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Due to a rapid increase in human population and development of neighborhood economy over the last few decades, nitrogen (N) and other nutrient inputs in Lake Dianchi drainage basin have increased dramatically, changing the lake's trophic classification from oligotrophic to eutrophic. Although human activities are considered as main causes for the degradation of water quality in the lake, a numerical analysis of the share of the effect of different anthropogenic factors is still largely unexplored. We use the net anthropogenic N input (NANI) method to estimate human-induced N inputs to the drainage basin from 1980 to 2010, which covers the period of dramatic socioeconomic and environmental changes. For the last three decades, NANI increased linearly by a factor of three, from 4700 kg km(-2)year(-1) in 1980 to 12,600 kg km(-2)year(-1) in 2010. The main reason for the rise of NANI was due to fertilizer N application as well as human food and animal feed imports. From the perspective of direct effects of food consumption on N inputs, contributions of drivers were estimated in terms of human population and human diet using the Logarithmic Mean Divisia Index (LMDI) factor decomposition method. Although human population density is highly correlated to NANI with a linear correlation coefficient of 0.999, human diet rather than human population is found to be the single largest driver of NANI change, accounting for 47% of total alteration, which illustrates that the role of population density in the change of NANI may be overestimated through simple relational analysis. The strong linear relationships (p<0.01) between NANI and total N concentrations in the lakes over time may indicate that N level in the lake is able to respond significantly to N inputs to the drainage basin., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The statistical technique self-organizing maps (SOM) was applied for stages analysis of Lake Dianchi sediments, southwestern China. The dataset of nine pollutants, including Cd, Cr, Cu, Hg, Pb, Sn, Zn, TP and KN, was observed and collected for 10 monitoring sites from 1991 to 2010. The results show that the 20 years' study could be divided into 4 stages. In stage one (1991 to 1995) , concentrations of sediments are relativity low. In the second stage (1996 to 2001) , concentrations of most sediments are higher than the stage before and show increasing trends. In the following stage (2002 to 2006), majority of the observed sediments exhibit fluctuation characteristics. Nevertheless, different concentration patterns exist among different pollutants, the concentration climaxes of most pollutants have been observed during the year 1996 to 1999 and 2005 to 2007. According to the relevant information gathering from yearly lake pollution load, the national survey of pollution sources etc., the reason for the above stages and concentration patterns of observed sediments are analyzed. The result shows that sediment concentration is sensitive to human activities in the basin, such as pollution emission as well as controlling.

To understand riverine process of non-point source effectively, first flush effects of storm events were investigated at Baoxiang River of Lake Dianchi Watershed. Three sampling stations were selected along Baoxiang River for observing the flow rate and pollutant concentrations of the first three storm events from June 2009 to August 2009. Net discharged volume, net discharged loading, and net event mean concentration (EMC(n)) were proposed with their calculation methods. According to the analysis of three storm events at three stations, the following results colcd be extracted: (1) the larger the percent of impervious land and population density were, the higher EMC(n) of TSS, TN, TP, permanganate index and their cumulative curves [M(V)] were along the river; (2) TSS, TP loadings as well as their M (V) were positively correlated to the storm intensity, while TN and permanganate index loadings were consistent with the total rainfall of each storm event, where the percent of NO3(-) -N in total nitrogen decreased gradually when the number of storm events increased; (3) compared to tradition EMC, EMC(n) was proven to be a better indicator to accurately uncover and magnify the differences in first flush effects of storm events among different stations or storm events.

Regarding the shortage and deficiency of traditional lake pollution control engineering techniques, a new lake pollution control engineering approach was proposed in this study, based on object-oriented intelligent design (OOID) from the perspective of intelligence. It can provide a new methodology and framework for effectively controlling lake pollution and improving water quality. The differences between the traditional engineering techniques and the OOID approach were compared. The key points for OOID were described as object perspective, cause and effect foundation, set points into surface, and temporal and spatial optimization. The blue algae control in lake was taken as an example in this study. The effect of algae control and water quality improvement were analyzed in details from the perspective of object-oriented intelligent design based on two engineering techniques (vertical hydrodynamic mixer and pumping algaecide recharge). The modeling results showed that the traditional engineering design paradigm cannot provide scientific and effective guidance for engineering design and decision-making regarding lake pollution. Intelligent design approach is based on the object perspective and quantitative causal analysis in this case. This approach identified that the efficiency of mixers was much higher than pumps in achieving the goal of low to moderate water quality improvement. However, when the objective of water quality exceeded a certain value (such as the control objective of peak Chla concentration exceeded 100 microg x L(-1) in this experimental water), the mixer cannot achieve this goal. The pump technique can achieve the goal but with higher cost. The efficiency of combining the two techniques was higher than using one of the two techniques alone. Moreover, the quantitative scale control of the two engineering techniques has a significant impact on the actual project benefits and costs.

Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003-2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August-September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, CODMn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH₄+-N, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, CODCr, CODMn, NH₄+-N, TP, and EC. In addition, box-whisker plots and GIS further facilitated and supported the multivariate analysis results.

The effect of water quality parameters and operation conditions of water treatment on the formation of trihalomethanes (THM) and haloacetic acids (HAA) in pilot-scale reactor and water distribution system were investigated. Results indicated that dissolved organic carbon and THM formation increased while the concentration of free chlorine decreased along the length of pipeline from the water treatment plant; but HAA formation showed no relationship with the pipeline length. THM concentration was predicted with hydraulic analytic software, namely WaterCAD. The predicted THM data were within +/-10% of measurements; HAA had a relatively high error of +/-16% due to complex bio-decomposition reactions occurred in the distribution system. Both the hazardous quotient and cancer risk of THM in the water distribution system of an advanced water treatment plant were much lower than those of a conventional water treatment plant; there was no significant difference in hazard quotient and cancer risk of HAA in the above two water distribution systems.