火焰原子吸收法测定水中铁、锰含量的不确定度评定

从样品消解、分析、计算全过程对火焰原子吸收法测定水中铁、锰进行分析,根据JJF 1059-1999对其测量不确定度进行评定。结果表明,测定水中铁的不确定度主要来源于消解过程、标准溶液及配制、重复测定样品;测定水中锰的不确定度主要来源于消解过程、工作曲线拟合、标准溶液及配制。水中铁、锰含量的结果可分别表示为：（2.58±...     

火焰原子吸收分光光度法测定饮用水中铁和锰的不确定度评定

分析了测试过程中不确定度影响因素,其主要来源为标准溶液、标准曲线拟合、样品重复测定、分光光度计4部分。本次测量结果为：铁（0.232±0.012）mg/L;锰（0.092±0.004）mg/L,k=2（约95%置信水平）。     

Hydrogeochemistry and Groundwater Quality Assessment of Lower Part of the Ponnaiyar River Basin, Cuddalore District, South India

The Lower Ponnaiyar River Basin forms an important groundwater province in South India constituted by Tertiary formations dominated by sandstones and overlain by alluvium. The region enjoyed artesian conditions 50 years back but at present frequent failure of monsoon and over exploitation is threatening the aquifer. Further, extensive agricultural and industrial activities and urbanization has resulted in the increase in demand and contamination of the aquifer. To identify the sources and quality of groundwater, water samples from 47 bore wells were collected in an area of 154 km² and were analysed for major ions and trace metals. The results reveal that the groundwater in many places is contaminated by higher concentrations of NO₃, Cl, PO₄ and Fe. Four major hydrochemical facies Ca–Mg–Cl, Na–Cl, Ca–HCO₃ and Na–HCO₃ were identified using Piper trilinear diagram. Salinity, sodium adsorption ratio, and sodium percentage indicate that most of the groundwater samples are not suitable for irrigation as well as for domestic purposes and far from drinking water standards. The most serious pollution threat to groundwater is from nitrate ions, which are associated with sewage and fertilizers application. The present state of the quality of the lower part of Ponnaiyar River Basin is of great concern and the higher concentration of toxic metals (Fe and Ni) may entail various health hazards.     

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Monitoring of ambient PM₁₀ (particulate matter which passes through a size selective impactor inlet with a 50% efficiency cut-off at 10 μm aerodynamic diameter) has been done at residential (Kasba) and industrial (Cossipore) sites of an urban region of Kolkata during November 2003 to November 2004. These sites were selected depending on the dominant anthropogenic activities. Metal constituents of atmospheric PM₁₀ deposited on glass fibre filter paper were estimated using Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES). Chromium (Cr), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), manganese (Mn) and iron (Fe) are the seven toxic trace metals quantified from the measured PM₁₀ concentrations. The 24 h average concentrations of Cr, Zn, Pb, Cd, Ni, Mn and Fe from ninety PM₁₀ particulate samples of Kolkata were found to be 6.9, 506.1, 79.1, 3.3, 7.4, 2.4 and 103.6 ng/m³, respectively. The 24 h average PM₁₀ concentration exceeded national ambient air quality standard (NAAQS) as specified by central pollution control board, India at both residential (Kasba) and industrial (Cossipore) areas with mean concentration of 140.1 and 196.6 μg/m³, respectively. A simultaneous meteorology study was performed to assess the influence of air masses by wind speed, wind direction, rainfall, relative humidity and temperature. The measured toxic trace metals generally showed inverse relationship with wind speed, relative humidity and temperature. Factor analysis, a receptor modeling technique has been used for identification of the possible sources contributing to the PM₁₀. Varimax rotated factor analysis identified four possible sources of measured trace metals comprising solid waste dumping, vehicular traffic with the influence of road dust, road dust and soil dust at residential site (Kasba), while vehicular traffic with the influence of soil dust, road dust, galvanizing and electroplating industry, and tanning industry at industrial site (Cossipore).     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.