Evaluation of ground water monitoring network by principal component analysis

Site prioritization and performance assessment of groundwater monitoring network by using information-based methodology

The arbitrary distribution of groundwater monitoring sites and the redundancy of observation data restrict the ability of monitoring network to provide reliable and effective data information. The purpose of this study is aimed at finding a quantitative method to screen ideal monitoring locations and evaluate the efficiency of the monitoring network. In terms of site selection, we use hydrogeological information, monitoring density and monitoring location to select the suitable site to monitor groundwater quality, understand the temporal trends and identify the abnormal signals of pollution sources. To evaluate the efficiency of monitoring network we used the groundwater quality data for consecutive years to evaluate the groundwater monitoring network based on information entropy and principal component analysis (PCA). The results show that the optimized groundwater monitoring network is comprised of 10 monitoring wells. The efficiency evaluation results of information entropy and PCA are basically consistent. The maximum mutual information (T) and comprehensive index of monitoring site (Laiguangying) were 1.29 and 3.25 respectively, while the minimum T and comprehensive index of monitoring site (Jinzhan) were 1.05 and -0.36 respectively, and the data efficiency was low. This study provides a good example for optimizing a groundwater pollution monitoring network.

PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies

In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. We demonstrate our methodology using the monitoring data at the Savannah River Site F-Area. Through this open-source PyLEnM package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relations.

Investigating Water Quality Data Using Principal Component Analysis and Granger Causality

This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective methods for water quality protection. The potential of using statistical tools, such as Principal Component (PC) and Granger causality analyses, for this purpose is assessed across 10 watersheds in the Eastern United States. The PC analysis shows consistency across the ten locations, with most of the variation explained by the first two PCs, except for the least developed watershed that presents three PCs. Results show that stronger Granger causality relationships and correlation coefficients are identified when considering a lag of one day, compared to longer lags. This is mainly due to the watersheds’ limited size and, thus, their fast hydrological response. The strongest Granger causalities are observed when water temperature and dissolved oxygen concentration are considered as the effect of the other variables, which corroborates the importance of these two water properties. This work also demonstrates how watershed size and land use can impact causalities between hydrometeorological variables and water quality, thus, highlighting how complex these relationships are even in a region characterized by overall similar climatology.

Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality-a case study of Kanavi Halla Sub-Basin, Belagavi, India

Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser-Meyer-Olkin and Bartlett's test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation.

Modelling Groundwater Hydraulics to Design a Groundwater Level Monitoring Network for Sustainable Management of Fresh Groundwater Lens in Lower Indus Basin, Pakistan

The over-extraction of groundwater from thin fresh groundwater lenses is a threat to the livelihood of farmers in the Lower Indus Basin (LIB). It is essential to monitor and regulate this pumping to sustain fresh groundwater lenses. In this study, we applied a modelling approach in combination with geostatistical analysis to identify the critical locations to monitor the groundwater levels for sustaining fresh groundwater in the LIB. Our approach included four steps: (i) simulating temporal heads using a calibrated hydrogeological model; (ii) sampling monitoring locations using a hexagonal pattern of sampling; (iii) applying principal component analysis (PCA) of the temporal head observations, and selecting high scoring locations from the PCA; and (iv) minimizing the observation points to represent the water level contours. The calibrated model was able to replicate the hydro-dynamic behavior of the study area, with a root mean square of 0.95 and an absolute residual mean of 0.74 m. The hexagonal pattern of spatial sampling resulted in a 195 point network, but PCA reduced this network to 135 points and contour classification reduced it even further to 59 points. The 195, 135, and 59 point networks represented the water levels with average standard errors of 0.098, 0.318, and 0.610 m, respectively. Long-term simulations with increased pumping showed that the water levels would best be assessed by 195 monitoring points, although 135 and 59 points would represent the depleting area but would not capture the water logging area.

Seasonal and Inter-Annual Variability of Groundwater and Their Responses to Climate Change and Human Activities in Arid and Desert Areas: A Case Study in Yaoba Oasis, Northwest China

Climate change and human activities have profound effects on the characteristics of groundwater in arid oases. Analyzing the change of groundwater level and quantifying the contributions of influencing factors are essential for mastering the groundwater dynamic variation and providing scientific guidance for the rational utilization and management of groundwater resources. In this study, the characteristics and causes of groundwater level in an arid oasis of Northwest China were explored using the Mann–Kendall trend test, Morlet wavelet analysis, and principal component analysis. Results showed that the groundwater level every year exhibited tremendous regular characteristics with the seasonal exploitation. Meanwhile, the inter-annual groundwater level dropped continuously from 1982 to 2018, with a cumulative decline depth that exceeded 12 m, thereby causing the cone of depression. In addition, the monthly groundwater level had an evident cyclical variation on the two time scales of 17–35 and 7–15 months, and the main periodicity of monthly level was 12 months. Analysis results of the climatic factors from 1954 to 2018 observed a significant warming trend in temperature, an indistinctive increase in rainfall, an inconspicuous decrease in evaporation, and an insignificant reduction in relative humidity. The human factors such as exploitation amount, irrigated area, and population quantity rose substantially since the development of the oasis in the 1970s. In accordance with the quantitative calculation, human activities were decisive factors on groundwater level reduction, accounting for 87.79%. However, climate change, including rainfall and evaporation, which contributed to 12.21%, still had the driving force to change the groundwater level in the study area. The groundwater level of Yaoba Oasis has been greatly diminished and the ecological environment has deteriorated further due to the combined effect of climate change and human activities.

Quantitative decision making for a groundwater monitoring and subsurface contamination early warning network

With the increased development of oil and gas activities in northern Colorado, public concerns over the environmental impacts associated with well drilling and hydraulic fracturing have continued to rise. Issues such as leakages of "toxic" products from oil and gas operations to the subsurface environment (such as groundwater contamination) have led to community action and state regulations related to the establishment of groundwater quality monitoring sites in oil and gas activity areas, particularly those adjacent to urban development. Colorado Water Watch was a groundwater quality monitoring network comprised of seven monitoring wells in northern Colorado to monitor groundwater quality near oil and gas wells and give early warnings of contamination. Our study is aimed at developing a quantitative methodology to find ideal monitoring locations as well as evaluate them. We utilized hydraulic and geological data to select the most preferred sites to monitor groundwater quality, understand the temporal trends and identify unique anomaly signals in the oil and gas active area (Wattenberg field, northern Colorado). In addition to the site selection methodology, water quality data from Colorado Water Watch over 2 years is used to do evaluate the performance using entropy information and Principal Component Analysis. The analysis indicates that the earliest functional monitoring site (CHILL) is the most informative monitoring well, and the most recently installed monitoring sites (Gilcrest and LaSalle) are the least informative and least important stations due to their low data efficiency.

A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks

We present a systematic approach for salinity sensor placement in a polder network, where the objective is to estimate the unmeasured salinity levels in the main polder channels. We formulate this problem as optimization of the estimated salinity levels using root mean square error (RMSE) as the “goodness of fit” measure. Starting from a hydrodynamic and salt transport model of the Lissertocht catchment (a low-lying polder in the Netherlands), we use principal component analysis (PCA) to produce a low-order PCA model of the salinity distribution in the catchment. This model captures most of the relevant salinity dynamics and is capable of reconstructing the spatial and temporal salinity variation of the catchment. Just using three principal components (explaining 93% of the variance of the dataset) for the low-order PCA model, three optimally placed sensors with a greedy algorithm make the placement robust for modeling and measurement errors. The performance of the sensor placement for salinity reconstruction is evaluated against the detailed hydrodynamic and salt transport model and is shown to be close to the global optimum found by an exhaustive search with a RMSE of 82.2 mg/L.

Environmental Groundwater Depth for Groundwater-Dependent Terrestrial Ecosystems in Arid/Semiarid Regions: A Review

Groundwater in arid/semiarid regions plays crucial roles in providing drinking water supply, supporting irrigated agriculture, and sustaining important native terrestrial ecosystems. Groundwater depth controls water availability to vegetation and is essential for conserving groundwater-dependent terrestrial ecosystems. Environmental groundwater depth can be defined as a mean depth or a range of depths, satisfying the growth of natural vegetation that is not under stress, either due to lack of water or anoxia or soil salinization. Five methodologies have been reported to estimate environmental groundwater depth: the direct ones rely on response functions that relate vegetation condition, e.g., physiological parameters, appearance frequency, community structure, and remotely sensed physical indexes, to changes in groundwater depth; the indirect one estimates environmental groundwater depth based on the threshold of soil moisture content. To fill a knowledge gap of unique recognized methodology, a conceptual framework was proposed, which involves initial estimation (data collection, response assessment, and estimation) and feedback adjustment (implementation and modification). A key component of the framework is to quantify the linkage between ecological conditions and geohydrological features. This review may provide references for groundwater resources management, ecological conservation, and sustainable development in arid/semiarid regions.

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.

Chlorate origin and fate in shallow groundwater below agricultural landscapes

In agricultural lowland landscapes, intensive agricultural is accompanied by a wide use of agrochemical application, like pesticides and fertilizers. The latter often causes serious environmental threats such as N compounds leaching and surface water eutrophication; additionally, since perchlorate can be present as impurities in many fertilizers, the potential presence of perchlorates and their by-products like chlorates and chlorites in shallow groundwater could be a reason of concern. In this light, the present manuscript reports the first temporal and spatial variation of chlorates, chlorites and major anions concentrations in the shallow unconfined aquifer belonging to Ferrara province (in the Po River plain). The study was made in 56 different locations to obtain insight on groundwater chemical composition and its sediment matrix interactions. During the monitoring period from 2010 to 2011, in June 2011 a nonpoint pollution of chlorates was found in the shallow unconfined aquifer belonging to Ferrara province. Detected chlorates concentrations ranged between 0.01 and 38 mg/l with an average value of 2.9 mg/l. Chlorates were found in 49 wells out of 56 and in all types of lithology constituting the shallow aquifer. Chlorates concentrations appeared to be linked to NO₃^-, volatile fatty acids (VFA) and oxygen reduction potential (ORP) variations. Chlorates behaviour was related to the biodegradation of perchlorates, since perchlorates are favourable electron acceptors for the oxidation of labile dissolved organic carbon (DOC) in groundwater. Further studies must take into consideration to monitor ClO₄^- in pore waters and groundwater to better elucidate the mass flux of ClO₄^- in shallow aquifers belonging to agricultural landscapes.

Seasonal changes of spatial variation of some groundwater quality variables in a large irrigated coastal Mediterranean region of Turkey

Soil and groundwater degradations have taken considerable attention, recently. We studied spatial and temporal variations of groundwater table depth and contours, and groundwater pH, electrical conductivity (EC), and nitrate (NO3) content in a large irrigated area in Western Mediterranean region of Turkey. These variables were monitored during 2009 and 2010 in previously constructed 220 monitoring wells. We analyzed the data by geostatistical techniques and GIS. Spatial variation of groundwater table depth (GTD) and groundwater table contours (GTC) remained similar across the four sampling campaigns. The values for groundwater NO3 content, EC, and pH values ranged from 0.01 to 454.1 gL(-1), 0.06 to 46.0 dS m(-1) and 6.53-9.91, respectively. Greatest geostatistical range (16,964 m) occurred for GTC and minimum (960 m) for groundwater EC. Groundwater NO3 concentrations varied both spatially and temporally. Temporal changes in spatial pattern of NO3 indicated that land use and farming practices influenced spatial and temporal variation of groundwater NO3. Several hot spots occurred for groundwater NO3 content and EC. These localities should be monitored more frequently and land management practices should be adjusted to avoid soil and groundwater degradation. The results may have important implications for areas with similar soil, land use, and climate conditions across the Mediterranean region.

Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region, Tunisia

In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group "A," water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group "B," the water level contains much less high-frequency variation. The wells of group "C" represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.

Improving the sampling strategy of the Joint Danube Survey 3 (2013) by means of multivariate statistical techniques applied on selected physico-chemical and biological data

The concept of basin-wide Joint Danube Survey (JDS) was launched by the International Commission for the Protection of the Danube River (ICPDR) as a tool for investigative monitoring under the Water Framework Directive (WFD), with a frequency of 6 years. The first JDS was carried out in 2001 and its success in providing key information for characterisation of the Danube River Basin District as required by WFD lead to the organisation of the second JDS in 2007, which was the world's biggest river research expedition in that year. The present paper presents an approach for improving the survey strategy for the next planned survey JDS3 (2013) by means of several multivariate statistical techniques. In order to design the optimum structure in terms of parameters and sampling sites, principal component analysis (PCA), factor analysis (FA) and cluster analysis were applied on JDS2 data for 13 selected physico-chemical and one biological element measured in 78 sampling sites located on the main course of the Danube. Results from PCA/FA showed that most of the dataset variance (above 75%) was explained by five varifactors loaded with 8 out of 14 variables: physical (transparency and total suspended solids), relevant nutrients (N-nitrates and P-orthophosphates), feedback effects of primary production (pH, alkalinity and dissolved oxygen) and algal biomass. Taking into account the representation of the factor scores given by FA versus sampling sites and the major groups generated by the clustering procedure, the spatial network of the next survey could be carefully tailored, leading to a decreasing of sampling sites by more than 30%. The approach of target oriented sampling strategy based on the selected multivariate statistics can provide a strong reduction in dimensionality of the original data and corresponding costs as well, without any loss of information.

Water quality assessment, by statistical analysis, on rural and urban areas of Chocancharava River (Río Cuarto), Córdoba, Argentina

Water quality has degraded dramatically in the Chocancharava River (Río Cuarto, Córdoba, Argentina) due to point and non-point sources. This paper aims to assess spatial and temporal variations of physical and chemical parameters of the river. Six sampling sites and six sampling campaigns were developed. During the period 2007-2008, wet and dry seasons were included. A statistical analysis was carried out with 23 physical and chemical variables. Then, a new statistical analysis was carried out including the Riparian Corridors Quality Index and the physical and chemical variables (24 variables). Considering a multivariate system, analysis of variance, principal component analysis and cluster analysis were used. From the statistical analysis, the river was divided into two zones with different degrees of contamination. The most polluted zone is due to pollution inputs of urban, industrial and agricultural sources. This area showed a remarkable deterioration in water quality, mainly due to wastewater discharges. According to Riparian Quality, better results were found in sections of poor water quality, due to the fact that the river bank forest was less degraded downstream of the sewage discharge.

Flocculation of metals during mixing of Siyahrud River water with Caspian Sea water

Flocculation of dissolved Cu, Mn, Ni, Zn, and Pb during mixing of Siyahrud River water with water sample of Caspian Sea at nine different salinity regimes was investigated. The maximum flocculation of elements occurs in the salinities 1.67‰ to 3.67‰ (except for Zn). The flocculation trend of Zn (80.9) >Mn (58.3) > Cu (30.5) > Ni (25.9) > Pb (19.5) indicates that flocculation of metals have nonlinear behavior towards salinity changes during estuarine mixing. Electrical conductivity shows a linear behavior in different proportions of salinity which is in contrast with the behavior of other studied parameters. Cluster analysis indicates that pH and NO(3) are governing factors in the flocculation of Cu, Mn, and Pb. The results of this research show that 80.9%, 19.5%, 25.9%, 30.5%, and 58.3% of dissolved Zn, Pb, Ni, Cu, and Mn flocculate during estuarine mixing. Total amount of studied dissolved element flowing in to the Caspian Sea would decrease from 5.62 to 2.76 t/year.

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.

Natural and anthropogenic factors affecting the groundwater quality in the Nandong karst underground river system in Yunan, China

The Nandong Underground River System (NURS) is located in a typical karst agriculture dominated area in the southeast Yunnan Province, China. Groundwater plays an important role for social and economical development in the area. However, with the rapid increase in population and expansion of farm land, groundwater quality has degraded. 42 groundwater samples collected from springs in the NURS showed great variation of chemical compositions across the study basin. With increased anthropogenic contamination in the area, the groundwater chemistry has changed from the typical Ca-HCO(3) or Ca (Mg)-HCO(3) type in karst groundwater to the Ca-Cl (+NO(3)) or Ca (Mg)-Cl (+NO(3)), and Ca-Cl (+NO(3)+SO(4)) or Ca (Mg)-Cl (+NO(3)+SO(4)) type, indicating increases in NO(3)(-), Cl(-) and SO(4)(2-) concentrations that were caused most likely by human activities in the region. This study implemented the R-mode factor analysis to investigate the chemical characteristics of groundwater and to distinguish the natural and anthropogenic processes affecting groundwater quality in the system. The R-mode factor analysis together with geology and land uses revealed that: (a) contamination from human activities such as sewage effluents and agricultural fertilizers; (b) water-rock interaction in the limestone-dominated system; and (c) water-rock interaction in the dolomite-dominated system were the three major factors contributing to groundwater quality. Natural dissolution of carbonate rock (water-rock interaction) was the primary source of Ca(2+) and HCO(3)(-) in groundwater, water-rock interaction in dolomite-dominated system resulted in higher Mg(2+) in the groundwater, and human activities were likely others sources. Sewage effluents and fertilizers could be the main contributor of Cl(-), NO(3)(-), SO(4)(2-), Na(+) and K(+) to the groundwater system in the area. This study suggested that both natural and anthropogenic processes contributed to chemical composition of groundwater in the NURS, human activities played the most important role, however.

Multivariate statistical study of seasonal variation of BTEX in the surface water of Savitri River

Volatile organic compounds (VOCs) analysis was carried out for the surface water of the Savitri river during the period of June 2005 to June 2007. BTEX compounds (Benzene, Toluene, Xylene & Ethyl benzene) were analyzed by using micro extraction technique (Purge & Trap). Concentrations of these BTEX compounds were ranging from 0.1 to 1.5 ppm during sampling period. Higher concentrations of BTEX were found at sampling location VI. Concentration of ethyl benzene was very low as compare to other compounds. However, the concentration of benzene was very high. Seasonal variations in conc. of BTEX compounds were observed and higher concentration was detected during the summer season. Salting-out effect had given higher quantification values. In PCA and PFA, the component loading for all the variables are positively correlated. Death of fishes was observed in the river that is indication of severe pollution problem.

Land use effects in groundwater composition of an alluvial aquifer (Trussu River, Brazil) by multivariate techniques

Multivariate statistical techniques, cluster analysis (CA) and factor analysis/principal component analysis (FA/PCA), were applied to analyze the similarities or dissimilarities among the sampling sites to identify spatial and temporal variations in water quality and sources of contamination (natural and anthropogenic). The aquifer under study is supplied by the Trussu River, which has a general direction from west to east, within Iguatu County, Ceará, Brazil. Groundwater samples were collected in four shallow wells, located at the Trussu River alluvial, from October 2002 to February 2004. The samples were analyzed for 13 parameters: pH, electrical conductivity (EC), Na, Ca, Mg, K, Cl, HCO(3), PO(4), NH(4)-N, NO(3)-N, SO(4), and sodium adsorption ratio (SAR). Two zones were very well differentiated based on cluster analysis results, and implied a relation to geographic position and time variation. One zone called UL-upland region-corresponds to upland of studied area, used mainly for irrigation and livestock activities. The other zone called DL-downland region-corresponds to the region downstream and is occupied by human settlements. These results may be used to reduce the number of samples analyzed both in space and time, without too much loss of information. Three major independent factors that define water quality in the UL region and four in DL region were identified in the PCA. At both regions, rotated component (RC) loadings identified that the variables responsible for water quality composition are mainly related to soluble salts variables (natural process) and nutrients (high loads of NO(3)-N, NH(4)-N), expressing anthropogenic activities. RC also revealed that hydrochemical processes were the major factors responsible for water quality.

Selected questions on biomechanical exposures for surveillance of upper-limb work-related musculoskeletal disorders

OBJECTIVE

Questionnaires for assessment of biomechanical exposure are frequently used in surveillance programs, though few studies have evaluated which key questions are needed. We sought to reduce the number of variables on a surveillance questionnaire by identifying which variables best summarized biomechanical exposure in a survey of the French working population.

METHODS

We used data from 2002 to 2003 French experimental network of Upper-limb work-related musculoskeletal disorders (UWMSD), performed on 2,685 subjects in which 37 variables assessing biomechanical exposures were available (divided into four ordinal categories, according to the task frequency or duration). Principal Component Analysis (PCA) with orthogonal rotation was performed on these variables. Variables closely associated with factors issued from PCA were retained, except those highly correlated to another variable (rho > 0.70). In order to study the relevance of the final list of variables, correlations between a score based on retained variables (PCA score) and the exposure score suggested by the SALTSA group were calculated. The associations between the PCA score and the prevalence of UWMSD were also studied. In a final step, we added back to the list a few variables not retained by PCA, because of their established recognition as risk factors.

RESULTS

According to the results of the PCA, seven interpretable factors were identified: posture exposures, repetitiveness, handling of heavy loads, distal biomechanical exposures, computer use, forklift operator specific task, and recovery time. About 20 variables strongly correlated with the factors obtained from PCA were retained. The PCA score was strongly correlated both with the SALTSA score and with UWMSD prevalence (P < 0.0001). In the final step, six variables were reintegrated.

CONCLUSION

Twenty-six variables of 37 were efficiently selected according to their ability to summarize major biomechanical constraints in a working population, with an approach combining statistical analyses and existing knowledge.

Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China

Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies.

Assessment of seasonal variations in surface water quality

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 16 physical and chemical parameters collected from 22 monitoring stations in a river during the years from 1998 to 2001 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons.