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Short-, medium-, and long-chain chlorinated paraffins (SCCPs, MCCPs, and LCCPs) were analyzed in human milk from the Yangtze River Delta (YRD) and Scandinavia. Individual samples were collected from Shanghai, Jiaxing, and Shaoxing (China), Stockholm (Sweden), and Bodø (Norway) between 2010 and 2016. Mean concentrations (range) of SCCPs, MCCPs, and LCCPs in samples from the YRD were 124 [

Per- and polyfluoroalkyl substances (PFAS) are found ubiquitously in wastewater treatment plants (WWTPs) due to their multiple sources in industry and consumer products. In Australia, limited spatial data are available on PFAS levels in WWTPs influent, while no temporal data have been reported. The aim of this study was to investigate the occurrence and temporal trend of PFAS in the influent of two large WWTPs in Australia (WWTP A and B) over a four-year period. Daily influent samples were collected over one week at different seasons from 2014 to 2017. Eleven perfluoroalkyl acids (PFAA) (i.e. seven perfluoroalkyl carboxylic acids (PFCAs) and four perfluoroalkyl sulfonic acids (PFSA)) were detected with mean Σ11PFAA concentrations of 57 ± 3.3–94 ± 17 ng/L at WWTP A, and 31 ± 6.1–142 ± 73 ng/L at WWTP B. The highest mean concentrations were observed for perfluorohexanoate (PFHxA) (20 ± 2 ng/L) in WWTP A, and perfluorooctane sulfonate (PFOS) (17 ± 13 ng/L) in WWTP B. The precursor 6:2 fluorotelomer sulfonate was detected over five sampling periods from Aug 2016 to Oct 2017, with mean concentrations of 37 ± 18–138 ± 51 ng/L for WWTP A and 8.8 ± 4.5–29 ± 5.1 ng/L for WWTP B. Higher concentration of 6:2 FTS (1.8–11 folds) than those of PFOA and PFOS in WWTP A indicate a likely substitution of C8 PFAA by fluorotelomer-based PFAS in this catchment. Temporal trends (annual and seasonal) in per-capita mass load were observed for some PFAA, increasing for PFPeA, PFHxA, PFHpA, PFNA, and PFHxS, while decreasing for PFBS and PFOS in either WWTPs. Notably, elevated levels of PFOS in October 2017 were observed at both WWTPs with the highest per capita mass load of up to 67 μg/day/inhabitant. For some PFAS release trends, longer sampling periods would be required to achieve acceptable statistical power.

Nondestructive sample cleanup and comprehensive two-dimensional gas chromatography (GC×GC) high-resolution mass spectrometry (HRMS) analysis generated a massive amount of data that could be used for nontarget screening purposes. We present a data reduction and prioritization strategy that involves time-trend analysis of nontarget data. Sewage sludge collected between 2005 and 2015 in Stockholm (Sweden) was retrieved from an environmental specimen bank, extracted, and analyzed by GC×GC-HRMS. After data alignment features with high blank levels, artifacts and low detection frequency were removed. Features that appeared in four to six out of ten years were reprocessed to fill in gaps. The total number of compounds was reduced by more than 97% from almost 60 000 to almost 1500. The remaining compounds were analyzed for monotonic (log–linear) and nonmonotonic (smoother) time trends. In total, 192 compounds with log–linear trends and 120 compounds with nonmonotonic trends were obtained, respectively. Most compounds described by a log–linear trend exhibited decreasing trends and were traffic-related. Compounds with increasing trends included UV-filters, alkyl-phenols, and flavor and fragrances, which often could be linked to trade statistics. We have shown that nontarget screening and stepwise reduction of data provides a simple way of revealing significant changes in emissions of chemicals in society.

Since the 1980s, lead levels decreased significantly in most marine biota from the Baltic Sea, the Kattegatt and the Skagerrack and in terrestrial biota from south and central Sweden, analysed in the National Swedish Environmental Monitoring Programme. In herring and cod liver, the decrease was 4.2–7.1 annually 1981–2003 and in perch liver and guillemot eggs analysed since the middle of the 1990s the decrease was 10–13. In kidneys of young starlings from south and central Sweden, lead levels decreased 6.2–12 annually and the annual decrease in liver and kidney from young moose was 8.8 and 6.5, respectively. In northern Sweden, lead levels decreased in pike liver by 2.8 between 1969 and 1994 and in reindeer liver by 3.5 annually while a significant increase 5 annually 1981–1994 has been detected in arctic char. In some marine biota, there were indications of a larger decrease during the last ten years compared to the whole period. The large decline in lead emission to the atmosphere during the last twenty years, mainly because of decreasing use of leaded gasoline has obviously resulted in decreasing lead levels in biota.

The goal of this paper is to describe and discuss statistical power with respect to mercury in Arctic biota, using data gathered during the past two or three decades, mostly under the auspices of AMAP Phases I and II. It will describe the current levels of power of existing data sets to detect temporal trends of Hg concentrations. If the desired power is fixed to an appropriate magnitude, the minimum size of a detectable trend within a specified time period or the number of years that is required to detect a certain trend could be estimated provided that the random between-year variation for the current time-series is known. These various measures of performance of the AMAP mercury time-series, derived from the power analysis, are discussed in some detail. The number of years required to detect a certain trend at a particular power at a specific Type I error rate α is compared with the actual number of years available when the AMAP Phase II assessment was carried out. In general the investigated time-series were too short to possess an acceptable statistical power. The effect of varyingthe Type-I error rate, the slope of a trend and the desired power is investigated to rank the importance of the various components regulating the statistical power. The consequence of sampling less frequently than once a year is considerable loss of power.

The goal of this paper is to describe and discuss statistical power with respect to mercury in Arctic biota, using data gathered during the past two or three decades, mostly under the auspices of AMAP Phases I and II. It will describe the current levels of power of existing data sets to detect temporal trends of Hg concentrations. If the desired power is fixed to an appropriate magnitude, the minimum size of a detectable trend within a specified time period or the number of years that is required to detect a certain trend could be estimated provided that the random between-year variation for the current time-series is known. These various measures of performance of the AMAP mercury time-series, derived from the power analysis, are discussed in some detail. The number of years required to detect a certain trend at a particular power at a specific Type I error rate (α) is compared with the actual number of years available when the AMAP Phase II assessment was carried out. In general the investigated time-series were too short to possess an acceptable statistical power. The effect of varying the Type-I error rate, the slope of a trend and the desired power is investigated to rank the importance of the various components regulating the statistical power. The consequence of sampling less frequently than once a year is considerable loss of power.

Available measurements of organochlorine concentrations in some biological matrices from the Baltic Sea stress the need for proper sampling and knowledge about biological matrices. To properly evaluate any analyte in biota, it is imperative to select and scrutinise the representativeness of the matrix and to select a relevant matrix. This article describes the importance of biological parameters in explaining the variation in concentrations of contaminants found in field-collected material. To disclose spatial and temporal variation in contaminant concentrations, annual analyses are required. Studies based on pooled samples are generally not recommended. The unexplained between-year variation found even in rather well-defined biological samples often implies that time trends showing changes of less than 5–10% are impossible to detect in experiments shorter than 10 years. Examples demonstrate the effect of this variation when interpreting temporal trends and spatial distribution. Normally most of this variation is due to biological factors. Any attempt to define spatial or temporal trends or differences between various ecological matrices with respect to contaminant concentrations requires a large number of chemical analyses, which often are very expensive. The development of methods for simplified chemical analysis would thus permit more individual analyses which will improve the utilisation of available resources.