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To estimate the oxidative potential (OP) of particulate matter (PM), two commonly used cell-free, molecular probes were applied: dithiothreitol (DTT) and dichloro-dihydro-fluorescein diacetate (DCFH-DA), and their performance was compared with 9,10-bis (phenylethynyl) anthracene-nitroxide (BPEAnit). To the best of our knowledge, this is the first study in which the performance of the DTT and DCFH has been compared with the BPEAnit probe. The average concentrations of PM, organic carbon (OC) and elemental carbon (EC) for fine (PM2.5) and coarse (PM10) particles were determined. The results were 44.8 ± 13.7, 9.8 ± 5.1 and 9.3 ± 4.8 µg·m−3 for PM2.5 and 75.5 ± 25.1, 16.3 ± 8.7 and 11.8 ± 5.3 µg·m−3 for PM10, respectively, for PM, OC and EC. The water-soluble organic carbon (WSOC) fraction accounted for 42 ± 14% and 28 ± 9% of organic carbon in PM2.5 and PM10, respectively. The average volume normalized OP values for the three assays depended on both the sampling periods and the PM fractions. The OPBPEAnit had its peak at 2 p.m.; in the afternoon, it was three times higher compared to the morning and late afternoon values. The DCFH and BPEAnit results were correlated (r = 0.64), while there was no good agreement between the BPEAnit and the DTT (r = 0.14). The total organic content of PM does not necessarily represent oxidative capacity and it shows varying correlation with the OP. With respect to the two PM fractions studied, the OP was mostly associated with smaller particles.

To estimate the oxidative potential (OP) of particulate matter (PM), two commonly used cell-free, molecular probes were applied: dithiothreitol (DTT) and dichloro-dihydro-fluorescein diacetate (DCFH-DA), and their performance was compared with 9,10-bis (phenylethynyl) anthracene-nitroxide (BPEAnit). To the best of our knowledge, this is the first study in which the performance of the DTT and DCFH has been compared with the BPEAnit probe. The average concentrations of PM, organic carbon (OC) and elemental carbon (EC) for fine (PM2.5) and coarse (PM10) particles were determined. The results were 44.8 ± 13.7, 9.8 ± 5.1 and 9.3 ± 4.8 µg·m−3 for PM2.5 and 75.5 ± 25.1, 16.3 ± 8.7 and 11.8 ± 5.3 µg·m−3 for PM10, respectively, for PM, OC and EC. The water-soluble organic carbon (WSOC) fraction accounted for 42 ± 14% and 28 ± 9% of organic carbon in PM2.5 and PM10, respectively. The average volume normalized OP values for the three assays depended on both the sampling periods and the PM fractions. The OPBPEAnit had its peak at 2 p.m.; in the afternoon, it was three times higher compared to the morning and late afternoon values. The DCFH and BPEAnit results were correlated (r = 0.64), while there was no good agreement between the BPEAnit and the DTT (r = 0.14). The total organic content of PM does not necessarily represent oxidative capacity and it shows varying correlation with the OP. With respect to the two PM fractions studied, the OP was mostly associated with smaller particles.

The current compliance networks of automatic air-quality monitoring stations in large urban environments are not sufficient to provide spatial and temporal measurement resolution for realistic assessment of personal exposure to pollutants. Small low-cost sensor platforms with greater mobility and expected lower maintenance costs, are increasingly being used as a supplement to compliance monitoring stations. However, low-cost sensor platforms usually provide data with uncertain precision. To improve the precision, these sensor platforms require in-field calibration. Our paper aims to demonstrate that data from each individual sensor system can be corrected using that sensor system's own data to achieve much improved data quality compared to a reference. However, in this procedure, there are practical difficulties such as individual sensor outputs from the multi-sensor system not being sufficiently available due to malfunctions for instance. We explore how this can be dealt with. In our opinion, this is a novel approach, of practical importance both to users and manufacturers. We present a detailed comparative analysis of Linear Regression (univariate), Multivariate Linear Regression and Artificial Neural Networks used with a specific aim of calibrating field-deployed low-cost CO and O3 sensors. For Artificial Neural Network models, the performance of three common training algorithms was compared (Levenberg-Marquardt, Resilient back-propagation and Conjugate Gradient Powell-Beale algorithm). Data for this study were obtained from two campaigns conducted with 25 multi-sensor AQMESH v.3.5 platforms used within the activities of the CITI-SENSE project. The platforms were co-located to reference gas monitors at the Automatic Monitoring Station Stari Grad, in Belgrade, Serbia. This paper demonstrates that Multivariate Linear Regression and Artificial Neural Network calibration models can improve the output signal. This improvement can be measured by changes in the median a

The International Workshop and Conference, Particulate Matter: Research and Management – WeBIOPATR is a biennial event held in Serbia since 2007. The conference rationale stems from the fact that particulate matter is the air quality constituent that currently is responsible for most instances of non-compliance with air quality directives in Europe. Particulate matter, arising both from primary emissions and as a result of secondary formation in the atmosphere, is also one of the least well understood issues. [from Preface]

CESTES is a global database for metaCommunity Ecology: Species, Traits, Environment and Space. It compiles 80 datasets from trait-based studies. Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. CESTES presents a harmonized structure and covers a diversity of ecosystem types (marine, terrestrial, freshwater), taxonomic groups (plants, vertebrates, invertebrates...), geographical regions, and spatial scales. The CESTES database is a live database: it will be maintained and expanded in the future as new datasets become available (https://icestes.github.io/sharedata). A zipped folder called “CESTES.zip” includes two alternative formats for the CESTES database: - a “xCESTES” folder that includes 80 Excel files (one file per dataset), each named according to the following structure: “AuthorPublicationYear.xlsx” - a “rCESTES” folder that includes the CESTES core processed database (comm, traits, envir, coord matrices) as an R list object “CESTES.RData” plus two R scripts, and two metadata tables for data processing and exploration. This “CESTES.zip” folder also includes: - an extended metadata table, “CESTES_metadata.xlsx”, that provides the general metadata information of all the datasets, - a tutorial document, “HOW_TO_SHARE_MY_DATA_FOR_CESTES.pdf”, that explains how to share data for integrating future datasets in the database. A second zipped folder, called "ceste.zip", corresponds to the non-spatial ancillary to CESTES. We provide access to 10 additional datasets that were not completely suitable for the CESTES database, due to the absence of spatial information or insufficient metadata but that were potentially valuable for their three other data matrices (comm, traits, envir). They follow the same structure as CESTES, except that they do not present the “coord” sheet and sometimes include only partial metadat, CESTES is a global database for metaCommunity Ecology: Species, Traits, Environment and Space. It compiles 80 datasets from trait-based studies. Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. CESTES presents a harmonized structure and covers a diversity of ecosystem types (marine, terrestrial, freshwater), taxonomic groups (plants, vertebrates, invertebrates...), geographical regions, and spatial scales. The CESTES database is a live database: it will be maintained and expanded in the future as new datasets become available (https://icestes.github.io/sharedata). A zipped folder called “CESTES.zip” includes two alternative formats for the CESTES database: - a “xCESTES” folder that includes 80 Excel files (one file per dataset), each named according to the following structure: “AuthorPublicationYear.xlsx” - a “rCESTES” folder that includes the CESTES core processed database (comm, traits, envir, coord matrices) as an R list object “CESTES.RData” plus two R scripts, and two metadata tables for data processing and exploration. This “CESTES.zip” folder also includes: - an extended metadata table, “CESTES_metadata.xlsx”, that provides the general metadata information of all the datasets, - a tutorial document, “HOW_TO_SHARE_MY_DATA_FOR_CESTES.pdf”, that explains how to share data for integrating future datasets in the database. A second zipped folder, called "ceste.zip", corresponds to the non-spatial ancillary to CESTES. We provide access to 10 additional datasets that were not completely suitable for the CESTES database, due to the absence of spatial information or insufficient metadata but that were potentially valuable for their three other data matrices (comm, traits, envir). They follow the same structure as CESTES, except that they do not present the “coord” sheet and sometimes include only partial metadat

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors performance. The SET was implemented in R and the code is available on the first authors website. (C) 2016 Elsevier B.V. All rights reserved.

The International Workshop and Conference, Particulate Matter: Research and Management – WeBIOPATR is a biennial event held in Serbia since 2007. The conference rationale stems from the fact that particulate matter is the air quality constituent that currently is responsible for most instances of non-compliance with air quality directives in Europe. Particulate matter, arising both from primary emissions and as a result of secondary formation in the atmosphere, is also one of the least well understood issues. [from Preface]

Exposure to increased concentrations of polycyclic aromatic hydrocarbons (PAHs) is associated with adverse health problems and specifically with carcinogenic and mutagenic effects. The major PAH sources outdoors are: stationary sources from industry (power plants, incineration, local industry) and domestic sources as the residential heating, burning and pyrolysis of coal, oil, gas, garbage, wood, or other organic substances mobile emissions (diesel and petrol engines), biomass burning and agricultural activities (e.g., open burning of brushwood, straw, stubble). The aim of this study was to assess potential differences in particle-bound PAH levels and source contribution between summer 2009 and 2012 sampling campaigns done at the same location in Belgrade urban area. The sampling location is considered representative for a mix of residential, business and industrial areas of New Belgrade, an urban area that has been under rapid development. The average concentrations of PM10 are slightly higher in summer 2012 than in 2009. PM-bound PAH follow the same trend as the PM indicating an increasing strength of PAH sources relative to all PM sources. Appling positive matrix factorization, three potential sources of PAHs in the atmosphere were distinguished: 1) stationary sources, 2) traffic (diesel and gasoline vehicle exhaust) and 3) local open burning sources (OBS). The analysis confirmed higher contribution of traffic and lower of OBS in summer 2012 than in 2009, reflecting higher traffic volumes and absence of or lower local OBS emissions due to burning wood, grass and domestic waste in 2012.