Effect of hydrocarbon pollution on the microbial properties of a sandy and a clay soil

The aim of this work was to ascertain the effects of different types of hydrocarbon pollution on soil microbial properties and the influence of a soil's characteristics on these effects. For this, toxicity bioassays and microbiological and biochemical parameters were studied in two soils (one sandy and one clayey) contaminated at a loading rate of 5% and 10% with three types of hydrocarbon (diesel oil, gasoline and crude petroleum) differing in their volatilisation potential and toxic substance content. Soils were maintained under controlled conditions (50-70% water holding capacity, and room temperature) for six months and several microbiological and toxicity parameters were monitored 1, 60, 120 and 180 days after contamination. The toxic effects of hydrocarbon contamination were greater in the sandy soil. Hydrocarbons inhibited microbial biomass, the greatest negative effect being observed in the gasoline-polluted sandy soil. In both soils crude petroleum and diesel oil contamination increased microbial respiration, while gasoline had little effect on this parameter, especially in the sandy soil. In general, gasoline had the highest inhibitory effect on the hydrolase activities involved in N, P or C cycles in both soils. All contaminants inhibited hydrolase activities in the sandy soil, while in the clayey soil diesel oil stimulated enzyme activity, particularly at the higher concentration. In both soils, a phytotoxic effect on barley and ryegrass seed germination was observed in the contaminated soils, particularly in those contaminated with diesel or petroleum.     

Geochemical evidence for the origin of vanadium in an urban environment

The city of Salamanca in central Mexico is surrounded by heavy industry, i.e., a refinery, a thermoelectric plant and chemical industries. Variable concentrations of vanadium (V) have been reported in the groundwater, and their presence has been related to particulates so this hypothesis was tested by sampling soil in the urban area and the surrounding uncontaminated country site. The 0-10-cm soil layer in the industrial and rural area was analyzed for V and other metal trace elements found in hydrocarbons, i.e., chromium (Cr), lead (Pb), zinc (Zn), and nickel (Ni). The concentrations of V were higher in the urban rather than in the rural soil, reaching values of >600 mg kg(-1) in the urban soils. In the rural area, V in the soil was related to regional geology, i.e., volcanic rocks such as basalts and rhyolites but not in the urban area where it was related to particulate distribution mostly emitted from the industries burning fuel oil number 6.     

Trace element concentration and speciation in selected urban soils in New York City

A long history of urbanization and industrialization has affected trace elements in New York City (NYC) soils. Selected NYC pedons were analyzed by aqua regia microwave digestion and sequential chemical extraction as follows: water soluble (WS); exchangeable (EX); specifically sorbed/carbonate bound (SS/CAR); oxide-bound (OX); organic/sulfide bound (OM/S). Soils showed a range in properties (e.g., pH 3.9 to 7.4). Sum of total extractable (SUM_TE) trace elements was higher in NYC parks compared to Bronx River watershed sites. NYC surface horizons showed higher total extractable (TE) levels compared to US non-anthropogenic soils. TE levels increased over 10 year in some of the relatively undisturbed and mostly wooded park sites. Surface horizons of park sites with long-term anthropogenic inputs showed elevated TE levels vs. subsurface horizons. Conversely, some Bronx River watershed soils showed increased concentrations with depth, reflective of their formation in a thick mantle of construction debris increasing with depth and intermingled with anthrotransported soil materials. Short-range variability was evident in primary pedons and satellite samples (e.g., Pb 253?±?143 mg/kg). Long-range variability was indicated by Pb_TE (348 versus 156 mg/kg) and Hg_TE (1 versus 0.3 mg/kg) concentrations varying several-fold in the same soil but in different geographic locations. Relative predominance of fractions: RES (37 %)?>?SS/CAR (22 %)?>?OX (20 %)?>?OM/S (10 %)?>?EX (7 %)?>?WS (4 %). WS and EX fractions were greatest for Hg (7 %) and Cd (14 %), respectively. RES was predominant fraction for Co, Cr, Ni, and Zn (41 to 51 %); SS/CAR for Cd and Pb (40 and 63 %); OM/S for Cu and Hg (36 and 37 %); and OX for As (59 %).     

Evaluation of genotoxic effects of benzene and its derivatives in workers of gas stations

The search for reliable biomarkers of human exposure to benzene and its derivatives is still subject of research. Many of the proposed biomarkers have limitations ranging from the low sensitivity to the wide variability of results. Thus, the aim of our study was to assess the frequencies of chromosomal abnormalities (CA) and sister chromatid exchanges (SCE) in workers of gas stations, with (cases, n?=?19) and without (local controls, n?=?6) risk of exposure to benzene and its derivatives, comparing them with the results from the general population (external controls, n?=?38). The blood dosages of benzene, toluene, and xylenes were measured in all participants. Blood solvent levels were compared with the findings obtained in cytogenetic evaluation and a research protocol which included data of the workplace, lifestyle, and health of the individuals. We did not detect the presence of benzene and its derivatives and did not find chromosomal damage that may be associated with the gas station activity in cases. Moreover, although we found an association of increased SCE and the working time in the local controls, the values found for SCE are within normal limits. Thus, our evaluation of SCE and CA reflected the levels of benzene and its derivatives observed in the blood. We believe, therefore, that SCE and CA may actually constitute possible tests for the evaluation of these exposures. However, we believe that further studies, including individuals at risk, are important to confirm this assertion.     

Assessment of the trophic status of four coastal lagoons and one estuarine delta, eastern Brazil

Anthropogenic eutrophication of aquatic ecosystems continues to be one of the major environmental issues worldwide and also of Brazil. Over the last five decades, several approaches have been proposed to discern the trophic state and the natural and cultural processes involved in eutrophication, including the multi-parameter Assessment of Estuarine Trophic Status (ASSETS) index model. This study applies ASSETS to four Brazilian lagoons (Mundaú, Manguaba, Guarapina, and Piratininga) and one estuarine delta (Paraíba do Sul River), set along the eastern Brazilian coast. The model combines three indices based on the pressure–state–response (PSR) approach to rank the trophic status and forecast the potential eutrophication of a system, to which a final ASSETS grade is established. The lagoons were classified as being eutrophic and highly susceptible to eutrophication, due primarily to their longer residence times but also their high nutrient input index. ASSETS classified the estuary of the Paraíba do Sul river with a low to moderate trophic state (e.g., largely mesotrophic) and low susceptibility to eutrophication. Its nutrient input index was high, but the natural high dilution and flushing potential driven by river flow mitigated the susceptibility to eutrophication. Eutrophication forecasting provided more favorable trends for the Mundaú and Manguaba lagoons and the Paraíba do Sul estuary, in view of the larger investments in wastewater treatment and remediation plans. The final ASSETS ranking system established the lagoons of Mundaú as “moderate,” Manguaba as “bad,” Guarapina as “poor,” and Piratininga as “bad,” whereas the Paraíba do Sul River Estuary was “good.”     

Seasonal trends of atmospheric nitrogen dioxide and sulfur dioxide over North Santa Clara, Cuba

Atmospheric nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) levels were monitored simultaneously by means of Radiello passive samplers at six sites of Santa Clara city, Cuba, in the cold and the warm seasons in 2010. The dissolved ionic forms of NO₂ and SO₂ as nitrate and sulfite plus sulfate, respectively, were determined by means of ion chromatography. Analysis of NO₂ as nitrite was also performed by UV–Vis spectrophotometry. For NO₂, significant t tests show good agreement between the results of IC and UV–Vis methods. The NO₂ and SO₂ concentrations peaked in the cold season, while their minimum levels were experienced in the warm season. The pollutant levels do not exceed the maximum allowable limit of the Cuban Standard 39:1999, i.e., 40 μg/m³ and 50 μg/m³ for NO₂ and SO₂, respectively. The lowest pollutant concentrations obtained in the warm season can be attributed to an increase in their removal via precipitation (scavenging) while to the decreased traffic density and industrial emission during the summer holidays (e.g., July and August).     

Floating aquatic macrophytes for the treatment of aquaculture effluents

Environmental Science and Pollution Research - Aquaculture is an activity with economic and social importance since it generates food, employment, and income. However, like other human activities,...     

A simple spectroscopic method to determine dimethoate in water samples by complex formation

Abstract

A simple and rapid method for the determination of dimethoate in water was developed based on the monitoring of the complex formation between bis 5-phenyldipyrrinate of nickel (II) and the herbicide dimethoate. The method showed a short response time (10?s), high selectivity (very low interference from other sulfate and salts), high sensitivity (limit of detection (LOD) 0.45?µM, limit of quantitation (LOQ) of 1.39?µM), and a K_d of 2.4?µM. Stoichiometry experiments showed that complex formation occurred with a 1:1 relation. The method was applied to different environmental water samples such as lagoon, stream, urban, and groundwater samples. The results indicated that independently from the water source, the method exhibited high precision (0.25–2.47% variation coefficient) and accuracy (84.42–115.68% recovery). In addition, the method was also tested using an effluent from a wastewater treatment plant from Mexico; however, the results indicated that the presence of organic matter had a pronounced effect on the detection.     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.