The AVI project: A bibliographical and archive inventory of landslides and floods in Italy

The AVI project was commissioned by the Minister of Civil Protection to the National Group for Prevention of Hydrogeologic Hazards to complete an inventory of areas historically affected by landslides and floods in Italy. More than 300 people, divided into 15 research teams and two support groups, worked for one year on the project. Twenty-two journals were systematically searched for the period 1918–1990, 350,000 newspaper issues were screened, and 39,953 articles were collected. About 150 experts on mass movement and floods were interviewed and 1482 published and unpublished technical and scientific reports were reviewed. The results of the AVI project, in spite of the limitations, represent the most comprehensive archiving of mass movement and floods ever prepared in Italy. The type and quality of the information collected and the methodologies and techniques used to make the inventory are discussed. Possible applications and future developments are also presented.     

Groundwater mapping: An analysis of the United States Hydrological Investigation Atlas Series

Groundwater mapping is a critical component of efficient water resource management today. Difficulties arise when highly specialized and complex groundwater data are collected and presented in map form. This is an analysis of the map content and its presentation in the USGS Hydrological Investigation Atlas Series. This analysis reveals the problems to be surmounted in mapping groundwater information. The technical nature of the groundwater data requires special design considerations.     

Information Management System for Site Remediation Efforts

/ Environmental regulatory agencies are responsible for protecting human health and the environment in their constituencies. Their responsibilities include the identification, evaluation, and cleanup of contaminated sites. Leaking underground storage tanks (USTs) constitute a major source of subsurface and groundwater contamination. A significant portion of a regulatory body's efforts may be directed toward the management of UST-contaminated sites. In order to manage remedial sites effectively, vast quantities of information must be maintained, including analytical dataon chemical contaminants, remedial design features, and performance details. Currently, most regulatory agencies maintain such information manually. This makes it difficult to manage the data effectively. Some agencies have introduced automated record-keeping systems. However, the ad hoc approach in these endeavors makes it difficult to efficiently analyze, disseminate, and utilize the data. This paper identifies the information requirements for UST-contaminated site management at the Waste Cleanup Section of the Department of Environmental Resources Management in Dade County, Florida. It presents a viable design for an information management system to meet these requirements. The proposed solution is based on a back-end relational database management system with relevant tools for sophisticated data analysis and data mining. The database is designed with all tables in the third normal form to ensure data integrity, flexible access, and efficient query processing. In addition to all standard reports required by the agency, the system provides answers to ad hoc queries that are typically difficult to answer under the existing system. The database also serves as a repository of information for a decision support system to aid engineering design and risk analysis. The system may be integrated with a geographic information system for effective presentation and dissemination of spatial data.     

Simulation modeling and resource management

Simulation models are becoming increasingly important as tools for synthesizing and applying information in almost all aspects of land management. They are particulary valuable for predicting and comparing outcomes of alternative decisions and assumptions. Models also permit managers to consider and integrate the potential influences of a large number of variables.     

地表水环境质量评价数据有效性探讨

通过对上海某区一年地表水手工常规监测与自动监测数据比较,对地表水环境质量评价的数据有效性进行分析,提出在丰水期(非威潮影响期)每月需监测8次(隔3日采样),在枯水期(成潮影响期)每月监测1次.定量解决了上海某区地表水环境质量评价数据有效性争议问题.     

Real time remote monitoring of air pollutants and their online transmission to the web using internet protocol

Recent advances in both information and sensor technologies have enabled the development of Real Time Remote Monitoring (RTRM) capabilities for environmental management. An online and real time remote monitoring system for air pollution has been designed and installed at a traffic police station at Punjagutta in Hyderabad, India. The system is optimized using electrochemical sensors and a real time particulate matter analyzer. The system also monitors meteorological parameters such as temperature, humidity, rainfall, barometric pressure, wind speed and wind direction. The system periodically monitors both pollution and meteorological parameters at pre- programmed intervals of hr during peak periods and 1 hr during non peak periods of the day and continuously uploads to a predestinated web site (www.appcb.org/home.htm) using File Transfer Protocol. The web site renders a quick, simple and graphical display of air pollution levels and meteorological parameters and their significance to humans. The present paper highlights design considerations of a pollution monitoring system, system hardware and software requirements and practical limitations and future directions for real time remote monitoring of air pollution.     

An approach to optimise nutrient management in environmental sanitation systems despite limited data

The material flow analysis method can be used to assess the impact of environmental sanitation systems on resource consumption and environmental pollution. However, given the limited access to reliable data, application of this data-intensive method in developing countries may be difficult. This paper presents an approach allowing to develop material flow models despite limited data availability. Application of an iterative procedure is of key importance: model parameter values should first be assessed on the basis of a literature review and by eliciting expert judgement. If model outputs are not plausible, sensitive input parameters should be reassessed more accurately. Moreover, model parameters can be expressed as probability distributions and variable uncertainty estimated by using Monte Carlo simulation. The impact of environmental sanitation systems on the phosphorus load discharged into surface water in Hanoi, Vietnam, is simulated by applying the proposed approach.     

Using data linkage to assess the impact of motorized recreational vehicle-related injuries in Ohio

Problem

Motorized recreational vehicle (MRV)-related injuries can result in severe medical and financial consequences. The objective of this study was to describe the epidemiology, and clinical and financial impact of MRV-related injuries in Ohio.

Method

Probabilistically linked statewide Emergency Medical Services (EMS) and hospital (inpatient and emergency department) data for 2003 and 2004 were examined. Record pairs with a MRV-related E-code (E821-E823, E825) were included in this study.

Results

There were 2,893 patients with MRV-related injuries, who had linked EMS and hospital records, resulting in more than $15 million in hospital charges and 1,921 inpatient days of hospitalization. The male-to-female ratio was nearly 4:1, and 19% were younger than 16. Almost 82% of cases were not wearing a helmet; there was a trend of decreasing helmet use with increasing age. Mean (SE) inpatient hospital charges and length of stay (LOS) were $22,218 ($1,290) and 3.8 (0.2) days, respectively. The mean (SE) Injury Severity Score (ISS) for inpatients was 9.2 (0.4). Individuals injured on a street/highway were 3.20 times more likely to sustain an ISS ≥ 16 (95% CI: 1.03, 9.88; p = 0.044) and 3.05 times more likely to sustain a traumatic brain injury (TBI) (95% CI: 1.17, 7.94; p = 0.024) than those who were injured at a place designated for sport or recreation. Children aged 12 to 15 and young adults aged 16 to 25 were 2.47 and 2.14 times more likely, respectively, to sustain a TBI than adults aged 36 or older (aged 12 to 15: 95% CI: 1.13, 5.38; p = 0.024; aged 16 to 25: 95% CI: 1.26, 3.64; p = 0.005). Higher ISS was associated with both higher total charges (p < 0.001) and longer LOS (p < 0.001).

Discussion

This study demonstrates that MRV-related injuries are an important public health problem in Ohio, with a substantial clinical and financial impact.

Impact on Industry

Enactment and enforcement of statewide MRV safety legislation and training of MRV users offer valuable opportunities to prevent these costly injuries.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.