A study of compliance with environmental regulations of ISO 14001 certified companies in Korea

This article investigates the impact of ISO 14001 certification on the compliance with environmental regulations by Korean companies. The impact of ISO 14001 certification on the industry was studied through a questionnaire survey and the compliance of environmental regulations were investigated using government-released data. The motivation for an environment management system was a result of the current international situation and the need to maintain fair competition. ISO 14001 certification has been recognized as an essential strategy for industrial competition and to improve company/product recognition. The certified and non-certified companies' environmental regulation violation (ERV) rates were 3.5% and 11.6%, respectively, in 1997. In 1998, the ERV rate had an eight-time difference with 1.0% and 8.5% for certified and non-certified companies, respectively. Annual regulation violation rates were reduced from 3.5% in 1997 to 1.0% in 1998 with certified companies and from 11.6% in 1997 to 8.5% in 1998 with their non-certified counterparts, respectively. ISO 14001 certified companies showed more improvement than non-certified companies in regards to environmental performance.     

Refuse treatment options: a case study

Records in public archives were evaluated for the period 1981-1991. Simple surveys of peoples and of technology and physical premises provided insight to gauge the attitudes of a local community in the Melbourne municipality and the Melbourne City Council towards composting of organic waste materials. There is a lag in perception and attitudes between the local community and the local government towards composting of organic refuse as a solid waste treatment option. The simple methodology of the study still made it possible to verify past and present perceptions and attitudes.     

ARCGIS‐SWAT: A GEODATA MODEL AND GIS INTERFACE FOR SWAT1

This paper presents ArcGIS‐SWAT, a geodata model and geographic information system (GIS) interface for the Soil and Water Assessment Tool (SWAT). The ArcGIS‐SWAT data model is a system of geodatabases that store SWAT geographic, numeric, and text input data and results in an organized fashion. Thus, it is proposed that a single and comprehensive geodatabase be used as the repository of a SWAT simulation. The ArcGIS‐SWAT interface uses programming objects that conform to the Component Object Model (COM) design standard, which facilitate the use of functionality of other Windows‐based applications within ArcGIS‐SWAT. In particular, the use of MS Excel and MATLAB functionality for data analysis and visualization of results is demonstrated. Likewise, it is proposed to conduct hydrologic model integration through the sharing of information with a not‐model‐specific hub data model where information common to different models can be stored and from which it can be retrieved. As an example, it is demonstrated how the Hydrologic Modeling System (HMS) ‐ a computer application for flood analysis ‐ can use information originally developed by ArcGIS‐SWAT for SWAT. The application of ArcGIS‐SWAT to the Seco Creek watershed in Texas is presented.     

MODELING FECAL COLIFORM CONTAMINATION IN THE RIO GRANDE1

This study examines sources of fecal coliform in Segment 2302 of the Rio Grande, located south of the International Falcon Reservoir in southern Texas. The watershed is unique because the contributing drainage areas lie in Texas and Mexico. Additionally, the watershed is mostly rural, with populated communities known as “colonias.” The colonias lack sewered systems and discharge sanitary water directly to the ground surface, thus posing an increased health hazard from coliform bacteria. Monitoring data confirm that Segment 2302 is not safe for contact recreation due to elevated fecal coliform levels. The goal of the study was to simulate the observed exceedences in Segment 2302 and evaluate potential strategies for their elimination. Fecal coliform contributions from ranching and colonia discharges were modeled using the Hydrologic Simulation Program‐Fortran (HSPF). Model results indicated that the regulatory 30‐day geometric mean fecal coliform concentration of 200 colony forming units (cfu) per 100 milliliters is exceeded approximately three times per year for a total of 30 days. Ongoing initiatives to improve wastewater facilities will reduce this to approximately once per year for 14 days. Best management practices will be necessary to reduce cattle access to streams and eliminate all exceedences. The developed model was limited by the relatively sparse flow and fecal coliform data.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

EVAPOTRANSPIRATION DYNAMICS AT AN ECOHYDROLOGICAL RESTORATION SITE: AN ENERGY BALANCE AND REMOTE SENSING APPROACH1

ABSTRACT: Little work is reported where spatial methods are employed to monitor evapotranspiration (ET) changes as a result of vegetation and wetland restoration. A remote sensing approach with the Surface Energy Balance Algorithm for Land (SEBAL) for estimating ET at The Nature Conservancy's Glacial Ridge prairie‐wetland restoration site in northwestern Minnesota is presented. The calibrated 24‐hour ET from SEBAL was estimated with an average error of prediction of ?4.3 percent. Monthly, interseasonal, and seasonal ET for the period of June to September (2000 to 2003) from three adjacent land‐uses: a hydrologic control preserved wetland; a treated or restored site; and a nontreated or impacted wetland, were used in the study. Results from comparing ET behavior to the preserve suggest restoration efforts have affected monthly and seasonal ET within the treated site. Spatial average standard deviations of the seasonal ET within the preserve, treated, and nontreated sites give 47.3, 75.7, and 109.9 mm, respectively, suggesting hydrologic stabilization within the treated site. Monthly and interseasonal comparisons show similar behavior to that of the seasonal data, where monthly correlations suggest increasing agreement within the treated site, approaching those within the preserve.     

微波消解石墨炉原子吸收法测定空气中的锰(铅)

本文介绍了用微波消解技术消解滤膜以测定空气中的锰（铅）的方法，与传统的稀硝酸浸出法、流酸-灰化法相比，微波消解更加简单、快速、节省、准确、沾污少、环境污染少，回收率高。     

Designating Air Quality Management Areas (AQMAs) in the UK Implications for Securing UK Air Quality Objectives

Across the United Kingdom, the majority of local authoritieshave now completed their first phase of local air qualityreview and assessment work, as required under the AirQuality Strategy for England, Scotland, Wales and NorthernIreland (DETR, 2000a). Emerging from this first phasework is an anticipated suite of over 110 Air QualityManagement Areas (AQMAs). These areas are identifiedlocations where one or more of the national air qualityobjectives are predicted to exceed by specific target dates,and their spatial extent and shape is emerging as highlyvariable. Local authorities are guided to use a variety ofscientific tools to underpin the scientific assessments, anda consideration of uncertainty in both the tools used andsubsequent delineation of AQMAs is likely to affect theemerging management areas significantly. With subsidiarity underpinning the process of local air qualitymanagement (LAQM), local decision-making is anticipated toinfluence the outcome of the LAQM process in its entirety,with the declaration of AQMAs necessitating the preparationand implementation of air quality action plans. UKexperience of the effective management of local air quality,through the designation of AQMAs, demonstrates a valuableframework for other European countries developing mechanismsto manage air quality locally.