Influence of Meteorological Factors NO2, SO2, CO and PM10 on the Concentration of O3 in the Urban Atmosphere of Eastern Croatia

Ozone, NO₂, SO₂, CO, PM₁₀ and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O₃ and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R ²?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R ²?=?0.82).     

成都双流国际机场大气污染物排放清单与时空分布特征

基于成都双流国际机场活动水平数据,采用排放因子法和计算模型等,编制了机场大气污染物排放清单,并完成了时空分配和不确定性分析,建立了高分辨率网格化排放清单。结果表明,成都双流国际机场标准起飞着陆(LTO)循环数为2.4×10~5次/a,CO、VOCs、NO_x、PM_(10)、PM_(2.5)、SO_2排放量分别为1.2×10~3、1.3×10~2、2.1×10~3、2.8×10、2.7×10、2.5×10~2t/a,且主要由飞机发动机排放;活动水平数据仅包括LTO循环数和地面保障设备两部分;污染物排放分布和跑道类型相关性较高;排放清单活动水平数据可靠性较高,而排放因子存在一定的不确定性。     

New research assessing the effect of engine operating conditions on regulated emissions of a 4-stroke motorcycle by test bench measurements

In the latest years the effect of powered two-wheelers on air polluting emissions is generally noteworthy all over the world, notwithstanding advances in internal combustion engines allowed to reduce considerably both fuel consumption and exhaust emissions of SI engines. Nowadays, in fact, these vehicles represent common means of quotidian moving, serving to meet daily urban transport necessities with a significant environmental impact on air quality. Besides, the emissive behavior of the two-wheelers measured under fixed legislative driving standards (and not on the local driving conditions) might not be sufficiently representative of real world motorcycle riding.The purpose of this investigation is a deeper research on emissive levels of in-use motorcycles equipped with last generation SI engines under real world driving behavior. In order to analyze the effect of vehicle instantaneous speed and acceleration on emissive behavior, instantaneous emissions of CO, HC and NO_X were measured in the exhaust of a four-stroke motorcycle, equipped with a three-way catalyst and belonging to the Euro-3 legislative category. Experimental tests were executed on a chassis dynamometer bench in the laboratories of the National Research Council (Italy), during the Type Approval test cycle, at constant speed and under real-world driving cycles. This analytical-experimental investigation was executed with a methodology that improves vehicles emission assessment in comparison with the modeling approaches that are based on fixed legislative driving standards. The statistical processing results so obtained are very useful also in order to improve the database of emission models commonly used for estimating emissions from road transport sector, then they can be used to evaluate the environmental impact of last generation medium-size motorcycles under real driving behaviors.     

Characteristics of ambient 1-min PM2.5 variation in Beijing

One-minute PM_2.5 concentration was obtained with LD-5C pocket microcomputer laser dust instrument from Dec. 15th, 2005 to Jan. 16th, 2006 and Mar. 17th to Apr. 28th, 2006 in Beijing. The concentration of SO₂, NO₂, O₃, CO, and PM₁₀ from Jan. 1st, 2001 to Dec. 31st, 2004 were obtained from the conversion of air pollution index. Results showed that all the pollutants showed cyclic characteristics. The longer yearly cycles was shown from SO₂, NO₂, O₃, CO, and PM₁₀, as the sampling time was 4-year long and daily collected. The shorter hourly and daily cycle was shown from 1-min PM_2.5, as the sampling time was about 1-month long and one collected at 1 min. The spectral density analysis confirmed this from the periodogram graphs. The longer yearly cycle (365, 180 days), the seasonal cycle (120, 60–90 days), and monthly cycle (21, 23, 27 days) of SO₂, NO₂, CO, O₃, and PM₁₀ were obviously shown. In addition, the shorter weekly cycle of 5–7 days is obviously shown, too. The shorter hourly cycle (8–12, 4–6, 3, 1–2 h, 20 min) of 1-min PM_2.5 was also indicated from spectral density analysis. Two major factors contribute the 1-min PM_2.5 cycles, i.e., the meteorological factors and source effects. Both the relative humidity and dew point showed consistent variation with PM_2.5, but the wind speed showed inverse variations with PM_2.5. Furthermore, the spectral density analysis of the meteorological factors (4–5, 2–2.5, 1–1.5 days, 12, 6–8, 3 h) may partially explain the cycles of PM_2.5. As for the sources effects, it can be shown from the strong dust storm of April 16–18th, 2006. PM_2.5 constantly increased tens and even hundreds of times high concentration within a few minutes due to the intensity of the dust sources.     

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

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

Factor analysis and linear regression model (LRM) of metal speciation and physico-chemical characters of groundwater samples

An approach is described for viewing the interrelationship between different variables and also tracing the sources of pollution of groundwater of north Chennai (India). The data set of 43 variables which include major ions, minor ions and trace metal speciation (Cu, Pb, Cd and Zn) collected during the pre-monsoon and post-monsoon seasons of the year 2000–2001, was subjected to R-mode factor analysis to comprehend the distribution pattern of the said variables. It was found that first factor measures salinity and hardness which explained 19.12% of the total variance (comprised of variables EC, TDS, Na⁺, K⁺, Ca²⁺, Mg²⁺, total hardness, Cl⁻ and SO₄ ²⁻) during pre-monsoon, while it was 25.08% during post-monsoon. The second and third factors were attributed to speciation of zinc and copper ions during both pre-monsoon and post-monsoon. Although there were two more factors, loaded with speciation parameters of lead and cadmium, the variance of them were less than 10%. From this study it is seen that sea water intrusion, municipal solid waste disposal are the identified sources of component of pollution. The importance of metal ions is taking a secondary role and the anthropogenic origin-industrial activity, is the reason in the evaluation of pollution status as they come in the second, third, fourth and fifth factors. As the trace metal speciation was grouped in separate factors, linear regression model (LRM) with correlation analysis was applied to check its validity for prediction of speciation and to apply LRM for rapid monitoring of water pollution.     

江苏省工地和裸地扬尘遥感监测方法研究与结果分析

应用卫星遥感影像结合无人机现场核查数据,对2020年江苏省各设区市主城区工地和裸地2类扬尘源的时空分布变化和污染、管控情况开展了连续性监测,为生态环境监测部门业务化应用提供了思路和方法.研究结果显示,遥感解译精度优于95％,扬尘源数量、面积均呈上升趋势,至第4季度总数达1760个、总面积162.53 km2,总体管控情...     

超低排放下燃煤电厂颗粒物排放特征分析研究

选取6家经过超低排放改造的燃煤电厂,对湿法脱硫(WFGD)和湿式电除尘器(WESP)进出口烟气中TPM、PM_(10)、PM_(2.5)、PM_1进行测试,分析研究超低排放下燃煤电厂颗粒物的排放特征及电除尘器后净化设备对颗粒物的脱除效果。结果表明,6家电厂TPM、PM_(10)、PM_(2.5)、PM_1排放浓度分别为0.75~2.36、0.71~2.12、0.65~1.96、0.51~1.57 mg/m~3。分析烟气中颗粒物粒径分布可知,除尘器后,PM10占TPM质量比低于40%,且比例随烟气经过WFGD和WESP而逐渐降低。WFGD对PM2.5有较好的脱除效果,而WESP对PM1脱除效果显著。为满足超低排放标准,6家电厂除尘器后脱除设备综合除尘效率大多在85%以上。计算得到6家电厂TPM、PM_(10)、PM_(2.5)、PM_1排放因子,与超低排放改造之前同等级燃煤电厂相比,6家电厂不同粒径颗粒物排放因子均显著降低,也远低于西方发达国家燃煤电厂颗粒物排放因子。     

Photoacoustic measurements of black carbon light absorption coefficients in Irbid city, Jordan

The goal of this work is the analysis of air quality levels in the area of Volos, a city of average size on the eastern seaboard of Central Greece. For this purpose, concentration measurements of sulfur dioxide, nitrogen oxide, and nitrogen dioxide, for a 4-year period (2001–2004) are analyzed. Air pollution data were obtained by a monitoring station, fully automated, which was established by the Hellenic Ministry of the Environment, Physical Planning, and Public Works, in order to measure air pollution levels in Volos, a medium-sized city, which faces the effects of industrialization. The main conclusions from the statistical analysis of the 4-year measurements of hourly SO₂, NO₂, and NO concentrations in the city of Volos, showed that the mean seasonal variation of the examined air pollutant concentration presents a minimum during the warm period of the year and a maximum during the cold period. Although the local geomorphology and meteorology encourage particularly the accumulation of air pollutants, the analysis shows that the SO₂ and NO₂ concentration levels remain lower than corresponding thresholds for human health protection set by the European Union, in this urban measuring site, during the examined period. The application of harmonic analysis revealed the difference between the annual variation of the SO₂ and NO _x concentrations. Regarding NO _x , the variation is mainly due to the first harmonic term (anthropogenic factor), while the SO₂ variation is interpreted by the two harmonic terms, which represent the anthropogenic and meteorological factors, respectively.     

Fluorescence spectroscopic characterization of dissolved organic matter fractions in soils in soil aquifer treatment

This work investigated the effect of soil aquifer treatment (SAT) operation on the fluorescence characteristics of dissolved organic matter (DOM) fractions in soils through laboratory-scale soil columns with a 2-year operation. The resin adsorption technique (with XAD-8 and XAD-4 resins) was employed to characterize the dissolved organic matter in soils into five fractions, i.e., hydrophobic acid (HPO-A), hydrophobic neutral (HPO-N), transphilic acid (TPI-A), transphilic neutral (TPI-N), and hydrophilic fraction (HPI). The synchronous fluorescence spectra revealed the presence of soluble microbial byproduct- and humic acid-like components and polycyclic aromatic compounds in DOM in soils, and SAT operation resulted in the enrichment of these fluorescent materials in all DOM fractions in the surface soil (0–12.5 cm). More importantly, the quantitative method of fluorescence regional integration was used in the analysis of excitation–emission matrix (EEM) spectra of DOM fractions in soils. The cumulative EEM volume (Φ _{T, n} ) results showed that SAT operation led to the enrichment of more fluorescent components in HPO-A and TPI-A, as well as the dominance of less fluorescent components in HPO-N, TPI-N, and HPI in the bottom soil (75–150 cm). Total Φ _{T, n} values, which were calculated as $ {\Phi_{{T,n}}} \times {\mathrm{DOC}} $ , suggested an accumulation of fluorescent organic matter in the upper 75 cm of soil as a consequence of SAT operation. The distribution of volumetric fluorescence among five regions (i.e., P _{i, n} ) results revealed that SAT caused the increased content of humic-like fluorophores as well as the decreased content of protein-like fluorophores in both HPO-A and TPI-A in soils.     

从大运会期间浓度变化来分析污染物削减措施效果

运用深圳以及周边城市的环境空气污染物浓度资料,分析了NO2、PM10、PM2.5的分布特征,重点讨论了大运会期间NO2、PM10和PM2.5浓度的变化规律,应用相似气象条件的概念,采取多种空间、时间对比分析的方法,排除客观因素,分析了大运会期间人为减排的具体效果,结果表明:大运会期间控制措施效果显著,PM10削减比例更大,也即大运会期间关停了一些高污染排放企业对污染浓度的影响更明显。大运会期间控制措施对各污染浓度的下降起一定作用,作用在白天时段更明显,且对PM2.5的控制效果更显著。大运会期间南山区管控效果最明显,中心福田区PM2.5控制最好。大运会期间的区域联合控制成效显著。大运会后期控制措施的解除,致使污染物浓度快速上升到较高水平,且工业布局密集的区域及机动车流量最大的区域上升幅度相应较大。     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

Dynamic global warming impact assessment integrating temporal variables: Application to a residential building in China

Climate warming is a global concern, and buildings have been recognized as a major contributor because the carbon emissions during their entire life cycles constitute a large share of the total value (almost 40% globally). Many life cycle assessments of buildings have been conducted to quantify the associated global warming impacts. However, few studies have considered the potential temporal variation over the long lifetimes of buildings. In this study, various temporal variables were combined to evaluate the dynamic life cycle global warming impact of a residential building in China. First, annual material and energy consumption data throughout the entire life cycle were acquired from questionnaire survey, statistical reports and literatures. Five dynamic variables (household size, usage behavior, replacement and improvement of components, waste treatment, and energy mix) and their effects on consumption levels were considered. Second, a dynamic inventory analysis tool (DyPLCA) was used to transform the temporal consumption data into dynamic greenhouse gas quantities. The global warming effects of these emissions were quantified using a dynamic characterization tool (DynCO₂). Finally, emission reduction targets for future decades were used to weight the severities of the impacts at different times. The dynamic instantaneous and cumulative global warming impacts of the building were calculated. Dynamic and static assessment results were compared. We also analyzed the contribution of each dynamic variable to the final results and found that the dynamic variables had very different effects (ranging from −42.00% to 45.34%). This study provided an operable dynamic assessment model and available dynamic data for the global warming impact assessment of buildings in China.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

Spatio-temporal variations of carbon dioxide and its gross emission regulated by artificial operation in a typical hydropower reservoir in China

Supersaturation and excess emission of greenhouse gases in freshwater reservoirs have received a great deal of attention in recent years. Although impoundment of reservoirs has been shown to contribute to the net emission of greenhouse gases, reservoir age, geographical distribution, submerged soil type and artificial regulation also have a great impact on their emissions. To examine how large scale reservoir operation impact the water column CO₂ and its air–water interface flux, a field study was conducted in 2010 to evaluate potential ecological processes that regulate the partial pressure of CO₂ (pCO₂) in the water column in the Pengxi River backwater area (PBA), a typical tributary in the Three Gorges Reservoir, China. Measurements of total alkalinity (TA), pH and water temperature were applied to compute the pCO₂. And this approach was also validated by calculation of pCO₂ from the dissolved inorganic carbon data of samples. Partial least squares (PLS) regression was used to determine how the dynamics of the water pCO₂ were related to the available variables. The estimated pCO₂ in our sample ranged from 26 to 4,087 μatm in the surface water. During low water operation from July to early September, there was an obvious pCO₂ stratification, and pCO₂ in the surface was almost unsaturated. This phenomenon was also observed in the spring bloom during discharge period. Conversely, there was no significant pCO₂ stratification and the entire water column was supersaturated during high water operation from November to the following February. Significant correlation was observed between the magnitude of pCO₂, DO and chlorophyll a, suggesting that phytoplankton dynamics regulate pCO₂ in the PBA. The average areal rate of CO₂ emissions from the Pengxi River ranged from 18.06 to 48.09 mmol m^?2 day^?1, with an estimated gross CO₂ emission from the water surface of 14–37 t day^?1 in this area in 2010. Photosynthesis and respiration rates by phytoplankton might be the dominant processes that regulated pCO₂ in the water column. We conclude that pCO₂ values in the surface water of Pengxi River could be regarded as potential sources of CO₂ to the atmosphere were smaller or similar to those that have been reported for many other reservoirs to date.     

Emissions of SO2, NO x and particulates from a pipe manufacturing plant and prediction of impact on air quality

Integrated pipe manufacturing industry is operation intensive and has significant air pollution potential especially when it is equipped with a captive power production facility. Emissions of SO₂, NO _x , and particulate matter (PM) were estimated from the stationary sources in a state-of-the-art pipe manufacturing plant in India. Major air polluting units like blast furnace, ductile iron spun pipe facility, and captive power production facility were selected for stack gas monitoring. Subsequently, ambient air quality modeling was undertaken to predict ground-level concentrations of the selected air pollutants using Industrial Source Complex (ISC 3) model. Emissions of SO₂, NO _x , and particulate matter from the stationary sources in selected facilities ranged from 0.02 to 16.5, 0.03 to 93.3, and 0.09 to 48.3 kg h^???1, respectively. Concentration of SO₂ and NO _x in stack gas of 1,180-kVA (1 KW = 1.25 kVA) diesel generator exceeded the upper safe limits prescribed by the State Pollution Control Board, while concentrations of the same from all other units were within the prescribed limits. Particulate emission was highest from the barrel grinding operation, where grinding of the manufactured pipes is undertaken for giving the final shape. Particulate emission was also high from dedusting operation where coal dust is handled. Air quality modeling indicated that maximum possible ground-level concentration of PM, SO₂, and NO _x were to the tune of 13, 3, and 18 μg/m³, respectively, which are within the prescribed limits for ambient air given by the Central Pollution Control Board.     

Application of multivariate statistical techniques in the assessment of groundwater quality in seawater intrusion area in Bafra Plain, Turkey

Multivariate statistical techniques such as cluster analysis and principal component analysis were performed on 28 groundwater wells in Bafra Plain. Cluster analysis results show that the groundwater in the study area is classified into three groups (A, B, and C), and factor analysis indicates that groundwater is composed of 89.64 % of total variance of 12 variables and is mainly affected by three factors. Factor 1 (seawater salinization) includes concentrations of electrical conductivity, TDS, Cl^?, Na⁺, and sodium adsorption ratio, factor 2 (mixing water) includes δ¹⁸O, δD, and T, and factor 3 (fresh) includes Ca²⁺. For determination of the source of water, Ca/Cl, Cl/HCO₃, Mg/Cl, and Ca/Na as initials and Mg/Ca and SO₄/Cl as molar rates which were identified, the rates had been found to be very useful. Cluster analysis was made by using these rates and the waters were classified in two groups (group 1 and group 2). First group waters were affected by seawater, and the second group were very less affected by freshwater or seawater. According to the comparison of two different parameters, group 1 comprised group A and group B-2, -3, and -4 from the same wells, and group 2 comprised group B-1 and group C from the same well. As a result of this study, it could be said that multivariate statistical methods gave very useful results for the determination of the source.     

杭州市大气污染物排放清单及特征

以杭州市区为研究区域,通过调查整合多套污染源数据库及其他统计资料,研究文献报道及模型计算的各种污染源排放因子,获得杭州市区各行业PM10、PM2.5、SO2、NOx、CO、VOCs、NH3等污染物的排放量,建立了杭州市区2010年1 km×1 km大气污染物排放清单。结果表明,2010年杭州市区PM10、PM2.5、SO2、NOx、CO、VOCs和NH3的排放总量分别为7.96×104、4.02×104、7.23×104、8.98×104、73.90×104、39.56×104、3.32×104t。从排放源的行业分布来看,机动车尾气排放是杭州市区大气污染物最重要排放源之一,对PM10、PM2.5、NOx、CO和VOCs的贡献分别达到14.4%、27.1%、40.3%、21.4%、31.1%。道路扬尘、电厂锅炉、工业炉窑、植被、畜禽养殖对不同污染物分别有着重要贡献,道路扬尘对PM10和PM2.5的贡献分别为44.6%和20.0%、电厂锅炉对SO2和NOx的贡献分别为37.0%和25.7%、工业炉窑对CO的贡献为41.5%、植被排放对VOCs的贡献为27.1%、畜禽养殖对NH3的贡献为76.5%。从空间分布来看,萧山区和余杭区对SO2、NH3和植被排放BVOC的贡献要显著高于主城区;而主城区机动车对PM2.5、NOx和VOCs的贡献分别达到36.3%、56.0%和47.4%,较市区范围内显著增加,表明机动车尾气排放已成为杭州主城区大气污染最重要的来源之一。     

Assessment of environmental impact on air quality by cement industry and mitigating measures: a case study

In this study, environmental impact on air quality was evaluated for a typical Cement Industry in Nigeria. The air pollutants in the atmosphere around the cement plant and neighbouring settlements were determined using appropriate sampling techniques. Atmospheric dust and CO₂ were prevalent pollutants during the sampling period; their concentrations were recorded to be in the range of 249–3,745 mg/m³ and 2,440–2,600 mg/m³, respectively. Besides atmospheric dust and CO₂, the air pollutants such as NO _x , SO _x and CO were in trace concentrations, below the safe limits approved by FEPA that are 0.0062–0.093 mg/m³ NO _x , 0.026 mg/m³ SO _x and 114.3 mg/m³ CO, respectively. Some cost-effective mitigating measures were recommended that include the utilisation of readily available and low-cost pozzolans material to produce blended cement, not only could energy efficiency be improved, but carbon dioxide emission could also be minimised during clinker production; and the installation of an advance high-pressure grinding rolls (clinker–roller-press process) to maximise energy efficiency to above what is obtainable from the traditional ball mills and to minimise CO₂ emission from the power plant.     

Statistical analysis of heavy metals in Cerastoderma edule glaucum and Venerupis aurea laeta from Ganzirri Lake, Messina (Italy)

Our study was carried out on two species of clams, Venerupis aurea laeta and Cerastoderma edule glaucum, from Ganzirri Lake considered as “environmental biomarkers” for their changes in physiology, morphology or distribution under the influence of substances in the environment. The aim of the present study was to conduct a statistical analysis on Venerupis and Cerastoderma to investigate the difference between the two autochthonous clams according to the presence of metals, and to link metal concentrations to the reproductive cycle of clam during 2009–2010. Metal analysis was carried out with inductively coupled plasma mass spectrometry (ICP-MS). Accuracy and precision were assessed by analyzing the certified standard matrix: mussel tissue NIST SRM 2976. The multivariate analysis was made using the SPSS 13.0 software package for Windows (SPSS Inc., Chicago, IL). In this study, the concentrations of some metals found in clam tissues showed seasonal cycles with higher values in summer than in winter. The significances of metal concentrations differences between Venerupis and Cerastoderma samples were estimated with Mann–Whitney U-test. The concentrations of Ag, As, Cd, Mn, Se and Zn show a significant p-level that suggests a difference between the two group samples. Statistical analysis showed that the link of metal concentrations to the reproductive cycles of Venerupis and Cerastoderma was not evident.