http://hdl.handle.net/123456789/16975 2026-04-04T12:27:32Z 2026-04-04T12:27:32Z Mr. Arjan Das, 02-282171-001 http://hdl.handle.net/123456789/19948 2025-09-24T14:03:51Z 2025-01-01T00:00:00Z

Spatio-Temporal Studies of Air Pollutants and Their Impacts on Karachi Region, Pakistan Mr. Arjan Das, 02-282171-001 Air pollution is becoming a larger concern for the health of people, biodiversity, and the environment in big cities. In order to fully understand the spatiotemporal variability and primary causes of urban air quality (UAQ), it is crucial to monitor and model the UAQ in these locations. Due to Karachi's absence of a reference network, it was customarily impossible to create high-resolution AQ maps in metropolitan regions. It is now important to configure and develop high-resolution AQ maps using remote sensing retrievals corresponding to the four-year availability of federal and provincial air quality ground base. This is what this research study was aimed to accomplish by examining the suitability for urban AQ monitoring with modelling and how measurements can be improved using advanced validation techniques, ground base concentration measurements, based on multiple criteria and using sensors of various grades, employing various AQ modelling and mapping techniques, including geospatial interpolations, Pearson coefficient correlation (PCC) land use regression (LUR) and Weather Research and Forecast (WRF) modelling. Using univariant and multivariant linear regression and a generalized linear model, universal references were employed as the Pakistan standards to analyze the ground base contaminations. In order to map air quality, validate models, analyses spatiotemporal variability of contaminants and model performance, data from the designed GB data was employed. This research study was designed to cover four components, geospatial and PCC analysis of criteria pollutants, integrating and correlate significance of remote satellite origin (OMI NO2) and ground base NO2 data and application of LUR and WRF techniques to nine years (2013-2021) PM10-AOD data with climatic variables to predict the surface PM10 concentration. Geospatial analysis to generated database of criteria pollutants to account the pollutants trend and distribution over Karachi urban area. PCC applied to three Criteria pollutants databases, correlate to each variable and estimate the influence to each other with correlation and significance levels. LUR and WRF univariant and multivariant linear regression analysis to formulate quantitative models to predict and forecast PM10 concentration and estimate the near surface PM10 values with MODIS AOD retrievals. The spatial analysis of NO, NO2, SO2, CO, O3, PM2.5 and PM10 suggested that Karachi urban area is distributed in three regions on the basis of air quality trends, industrial, commercial with high traffic flows and elite residential localities. PCC statistically correlate the alldesignated criteria pollutants and estimated the significance among them, the results were acceptable and there was moderately correlation with high level of significance between the major pollutants, NOx and PMs. This study investigates the air quality in Karachi over the years 2015, 2017, and 2018, focusing on all criteria pollutants such as NO, NO2, SO2, CO, O3, PM10 and PM2.5. Using Pearson correlation analysis, the study highlights the strong and increasing correlations between NO, NO2, SO2, CO and PM2.5 pollutants, indicating common sources such as vehicular and industrial emissions. The concentration of various air pollutants in Karachi's atmosphere showed a significant positive correlation with each other except the ground-level Ozone (O3) which displayed a marked deviation in behavior from other pollutants. NO, NO2 has weak correlation coefficient with O3. O3 demonstrated high insignificant negative correlation with SO2, CO and PM10. Only PM2.5 has moderate correlation with O3 during 2018. O3 showed insignificant very weak correlations with other pollutants in Karachi region in all three study years, indicating it is due to unfavorable climatic conditions to restrict photochemical reactions to form this secondary pollutant. However, extremely polluted areas continue to be a source of bias and uncertainties due to meteorological variables, land use land cover and socioeconomic indicators. Monthly average OMI NO2 concentrations from space borne tools with GBNO2 observations were used to train and validate by PCC correlation. Comparing estimated and measured concentrations allowed for cross-validation of the models. Planet Boundary Layer Height (PBLH) had a significant effect on NO2 concentrations, whereas major roads intersections, commercial and industrial areas had a positive significant effect on NO2 growth in air Karachi. This technique estimated realistic (based on prior expectations) NO2 data measurements at GB sites are compared and correlated with Ozone Monitoring Instrument (KNMI TEMIS OMI NO2) retrievals over Karachi, together with validation against ground measurements. The approach proved effective in emphasizing the hotspots of NO2 concentrations in Karachi and illustrating the regional heterogeneity. In Karachi Metropolitan, LUR models were created for the first time incorporating a major variable (PM10) of criteria pollutants and climatic related factors. Here, univariant and multivariate regression procedures were also employed to build LUR and WRF models, which performed better than their linear versions, in contrast to earlier studies that mostly used linear techniques. In addition, the development and testing of an WRF forecasting model revealed that whereas point sources were mostly responsible for controlling PM10 concentrations, road traffic was primarily responsible for controlling PM10 concentrations. The study's key findings showed that over a nine-year period, the average values of observed PM10 and AOD increased by approximately 182% and 208%, respectively. However, during the COVID-19 lockdown period, these values experienced a significant reduction, ranging from approximately 28% to 35%. In Karachi, using the data fusion technique known as natural Neighbor (ArcMap), modelled and measured concentrations were fused (integrated) to produce high-resolution AQ maps. The key conclusions of this study are as follows: (a) It suggested a geospatial and temporal model for AP in ambient air quality. Although in-situ measurements are a relatively expensive source of AP data, they necessitate reliable outfield instrumentation. (b) Pearson Coefficient Correlation (PCC) two-dimensional generalization of temporal auto-correlation in which the correlation (r) and degree of significance (p) between multiple air quality variables. (c) insitu NO2 concentrations are integrate and correlate (PCC) with OMI NO2 retrievals, identified the emission and dispersion of NOx (NO+NO2), (d) statistical modelling estimations and observed concentrations were combined using data fusion techniques (LUR and WRF). These data fusion techniques are helpful tools for enhancing data quality and creating high-resolution predictions and predicting spatial missing data to fill in spatial missing PM10 data. The all studies are suggested that gray pollutants (PM) are the major portion of air pollutants in Karachi urban region and have adverse impacts on individual health and economic losses. There is a dearth of trace gaseous and particulate matter (fine and ultrafine particles) concentrations data continuously, therefore intensive work is needed on monitoring, modelling and management of NOx (NO+NO2) and PMs in Karachi region. To conclude, human activities have had impact on the air quality contamination in Karachi urban areas, in the meantime, the quality and quantity of fossil fuel as energy surging consumption may add significantly air pollution in Karachi s atmosphere. Based on multiple criteria air pollutants quantification studies concluded and proposed for future to structuring an AAQMN in Karachi urban settlements to record inventories continuously minutely, hourly, daily, monthly and yearly to helping in establishment of management policies on ground reality basis and enforce regulations properly. The future trajectory of air pollutant research in Karachi region needs the effective implementation of regulations and policies to reduce air pollution, addressing poverty, illiteracy and public awareness. It also discusses the development of better modeling and prediction techniques, improving satellite-based estimation, geostatistical hybrid modelling and exploring AI modeling approaches for future research. Supervised by Prof. Dr. Yasmin Nergis

2025-01-01T00:00:00Z Waqar Ali, 01-286172-009 http://hdl.handle.net/123456789/20045 2025-11-17T10:09:29Z 2025-01-01T00:00:00Z

SPATIO-Temporal Change Analysis of Climate Induced Hydrological Extreme Events in the Swat River Basin, Khyber Pakhtunkhwa, Pakistan Waqar Ali, 01-286172-009 The study deals with spatio-temporal change analysis of temperature, precipitation and streamflow extreme events in the Swat River Basin, Pakistan. The impacts of climate change particularly extreme events are not uniform: in fact, some regions are more affected than others, so it is important to know that when and which area is how much vulnerable to climate change? Hence, spatio-temporal analysis. Similarly, the study area is highly susceptible to flood due to ice/snow melting, rough terrain and heavy rainfall in monsoon. Three hydropower plants are there on the river with total operational capacity of around 123 MW and has a further potential of almost 1000 MW. Likewise, the area is important from tourism point of view and is known as “Eastern Switzerland” throughout the country. Data collection involve two datasets from two different sources. The daily temperature and precipitation data from the year 1989 to 2018 of Dir, Kalam, Malam Jabba and Saidu Sharif station was provided by Pakistan Meteorological Department, while the streamflow data of “Chakdara” gauge was taken from WAPDA. For trend analysis in observed data, the study employed Mankendall and a Spearman’s Rho tests while for projection of temperature/precipitation, SDSM (Statistical Downscaling Model) with CanESM2 GCM was used. Adaptation capacity of the people of the area was gauged by using stratified random sampling technique to assess the current adaptation capacity, gaps in the capacity and actions required to fill those gaps. R2 between monthly simulated and historical temperature ranged between 0.82 and 0.91 and 0.92 to 0.96 for calibration and validation periods, correspondingly. Areal precipitation experienced R2 of 0.49 for training and 0.35 for confirmation period. Historical temperature exhibited insignificant declining trend at every station excluding Saidu Sharif, while precipitation experienced rising trend at Kalam and Malam Jabba and diminishing trend at Saidu Sharif and Dir. A remarkable increasing trend was found in the discharge on annual maximum time-series and a notable rise was there on monthly scale in October, November, December, and January. From March till August, the runoff has shown a decreasing trend where a remarkable decreasing tendency was noticed for June/July. More than 2 ºC increase was observed in projected annual maximum (2041 to 2060) temperature (Areal and Dir), while for Malam Jabba, Kalam, and Saidu Sharif: about 1 ºC increase was noticed. Similarly, about 12% rise was noticed in seasonal precipitation (summer and autumn) and annual maximum (areal) under all scenarios except RCP 4.5 where 20% and 32% rise was observed in summer and autumn seasons, correspondingly. While simulating maximum precipitation/temperature, the performance of SDSM was satisfactory. Adaptation options for safeguarding lives/livelihood and environmental/ecological resources of the area include: formulation of appropriate risk management system for protecting crops, development of livestock surveillance/disease detecting system, institutional reforms, raising awareness and promoting innovative research, forest management, enhancing adaptation capacity and reducing poverty. Future research aims by including other sources of climate data (like remotely sensed climate data appropriate for hilly watersheds) to reduce the uncertainties in the results due to limited weather stations in the study area and adopting a multi-model approach. The study will help in evaluating impact of climate change on environment, agriculture, human health, tourism and water resources of the area for strengthening local decision-making, adaptive capacity and strategic planning Supervised by Dr. Asma Jamil

2025-01-01T00:00:00Z Aneela Khan, 01-286172-002 http://hdl.handle.net/123456789/20046 2025-11-17T10:18:51Z 2025-01-01T00:00:00Z

Hydroclimatological Response of Selected Subbasins of the Upper Indus Basin for Mid and End Century Climate Change Aneela Khan, 01-286172-002 Variability in the radiative balance of the earth resulting in climate change has posed immense challenges, such as rising temperatures, altered precipitation patterns and increased frequency of extreme weather events. These have jeopardized the availability of water, food and health conditions for the humans. Pakistan’s geographical location makes it one of the most affected countries by climate change. Hence the study was conducted to investigate the climate variability and the impact of projected climate change on the future streamflow of the Hunza, Astore and Shigar River basin located in the Hindukush Karakorum Himalaya (HKH) region. To examine the climate variability annual and seasonal (winter & spring, pre-monsoon and monsoon).trend and correlation analysis of the temperature and precipitation records, river discharge and basin-wide (BW) and altitudinal zonewise (z1, z2 & z3) snow cover area (SCA) was carried out covering a period from 2000 to 2018 . The Modified Mann Kendall (MKK) test was utilized to analyze the trends. The Kendall Rank, Spearman and Pearson correlation tests were employed to conduct correlation analysis. The snow images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Four CMIP6 based GCMs, including MPI-ESM1-2-HR, MIROC6, NEMS3 and INM-CM3 under Shared Socioeconomic Pathway (SSP245 and SSP585) were utilized to investigate the future changes in annual and seasonal temperature and precipitation across the Early century (2025-2050), Midcentury (2050-2075), and Late century (2075-2100)) in the study area. Moreover, the hydrological model Snowmelt Runoff Model (SRM) was applied over the study basins to investigate future changes in hydrological components over the study basins till the end of 21st century. The MODIS investigation suggested an overall BW and ZW decline in SCA in the three basins. Moreover, the temperature trend analysis suggested an increase in annual and seasonal BW temperature indicating warming in the three basins. Further, the CMIP6 based simulations exhibited an increase in mean annual temperature under SSP245 and SSP585, throughout the 21st century. Based on average temperature change for all scenario periods the monsoon, pre-monsoon, and winter & spring are categorized as first, second and third respectively, for the rise in annual precipitation under SSP585 for 21st end century. Significant heterogeneity in projected precipitation has been observed in the three basins and even at different stations within the same basin. The Astore, Hunza and Shigar basins are classified as first, second and third, respectively, for the rise in annual precipitation under SSP585 for 21st end century. The SRM generated future streamflow projections presented a substantial shift in hydrological response of the study basins. For all the scenario periods an increase in average annual streamflow in the basins has been observed. This is in response to the rise in temperature that is instigating a significant rise in snow and glacier-melt runoff. The stream flow projections for the pre-monsoon season indicated an earlier offset and considerable surge in the three basins in coming decades for all the scenarios, especially for the end-century simulations. Hence the study results reveal that climate change will significantly affect the hydroclimatology of the study basins. This could severely impact water availability across the Indus River Basin, both upstream and downstream areas. The objective of the research conducted was to interpret the consequences of climate change on SCA and water resources hence the research results are highly beneficial for effective sustainable water resource management and flood risk assessment in the study area. The study undertaken closely aligns with several United Nations Sustainable Development Goals (UNSDGs), particularly SDG 13 (Climate Action) and SDG 6 (Clean Water and Sanitation). Supervised by Dr. Humera Farah

2025-01-01T00:00:00Z Zainab Wahab, 01-286172-008 http://hdl.handle.net/123456789/20044 2025-11-17T09:55:30Z 2025-01-01T00:00:00Z

Effect of Urbanization and Climate Change On Metropolitan Environment In Islamabad, Pakistan Zainab Wahab, 01-286172-008 Urbanization is a global and diverse phenomenon characterized by a rapid rise in the density of human population and land use and land cover changes. It has a substantial effect on the interaction between urban and rural regions. The relationship between urbanization and climate change is important for promoting sustainable development, especially evident in metropolitan centers like Islamabad. This study examines the significant effects of urbanization and climate change on the metropolitan environment of Islamabad. Over the span of 42 years (1979-2020), the study thoroughly investigates deviations in Land Use and Land Cover (LULC) demography and dynamics shifts utilizing the Maximum Likelihood Classification (MLC) algorithm. The study focuses on four primary land use and land cover categories: Bare soil, Vegetation, Built-up Area, and Water. In addition, a detailed assessment of the regional climate is presented, with particular attention to temperature and precipitation trends during the previous 58 years (1960-2018). The study also estimates the combined impact of urbanization and climate change on the metropolitan environment using the Ridge Regression Model. Finally, the research contributes to practical solutions for creating a resilient and sustainable environment, utilizing Sustainable Development Goals (SDGs) tailored to the results observed in Islamabad. According to the results, there has been a significant rise in the built-up area by 111.20 km2 over the last four decades. Population growth was also detected over time from 168,745 to 1,129,198 individuals, indicating a major contribution to the extension of urbanization. The results of the current study indicate that demographic expansion intensifies the enlargement of the built-up areas, indicating a strong correlation between these two variables. The mean annual precipitation, mean annual minimum temperature, and mean annual maximum temperature show an increasing trend, which points to a significant change in the city's climate. The ridge regression model results explained that the combination of climate variables (Minimum Temperature, Maximum Temperature, and Rainfall) and population data contributes significantly to understanding changes in urban areas with an R2 of 0.98. This shows that population and climate variables all have an impact on the dependent variable i.e., urban area. The findings of the current research can help develop strategies for monitoring sustainable urbanization that comply with the SDGs. It leverages the potential of Geographic Information System (GIS) applications and satellite imagery. Supervised by Dr. Humera Farah

2025-01-01T00:00:00Z