An automated system for continuous measurement of N₂O fluxes on an hourly basis was employed to study N₂O emissions in an intensively managed low carbon calcareous soil under sub-humid temperate monsoon conditions. N₂O emissions occurred mainly within two weeks of application of NH₄(+) based fertilizer and total N₂O emissions in wheat (average 0.35 or 0.21 kg N ha⁻¹ season⁻¹) and maize (average 1.47 or 0.49 kg N ha⁻¹ season⁻¹) under conventional and optimum N fertilization (300 and 50-122 kg N ha⁻¹, respectively) were lower than previously reported from low frequency measurements. Results from closed static chamber showed that N₂O was produced mainly from nitrification of NH₄(+)-based fertilizer, with little denitrification occurring due to limited readily oxidizable carbon and low soil moisture despite consistently high soil nitrate-N concentrations. Significant reductions in N₂O emissions can be achieved by optimizing fertilizer N rates, using nitrification inhibitors, or changing from NH₄(+)- to NO₃(-)-based fertilizers.

Nitrogen (N) and phosphorus (P) use and losses in China's food chain have accelerated in the past three decades, driven by population growth, rapid urbanization, dietary transition, and changing nutrient management practice. There has been little detailed quantitative analysis of the relative magnitude of these driving forces throughout this period. Therefore, we analyzed changes in N and P flows and key drivers behind changes in the food (production and consumption) chain at the national scale from 1980 to 2010. Food (N and P) consumption increased by about fivefold in urban settings over this period but has decreased in rural settings since the 1990s. For urban settings, the integrated driving forces for increased food consumption were population growth, which accounted for ∼60%, and changing urban diets toward a greater emphasis on the consumption of animal products. Nutrient inputs and losses in crop and animal productions have continuously increased from 1980 to 2010, but the rates of decadal increase were greatly different. Increased total inputs and losses in crop production were primarily driven by increased crop production for food demand (68-96%) in the 1980s but were likely offset in the 2000s by improved nutrient management practices, as evidenced by decreased total inputs to and losses from cropland for harvesting per nutrient in crop. The contributions of animal production to total N and P losses to waters from the food chain increased by 34 and 60% from 1980 to 2010. These increases were caused mainly by decreased ratios of manure returned to cropland. Our study highlights a larger impact of changing nutrient management practice than population growth on elevated nutrient flows in China's food chain.

Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A "progressive" layout is the next best: one sensor is positioned to "see" only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.

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