A large data set including surface, aircraft, and laboratory observations of the atomic oxygen-to-carbon (O:C) and hydrogen-to-carbon (H:C) ratios of organic aerosol (OA) is synthesized and corrected using a recently reported method. The whole data set indicates a wide range of OA oxidation and a trajectory in the Van Krevelen diagram, characterized by a slope of -0.6, with variation across campaigns. We show that laboratory OA including both source and aged types explains some of the key differences in OA observed across different environments. However, the laboratory data typically fall below the mean line defined by ambient observations, and little laboratory data extend to the highest O:C ratios commonly observed in remote conditions. OA having both high O:C and high H:C are required to bridge the gaps. Aqueous-phase oxidation may produce such OA, but experiments under realistic ambient conditions are needed to constrain the relative importance of this pathway.
Elemental compositions of organic aerosol (OA) particles provide useful constraints on OA sources, chemical evolution, and effects. The Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) is widely used to measure OA elemental composition. This study evaluates AMS measurements of atomic oxygen-to-carbon (O : C), hydrogen-to-carbon (H : C), and organic mass-to-organic carbon (OM : OC) ratios, and of carbon oxidation state ((OS) over bar (C))for a vastly expanded laboratory data set of multifunctional oxidized OA standards. For the expanded standard data set, the method introduced by Aiken et al. (2008), which uses experimentally measured ion intensities at all ions to determine elemental ratios (referred to here as "Aiken-Explicit"), reproduces known O: C and H: C ratio values within 20% (average absolute value of relative errors) and 12%, respectively. The more commonly used method, which uses empirically estimated H2O+ and CO+ ion intensities to avoid gas phase air interferences at these ions (referred to here as "Aiken-Ambient"), reproduces O: C and H: C of multifunctional oxidized species within 28 and 14% of known values. The values from the latter method are systematically biased low, however, with larger biases observed for alcohols and simple diacids. A detailed examination of the H2O+, CO+, and CO2+ fragments in the high-resolution mass spectra of the standard compounds indicates that the Aiken-Ambient method underestimates the CO C and especially H2O+ produced from many oxidized species. Combined AMS-vacuum ultraviolet (VUV) ionization measurements indicate that these ions are produced by dehydration and decarboxylation on the AMS vaporizer (usually operated at 600 degrees C). Thermal decomposition is observed to be efficient at vaporizer temperatures down to 200 degrees C. These results are used together to develop an "Improved-Ambient" elemental analysis method for AMS spectra measured in air. The Improved-Ambient method uses specific ion fragments as markers to correct for molecular functionality-dependent systematic biases and reproduces known O : C (H : C) ratios of individual oxidized standards within 28% (13 %) of the known molecular values. The error in Improved-Ambient O : C (H : C) values is smaller for theoretical standard mixtures of the oxidized organic standards, which are more representative of the complex mix of species present in ambient OA. For ambient OA, the Improved-Ambient method produces O : C (H : C) values that are 27% (11 %) larger than previously published Aiken-Ambient values; a corresponding increase of 9% is observed for OM : OC values. These results imply that ambient OA has a higher relative oxygen content than previously estimated. The (OS) over bar (C) values calculated for ambient OA by the two methods agree well, however (average relative difference of 0.06 (OS) over bar (C) units). This indicates that (OS) over bar (C) is a more robust metric of oxidation than O : C, likely since (OS) over bar (C) is not affected by hydration or dehydration, either in the atmosphere or during analysis.
Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)each with multiple Total Maximum Daily Loads (TMDL) targetswere selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of \$67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of \$67.7 millionmarginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.
The brain site of perceptual learning has been frequently debated. Recent psychophysical evidence for complete learning transfer to new retinal locations and orientations/directions suggests that perceptual learning may mainly occur in high-level brain areas. Contradictorily, ERP C1 changes associated with perceptual learning are cited as evidence for training-induced plasticity in the early visual cortex. However, C1 can be top-down modulated, which suggests the possibility that C1 changes may result from top-down modulation of the early visual cortex by high-level perceptual learning. To single out the potential top-down impact, we trained observers with a peripheral orientation discrimination task and measured C1 changes at an untrained diagonal quadrant location where learning transfer was previously known to be significant. Our assumption was that any C1 changes at this untrained location would indicate top-down modulation of the early visual cortex, rather than plasticity in the early visual cortex. The expected learning transfer was indeed accompanied with significant C1 changes. Moreover, C1 changes were absent in an untrained shape discrimination task with the same stimuli. We conclude that ERP C1 can be top-down modulated in a task-specific manner by high-level perceptual learning, so that C1 changes may not necessarily indicate plasticity in the early visual cortex. Moreover, learning transfer and associated C1 changes may indicate that learning-based top-down modulation can be remapped to early visual cortical neurons at untrained locations to enable learning transfer.
The environmental degradation of lakes in China has become increasingly serious over the last 30 years and eutrophication resulting from enhanced nutrient inputs is considered a top threat. In this study, a quasi-mass balance method, net anthropogenic N inputs (NANI), was introduced to assess the human influence on N input into three typical Chinese lake basins. The resultant NANI exceeded 10 000 kg N km(-2) year(-1) for all three basins, and mineral fertilizers were generally the largest sources. However, rapid urbanization and shrinking agricultural production capability may significantly increase N inputs from food and feed imports. Higher percentages of NANI were observed to be exported at urban river outlets, suggesting the acceleration of NANI transfer to rivers by urbanization. Over the last decade, the N inputs have declined in the basins dominated by the fertilizer use but have increased in the basins dominated by the food and feed import. In the foreseeable future, urban areas may arise as new hotspots for nitrogen in China while fertilizer use may decline in importance in areas of high population density.