Spatial analysis and eutrophication assessment for chlorophyll a in Taihu Lake

Assessment of the trophic state of the Soła River dam cascade, Polish Carpathians: a comparison of the methodology

The aim of this research was to determine the trophic state of mountain dam reservoirs, which are characterized by greater hydrological and ecological dynamics than lowland reservoirs. The trophic state of three dam reservoirs forming a cascade system was investigated. Trophic evaluation was carried out based on multiple criteria, i.e., (1) the content of chlorophyll a in the water, (2) planktonic algal biomass, (3) groups and species of algae, (4) the total phosphorus concentration in the water, and (5) the Integral Trophic State index (ITS). The analyzed parameters were characterized by high variability during the study period, which to a large extent may have resulted from the mountain environmental conditions. The greatest dynamics concerned parameters related to phytoplankton development. Unequivocal determinations of the trophic states of the reservoirs were difficult; however, it was found that in successive reservoirs of the cascade (from the highest to the lowest), a reduction in water fertility occurred.

Evaluation of Chlorophyll-a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan

Harmful algal blooms (HABs) occur frequently in the Seto Inland Sea, bringing significant economic and environmental losses for the area, which is well known as one of the world's most productive fisheries. Our objective was to develop a quantitative model using in situ hyperspectral measurements in the Seto Inland Sea to estimate chlorophyll a (Chl-a) concentration, which is a significant parameter for detecting HABs. We obtained spectra and Chl-a data at six stations from 12 ship-based surveys between December 2015 and September 2017. In this study, we used an iterative stepwise elimination partial least squares (ISE-PLS) regression method along with several empirical and semi-analytical methods such as ocean chlorophyll, three-band model, and two-band model algorithms to retrieve Chl-a. Our results showed that ISE-PLS using both the water-leaving reflectance (RL) and the first derivative reflectance (FDR) had a better predictive ability with higher coefficient of determination (R²), lower root mean squared error (RMSE), and higher residual predictive deviation (RPD) values (R² = 0.77, RMSE = 1.47 and RPD = 2.1 for RL; R² = 0.78, RMSE = 1.45 and RPD = 2.13 for FDR). However, in this study the ocean chlorophyll (OC) algorithms had poor predictive ability and the three-band and two-band model algorithms did not perform well in areas with lower Chl-a concentrations. These results support ISE-PLS as a potential coastal water quality assessment method using hyperspectral measurements.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

Background

This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.

Results

Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.

Conclusions

Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.

Spatial distribution of Cd and Cu in soils in Shenyang Zhangshi Irrigation Area (SZIA), China

Heavy metal contamination of soils, derived from sewage irrigation, mining and inappropriate utilization of various agrochemicals and pesticides, and so on, has been of wide concern in the last several decades. The Shenyang Zhangshi Irrigation Area (SZIA) in China is a representative area of heavy metal contamination of soils resulting from sewage irrigation for about 30 years. This study investigated the spatial distribution and temporal variation of soil cadmium (Cd) and copper (Cu) contamination in the SZIA. The soil samples were collected from the SZIA in 1990 and 2004; Cd and Cu in soils was analyzed and then the spatial distribution and temporal variation of Cd and Cu in soils were modeled using Kriging methods. The results show that long-term sewage irrigation had caused serious Cd and Cu contamination in soils. The mean and the maximum of soil Cd are markedly higher than the levels in second grade standard soil (LSGSS) in China, and the maximum of soil Cu is close to the LSGSS in China in 2004 and is more than the LSGSS in China in 1990. The contamination magnitude of soil Cd and the soil extent of Cd contamination had evidently increased since sewage irrigation ceased in 1992. The contamination magnitude of soil Cu and the soil extent of Cu contamination had evidently increased in topsoil, but obviously decresed in subsoil. The soil contamination of Cd and Cu was mainly related to Cd and Cu reactivation of contaminated sediments in Shenyang Xi River and the import of Cd and Cu during irrigation. The eluviation of Cd and Cu in contaminated topsoil with rainfall and irrigation water was another factor of temporal-spatial variability of Cd and Cu contamination in soils.