Assessment of temporal and spatial water quality in international Gomishan Lagoon,Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.     

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.     

Emission Factors Determination of Euro III 1,200- to 1,400-cc Petrol Passenger Cars with a PLS Multivariate Regression Model

This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.     

Simultaneous polarographic determination of 2-nitrophenol and 4-nitrophenol by differential pulse polarography method and support vector regression

A differential pulse polarography (DPP) for the simultaneous determination of 2-nitrophenol and 4-nitrophenol was proposed. It was found that under optimum experimental conditions (pH = 5, scan rate = 5 mV/s, pulse amplitude = −50 mV), 2-nitrophenol and 4-nitrophenol had well-defined polarographic reduction waves with peak potentials at −317 and −406 mV, respectively. In the mixture of two compounds overlapping polarographic peaks were observed. In this study, support vector regression (SVR) was applied to resolve the overlapped polarograms. Furthermore, a comparison was made between the performance of SVR and partial least square (PLS) on data set. The results demonstrated that SVR is a better well-performing alternative for the analysis and modeling of DPP data than the commonly applied PLS technique. The proposed method was used for the determination of 2-nitrophenol and 4-nitrophenol in industrial waste water.     

Air quality and ventilation fan control based on aerosol measurement in the bi-directional undersea Bømlafjord tunnel

Aerosol, NO and CO concentration, temperature, air humidity, air flow and number of running ventilation fans were measured by continuous analysers every minute for a whole week for six different one-week periods spread over ten months in 2001 and 2002 at measuring stations in the 7860 m long tunnel. The ventilation control system was mainly based on aerosol measurements taken by optical scatter sensors. The ventilation turned out to be satisfactory according to Norwegian air quality standards for road tunnels; however, there was some uncertainty concerning the NO2 levels. The air humidity and temperature inside the tunnel were highly influenced by the outside metrological conditions. Statistical models for NO concentration were developed and tested; correlations between predicted and measured NO were 0.81 for a partial least squares regression (PLS1) model based on CO and aerosol, and 0.77 for a linear regression model based only on aerosol. Hence, the ventilation control system should not solely be based on aerosol measurements. Since NO2 is the hazardous polluter, modelling NO2 concentration rather than NO should be preferred in any further optimising of the ventilation control.     

Assessing possible visitor-use impacts on water quality in Yosemite National Park, California

There is concern that visitor-use associated activities, such as bathing, dish washing, wastewater production, and stock animal use near lakes and streams, could cause degradation of water quality in Yosemite National Park. A study was conducted during 2004-2007 to assess patterns in nutrient and Escherichia coli (E. coli) concentrations in the Merced and Tuolumne Rivers and characterize natural background concentrations of nutrients in the park. Results indicated that nutrient and E. coli concentrations were low, even compared to other undeveloped sites in the United States. A multiple linear regression approach was used to model natural background concentrations of nutrients, with basin characteristics as explanatory variables. Modeled nitrogen concentrations increased with elevation, and modeled phosphorus concentrations increased with basin size. Observed concentrations (±uncertainty) were compared to modeled concentrations (±uncertainty) to identify sites that might be impacted by point sources of nutrients, as indicated by large model residuals. Statistically significant differences in observed and modeled concentrations were observed at only a few locations, indicating that most sites were representative of natural background conditions. The empirical modeling approach used in this study can be used to estimate natural background conditions at any point along a study reach in areas minimally impacted by development, and may be useful for setting water-quality standards in many national parks.     

Environmetrics to evaluate marine environment quality

The environmetric data analysis of analytical datasets from sediment and benthic organisms samples collected from different sampling sites along the coast of Black Sea near to City of Varna, Bulgaria has given some important indications about the bioindication properties of both type of samples. Various multivariate statistical methods like cluster analysis, principal components analysis, source apportioning modeling and partial least square (PLS) modeling were used in order to classify and interpret the parameters describing the chemical content of the coastal sediments (major components, heavy metals and total organic carbon) and benthic organisms (heavy metals). It has been shown that seriously polluted coastal zones are indicated in the same way by all benthic species, although some specificity could be detected for moderate polluted regions' e.g. polychaeta accumulated preferably Co, Cr, Cu, and Pb; crustacea - As, Cd, and Ni; mollusca - Zn. The identified latent factors responsible for the dataset structure are clearly indicated and apportioned with respect to their contribution to the total mass or total concentration of the species in the samples. The linear regression and PLS models indicated that a reliable forecast about the relation between naturally occurring chemical components and polluting species accumulated in the benthic organisms is possible.     

Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model

This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. The datasets indicate that Lower Tampa Bay is an area with limited nutrient input throughout the year. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high values of colored dissolved organic matter are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. With this new integration, improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a critical coastal bay, the Gulf of Mexico.     

Modeling of NO x Emissions from Oil Refinery Furnaces

The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO _x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO _x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO _x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO _x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO _x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper. This revised version was published online in July 2006 with corrections to the Cover Date.     

Quantitative PCR analysis of house dust can reveal abnormal mold conditions

Indoor mold concentrations were measured in the dust of moldy homes (MH) and reference homes (RH) by quantitative PCR (QPCR) assays for 82 species or related groups of species (assay groups). About 70% of the species and groups were never or only rarely detected. The ratios (MH geometric mean : RH geometric mean) for 6 commonly detected species (Aspergillus ochraceus, A. penicillioides, A. unguis, A. versicolor, Eurotium group, and Cladosporium sphaerospermum) were >1 (Group I). Logistic regression analysis of the sum of the logs of the concentrations of Group I species resulted in a 95% probability for separating MH from RH. These results suggest that it may be possible to evaluate whether a home has an abnormal mold condition by quantifying a limited number of mold species in a dust sample. Also, four common species of Aspergillus were quantified by standard culturing procedures and their concentrations compared to QPCR results. Culturing underestimated the concentrations of these four species by 2 to 3 orders of magnitude compared to QPCR.     

Modelling nutrient loads to Sydney estuary (Australia)

Sydney estuary (Australia) catchment is substantially urbanised (80%) and small (480 km²) with a large population (2.5 million) and is therefore highly sensitive to anthropogenic influence. The Model for Urban Stormwater Improvement Conceptualisation used to model nutrient export to the estuary determined an average annual load of 475 t total nitrogen, 63.5 t total phosphorus and 343,000 t total suspended solids. Model verification included intense, short-term water sampling and analysis undertaken in the current project and use of published data spanning 10 years. Under high-rainfall conditions (>50 mm day^???1), the estuary becomes stratified and nutrients are either removed from the estuary directly in a plume or indirectly by advective/dispersive remobilisation. The majority of the nutrient load is delivered during moderate rainfall (5–50 mm day^???1) conditions and accumulates close to discharge points and remains in the estuary. To significantly reduce nutrient load, management strategies should aim to minimise low and moderate rainfall pollutant loads.     

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.     

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).     

High-frequency nutrient monitoring to infer seasonal patterns in catchment source availability, mobilisation and delivery

To explore the value of high-frequency monitoring to characterise and explain riverine nutrient concentration dynamics, total phosphorus (TP), reactive phosphorus (RP), ammonium (NH₄-N) and nitrate (NO₃-N) concentrations were measured hourly over a 2-year period in the Duck River, in north-western Tasmania, Australia, draining a 369-km² mixed land use catchment area. River discharge was observed at the same location and frequency, spanning a wide range of hydrological conditions. Nutrient concentrations changed rapidly and were higher than previously observed. Maximum nutrient concentrations were 2,577 μg L^?1 TP, 1,572 μg L^?1 RP, 972 μg L^?1 NH₄-N and 1,983 μg L^?1 NO₃-N, respectively. Different nutrient response patterns were evident at seasonal, individual event and diurnal time scales—patterns that had gone largely undetected in previous less frequent water quality sampling. Interpretation of these patterns in terms of nutrient source availability, mobilisation and delivery to the stream allowed the development of a conceptual model of catchment nutrient dynamics. Functional stages of nutrient release were identified for the Duck River catchment and were supported by a cluster analysis which confirmed the similarities and differences in nutrient responses caused by the sequence of hydrologic events: (1) a build-up of nutrients during periods with low hydrologic activity, (2) flushing of readily available nutrient sources at the onset of the high flow period, followed by (3) a switch from transport to supply limitation, (4) the accessibility of new nutrient sources with increasing catchment wetness and hydrologic connectivity and (5) high nutrient spikes occurring when new sources become available that are easily mobilised with quickly re-established hydrologic connectivity. Diurnal variations that could be influenced by riverine processes and/or localised point sources were also identified as part of stage (1) and during late recession of some of the winter high flow events. Illustrated by examples from the Duck River study, we demonstrate that the use of high-frequency monitoring to identify and characterise functional stages of catchment nutrient release is a constructive approach for informing and supporting catchment management and future nutrient monitoring strategies.     

Assessing the influence of nutrient reduction on water quality using a three-dimensional model: case study in a tidal estuarine system

A coupled three-dimensional hydrodynamic and water quality model has been developed and applied to the Danshuei River estuarine system and adjacent coastal sea. The water quality model considers various species of nitrogen, phosphorus, organic carbon, and phytoplankton as well as dissolved oxygen and is driven by a three-dimensional hydrodynamic model. The hydrodynamic and water quality models were validated with observations of water surface elevation, velocity, salinity distribution, and water quality parameters. Statistical error analysis shows that predictions of hydrodynamics, salinity, dissolved oxygen, and nutrients from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict water quality conditions as a result of a reduction in nutrient loadings based on different engineering strategies. The simulated results revealed that the dissolved oxygen concentration would increase significantly and would be higher than 2 mg/L in the main stream and in three tributaries to meet the minimum statutory requirement for dissolved oxygen. Active estuarine management focused on the reduction of anthropogenic nutrient loads is needed for improvement in water quality.     

A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay Watershed

The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.     

基于KZ滤波法的江淮地区PM2.5浓度变化影响分析

基于2018—2020年合肥、芜湖和马鞍山3个城市国控站点的PM_2.5逐日监测数据和同期地面气象观测资料,利用Kolmogorov-Zurbenko(KZ)滤波对PM_2.5日浓度的原始时间序列进行分解,获取短期分量、季节分量和长期分量,并进行多元线性逐步回归构建各分量与气象因子的模型,最后依据短期分量和基线分量的回归模型和残差分析,对序列进行重建,获取消除气象条件影响的PM_2.5长期分量。KZ滤波分析结果表明：2018—2020年气象条件对江淮区域PM_2.5污染改善影响存在波动,在2018—2019年为负贡献,而在2020年秋冬季则变为正贡献;江淮地区3个城市2018年和2020年PM_2.5修正后的长期分量均值表明气象条件对各市PM_2.5改善影响存在差异较大,气象条件对合肥PM_2.5改善的贡献仅为1.0%,芜湖为7.8%,马鞍山为21.0%;NAQPMS数值模式情景分析结果显示,减排措施对江淮之间PM_2.5浓度改...