Attributes of reliable long-term landscape-scale studies: Malpractice insurance for landscape ecologists

Monitoring long-term change in forested landscapes is an intimidating challenge with considerable practical, methodological, and theoretical limitations. Current field approaches used to assess vegetation change at the plot-to-stand scales and nationwide forest monitoring programs may not be appropriate at landscape scales. We emphasize that few vegetation monitoring programs (and, thus, study design models) are designed to detect spatial and temporal trends at landscape scales. Based primarily on advice from many sources, and trial and error, we identify 14 attributes of a reliable long-term landscape monitoring program: malpractice insurance for landscape ecologists. The attributes are to: secure long-term funding and commitment; develop flexible goals; refine objectives; pay adequate attention to information management; take an experimental approach to sampling design; obtain peer-review and statistical review of research proposals and publications; avoid bias in selection of long-term plot locations; insure adequate spatial replication; insure adequate temporal replication; synthesize retrospective, experimental, and related studies; blend theoretical and empirical models with the means to validate both; obtain periodic research program evaluation; integrate and synthesize with larger and smaller scale research, inventory, and monitoring programs; and develop an extensive outreach program. Using these 14 attributes as a guide, we describe one approach to assess the potential effect of global change on the vegetation of the Front Range of the Colorado Rockies. This self-evaluation helps identify strengthes and weaknesses in our program, and may serve the same role for other landscape ecologists in other programs.     

Establishing a standard Sonoran Reference Plant and its application in monitoring industrial and urban pollution throughout the Sonoran Desert

Using analyses from honey mesquite (Prosopis glandulosa) leaves from nearly pristine locations in Arizona, an inorganic plant concentration standard is proposed for use as a Sonoran Reference Plant (SRP). Such a standard can be used as a monitoring device for different anthropogenic pollution sources throughout the Sonoran Desert. To test the application of the SRP, honey mesquite leaves were collected at different sites in southern Arizona and their chemical fingerprints compared to SRP. Sources of element origin were identified through factor analysis and correlation matrices. A terrestrial factor was found in leaves from all sites. Anthropogenic factors varied for different sites. Mesquite leaves accumulated significant amounts of elements originating from copper smelters (As, Sb) and urban pollution (La, Sm, Ce, V). These pollutants are small-sized and have been linked to human lung diseases. Mesquite is a deciduous tree; the yearly comparison of mesquite leaf element concentration spectra to the SRP standard can be used to determine seasonal improvement or deterioration of environmental quality in a particular area.     

The analysis of monitoring data with the aid of time-series analysis

Methods derived from time-series analysis are proposed for processing monitoring data. The necessity for the use of these methods is demonstrated. In a case study time-series analysis was applied to assess the impact of the closure of the Grevelingen estuary (S. W. Netherlands) in 1971 on a local wintering population of Oystercatchers (Haematopus ostralegus) in the adjacent Oosterschelde estuary.Communication No. 328 of the Delta Institute for Hydrobiological Research.     

Environmental and biological monitoring of methyl ethyl ketone (MEK)

This study was conducted to evaluate the usefulness of various biological parameters for monitoring of workers exposed to methyl ethyl ketone (MEK). Fifty male workers from a large magnetic videotape factory participated in this study. Personal air samples were collected using 3M organic vapor monitors and analysed for MEK by gas chromatography with flame ionisation detector (FID). 10 mL of urine; blood (1 mL) and exhaled air were also collected at the end of an 8-hour workshift. The headspace GC method was applied for measurement of urinary and blood MEK. MEK in expired air was analysed directly by using a GC/FID.The correlation coefficients (r) between environmental MEK and all other biological parameters measured show significant positive relationships. The r for environmental MEK and urine MEK was 0.84; for blood 0.73 and for breath 0.64. The correlation coefficients between blood and urine was 0.72; blood and breath was 0.88 and urine and breath 0.60. These findings suggest that measurements of unmetabolised MEK in blood, exhaled air and urine can be used for biological monitoring of MEK exposure. Nevertheless, laboratory methodological assessment is in favour of measuring urinary MEK as it is non-invasive and does not have to be analysed immediately after collection.     

Quantification of protein adducts of hexahydrophthalic anhydride and methylhexahydrophthalic anhydride in human plasma

Hexahydrophthalic anhydride (HHPA) and methylhexahydrophthalic anhydride (MHHPA) are two highly allergenic compounds used in the chemical industry. A method was developed for quantification of protein adducts of HHPA and MHHPA in human plasma. The plasma was dialysed and the anhydrides were hydrolysed from the proteins at mild acidic conditions. The released hexahydrophthalic acid (HHP acid) and methylhexahydrophthalic acid (MHHP acid) were purified by reversed solid phase extraction followed by derivatisation with pentafluorobenzyl bromide. The derivatives were analysed using GC-MS in negative ion chemical ionisation mode with ammonia as moderating gas. As internal standards, deuterium labelled HHP and MHHP acids were used. The detection limits were 0.06 pmol mL(-1) plasma for HHP acid and 0.03 pmol mL(-1) plasma for MHHP acid. The between-day precisions for HHP acid were 18% at 0.3 pmol mL(-1) and 8% at 4 pmol mL(-1). For MHHP acid, the precisions were 13% at 0.3 pmol mL(-1) and 9% at 4 pmol mL(-1). There were strong correlations (r=0.94 for HHPA and 0.99 for MHHPA) between total plasma protein adduct concentrations and serum albumin adduct levels. Workers exposed to time-weighted average air levels of HHPA between < 1 and 340 microg m(-3) and between 2 and 160 microg m(-3) for MHHPA had plasma adduct levels between the detection limits of the methods and 8.40 and 19.0 pmol mL(-1), respectively.     

Development of an ombrotrophic peat bog (low ash) reference material for the determination of elemental concentrations

Given the increasing interest in using peat bogs as archives of atmospheric metal deposition, the lack of validated sample preparation methods and suitable certified reference materials has hindered not only the quality assurance of the generated analytical data but also the interpretation and comparison of peat core metal profiles from different laboratories in the international community. Reference materials play an important role in the evaluation of the accuracy of analytical results and are essential parts of good laboratory practice. An ombrotrophic peat bog reference material has been developed by 14 laboratories from nine countries in an inter-laboratory comparison between February and October 2002. The material has been characterised for both acid-extractable and total concentrations of a range of elements, including Al, As, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Na, Ni, P, Pb, Ti, V and Zn. The steps involved in the production of the reference material (i.e. collection and preparation, homogeneity and stability studies, and certification) are described in detail.     

Natural mercury enrichment in a minerogenic fen--evaluation of sources and processes

Mercury (Hg) records in natural archives such as peat bogs are often used to evaluate anthropogenic or climatic influences on atmospheric Hg deposition. In this context, there is an ongoing discussion about natural sources or processes of Hg enrichment in natural archives. In the present study we estimated Hg fluxes from rock weathering, direct atmospheric deposition and from indirect atmospheric deposition in the catchment of a pristine minerogenic fen (GC2) located in the Magellanic Moorlands, southernmost Chile. The Hg record in the bog covers 11 174 cal. (14)C years and shows Hg concentrations of up to 570 [micro sign]g kg(-1) with an average of 268 [micro sign]g kg(-1). Hg was found to be enriched in the peat by a factor of 81 if compared to the mean Hg concentrations in the rocks of the catchment (3.2 [micro sign]g kg(-1)). Hg and also Pb, Fe, and As were found to be enriched predominately in goethite layers indicating high retention of these elements in the bog by iron oxyhydrates. It could also be demonstrated that the high peat decomposition rates in minerogenic bogs can increase the Hg concentrations in the minerogenic peat by a factor of approximately 2 at the same atmospheric Hg deposition rate if compared to ombrotrophic sites. This study has shown that Hg in minerogenic peat can be naturally enriched especially through the retention by autochthonous formed goethite and can be a solely internal process which does not require increased external Hg fluxes.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.