基于可靠度的变压器声功率遗传算法研究

换流变压器是高压直流换流站中主要的噪声源,对换流变压器噪声的预测精度直接影响到后期整个换流站噪声预测模型的准确性,因此,准确获得换流变压器的声功率至关重要。根据《声学 户外声传播衰减 第2部分:一般计算方法》(ISO 9613-2:1996)中的户外声传播声功率计算方法,构建了声源声功率反推数学模型。利用某变压器周围大量测点现场实测数据,结合可靠度理论,识别并剔除了实测点中受相干声场影响较大的点,实现了换流变压器声功率反演计算,并对测点个数的选取进行了初步研究及优化。与其他声功率测量方法相比,该方法操作简单,对仪器设备要求较低,适用于复杂的声学环境,具有一定的应用前景。     

Urban daytime traffic noise prediction models

An evaluation was made of the acoustic environment generated by an urban highway using in situ measurements. Based on the data collected, a mathematical model was designed for the main sound levels (L _eq, L ₁₀, L ₅₀, and L ₉₀) as a function of the correlation between sound levels and between the equivalent sound pressure level and traffic variables. Four valid groups of mathematical models were generated to calculate daytime sound levels, which were statistically validated. It was found that the new models can be considered as accurate as other models presented in the literature to assess and predict daytime traffic noise, and that they stand out and differ from the existing models described in the literature thanks to two characteristics, namely, their linearity and the application of class intervals.     

Total suspended matter observation in the Pearl River estuary from in situ and MERIS data

Based on the cruise data collected in the Pearl River estuary (PRE) in May 2008, an empirical two-band model by using the ratio of R _rs at 629 and 671 nm was established to retrieve total suspended matter (TSM) concentration with the determination coefficient (R²) of 0.854, mean relative error (MRE) of 7.483%, and root-mean-square error (RMSE) of 1.295 mg L^???1. To match with medium resolution imaging spectrometer (MERIS) bands, in situ remote sensing reflectance was re-sampled to the bandwidth of 10 nm. The relationship between TSM and re-sampled R _rs at 620 nm (MERIS band 6) and 665 nm (MERIS band 7) are obtained (R² = 0.748, RMSE = 1.697 mg L^???1, MRE = 8.785%, n = 13). Additionally, to map the spatial distribution of TSM in the PRE, MERIS level_1B data were calibrated using a multiple linear regression model based on in situ R _rs. Another dataset collected in the PRE in January 2004 was used to validate the two-band model and also applied to map TSM distribution from MERIS image. The comparison between measured TSM values and modeled ones showed satisfactory results (R² = 0.753, MRE = 22.199%, and RMSE = 2.603 mg L^???1).     

Confidence intervals of Leq in the case of stationary random noise measurements

A common practice in random noise measurements is to assume that the noise samples are normally distributed. Since the sound pressure level (L _p) is related with the sound energy by a non linear equation, the distribution of the two variables will be different. If one assumes that the probability density function (pdf) of L _p is normal, the pdf of noise energy is not and vice versa. A simple criterion based on statistical properties of sampled noise can be used for the determination of the kind of distribution of the measured noise. The case in which the noise energy emitted by the source, and the case in which the measured noise level is normally distributed are examined. For each case confidence intervals of the calculated equivalent level are calculated.     

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R _net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R _net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R ² 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R ² = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R ² = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.     

Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R ² (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.     

A new <Emphasis Type="Italic">P. putida</Emphasis> instrumental toxicity bioassay

Here, we present a new toxicity bioassay (CO₂-TOX), able to detect toxic or inhibitory compounds in water samples, based on the quantification of Pseudomonas putida KT2440 CO₂ production. The metabolically produced CO₂ was measured continuously and directly in the liquid assay media, with a potentiometric gas electrode. The optimization studies were performed using as a model toxicant 3,5-DCP (3,5-dichlorophenol); later, heavy metals (Pb²⁺, Cu²⁺, or Zn²⁺) and a metalloid (As⁵⁺) were assayed. The response to toxics was evident after 15 min of incubation and at relatively low concentrations (e.g., 1.1 mg/L of 3,5-DCP), showing that the CO₂-TOX bioassay is fast and sensitive. The EC₅₀ values obtained were 4.93, 0.12, 6.05, 32.17, and 37.81 mg/L for 3,5-DCP, Cu²⁺, Zn²⁺, As⁵⁺, and Pb²⁺, respectively, at neutral pH. Additionally, the effect of the pH of the sample and the use of lyophilized bacteria were also analyzed showing that the bioassay can be implemented in different conditions. Moreover, highly turbid samples and samples with very low oxygen levels were measured successfully with the new instrumental bioassay described here. Finally, simulated samples containing 3,5-DCP or a heavy metal mixture were tested using the proposed bioassay and a standard ISO bioassay, showing that our test is more sensible to the phenol but less sensible to the metal mixtures. Therefore, we propose CO₂-TOX as a rapid, sensitive, low-cost, and robust instrumental bioassay that could perform as an industrial wastewater-process monitor among other applications.     

Impact of Diwali celebrations on urban air and noise quality in Delhi City,India

A study was conducted in the residential areas of Delhi, India, to assess the variation in ambient air quality and ambient noise levels during pre-Diwali month (DM), Diwali day (DD) and post-Diwali month during the period 2006 to 2008. The use of fireworks during DD showed 1.3 to 4.0 times increase in concentration of respirable particulate matter (PM₁₀) and 1.6 to 2.5 times increase in concentration of total suspended particulate matter (TSP) than the concentration during DM. There was a significant increase in sulfur dioxide (SO₂) concentration but the concentration of nitrogen dioxide (NO₂) did not show any considerable variation. Ambient noise level were 1.2 to 1.3 times higher than normal day. The study also showed a strong correlation between PM₁₀ and TSP (R ² ≥ 0.9) and SO₂ and NO₂ (R ² ≥ 0.9) on DD. The correlation between noise level and gaseous pollutant were moderate (R ² ≥ 0.5). The average concentration of the pollutants during DD was found higher in 2007 which could be due to adverse meteorological conditions. The statistical interpretation of data indicated that the celebration of Diwali festival affects the ambient air and noise quality. The study would provide public awareness about the health risks associated with the celebrations of Diwali festival so as to take proper precautions.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

利用阈值浮动技术实现机场周围飞机噪声的自动测量

介绍一种自动、高效测量机场周围飞机噪声的技术方法"阈值浮动技术",即噪声测量仪的运行阈值随环境背景噪声的高低而自主浮动的技术。利用该技术,当被测飞机噪声声级高于运行阈值时,噪声测量仪启动"记录、分析、录音"等程序的运行;当被测飞机噪声声级低于运行阈值时,停止相关程序的运行。由于运行阈值是浮动的,因而实现了机场周围飞机噪声的自动、高效和准确测量。给出了实现"阈值浮动技术"的计算机算法,并列举了测量实例对该项技术加以验证,证明了该技术方法行之有效。     

Characteristics of atmospheric non-methane hydrocarbons during haze episode in Beijing, China

This study firstly focused on non-methane hydrocarbons (NMHCs) during three successive days with haze episode (16–18 August 2006) in Beijing. Concentrations of alkanes, alkenes, aromatic hydrocarbons, and ethyne all peaked at traffic rush hour, implying vehicular emission; and alkanes also peaked at non-traffic rush hour in the daytime, implying additional source. Especially, alkanes and aromatics clearly showed higher levels in the nighttime than that in the daytime, implying their active photochemical reactions in the daytime. Correlation coefficients (R ²) showed that propane, n-butane, i-butane, ethene, propene, and benzene correlated with ethyne (R ²?=?0.61–0.66), suggesting that their main source is vehicular emission; 2-methylpentane and n-hexane correlated with i-pentane (R ²?=?0.61–0.64), suggesting that gasoline evaporation is their main source; and ethylbezene, m-/p-xylene, and o-xylene correlated with toluene (R ²?=?0.60–0.79), suggesting that their main source is similar to that of toluene (e.g., solvent usage). The R ² of ethyne, i-pentane, and toluene with total NMHCs were 0.58, 0.76, and 0.60, respectively, indicating that ambient hydrocarbons are associated with vehicular emission, gasoline evaporation, and solvent usage. The sources of other hydrocarbons (e.g., ethane) might be natural gas leakage, biogenic emission, or long-range transport of air pollutants. Measured higher mean B/T ratio (0.78?±?0.27) was caused by the more intensive photochemical activity of toluene than benzene, still indicating the dominant emission from vehicles.     

化学发光法臭氧分析仪的研制与性能测试

基于化学发光臭氧(O₃)检测方法,将氮氧化物分析仪(Thermo Model 42i-TL)改装为O₃分析仪,测试其性能,并与紫外光度法O₃分析仪(Thermo Model 49i)同时应用于外场观测,开展比对测试试验。结果显示：该改装仪器的测量性能优于Thermo Model 49i,其零点噪声为0.10×10^-9(体积分数,下同),最低检出限为0.20×10^-9,量程噪声为0.42×10^-9,示值误差为0.1%满量程(F.S.)。在比对试验过程中,化学发光法O₃分析仪运行稳定,测量结果与商品化的Thermo Model 49i所测定的O₃浓度数值的变化趋势高度一致(R²=0.998)。化学发光法O₃分析仪相较于Thermo Model 49i具有更低的噪声,其日间(09:00—17:00)O₃浓度示值显著低于Thermo Model 49i,且...     

Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection

Statistical models of microbial water quality inform risk management for water recreation. Current research focuses on resource-intensive, location-specific data collection and water quality modeling, but this approach may be cost-prohibitive for risk managers responsible for numerous recreation sites. As an alternative, we tested the ability of two data-driven models, tree regression and random forests with conditional inference trees, to select readily available hydrometeorological variables for use in linear mixed effects (LME) models predicting bacterial density. The study included the Chicago Area Waterway System (CAWS) and Lake Michigan beaches and harbors in Chicago, Illinois, at which Escherichia coli and enterococci were measured seasonally in 2007–2009. Tree regression node variables reduced data dimensionality by >50 %. Variable importance ranks from random forests were used in a forward-step selection based on R ² and root mean squared prediction error (RMSPE). We found two to three variables explained bacteria densities well relative to random forests with all variables. LME models with tree- or forest-selected variables performed reasonably well (0.335?<?R ²?<?0.658). LME models for Lake Michigan had good prediction accuracy with respect to the single sample maximum standard (72–77 %), but limited sensitivity (23–62 %). Results suggest that our alternative approach is feasible and performs similarly to more resource-intensive approaches.     

Temporal changes of soil respiration under different tree species

Soil respiration rates were measured monthly (from April 2007 to March 2008) under four adjacent coniferous plantation sites [Oriental spruce (Picea orientalis L.), Austrian pine (Pinus nigra Arnold), Turkish fir (Abies bornmulleriana L.), and Scots pine (Pinus sylvestris L.)] and adjacent natural Sessile oak forest (Quercus petraea L.) in Belgrad Forest—Istanbul/Turkey. Also, soil moisture, soil temperature, and fine root biomass were determined to identify the underlying environmental variables among sites which are most likely causing differences in soil respiration. Mean annual soil moisture was determined to be between 6.3 % and 8.1 %, and mean annual temperature ranged from 13.0°C to 14.2°C under all species. Mean annual fine root biomass changed between 368.09 g/m² and 883.71 g/m² indicating significant differences among species. Except May 2007, monthly soil respiration rates show significantly difference among species. However, focusing on tree species, differences of mean annual respiration rates did not differ significantly. Mean annual soil respiration ranged from 0.56 to 1.09 g?C/m²/day. The highest rates of soil respiration reached on autumn months and the lowest rates were determined on summer season. Soil temperature, soil moisture, and fine root biomass explain mean annual soil respiration rates at the highest under Austrian pine (R ²?=?0.562) and the lowest (R ²?=?0.223) under Turkish fir.     

Monitoring of arsenic in aquatic plants, water, and sediment of wastewater treatment ponds at the Mae Moh Lignite power plant, Thailand

Mae Moh is a risky area for arsenic contamination caused by the effluent from biowetland ponds in Mae Moh lignite-fuelled power plant. The objective of this study was to investigate the arsenic concentrations of Mae Moh biowetland ponds and determine the main factors which are important for arsenic phytoremediation in the treatment system. The result revealed that arsenic concentrations in the supernant were in the range of less than 1.0 μg As L^???1 to 2.0 μg As L^???1 while those in the sediment were in the range of 25–200 μg As kg soil^???1. Both values were below the Thailand national standard of 0.25 mg As L^???1 for water and 27 mg As kg soil^???1 for the soil. Arsenic accumulation in the biomass of 5 aquatic plants at the biowetland ponds ranged from 123.83 to 280.53 mg As kgPlant^???1. Regarding the result of regression analysis (R ²?= 0.474 to 0.954), high concentrations of organic matter and other soluble ions as well as high pH value in the sediment could significantly enhance the removal of soluble arsenic in the wetland ponds. From the regression equation of accumulated arsenic concentration in each aquatic plant, Eichhornia crassipes (Mart.) Solms. (R ²?= 0.954), Ipomoea aquatica Forsk. (R ²?= 0.850), and Typha angustifolia (L.) (R ²?= 0.841) were found to be preferable arsenic removers for wastewater treatment pond in the condition of low Eh value and high content of solid phase EC and phosphorus. On the other hand, Canna glauca (L.) (R ²?= 0.749) appeared to be favorable arsenic accumulator for the treatment pond in the condition of high Eh value and high concentration of soluble EC.     

Influence of Meteorological Factors NO2, SO2, CO and PM10 on the Concentration of O3 in the Urban Atmosphere of Eastern Croatia

Ozone, NO₂, SO₂, CO, PM₁₀ and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O₃ and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R ²?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R ²?=?0.82).     

特高压换流站噪声源测试与预测技术研究

科学识别和获取特高压换流站内主要噪声源特性,在此基础上对换流站噪声影响进行分析预测,用于换流站噪声评价、设计和防护。在复杂声场环境条件下,通过现场录波、噪声与振动同步测试等方法,结合噪声理论知识,识别出换流变压器的优势谱点为400 Hz,该点占整个频谱噪声贡献值90%以上,同时识别出交流滤波器场电抗器特征频率点与电抗器所滤除的谐波频率相一致,且换流变压器和交流滤波器场电抗器的声压与振动优势频点具有较强的相关性。根据噪声源特性,对其计算模型进行相应的改进,可提高换流站噪声预测结果的准确性。     

Mercury levels in myliobatid stingrays (Batoidea) from the Gulf of California: tissue distribution and health risk assessment

With the aim of knowing Hg distribution in selected tissues of myliobatid stingrays and assessing health risk to Mexican population, Hg concentration was determined in the muscle and liver of four ray species. Total Hg levels were determined by cold vapor atomic absorption spectrophotometry. With respect to the muscle, devil rays (Mobula spp.) showed lower Hg levels (<0.22 μg g^?1) than Rhinoptera steindachneri (0.37?±?0.25 μg g^-1 wet weight). In the case of the liver, the highest Hg concentration was found in Mobula japanica (0.22?±?0.01 μg g^?1). Hg levels in the muscle and liver varied according to the species; in some case, the liver accumulated more Hg than the muscle and the opposite pattern in other cases. R. steindachneri showed a significant difference between both tissues. No significant differences of Hg levels between males and females and between juveniles and adult specimens of R. steindachneri were found. Positive correlation between Hg concentrations and disc width and total weight was not significant for R. steindachneri (Rs?<?0.36, p?>?0.05). Batoids showed Hg values below the Mexican (NOM-027-SSA1-1993) limits (1.0 μg g^?1) in fishes for human consumption. The species with the highest potential of Hg transfer to human population is R. steindachneri; however, an adult (70 kg) could consume approximately 943 g per week without representing a health risk. Nevertheless, further and continuous monitoring is needed since batoids support an important fishery in Mexican waters, being a food resource and income to coastal communities.     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications

Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R ²) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available.