Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients, but their physiological regulations (responses to nutrients, pH, temperature and salinity) are mainly responsible for the increased biological production. The model explaining 77% of variability for chlorophyll a production is indicative of preconditioning of the coastal waters. The phytoplankton production is found to be sensitive to the environment, which varies seasonally. Further, the study suggests that supply of organic matter and grazing of zooplankton (not included) would improve the model efficiency. Despite this, the good agreement in the computed and measured chlorophyll a values shows that step-up multiple regression model is a useful tool to understand the influence of environmental variables on the production of phytoplankton in these coastal waters.     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland

A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment’s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations, of 0.6–1.1 and 0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class.     

Grid search: an innovative method for the estimation of the rates of lead exchange between body compartments

This paper describes a new metabolic model for lead in humans and a numerical method to solve the differential equations governing the transfer of lead between body compartments. The model includes 3 compartments-cortical bone, trabecular bone and blood-and accounts for absorption from external sources and release through excreta. Estimation of the lead kinetics parameters was performed using the grid search method. Grid search is a simple procedure that allows the fit of an arbitrary function to data. When applied to data from occupationally exposed populations, the method demonstrated the exposure dependence of the rate of lead uptake and release by the compartments in the model. The results confirm and refine previous observations of the significant decrease of the transfer rate of lead from cortical bone to blood with increasing exposure, as expressed by half-lives of (in years): 6.5 +/- 0.7, 13.6 +/- 1.0 and 47.5 +/- 2.3, in subgroups of low, intermediate and high long-term lead exposure. A similar trend was observed for the transfer rate from trabecular bone, which could be statistically supported for the first time. Reduction by a factor of 7 to 10 in the default values assigned to the fractional removal of lead from cortical bone to plasma in existing metabolic models was also predicted. These results can be used in the review of current metabolic models for lead, which are still based on the assumption of a constant rate of lead removal from bone, independently of the level of exposure.     

Development of GP and GEP models to estimate an environmental issue induced by blasting operation

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.     

Stationary and Non-stationary Extreme Value Approaches for Modelling Extreme Temperature: the Case of Riyadh City,Saudi Arabia

Climate change is one of the most fiercely debated scientific issues in recent decades, and the changes in climate extremes are estimated to have greater negative impacts on human society and the natural environment than the changes in mean climate. Extreme value theory is a well-known tool that attempts to best estimate the probability of adversarial risk events. In this paper, the focus is on the statistical behaviour of extreme maximum values of temperature. Under the framework of this theory, the methods of block maxima and threshold exceedances are employed. Due to the non-stationary characteristic of the series of temperature values, the generalized extreme value distribution and the generalized Pareto distribution were extended to the non-stationary processes by including covariates in the parameters of the models. For the purpose of obtaining an approximately independent threshold excesses, a declustering method was performed and then the de-clustered peaks were fitted to the generalized Pareto distribution. The stationary Gumbel distribution was found a reasonable model for the annual block maxima; however, a non-stationary generalized extreme value distribution with quadratic trend in the location was recommended for the half-yearly period. The findings also show that there is an improvement in modelling daily maxima temperature when it is applied to the declustered series and the given model outperforms the non-stationary generalized Pareto distribution models. Furthermore, the retained generalized Pareto distribution model proved better than the generalized extreme value distribution. Estimates of the return levels obtained from both extreme value models show that new records on maximum temperature event could appear within the next 20, 50 and 100 years.     

Modified Grey Model and its application to groundwater flow analysis with limited hydrogeological data: a case study of the Nubian Sandstone, Kharga Oasis, Egypt

Groundwater flow at Kharga Oasis, located in the western desert of Egypt, was previously analyzed using numerical models; however, the lack of basic data often limits the implementation of these models, as well as introducing a problem for model calibration and validation. The Grey Model (GM) was used to overcome these difficulties of data limitation and uncertainty of hydrogeological conditions. However, no clear theories exist for selecting the number of input model trends and the most suitable values of input parameters. Therefore, in the current study, a modification of the GM is newly proposed and called the Modified Grey Model (MGM) in an attempt to determine a process for selecting the best input models' trends with the appropriate values of input parameters to achieve acceptable fitting to observations. The sensitivity analysis results showed that the MGM produced more stable results than the GM using a wide range of values for input parameters. Moreover, the MGM reduced the calculation time required for fitting the measured piezometric level trends by 99.8 %. Three development scenarios of groundwater withdrawal were proposed that involved either expanding the present extraction rate or redistributing the groundwater withdrawal over the recent working production wells (RWPWs). The results concluded that the groundwater table in the northern part of the oasis could be temporally recovered to an economical piezometric level; however, the table in the southern part is severely decreased. Therefore, new production wells are recommended to be constructed in the southern part far enough from the RWPWs.     

Estimation of chlorophyll-a concentration using field spectral data: a case study in inland Case-II waters, North China

In the remote sensing of chlorophyll-a (Chla) in inland Case-II waters, the assumption that the optical parameter of Chla specific absorption coefficient a*ph remains constant usually restrains application of many models. In this paper, we presented a newly developed model [Rrs(-1)(lambda1) - Rrs(-1)(lambda2)] x Rrs(lambda3) x a*ph(-1)(lambda1) which was improved on a previous three-band model to isolate interferences from a*ph. In terms of the importance of water optical properties in the model development, spectral and absorption characteristics were analyzed for Shitoukoumen Reservoir and Songhua Lake in Northeast China, as typical examples of inland Case-II waters. Both waters showed overwhelming absorption sum of tripton and chromophoric dissolved organic matter (CDOM) owing to their relatively low Chla contents (1.53 to 19.35 microgl(-1)). According to the optical characteristics of waters studied, optimal positions for lambda (1), lambda (2) and lambda (3) were spectrally tuned to be at 664, 684 and 705 nm, respectively. The model allowed accurate Chla estimation with a determination coefficient (R (2)) close to 0.98 and a root mean square error (RMSE) of 0.87 microgl(-1). Comparison of different models further showed the stability of the improved model, implying its potential use in water color remote sensing. Although the findings underline the rationale behind the improved model, an extensive database containing data in different water conditions and water types is required to generalize its application.     

Feasibility of On-line Measurement of Sewage Components Using the UV Absorbance and the Neural Network

The objective of this study was to test the feasibility of a new method to improve the accuracy in the estimation of sewage components. Adding to the regression of sewage components with UV (ultraviolet) absorbance values, a proposed method considered an unclear but existing relationship among characteristic of sewage production. Sewage production showed very defined profiles due to the daily human activities. So the main idea was the combination of measuring the UV absorbance values and analyzing the characteristics of the sewage production. For this purpose, 446 sewage samples taken at every 2-h interval for 51 days at a wastewater treatment plant were statistically analyzed using neural network (NN). NN was trained with 350 data sets (about 29 days) of UV absorbance values, flow rate and time. And as a result, it could predict 96 data (12 days) as a validation, indicating that estimation accuracies were improved to higher level than those of the linear regressions. The proposed method could estimate concentrations of total nitrogen (TN) and total phosphate (TP) within practical accuracies as well as total suspended solid.     

Comparative Assessment of Climate Change Scenarios Based on Aquatic Food Web Modeling

In the years 2004 and 2005, we collected samples of phytoplankton, zooplankton, and macroinvertebrates in an artificial small pond in Budapest (Hungary). We set up a simulation model predicting the abundances of the cyclopoids, Eudiaptomus zachariasi, and Ischnura pumilio by considering only temperature and the abundance of population of the previous day. Phytoplankton abundance was simulated by considering not only temperature but the abundances of the three mentioned groups. When we ran the model with the data series of internationally accepted climate change scenarios, the different outcomes were discussed. Comparative assessment of the alternative climate change scenarios was also carried out with statistical methods.     

A multi criteria analog model for assessing the vulnerability of rural catchments to road spills of hazardous substances

Road spills of hazardous substances are common in developing countries due to increasing industrialization and traffic accidents, and represent a serious threat to soils and water in catchments. There is abundant literature on equations describing the wash-off of pollutants from roads during a storm event and there are a number of watershed models incorporating those equations in storm water quality algorithms that route runoff and pollution yields through a drainage system towards the catchment outlet. However, methods describing catchment vulnerability to contamination by road spills based solely on biophysical parameters are scarce. These methods could be particularly attractive to managers because they can operate with a limited amount of easily collectable data, while still being able to provide important insights on the areas more prone to contamination within the studied watershed. The purpose of this paper was then to contribute with a new vulnerability model. To accomplish the goal, a selection of medium properties appearing in wash-off equations and routing algorithms were assembled and processed in a parametric framework based on multi criteria analysis to define the watershed vulnerability. However, parameters had to be adapted because wash-off equations and water quality models have been developed to operate primarily in the urban environment while the vulnerability model is meant to run in rural watersheds. The selected parameters were hillside slope, ground roughness (depending on land use), soil permeability (depending on soil type), distance to water courses and stream density. The vulnerability model is a spatially distributed algorithm that was prepared to run under the IDRISI Selva software, a GIS platform capable of handling spatial and alphanumeric data and execute the necessary terrain model, hydrographic and thematic analyses. For illustrative purposes, the vulnerability model was applied to the legally protected Environmental Protection Area (APA), located in the Uberaba region, state of Minas Gerais, Brazil. In this region, the risk of accidents causing chemical spills is preoccupying because large quantities of dangerous materials are transported in two important distribution highways while the APA is fundamental for the protection of water resources, the riverine ecosystems and remnants of native vegetation. In some tested scenarios, model results show 60% of vulnerable areas within the studied area. The most sensitive parameter to vulnerability is soil type. To prevent soils from contamination, specific measures were proposed involving minimization of land use conflicts that would presumably raise the soil's organic matter and in the sequel restore the soil's structural functions. Additionally, the present study proposed the preservation and reinforcement of riparian forests as one measure to protect the quality of surface water.     

Environmental influence on coastal phytoplankton and zooplankton diversity: a multivariate statistical model analysis

In a marine ecosystem, the diversity of phytoplankton can influence the diversity of zooplankton, or vice versa, and both can be affected by the environmental factors. In this study, we used principal component analysis (PCA) to identify the major sources of influence on the coastal water near an industrial park, following by construction of structural equation model (SEM) to determine the direct and indirect effect of the factors on phytoplankton and zooplankton diversity. PCA results indicated that the coastal area was mainly affected by riverine discharge (represented by high PC factor loadings of transparency and turbidity) and seasonal change (represented by temperature). SEM further suggested that both riverine discharge and seasonal influences can directly affect phytoplankton diversity, but indirectly affected zooplankton diversity via changes in phytoplankton. Using PCA to determine the sources of influence followed by construction of SEM allowed us to understand the relative importance of the environmental factors, direct or indirect, on phytoplankton and zooplankton diversity. When environmental changes occur, a new SEM could be constructed using the same category of physical and biological data and then compared to the current model to verify whether the environmental changes were the cause of alterations in planktonic communities in the area.     

Modelling the Effects of Inflow Parameters on Lake Water Quality

A one-dimensional lake water quality model which includes water temperature, phytoplankton, phosphorus as phosphate, nitrogen as ammonia, nitrogen as nitrate and dissolved oxygen concentrations, previously calibrated for Lake Calhoun (USA) is applied to Uokiri Lake (Japan) for the year 1994. The model simulated phytoplankton and nutrient concentrations in the lake from July to November. Most of the water quality parameters are found to be the same as for Lake Calhoun. To predict probable lake water quality deterioration from algal blooming due to increased nutrient influx from river inflow, the model was run for several inflow water conditions. Effects of inflow nutrient concentration, inflow volume, inflow water temperatures are presented separately. The effect of each factor is considered in isolation although in reality more than one factor can change simultaneously. From the results it is clear that inflow nutrient concentration, inflow volume and inflow water temperature show very regular and reasonable impacts on lake water quality.     

The trophic status of Suwałki Landscape Park lakes based on selected parameters (NE Poland)

This study describes changes in the trophic status of 12 lakes within Suwa?ki Landscape Park (SLP). All of the trophic classifications of the lakes were based on the trophic continuum division. Trophic status was determined by means of multiparameter indices using several diverse criteria. In this study, the assessment of the trophic status of lakes included water quality; abundance and biomass of bacterioplankton, phytoplankton, and zooplankton; and primary production of phytoplankton. The Carlson trophic state index (TSI) describes the level of water fertility and indicated the dominance of moderately eutrophic waters. Lakes Perty, Jeglówek, and Hańcza have a trophic status that indicates mesotrophy (TSI <50). The trophic status of the studied lakes was determined based on the bacterial abundance and clearly showed a lack of oligotrophic lakes in SLP. Based on the number of bacteria, only Lake Szurpi?y can be classified as β-mesotrophic, whereas Lake Linówek can be characterized as hypertrophic with some features typical for humic waters. The greatest value of gross primary production was observed in Lake Linówek (126.4 mg C/m³/h). The phytoplankton trophy index varied from 1.59 to 2.28, and its highest value, which indicated eutrophy, was determined for Lake Udziejek. In the case of Lakes Hańcza, Szurpi?y, Perty, Jeglówek, and Kojle, the index ranged from 1.25 to 1.74, which indicated mesotrophy. The majority of the lakes were classified as mesoeutrophic (1.75–2.24). The highest trophic status was assessed for lakes with a marked dominance of cyanobacteria (Lake Przechodnie, Lake Krajwelek, Lake Udziejek, and Lake Pogorza?ek), which is commonly recognized as an indicator of high trophic status. Considering all of the indices of trophic status, the analysis of rotifer community structure indicates that the studied group of lakes is mesoeutrophic or eutrophic. The values of crustacean zooplankton indices indicated that the trophic status of the studied lakes was close to that determined using a TSI. The parameters of zooplankton abundance and species structure allowed for the observance of changes in the tropic levels of lakes, which are difficult to detect by a chemical assay alone.     

Simulation of the seasonal evolution of macroalgae in the lagoon of Venice

A 3D reaction‐diffusion model is presented, which describes the dynamics of two communities of primary producers, phytoplankton and macroalgae, and their effects on the oxygen balance in the lagoon of Venice. The model includes ten state variables: zooplankton, phytoplankton and Ulva rigida densities, nitrogen concentration in Ulva (quota), ammonium, nitrate and orthophosphate concentration in water, detritus, dissolved oxygen and water temperature. The spatial distribution of Ulva shows a well defined pattern, also with homogeneous initial conditions. Such distributions become closer to the real ones when the initial conditions for Ulva are obtained by processing remote sensing data. The massive presence of Ulva greatly affects the DO balance, as hypossic crises occur in the core of Ulva colonies in the early morning, as a consequence of respiration.     

Simulating the production and dispersion of environmental pollutants in aerosol phase in an urban area of great historical and cultural value

Mathematical models were developed to simulate the production and dispersion of aerosol phase atmospheric pollutants which are the main cause of the deterioration of monuments of great historical and cultural value. This work focuses on Particulate Matter (PM) considered the primary cause of monument darkening. Road traffic is the greatest contributor to PM in urban areas. Specific emission and dispersion models were used to study typical urban configurations. The area selected for this study was the city of Florence, a suitable test bench considering the magnitude of architectural heritage together with the remarkable effect of the PM pollution from road traffic. The COPERT model, to calculate emissions, and the street canyon model coupled with the CALINE model, to simulate pollutant dispersion, were used. The PM concentrations estimated by the models were compared to actual PM concentration measurements, as well as related to the trend of some meteorological variables. The results obtained may be defined as very encouraging even the models correlated poorly: the estimated concentration trends as daily averages moderately reproduce the same trends of the measured values.     

Analysis of the land use structure changes based on Lorenz curves

Land use change has become a new field in global change research, and it is closely related to land resource security and sustainable development. The environmental simulation model was mostly used to study the land-use change before. In this paper, we use the Economic Lorenz curve and Gini coefficient of economic model to study the land use structure changes in the western plain of Jilin Province, China. The results showed that the Lorenz curve has the phenomenon of "two near and two far" and Gini coefficient "two increased and two decreased". That is to say, during the 15 years from 1989 to 2004, the areas of cultivated land and unused land had increased, Lorenz curves were near to the perfect equality line, and the distribution tended to be uniform; the areas of grassland and water body were reduced, the Lorenz curves were away from the perfect equality line, and the distribution tended to be increasingly uneven, which reflects the main process of the land use structure changes. Studying on the land use changes based on Economic model is simpler and more intuitionistic, and had the generalization values.     

Spatio-temporal variation in physicochemical properties of coastal waters off Kalpakkam, southeast coast of India, during summer, pre-monsoon and post-monsoon period

Seasonal observations on water-quality parameters and chlorophyll-a in the coastal waters off Kalpakkam, southeast coast of India, was carried out covering an area of about 30 km(2) to find out the variations in physicochemical properties during a monsoonal cycle of the year. Most of the parameters exhibited a significant spatial and seasonal variation. It revealed that the coastal water was significantly influenced by freshwater input from the nearby backwaters during North-east monsoon and post-monsoon periods. A marginal increase in pH from coast towards offshore was noticed during the observation. Relatively low salinity values were observed during pre and post monsoon when compared to summer. Bottom water was found to be highly turbid during summer and pre-monsoon conditions when compared to surface. This could be attributed to the strong northerly wind and northward current prior to the onset of southwest monsoon. N, P and Si based nutrients are relatively high in their concentration in the bottom water. Nitrate was significantly high during post-monsoon and contributed greatly towards total nitrogen as evident from the statistical correlation. Ammonia concentration was relatively high in the bottom samples during all the seasons except on a few occasions during post-monsoon. In general, phosphate and total phosphorous values remained low and particularly so in the surface water. Higher silicate concentration was observed in the bottom water, and there was a reducing trend towards offshore. High chlorophyll-a values were observed during summer and surface water was found to have higher pigment concentrations as compared to the bottom. Results show that phosphate acts as the limiting factor for phytoplankton production particularly during post-monsoon period whereas; none of the nutrients were found to be limiting the phytoplankton growth during other seasons.