Bin Wang

Global atmospheric emissions of 16 polycyclic aromatic hydrocarbons (PAHs) from 69 major sources were estimated for a period from 1960 to 2030. Regression models and a technology split method were used to estimate country and time specific emission factors, resulting in a new estimate of PAH emission factor variation among different countries and over time. PAH emissions in 2007 were spatially resolved to 0.1° × 0.1° grids based on a newly developed global high-resolution fuel combustion inventory (PKU-FUEL-2007). The global total annual atmospheric emission of 16 PAHs in 2007 was 504 Gg (331-818 Gg, as interquartile range), with residential/commercial biomass burning (60.5%), open-field biomass burning (agricultural waste burning, deforestation, and wildfire, 13.6%), and petroleum consumption by on-road motor vehicles (12.8%) as the major sources. South (87 Gg), East (111 Gg), and Southeast Asia (52 Gg) were the regions with the highest PAH emission densities, contributing half of the global total PAH emissions. Among the global total PAH emissions, 6.19% of the emissions were in the form of high molecular weight carcinogenic compounds and the percentage of the carcinogenic PAHs was higher in developing countries (6.22%) than in developed countries (5.73%), due to the differences in energy structures and the disparities of technology. The potential health impact of the PAH emissions was greatest in the parts of the world with high anthropogenic PAH emissions, because of the overlap of the high emissions and high population densities. Global total PAH emissions peaked at 592 Gg in 1995 and declined gradually to 499 Gg in 2008. Total PAH emissions from developed countries peaked at 122 Gg in the early 1970s and decreased to 38 Gg in 2008. Simulation of PAH emissions from 2009 to 2030 revealed that PAH emissions in developed and developing countries would decrease by 46-71% and 48-64%, respectively, based on the six IPCC SRES scenarios.

Abstract The development of active and durable bifunctional electrocatalysts for overall water splitting is mandatory for renewable energy conversion. This study reports a general method for controllable synthesis of a class of IrM (M = Co, Ni, CoNi) multimetallic porous hollow nanocrystals (PHNCs), through etching Ir‐based, multimetallic, solid nanocrystals using Fe 3+ ions, as catalysts for boosting overall water splitting. The Ir‐based multimetallic PHNCs show transition‐metal‐dependent bifunctional electrocatalytic activities for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in acidic electrolyte, with IrCo and IrCoNi PHNCs being the best for HER and OER, respectively. First‐principles calculations reveal a ligand effect, induced by alloying Ir with 3d transition metals, can weaken the adsorption energy of oxygen intermediates, which is the key to realizing much‐enhanced OER activity. The IrCoNi PHNCs are highly efficient in overall‐water‐splitting catalysis by showing a low cell voltage of only 1.56 V at a current density of 2 mA cm −2 , and only 8 mV of polarization‐curve shift after a 1000‐cycle durability test in 0.5 m H 2 SO 4 solution. This work highlights a potentially powerful strategy toward the general synthesis of novel, multimetallic, PHNCs as highly active and durable bifunctional electrocatalysts for high‐performance electrochemical overall‐water‐splitting devices.

The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.