Dustfall Heavy Metal Pollution During Winter in North China.

In order to study heavy metal pollution in dustfall during Winter in North China, forty-four dustfall samples were collected in North China Region from November 2013 to March 2014. Then forty trace elements content were measured for each sample by inductively coupled plasma-mass spectrometry. Finally, the contamination characteristics of the main heavy metals were studied through a multi-method analysis, including variability analysis, Pearson correlation analysis and principal component analysis. Results showed that the relative contents of cadmium (Cd), zinc (Zn), copper (Cu), bismuth (Bi), lead (Pb) exceeded the standards stipulated in Chinese soil elements background values by amazing 4.9 times. In this study, conclusions were drawn that dustfall heavy metal pollution in the region was mainly caused by transport pollution, metallurgy industrial pollution, coal pollution and steel industrial pollution.

[Typical plant spectral characteristics analysis in wild duck lake along water environment gradient].

Typical submerged plants, floating plants, emerged plants, hygrophyte plants, and mesophyte plants were chosen, and derivative method and continuum removal method were used to analyze the spectral characteristics and changing trend of plants along water environment gradient. Emerged plants and hygrophyte plants have the highest reflectance value; floating plants have lower value, while submerged plants take the lowest reflectance value due to the effect of water surface. Derivative method could emphasize the changed trends of original spectral curve, thus more characteristic bands could take on. Spectral curves reached the fastest increasing points around 520 and 710 nm, which could be considered as characteristics bands to distinguish submerged plants and others. Emerged plants and hygrophyte plants have the peak green value. According to water environment gradient from high to low, the red edges of submerged plants, floating plants and emerged plants increase, while hygrophyte plants and mesophyte plants have lower red edge value. Original spectral curves were translated to absorption curves by continuum removal, the absorption depth changes from low to high as follows: submerged plants < floating plants < merged plants < hygrophyte plants, while that is lower for mesophyte plants compared to hygrophyte plants. Absorption area increased along water environment gradient from high to low except mesophyte plants.

Fluorescence detection of malachite green and cations (Cr3+, Fe3+ and Cu2+) by a europium-based coordination polymer.

Due to remarkable fluorescence characteristics, lanthanide coordination polymers (CP) have been widely employed in fluorescence detection, but it is rarely reported that they act as multifunctional luminescent probes dedicated to detecting malachite green (MG) and various metal ions. A europium-based CP fluorescent probe, Eu(PDCA)2(H2O)6 (PDCA = 2,6-pyridinedicarboxylic acid), has been synthesized and exhibited excellent recognition ability for malachite green and metal cations (Cr3+, Fe3+ and Cu2+) among 11 metal cations, 13 anions and six other compounds. The recognition was achieved by fluorescence quenching when MG, Cr3+, Fe3+ and Cu2+ were added to a suspension of Eu(PDCA)2(H2O)6 respectively. Eu(PDCA)2(H2O)6 is a multifunctional luminescent probe, and displayed high quenching efficiencies K sv (2.10 × 106 M-1 for MG; 1.46 × 105 M-1 for Cr3+; 7.26 × 105 M-1 for Fe3+; 3.64 × 105 M-1 for Cu2+), and low detection limits (MG: 0.039 µM; Cr3+: 0.539 µM; Fe3+: 0.490 µM; Cu2+: 0.654 µM), presenting excellent selectivity and sensitivity, especially for MG. In addition, Eu(PDCA)2(H2O)6 was also made into fluorescent test strips, which can rapidly and effectively examine trace amounts of MG, Cr3+, Fe3+ and Cu2+ in aqueous solutions. This work provides a new perspective for detecting malachite green in fish ponds and heavy metal ions in waste water.

[Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.] / 异质背景下黄河三角洲潮沟的遥感提取方法.

The Yellow River Delta exhibits irregular tidal flat, with tidal creeks that vary in width and experience tidal creek current anisotropy. Given such characteristics, the GF-2 multi-spectral image was selected as the data source to characterize the details of tidal creeks. First, the normali-zed difference water index (NDWI) and OTSU classification were used to delineate the wide tidal creeks. Second, the modified fuzzy C-means clustering algorithm (MFCM) and multi-scale Gaussian matching filter (MGMF) were used to enhance the narrow tidal creeks on the basis of weakening the heterogeneity of tidal flat background. Then, the adaptive threshold segmentation was conducted to delineate the narrow tidal creeks. Finally, the complete tidal creek networks were delineated by combining the wide and narrow tidal creeks. We fully used the spatial resolution and spectral information of the GF-2 image and took into account the geometric features of the linear features, ensuring the spatial continuity of the tidal creek extraction results. In the four tested areas, the Kappa coefficient was greater than 0.8 and the overall accuracy was greater than 97%, which performed better than the maximum likelihood method and support vector machine. The results showed that the proposed method could completely differentiate different types of tidal creeks, with good extraction accuracy and stability. The method could provide scientific reference for real-time dynamic monitoring of tidal creek and its development and evolution.

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.] / åŸºäºŽå¯è§å ‰æ¤è¢«æŒ‡æ•°çš„é¢å‘å¯¹è±¡æ¹¿åœ°æ°´ç”Ÿæ¤è¢«æå–æ–¹æ³•.

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

[Estimating total nitrogen content in reclaimed water based on hyperspectral reflectance information from emergent plants: a case study of Mencheng Lake Wetland Park in Beijing, China].

Hyperspectral reflectance information is a crucial method to detect total nitrogen content in plant leaves, meanwhile, vegetation nitrogen content has a strong relationship with nitrogen in water. Taking Mencheng Lake Wetland Park supplied with reclaimed water as study area, the vegetation hyperspectral data (Phragmites australis and Typha angustifolia), and the content of total nitrogen in water were detected to investigate the feasibility of estimating total nitrogen content in reclaimed water based on hyperspectral reflectance information from emergent plants. We established simple linear regression model, stepwise multiple linear regression model and partial least square regression model based on four hyperspectral indices (spectral indices, normalized difference indices, trilateral parameters, absorption feature parameters), respectively. The accuracy of these models was coefficient of determination (R2) and root mean square error (RMSE). The results showed that stepwise multiple linear regression model and partial least square regression model predicted more accurately than simple linear regression model, and the accuracy of prediction models based on P. australis reflectance spectra was higher than those on T. angustifolia. Partial least square regression model was the most useful explorative tool for unraveling the relationship between spectral reflectance of P. australis and total nitrogen content in water with R2 of 0.854 and RMSE of 0.647. 500-700 nm was the best band range for detecting water total nitrogen content. The reflectance ratio of green peak and red valley could be effectively predicted by the absorption feature parameters.

[Study on influence of traffic and meteorological factors on inhalable particle matters of different size].

To study the effects of meteorological and traffic factors on the PM2.5 and PM10 concentrations, 28 samples were taken in the Third Ring Road of Beijing, and dust fall weight, velocity of vehicle, traffic volume, temperature, humidity, wind speed, PM2.5 and PM10 concentration data were collected for these samples. Analysis of the collected data on dust fall weight indicated that the traffic had a significant impact on the air quality. The average dust fall weights in the road and away from the traffic source were 0. 284g and 0. 016 g, respectively. The results of the partial experiment indicated that concentrations of PM2.5 and PM10 in residential areas were lower than those in road, furthermore, the PM2.5 at night was often higher than that during daytime, and the mean values of the difference in PM2.5 and PM10 were 101074 n.(cf)-1 and 15386 n.(cf)-1, respectively. Through analysis using the best subset prediction model, it was indicated that PM2.5 and PM10 were both most significantly influenced by temperature and humidity, followed by wind speed, velocity of vehicle and traffic volume. Comparing with PM10, the velocity of vehicle, traffic volume and wind speed had a more significant influence on PM2.5.

[Spatial distribution of inhalable particulate and association with respiratory disease in Beijing City].

Inhalable particulate has become the premier pollutant of Beijing, which has enormous influence on the environmental quality of the city and health of the residents. Inhalable particle pollutants (particulate matter of 0.3, 0.5, 3.0 and 5.0 microm) during the heating period and the non-heating period in 2008 and 2009 were collected, and spatial analysis was used to study the spatial distribution of each pollutant. Meanwhile, the hospital data about respiratory disease during the same time was gathered and counted. Then the relativity between inhalable particulate pollutants and respiratory disease was studied by grey correlation analysis on the base of regression analysis. The results showed that spatial distribution of fine particle was diverse but the pattern of coarse particle was similar. There was certain association between respiratory disease and inhalable particle pollutants. Heating period was the highest incidence period of respiratory disease. The prevalence of respiratory disease was higher in heating period than non-heating period. The concentration of fine particle was higher than that of coarse particle both in heating and non-heating periods, and fine particle had more effects on the respiratory system disease than coarse particle.