Spatial variation in soil carbon in the organic layer of managed boreal forest soil—implications for sampling design

We studied within-site spatial variation of the carbon stock in the organic layer of boreal forest soil. A total of 1,006 soil samples were taken in ten forest stands (five Scots pine stands and five Norway spruce stands). Our results indicate that the spatial autocorrelation disappears at a distance of 75-225 cm. This spatial autocorrelation should be taken into account in the sampling design by locating the sampling points at adequate intervals. With a sample size of over 20-30 samples per site, additional soil samples do not notably improve the precision of the site mean estimate. An adequate sample size is dependent on the purpose of sampling and on the site-specific soil variation. Our results on the dependence between sample size and precision of the mean estimates can be applied in designing efficient soil monitoring in boreal coniferous forests.     

Global warming mitigation potential of biogas plants in India

Biogas technology, besides supplying energy and manure, provides an excellent opportunity for mitigation of greenhouse gas (GHG) emission and reducing global warming through substituting firewood for cooking, kerosene for lighting and cooking and chemical fertilizers. A study was undertaken to calculate (1) global warming mitigation potential (GMP) and thereby earning carbon credit of a family size biogas plant in India, (2) GMP of the existing and target biogas plants in the country and (3) atmospheric pollution reduction by a family size biogas plant. The GMP of a family size biogas plant was 9.7 t CO(2) equiv. year( - 1) and with the current price of US $10 t( - 1) CO(2) equiv., carbon credit of US $97 year( - 1) could be earned from such reduction in greenhouse gas emission under the clean development mechanism (CDM). A family size biogas plant substitutes 316 L of kerosene, 5,535 kg firewood and 4,400 kg cattle dung cake as fuels which will reduce emissions of NOx, SO(2), CO and volatile organic compounds to the atmosphere by 16.4, 11.3, 987.0 and 69.7 kg year( - 1), respectively. Presently 3.83 million biogas plants are operating in the country, which can mitigate global warming by 37 Mt CO(2) equiv. year( - 1). Government of India has a target of installing 12.34 million biogas plants by 2010. This target has a GMP of 120 Mt CO(2) equiv. year( - 1) and US $1,197 million as carbon credit under the CDM. However, if all the collectible cattle dung (225 Mt) produced in the country is used, 51.2 million family size biogas plants can be supported which will have a GMP of 496 Mt of CO(2) equiv. year( - 1) and can earn US $4,968 million as carbon credit. The reduction in global warming should encourage policy makers to promote biogas technology to combat climate change and integration of carbon revenues will help the farmers to develop biogas as a profitable activity.     

Multivariate statistical study of seasonal variation of BTEX in the surface water of Savitri River

Volatile organic compounds (VOCs) analysis was carried out for the surface water of the Savitri river during the period of June 2005 to June 2007. BTEX compounds (Benzene, Toluene, Xylene & Ethyl benzene) were analyzed by using micro extraction technique (Purge & Trap). Concentrations of these BTEX compounds were ranging from 0.1 to 1.5 ppm during sampling period. Higher concentrations of BTEX were found at sampling location VI. Concentration of ethyl benzene was very low as compare to other compounds. However, the concentration of benzene was very high. Seasonal variations in conc. of BTEX compounds were observed and higher concentration was detected during the summer season. Salting-out effect had given higher quantification values. In PCA and PFA, the component loading for all the variables are positively correlated. Death of fishes was observed in the river that is indication of severe pollution problem.     

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.     

A comparison of techniques to sample salamander assemblages along highland streams of Maryland

Amphibians may be useful indicators of biological condition in small streams so determining which sampling technique maximizes encounters at the least cost and at the optimal time of year is important. Area constrained surveys (ACS), used by the Maryland Biological Stream Survey, were tested against cover board surveys, drift fences with pitfall and funnel traps, quadrat leaf litter searches, and leaf litter bags. Sixteen, 100 m-long sites were established in headwater streams in the Savage River State Forest in Garrett County, Maryland. Each technique was randomly assigned to a 25 m stream section within each overall sampling site, and sites were sampled once each month from May to October (2005) with additional sampling in March and April (2006). Area constrained surveys yielded means of 2.7 taxa and 14.9 total individuals per sampling visit, which was significantly higher than the yield of all other methods in all months except October and March, when yields were low for all techniques. Area constrained surveys were also significantly more cost-effective per taxon and per individual compared to all other methods. September produced the most taxa and individuals, October and March produced the least, and yields for April through August were similar to September. We employed removal sampling at four sites in April 2006, but abundance could not be estimated because a significant linear decrease in the accumulated catch versus catch per unit effort did not occur for three of the sites.     

Utilization of statistics based classification approach to investigate water supply profile of Turkey

In the paper water supply profile of Turkey was examined. In this scope, the questionnaire survey conducted by Turkish Statistical Institute in 2004 to investigate annual amount of water abstracted to drinking water networks by type of resources in 81 provinces was evaluated. In the questionnaire, sources were grouped under five categories as spring, (artificial) lake, river, reservoir and well. Due to the complex and multivariate characteristics of the data sets, to replace a large collection of variables with a smaller number of factors the statistical method "factor analysis" was performed. Results revealed that, water supply systems in the country were mainly governed by groundwater sources (well and/or spring). However, in the northeastern part of the country, rivers were allocated for drinking water supply. On the other hand, reservoir dependent cities were densely located in Marmara, Central Anatolia and Southeast Anatolia Regions. This study showed that statistics based classification methods assist decision makers to extract information from multidimensional complex data sets representing environmental conditions.     

An appraisal of changes in seasonal water quality during passage through a shallow reservoir in Western Poland

Following restoration changes in Antoninek Reservoir physico-chemical and biological processes in the water column and bottom sediments were measured to outline mechanisms of changes in nitrogen, phosphorus and organic matter concentrations during water flow through this reservoir. Intensive mineralisation of organic matter in the shallow sediments stimulated primary production and influenced increasing ammonia and nitrite nitrogen concentrations. Two main factors affected concentrations of phosphorus: (1) its presence in the external loads of river waters entering the reservoir, more important in the colder seasons as the water discharge was higher and (2) from the internal loads coming from bottom sediments. The quality of the river water during its flow through this reservoir improved for most parameters and seasons. However, concentrations of nutrients were still high in waters flowing out from the reservoir and in some months they were higher in the outflow than in waters entering the reservoir.     

Risk assessment and distribution of soil Pb in Guangdong, China

In this study, the spatial distributions of soil lead (Pb) concentration in three horizontal soils in Guangdong, China, were surveyed and analyzed using geostatistics and geography information systems (GIS). Findings indicated that the Pb geometric mean concentration of 23.3 mg/kg in surface soils was found to be higher than those in global soils, which ranged from 2.3–235 mg/kg. In addition, the Pb geometric mean concentrations from A- to C-horizon were found to be 23.3, 27.2, and 28.6 mg/kg, respectively. The classification of a soil Pb environmental risk in an area was likewise presented based on the different levels of environmental quality of Pb and was done by GIS technology. Accordingly, there is a higher local concentration of Pb in the surrounding areas of Guangzhou where there is higher population density and in the north of Guangdong, which is a historic mining area. The results obtained by the environmental risk assessment reveal that about 46% of total surveyed area was above the background value, that is, 2.7% of the total area was at risk of pollution.     

Assessing benthic community condition in Chesapeake Bay: does the use of different benthic indices matter?

Federal and state environmental agencies conduct several programs to characterize the environmental condition of Chesapeake Bay. These programs use different benthic indices and survey designs, and have produced assessments that differ in the estimate of the extent of benthic community degradation in Chesapeake Bay. Provided that the survey designs are unbiased, differences may exist in the ability of these indices to identify environmental degradation. In this study we compared the results of three indices calculated on the same data, and the assessments of two programs: the Chesapeake Bay Program and the Mid-Atlantic Integrated Assessment (MAIA). We examined the level of agreement of index results using site-based measures of agreement, evaluated sampling designs and statistical estimation methods, and tested for significant differences in assessments. Comparison of ratings of individual sites was done within separate categories of water and sediment quality to identify which indices summarize best pollution problems in Chesapeake Bay. The use of different benthic indices by these programs produced assessments that differed significantly in the estimate of degradation. A larger fraction of poor sites was classified as good by the Environmental Monitoring and Assessment Program’s Virginian Province and MAIA benthic indices compared to the Chesapeake Bay benthic index of biotic integrity, although overall classification efficiencies were similar for all indices. Differences in survey design also contributed to differences in assessments. The relative difference between the indices remained the same when they were applied to an independent dataset, suggesting that the indices can be calibrated to produce consistent results.     

IFTCIP: An Integrated Optimization Model for Environmental Management under Uncertainty

In this study, an interval-fuzzy two-stage chance-constrained integer programming (IFTCIP) method is developed for supporting environmental management under uncertainty. The IFTCIP improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within a general mixed integer linear programming framework. It has advantages in uncertainty reflection, policy investigation, risk assessment, and capacity-expansion analysis in comparison to the other optimization methods. Moreover, it can help examine the risk of violating system constraints and the associated consequences. The developed method is applied to the planning for facility expansion and waste-flow allocation within a municipal solid waste management system. Violations of capacity constraints are allowed under a range of significance levels, which reflects tradeoffs between the system cost and the constraint-violation risk. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different risk levels. They are useful for generating desired decision alternatives with minimized system cost and constraint-violation risk under various environmental, economic, and system-reliability conditions. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost.