Determination of mercury in airborne particulate matter collected on glass fiber filters using high-resolution continuum source graphite furnace atomic absorption spectrometry and direct solid sampling

A study has been undertaken to assess the capability of high-resolution continuum source graphite furnace atomic absorption spectrometry for the determination of mercury in airborne particulate matter (APM) collected on glass fiber filters using direct solid sampling. The main Hg absorption line at 253.652 nm was used for all determinations. The certified reference material NIST SRM 1648 (Urban Particulate Matter) was used to check the accuracy of the method, and good agreement was obtained between published and determined values. The characteristic mass was 22 pg Hg. The limit of detection (3σ), based on ten atomizations of an unexposed filter, was 40 ng g- 1, corresponding to 0.12 ng m- 3 in the air for a typical air volume of 1440 m3 collected within 24 h. The limit of quantification was 150 ng g-1, equivalent to 0.41 ng m-3 in the air. The repeatability of measurements was better than 17% RSD (n = 5). Mercury concentrations found in filter samples loaded with APM collected in Buenos Aires, Argentina, were between < 40 ng g-1 and 381 ± 24 ng g-1. These values correspond to a mercury concentration in the air between < 0.12 ng m-3 and 1.47 ± 0.09 ng m-3. The proposed procedure was found to be simple, fast and reliable, and suitable as a screening procedure for the determination of mercury in APM samples.Fil: Araujo, Rennan G. O.. Universidade Federal de Santa Catarina; BrasilFil: Vignola, Fabíola. Universidade Federal de Santa Catarina; BrasilFil: Castilho, Ivan N. B.. Universidade Federal de Santa Catarina; BrasilFil: Borges, Daniel L. G.. Universidade Federal de Santa Catarina; Brasil. Universidade Federal da Bahia; BrasilFil: Welz, Bernhard. Universidade Federal de Santa Catarina; Brasil. Universidade Federal da Bahia; BrasilFil: Vale, Maria Goreti R.. Universidade Federal do Rio Grande do Sul; Brasil. Universidade Federal da Bahia; BrasilFil: Smichowski, Patricia Nora. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ferreira, Sérgio L. C.. Universidade Federal da Bahia; BrasilFil: Becker Ross, Helmut. Leibniz-Institut für Analytische Wissenschaften; Alemani

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Determination of Polycyclic Aromatic Hydrocarbons in Groundwater Samples by Gas Chromatography-Mass Spectrometry After Pre-Concentration Using Cloud-Point Extraction with Surfactant Derivatization

A cloud-point extraction (CPE) method using the surfactant (30)p-tert-octylphenol polyoxyethylene (OPEO30) was proposed as the preceding step for the determination of polycyclic aromatic hydrocarbons (PAHs) by gas chromatography-mass spectrometry (GC-MS). Given the need for surfactant derivatization before the chromatographic analysis, the reaction conditions of coacervate derivatization were studied. The extraction process was also optimized, where surfactant concentration, temperature and time were analyzed as variables. The limits of detection obtained were between 0.02 and 0.05 µg L-1, and the recoveries of analytes were between 70 and 98%, with coefficient of variation better than 10.3%. The analytical method developed provides an efficient, precise and accurate method for the determination of the 16 priority PAHs, generating results in accordance with the USEPA 3510C method. The method was applied to the analysis of groundwater samples collected from artesian wells located at retail fuel stations.Journal of the Brazilian Chemical Societ

Assessment of the Halogen Content of Brazilian Inhalable Particulate Matter (PM₁₀) Using High Resolution Molecular Absorption Spectrometry and Electrothermal Vaporization Inductively Coupled Plasma Mass Spectrometry, with Direct Solid Sample Analysis

Halogens in the atmosphere play an important role in climate change and also represent a potential health hazard. However, quantification of halogens is not a trivial task, and methods that require minimum sample preparation are interesting alternatives. Hence, the aim of this work was to evaluate the feasibility of direct solid sample analysis using high-resolution continuum source molecular absorption spectrometry (HR-CS MAS) for F determination and electrothermal vaporization-inductively coupled plasma mass spectrometry (ETV-ICP-MS) for simultaneous Cl, Br, and I determination in airborne inhalable particulate matter (PM₁₀) collected in the metropolitan area of Aracaju, Sergipe, Brazil. Analysis using HR-CS MAS was accomplished by monitoring the CaF molecule, which was generated at high temperatures in the graphite furnace after the addition of Ca. Analysis using ETV-ICP-MS was carried out using Ca as chemical modifier/aerosol carrier in order to avoid losses of Cl, Br, and I during the pyrolysis step, with concomitant use of Pd as a permanent modifier. The direct analysis approach resulted in LODs that were proven adequate for halogen determination in PM₁₀, using either standard addition calibration or calibration against a certified reference material. The method allowed the quantification of the halogens in 14 PM₁₀ samples collected in a northeastern coastal city in Brazil. The results demonstrated variations of halogen content according to meteorological conditions, particularly related to rainfall, humidity, and sunlight irradiation