Surveys of rice sold in Canada for aflatoxins, ochratoxin A and fumonisins

Approximately 200 samples of rice (including white, brown, red, black, basmati and jasmine, as well as wild rice) from several different countries, including the United States, Canada, Pakistan, India and Thailand, were analysed for aflatoxins, ochratoxin A (OTA) and fumonisins by separate liquid Chromatographic methods in two different years. The mean concentrations for aflatoxin B1 (AFB1) were 0.19 and 0.17 ng g−1 with respective positive incidences of 56% and 43% (≥ the limit of detection (LOD) of 0.002 ng g−1). Twenty-three samples analysed in the second year also contained aflatoxin B2 (AFB2) at levels ≥LOD of 0.002 ng g−1 The five most contaminated samples in each year contained 1.44–7.14 ng AFB1 g−1 (year 1) and 1.45–3.48 ng AFB1 g−1 (year 2); they were mostly basmati rice from India and Pakistan and black and red rice from Thailand. The average concentrations of ochratoxin A (OTA) were 0.05 and 0.005 ng g−1 in year 1 and year 2, respectively; incidences of samples containing ≥LOD of 0.05 ng g−1 were 43% and 1%, respectively, in the 2 years. All positive OTA results were confirmed by LC-MS/MS. For fumonisins, concentrations of fumonisin B1 (FB1) averaged 4.5 ng g−1 in 15 positive samples (≥0.7 ng g−1) from year 1 (n = 99); fumonisin B2 (FB2) and fumonisin B3 (FB3) were also present (≥1 ng g−1). In the second year there was only one positive sample (14 ng g−1 FB1) out of 100 analysed. All positive FB1 results were confirmed by LC-MS/MS

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018)

A META-ANALYSIS OF ENVIRONMENTAL KUZNETS CURVE STUDIES

An understanding of the empirical relationship between income and environmental quality is evolving through recent studies investigating the Environmental Kuznets Curve (EKC). The EKC represents an inverted-U relationship between income and environmental degradation. However, studies may employ different methods, evaluate different environmental indicators, and use different data, resulting in a broad spectrum of findings and leading to sometimes conflicting interpretations. The purpose of this paper is to synthesize the results of existing EKC findings by conducting a statistical meta-analysis, and to predict new income turning points (ITP). Results indicate how both methodological choices and pollutant types affect ITPs

A seemingly unrelated Poisson model for revealed and stated preference data

A seemingly unrelated Poisson regression model is proposed as an alternative to single equation Poisson models for joint estimation of revealed preference (RP) and contingent behaviour (CB) trip demand. Findings indicate that RP and CB data should be estimated jointly.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha-1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426 - 571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country’s national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120% of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018)