Grouping and representativeness of monitoring stations based on wind speed and wind direction data in urban areas of Japan

Data on wind speed and wind direction from 27 air pollution monitoring stations in an urban area of Japan were analyzed and studied for the future assessment of the urban heat island phenomenon. A cluster analysis clarified the regionality of the wind speed and wind direction. In the study area, land and sea breeze developed and controlled the wind direction. On the other hand, the mountain and valley wind should be studied to determine their typical behavior.     

2009年广州能见度变化规律及主要影响因素分析

通过对2009年的广州大气能见度和影响因素的数据资料统计,结合定性分析、相关性计算及各时间尺度中物理量的相互作用过程研究,得出能见度的变化规律和各因素的相关性特征。结果表明,2009年整体能见度水平略有提高;24h的降雨量、气温差值较大时,该日的能见度小时均值波动较大;风速、相对湿度与中尺度(24h)能见度的相关性较强;降雨量、气温对小尺度(1h、5min)能见度影响显著;相对湿度水平能制约风速对能见度的改善作用。灰霾现象等与能见度水平密切相关。     

PPM htv型甲醛速测仪与AHMT分光光度法的可比性研究

按照《民用建筑工程室内环境污染控制规范》(GB 50325-2006)的布点要求,本文采用AHMT分光光度法和PPM htv型甲醛速测仪同步采样,并对AHMT分光光度法与PPM htv型甲醛速测仪的性能进行了系统的比较。     

风机变频改造及自动切换功能的应用

火电厂既是电能生产企业,也是耗能大户,要提高电厂综合效益,必须降低厂用电率和发电煤耗。电厂辅机出力为长期连续运行并常常处于变负荷运行状态,送、引风机作为主要辅机,采用控制风机挡板的开度来调节出力,而作为电动机消耗的能量变化则不大,造成较大能量损耗,直接影响到厂用电率。改为变频调节后,通过改变电机转速控制出力,实现调整需要,达到节电目的。     

高速公路汽车防碰撞模型仿真研究

随着高速公路的快速建设,交通事故迅猛增长.保证适当的车辆行驶距离是预防高速公路事故的有效手段.论文根据车辆制动规律和运行状态,得出行车安全距离模型,同时对模型中的参数进行了说明和分析.采用MATLAB软件对高速公路车辆的安全距离模型进行仿真分析,得出安全距离随车速和附着系数的变化规律,寻求既保证车辆安全行驶又不影响道路通行能力的合理的安全距离值,为降低高速事故率提供一定的依据.     

Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function

The present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.     

加药量和水力搅拌速度对雨水混凝效果的影响

为降低分流制雨水中悬浮颗粒物及其他污染物浓度,减轻城市景观河道的水体富营养化程度,对取自泵站的雨水进行混凝沉淀工艺优化实验。以PAC为混凝剂,采用Zeta电位仪、激光粒度仪和iPDA在线监测技术对混凝过程进行监测,考察了混凝剂投加量和水力搅拌速度对絮体形成和分流制雨水处理效果的影响,结果表明,混凝剂投加量和混合水力搅拌速度直接影响絮体Zeta电位和聚沉特性;混合搅拌速度控制混凝反应速率,絮凝速度梯度影响絮体形成粒径。FI曲线特征参数对控制混凝工艺具有指导意义。PAC投加量为35 mg/L,混合阶段搅拌速度800 r/min,搅拌30 s,絮凝阶段采用150、108和60 r/min的转速各自搅拌5 min,沉后水中剩余颗粒总数最少,浊度、COD和总磷去除效果最佳。     

Numerical Modeling of Particulate Matter Dispersion from Kerman Cement Plant, Iran

A three dimensional diffusion model has been developed for computing the concentration of PM10 from Kerman Cement Plant, Iran. This model incorporates source-related factors, meteorological factors, surface roughness, and settling particles to estimate pollutant concentration from continuous sources. The study focused on the local environmental impact of Kerman Cement Plant. The performance of the model was found to be in good agreement with measured data; the average absolute percent deviation is 25.53%. In addition, the result of this modeling shows that the PM10 concentration in the ambient air at distances of about 600–1,400 m from the stacks is higher than the WHO guidelines of an annual average of 260 μg/m³.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.