A new approach of drawing airport noise contours on computer based on Surfer

Noise contours are used to describe the extent of airport noise pollution and to plan land use around airports. The LWECP, (weighted equivalent continuous perceive noise level) recommended by ICAO( International Civil Aviation Organization) is adopted as airport noise rating parameter in this paper. With the help of various mathematical models in the software Surfer, noise contours can be drawn automatically by the completed program in Visual C Code. Corrections for thrust, velocity, atmospheric temperature, humidity and lateral ground attenuation are also considered in the new method, which can improve the efficiency of drawing contours. An example of its use for drawing noise contours of an airport in Zhejiang Province of China is proposed and the predictions and the measurements show agreements well.     

区域高速公路网络构建对可达性空间格局的影响——以安徽沿江地区为实证 

以安徽沿江为实证，采用加权平均旅行时间指标，分析了高速公路网构建对节点区内联系及区外联系可达性格局的影响，并以此为基础，综合分析了节点总体可达性格局的变动；随后对总体可达性的变动幅度及其相对可达性变动格局进行了探讨。结果表明：安徽沿江高速公路网络的构建显著提高了区域整体可达性，且促进了可达性的均衡化；区内联系可达性、区外联系可达性及总体可达性三者格局特征不同，路网构建所形成的影响也各有差别；东部及北部地区可达性变动幅度小于西南部地区；根据可达性系数的变化幅度及方向，可将节点归为相对可达性下降、相对可达性稳定及相对可达性上升3类。     

1990~2020年阿克苏河流域土地利用碳排放时空轨迹与影响因素

土地利用变化会导致不同类型碳源和碳汇功能变化,是碳排放的关键来源.从土地利用变化的角度开展阿克苏河流域碳排放及其影响因素研究,对于促进流域山水林田湖草沙冰一体化保护修复、助力碳达峰与碳中和目标实现具有重要意义.基于1990~2020年的4期土地利用数据与同期社会经济数据,测算土地利用碳排放总量,探究土地利用碳排放时空轨迹及其影响因素.结果表明:①1990~2020年,耕地、林地、建设用地和未利用地整体呈增加趋势,草地和水域呈减少态势.土地利用类型空间变化特征主要表现为草地、未利用地转换为耕地;②1990~2020年流域净碳总排放量呈现持续上升趋势,累计增加了14.78×10⁴ t,耕地面积增加是引起流域净碳排放量增长的关键因素;③流域土地利用碳排放量空间上呈中间高四周低的分布格局,净碳排放量显著变化区域主要分布在温宿县南部、阿克苏市、阿瓦提县及阿拉尔市;④人类活动对土地利用碳排放驱动作用最强且其影响由东部向西部逐渐增大,年均气温对土地利用碳排放影响贡献主要集中在阿克苏市东部和阿瓦提县北部,年均降雨量对温宿县北部和阿合奇县西部的抑制作用较强.     

中国乡镇企业环境管理区划研究

研究了全国乡镇企业环境管理区划,选择了13个指标：（1）枯水年径流深,（2）降水量,（3）大气污染系数,（4）森林覆盖率,（5）人均耕地面积,（6）县级环境管理机构人数,（7）人口密度,（8）人均国民收入,（9）乡镇企业经济密度,（10）乡镇企业行业结构的环境效应,（11）污径比,（12）原煤消耗密度,（13）乡镇企业技术进步。以模糊聚类数学方法作数量区划,把全国分为4个区、7个亚区。所作区划结果能基本满足国家级分区环境管理的需要。     

潜在有毒化学品优先控制名单筛选方法研究

在收集了国内外共37个不同化学品名单基础上,研究了我国潜在有毒化学品的筛选程序和评分系统。根据环境的9项指标和毒性的7项指标共16个参数,运用模糊数学方法计算了化学品的综合危害分值,藉此按其危害程度予以排序,从而选定了52种优先控制的有毒化学品名单,旨在对化学品应采取的适宜管理对策提供有效的科学依据。      

基于改进水平集算法的锅炉承压视觉预警技术

传统锅炉承压预警技术主要依靠人工巡查发现问题,在检测过程中受工作人员自身操作不稳定因素影响,存在故障预警实时性差、盲点多等问题,达不到相应的预警要求.针对此问题,提出基于改进采用水平集算法的锅炉承压预警技术.利用图像处理技术从图像中分割出锅炉承压关键部件,统计关键部件灰度值,并利用灰度值设定特征图像及初始轮廓,设计采用...     

城市控规单元防灾能力评价的有向加权图模型研究

为从城市具体规划建设的视角进行防灾能力评价,找到城市空间与设施的防灾薄弱环节,提升城市的防灾减灾能力,首先,在深入分析城市抗灾-救灾理念的基础上,从抗灾能力和救灾能力2个方面,建立适用于控制性详细规划层面的防灾能力评价指标体系;其次,基于防灾能力评价指标体系构建城市防灾有向加权图模型,通过PageRank算法对用地单元防灾能力值进行排序,实现防灾减灾能力的定量评价;最后,通过某市规划实例检验评价模型,针对控规单元的防灾问题提出规划提升措施。研究结果表明：所构建模型能够实现对各控规单元的防灾能力等级评价,反映城市控规单元防灾能力的分布情况,发现用地单元规划建设中的防灾减灾问题。     

A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields

A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey

Introduction

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey.

Method

Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P < 0.05 with spatial autocorrelation analyses.

Results

Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P < 0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R² values. Geographically weighted regression provided values of 0.89-0.99 adjusted R² for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions.

Impact on industry

Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.