Mercury contamination in human hair and some marine species from Sfax coasts of Tunisia: levels and risk assessment

The aim of this study was to measure the mercury (Hg) contents of three marine fish and common seafood species (Diplodus annularis, Sarpa salpa and Sepia officinalis) at two sampling sites in the gulf of Gabes, i.e. Sidi Mansour (polluted site) and Kerkennah (control site). These species are frequently consumed by the population living at the Sfax coasts of Tunisia, particularly by the families of fisherman. Additionally, the hair mercury levels of 55 volunteers (28 women, 27 men) were analysed and the daily total mercury intake through the fish and seafood diet was estimated. The key findings were: (1) the mercury contents of the examined fish and seafood species frequently exceeded the regulatory guideline value of 0.5 mg/kg, (2) no site-specific differences in hair mercury contents were found, (3) fish and seafood consumption is probably the major contributor of mercury exposure in this population, (4) the daily mercury intake through frequent consumption of D. annularis exceeds the US EPA reference dose. Further studies are necessary to evaluate the health risks associated with such high mercury exposure levels in order to allow optimal counseling and therapy of the concerned population and to avoid future impairment of human health, particularly children's health.     

Long-term monitoring of streambed sedimentation and scour in a dynamic stream based on streambed temperature time series

This study presented the monitoring and quantification of streambed sedimentation and scour in a stream with dynamically changing streambed based on measured phase and amplitude of the diurnal signal of sediment temperature time series. With the applied method, changes in streambed elevation were estimated on a sub-daily scale with 2-h intervals without continuous maintenance of the measurement system, thus making both high temporal resolution and long-term monitoring of streambed elevations possible. Estimates of streambed elevation showed that during base flow conditions streambed elevation fluctuates by 2–3 cm. Following high stream stages, scouring of 2–5 cm can be observed even at areas with low stream flow and weak currents. Our results demonstrate that weather variability can induce significant changes in the stream water and consequently sediment temperatures influencing the diurnal temperature signal in such an extent that the sediment thickness between paired temperature sensors were overestimated by up to 8 cm. These observations have significant consequences on the design of vertical sensor spacing in high-flux environments and in climates with reduced diurnal variations in air temperature.     

Groundwater geochemistry and identification of hydrogeochemical processes in a hard rock region, Southern India

Hydrogeochemical investigations were carried out in Chithar River basin, Tamil Nadu, India to identify the major geochemical processes that regulate groundwater chemistry. For this study, long-term (1991–1997) and recent water quality data (2001–2002) for 30 groundwater wells spread over the study area were used to understand the groundwater geochemistry and hydrogeochemical process regulating groundwater quality. Groundwater quality data obtained from more than 400 water samples were employed. Results of electrical conductivity and chloride express large variation between minimum and maximum values and high standard deviation, which suggests that the water chemistry in the study region is not homogeneous and influenced by complex contamination sources and geochemical process. Nitrate and depth to water table expose the influences of surface contamination sources, whereas dissolved silica, fluoride and alkalinity strongly suggest the effect of rock–water interaction. In the study region, weathering of carbonate and silicate minerals and ion exchange reactions predominantly regulate major ion chemistry. Besides, the concentrations of sulphate, chloride and nitrate firmly suggest the impact of agricultural activities such as irrigation return flow, fertiliser application, etc on water chemistry in the study region.     

Spatiotemporal distribution and the characteristics of the air temperature of a river source region of the Qinghai-Tibet Plateau

As the backland of the Qinghai-Tibet Plateau, the river source region is highly sensitive to changes in global climate. Air temperature estimation using remote sensing satellite provides a new way of conducting studies in the field of climate change study. A geographically weighted regression model was applied to estimate synchronic air temperature from 2001 to 2015 using Moderate-Resolution Imaging Spectroradiometry (MODIS) data. The results were R²?=?0.913 and RMSE?=?2.47 °C, which confirmed the feasibility of the estimation. The spatial distribution and variation characteristics of the average annual and seasonal air temperature were analyzed. The findings are as follows: (1) the distribution of average annual air temperature has significant terrain characteristics. The reduction in average annual air temperature along the elevation of the region is 0.19 °C/km, whereas the reduction in the average annual air temperature along the latitude is 0.04 °C/degree. (2) The average annual air temperature increase in the region is 0.37 °C/decade. The average air temperature increase could be arranged in the following decreasing order: Yangtze River Basin > Mekong River Basin > Nujiang River Basin > Yarlung Zangbo River Basin > Yellow River Basin. The fastest, namely, Yangtze River Basin, is 0.47 °C/decade. (3) The average air temperature rise in spring, summer, and winter generally increases with higher altitude. The average annual air temperature in different types of lands following a decreasing order is as follows: wetland > construction land > bare land glacier > shrub grassland > arable land > forest land > water body and that of the fastest one, wetland, is 0.13 °C/year.     

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.     

Polycyclic aromatic hydrocarbons (PAHs) in surface sediments from the Bizerte Lagoon,Tunisia: levels,sources, and toxicological significance

To assess the status of polycyclic aromatic hydrocarbon (PAH) contamination in sediments from the Bizerte Lagoon (northern Tunisia), 18 surface sediment samples were collected in March 2011 and analyzed for 14 US Environmental Protection Agency priority PAHs by high-performance liquid chromatography. The total concentrations of the 14 PAHs (ΣPAHs) ranged from 16.9 to 394.1 ng g^?1 dry weight (dw) with a mean concentration of 85.5 ng g^?1 dw. Compared with other lagoons, coasts, and bays in the world, the concentrations of PAHs in surface sediments of the Bizerte Lagoon are low to moderate. The PAHs’ composition pattern was dominated by the presence of four-ring PAHs (45.8 %) followed by five-ring (26.8 %) and three-ring PAHs (12.7 %). The PAH source analysis suggested that the main origin of PAHs in the sediments of the lagoon was mainly from pyrolytic sources. According to the numerical effect-based sediment quality guidelines of the USA, the levels of PAHs in the Bizerte Lagoon should not exert adverse biological effects. The total benzo[a]pyrene toxicity equivalent values calculated for the samples varied from 3.1 to 53.7 ng g^?1 dw with an average of 10.6 ng g^?1 dw.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Community air monitoring for pesticides. Part 3: using health-based screening levels to evaluate results collected for a year

The CA Department of Pesticide Regulation (CDPR) and the CA Air Resources Board monitored 40 pesticides, including five degradation products, in Parlier, CA, to determine if its residents were exposed to any of these pesticides and, if so, in what amounts. They included 1,3-dichloropropene, acrolein, arsenic, azinphos-methyl, carbon disulfide, chlorpyrifos and its degradation product, chlorthalonil, copper, cypermethrin, diazinon and its degradation product, dichlorvos, dicofol, dimethoate and its degradation product, diuron, endosulfan and its degradation product, S-ethyl dipropylcarbamothioate (EPTC), formaldehyde, malathion and its degradation product, methyl isothiocyanate (MITC), methyl bromide, metolachlor, molinate, norflurazon, oryzalin, oxyfluorfen, permethrin, phosmet, propanil, propargite, simazine, SSS-tributylphosphorotrithioate, sulfur, thiobencarb, trifluralin, and xylene. Monitoring was conducted 3 days per week for a year. Twenty-three pesticides and degradation products were detected. Acrolein, arsenic, carbon disulfide, chlorpyrifos, copper, formaldehyde, methyl bromide, MITC, and sulfur were detected in more than half the samples. Since no regulatory ambient air standards exist for these pesticides, CDPR developed advisory, health-based non-cancer screening levels (SLs) to assess acute, subchronic, and chronic exposures. For carcinogenic pesticides, CDPR assessed risk using cancer potency values. Amongst non-carcinogenic agricultural use pesticides, only diazinon exceeded its SL. For carcinogens, 1,3-dichloropropene concentrations exceeded its cancer potency value. Based on these findings, CDPR has undertaken a more comprehensive evaluation of 1,3-dichloropropene, diazinon, and the closely related chlorpyrifos that was frequently detected. Four chemicals—acrolein, arsenic, carbon disulfide, and formaldehyde—sometimes used as pesticides were detected, although no pesticidal use was reported in the area during this study. Their presence was most likely due to vehicular or industrial emissions.     

PAHs in the urban air of Sarajevo: levels,sources, day/night variation,and human inhalation risk

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants derived from pyrolysis and pyrosynthesis processes. Industrial activity, motor vehicle emission, and domestic combustion are the main sources of PAHs in the urban atmosphere. In this work, samples collected during the day and night in the urban area of Sarajevo are analyzed separately for gaseous and particle-bound PAHs; the possible origin of PAHs at the receptor site was suggested using different methods applied to the solid phase and to the total PAHs (gaseous + particulate phase). Finally, the risk level in Sarajevo associated to the carcinogenic character of the studied PAHs has been assessed. The result of this study suggests that (a) the total PAH concentrations were higher than those reported in other European cities; (b) the PAH daytime concentrations are higher than nocturnal concentrations: the sum of the PAH day/night ratios is 1.52 (gas) and 1.45 (particle phase); (c) stationary combustion and traffic were suggested to be the main sources of PAHs; (d) the average particle-bound benzo(a)pyrene (BaP) concentration (5.4 ng/m³) is higher than EU target annual value (1 ng/m³); and (e) PAH cancer risk exceeds the carcinogenic benchmark level recommended by the EPA mainly due to BaP during both the day and night periods.     

Water quality time series for Big Melen stream (Turkey): its decomposition analysis and comparison to upstream

Big Melen stream is one of the major water resources providing 268 km³ year^???1 of drinking and municipal water for Istanbul. Monthly time series data between 1991 and 2004 for 25 chemical, biological, and physical water properties of Big Melen stream were separated into linear trend, seasonality, and error components using additive decomposition models. Water quality index (WQI) derived from 17 water quality variables were used to compare Aksu upstream and Big Melen downstream water quality. Twenty-six additive decomposition models of water quality time series data including WQI had R ² values ranging from 88% for log(water temperature) (P?≤?0.001) to 3% for log(total dissolved solids) (P?≤?0.026). Linear trend models revealed that total hardness, calcium concentration, and log(nitrite concentration) had the highest rate of increase over time. Tukey’s multiple comparison pointed to significant decreases in 17 water quality variables including WQI of Big Melen downstream relative to those of Aksu upstream (P?≤?0.001). Monitoring changes in water quality on the basis of watersheds through WQI and decomposition analysis of time series data paves the way for an adaptive management process of water resources that can be tailored in response to effectiveness and dynamics of management practices.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Evaluation of levels, sources and distribution of toxic elements in PM10 in a suburban industrial region, Rio de Janeiro, Brazil

The Oswaldo Cruz Foundation Campus (FIOCRUZ), in a suburban region of the city of Rio de Janeiro, was selected as a case study to assess the pollution released from vehicle and industrial facilities in Basin III, the most polluted area of the city. Concentrations of particulate matter (PM10) and trace metals in airborne particles were determined in an intensive field campaign. The samplings were performed every six days for 24 h periods, using a PM10 high volume sampler, from September 2004 to August 2005. PM10 mass concentrations were determined gravimetrically and the metals by ICP-OES. For PM10, the arithmetic mean for the period is 169 ± 42 μg m⁻³ which is 3.4 times the national recommended standard of 50 μg m⁻³. Additionally, 51% of the samplings exceeded the recommended 24 h limit of 150 μg m⁻³. Ca, Mg, Fe, Zn and Al were the metals that presented the higher concentrations. The correlation matrix gave two main clusters and three significant principal components (PC). Both PC1 and PC2 are associated to crustal, vehicular and industrial emissions while PC3 is mainly associated to geological material. Enrichment factors for Zn, Cu, Cd and Pb indicate that for these elements, anthropic sources prevail over natural inputs. PM10 levels showed a good correlation with hospital admissions for respiratory diseases in children and elderly people.     

Strategies to optimize monitoring schemes of recreational waters from Salta,Argentina: a multivariate approach

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.     

Groundwater quality assessment and its correlation with gastroenteritis using GIS: a case study of Rawal Town,Rawalpindi, Pakistan

Majority of the people of Pakistan get drinking water from groundwater source. Nearly 40 % of the total ailments reported in Pakistan are the result of dirty drinking water. Every summer, thousands of patients suffer from acute gastroenteritis in the Rawal Town. Therefore, a study was designed to generate a water quality index map of the Rawal Town and identify the relationship between bacteriological water quality and socio-economic indicators with gastroenteritis in the study area. Water quality and gastroenteritis patient data were collected by surveying the 262 tubewells and the major hospitals in the Rawal Town. The collected spatial data was analyzed by using ArcGIS spatial analyst (Moran’s I spatial autocorrelation) and geostatistical analysis tools (inverse distance weighted, radial basis function, kriging, and cokriging). The water quality index (WQI) for the study area was computed using pH, turbidity, total dissolved solids, calcium, hardness, alkalinity, and chloride values of the 262 tubewells. The results of Moran’s I spatial autocorrelation showed that the groundwater physicochemical parameters were clustered. Among IDW, radial basis function, and kriging and cokriging interpolation techniques, cokriging showed the lowest root mean square error. Cokriging was used to make the spatial distribution maps of water quality parameters. The WQI results showed that more than half of the tubewells in the Rawal Town were providing “poor” to “unfit” drinking water. The Pearson’s coefficient of correlation for gastroenteritis with fecal coliform was found significant (P?P?P?相似文献   

Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: a PSCF model approach

The contributions of long range transported aerosol in East Asia to carbonaceous aerosol and particulate matter (PM) concentrations in Seoul, Korea were estimated with potential source contribution function (PSCF) calculations. Carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)), PM(2.5), and PM(10) concentrations were measured from April 2007 to March 2008 in Seoul, Korea. The PSCF and concentration weighted trajectory (CWT) receptor models were used to identify the spatial source distributions of OC, EC, PM(2.5), and coarse particles. Heavily industrialized areas in Northeast China such as Harbin and Changchun and East China including the Pearl River Delta region, the Yangtze River Delta region, and the Beijing-Tianjin region were identified as high OC, EC and PM(2.5) source areas. The conditional PSCF analysis was introduced so as to distinguish the influence of aerosol transported from heavily polluted source areas on a receptor site from that transported from relatively clean areas. The source contributions estimated using the conditional PSCF analysis account for not only the aerosol concentrations of long range transported aerosols but also the number of transport days effective on the measurement site. Based on the proposed algorithm, the condition of airmass pathways was classified into two types: one condition where airmass passed over the source region (PS) and another condition where airmass did not pass over the source region (NPS). For most of the seasons during the measurement period, 249.5-366.2% higher OC, EC, PM(2.5), and coarse particle concentrations were observed at the measurement site under PS conditions than under NPS conditions. Seasonal variations in the concentrations of OC, EC, PM(2.5), and coarse particles under PS, NPS, and background aerosol conditions were quantified. The contributions of long range transported aerosols on the OC, EC, PM(2.5), and coarse particle concentrations during several Asian dust events were also estimated. We also investigated the performance of the PSCF results obtained from combining highly time resolved measurement data and backward trajectory calculations via comparison with those from data in low resolutions. Reduced tailing effects and the larger coverage over the area of interest were observed in the PSCF results obtained from using the highly time resolved data and trajectories.     

Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China

The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8 % of total district area) in 1974 to 1,128,380 ha (43.9 %) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1 % and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2 % and 86.5 % of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities.