Quantitative PCR analysis of house dust can reveal abnormal mold conditions

Indoor mold concentrations were measured in the dust of moldy homes (MH) and reference homes (RH) by quantitative PCR (QPCR) assays for 82 species or related groups of species (assay groups). About 70% of the species and groups were never or only rarely detected. The ratios (MH geometric mean : RH geometric mean) for 6 commonly detected species (Aspergillus ochraceus, A. penicillioides, A. unguis, A. versicolor, Eurotium group, and Cladosporium sphaerospermum) were >1 (Group I). Logistic regression analysis of the sum of the logs of the concentrations of Group I species resulted in a 95% probability for separating MH from RH. These results suggest that it may be possible to evaluate whether a home has an abnormal mold condition by quantifying a limited number of mold species in a dust sample. Also, four common species of Aspergillus were quantified by standard culturing procedures and their concentrations compared to QPCR results. Culturing underestimated the concentrations of these four species by 2 to 3 orders of magnitude compared to QPCR.     

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.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

Linking Land Cover and Water Quality in New York City’S Water Supply Watersheds

The Catskill/Delaware reservoirs supply 90% of New York City’s drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subjected to Agency’s administrative review and approved for publication as an EPA document.     

A Spatial Model to Estimate Habitat Fragmentation and Its Consequences on Long-Term Persistence of Animal Populations

The increasing use of the landscape by humans has led to important diminutions of natural surfaces. The remaining patches of wild habitat are small and isolated from each other among a matrix of inhospitable land-uses. This habitat fragmentation, by disabling population movements and stopping their spread to new habitats, is a major threat to the survival of numerous plant and animal species. We developed a general model, adaptable for specific species, capable of identifying suitable habitat patches within fragmented landscapes and investigating the capacity of populations to move between these patches. This approach combines GIS analysis of a landscape, with spatial dynamic modeling. Suitable habitat is identified using a threshold area to perimeter ratio. Potential movement pathways of species between habitat patches are modeled using a cellular automaton. Habitat connectivity is estimated by overlaying habitat patches with movement pathways. The maximum potential population is calculated within and between connected habitat patches and potential risk of inbreeding within meta-populations is considered. The model was tested on a sample map and applied to scenario maps of predicted land-use change in the Peoria Tri-county region (IL). It (1) showed area of natural area alone was insufficient to estimate the consequences on animal populations; (2) underscored the necessity to use approaches investigating the effect of land-use change spatially through the landscape and the importance of considering species-specific life history characteristics; and (3) highlighted the model's potential utility as an indicator of species likelihood to be affected negatively by land-use scenarios and therefore requiring detailed investigation.     

The study of age influence on human bone lead metabolism by using a simplified model and X-ray fluorescence data

Long term lead metabolism in the human body has never been fully understood due to the lack of human data in this area. The technological improvement of bone lead measurement systems has made bone lead data of substantial populations available. In this study, a set of X-ray fluorescence bone lead data was used to test Leggett's lead metabolism model (R. W. Leggett, Environ. Health Perspect., 1993a, 101, 598-616), especially the model of metabolism in bone. The data set includes the bone lead concentration of 539 occupationally exposed workers, of whom 327 were measured twice in five years. The bone lead concentrations of both cortical bone (tibia) and trabecular bone (calcaneus) were obtained by Cd-109 gamma-ray induced XRF measurement. The histories of blood lead concentration for these workers were used to regulate the input file of the model. The results show that the bone lead concentrations predicted by Leggett's model greatly underestimate the measured values, especially for older workers. This data set was then organized into five age groups. A new simplified model was applied to estimate the lead transfer rates between blood and lead compartments for these age groups. The original transfer rates and the new transfer rates are compared, and the differences are discussed. When the transfer rates derived from measured bone lead data were put into the input file of the model to replace the existing parameters, the predicted values were much closer to the measured values for both cortical bone and trabecular bone.     

Formation and Emission of N2O and CH4 from Compost Heaps of Organic Household Waster

Composting can be a source of N₂O andCH₄ production. In this investigation, differentcompost heaps of organic household waste weremonitored with the focus on potential formation ofCH₄ and N₂O in the heaps and emission ofthese gases from the heaps. The studied compost heapshad different compost ages, turning intervals andcompost sizes. The analysed compost gases containedbetween 1–3421 L of N₂O-N L^-1 and 0–470 mL of CH₄ L^-1. The emission rates ofN₂O and CH₄ from the compost heaps werebetween 1–1464 mg N₂O m^-2 day^-1 and0–119 000 mg CH₄ m^-2 day^-1. These verylarge differences in compost gas composition andemission indicate the importance of compostmanagement. The results also give an understanding ofwhere in the composting process an increasing emissionof N₂O and CH₄ can occur.     

Assessing the Effects of Atmospheric Ethylene on Epinasty and Tuber Yield of Potato (Solanum Tuberosum L.) Near Polyethylene Manufacturing Plants

A multi-year programme was performed to assess the effects of atmospheric ethylene on potato (Solanum tuberosum L.) in the vicinity of polyethylene manufacturing plants. There was a strong temporal variation of the hourly ethylene concentrations measured close to the sources from 1982 through 1991. Growing seasonal means exceeded 12 g m^–3 ethylene, the threshold for phytotoxic effects under laboratory conditions. Young test plants of potato showed an epinastic response to enhanced levels of ethylene. This response was reversible and did not occur when atmospheric ethylene was not detected. Based on hourly observations for the growing seasons of 1984 through 1991, epinasty occurred on average during circa 5% of the growing season and varied from circa 1% in 1985 to circa 18% in 1991. At night, ethylene concentrations were higher and epinasty was more frequent than during daylight hours. The intermittent exposures to ethylene did not affect tuber yield for the growing seasons of 1982 through 1990. The occurrence of epinasty indicated that ethylene exposure levels in the vicinity of the industrial sources might be sufficiently high to affect sensitive plants.     

Forest Area in Costa Rica: A Comparative Study of Tropical Forest Cover Estimates over Time

Forest area figures, at a given point in time and for a given region of interest, differ considerably, affecting the calculation of deforestation rates and thus confuse the political and scientific discussion on the state and change of the resource forest. This article discusses the variation of published forest cover figures, using Costa Rica as an example. A list of published figures on the forest cover of Costa Rica from 1940 onwards is analyzed. Reasons for the differences are hypothesized and discussed. These differences are mainly in the definition of forest and forest classes included, in the type of the studies conducted (mapping and/or sampling), in the precision of the estimates, and in the information sources used. It is concluded that part of the problem is inherent in the nature of the resource `forest'. Quality and completeness of the presentation of the forest cover estimates are a clue to their correct understanding and interpretation. The latter point being especially relevant, as forest cover data have both a technical-scientific and a political meaning and are used as relevant arguments in many discussions. In the example of Costa Rica, a general downward trend is observed up to about 1985/1990, whereas after that forest area figures are on the average at a markedly higher level. Some hypotheses for this change in the trend are discussed.