Changchun Huang

Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009

Human activities contribute highly to water eutrophication. In this study, the relationship between human activities and water eutrophication in Dianchi Lake in China was characterized using a combination of satellite imaging, sedimentary physicochemical and meteorological data analyses. The heavy eutrophic status and algal bloom in Dianchi Lake were first observed by satellite in 1977 and 1989, respectively. The C/N ratio, an indicator of organic sources in sediments, also showed that the planktonic organic source in the sediment significantly increased beginning in 1989. The land use cover in the Dianchi basin showed that both farm lands and forests, but particularly farmlands, were reduced during the period from 1974 to 2009. The urbanized land area increased from 1974 to 2009, particularly after 2000. The effects of human activities on water eutrophication were expressed by land use cover, population, gross domestic product (GDP; separated into primary, secondary and tertiary industries) and wastewater discharge. For land use cover, farm and urbanized lands were the main sources of water nutrients; forest contributed slightly to these nutrients. For GDP, primary (correlation coefficient=0.94, P<0.001) and tertiary (correlation coefficient=0.95, P<0.001) industries were highly correlated with total nitrogen. Secondary (correlation coefficient=0.95, P<0.001) and tertiary (correlation coefficient=0.96, P<0.001) industries were highly correlated with total phosphorus. The algal bloom area was significantly correlated with wastewater discharge (correlation coefficient=0.78, P<0.005) (except industrial wastewater), which was primarily led by the non-agricultural population, from 2000 to 2009. This study suggests that the protection of water environments requires a comprehensive protection policy in addition to a unilateral protection policy.

Light scattering properties and their relation to the biogeochemical composition of turbid productive waters: a case study of Lake Taihu

Light scattering properties in such a highly turbid productive lake as Lake Taihu in China were examined through 118 samples collected during three cruises in November 2006, March 2007, and November 2007. The particulate scattering and backscattering coefficients were observed using WETLabs AC-S and ECO-BB9. A power model with a spectral exponent of -0.729 was used to simulate the particulate scattering coefficient (b(p)) spectra. It has a better performance than the linear model. Scattering parameters are more closely related to inorganic suspended matter (ISM) concentration than to other water components, such as total suspended matter (TSM), organic suspended matter (OSM), and chlorophyll a (Chla). This indicates that ISM dominates the scattering signal in the lake. Three discrepancies with oceanic/coastal waters are observed: (a) the backscattering ratio (b (bp)) decreases with an increase in the ISM concentration because of a highly strong contribution by ISM to b(p); (b) the mass-specific scattering coefficient (b(p) (m)) exhibits a wider range of variability than that reported in previous studies, which can be attributed to considerable variation in the OSM and ISM distributions; (c) the particle size distribution slope (xi) is mostly larger than 4.0 in Lake Taihu, whereas it is usually within 3.5-4.0 for marine particles. In addition, the bulk refractive index (n (p)) calculated according to the Twardowski et al. model [J. Geophys. Res. 106, 14129 (2001)JGREA20148-0227] indicates that some stations (n (p)<1.07) can be regarded as organic-particle dominant. Other stations with high ISM concentrations have a very small n (p) value mostly within 1.10-1.17. Overall, the knowledge on the scattering properties gained in this study broadens our understanding of water optics in highly turbid productive water columns.

Long-Term Satellite Observations of Microcystin Concentrations in Lake Taihu during Cyanobacterial Bloom Periods.

Microcystins (MCs) produced by cyanobacteria pose a serious threat to public health. Intelligence on MCs distributions in freshwater is therefore critical for environmental agencies, water authorities, and public health organizations. We developed and validated an empirical model to quantify MCs in Lake Taihu during cyanobacterial bloom periods using the atmospherically Rayleigh-corrected moderate resolution imaging spectroradiometer (MODIS-Aqua) (Rrc) products and in situ data by means of chlorophyll a concentrations (Chla). First, robust relationships were constructed between MCs and Chla (r = 0.91; p < 0.001; t-test) and between Chla and a spectral index derived from Rrc (r = -0.86; p < 0.05; t-test). Then, a regional algorithm to analyze MCs in Lake Taihu was constructed by combining the two relationships. The model was validated and then applied to an 11-year series of MODIS-Aqua data to investigate the spatial and temporal distributions of MCs. MCs in the lake were markedly variable both spatially and temporally. Cyanobacterial bloom scums, temperature, wind, and light conditions probably affected the temporal and spatial distribution of MCs in Lake Taihu. The findings demonstrate that remote sensing reconnaissance in conjunction with in situ monitoring can greatly aid MCs assessment in freshwater.

Estimation of Chlorophyll a Concentration Using NIR/Red Bands of MERIS and Classification Procedure in Inland Turbid Water

The classification criteria are established to classify the water of Taihu Lake into four classes based on above-water remote sensing reflectance (R_rs), i.e., types A to D. Among the four water types, type A spectra represented the case of waters where algal blooms or aquatic plants appeared, while type B is referred to the water with high suspended matter concentration and low chlorophyll a concentration (C_chla). Both types A and B were not suitable for retrieving C_chla from image data. Hence, three-band, four-band, and two-band ratio algorithms were constructed to retrieve C_chla from water types C and D. The obtained results showed that the relation trends between C_chla and R_rs were different between type C and type D waters. By using Medium Resolution Imaging Spectrometer images, acquired on November 11, 2007 and November 20, 2008, the C_chla of Taihu Lake was mapped by band 9/band 7 models; it could be concluded that the C_chla mainly ranged from 0 to 20 mg · m^-3, accounting for 83.70% of the whole lake area in 2007 image, while the area was 86.63% in 2008 image. The estimation accuracies varied from different C_chla ranges. The mean absolute percent errors obtained by band 9/band 7 models were 106.23%, 56.79%, 38.04%, 33.80%, and 58.74% for the ranges 0 mg · m^-3 <; C_chla <; = 5 mg · m^-3, 5 mg · m^-3 <; C_chla <; = 10 mg · m^-3, 10 C_chla <; = 20 mg · m^-3, 20 mg · m^-3 <; C_chla <; = and 30 mg · m^-3 <; C_chla, respectively. Correspondingly, the root-mean-square errors were 5.02, 4.45, 5.59, 8.72, and 32.55 mg · m^-3, respectively.

Parameterization of water component absorption in an inland eutrophic lake and its seasonal variability: a case study in Lake Taihu

Water spectral absorption characteristics of eutrophic lakes are largely different from those of ocean and coastal waters. We therefore studied them with the aim of establishing an analytical model for inland water colour, to be used in remote sensing. Field measurements were carried out on 16 and 17 August 2006 (summer), 5 and 6 November 2006 (winter), and 29 and 30 March 2007 (spring) at 15 stations in northern Lake Taihu (China). Chromophoric dissolved organic matter (CDOM) absorption coefficients (a CDOM) are higher in summer than in spring and winter, with the ratios of a CDOM in spring, summer and winter being approximately 1 : 4.0 : 1.2 at both UV‐C and UV‐B. The spectral slope S CDOM values change with wavelength and season, and covary with CDOM concentration, as shown by regression analysis. For the CDOM absorption spectrum in the wavelength region 500–700 nm (important for water colour remote sensing), a linear method simulates better than an exponential method. Seasonal variations in non‐algal particulate (NAP) absorption (a NAP) at blue, green and red wavelengths show better consistency, in the order winter>spring>summer. The average S NAP is 0.0065±0.0009 nm−1, which is lower than that in other types of waters. Phytoplankton absorption (a ph) peak height changes with the season, with the pattern summer>winter>spring, and phytoplankton absorption spectra can be expressed with high accuracy by a quadratic model. CDOM absorption contributions in the three seasons are low compared to phytoplankton and NAP.

Molecular biomarkers for sources of organic matter in lacustrine sediments in a subtropical lake in China

N-alkanes distributions and stable isotopic compositions (δ(13)C and δ(15)N) in the lacustrine sediments of Shijiu lake were measured to assess whether biological source information was recorded in the molecular biomarker. Results showed regular unimodal n-alkanes distribution in range of C16-C33 with strong predominance of odd-numbered n-alkanes, maximizing at C29. The δ(15)N for SON were uniformly low, ranging from -6.7‰ to 3.8‰ and C/N ratios ranged from 6.6 to 10.0, suggesting that most of organic matter was influenced by terrestrial characteristics of the watershed. The δ(13)C for C27 to C31n-alkanes and for SOC varied from -32.9‰ to -26.6‰ and -23.4‰ to -21.6‰, respectively, falling within the range of corresponding n-alkanes in leaves mainly from C3 land plants. The values of C/N, CPI, OEP, ACL and C27/C31 exhibit similar temporal changes with the primary production, showing enhanced eutrophication resulted from increased anthropogenic activities in Shijiu lake from 1852 to 2010.

Retrieval of Microcystis aentginosa Percentage From High Turbid and Eutrophia Inland Water: A Case Study in Taihu Lake

Microcystis aentginosa (MA), which is one kind of cyanobacteria, is the primary algal species in Taihu Lake. The MA bloom has a significantly negative effect on the human health and water environment ecosystem. The monitoring and prediction of MA bloom become more and more critical for the security of drinking water source and environment in the Taihu Lake area. In this paper, the percentage of MA was estimated from remote-sensing reflectance via a novel spectral shape genetic optimization algorithm. This algorithm focuses on the shape of remote-sensing reflectance, and it can remove the influence of the amplitude of remote-sensing reflectance from the retrieval result. The accuracy of this optimization algorithm is acceptable according to both simulated and in situ data. The percentage of mean square root (RMSP) of the percentage of the phytoplankton absorption coefficient to the total absorption coefficient at 440 nm [ar (440 nm)] between the retrieved and the simulated is 18.39%. The RMSP of the total absorption coefficient at 440 nm [a (440 nm)] between the retrieved and the simulated is 3.65%. The RMSP of the percentage of MA [Sf] between the retrieved and the simulated is 13.60%. The RMSP of the colored dissolved organic matter (CDOM) absorption coefficient slope [S] between the retrieved and the simulated is 5.89%. The RMSP of the particle backscatter coefficient slope [Y] between the retrieved and the simulated is 30.46%. In Taihu Lake, the RMSPs of ar (440 nm), a (440 nm), Sf , and S between the retrieved and the measured are 36.59%, 35.70%, 19.25%, and 16.80%, respectively. The retrieved percentage of MA (Sf) and Scenedesmus obliquus (1 - Sf) by this model from August 2006, October 2006, to November 2008 indicates the variation trend of algal species in different seasons. This trend accords with the results from previous studies and observations. This paper extends and advances the previous retrieval methods and confirms that the genetic optimization algorithm can be used to retrieve the information of water constituents in the high turbid and eutrophic inland water mass.

Changes in land use, climate and the environment during a period of rapid economic development in Jiangsu Province, China

Understanding changes in climate and environment on a regional scale can provide useful guidance for regional socio-economic development. The present study characterizes changes in the environment, climate, land use and cover types via in situ observed, statistical data and remote sensing images for Jiangsu Province, China, during the period 1980-2012. Statistical and spatial analyses indicate that the pace of urbanization in southern Jiangsu is more rapid than that in northern Jiangsu. Urbanization (92.7%) results primarily from the loss of farmland. While emissions of pollutants from industrial sources were well controlled, and wastewater, which more frequently derives from urban domestic sources, was found to be increasing. The rates of wastewater to population increased from 0.17±0.017 to 0.32±0.090 (billion ton/million persons) during the two periods of 1980-2000 and 2000-2012. However, the rates of wastewater to Gross Domestic Product (GDP) decreased from 0.26±0.20 to 0.014±0.009 (billion ton/billion Yuan), respectively. The significant increase in scattering radiance and Earths albedo caused by the urbanization and its process (Pearson correlation coefficient (r) between urban land and scattering radiance=0.86, p<0.0001; r between farmland and scattering radiance=-0.92, p<0.0001) aggravates the warming in the regional scale. This correlation analysis indicates that temperature will decrease with the increase of woodland, grassland and farmland, and will increase with the increase of urbanized and unexploited lands. Added to warming caused by an increase in CO2, land use/cover change and human activities may be the primary reason for the rising temperatures in Jiangsu Province. The change in regional thermal conditions reduces both local humidity and land atmosphere flux exchange. The low atmosphere flux exhange contributes to the spread of atmospheric pollutants and the deposition of atmospheric particles.

A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance

This study develops a novel support vector regression (SVR) model for retrieving the specific cyanobacterial pigment C-phycocyanin (C-PC) concentrations in cyanobacteria-dominated large turbid lakes of China. Lake Taihu, Lake Chaohu, and Lake Dianchi in China were our study areas. Five field cruises were carried out to collect data sets of optical and water quality parameters. To retrieve the C-PC, three types of reflectance forms, including single band, band ratio, and three-band-combination, were compared. The band ratio was the best candidate to serve for algorithm development. On this basis, two types of models, including linear models and a SVR model, were originally established. The previous typical algorithms were also examined. The obtained results showed that the best-performing model was the SVR model. By our validation data set, the proposed SVR model also presented accurate prediction results, with the lowest errors among all methods. The novelty of the SVR model compared to the previous ones lies in the inclusion of band ratios that are located outside of the main pigment absorption peaks but hold information on inflection points, curvature, etc., into empirical optimization. The implications of these findings indicates the potential applicability of the SVR models in lakes of the similar type.

Hyperspectral Remote Sensing of the Pigment C-Phycocyanin in Turbid Inland Waters, Based on Optical Classification

Pigment C-phycocyanin (C-PC) is a useful indicator for the presence of cyanobacteria in inland waters, which has been well known as a phytoplankton group with many negative effects on human, animal, and aquatic ecosystem health. In recent years, the remote detection of the C-PC concentrations for inland waters has received much attention. However, their accurate quantification by means of remote sensing is still a challenge due to the significant bio-optical complexity of turbid inland waters. In this paper, three typical turbid inland lakes in China were investigated through in situ observed data sets containing optical and water quality parameters. By using a recently proposed TD680 optical classification method, all collected samples were first classified into three types. For each type of water, we determined specific spectral sensitive regions for the pigment C-PC. Then, we developed three type-specific support vector regression (SVR) algorithms and an aggregated SVR algorithm. The performances of these algorithms were evaluated through the validation data sets. The results show that the type-specific algorithms generally have significantly improved performance over the aggregated SVR algorithm. Their assessment errors [mean absolute percentage error (MAPE) and root-mean-square error ( rmse)] were as follows: 1) MAPE = 15.6% and rmse = 30.6 mg·m-3 for Type 1 water; 2) MAPE = 47.1% and rmse = 61.5 mg·m-3 for Type 2 water; and 3) MAPE = 26.4% and rmse = 19.1 mg·m-3 for Type 3 water. The findings in this paper demonstrate that a prior water classification is needed for the development of accurate C-PC retrieval algorithms. This paper provides a valid strategy for improving C-PC estimation accuracy and enhancing algorithm commonality for optically complex turbid waters.