Moazzam Ali Khan

Domestic water contamination in rapidly growing megacities of Asia: case of Karachi Pakistan.

The development of essential services including water and sanitation in many megacities of the economically developing countries of Asia cannot keep pace with their rapidly growing population and accompanying urban and industrial development. The inadequate water supply and poor sanitation services lead to contamination of their water supply. It also leads to the input of sewage water into the groundwater. The problem is seriously acute in Karachi, the largest city in Pakistan with a population of over 12 million and growing at 6 percent. This paper examines the problem of water contamination in Karachi. The paper presents the data on water quality from various sources, mainly municipal water supply, vendors and well water; the three major sources of water for domestic use in Karachi. Except municipal water from some areas and during certain periods, water from most other sources contain coliform bacteria, and in many cases faecal coliform, in amounts several magnitudes higher than any standards permit. Many samples have also been found to contain heavy metals including Chromium, Lead, Nickel and Arsenic in amounts excessive of permitted standards. The probable sources of contaminants for the various types of water (piped, vendors, wells) indicate that groundwater may be the main contributor. The very source of this groundwater is predominantly from sewage. The health hazards from consuming such contaminated water are obvious. The paper also evaluates the solutions that are being practiced, proposed or may be feasible, as well as those that are evolving.

Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

Abstract In this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50) on the eigenvalues and eigenvectors resulting from principal component analysis (PCA). For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22) of a small data set comprising of 55 samples (stations from where water samples were collected). Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

Vulnerability to climate change of surface water resources of coastal areas of Sindh, Pakistan

AbstractThe coastal district Thatta of Sindh Province is the area where availability of freshwater is a critical factor for the human survival and livelihood. As such, there are no perennial surface water resources available in the district except of River Indus and its canal system. In view of the changing climate change scenario of the coastal areas of Sindh, this study was undertaken to determine the availability and quality of surface water available in Thatta district. Results of the present investigation indicate that the water from the canals was not suitable for drinking as per WHO guidelines and National Standards for Drinking Water Quality Pakistan. Agriculture run-off, sewage leaks and metals quite often contaminate the surface water resources. However, users have no method to judge its quality except for the taste. Thus, most of the residents drink polluted water unknowingly, and do not question its quality. Increasing variability in rainfall pattern and reduced flow in the River Indus at down...

Estimation of Environmental Pollutants in Vegetables

ABSTRACT The Karachi area is drained by two major river basins used for dumping solid, industrial, agricultural, and domestic waste. The banks of the rivers are used for agricultural purposes and crops irrigated by contaminated river water. The industrial and domestic wastewater contains pollutants that are toxic to environmental and human health. Microbial loads and toxic heavy metals were determined in vegetables grown in the area. High levels of total coliforms count were found in lucerene (Portulaca oleracea L), amaranthus (Amaranthus spp. L.), and spinach (Spinacia oleracea L.) as well as chilies (Capsicum annum L.). Total fecal coliforms were highest in amaranthus, maize (Zea mays L), and gourd (Lagenaria siceraria L). Total fecal streptococci count were high in spinach, maize, and okra. Low levels of bacteria were found in bitter gourd (Momordica charantia L.), luffa (Luffa cylindrica L.), and gourd. There was no Cr and Cd in samples; Cu concentration was highest in beetle leaf (Piper betle L.) and spinach and lowest in luffa. Lead was highest in lucerne and spinach and lowest in bitter gourd. Nickel concentration was highest in okra (Abelmoschus esculentus L.) and beetle leaf and lowest in luffa. Regardless of vegetable tested, none were considered safe for consumption based on levels of toxins and/or bacteria.

An Application of Principal Component Analysis to The Study of Activated Sludge Treatment System Performance Efficiency For the Degradation of Malathion

The biosimulator system (activated sludge system) have proved to be very effective in the treatment of wastewater containing high content of pesticide and Chemical Oxygen Demand (COD) in terms of organic load and the quality of effluent being obtained after treatment was acceptable for landscape irrigation and for growing plants of ornamental value for commercial use. The biosimulator can be operated without being an aesthetic nuisance at a considerably low Dissolved Oxygen (DO). The efficiency of the biosimulator at high organic load (pesticide inobulated) and the inoculation of strain of Pseudomonas capable of degrading malathion, could be used to minimize time for treating wastewater containing high content of hazardous pesticide.The principal component analysis exposed the groups of correlated variables and their importance in the data structure. MalIN -MalOUT, TMC, MalOUT/MalIN and CODOUT/CODIN were highly correlated with each other and emerged as the variables controlling the first component. DO, pH and retention time governed the second component. The third component of PCA essentially repeated the trend exhibited by the first two components. The presence of pesticides or their metabolites, emphasizes the need for conducting monitoring studies, in order to draw a national picture for overall assessment of the situation.