The measurement of place attachment: Personal,community, and environmental connections

Place attachment has been researched extensively in the social and behavioural sciences over the past two decades. However, it is challenging for researchers to assimilate the mixed messages presented in the place attachment literature and to understand the multiple place attachment terms. In this study, a four-dimensional model of rural landholder attachments to their natural resource management region was conceptually and empirically developed with the aim of developing an integrated approach to the measurement of place attachment that clearly distinguishes between different elements of place scholarship. A 29-item place attachment scale with the dimensions of place identity, place dependence, nature bonding, and social bonding was tested on a random sample of rural landholders in the Adelaide and Mount Lofty Ranges in South Australia (N = 320). The majority of respondents were male (69.3%) and the average age was 59 years. The scale was reduced to 20-items and then administered simultaneously and in the same response format to rural landholders in two other areas of South Australia: the Northern and Yorke region (N = 664) and South Australian Murray-Darling Basin region (N = 659). In both studies, the majority of respondents were male (85%) and the average age was 55 years. Exploratory factor and reliability analyses of Adelaide and Mount Lofty Ranges and Northern and Yorke datasets produced a five-dimensional model of place attachment with high reliabilities. Social bonding divided into the constructs of family bonding and friend bonding. The refined five-dimensional model was then examined for convergent validity, with moderate but significant correlations found between individual attachment constructs and dependent variables expected to be related to the construct, such as place identity and length of residence, and nature bonding and time currently spent in nature. We used confirmatory factor analysis to test the goodness-of-fit of the South Australian Murray-Darling Basin (SAMDB) dataset to the proposed five-dimensional model and then compared its fit to the traditional two-dimensional model of place identity and place dependence. The five-dimensional model provided moderate fit for the SAMDB data. We conclude with a discussion of the validity and reliability of the five-dimensional model and its future role in place attachment research.     

Trend, seasonal and multivariate modelling study of wet precipitation data from the Austrian Monitoring Network (1990-1997)

The aim of the present study was to analyse the data structure of a large data set from rainwater samples collected during a long-term interval (1990-1997) by the Austrian Precipitation Monitoring Network. Eleven sampling sites from the network were chosen as data sources (chemical concentrations of major ions only) covering various location characteristics (height above sea level, rural and urban sampling positions, Alpine rim and Alpine valley disposition, etc.). The analytical results were treated by the application of already classical environmetric approaches, such as linear regression analysis, time-series analysis and principal components analysis (PCA). For most of the sampling sites, a distinct trend of acidity decrease of the wet precipitation was observed. An overall decrease in sulfate concentration for the whole period and all sites of 3.9% year(-1) (2.0 muequiv. L(-1) year(-1)) was found. The free acidity decrease for most of the sites was between 3.5 and 10.9% year(-1). No significant linear trends were found for nitrate. Base cations either decreased (mean percentage decrease for calcium was 5.4% year(-1) and for magnesium 4.4% year(-1)) or did not show any significant change (sodium, potassium). The overall decrease in ammonium concentration was 2.3% year(-1). Further, some typical "rural" (summer minima and winter maxima) and "urban" (winter minima and spring maxima) seasonal behaviour for the majority of the sites in consideration could be defined, indicating the influence of local emission sources. Several latent factors, named "anthropogenic", "crustal" and "mixed salt", were revealed by the multivariate modelling procedure (PCA) possessing a similar structure for most of the sites. The unavoidable exceptions observed were indications of the influence of sporadic local events (construction and agricultural activities, secondary emission sources, etc.), and an effort was made to explain these exceptions.     

The use of leeches and logit log-linear contingency models to assess and monitor aquatic PCB contamination originating from mid-Canada radar line site 050

Abandoned military sites in northern North America are relics of the Cold War and sources of polychlorinated biphenyls (PCBs). In the late 1990s, the Canadian federal and provincial governments began the cleanup of the mid-Canada radar line in Ontario, Canada. The first site to be remediated was Site 050 (Fort Albany First Nation) in 2001; however, the community remains concerned that contaminants may have moved prior to, during, and after remediation into the Albany River directly adjacent to Site 050. Thus, the Albany River was monitored (1999, 2001, 2002) during the remediation process to determine if the cleanup itself further contaminated the aquatic compartment, using leeches (Haemopis spp.) as bioindicators. Few organochlorines were found in leeches at levels higher than the detection limit, aside from PCBs. Leech data from the present study indicated that PCB levels were significantly higher near Site 050 than the control site upstream, indicating point source contamination from Site 050. The temporal trend in leech contaminant data indicated an increase in PCB contaminant load from 1999 (pre-remediation) to 2001 (immediately post-remediation), but this difference was not statistically significant due to high variances. Nevertheless, logit log-linear contingency modeling did reveal that immediately after cleanup (2001), contaminants (CBs 99, 118, 128, 156, 170, 183) in leeches were detected significantly more frequent than expected. When taken together, leech body burden and frequency of detection data suggest that the remediation process itself further contaminated the aquatic environment, if only temporarily. Lastly, the removal of the terrestrial source of PCBs during remediation did remove the source of aquatic contaminants in that body burden of contaminants in leeches were significantly lower a year after cleanup.     

Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta

In the Southeastern US, organic carbon (OC) comprises about 30% of the PM_2.5 mass. A large fraction of OC is estimated to be of secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM_2.5 chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC as 30% of OC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. CMB had the highest estimate of SOC (46% of OC) while PMF led to the lowest (26% of OC). The comparison of SOC uncertainties, estimated based on propagation of errors, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC, which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the strongest correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates.     

PROBLEMS IN FORECASTING WATER REQUIREMENTS1

ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long-range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.     

MANAGING CRISIS: THE EFFECTIVENESS OF LOCAL DISTRICTS FOR CONTROL OF GROUND WATER MINING1

: This article examines the willingness and capacity of local districts to control ground water mining of the Ogallala Aquifer in the High Plains. The questions of willingness and capacity were approached through extensive field interviews and a survey of all district board members and managers. The analysis focuses on the policy alternatives board members and managers perceive and how they evaluate these alternatives. Methodologically, the study utilized factor analysis of responses rating the desirability of various policy alternatives to ascertain what alternatives were perceived by the sample. Then the sample's preferences for each of the identified factors were calculated. The results demonstrate that the sample of those who must regulate if ground water mining is to be controlled at the substate level are not oriented to regulatory policies and therefore lack the willingness to deal with ground water mining.