544 research outputs found

    Variations of China's emission estimates:Response to uncertainties in energy statistics

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    The accuracy of China's energy statistics is of great concern because it contributes greatly to the uncertainties in estimates of global emissions. This study attempts to improve the understanding of uncertainties in China's energy statistics and evaluate their impacts on China's emissions during the period of 1990-2013. We employed the Multi-resolution Emission Inventory for China (MEIC) model to calculate China's emissions based on different official data sets of energy statistics using the same emission factors. We found that the apparent uncertainties (maximum discrepancy) in China's energy consumption increased from 2004 to 2012, reaching a maximum of 646Mtce (million tons of coal equivalent) in 2011 and that coal dominated these uncertainties. The discrepancies between the national and provincial energy statistics were reduced after the three economic censuses conducted during this period, and converging uncertainties were found in 2013. The emissions calculated from the provincial energy statistics are generally higher than those calculated from the national energy statistics, and the apparent uncertainty ratio (the ratio of the maximum discrepancy to the mean value) owing to energy uncertainties in 2012 took values of 30.0, 16.4, 7.7, 9.2 and 15.6%, for SO2, NOx, VOC, PM2.5 and CO2 emissions, respectively. SO2 emissions are most sensitive to energy uncertainties because of the high contributions from industrial coal combustion. The calculated emission trends are also greatly affected by energy uncertainties - from 1996 to 2012, CO2 and NOx emissions, respectively, increased by 191 and 197% according to the provincial energy statistics but by only 145 and 139% as determined from the original national energy statistics. The energy-induced emission uncertainties for some species such as SO2 and NOx are comparable to total uncertainties of emissions as estimated by previous studies, indicating variations in energy consumption could be an important source of China's emission uncertainties

    NOx Emission Trends over Chinese Cities Estimated from OMI Observations During 2005 to 2015

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    Satellite NO2 observations have been widely used to evaluate emission changes. To determine trends in NOx emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations during 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52 from 2005 to 2011 and decreased by 21 from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r = 0.8 on average), but not for some cities (r = 0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies

    COVID-19 causes record decline in global CO2 emissions

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    The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

    Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020

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    Vehicles are among the most important contributors to global anthropogenic CO2 emissions. However, the lack of fuel-, vehicle-type-, and age-specific information about global on-road CO2 emissions in existing datasets, which are available only at the sector level, makes these datasets insufficient for supporting the establishment of emission mitigation strategies. Thus, a fleet turnover model is developed in this study, and CO2 emissions from global on-road vehicles from 1970 to 2020 are estimated for each country. Here, we analyze the evolution of the global vehicle stock over 50 years, identify the dominant emission contributors by vehicle and fuel type, and further characterize the age distribution of on-road CO2 emissions. We find that trucks accounted for less than 5 % of global vehicle ownership but represented more than 20 % of on-road CO2 emissions in 2020. The contribution of diesel vehicles to global on-road CO2 emissions doubled during the 1970–2020 period, driven by the shift in the fuel-type distribution of vehicle ownership. The proportion of CO2 emissions from vehicles in developing countries such as China and India in terms of global emissions from newly registered vehicles significantly increased after 2000, but global CO2 emissions from vehicles that had survived more than 15 years in 2020 still originated mainly from developed countries such as the United States and countries in the European Union. The data are publicly available at https://doi.org/10.6084/m9.figshare.24548008 (Yan et al., 2024).</p

    Strategies for controlling pollution from vehicular emissions in Beijing

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    This paper describes the severe situation of vehicular emission pollution in Beijing, and discusses the following mitigation strategies: improving fuel quality, controlling the exhaust from new vehicles, controlling the emissions from vehicles in use through, e.g., Inspection/Maintenance (I/M), renovating in-use vehicles and scrapping of old vehicles, and road infrastructure and traffic policies

    Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

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    Water-soluble organic aerosol (WSOA) in fine particles (PM2.5) collected during wintertime in a polluted city (Handan) in Northern China was characterized using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS). Through comparing with real-time measurements from a collocated Aerosol Chemical Speciation Monitor (ACSM), we determined that WSOA on average accounts for 29% of total organic aerosol (OA) mass and correlates tightly with secondary organic aerosol (SOA; Pearson's r = 0.95). The mass spectra of WSOA closely resemble those of ambient SOA, but also show obvious influences from coal combustion and biomass burning. Positive matrix factorization (PMF) analysis of the WSOA mass spectra resolved a water-soluble coal combustion OA (WS-CCOA; O/C = 0.17), a water-soluble biomass burning OA (WS-BBOA; O/C = 0.32), and a water-soluble oxygenated OA (WS-OOA; O/C = 0.89), which account for 10.3%, 29.3% and 60.4% of the total WSOA mass, respectively. The water-solubility of the OA factors was estimated by comparing the offline AMS analysis results with the ambient ACSM measurements. OOA has the highest water-solubility of 49%, consistent with increased hygroscopicity of oxidized organics induced by atmospheric aging processes. In contrast, CCOA is the leastwater soluble, containing 17% WS-CCOA. The distinct characteristics of WSOA from different sources extend our knowledge of the complex aerosol chemistry in the polluted atmosphere of Northern China and the water-solubility analysis may help us to understand better aerosol hygroscopicity and its effects on radiative forcing in this region. (C) 2020 Published by Elsevier B.V.Peer reviewe
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