Application of GIS in the study of mass transport of pollutants by Adyar and Cooum Rivers in Chennai, Tamilnadu

Residential, industrial, commercial, institutional and recreational activities discharge degradable and non-degradable wastes that reach the coastal water through rivers and cause coastal pollution. In the present study, mass transport of pollutants by Adyar and Cooum Rivers to the coastal water as a result of land-based discharges was estimated during low tide. The lowest and the highest flow recorded in Adyar varied from 514.59 to 2,585.08×10⁶ litres/day. Similarly, the flow in Cooum River fluctuated between 266.45 and 709.34×10⁶ litres/day. The present study revealed that the Adyar River transported 53.89–454.11 t/d of suspended solids, 0.06–19.64 t/d of ammonia, 15.95–123.24 t/d of nitrate and 0.4–17.86 t/d of phosphate, 0.004–0.09 kg/d of cadmium, 0.15–1.29 kg/d of lead and 3.03–17.58 kg/d of zinc to the coastal water owing to its high discharge. Similarly, the Cooum River transported 11.87–120.06 t/d of suspended solids, 0.08–58.7 t/d of ammonia, 6.11–29.25 t/d of nitrate and 0.66–10.73 t/d of phosphate, 0.003–0.021 kg/d of cadmium, 0.02–0.44 kg/d of lead and 1.36–3.87 kg/d of zinc. A higher concentration of suspended solids was noticed in post monsoon and summer months. An increase in the mass transport of ammonia, nitrate, phosphate in summer months (April and May) and an increase in the mass transport of cadmium, lead and zinc were observed in monsoon months (October–December) in both the rivers. Thus mass transport of pollutants study reveal that Cooum and Adyar Rivers in Chennai contribute to coastal pollution by transporting inorganic and trace metals significantly through land drainage.     

Development of an integrated sensor to measure odors

Odorous air samples collected from several sources were presented to an olfactometer, an electronic nose, a hydrogen sulfide (H(2)S) detector and an ammonia (NH(3)) detector. The olfactometry measurements were used as the expected values while measurements from the other instrumentation values became input variables. Five hypotheses were established to relate the input variables and the expected values. Both linear regression and artificial neural network analyses were used to test the hypotheses. Principal component analysis was utilized to reduce the dimensionality of the electronic nose measurements from 33 to 3 without significant loss of information. The electronic nose or the H(2)S detector can individually predict odor concentration measurements with similar accuracy (R (2) = 0.46 and 0.50, respectively). Although the NH(3) detector alone has a very poor relationship with odor concentration measurements, combining the H(2)S and NH(3) detectors can predict odor concentrations more accurately (R (2) = 0.58) than either individual instrument. Data from the integration of the electronic nose, H(2)S, and NH(3) detectors produce the best prediction of odor concentrations (R (2) = 0.75). With this accuracy, odor concentration measurements can be confidently represented by integrating an electronic nose, and H(2)S and NH(3) detectors.     

Passive aerosol sampler for particle concentrations and size distributions

This research evaluated the UNC passive aerosol sampler as a tool to measure particle mass concentrations and size distributions. The exposure scenario represented high concentrations and exposure periods of a few hours. Mass concentrations measured with the passive sampler were compared to concentrations measured using both a dichotomous sampler and an aerodynamic particle sizer (APS). In addition, the size distributions measured with the passive sampler were compared to those measured using the APS. Mass concentrations measured using the dichotomous sampler and the APS agreed well. The passive sampler tracked, but tended to overestimate, mass concentrations measured by the other two instruments. Size distributions measured with the passive sampler followed the general pattern of those measured using the APS. Overall, the passive sampler demonstrated both its utility and its limitations in these tests. The concentration measurements and size distributions found using passive samplers were more variable than those of the other instruments, but generally followed the data taken using the other methods. The advantages of low cost and ease of use offset the limitations in data quality with the passive sampler; these advantages are particularly welcome for sampling situations where aerosol properties vary over space or time.     

Coupling upland watershed and downstream waterbody hydrodynamic and water quality models (SWAT and CE-QUAL-W2) for better water resources management in complex river basins

Effective water resources management programs have always incorporated detailed analyses of hydrological and water quality processes in the upland watershed and downstream waterbody. We have integrated two powerful hydrological and water quality models (SWAT and CE-QUAL-W2) to simulate the combined processes of water quantity and quality both in the upland watershed and downstream waterbody. Whereas the SWAT model outputs water quality variables in its entirety, the CE-QUAL-W2 model requires inputs in various pools of organic matter contents. An intermediate program was developed to extract outputs from SWAT at required subbasin and reach outlets and converts them into acceptable CE-QUAL-W2 inputs. The CE-QUAL-W2 model was later calibrated for various hydrodynamic and water quality simulations in the Cedar Creek Reservoir, TX, USA. The results indicate that the two models are compatible and can be used to assess and manage water resources in complex watersheds comprised of upland watershed and downstream waterbodies.     

Quantitative arsenic speciation in two species of earthworms from a former mine site

The relationship between the total arsenic concentration and the chemical speciation of arsenic in two species of earthworm (Lumbricus rubellus and Dendrodrilus rubidus) in relation to the host soil, was investigated for 13 sites of varying arsenic content, including a background level garden soil and a former mine site at the Devon Great Consols, UK. Earthworms were collected with the host soil (As soil concentration range 16-12, 466 mg kg(-1) dry weight) and measured for their total arsenic (concentration range 7-595 mg kg(-1) dry weight) using inductively coupled plasma mass spectrometry (ICP-MS). A methanol-water mixture was used to extract arsenic species from the earthworms prior to determination of the individual arsenic species by a combination of anion and cation exchange high performance liquid chromatography coupled to inductively coupled plasma mass spectrometry (HPLC-ICP-MS). A gradient elution anion exchange method is presented whereby nine arsenic species could be measured in one sample injection. Arsenic species were identified by comparison of retention times and sample spiking with known standards and a fully characterised seaweed extract. Arsenic was generally present in the earthworm as arsenate (As(V)) or arsenite (As(III)) and arsenobetaine (AB). Methylarsonate (MA), dimethylarsinate (DMA) and three arsenosugars (glycerol, phosphate, sulfate) were present as minor constituents. These results are discussed in relation to the mechanisms for coping with exposure to soil bound arsenic.     

A comparison of speciated atmospheric mercury at an urban center and an upwind rural location

Gaseous elemental mercury (GEM), particulate mercury (PHg) and reactive gaseous mercury (RGM) were measured every other hour at a rural location in south central Wisconsin (Devil's Lake State Park, WI, USA) between April 2003 and March 2004, and at a predominantly downwind urban site in southeastern Wisconsin (Milwaukee, WI, USA) between June 2004 and May 2005. Annual averages of GEM, PHg, and RGM at the urban site were statistically higher than those measured at the rural site. Pollution roses of GEM and reactive mercury (RM; sum of PHg and RGM) at the rural and urban sites revealed the influences of point source emissions in surrounding counties that were consistent with the US EPA 1999 National Emission Inventory and the 2003-2005 US EPA Toxics Release Inventory. Source-receptor relationships at both sites were studied by quantifying the impacts of point sources on mercury concentrations. Time series of GEM, PHg, and RGM concentrations were sorted into two categories; time periods dominated by impacts from point sources, and time periods dominated by mercury from non-point sources. The analysis revealed average point source contributions to GEM, PHg, and RGM concentration measurements to be significant over the year long studies. At the rural site, contributions to annual average concentrations were: GEM (2%; 0.04 ng m(-3)); and, RM (48%; 5.7 pg m(-3)). At the urban site, contributions to annual average concentrations were: GEM (33%; 0.81 ng m(-3)); and, RM (64%; 13.8 pg m(-3)).     

Origin and characterisation of microparticles in an ice core from the Central Dronning Maud Land, East Antarctica

The scanning electron microscopy-energy dispersive spectroscopic (SEM-EDS) study of selected samples from an ice core collected from Central Dronning Maud Land (CDML), East Antarctica, revealed several microparticles. They are mainly siliceous and carbonaceous particles and have distinct variations in their shape and composition. The morphology and major element chemistry of the particles suggest their origin from either volcanic eruptions or continental dust. The EDS analysis revealed that the volcanic particles are enriched in silica (average SiO2 62%), compared to the continental dust particle (average SiO2 56%). We found that the tephra relating to Agung (1963) and Karkatau (1883) volcanic eruptions, as recorded, in the ice core harbored microbial cells (both coocoid and rods). The occurrence of organic and inorganic particles which bear relation to volcanic eruption and continental dust implies significant environmental changes in the recent past.     

An assessment of fluoride in the coastal aquifer of the Bara tract in Bharuch district, Gujarat (India)

The fluoride (F) concentration in the coastal aquifers of Bara tract in Bharuch district, Gujarat was determined by potentiometric method using an ion-selective electrode. The fluoride concentration in these aquifers varies between 0.060 to 3.51 mg/L. It was also found that F has a positive correlation with pH and HCO(3)(-) whereas negatively correlated with Ca(2+), indicating that high fluoride in ground water is associated with low calcium content. This suggests that the higher pH of water promotes the leaching of fluoride and thus affects the concentration of fluoride in the ground water. It was also found that there exists no relationship between F, EC, and ground water table from surface in the ground water.     

Statistical approaches for hydrogeochemical characterization of groundwater in West Delhi, India

Parametric statistical approaches, correlations and multiple linear regressions were used to develop models for the interpretation of hydrogeochemical parameters in the Western part of Delhi state, India. The hydrogeochemical parameters indicated that the groundwater quality is not safe for consumption. The water is moderately saline and the salinity level is increasing over time. There is also the problem of nitrate pollution. The correlation between electrical conductivity (EC) and other water quality parameters except potassium (K(+)), nitrate (NO(3)(-)) and bicarbonate (HCO(3)(-)) is significantly positive and Ca(++)+ Mg(++)/Na(+)+ K(+) is significantly negative. In predicting EC, the multiple R(2) values of 0.996 and 0.985 indicate that 99.6% and 98.5% variability in the observed EC could be ascribed to the combined effect of Na(+), HCO(3)(-), Cl(-), SO(4)(--), NO(3)(-) and Ca(++)+ Mg(++) for the year of 2005 and 2006 respectively. Out of 99.6% of the variability in EC in 2005, 51.2% was due to Cl(-) alone, and 8.5%, 12.5%, 6.1%, 14.7% and 6.7% were due to Na(+), HCO(3)(-), SO(4)(--), NO(3)(-) and Ca(++) + Mg(++). Similarly in 2006, out of 98.5% of the variability in EC, 48.5% was due to Cl(-) alone, and 10.4%, 12.7%, 5.3%, 17.2% and 4.4% were due to Na(+), HCO(3)(-), SO(4)(--), NO(3)(-) and Ca(++)+ Mg(++). The analysis shows that a good correlation exists between EC, Cl(-) and SO(4)(--) either individually or in combination with other ions and the multiple regression models can predict EC at 5% level of significance.