Anticoagulant rodenticide exposure in raptors from Ontario,Canada

Environmental Science and Pollution Research - Anticoagulant rodenticides (ARs) are used globally to control rodent pest infestations in both urban and agricultural settings. It is well documented...     

Invasion in a diversity hotspot: exotic cover and native richness in the Californian serpentine flora

Exotic species have been observed to be more prevalent in sites where the richness of native species is highest, possibly reflecting variation among sites in resources, propagule supply, heterogeneity, or disturbance. However, such a pattern leaves unclear whether natives at species-rich sites are subject to especially severe impacts from exotics as a result. We considered this question using path models in which relationships between exotic cover and native richness were evaluated in the presence of correlated environmental factors. At 109 sites on serpentine soils across California, USA, exotic cover was positively correlated with total native herbaceous richness and was negatively correlated with the richness of both serpentine-endemic and rare native herbs. However, in path models that accounted for the influences of soil chemistry, disturbance, overstory cover, and regional rainfall and elevation, we found no indication that exotic cover reduced any component of native herb richness. Rather, our results indicated similarities and differences in the conditions favoring exotic, native, endemic, and rare species. Our results suggest that, in spite of some localized impacts, exotic species are not exerting a detectable overall effect on the community richness of the unique native flora of Californian serpentine.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

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.     

Incorporating green and sustainable remediation analysis in coal combustion residuals (CCR) surface impoundment closure decision making

In response to new coal combustion residuals (CCR) disposal regulations, many coal‐fired utilities have closed existing unlined surface impoundments (SIs) that were traditionally used for disposal of CCR. The two primary closure options are closure‐in‐place (CIP), which involves dewatering and capping, and closure‐by‐removal (CBR), which includes excavation, transportation, and disposal of the CCR into a lined landfill. This article provides a methodology and a case study of how green and sustainable remediation concepts, including accounting for the life cycle environmental footprints and the physical risks to workers and community members, can be incorporated into the closure decision‐making process. The environmental impacts, occupational risks, and traffic‐related fatalities and injuries to workers and community members were calculated and compared for closure alternatives at a hypothetical site. The results demonstrated that the adverse impacts of the CBR option were significantly greater than those of the CIP option with respect to the analyzed impact pathways.     

A structural equation model analysis of postfire plant diversity in California shrublands.

This study investigates patterns of plant diversity following wildfires in fire-prone shrublands of California, seeks to understand those patterns in terms of both local and landscape factors, and considers the implications for fire management. Ninety study sites were established following extensive wildfires in 1993, and 1000-m(2) plots were used to sample a variety of parameters. Data on community responses were collected for five years following fire. Structural equation modeling (SEM) was used to relate plant species richness to plant abundance, fire severity, abiotic conditions, within-plot heterogeneity, stand age, and position in the landscape. Temporal dynamics of average richness response was also modeled. Richness was highest in the first year following fire, indicating postfire enhancement of diversity. A general decline in richness over time was detected, with year-to-year variation attributable to annual variations in precipitation. Peak richness in the landscape was found where (1) plant abundance was moderately high, (2) within-plot heterogeneity was high, (3) soils were moderately low in nitrogen, high in sand content, and with high rock cover, (4) fire severity was low, and (5) stands were young prior to fire. Many of these characteristics were correlated with position in the landscape and associated conditions. We infer from the SEM results that postfire richness in this system is strongly influenced by local conditions and that these conditions are, in turn, predictably related to landscape-level conditions. For example, we observed that older stands of shrubs were characterized by more severe fires, which were associated with a low recovery of plant cover and low richness. These results may have implications for the use of prescribed fire in this system if these findings extrapolate to prescribed burns as we would expect.