Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.     

Strontium-90 in Canada goose eggshells and reed canary grass from the Columbia River,Washington

Strontium-90 (⁹⁰SR) released to the ground near the N Reactor at the U.S. Department of Energy's Hanford Site enters the Columbia River through shoreline seeps. The ⁹⁰Sr is then potentially available for uptake by plants and animals. The life history and foraging behavior of nesting Canada geese is such that female geese could ingest ⁹⁰Sr while foraging on shoreline plants. Radichemical analyses showed that goose eggshells taken from an island, downstream from the N Reactor, contained more ⁹⁰Sr than did eggshells collected from other downstream islands. Reed canary grass samples taken from shoreline areas immediately downstream from the N Reactor contained higher concentrations of ⁹⁰Sr than samples from other downstream areas. All goose eggshells did not contain enhanced levels of ⁹⁰Sr, and all reed canary grass samples did not contain enhanced levels of ⁹⁰Sr, but a relationship exists between the releases of ⁹⁰Sr to the Columbia River and the enhanced levels of ⁹⁰Sr in some of the environmental samples analyzed.Pacific Northwest Laboratory is operated by Battelle Memorial Institute for the U.S. Department of Energy under contract DE-ACO6-76RLO 1830.     

The probabilistic tsunami hazard assessment along Karnataka Coast from Makran Subduction Zone,west coast of India

Coastal tsunami amplitudes were calculated to identify areas susceptible to tsunami hazard at selected locations of the coast of Karnataka, west of India, due to earthquakes in the Makran Subduction Zone. This is the first time that the probabilistic tsunami hazard assessment along this study area has been attempted. A series of earthquake source scenarios with magnitudes of Mw 8.0, Mw 8.5, and a mega thrust of Mw 9.1 were modeled by using the Community Model Interface for Tsunami interface with the MOST model of Titov and Synolakis (J. Waterway, Port, Coastal and Ocean Engineering, 121(6), 308–316, 1995). As per the previous occurrences, the two least magnitudes are probable, while Mw 9.1 is a worst-case scenario as described by Heidarzadeh et al. (Ocean Engineering, 36(5), 368–376, 2009) and the same is reported by Burbidge et al. (Geoscience Australia Professional Opinion No. 2009/11) as high magnitude. These are not at all historical earthquakes or specifically from historical catalogues. The results of modeling show that all the seven coastal locations are inundated barely in our worst-case scenario with maximum water levels in the range of 100–200 cm. The first tsunami wave strikes the coast within 4–5 h of earthquake occurrence.     

Dose-time-response functions for toxic chemicals

In many situations, the effect of a toxic chemical on a biological system depends on both the intensity and the duration of exposure. The dependence on the time dimension can be the expression of a range of processes including the physical accumulation of toxic chemicals or their metabolites and the functional accumulation of damage. Measures and functions that have been used to describe this dependence are reviewed.Some of these functions are compared through a case study of the neurotoxicity of methylmercury. Use is made of data that indicates a dependency between the blood concentration at which monkeys were exposed and the length of time before damage was detected. Several exposure functions are fitted to these data and their appropriateness is compared. Using the most appropriate function, an exposure-response relationship is developed using probit analysis. An alternative data analysis procedure is also investigated. The apparent threshold after a 100 day exposure is estimated to be greater by a factor of 3–5 compared to the threshold for chronic exposure. Applying this factor to man, the blood concentration threshold for chronic exposure is estimated to be 40–170 ppb, a finding consistent with recent reports of neurological damage in humans exposed below the generally accepted threshold.     

Outline of an environmental information system

The development of an environmental information system necessitates a phased implementation approach. Phase 1 includes the elements that are traditionally viewed as comprising monitoring and assessment activities. Analysis tools for interpretive work are identified including statistics, modelling, and GIS. Phase 2 follows the information flow beyond project reporting to examine the process of decision making. The inclusion of other forms of knowledge beyond the strictly scientific is necessary where the development of multi-sectoral decisions must be made. Phase 3 extends the decision-making process to the policy development and implementation field. This is accomplished by the inclusion of expert systems as advanced decision support systems which enable the manager to test various hypotheses and policy options prior to commitment. In addressing water resource issues, the importance of setting achievable and enforceable sectoral criteria and standards for industrial, agricultural, and drinking water supplies is discussed with reference to both usage and effluent criteria. Quality assurance and control is an area which must be critically addressed in any water resource project. The implementation of quality control programs must extend from the field sampling procedures and laboratory standard methods to both inter- and intra-laboratory tests and the development and maintenance of databases.     

Metals and selenium in wild animals from Norwegian areas close to Russian nickel smelters

This paper reports the concentrations of metals (Al, As, Cd, Cr, Cu, Hg, Ni, Pb, and/or Zn) and selenium (Se) in kidney and/or liver samples from capercaillie (Tetrao urogallus), willow ptarmigan (Lagopus lagopus), hares (Lepus timidus), common shrews (Sorex araneus) and grey-sided voles (Clethrionomus rufocanus) from the Norwegian areas most heavily contaminated by pollutants from the Russian smelters on the Kola peninsula. In addition to comparing areas that differed in expected pollution rate within Sør-Varanger, comparisons are drawn with reference data from other parts of Norway. The relatively highest levels of metals were found for Cu and Ni in the sub-area most heavily exposed to pollution from the smelters. Also the highest Cr concentrations were found in the areas closest to the smelters. In this study, there is evidence for a direct link between increased metal concentrations in wild animals and pollution from the Russian smelters for Cu and Ni and to some extent for Cr. relatively high concentration of Hg and slight increases in Pb are also documented, but regional differences within Sør-Varanger indicate no direct relationship to the Russian smelters. No samples showed concentrations of any of the analysed metals in excess of the limits where negative effects on animals can be measured.     

Status of air pollution caused by coal washery projects in India

The importance of coal washeries in India is growing as local coals have a high ash content. At present, there are 23 coal washeries with an annual rated input of 45 Million Tonnes. During the various operations in washeries, large amounts of dusts and gaseous pollutants are generated. Four coal washery projects were surveyed to study their air pollution characteristics. Air monitoring stations were set up in local industrial, residential and sensitive areas and air pollution samples were collected along with micro-meteorological data. Diurnal variations of SPM, SO₂ NO_x and CO are discussed. SPM concentrations were found to exceed the permissible limits at all locations. SO₂ and NO_x were also found to exceed the permissible limit in residential and sensitive areas. It was observed that about 50% of the dust particles were less than 10 µ in diameter. Benzene soluble matter in SPM ranged from 45–62%.