Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

The Interactive Effects of Temperature and Osmotic Potential on the Growth of Aquatic Isolates of Fusarium culmorum.

The mycelial growth of 10 Fusarium culmorum strains isolated from water of the Andarax riverbed in the provinces of Granada and Almeria in southeastern Spain was tested on potato-dextroseagar adjusted to different osmotic potentials with either KCl or NaCl (−1.50 to−144.54 bars) at 10◦C intervals ranging from15◦ to 35◦C. Fungal growth was determined by measuring colony diameter after 4 d of incubation. Mycelial growth was maximal at 25◦C. The quantity and capacity of mycelial growth of F. culmorum were similar at 15 and 25◦C, with maximal growth occurring at −13.79 bars water potential and a lack of growth at 35◦C. The effect of water potential was independent of salt composition. The general growth pattern of Fusarium culmorum growth declined at potentials below −13.79 bars. Fungal growth at 25◦C was always greater than growth at 15◦C, at all of the water potentials tested. Significant differences were observed in the response ofmycelia to water potential and temperature as main and interactive effects. The number of isolates that showed growth was increasingly inhibited as the water potential dropped, but some growth was still observable at −99.56 bars. These findings could indicate that F. culmorum strains isolated from water have a physiological mechanism that permits survival in environments with low water potential. Propagules of Fusarium culmorum are transported long distances by river water, which could explain the severity of diseases caused by F.culmorum on cereal plants irrigated with river water and its interaction under hydric stress ormoderate soil salinity. The observed differences in growth magnitude and capacity could indicate that the biological factors governing potential and actual growth are affected by osmotic potential in different ways.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Toxicity of ZnO nanoparticles, ZnO bulk and ZnCl2 on earthworms in a spiked natural soil and toxicological effects of leachates on aquatic organisms

The present study assessed the uptake and toxicity of ZnO nanoparticles (NPs), ZnO bulk, and ZnCl2 salt in earthworms in spiked agricultural soils. In addition, the toxicity of aqueous extracts to Daphnia magna and Chlorella vulgaris was analyzed to determine the risk of these soils to the aquatic compartment. We then investigated the distribution of Zn in soil fractions to interpret the nature of toxicity. Neither mortality nor differences in earthworm body weight were observed compared with the control. The most sensitive end point was reproduction. ZnCl2 was notably toxic in eliminating the production of cocoons. The effects induced by ZnO-NPs and bulk ZnO on fecundity were similar and lower than those of the salt. In contrast to ZnO bulk, ZnO-NPs adversely affected fertility. The internal concentrations of Zn in earthworms in the NP group were greater than those in the salt and bulk groups, although bioconcentration factors were consistently <1. No relationship was found between toxicity and internal Zn amounts in earthworms. The results from the sequential extraction of soil showed that ZnCl2 displayed the highest availability compared with both ZnO. Zn distribution was consistent with the greatest toxicity showed by the salt but not with Zn body concentrations. The soil extracts from both ZnO-NPs and bulk ZnO did not show effects on aquatic organisms (Daphnia and algae) after short-term exposure. However, ZnCl2 extracts (total and 0.45-μm filtered) were toxic to Daphnia.

Simultaneous determination of multiclass emerging contaminants in aquatic plants by ultrasound assisted matrix solid phase dispersion and GC-MS

A multiresidue method was developed for the simultaneous determination of 31 emerging contaminants (pharmaceutical compounds, hormones, personal care products, biocides and flame retardants) in aquatic plants. Analytes were extracted by ultrasound assisted-matrix solid phase dispersion (UA-MSPD) and determined by gas chromatography-mass spectrometry after sylilation. The method was validated for different aquatic plants (Typha angustifolia, Arundo donax and Lemna minor) and a semiaquatic cultivated plant (Oryza sativa) with good recoveries at concentrations of 100 and 25 ng g-1 wet weight, ranging from 70 to 120 %, and low method detection limits (0.3 to 2.2 ng g-1 wet weight). A significant difference of the chromatographic response was observed for some compounds in neat solvent versus matrix extracts and therefore quantification was carried out using matrix-matched standards in order to overcome this matrix effect. Aquatic plants taken from rivers located at three Spanish regions were analyzed and the compounds detected were parabens, bisphenol A, benzophenone-3, cyfluthrin and cypermethrin. The levels found ranged from 6 to 25 ng g-1 wet weight except for cypermethrin that was detected at 235 ng g-1 wet weight in Oryza sativa samples.