Valuation of socio-economic indicators for progressing urban sustainability under distinctive rubrics

The key objective of the study is to collect the factors which play an important role in the city's sustainability and implementation advantages for the development of cities in future. This article develops an urban sustainability assessment framework by giving GIS-based decision support tools to guide cities toward sustainability. Multicriteria analysis was used as the decision support system and provides an analytical framework for assessing differences in the level of criteria and ranking decision options. It has the capability for assessment of urban sustainability because it brings sustainability criteria from three pillars of sustainability, environmental, social, and economic, which provide an integrated approach for assessment of urban sustainability. The GIS-based multicriteria analysis serves as a sustainability support system that maps urban sustainability and the underlying environmental, social, and economic conditions. The results from the study show that the four cities - Faisalabad, Lahore, Gujranwala, and Multan - have better economic conditions while only Lahore and Faisalabad showed social progress and the remaining cities showed less suitability. For the environmental index, none of the cities attained high suitability. Lahore, Faisalabad, and Rawalpindi showed better conditions than Gujranwala and Multan. It is demonstrated that Punjab cities have made progress in economic condition and improvement in social condition but have poor environmental condition. In the study, the environmental dimension has indicators which have more impact on the urban sustainability. Environmental degradation is observed in all selected regions and not a single region showed suitability toward its environmental condition and due to this none of the cities gained suitable scores for urban sustainability. Consequently, urban sustainability is a multidimensional and dynamic process that needs regular evaluation and monitoring. Thus, this article contends that the tools help to highlight and emphasize those areas that need guidance in achieving urban sustainability.     

Socio-economic drivers of deforestation in Roghani Valley,Hindu-Raj Mountains,Northern Pakistan

Deforestation and associated ecological disturbances are the issues of global concern.Researchers have investigated a number of driving forces which accelerate the rate of deforestation at local and regional levels.These include poverty,population growth,market demand and prices,political instability,agricultural expansion and changes in property right and ownership regimes.This paper seeks to explore the impacts of population growth,changing tenure system and other socioeconomic factors on the forest cover of Roghani Valley,located in Hindu Raj Mountains,Northern Pakistan.The present study is mainly based on information collected through participatory observation,selfadministered interviews and questionnaire survey.Geographical Information System（GIS） database is also used for mapping and quantification.The results reveal that in the past three to four decades the study area has been subjected to severe deforestation and about half of the forest area has been converted into barren land.Thus,the area under natural forests decreased from 2099 to 1444 hectares in four decades.This large-scale deforestation is attributed to both proximate and under lying causes particularly traditional land tenure system and demographic development.Consequently,forest resources have been degraded and a number of plant species have disappeared from the forests of the study area while several others are in the process of disappearance.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

On rising temperature trends of Karachi in Pakistan

Karachi is the largest city of Pakistan. The temperature change in Karachi is studied in this research by analyzing the time series data of mean maximum temperature (MMxT), mean minimum temperature (MMiT) and mean annual temperature (MAT) from 1947 to 2005 (59 years). Data is analyzed in three parts by running linear regression and by taking anomalies of all time periods: (a) whole period from 1947–2005; (b) phase one 1947–1975 and (c) phase two 1976–2005. During 1947 to 2005 MMxT has increased about 4.6°C, MMiT has no change and MAT has increased 2.25°C. During 1947–1975, MMxT increased 1.9°C, in this period there is − 1.3°C decrease in MMiT and MAT has raised upto 0.3°C. During 1976–2005, the MMxT, MMiT and MAT increased 2.7°C, 1.2°C and 1.95°C, respectively. The analysis shows significantly the role of extreme vulnerability of MMxT in rising the temperature of Karachi than the MMiT.     

BIFoR FACE: Water–soil–vegetation–atmosphere data from a temperate deciduous forest catchment,including under elevated CO2

The ecosystem services provided by forests modulate runoff generation processes, nutrient cycling and water and energy exchange between soils, vegetation and atmosphere. Increasing atmospheric CO₂ affects many linked aspects of forest and catchment function in ways we do not adequately understand. Global levels of atmospheric CO₂ will be around 40% higher in 2050 than current levels, yet estimates of how water and solute fluxes in forested catchments will respond to increased CO₂ are highly uncertain. The Free Air CO₂ Enrichment (FACE) facility of the University of Birmingham's Institute of Forest Research (BIFoR) is the only FACE in mature deciduous forest. The site specializes in fundamental studies of the response of whole ecosystem patches of mature, deciduous, temperate woodland to elevated CO₂ (eCO₂). Here, we describe a dataset of hydrological parameters – seven weather parameters at each of three heights and four locations, shallow soil moisture and temperature, stream hydrology and CO₂ enrichment – retrieved at high frequency from the BIFoR FACE catchment.     

Spatial and temporal dynamics of nitrogen exchange in an upwelling reach of a groundwater-fed river and potential response to perturbations changing rainfall patterns under UK climate change scenarios

We report the complex spatial and temporal dynamics of hyporheic exchange flows (HEFs) and nitrogen exchange in an upwelling reach of a 200 m groundwater-fed river. We show how research combining hydrological measurement, geophysics and isotopes, together with nutrient speciation techniques provides insight on nitrogen pathways and transformations that could not have been captured otherwise, including a zone of vertical preferential discharge of nitrate from deeper groundwater, and a zone of rapid denitrification linking the floodplain with the riverbed. Nitrate attenuation in the reach is dominated by denitrification but is spatially highly variable. This variability is driven by groundwater flow pathways and landscape setting, which influences hyporheic flow, residence time and nitrate removal. We observed the spatial connectivity of the river to the riparian zone is important because zones of horizontal preferential discharge supply organic matter from the floodplain and create anoxic riverbed conditions with overlapping zones of nitrification potential and denitrification activity that peaked 10–20 cm below the riverbed. Our data also show that temporal variability in water pathways in the reach is driven by changes in stage of the order of tens of centimetres and by strength of water flux, which may influence the depth of delivery of dissolved organic carbon. The temporal variability is sensitive to changes to river flows under UK climate projections that anticipate a 14%–15% increase in regional median winter rainfall and a 14%–19% reduction in summer rainfall. Superimposed on seasonal projections is more intensive storm activity that will likely lead to a more dynamic and inherently complex (hydrologically and biogeochemically) hyporheic zone. We recorded direct evidence of suppression of upwelling groundwater (flow reversal) during rainfall events. Such flow reversal may fuel riverbed sediments whereby delivery of organic carbon to depth, and higher denitrification rates in HEFs might act in concert to make nitrate removal in the riverbed more efficient.     

Flood risk perception and its determinants among rural households in two communities in Khyber Pakhtunkhwa,Pakistan

Natural Hazards - Risk perception plays a vital part in flood risk management and mitigation strategies. Therefore, this study aims at first to measure the risk perception of the vulnerable...     

Feasibility,emission and fuel requirement analysis of hybrid car versus solar electric car: a comparative study

The problem regarding environment has been considered as contemporary issue, and to cater this, various technologies have been revolutionized in vehicle transport field. Efforts have been made to make vehicle engine efficient and introducing hybridized vehicles with the aim of reducing emissions and less fuel dependency. In essence of this, trends of solar electric cars in different countries have been reviewed. Feasibility analysis is done by doing fuel cost analysis of two cases, i.e., simple hybrid vehicle and hybrid vehicle equipped with solar module and increased battery energy storage capacity for a specific round trip distance between two cities, i.e., Rawalpindi and Islamabad in comparison with feasibility of third case, i.e., proposed solar electric car model. The solar module selection along with desired number of batteries with charging and discharging time and motor power required to carry five persons weight (70 kg each) is calculated for third case. Moreover, total carbon dioxide emission analysis has been carried out from car material production to its assembly, manufacturing solar module and nickel metal hydride battery for each case. The annual carbon dioxide emitted from fuel in first two cases relative to electric outlet in third case for specific distance has been analyzed. On large scale, emission analyses for hundred cars of each case have been done at 100 km distance. From calculations, it is revealed that overall emissions in third case on large scale and from its material production, assembly, solar module and batteries manufacturing perspective are comparatively less than other cases.