Development of GP and GEP models to estimate an environmental issue induced by blasting operation

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.     

Classification of air quality using fuzzy synthetic multiplication

Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.     

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO₄ phosphorus, NO₃ nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.     

How many samples are required? Evaluating a model for verification of carbon sequestration in a hybrid poplar buffer strip

A model is developed to predict annual and total above-ground carbon storage within a hybrid poplar stream buffer. The regression model predicts tree wet weight based upon circumference at breast-height (137 cm) with an r-square value of 0.9922. Carbon storage in above-ground biomass is estimated to be 3.57 to 3.71 metric tons per hectare, with a measured annual increment of 0.92 to 1.37 metric ton per hectare per year. The variability of carbon storage within this biological system, including soil organic matter, is explored, and the number of samples required to achieve a desired level of statistical certainty are predicted. As has been investigated previously for other biological systems (Garten and Wullschleger, 1999), the study shows that a prohibitively large number of samples must be taken in order to achieve high degrees of certainty about mean carbon storage values. The study also shows, however, that mean values with somewhat greater uncertainties can easily be achieved with much smaller sample sizes. Thus carbon sequestration verification might be accomplished cost-effectively if the degree of certainty required is not unrealistically high for highly variable natural systems.     

Assessment of respirable dust exposures in an opencast coal mine

All major opencast mining activities produce dust. The major operations that produce dust are drilling, blasting, loading, unloading, and transporting. Dust not only deteriorates the environmental air quality in and around the mining site but also creates serious health hazards. Therefore, assessment of dust levels that arise from various opencast mining operations is required to prevent and minimize the health risks. To achieve this objective, an opencast coal mining area was selected to generate site-specific emission data and collect respirable dust measurement samples. The study covered various mining activities in different locations including overburden loading, stock yard, coal loading, drilling, and coal handling plant. The dust levels were examined to assess miners' exposure to respirable dust in each of the opencast mining areas from 1994 to 2005. The data obtained from the dust measurement studies were evaluated by using analysis of variance (ANOVA) and the Tukey-Kramer procedure. The analyses were performed by using Minitab 14 statistical software. It was concluded that, drilling operations produce higher dust concentration levels and thus, drill operators may have higher incidence of respiratory disorders related to exposure to dust in their work environment.     

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran)

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.     

Assessment of cadmium content of potato grown in Weining County,Guizhou Province,China

A total of 54 soil and 54 potato samples have been collected from Weining County to evaluate the accumulation of cadmium in potatoes. The concentrations of the total Cd and the available Cd in the soil samples have been detected. The total concentrations of Cd were from 0.41 to 10.0 mg/kg with an average value of 2.60 mg/kg in soil. The concentrations of available Cd in the soil were 0.07 to 3.47 mg/kg with an average value of 0.59 mg/kg. The concentration of the available Cd showed a good linear positive correlation with the total Cd content in the soil. For the 54 potato samples, the Cd concentrations were from 0.023 to 0.18 mg/kg with an average value 0.083 mg/kg (fresh weight).The bioconcentration factor (BCF) values of Cd in potatoes, based on dry weight, were from 0.02 to 0.96 with an average value 0.24. The uptake of cadmium by plants is dependent on various soil and environmental factors. A regression model to predict the concentration of cadmium in Weining potatoes based on soil properties and elevation was developed. The results showed the elevation and the soil pH played an important role and had a negative influence on the uptake of Cd by potato in Weining County. The mean intake of Cd by adults through consumption of potato from Weining would be 5.9 μg/day, and it is well below the provisionally tolerable daily intake for Cd (70 μg/day).     

Ground temperature estimations for Bolu,Turkey

The approximation of the soil temperature profile may be considered for many energy, environmental, and agriculture applications. In this study, temperature approximation model of ground in Bolu is developed. Unsteady state one-dimensional semi-infinite solid model is used to calculate temperature variation in ground. The results of the temperature equation which are obtained by using analytical method are compared with experimental data obtained from The Turkish State Meteorological Service. Developed model to predict soil temperature variation for Bolu has been solved by using MATLAB computer program. Satisfactory agreement is observed between them.     

地下水环境质量评价方法

分析了ＧＢ／Ｔ１４８４８－９３《地下水质量标准》提出的评价方法和使用范围的局限性，根据建设项目环境影响评价实际，提出采用模糊综合评判结合单因子污染指数法描述污控因子，评价地下水环境质量的方法，给出模糊综合评判方法步骤，并以实例说明其应用情况。     

Development of fuzzy air quality index using soft computing approach

Proper assessment of air quality status in an atmosphere based on limited observations is an essential task for meeting the goals of environmental management. A number of classification methods are available for estimating the changing status of air quality. However, a discrepancy frequently arises from the quality criteria of air employed and vagueness or fuzziness embedded in the decision making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies like air quality index when describing integrated air quality conditions with respect to various pollutants parameters and time of exposure. In recent years, the fuzzy logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental issues. In the present study, a methodology based on fuzzy inference systems (FIS) to assess air quality is proposed. This paper presents a comparative study to assess status of air quality using fuzzy logic technique and that of conventional technique. The findings clearly indicate that the FIS may successfully harmonize inherent discrepancies and interpret complex conditions.     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

环境振动的灰色预测模型

利用GIM(1)的非时序直接建模法预测研究建筑施工的环境振动 ,并将GIM(1)模型与GM (1,1)模型进行比较分析 ,结果表明GIM(1)模型的拟合精度优良 ,对原始资料中白化信息的利用更加丰富 ,拓宽了GIM (1)模型在环境科学领域中的应用范围     

Comparison and Verification of Bacterial Water Quality Indicator Measurement Methods Using Ambient Coastal Water Samples

During the summers of 2002 and 2004, in-stream integrated flow and concentration measurements for the total dissolved solids in the Cheyenne River, South Dakota, USA was conducted in order to compare the obtained actual field measurements with the predictions values made by the Bureau of Reclamation in the Environmental Impact Statement. In comparison to the actual field measurements conducted in this study, The Bureau of Reclamation extension of a small database used in the analysis for the impact of operations at the Angostura Unit over the past 50 years and into the future to predict the annual total dissolved solid loadings doesn't represent the actual loading values and various conditions in the study area. Additional integrated flow and concentration sampling is required to characterize the impact of the current Angostura Dam operations and Angostura Irrigation District return flows on the Cheyenne River in different seasons of the year.     

Qualitative time trend analysis of ground water monitoring networks

The detection of significant (short-term) time trends is one of the major goals of ground water monitoring networks. These trends can be used to recognize active geochemical processes and potential environmental threats. This paper presents a case history of time trend analysis on macrochemical parameters of ground water quality. It shows the difficulties and traps that are generally encountered in such studies. The data used originated from the Dutch National Groundwater Quality Monitoring Network. This network is operative since 1979, and keeps track of the ground water composition at 350 locations at two depths (ca. 10 and 25 m below surface; general density, one location per 100 km²). Prior to the trend analysis the data set was divided into geochemically homogeneous groups using fuzzy c-means clustering. Each group represents a specific ground water type, characterized by a distinct source (seawater, surface water or precipitation) and a unique combination of dominant geochemical processes (e.g. mineralization of organic matter, carbonate dissolution and cation exchange).To study trends qualitatively, the concentrations of the various macro-constituents in ground water are correlated with time of sampling. The nonparametric and outlier insensitive Spearman rank correlation coefficient is computed per well screen. A frequency distribution of correlation coefficients is formed by combining the Spearman correlation coefficients of all individual wells within a homogeneous group. This distribution is tested for trends against the appropriate theoretical distribution of zero correlation by use of the Kolmogorov-Smirnov one-sample test. The type of trend is derived from the shape of the distribution.Most ground water types show statistically significant qualitative trends, of which many, however, are caused by changes in the sampling and analytical procedures over the monitoring period. After elimination of differences in limits of detection for NO₃, total-P, and NH₄, most trends in these compounds disappeared. In some water types trends for alkalinity, apparent trends for pH, Ec, and total-P are caused by variations in the laboratory practice, e.g. varying storage procedures, leading to erroneous analyses. Other parameters showed statistically significant trends, related to geochemical processes.The most interesting and most substantial trends are observed in the water type characterized by infiltrating rainwater with agricultural pollutants. In this water type the lowering ground water table induces lower rates of evapotranspiration, giving lower concentrations in time of conservative parameters (Cl, Na, Ca). The aerated zone is enlarged, resulting in increased oxidation of organic material, less efficient nutrient (NO₃, K) uptake by plant roots, leading to increased ground water concentrations of nutrients. In other water types trends are quantitatively small. However, trends are not necessarily linear, and all should be closely monitored in future.     

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Environmental quality assessment of Port Blair in Andaman islands

The Andman and Nicobar archipelago comprises of about 556 small and big islands covering an area of 8493 sq. kms in the Bay of Bengal. The very remoteness of these islands from the mainland has preserved their pristine environment and spectacular natural beauty. The Andman and Nicobar Administration is going for major developmental projects to cope with the increasing needs of the people, which ultimately results in significant changes in environmental quality. This paper describes the existing environmental quality around Port Blair city, which will give baseline scenario to assess the environmental impacts due to developments in the future.In order to monitor the air quality of the region, sampling stations were selected based on the locations of various industries and domestic activities. Suspended Particulate Matter (SPM), Sulphur dioxide (SO₂) and Nitrogen Oxides (NOx) were monitored for a period of one month during winter season. In addition, micrometeorological data, viz. wind speed and direction were also recorded and analysed to obtain the representative meteorological scenario of the air basin. The monitored values of ambient air quality was found to be within the NAAQ standards of India.Similarly, noise levels were also measured at various locations viz., residential areas, commercial centres, villages, stone quarry sites and construction sites. Noise levels were found to exceed the standards at stone quarry, construction sites and other locations.Water quality studies were carried out with respect to surface and ground water. The various physico- chemical and bacteriological parameters were analysed. It was observed that the physico-chemical parameters of surface and ground water lie within the standards stipulated for Indian subcontinent except for heavy metals which exceed the limits in ground water samples. Bacteriological analysis of sea water and ground water indicate that they are contaminated with faecal matters. Further, the ground water can be used for drinking purposes only after adequate treatment.     

Estimating the take of migrating adult chum salmon (Oncorhynchus keta) caused by confined underwater rock blasting

This paper describes a methodology for estimating the take of upstream migrating adult chum salmon (Oncorhynchus keta) caused by confined underwater rock blasting. Because these fish are listed under the Endangered Species Act, it is unlawful to take (i.e., harm, capture, collect, injure, kill, etc.) them without a federal permit. In the permit for an underwater blasting project to deepen a 2 km section of the Columbia River navigation channel linking Portland, Oregon, to the Pacific Ocean, regulators defined take as the mortality of adult chum salmon due to underwater blasting. They required monitoring to estimate take to track compliance with the permit. Conventional predictive models of fish mortality from underwater blasting depend on data about the explosive charges; however, such data for this project were not available for proprietary reasons. Therefore, an innovative approach had to be conceived. The dose-exposure-response methodology we developed provided an unobtrusive, science-based methodology for monitoring and near real-time reporting of adult chum salmon take. We applied the methodology for 99 blasting events from November 1, 2009, through February 5, 2010, in the lower Columbia River (rkm 139–141). The mean absolute peak pressure in underwater sound generated by blast events was 151,685 Pa (22 psi) at a range of 42.7 m. The estimated cumulative take for the project was 0.126 adult chum salmon, far below the 10-fish mortality limit regulators set for the project. We propose that this dose-exposure-response methodology be considered wherever underwater blasting has the potential to have an adverse effect on important fish species.     

用二级模糊评判模型评价环境质量状况

用二级模糊评判模型评价区域环境质量状况,弥补了用一级模糊综合评判只可评价各点位环境质量状况的不足,是一种新的环境评价方法,评价结果与实际情况相符,效果良好,方法实用.     

An air quality balance index estimating the total amount of air pollutants at ground level

A new index named Air Quality Balance Index (AQBI), which is able to characterise the amount of pollution level in a selected area, is proposed. This index is a function of the ratios between pollutant concentration values and their standards; it aims at identifying all situations in which there is a possible environmental risk even when several pollutants are below their limit values but air quality is reduced. AQBI is evaluated by using a high-resolution three-dimensional dispersion model: the air concentration for each substance is computed starting from detailed emissions sources: point, line and area emissions hourly modulated. This model is driven with accurate meteorological data from ground stations and remote sensing systems providing vertical profiles of temperature and wind; these data are integrated with wind and temperature profiles at higher altitudes obtained by a Local Area Model. The outputs of the dispersion model are compared with pollutant concentrations provided by measuring stations, in order to recalibrate emission data. A three-dimensional high resolution grid of AQBI data is evaluated for an industrial area close to Alessandria (Northern Italy), assessing air quality and environmental conditions. Performance of AQBI is compared with the Air Quality Index (AQI) developed by the U.S. Environmental Protection Agency. AQBI, computed taking into account all pollutants, is able to point out situations not evidenced by AQI, based on a preset limited number of substances; therefore, AQBI is a good tool for evaluating the air quality either in urban and in industrial areas. The AQBI values at ground level, in selected points, are in agreement with in situ observations.