Atmospheric influences on water quality: a simulation of nutrient loading for the Pearl River Basin, USA

Knowledge of water quality conditions is essential in assessing the health of riverine ecosystems. The goal of this study is to determine the degree to which water quality variables are related to precipitation and air temperature conditions for a segment of the Pearl River Basin near Bogalusa, LA, USA. The AQUATOX ecological fate simulation model is used to estimate daily total nitrogen, total phosphorus, and dissolved oxygen concentrations over a 2-year period. Daily modeled output for each variable was calibrated against reliably measured data to assess the accuracy. Observed data were plotted against simulated data for controlled and perturbed models for validation, and stepwise multiple regression analysis was used to quantify the relationships between the water quality and meteorological variables. Results suggest that daily dissolved oxygen is significantly negatively correlated to concurrent daily mean air temperature with a total explained variance of 0.679 (p?<?0.01), and monthly dissolved oxygen is significantly negatively correlated to monthly mean air temperature with a total explained variance of 0.567 (p?<?0.01). Total mean monthly phosphorus concentration is significantly positively related to the previous month's precipitation with a total explained variance of 0.302 (p?<?0.01). These relationships suggest that atmospheric conditions have a strong influence on water quality in the Pearl Basin. Therefore, environmental planners should expect that future climatic changes are likely to alter water quality.     

模糊数学在丹江口水库富营养化评价中的应用

丹江口水库是南水北调中线工程水源地,其水质安全将直接影响调水工程的成败。基于2013年6月的水质监测结果,通过对水质指标的聚类,将丹江口水库划分子库区,并尝试使用模糊数学方法进行水质富营养化评价。结果表明,丹江口库区可分为6个子库区,各库区在透明度、溶解氧、浊度、高锰酸盐指数、总磷、硝酸盐氮和叶绿素a等理化指标均有显著差异;富营养化评价显示,库区总体营养状态级别为中营养。其中,坝前区域(库区Ⅰ)和丹库主体部分(库区Ⅵ)营养状态级别为贫营养,汉库原河道区(库区Ⅱ)为中营养,汉江入库口(库区Ⅲ)、汉库最大库湾(库区Ⅳ)、丹江入库口(库区Ⅴ)为轻度富营养。模糊综合评价法较好反映出水质的模糊性、连续性,使评价结果更加准确可靠。     

Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications

Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R ²) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available.     

Mapping chlorophyll-a through in-situ measurements and Terra ASTER satellite data

This paper presents an application of water quality mapping through real-time satellite and ground data. The Lake Beysehir which is the largest freshwater lake and drinking water reservoir in Turkey was selected as the study area. Terra ASTER satellite image is used as remote sensing data source for water quality mapping in addition to simultaneously performed in-situ measurements. Ground data is collected simultaneously with the ASTER overpass on June 09, 2005 over the Lake Beysehir. The spatial distribution map is developed by using multiple regression (MR) technique for water quality parameter, which is chlorophyll-a (chl-a). The results indicate that simultaneous ground and satellite remote sensing data are highly correlated (R (2) > 0.86). In the image processing step, geometric correction, image filtering and development of water quality map procedures are performed with the ERDAS Imagine and ArcGIS 9.0 software. The trophic status of Lake Beysehir is considered to be oligotrophic with an average 1.55 microg/l chl-a concentration.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

Water Quality Changes in Chini Lake, Pahang, West Malaysia

A study of the water quality changes of Chini Lake was conducted for 12 months, which began in May 2004 and ended in April 2005. Fifteen sampling stations were selected representing the open water body in the lake. A total of 14 water quality parameters were measured and Malaysian Department of Environment Water Quality Index (DOE-WQI) was calculated and classified according to the Interim National Water Quality Standard, Malaysia (INWQS). The physical and chemical variables were temperature, dissolved oxygen (DO), conductivity, pH, total dissolved solid (TDS), turbidity, chlorophyll-a, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), ammonia-N, nitrate, phosphate and sulphate. Results show that base on Malaysian WQI, the water in Chini Lake is classified as class II, which is suitable for recreational activities and allows body contact. With respect to the Interim National Water Quality Standard (INWQS), temperature was within the normal range, conductivity, TSS, nitrate, sulphate and TDS are categorized under class I. Parameters for DO, pH, turbidity, BOD, COD and ammonia-N are categorized under class II. Comparison with eutrophic status indicates that chlorophyll-a concentration in the lake was in mesotrophic condition. In general water quality in Chini Lake varied temporally and spatially, and the most affected water quality parameters were TSS, turbidity, chlorophyll-a, sulphate, DO, ammonia-N, pH and conductivity.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

改进的模糊综合评价法在太湖水质类别判定中的应用

水质判别是一个多参数多级别的模糊概念，基础的模糊综合评价法存在局限性，对基础模糊数学法中隶属度函数及最大隶属度原则进行改进，建立一种改进的模糊综合评价法。以太湖流域水质判别为实例验证了此方法比传统的方法更有效，且适合在太湖流域应用。     

太湖蓝藻水华暴发程度年度预测

近年来,尽管太湖主要水质指标有所改善,但蓝藻水华暴发的频次和面积并未明显减少.为了探讨太湖蓝藻水华暴发的环境驱动因子,统计了2012—2020年历年4—10月预警期间的太湖蓝藻水华发生规模与频次,结合同步浮标自动监测数据和实验室分析数据,构建了蓝藻水华预测模型.以太湖蓝藻水华综合指数(Ic)表征蓝藻水华强度,并通过Ic...     

Landsat and limnologically derived water quality data: A perspective

Fixed station sampling is the conventional method used to obtain data on the median water quality of reservoirs. A major source of uncertainty associated with this technique is that water quality at the fixed stations may not be representative of the ambient water quality in the reservoir at the time of sampling. This problem is particularly relevant for water quality variables such as chlorophyll, which have a markedly patchy spatial distribution. The use of Landsat reflectance data to estimate median chlorophyll concentrations in Roodeplaat Dam was investigated. A linear polynomial regression model for estimating chlorophyll concentrations from Landsat reflectance data, was firstly calibrated with chlorophyll concentration data obtained by sampling seven fixed stations on the reservoir at the time of the satellite overflight to produce an individual calibration. Secondly, the model was calibrated with a pooled set of sampled data obtained from five separate overflights, to obtain a generalised calibration.It was found that median chlorophyll concentrations determined from Landsat-derived data were similar to median chlorophyll concentrations estimated from fixed station data. However, the range of chlorophyll concentrations in the reservoir estimated from Landsat data was considerably larger than that estimated from fixed station data. Landsat derived estimates of chlorophyll concentrations have the added advantage of providing information on the spatial distribution of chlorophyll in the reservoir.     

Multivariate Analysis of Interactions Between Phytoplankton Biomass and Environmental Variables in Taihu Lake, China

Phytoplankton variation in large shallow eutrophic lakes is characterized by high spatial and temporal heterogenity. Understanding the pattern of phytoplankton variation and the relationships between it and environmental variables can contribute to eutrophic lakes management. In this study Taihu Lake, one of the largest eutrophic fresh water lake in China, was taken as study area. The water body of Taihu Lake was divided into five regions viz. Wuli bay (WB), Meilian Bay (MB), West Taihu Lake (WTL), Main Body of Taihu Lake (MBTL) and East Taihu Lake (ETL). Concentrations of chlorophyll-a and the related environmental variables were determined in each region in the period 2000–2003. Factor analysis and multivariate analysis were applied to evaluate the interactions between phytoplankton variation and environmental variables. Results showed that the highest average concentrations of TN, TP and Chl-a were observed in WB, followed in a descending order by MB and WTL, and the lowest concentrations of TN, TP and Chl-a were observed in MBTL and ETL. Chl-a and TP concentrations in most regions (except ETL) declined during the study period. It suggested that to some extent the lake was recovering from eutrophication. However, persistent ascending of TN and NH₄–N in all five regions indicated the deteriorating of water quality in the study period. Results of multivariate showed that the relationships between phytoplankton biomass and environmental variables varied among regions. TP illustrated itself a controlling role on phytoplankton in WB, MB, WTL and MBTL according to the significant positive relations to phytoplankton biomass in these regions. Nitrogen could be identified as a limiting factor to phytoplankton biomass in ETL in view of the positive correlations between TN and phytoplankton and between NH₄–N and phytoplankton. Spatial variation of interactions between phytoplankton and environmental parameters suggested proper eutrophication control measures were needed to restore ecological system in each region of Taihu Lake.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO₂) into ozone (O₃) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O₃ in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O₃ concentration was tested. Results indicated that the presence of NO₂ and sunshine influence the concentration of O₃ in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.     

Probability-based nitrate contamination map of groundwater in Kinmen

Groundwater supplies over 50 % of drinking water in Kinmen. Approximately 16.8 % of groundwater samples in Kinmen exceed the drinking water quality standard (DWQS) of NO₃ ^?-N (10 mg/L). The residents drinking high nitrate-polluted groundwater pose a potential risk to health. To formulate effective water quality management plan and assure a safe drinking water in Kinmen, the detailed spatial distribution of nitrate–N in groundwater is a prerequisite. The aim of this study is to develop an efficient scheme for evaluating spatial distribution of nitrate–N in residential well water using logistic regression (LR) model. A probability-based nitrate–N contamination map in Kinmen is constructed. The LR model predicted the binary occurrence probability of groundwater nitrate–N concentrations exceeding DWQS by simple measurement variables as independent variables, including sampling season, soil type, water table depth, pH, EC, DO, and Eh. The analyzed results reveal that three statistically significant explanatory variables, soil type, pH, and EC, are selected for the forward stepwise LR analysis. The total ratio of correct classification reaches 92.7 %. The highest probability of nitrate–N contamination map presents in the central zone, indicating that groundwater in the central zone should not be used for drinking purposes. Furthermore, a handy EC–pH-probability curve of nitrate–N exceeding the threshold of DWQS was developed. This curve can be used for preliminary screening of nitrate–N contamination in Kinmen groundwater. This study recommended that the local agency should implement the best management practice strategies to control nonpoint nitrogen sources and carry out a systematic monitoring of groundwater quality in residential wells of the high nitrate–N contamination zones.     

洞庭湖水体叶绿素a时空分布及与环境因子的相关性

2013年6月至2014年5月逐月对洞庭湖水体叶绿素a质量浓度和主要环境因子进行测定,分析洞庭湖水体叶绿素a质量浓度的时空分布特征,探讨洞庭湖水体叶绿素a质量浓度与环境因子的相关性。结果表明,洞庭湖水体叶绿素a质量浓度为0.11~8.62 mg/m~3,年均值为(1.89±1.23)mg/m~3,属贫营养;叶绿素a质量浓度随季节变化明显,总体呈现夏、秋季明显大于冬、春季的规律;在空间上,总体表现为西洞庭湖和东洞庭湖明显大于南洞庭湖。全湖叶绿素a质量浓度与水温、电导率、COD和TP呈极显著正相关,与DO、NH3-N、TN和TN/TP呈极显著负相关,与NO-3-N呈显著负相关,与p H和透明度无显著相关性。全湖TN/TP的年均值为28.5,磷可能是洞庭湖水体浮游植物生长的限制性营养盐。     

水体中泥沙对藻类光谱特征位置的影响分析

自然水体叶绿素a浓度的遥感反演中,泥沙的存在影响着反演精度.如何消除这种影响是提高叶绿素a遥感反演精度的关键,而了解泥沙对藻类光谱特征的影响是消除影响的前提.文章在人工控制条件下获取了不同泥沙浓度下藻类光谱曲线,通过分析光谱曲线特征位置的漂移和数值变化,总结泥沙对藻类光谱的影响并提出了消除影响的可能性.     

Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Air pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritizing the monitoring locations. The exposure-based approach prioritizes the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritizes the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), respectively. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence).This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NO_x, PM₁₀ and SO_x are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritized based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.     

Modeling trihalomethane formation for Jabal Amman water supply in Jordan

A mathematical model that expresses Total trihalomethane (TTHM) concentration in terms of initial chlorine concentration, total organic carbon, bromide ion concentration, contact time, and pH is developed for Zai water treatment plant which supplies water to Jabal Amman. The developed mathematical model is for constant temperature of 20°C. To adjust model calculated TTHM concentrations for temperatures other than 20°C, another mathematical model that expresses TTHM growth rate as function of temperature is also developed. To test the ability of the two developed models in predicting TTHM concentrations throughout water supplies, a sampling program that aimed at measuring TTHM concentrations in addition to the predictors in the two developed mathematical models namely; chlorine concentration, bromide ion concentration, total organic carbon, temperature and pH throughout Jabal Amman water supply was conducted. The two developed mathematical models and WaterCad, which was used to determine water age, were used to predict TTHM concentrations throughout Jabal Amman water supply. Predicted TTHM concentrations were compared to actual TTHM concentrations measured during the sampling program. Results showed that there is good agreement between measured and, calculated TTHM concentrations, which means that the method presented in this paper, can be used to obtain good estimates of TTHM concentrations throughout networks.     

Elements of a Predictive Model for Determining Beach Closures on a Real Time Basis: The Case of 63rd Street Beach Chicago

Data on hydrometeorological conditions and E. coli concentration were simultaneously collected on 57 occasions during the summer of 2000 at 63rd Street Beach, Chicago, Illinois. The data were used to identify and calibrate a statistical regression model aimed at predicting when the bacterial concentration of the beach water was above or below the level considered safe for full body contact. A wide range of hydrological, meteorological, and water quality variables were evaluated as possible predictive variables. These included wind speed and direction, incoming solar radiation (insolation), various time frames of rainfall, air temperature, lake stage and wave height, and water temperature, specific conductance, dissolved oxygen, pH, and turbidity. The best-fit model combined real-time measurements of wind direction and speed (onshore component of resultant wind vector), rainfall, insolation, lake stage, water temperature and turbidity to predict the geometric mean E. coli concentration in the swimming zone of the beach. The model, which contained both additive and multiplicative (interaction) terms, accounted for 71% of the observed variability in the log E. coli concentrations. A comparison between model predictions of when the beach should be closed and when the actual bacterial concentrations were above or below the 235 cfu 100 ml(-1) threshold value, indicated that the model accurately predicted openings versus closures 88% of the time.