[Assessment of Potential Risk of Diffuse Pollution in Haihe River Basin Based Using DPeRS Model].

Considering the Haihe River Basin as an example, the DPeRS model was used to analyze the spatial distribution characteristics and pollution sources of the diffuse pollution by remote sensing pixel scale. Combined with the evaluation standard of surface water quality, a potential risk grading method for diffuse pollution was constructed to assess the potential risk of diffuse pollution in Haihe River Basin. The results showed that, in 2016, the diffuse discharge loads of total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4+-N), and chemical oxygen demand (COD) were 429.2, 25.7, 288.3, and 1017.0 kg ·km-2, respectively, with the amount of river entry being 2.5×104 ton, 1597.2 ton, 1.7×104 ton, and 6.6×104 ton in Haihe River Basin, respectively. Farmland runoff is the most important source of diffuse pollution of TN, TP and NH4+-N in the Haihe River Basin. For COD index, urban life is the primary type of pollution, followed by livestock. The diffuse pollution is relatively severe in the central and southern areas of Haihe River Basin, and this area is also a high-risk concentrated distribution area of diffuse pollution in the basin. The distribution of high-risk areas of nitrogen-phosphorus diffuse pollution are relatively concentrated, and the chemical oxygen demand is relatively scattered. More than 36% of the Haihe River Basin has a nitrogen-phosphorus diffuse pollution risk, and 2.94% of the area has a chemical oxygen demand diffuse pollution risk.

[Muti-model collaborative retrieval of chlorophyll a in Taihu lake based on data assimilation ].

Under the efforts of many scholars, large amount of remote retrieval models of water quality parameters have been developed. However, each model could only reflect the "true value" from one level because of the natural limitation of remote sensing. To get the relatively true value by combining various retrieval models, in this work, we developed a multi-model collaborative retrieval algorithm for retrieving the concentration of Chlorophyll a based on data assimilation. We measured water quality parameters and water reflectance spectra in Taihu Lake during 2006 to 2009. There were seven retrieve models established and six models were selected to participate in the multi-model collaborative retrieval algorithm. Then these selected models were combined to establish a multi-model for retrieving the concentration of Chlorophyll a. The results indicated: (1) the accuracy of multi-model retrieval algorithm was better than that of single-model retrieval method, with an optimal MAPE of only 22. 4% ; (2) with more models participating in the multi-model collaborative retrieval algorithm, the accuracy became better, the average MAPE was decreased from 25. 6% to 23. 4% , the average RMSE was decreased from 15. 082 µg.L-1 to 14. 575 µg.L-1, and the average correlation coefficient was improved from 0.91 to 0. 92; (3) the accuracy and errors of retrieval products could be effective evaluated through calculating the confidence interval, which makes possible the acquirement of spatial and temporal error distribution of Chlorophyll a concentration retrieval in Taihu Lake.

[Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat transfer resulting from the thermal plume is the primary reason leading to the temperature variations. Temperature rising (TR) distributions obtained from remote sensing data and in-situ measurements are consistent, except that the interpolated BT shows more level details (> 5 levels) than that of the ST (up to 4 levels). The areas with higher TR levels (> 2) are larger on BT maps, while for lower TR levels (≤ 2), the two methods perform with no obvious differences. Minimal errors for satellite-derived SST occur regularly around local time 10 a. m. This makes the remote sensing results to be substitutes for in-situ measurements. Therefore, for operational applications of HJ-1B IRS4, remote sensing technique can be a practical approach to monitoring the nuclear plant thermal pollution around this time period.

[Review of change detection methods using multi-temporal remotely sensed images].

With the development of platforms and sensors, continuous repetition of remote sensing observation of the earth surface has been realized, and a mass of multi-source, multi-scale, multi-resolution remote sensing data has been accumulated. Those images have detailedly recorded the changing process of ground objects on the earth, which makes the long term global change research, such as change detection, based on remote sensing become possible, and greatly push forward the research on image processing and application. Although plenty of successful research has been reported, there are still enormous challenges in multi-temporal imagery change detection. A relatively complete mature theoretical system has not formed, and there is still a lack of systematic summary of research progress. Firstly, the current progress in change detection methods using multi-temporal remotely sensed imagery has been reviewed in this paper. Then, the methods are classified into three categories and summarized according to the type and amount of the input data, single-phase post-classification comparison, two-phase comparison, and time series analysis. After that, the possible existing problems in the current development of multi-temporal change detection are analyzed, and the development trend is discussed finally.

[Spectral absorption coefficients of optically active substances in Lake Dianchi].

A field investigation in Lake Dianchi was carried out in November, 2009, and a dataset including absorption coefficients and concentration of water components was collected. Absorption properties and its spatial distribution of Lake Dianchi have been systematically analyzed, the results show that: (1) Absorption spectral curve of total particles (a(p)) are similar with that of phytoplankton (a(ph)), which indicates that phytoplankton absorption dominates absorption of total suspended particles; (2) Significant relationships are found between a(ph) (440), a(ph) (624), a(ph) (675) and concentration of chla, and cyanophyta dominats the quantity of the algae in water columns; (3) There are more phytoplankton existed in northern lake than other areas because of higher level of eutrophication. Comparing with other water columns, discrepancies mainly appear as below: (1) The mean value of slope of absorption spectral curve of non-algal particles (S(d)) in Lake Dianchi is much bigger than that in other case-II waters; (2) The mean value of slope of absorption spectral curve of chromophoric dissolved organic matter (CDOM) S(g) in Lake Dianchi is much smaller than that in other case-II waters; (3) Specific absorption of phytoplankton (a(ph)*) at 440 nm and 675 nm is at intermediate level, and a(ph)* value is susceptible to different regions, seasons, population structure of phytoplankton and so on.

[Retrieve phycocyanin concentrations based on semi-analytical model in the Dianchi Lake, China].

Phycocyanin (PC) in the blue-green algae is usually used to detective the quantity of the blue-green algae, because of its special absorption at band 620 nm. A semi-analytical model retrieving phycocyanin concentrations is been built, based on a nested semi-empirical band ratio algorithm, using the data sets collected in September 19 and September 20, 2009 from Dianchi Lake. The empirical relationship between the specific absorption coefficient at band 620 nm [a(PC)* (620)] and the absorption coefficient at band 620 nm [a(PC) (620)] reduces the impact of the variability of a(PC)* (620) in the model built by Simis. The new semi-analytical model is proved well done in retrieving phycocyanin concentrations and has a mean relative error (MRE) 21.63% by the dataset collected on December 1, 2009 from Dianchi Lake. The model error analysis prove that the main reason of the error is caused by the component and concentrations of pigments changing seasonally in the blue-green algae.

[Laparoscopic-assisted modified Soave procedures for adult Hirschsprung disease].

OBJECTIVE: To evaluate the clinical value of laparoscopy-assisted modified Soave procedure for Hirschsprung disease in adults. METHODS: Twenty-eight patients with a preoperative diagnosis of Hirschsprung disease underwent laparoscopy-assisted modified Soave procedure between March 2005 and December 2009. Clinical data were retrospectively analyzed. RESULTS: There were no conversions to open surgery. The mean operative time was (165±12) minutes (range: 135-185 minutes). Estimated blood loss ranged from 50 to 250 ml, and no patients required intraoperative blood transfusion. Postoperative pathologic examination showed Hirschsprung diseases in 19 patients and Hirschsprung allied diseases in 9. Only two patients developed rectal cuff infection and three mild seepage. Other patients had no postoperative complications. The mean hospital stay was (17.5±1.0) days. No fecal incontinence or recurrent constipation occurred during follow-up. CONCLUSION: Laparoscopy- assisted modified Soave procedure is safe and effective for Hirschsprung disease.

[Estimating of suspended matter concentration based on bio-optical model in Chaohu Lake].

Remote sensing reflectivity (R(rs)), total suspended matter (TSM), absorption and scattering properties of 32 sampling sites in situ were measured in June 2009. The inherent optical properties (IOPs), such as absorption and scattering properties of different components in Chaohu Lake, were analyzed according to these data. And specific scattering coefficient of suspended matters and the backscattering ratio which were the indispensable inherent optical parameters were identified. Then an inversion model based on bio-optical model was constructed and quasi-synchronous HJ-1 CCD images were used to inverse the concentration of suspended matters in Chaohu Lake. Results showed that, the average specific scattering coefficient of suspended matters at the wavelength of 555 nm was 0.48 m2/g. A power model about specific scattering coefficient of suspended matters was constructed by choosing 555 nm as a reference wave band with determination coefficient of 0.99. In addition, the backscattering ratio didn't rely on the wavelength from 700 nm to 900 nm (Band 4), and its value is stable at 0.051. A remote sensing reflectivity model was constructed by IOPs and apparent optical properties (AOPs) to obtain TSM in Chaohu Lake. The trend of relative error between measured values and inversed values decreased with the concentration of TSM increasing. And the average relative error value was 17.25% by using this model. This showed that this method was applicable to inverse TSM in inland lake with high concentration suspended matters. Based on two CCD image data of HJ-1 satellite, the inversion of suspended matter concentration mainly changed between 0 and 100 mg/L in Chaohu Lake. The suspended matter concentration higher than 40 mg/L covered 54.37% area of Chaohu Lake in June 13 and that of 61.62% in June 15. As well as, the distribution of suspended matter concentration inversed by using two HJ-1 CCD images of Chaohu Lake kept correspondence with climate changes at that time.

[Three wavelengths factor model of chlorophyll concentrations inversion based on different spectral dominating factors for inland lake].

A hierarchical cluster analysis is applied to the data set of R(rs) spectra. Combing the inherent optical and measured water color characteristics, we determine factors of the variability for each class of R(rs) spectra. The R(rs) measured from Lake Taihu, Chaohu, Dianchi and Three Gorges Reservoir can be divided into three spectrally distinct classes: class 1, class 2 and class 3. Class 1 is associated with water optical property dominated by total suspended matter and phytoplankton. Class 2 is associated with water optical property dominated by phytoplankton. And Class 3 is associated with water optical property dominated by total suspended matter. The three wavelengths factor Chlorophyll a concentration inversion models were developed according to different class water optical characteristics, which means relative error and RMSE are 23.8% and 8.5 mg/m3, and have higher accuracy than the model developed without classification.