Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.     

A measurement of community disaster resilience in Korea

Building a community that is resilient to disasters has become one of the main goals of disaster management. Communities that are more disaster resilient often experience less impact from the disaster and reduced recovery periods afterwards. This study develops a methodology for constructing a set of indicators measuring Community Disaster Resilience Index (CDRI) in terms of human, social, economic, environmental, and institutional factors. In this study, the degree of community resilience to natural disasters was measured for 229 local municipalities in Korea, followed by an examination of the relationship between the aggregated CDRI and disaster losses, using an ordinary least squares (OLS) regression method and a geographically weighted regression (GWR) method. Identifying the extent of community resilience to natural disasters would provide emergency managers and decision-makers with strategic directions for improving local communities' resilience to natural disasters while reducing the negative impacts of disasters.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.     

An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.     

Land use planning and wildfire risk mitigation: an analysis of wildfire-burned subdivisions using high-resolution remote sensing imagery and GIS data

This paper evaluates risk factors that influence the probability that a house will burn from wildfire. A logistic regression is used to analyse data processed from pre-fire and post-fire IKONOS images and other geo-referenced data. The dependent variable is the probability that a given house will burn. A total of 12 independent variables are evaluated: vegetation density; area of defensible space; adjacency of a parcel to public lands; proximity of a house to fire station; road width; road type; parcel size; subdivision morphology; assessed value; elevation; slope and aspect. Model results generally support dominant land use planning and design strategies for wildfire risk reduction including vegetation treatments, site selection with respect to topography, and improving access to fire stations.     

Assessment Tools for Urban Catchments: Defining Observable Biological Potential1

Abstract: Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts.     

Modeling perceived collision risk in port water navigation

An increase in the likelihood of navigational collisions in port waters has put focus on the collision avoidance process in port traffic safety. The most widely used on-board collision-avoidance system is the automatic radar plotting aid which is a passive warning system that triggers an alert based on the pilot’s pre-defined indicators of distance and time proximities at the closest point of approaches in encounters with nearby vessels. To better help pilot in decision making in close quarter situations, collision risk should be considered as a continuous monotonic function of the proximities and risk perception should be considered probabilistically. This paper derives an ordered probit regression model to study perceived collision risks. To illustrate the procedure, the risks perceived by Singapore port pilots were obtained to calibrate the regression model. The results demonstrate that a framework based on the probabilistic risk assessment model can be used to give a better understanding of collision risk and to define a more appropriate level of evasive actions.     

Long-Term Relationship between Phosphorus Inputs and Wetland Phosphorus Concentrations in a Northern Everglades Marsh

Assessments of long-term relationships between changes innutrient inputs and wetland nutrient concentrations can becomplicated by fluctuations in other environmental factors aswell as by problems typical of long-term monitoring data.Consequently, statistical analysisof these types of data sets requirescareful consideration of environmental covariates, potentialbiases in the monitoring design, and irregularities caused bychanges in field sampling protocols. We evaluated therelationship between anthropogenic phosphorus (P) inputs andwater-column total P (TP) concentrations in a northernEverglades marsh by statistically analyzing available datacollected from several sampling programs over the past 20 years(1978–1997). Canal inputs of agricultural runoff contributemost of the P to the marsh and have produced a zone ofenrichment within the marsh during the past few decades.Regression analyses showed that both canal and marsh TPconcentrations increased during the 1980s and then decreased inthe 1990s. However, the statistical relationship between canal Pinputs and marsh TP, while significant, generally was weakexcept for marsh locations adjacent to the canal. Strongerrelationships existed between marsh TP and hydrologic parameterssuch as marsh water depth, which is controlled by changes inweather patterns and marsh management. In particular, dryconditions during the 1980s may have contributed to observedincreases in marsh P concentrations and the movement of a P`front' further into the marsh. Higher rainfall and water depthsand agricultural best management programs initiated during the1990s have been associated with reduced P concentrations incanal waters entering the marsh. While it is anticipated thatthis reduction eventually will result in lower marsh TPconcentrations, this effect is not yet evident, possibly due tointernal loading of P from enriched marsh soils. Our findingsillustrate some of the environmental factors that can complicateattempts to develop empirical relationships between P inputs andwetland P concentrations and to use such relationships to forecast changesin marsh concentrations based on past monitoring data alone.     

关于植物中氟化物测定结果的计算

针对植物中氟化物测定时由于绘制校准曲线的标准溶液体积与分析样品的定容体积不一样而引出的结果计算时的某些问题,提出了正确的计算方法和建议。