to the tested soil samples. The higher concentration of Zn was responsible for increasing the DIM of Zn where, in average, the highest value was reported, particularly in 75 % SW treatment. This was reflected also in HRI where the maximum value was reported for Zinc under the same treatment. Higher value of HRI for wheat cultivated on polluted soils suggested that appropriate management of cultivated area is necessary for food safety and thus for public health. The results are expected to create awareness among the public on the safety of consuming food products grown in particular areas.

Health risk assessment of heavy metals in wheat using different water qualities: implication for human health

The Ramganga River flows from the mountainous regions of Kumaon Himalayas, through the forests of Jim Corbett National Park and the Ganga flood plains. It is the first major tributary of the Ganga River, carrying high sediment load causing frequent floods in major cities of Uttar Pradesh. The water discharge of the river is controlled by glacial melt as well as precipitation, making it a perennial river. This study is on the temporal and spatial variation of water discharge and sediment flux of the Ramganga River and identifies the factors which control them. In this study, 84 samples were collected from different locations over the 642 km stretch of the river and its major tributaries to observe the temporal and spatial variation of suspended matter in river water. In addition, daily water flow and sediment concentration data of two locations, e.g. Bareilly and Dabri, for a duration of 10 years were used to understand the variation in those parameters over an extended time period. An attempt was also made to relate meandering to the change in water discharge and sediment flux in the Ganga flood plains. Human activities also contribute to the sediment concentration. The results of this study showed that a significant amount of water flow and sediment flux (>75 %) were attributed to the monsoon months. However, in 2009, the results were not similar to other years, probably because of low rainfall due to the occurrence of an El Nino.

Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India

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Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India