Removal characteristics of 53 micropollutants during ozonation, chlorination, and UV/H2O2 processes used in drinking water treatment plant

The removal of 53 emerging micropollutants (MPs), including 10 per- and polyfluorinated substances (PFASs), 25 pharmaceuticals and personal care products (PPCPs), 7 pesticides, 5 endocrine disrupters (EDCs), 3 nitrosamines, and 3 taste and odor compounds (T&Os), by chlorination, ozonation, and UV/H2O2 treatment was examined in deionized water and surface waters used as the raw waters in drinking water treatment plants (DWTPs) in South Korea. The UV/H2O2 treatment was effective in the removal of most MPs, whereas chlorination was selectively effective for 19 MPs, including EDCs (>70 %). MPs containing aromatic ring with electron-donating functional group, or primary and secondary amines were effectively removed by chlorination immediately upon reaction initiation. The removal of MPs by ozonation was generally lower than that of the other two processes at a low ozone dose (1 mg L-1), but higher than chlorination at a high ozone dose (3 mg L-1), particularly for 16 MPs, including T&Os. Compared in deionized water, the removals of MPs in the raw water samples were lower in all three processes. The regression models predicting the rate constants (kobs) of 53 MPs showed good agreement between modeled and measured value for UV/H2O2 treatment (R2 = 0.948) and chlorination (R2 = 0.973), despite using only dissolved organic carbon (DOC) and oxidant concentration as variables, whereas the ozonation model showed a variation (R2 = 0.943). Our results can provide the resources for determining which oxidative process is suitable for treating specific MPs present in the raw waters of DWTPs.

Estimation of missing mandible from the skull using postmortem CT images

Skeletal remains often have missing mandibles owing to the environment in which the corpse was placed or damage caused by an animal. Loss of the mandible reduces the accuracy of skull identification. Although several studies have validated methods for estimating mandibular morphology from conventional anthropological measurements using skull specimens, there are no reports using three-dimensional computed tomography (3DCT) images. Here, we examined methods for establishing the mandibular morphology from the remaining skulls using postmortem computed tomography (PMCT) images. We used PMCT images from 200 Japanese subjects as samples. After verifying the morphological correlation between the cranium and mandible, we created and validated estimation models using multiple regression analysis (stepwise method) for seven sites that were necessary for understanding the morphology of the mandible. Among the regression models, the estimated model for bicondylar breadth had the highest coefficient of determination (adjR2 = 0.53). We verified the accuracy of the model on a sample independent from the specimen used to create the estimation model and found that the formulated model of bicondylar breadth had good estimation accuracy, with a high correlation coefficient between the measured and predicted values of 0.82 and a mean absolute error of 3.582 mm, indicating that the model had good estimation accuracy. Here, we established a novel method for estimating the missing mandibular morphology based on PMCT data from the Japanese population. Our estimation model can help determine the missing mandibular morphology in a cranium remnant.

The effects of the size and strength of food on jaw motion and temporomandibular joints

Mastication displays much importance in people's lives. The masticatory mandibular motion associated with dental kinematics also impacts temporomandibular joint (TMJ) kinematics and even TMJ health status. How food properties impact kinematical parameters of TMJs is a meaningful question for the conservative treatment of temporomandibular disorders (TMD) and evidence for the diet recommendation of TMD patients. The aim of this study was to find the primary mechanical properties influencing the masticatory motion. The potato boluses with different boiling times and sizes were chosen. The optical motion tracking system was adopted to record the masticatory trials of chewing boluses with various mechanical properties. The mechanical experiments revealed that increasing boiling time could reduce compressive strength. Moreover, multiple regression models were built to find the primary property of food influencing the TMJ kinematics, including condylar displacement, velocity, acceleration, and crushing time. The results showed that the bolus size had a significant primary influence on condylar displacements. The chewing times had a significantly minor influence on condylar displacements, while bolus strength had only a small impact on condylar displacements. Furthermore, condylar displacements on the non-working side were more affected by bolus size and chewing times than on the working sides. The crushing time of the bolus was significantly influenced by the compressive strength. Meals with small sizes and soft properties were therefore advised to lessen condylar displacements and relax the crushing process, and further reduce the loadings in the TMJ.

Influencing factors and prediction of net carbon sink in the primary sector of the coastal city in China

To achieve the goal of urban carbon dioxide emission reduction, how to increase carbon sequestration has become a top priority. The biological sink is mainly divided into green carbon sink and blue carbon sink. Coastal cities have two kinds of carbon sinks. There, the study of carbon sinks in coastal cities is the primary choice to cope with climate change. Therefore, this study chooses coastal cities with primary industries including agriculture, fishery, and forestry as the study subjects. The LMDI (Log-Mean Divisia Index) method and multiple regression prediction models were used to explore the low-carbon countermeasures which increase urban net carbon sink from the perspective of influencing factors and future potential. The study found that the average output value of employees in the primary industry is the main driving factor, and the change in the purchasing power of unit carbon sinks and the change in the proportion of employees in the primary industry have inhibited the increase in net carbon sinks. Projections based on the primary industry's output and afforestation area as independent variables show an overall upward trend in net carbon sinks, reaching 15.70 million tons of net carbon sinks in 2060, offsetting 10-20% of total carbon emissions in the same year. Based on the calculation results, this paper puts forward some corresponding countermeasures to increase carbon sinks. This paper provides a theoretical reference for the low-carbon development of coastal cities in China, and the strategies can be also expanded to other cities with similar resources around the world.

Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models

BACKGROUNDS

Evaluating the growth performance of pigs in real-time is laborious and expensive, thus mathematical models based on easily accessible variables are developed. Multiple regression (MR) is the most widely used tool to build prediction models in swine nutrition, while the artificial neural networks (ANN) model is reported to be more accurate than MR model in prediction performance. Therefore, the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.

RESULTS

Body weight (BW), net energy (NE) intake, standardized ileal digestible lysine (SID Lys) intake, and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables. In the training phase, MR models showed high accuracy in both ADG and F/G prediction (R²_ADG = 0.929, R²_F/G = 0.886) while ANN models with 4, 6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction (R²_ADG = 0.964, R²_F/G = 0.932). In the testing phase, these ANN models showed better accuracy in ADG prediction (CCC: 0.976 vs. 0.861, R²: 0.951 vs. 0.584), and F/G prediction (CCC: 0.952 vs. 0.900, R²: 0.905 vs. 0.821) compared with the MR models. Meanwhile, the "over-fitting" occurred in MR models but not in ANN models. On validation data from the animal trial, ANN models exhibited superiority over MR models in both ADG and F/G prediction (P < 0.01). Moreover, the growth stages have a significant effect on the prediction accuracy of the models.

CONCLUSION

Body weight, NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs, with trained ANN models are more flexible and accurate than MR models. Therefore, it is promising to use ANN models in related swine nutrition studies in the future.

A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data

Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R² = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R² = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors.

The use of statistical methods to assess groundwater contamination in the Lower Tano river basin, Ghana, West Africa

In this study, descriptive statistics, correlation matrix, multiple regression model, and geostatistical models were used to assess the contamination of groundwater with respect to trace elements in the Lower Tano river basin, Ghana, West Africa. A total number of 48 boreholes drilled across the basin with depths ranging from 18 to 60 m were used as data sources in this study. The results of the descriptive statistics showed that the average lead, iron, and aluminium concentrations exceeded the WHO permissible limits of 0.3 mg/L, 0.01 mg/L, and 0.2 mg/L respectively. Furthermore, copper, chromium, aluminium, zinc, manganese, nickel, iron, arsenic, electrical conductivity, and total dissolved solids were found to be extreme and highly positively skewed. Even though significant correlations exist among some variables, the statistical results showed that the quality of the boreholes drilled across the basin was mainly originating from geogenic and anthropogenic sources. In addition, each pair of correlated physical parameters and trace elements in the drilled boreholes were predicted using multiple regression models. Likewise, geostatistical modelling was used to assess the spatial analysis of each pair of correlated physical parameters and trace elements in the drilled boreholes. The cross-validation results revealed kriging model, as the most precise model for the spatial distribution maps for the correlated physical parameters, and correlated trace elements concentration in the boreholes drilled across the study region. The semivariogram models showed that most of the correlated physical parameters and correlated trace elements were weak moderately and strongly spatially dependent, suggesting fewer agronomic influences. The results of the spatial analysis were consistent with the multiple regression model and the Pearson correlation matrix.

Predicting accumulation of Cd in rice (Oryza sativa L.) and soil threshold concentration of Cd for rice safe production

Rice contamination by cadmium (Cd) poses a serious threat to human health, which has attracted widespread concerns in China. It is imperative to determine major soil factors influencing the accumulation of Cd in rice and develop prediction models to derive the threshold concentration of Cd in soil for rice food safety. In this study, the bioavailability, accumulation, and transfer of Cd in the 18 typical paddy soil-rice systems with a wide range of soil properties was investigated using pot experiments. The regression-based models incorporated with total or extractable Cd and soil properties were constructed to predict Cd content of rice grain. Pot experimental results indicated that rice showed a high accumulation potential for Cd, while rice grains grown in acid soils displayed larger Cd contents than those in neutral and alkaline soils. The pH and MnO content were major soil factors influencing the Cd accumulation of rice. Multiple regression models based on the total Cd, extractable Cd, pH, and MnO content in soils could well describe the Cd content in rice grain. Measured Cd content of rice grains from field samples demonstrated that the empirical models could quantitatively predict the Cd content of rice grains. The threshold concentrations of Cd in soils for rice food safety could be back-calculated by both EDTA-extractable and total Cd contents in soils. The EDTA-extractable Cd in soils could use as an indication to derive the threshold concentrations of Cd for rice food safety. In conclusions, multiple regression models proved reliable and practical in predicting Cd accumulation in rice grain. These empirical models could well predict the content of Cd in rice grain and deduce soil Cd threshold criteria. These results could help to quantitatively evaluate the health risk of Cd accumulation in rice crop and provide a useful reference for safe production of rice.

Multiple regression analysis to assess the contamination with metals and metalloids in surface sediments (Aveiro Lagoon, Portugal)

An innovative multiple regression analysis was used to evaluate metal/metalloid contamination in the surface sediments of a coastal lagoon. The concentrations of metals/metalloids were represented as a function of geochemical characteristics of the sediments (fine fraction, concentrations of organic carbon, Ca, Al, Mn) and distances between sampling points. The effect of distances on the concentrations were negligible for Li, Co, Ni, Ba, V, Cr, and only geochemical variables specific for each element explained its spatial variation. The concentrations of As, Cu, Zn and Pb were influenced by both geochemical and geographical distance variables, the latter representing the anthropogenic influence and the extent of transport of contaminants away from the upstream source. Enrichment of the sediment with Ba, As, Co, Cr and V was determined mainly by enrichment with Mn. The proposed approach is supplementary to the traditional utilization of enrichment factors, and is better suited for systems with anthropogenic influence.

QSAR study for predicting the ecotoxicity of NADES towards Aliivibrio fischeri. Exploring the use of mixing rules

(Eco)toxicological information of natural deep eutectic solvents (NADES) is scarce, and thus, quantitative structure activity relationship (QSAR) models are an important tool for achieving the prediction of toxicity in this case. For that reason, in this manuscript, a new QSAR model for predicting the ecotoxicity of NADES towards the Aliivibrio fischeri biomodel, using mixing rules, is proposed. The main advantage of the method is that the individual components of the mixtures are molecularly modelled, and then, a mixing rule is used, which simplifies the process. For developing the model, a total of 11 descriptors for each component is used: the dissociation constant, partition coefficient, Van der Waals volume, Van der Waals surface area, topological polar surface area, solvent accessible surface area, minimum projection area, maximum projection area, minimum projection radius, maximum projection radius and molecular weight. The final obtained model includes the topological polar surface area and the dissociation constant, mechanistically interpreted as the ability of a NADES to transport through biological membranes and the severe negative effect of pH on the toxicity and biological response of Aliivibrio fischeri bacteria. The OECD Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships is followed to develop the mathematical model.

Factors controlling dissolved 137Cs concentrations in east Japanese Rivers

To investigate the main factors that control the dissolved radiocesium concentration in river water in the area affected by the Fukushima Daiichi nuclear power plant accident, the correlations between the dissolved ¹³⁷Cs concentrations at 66 sites normalized to the average ¹³⁷Cs inventories for the watersheds with the land use, soil components, topography, and water quality factors were assessed. We found that the topographic wetness index is significantly and positively correlated with the normalized dissolved ¹³⁷Cs concentration. Similar positive correlations have been found for European rivers because wetland areas with boggy organic soils that weakly retain ¹³⁷Cs are mainly found on plains. However, for small Japanese river watersheds, the building area ratio in the watershed strongly affected the dissolved ¹³⁷Cs concentration. One reason for this would be because the high concentrations of solutes, such as K⁺ and dissolved organic carbon, discharged in urban areas would inhibit ¹³⁷Cs absorption to soil particles. A multiple regression equation was constructed to predict the normalized dissolved ¹³⁷Cs concentration with the topography, land use, soil component, and water quality data as explanatory variables. The best model had the building land use as the primary predictor. When comparing two multiple regression models in which the explanatory variables were limited to (1) the land use and soil composition and (2) the water quality, the water quality model underestimated the high normalized dissolve ¹³⁷Cs concentration in urban areas. This poor reproducibility indicates that the dissolved ¹³⁷Cs concentration value in urban areas cannot be solely explained by the solid-liquid distribution of ¹³⁷Cs owing to the influence of the water quality, but some specific ¹³⁷Cs sources in urban areas would control the dissolved ¹³⁷Cs concentration.

Use of multiple regression models for predicting the formation of bromoform and dibromochloromethane during ballast water treatment based on an advanced oxidation process

Disinfection byproducts (DBPs) generated by ballast water treatment have become a concern worldwide because of their potential threat to the marine environment. Predicting the relative DBP concentrations after disinfection could enable better control of DBP formation. However, there is no appropriate method of evaluating DBP formation in a full-scale ballast water treatment system (BWTS). In this study, multiple regression models were developed for predicting the dibromochloromethane (DBCM) and bromoform (TBM) concentrations produced by an emergency BWTS using field experimental data from ballast water treatments conducted at Dalian Port, China. Six combinations of independent variables [including several water parameters and/or the total residual oxidant (TRO) concentration] were evaluated to construct mathematical prediction formulas based on a polynomial linear model and logarithmic regression model. Further, statistical analyses were performed to verify and determine the appropriate mathematical models for DBCM and TBM formation, which were ultimately validated using additional field experimental data. The polynomial linear model with four variables (temperature, salinity, chlorophyll, and TRO) and the logarithmic regression model with seven variables (temperature, salinity, dissolved oxygen, pH, turbidity, chlorophyll, and TRO) exhibited good reproducibility and could be used to predict the DBCM and TBM concentrations, respectively. The validation results indicated that the developed models could accurately predict DBP concentrations, with no significant statistical difference from the measured values. The results of this work could provide a theoretical basis and data reference for ballast water treatment control in engineering applications of emergency BWTSs.

Multiple regression analysis to assess the spatial distribution and speciation of mercury in surface sediments of a contaminated lagoon

The concentrations of inorganic mercury (IHg) and methylmercury (MeHg) in surface sediments from the contaminated Aveiro Lagoon (Portugal) were determined by species-specific isotope dilution analysis. Different behaviour of IHg and MeHg was observed based upon multiple regression analysis, including geochemical characteristics of the surface sediments (fine fraction, concentrations of organic matter and metals) and non-Euclidean distances between sampling points. This data treatment method was valid over the entire concentration range of IHg and MeHg, allowing robust quantitative evaluation with respect to extrapolation. For IHg, there was statistical separation of the dispersion away from the contamination source and of Al concentration in the sediments. The MeHg concentrations followed those of IHg at high concentrations. The geochemical variables, such as concentrations of Ca (marine influence proxy), Mn and organic matter, were necessary to describe the behaviour of MeHg across the whole concentration range. The models for MeHg demonstrated that, close to the mouth of the lagoon, net production of MeHg was higher. In future, multiple regression analysis could be applied to separate and to evaluate quantitatively the effects of geochemistry and dispersion away from the contamination source in sediments contaminated with other substances.

On-site assessments on variations of PM2.5, PM10, CO2 and TVOC concentrations in naturally ventilated underground parking garages with traffic volume

There have been an increasing number of automobile vehicles in cities, so that newly developed residential areas are mostly designed with underground parking garages (UPGs). For naturally ventilated UPGs, the ventilation performance may be insufficient to discharge totally vehicle-induced pollutants out of the enclosed underground spaces, which consequently results in threats to residents' health. This study, therefore, aims at examining the patterns of pollutant concentrations in naturally ventilation UPGs as well as their sensitivities to traffic volume. In particular, the naturally ventilated UPGs' weekday particulate matters (PM_2.5 and PM₁₀), CO₂ and TVOC concentration as well as their relationships between traffic volume were quantitively evaluated based on field measurements in eight residential areas in Baoding, China. Results indicated that daily average PM_2.5, PM₁₀, CO₂ and TVOC concentrations in studied UPGs were 105.81 μg/m³, 464.17 μg/m³, 571 ppm and 24 ppb, respectively. The PM_2.5 concentrations in UPGs were slightly higher than that in ambient environments, while the PM₁₀ concentrations in UPGs were significantly higher. Furthermore, both PM₁₀ and TVOC concentrations in UPGs were in significant relationships with traffic volume at the p < 0.01 level, while the concentration of UPG PM_2.5 generally exhibited a significant correlation (p < 0.01) with that of the ambient. Nevertheless, a combination of traffic volume, the ambient and accumulative effect was much better to explain the hourly PM₁₀ concentration in UPGs. These findings will be conducive to instruct engineers with fundamental knowledge of UPG ventilation design.

Removal of phosphate from River water using a new baffle plates electrochemical reactor

During the last 50 years, the human activities have significantly altered the natural cycle of phosphate in this planet, causing phosphate to accumulate in the freshwater ecosystems of some countries to at least 75% greater than preindustrial levels, which indicates an urgent need to develop efficient phosphate treatment methods. Therefore, the current study investigates the removal of phosphate from river water using a new electrochemical cell (PBPR). This new cell utilises perforated baffle plates as a water mixer rather than magnetic stirrers that require power to work. This study investigates the influence of key operational parameters such as initial pH (ipH), current density (Ј), inter-electrode distance (ID), detention time (t) and initial phosphate concentration (IC) on the removal efficiency, and influence of the electrocoagulation process on the morphology of the surface of electrodes.

Overall, the results showed that the new reactor was efficient enough to reduce the concentration of phosphate to the permissible limits. Additionally, SEM images showed that the Al anode became rough and nonuniform due to the production of aluminium hydroxides. The main advantages of the electrocoagulation technique are:

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The EC method does not produce secondary pollutants as it does not required chemical additives, while other traditional treatment methods required either chemical or biological additives [[1], [2], [3], [4]].

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It has a large treatment capacity and a relatively short treatment time in comparison with other treatment methods, such as the biological methods [1,[5], [6], [7]].

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The EC method produces less sludge than traditional treatment traditional chemical and biological treatment methods [8,9].

EC technology, like any other treatment method, has some drawbacks that could limit its performance. For instance, it still has a clear deficiency in the variety of reactor design, and the electrodes should be periodically replaced as they dissolve into the solution due to the oxidation process [2,10].

Main controlling factors and forecasting models of lead accumulation in earthworms based on low-level lead-contaminated soils

To explore the main controlling factors in soil and build a predictive model between the lead concentrations in earthworms (Pb_earthworm) and the soil physicochemical parameters, 13 soils with low level of lead contamination were used to conduct toxicity experiments using earthworms. The results indicated that a relatively high bioaccumulation factor appeared in the soils with low pH values. The lead concentrations between earthworms and soils after log transformation had a significantly positive correlation (R² = 0.46, P < 0.0001, n = 39). Stepwise multiple linear regression analysis derived a fitting empirical model between Pb_earthworm and the soil physicochemical properties: log(Pb_earthworm) = 0.96log(Pb_soil) - 0.74log(OC) - 0.22pH + 0.95, (R² = 0.66, n = 39). Furthermore, path analysis confirmed that the Pb concentrations in the soil (Pb_soil), soil pH, and soil organic carbon (OC) were the primary controlling factors of Pb_earthworm with high pathway parameters (0.71, - 0.51, and - 0.49, respectively). The predictive model based on Pb_earthworm in a nationwide range of soils with low-level lead contamination could provide a reference for the establishment of safety thresholds in Pb-contaminated soils from the perspective of soil-animal systems.

Analysis of the Environmental Management System based on ISO 14001 on the American continent

The American continent is in broad economic and industrial development. Consequently, a more detailed discussion of the impacts generated by such development is needed. Moreover, there is an increase in the number of ISO 14001 certificates issued to this continent. Given the above, no studies were found that bridge the gap to identify the influence of different factors on ISO 14001 in the Americas. Thus, this article has as its main aim to check which economic, environmental and cultural factors have influence on ISO 14001 Certification in the American Continent. The data were collected in the ISO Survey, World Bank, United Nations Development Programme and International Energy Agency. Among the countries of that continent, thirteen were analyzed and only two did not show the economic factors as the influence factor in the multiple regression models fitted with Brazil and the United State. In these models, all presented environmental factors as influencing factors. Only in Brazil the index HDI presented as cultural factor in multiple regression model fitted. The economic factors: Gross Domestic Product and exports of goods and services and environmental: Carbon Dioxide (CO₂) and fossil fuel consumption were the most influential in ISO 14001 certification. Venezuela, Uruguay, Colombia and the United States were countries that had factors dependent on each other, featuring the environmental marketing. Briefly, this study brings up several implications: to the academy, with the proposal of new concepts and guidance on the factors that assist in ISO 14001 certification in the American Continent. Additionally, taking into account the industry, the factors serve as efficiency parameters for the implementation of ISO 14001 standard, and for the Government to improve through factors that do not fit in multiple regression models.

Gas chromatography-mass spectrometry and high-performance liquid chromatography-diode array detection for dating of paper ink

An extraction and determination method is shown for the analysis of dyes and solvents present in two types of ballpoint pen inks that are deposited onto paper. Ink extracts are analysed using a combination of gas chromatography with mass spectrometry (GC-MS), and high-pressure liquid chromatography with photodiode array detection (HPLC-DAD), within a single sample extraction procedure. Seventeen solvents and thirteen dyes contained in two Montblanc^® inks (black and blue) were monitored for 45 months at monthly intervals, in order to determine variations in the concentrations of the compounds over time. We also studied the relative variations between different compounds and the generation of degradation products such as phenol. The concentration data obtained from these compounds during their exposure have been analysed and a multiple regression model is developed for each ink type that allows an estimate of the exposure time of the ink on paper with a maximum error of between 4 and 7 months.

Multiple regression analysis to assess the role of plankton on the distribution and speciation of mercury in water of a contaminated lagoon

Spatial and seasonal variation of mercury species aqueous concentrations and distributions was carried out during six sampling campaigns at four locations within Laranjo Bay, the most mercury-contaminated area of the Aveiro Lagoon (Portugal). Inorganic mercury (IHg(II)) and methylmercury (MeHg) were determined in filter-retained (IHgPART, MeHgPART) and filtered (<0.45μm) fractions (IHg(II)DISS, MeHgDISS). The concentrations of IHgPART depended on site and on dilution with downstream particles. Similar processes were evidenced for MeHgPART, however, its concentrations increased for particles rich in phaeophytin (Pha). The concentrations of MeHgDISS, and especially those of IHg(II)DISS, increased with Pha concentrations in the water. Multiple regression models are able to depict MeHgPART, IHg(II)DISS and MeHgDISS concentrations with salinity and Pha concentrations exhibiting additive statistical effects and allowing separation of possible addition and removal processes. A link between phytoplankton/algae and consumers' grazing pressure in the contaminated area can be involved to increase concentrations of IHg(II)DISS and MeHgPART. These processes could lead to suspended particles enriched with MeHg and to the enhancement of IHg(II) and MeHg availability in surface waters and higher transfer to the food web.

Predicting concrete corrosion of sewers using artificial neural network

Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable

Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available.

Relating clinical measures of pain with experimentally assessed pain mechanisms in patients with knee osteoarthritis

Background Peripheral and central sensitisation is prominent in knee osteoarthritis (KOA) and could be important for the reduced efficacy in some cases after as well surgery as pharmacological interventions. Although sensitisation is important in KOA it is not known to what degree it contributes to the overall clinical pain problem. The aim was therefore to investigate how much a combination of quantitative pain measures assessing various pain mechanisms (local and spreading hyperalgesia, temporal and spatial summation, descending inhibition) could predict peak pain intensity in patients with KOA. Methods While resting in a comfortable recumbent position the pressure pain thresholds (PPT) in the peripatellar region (eight locations) and at the tibialis anterior muscle (TA) were assessed by handheld pressure algometry, computer-controlled pressure algometry and cuff-algometry in the affected leg of 17 KOA patients without pain or sensory dysfunctions in other regions than the knee. Cuff-algometry was used to detect spatial pain summation of the lower leg. Temporal pain summation was assessed by repeated pressure stimulation on the TA muscle. The conditioning pain modulation (CPM) was evaluated by conditioning tonic arm pain and by PPT from the peripatellar region. The participants rated their peak pain intensity in the previous 24 h using on a 10 cm visual analogue scale. Results A multiple-regression model based on TA pressure pain sensitivity (spreading sensitisation) and temporal pain summation on the lower leg accounted for 55% of the variance in peak pain intensity experienced by the patients (P=0.001). Significant correlations (P< 0.05) were found between PPTs assessed by handheld pressure algometry in the peripatellar region and at TA (R = 0.94), PPTs assessed by computer-controlled pressure algometry and handheld pressure algometry in the peripatellar region (R = 0.71), PPTs assessed by computer-controlled pressure algometry in the peripatellar region and handheld pressure algometry at TA (R = 0.71) and temporal summation at the knee and at TA (R = 0.73). Conclusion Based on the multiple regression model 55% variance of the perceived maximal pain intensity in painful KOA could be explained by the quantitative experimental pain measures reflecting central pain mechanisms (spreading sensitisation, temporal summation). The lack of other correlations between the methods used in assessing pain mechanisms in this study highlights the importance of applying different tests and different pain modalities when assessing the sensitised pain system as different methods add complementary information. Implications Clinical pain intensity can be explained by influences of different central pain mechanisms in KOA. This has implications for pain management in KOA where treatment addressing central pain components may be more important than previously acknowledged.