Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

Degradation kinetics of forchlorfenuron in typical grapevine soils of India and its influence on specific soil enzyme activities

The rate of degradation of forchlorfenuron, a cytokinin-based plant growth regulator (PGR) was explored in typical grapevine soils of India with simultaneous evaluation of its effect on biochemical attributes of the test soils in terms of the activities of specific soil microbial enzymes. In all the test soils, namely clay, sandy-loam and silty-clay, the dissipation rate was faster at the beginning, which slowed down with time, indicating a non-linear pattern of degradation. Degradation in soils could best be explained by two-compartment 1st + 1st order kinetics with half-life ranging between 4–10 days. The results suggest that organic matter might be playing a major role in influencing the rate of degradation of forchlorfenuron in soil. The rate of degradation in sandy-loam soil was fastest followed by clay and silty-clay soils, respectively. Comparison of the rate of degradation in natural against sterilized soils suggests that microbial degradation might be the major pathway of residue dissipation. Changes in soil enzyme activities as a consequence of forchlorfenuron treatment were studied for extra-cellular enzymes namely acid phosphatase, alkaline phosphatase and β -glucosidase and intracellular enzyme-dehydrogenase. Although small changes in enzyme activities were observed, forchlorfenuron did not have any significant deleterious effect on the enzymatic activity of the test soils. Simple correlation studies between degradation percentage and individual enzyme activities did not establish any significant relationships. The pattern and change of enzyme activity was primarily the effect of the incubation period rather than the effect of forchlorfenuron itself.     

Degradation of tannic acid by cold-adapted <Emphasis Type="BoldItalic">Klebsiella</Emphasis> sp NACASA1 and phytotoxicity assessment of tannic acid and its degradation products

Background, aim, and scope  

The focus of the present study is to know the potential of bacterial isolate for tannic acid degradation at low temperature. Also, we tried to evaluate the suitability of phytotoxicity testing protocol for the determination of tannic acid toxicity.     

Use of best linear unbiased prediction for hot spot identification in two-way compositing

Compositing of individual samples is a cost-effective method for estimating a population mean, but at the expense of losing information about the individual sample values. The largest of these sample values (hotspot) is sometimes of particular interest. Sweep-out methods attempt to identify the hotspot and its value by quantifying a (hopefully, small) subset of individual values as well as the usual quantification of the composites. Sweep-out design is concerned with the sequential selection of individual samples for quantification on the basis of all earlier quantifications (both composite and individual). The design-goal is for the number of individual quantifications to be small (ideally, minimal). Previous sweep-out designs have applied to traditional (i.e., disjoint) compositing. This paper describes a sweep-out design suitable for two-way compositing. That is, the individual samples are arranged in a rectangular array and a composite is formed from each row and also from each column. At each step, the design employs all available measurements (composite and individual) to form the best linear unbiased predictions for the currently unquantified cells. The cell corresponding to the largest predicted value is chosen next for individual measurement. The procedure terminates when the hotspot has been identified with certainty.     

Moving beyond traditional macrofaunal community structure studies in the Indian Ocean continental shelf: a research synthesis based on research weaving

Environmental Science and Pollution Research - The Indian Ocean (IO) continental shelf characterized by unique oceanographic and meteorological features and extreme habitat is a biodiversity...     

Propagation functions for monitoring distributional changes

In environmental assessment and monitoring, a primary objective of the investigator is to describe the changes occurring in the environmentally important variables over time. Propagation functions have been proposed to describe the distributional changes occurring in the variable of interest at two different times. McDonald et al. (1992, 1995) proposed an estimator of propagation function under the assumption of normality. We conduct a detailed sensitivity analysis of inference based on the normal model. It turns out that this model is appropriate only for small departures from normality whereas, for moderate to large departures, both estimation and testing of hypothesis break down. Non-parametric estimation of the propagation function based on kernel density estimation is also considered and the robustness of the choice of bandwidth for kernel density estimation is investigated. Bootstrapping is employed to obtain confidence intervals for the propagation function and also to determine the critical regions for testing the significance of distributional changes between two sampling epochs. Also studied briefly is the mathematical form and graphical shape of the propagation function for some parametric bivariate families of distributions. Finally, the proposed estimation techniques are illustrated on a data set of tree ring widths.     

Water quality assessment of Fort lake of Belgaum (Karnataka) with special reference to zooplankton

Study on limnobiotic status of the Fort lake water was made to assess the potability of water from Feb 1998 to Jan 1999. Fort lake exhibits high alkalinity with pH ranging from 8.36 (June) to 10.6 (Feb and March), total alkalinity (TA) from 88 to 188 mg/l, total hardness (TH) from 59.8-217 mg/l, PO4 (7.2 to 13.6 mg/l and BOD (42.6-60.6 mg/l). From the observations it can be known that the Fort lake water is inferior and not suitable for drinking purposes. The possible factors for its poor quality have been discussed.     

Editorial: Composite sampling

The high costs of laboratory analytical procedures frequently strain environmental and public health budgets. Whether soil, water or biological tissue is being analysed, the cost of testing for chemical and pathogenic contaminants can be quite prohibitive.Composite sampling can substantially reduce analytical costs because the number of required analyses is reduced by compositing several samples into one and analysing the composited sample. By appropriate selection of the composite sample size and retesting of select individual samples, composite sampling may reveal the same information as would otherwise require many more analyses.Many of the limitations of composite sampling have been overcome by recent research, thus bringing out more widespread potential for using composite sampling to reduce costs of environmental and public health assessments while maintaining and often increasing the precision of sample-based inference.     

Spatially constrained clustering and upper level set scan hotspot detection in surveillance geoinformatics

We discuss upper level set (ULS) scan as a type of spatially constrained clustering in relation to two ways of imposing the spatial constraint, retrospectively versus progressively. We show that ULS scan produces the same results both ways; whereas two popular clustering techniques, single-linkage and K-means, can yield different results when spatial constraints are imposed retrospectively versus progressively. The ULS scan approach examines spatially connected components of a tessellation as a threshold is moved from the highest level (value) in the data to the lowest level. When the variable of interest on the tessellation is a rate of incidence, then a significance test is available based on binomial or Poisson null models and Monte Carlo techniques. This is a common context for detecting hotspots of diseases in epidemiological work. We also discuss an approach for extending the univariate methodology to accommodate multivariate contexts. Received: September 2005 / Revised: February 2006 This material is based upon work supported by (i) the National Science Foundation under Grant No. 0307010, (ii) the United States Environmental Protection Agency under Grant No. CR-83059301 and (iii) the Pennsylvania Department of Health using Tobacco Settlement Funds under Grant No. ME 01324. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the agencies.