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An important new book on medical imaging, explaining the physics and engineering principles behind two of today's major functional imaging methods-PET and SPECT.

Despite the emergence of regional climate policies, growth in global CO₂ emissions has remained strong. From 1990 to 2008 CO₂ emissions in developed countries (defined as countries with emission-reduction commitments in the Kyoto Protocol, Annex B) have stabilized, but emissions in developing countries (non-Annex B) have doubled. Some studies suggest that the stabilization of emissions in developed countries was partially because of growing imports from developing countries. To quantify the growth in emission transfers via international trade, we developed a trade-linked global database for CO₂ emissions covering 113 countries and 57 economic sectors from 1990 to 2008. We find that the emissions from the production of traded goods and services have increased from 4.3 Gt CO₂ in 1990 (20% of global emissions) to 7.8 Gt CO₂ in 2008 (26%). Most developed countries have increased their consumption-based emissions faster than their territorial emissions, and non-energy-intensive manufacturing had a key role in the emission transfers. The net emission transfers via international trade from developing to developed countries increased from 0.4 Gt CO₂ in 1990 to 1.6 Gt CO₂ in 2008, which exceeds the Kyoto Protocol emission reductions. Our results indicate that international trade is a significant factor in explaining the change in emissions in many countries, from both a production and consumption perspective. We suggest that countries monitor emission transfers via international trade, in addition to territorial emissions, to ensure progress toward stabilization of global greenhouse gas emissions.

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population–weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3–70.0) to 42.0 μg/m³ (95% CI: 35.7–48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9–7.1), 4.4- (95% CI: 3.8–4.9), 2.8- (95% CI: 2.5–3.0), and 2.2- (95% CI: 2.0–2.5) μg/m³ declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35–0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China’s recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

The EDGARv4.3.1 (Emissions Database for Global Atmospheric Research) global anthropogenic emissions inventory of gaseous (SO2, NOx, CO, non-methane volatile organic compounds and NH3) and particulate (PM10, PM2.5, black and organic carbon) air pollutants for the period 1970–2010 is used to develop retrospective air pollution emissions scenarios to quantify the roles and contributions of changes in energy consumption and efficiency, technology progress and end-of-pipe emission reduction measures and their resulting impact on health and crop yields at European and global scale. The reference EDGARv4.3.1 emissions include observed and reported changes in activity data, fuel consumption and air pollution abatement technologies over the past 4 decades, combined with Tier 1 and region-specific Tier 2 emission factors. Two further retrospective scenarios assess the interplay of policy and industry. The highest emission STAG_TECH scenario assesses the impact of the technology and end-of-pipe reduction measures in the European Union, by considering historical fuel consumption, along with a stagnation of technology with constant emission factors since 1970, and assuming no further abatement measures and improvement imposed by European emission standards. The lowest emission STAG_ENERGY scenario evaluates the impact of increased fuel consumption by considering unchanged energy consumption since the year 1970, but assuming the technological development, end-of-pipe reductions, fuel mix and energy efficiency of 2010. Our scenario analysis focuses on the three most important and most regulated sectors (power generation, manufacturing industry and road transport), which are subject to multi-pollutant European Union Air Quality regulations. Stagnation of technology and air pollution reduction measures at 1970 levels would have led to 129 % (or factor 2.3) higher SO2, 71 % higher NOx and 69 % higher PM2.5 emissions in Europe (EU27), demonstrating the large role that technology has played in reducing emissions in 2010. However, stagnation of energy consumption at 1970 levels, but with 2010 fuel mix and energy efficiency, and assuming current (year 2010) technology and emission control standards, would have lowered today's NOx emissions by ca. 38 %, SO2 by 50 % and PM2.5 by 12 % in Europe. A reduced-form chemical transport model is applied to calculate regional and global levels of aerosol and ozone concentrations and to assess the associated impact of air quality improvements on human health and crop yield loss, showing substantial impacts of EU technologies and standards inside as well as outside Europe. We assess that the interplay of policy and technological advance in Europe had substantial benefits in Europe, but also led to an important improvement of particulate matter air quality in other parts of the world.

China is the largest cement producer and consumer in the world. Cement manufacturing is highly energy-intensive and is one of the major contributors to carbon dioxide (CO2) and air pollutant emissions, which threatens climate mitigation and air quality improvement. In this study, we investigated the decadal changes in carbon dioxide and air pollutant emissions for the period of 1990–2015 based on intensive unit-based information on activity rates, production capacity, operation status, and control technologies which improved the accuracy of the cement emissions in China. We found that, from 1990 to 2015, accompanied by a 10.3-fold increase in cement production, CO2, SO2, and NOx emissions from China's cement industry increased by 627 %, 56 %, and 659 %, whereas CO, PM2.5, and PM10 emissions decreased by 9 %, 63 %, and 59 %, respectively. In the 1990s, driven by the rapid growth of cement production, CO2 and air pollutant emissions increased constantly. Then, the technological innovation in production of replacing traditional shaft kilns with the new precalciner kilns equipped with high-efficiency control facilities in the 2000s markedly reduced SO2, CO, and PM emissions in the cement industry. In 2010, nationwide, 39 % and 31 % of the nationwide PM2.5 and NOx emission were produced by 3 % and 15 % of the total capacity of the production lines, indicating the disproportionately high emissions from a small number of the super-polluting units. Since 2010, the growing trend of emissions has been further curbed by a combination of measures, including promoting large-scale precalciner production lines and phasing out small ones, upgrading emission standards, installing low NOx burners (LNB), and selective non-catalytic reduction (SNCR) to reduce NOx emissions, as well as adopting more advanced particulate matter control technologies. Our study highlights the effectiveness of advanced technologies on air pollutant emission control; however, CO2 emissions from China's cement industry kept growing throughout the period, posing challenges to future carbon emission mitigation in China.

This paper, which focuses on emissions from China's coal-fired power plants during 1990–2010, is the second in a series of papers that aims to develop a high-resolution emission inventory for China. This is the first time that emissions from China's coal-fired power plants were estimated at unit level for a 20-year period. This inventory is constructed from a unit-based database compiled in this study, named the China coal-fired Power plant Emissions Database (CPED), which includes detailed information on the technologies, activity data, operation situation, emission factors, and locations of individual units and supplements with aggregated data where unit-based information is not available. Between 1990 and 2010, compared to a 479 % growth in coal consumption, emissions from China's coal-fired power plants increased by 56, 335, and 442 % for SO2, NOx, and CO2, respectively, and decreased by 23 and 27 % for PM2.5 and PM10 respectively. Driven by the accelerated economic growth, large power plants were constructed throughout the country after 2000, resulting in a dramatic growth in emissions. The growth trend of emissions has been effectively curbed since 2005 due to strengthened emission control measures including the installation of flue gas desulfurization (FGD) systems and the optimization of the generation fleet mix by promoting large units and decommissioning small ones. Compared to previous emission inventories, CPED significantly improved the spatial resolution and temporal profile of the power plant emission inventory in China by extensive use of underlying data at unit level. The new inventory developed in this study will enable a close examination of temporal and spatial variations of power plant emissions in China and will help to improve the performances of chemical transport models by providing more accurate emission data.

Severe surface ozone pollution over major Chinese cities has become an emerging air quality concern, raising a new challenge for emission control measures in China. In this study, we explore the source contributions to surface daily maximum 8 h average (MDA8) ozone over China in 2016 and 2017, the 2 years with the highest surface ozone averaged over Chinese cities in record. We estimate the contributions of anthropogenic, background, and individual natural sources to surface ozone over China using the GEOS-Chem chemical transport model at 0.25∘×0.3125∘ horizontal resolution with the most up-to-date Chinese anthropogenic emission inventory. Model results are evaluated with concurrent surface ozone measurements at 169 cities over China and show generally good agreement. We find that background ozone (defined as ozone that would be present in the absence of all Chinese anthropogenic emissions) accounts for 90 % (49.4 ppbv) of the national March–April mean surface MDA8 ozone over China and 80 % (44.5 ppbv) for May–August. It includes large contributions from natural sources (80 % in March–April and 72 % in May–August). Among them, biogenic volatile organic compound (BVOC) emissions enhance MDA8 ozone by more than 15 ppbv in eastern China during July–August, while lightning NOx emissions and ozone transport from the stratosphere both lead to ozone enhancements of over 20 ppbv in western China during March–April. Over major Chinese city clusters, domestic anthropogenic sources account for about 30 % of the May–August mean surface MDA8 ozone and reach 39–73 ppbv (38 %–69 %) for days with simulated MDA8 ozone > 100 ppbv in the North China Plain, Fenwei Plain, Yangtze River Delta, and Pearl River Delta city clusters. These high ozone episodes are usually associated with high temperatures, which induce large BVOC emissions and enhance ozone chemical production. Our results indicate that there would be no days with MDA8 ozone > 80 ppbv in these major Chinese cities in the absence of domestic anthropogenic emissions. We find that the 2017 ozone increases relative to 2016 are largely due to higher background ozone driven by hotter and drier weather conditions, while changes in domestic anthropogenic emissions alone would have led to ozone decreases in 2017. Meteorological conditions in 2017 favor natural source contributions (particularly soil NOx and BVOC ozone enhancements) and ozone chemical production, increase the thermal decomposition of peroxyacetyl nitrate (PAN), and further decrease ozone dry deposition velocity. More stringent emission control measures are thus required to offset the adverse effects of unfavorable meteorology, such as high temperature, on surface ozone air quality.

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.

PurposeImage quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsupervised deep learning, where no training pairs are needed.MethodsIn this method, the prior high-quality image from the patient was employed as the network input and the noisy PET image itself was treated as the training label. Constrained by the network structure and the prior image input, the network was trained to learn the intrinsic structure information from the noisy image and output a restored PET image. To validate the performance of the proposed method, a computer simulation study based on the BrainWeb phantom was first performed. A 68Ga-PRGD2 PET/CT dataset containing 10 patients and a 18F-FDG PET/MR dataset containing 30 patients were later on used for clinical data evaluation. The Gaussian, non-local mean (NLM) using CT/MR image as priors, BM4D, and Deep Decoder methods were included as reference methods. The contrast-to-noise ratio (CNR) improvements were used to rank different methods based on Wilcoxon signed-rank test.ResultsFor the simulation study, contrast recovery coefficient (CRC) vs. standard deviation (STD) curves showed that the proposed method achieved the best performance regarding the bias-variance tradeoff. For the clinical PET/CT dataset, the proposed method achieved the highest CNR improvement ratio (53.35% ± 21.78%), compared with the Gaussian (12.64% ± 6.15%, P = 0.002), NLM guided by CT (24.35% ± 16.30%, P = 0.002), BM4D (38.31% ± 20.26%, P = 0.002), and Deep Decoder (41.67% ± 22.28%, P = 0.002) methods. For the clinical PET/MR dataset, the CNR improvement ratio of the proposed method achieved 46.80% ± 25.23%, higher than the Gaussian (18.16% ± 10.02%, P < 0.0001), NLM guided by MR (25.36% ± 19.48%, P < 0.0001), BM4D (37.02% ± 21.38%, P < 0.0001), and Deep Decoder (30.03% ± 20.64%, P < 0.0001) methods. Restored images for all the datasets demonstrate that the proposed method can effectively smooth out the noise while recovering image details.ConclusionThe proposed unsupervised deep learning framework provides excellent image restoration effects, outperforming the Gaussian, NLM methods, BM4D, and Deep Decoder methods.

Energy and emission data are crucial to climate change research and mitigation efforts. The accuracy of energy statistics is essential for mitigation strategies and evaluating the performance of low carbon energy transition efforts. This study provides the most up‐to‐date emission inventories for China and its provinces for 2018 and 2019. We also update the carbon dioxide (CO2) emission inventories of China and 30 provinces since 2012 based on the newly revised energy statistics. The inventories are compiled in a combined accounting approach of scope 1 (Intergovernmental Panel on Climate Change territorial emissions from 17 types of fossil fuel combustion and cement production by 47 socioeconomic sectors) and scope 2 (emissions from purchased electricity and heat consumption). The most recent energy revision led to an increase in reported national CO2 emissions by an average of 0.3% from 2014 to 2017. The results show that data revisions raised China's carbon intensity mitigation baseline (in 2005) by 5.1%–10.8% and thus made it more challenging to fulfill the mitigation pledges. However, the 2020 carbon intensity mitigation target was achieved ahead of schedule in 2018. A preliminary estimate of China's national emissions for 2020 shows that the COVID‐19 pandemic and lockdown was not able to offset China's annual increase in CO2 emissions. These emissions inventories provide an improved evidence base for China's policies toward net‐zero emissions. Key Points The most up‐to‐date emission inventories for China and its provinces A combined accounting approach of scope 1 and 2 emissions China's 2020 carbon intensity mitigation target was achieved ahead of schedule