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This paper proposes a convolutional neural network (CNN) method to estimate subsurface temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing measurements. These include sea surface temperature(SST), sea surface height (SSH), and sea surface salinity (SSS). We propose using the multisource sea surface parameters to establish a monthly CNN model to reconstruct the ocean subsurface temperature (ST) and use Argo data for accurate validation. The results show that the CNN can accurately estimate the ST of the Pacific Ocean by using the model. We trained the model for 12 months. The most prominent months are January, April, July, and October with average mean square error (MSE) values of 0.2659, 0.3129, 0.5318, and 0.5160, and the average coefficients of determination (R 2 ) were 0.968, 0.971, 0.949, and 0.967, respectively. This study improves the accuracy of ST estimation and the good results based on reanalysis indicate that the model is promising to be applied to satellite observations. INDEX TERMS Convolutional neural network, ocean data, satellite measurements, subsurface temperature.

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Ammonia (NH3) emissions, mainly from agricultural sources, generate substantial health damage due to the adverse effects on air quality. NH3 emission reduction strategies are still far from being effective. In particular, a growing trade network in this era of globalization offers untapped emission mitigation potential that has been overlooked. Here we show that about one-fourth of global agricultural NH3 emissions in 2012 are trade-related. Globally they induce 61 thousand PM2.5-related premature mortalities, with 25 thousand deaths associated with crop cultivation and 36 thousand deaths with livestock production. The trade-related health damage network is regionally integrated and can be characterized by three trading communities. Thus, effective cooperation within trade-dependent communities will achieve considerable NH3 emission reductions allowed by technological advancements and trade structure adjustments. Identification of regional communities from network analysis offers a new perspective on addressing NH3 emissions and is also applicable to agricultural greenhouse gas emissions mitigation.

Reliable inventory information is critical in informing emission mitigation efforts. Using the latest officially released emission data, which is production based, we take a consumption perspective to estimate the non‐CO2 greenhouse gas (GHG) emissions for China in 2012. The non‐CO2 GHG emissions, which cover CH4, N2O, HFCs, PFCs, and SF6, amounted to 2003.0 Mt. CO2‐eq (including 1871.9 Mt. CO2‐eq from economic activities), much larger than the total CO2 emissions in some developed countries. Urban consumption (30.1%), capital formation (28.2%), and exports (20.6%) derived approximately four fifths of the total embodied emissions in final demand. Furthermore, the results from structural path analysis help identify critical embodied emission paths and key economic sectors in supply chains for mitigating non‐CO2 GHG emissions in Chinese economic systems. The top 20 paths were responsible for half of the national total embodied emissions. Several industrial sectors such as Construction, Production and Supply of Electricity and Steam, Manufacture of Food and Tobacco and Manufacture of Chemicals, and Chemical Products played as the important transmission channels. Examining both production‐ and consumption‐based non‐CO2 GHG emissions will enrich our understanding of the influences of industrial positions, final consumption demands, and trades on national non‐CO2 GHG emissions by considering the comprehensive abatement potentials in the supply chains.

Global anthropogenic CH4 emissions have witnessed a rapid increase in the last decade. However, how this increase is connected with its socioeconomic drivers has not yet been explored. In this paper, we highlight the impacts of final demand and international trade on global anthropogenic CH4 emissions based on the consumption‐based accounting principle. We find that household consumption was the largest final demand category, followed by fixed capital formation and government consumption. The position and function of nations and major economies to act on the structure and spatial patterns of global CH4 emissions were systematically clarified. Substantial geographic shifts of CH4 emissions during 2000–2012 revealed the prominent impact of international trade. In 2012, about half of global CH4 emissions were embodied in international trade, of which 77.8% were from intermediate trade and 22.2% from final trade. Mainland China was the largest exporter of embodied CH4 emissions, while the United States was the largest importer. Developed economies such as Western Europe, the United States, and Japan were major net receivers of embodied emission transfer, mainly from developing countries. CH4 emission footprints of nations were closely related to their human development indexes and per capita gross domestic products. Our findings could help to improve current understanding of global anthropogenic CH4 emission increases and to pinpoint regional and sectoral hotspots for possible emission mitigation in the entire supply chains from production to consumption.

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