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Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China

机译:量化中国人为大气污染物自下而上排放清单的不确定性

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The uncertainties of a national, bottom-up inventory of Chinese emissions of anthropogenic SO_2, NO_x, and particulate matter (PM) of different size classes and carbonaceous species are comprehensively quantified, for the first time, using Monte Carlo simulation. The inventory is structured by seven dominant sectors: coal-fired electric power, cement, iron and steel, other industry (boiler combustion), other industry (non-combustion processes), transportation, and residential. For each parameter related to emission factors or activity-level calculations, the uncertainties, represented as probability distributions, are either statistically fitted using results of domestic field tests or, when these are lacking, estimated based on foreign or other domestic data. The uncertainties (i.e., 95% confidence intervals around the central estimates) of Chinese emissions of SO_2, NO_x, total PM, PM_(10), PM_(2.5), black carbon (BC), and organic carbon (OC) in 2005 are estimated to be 14%~13%, 13%~37%, 11%~38%, 14%~45%, 17%~54%, 25%~136%, and 40%~121%, respectively. Variations at activity levels (e.g., energy consumption or industrial production) are not the main source of emission uncertainties. Due to narrow classification of source types, large sample sizes, and relatively high data quality, the coal-fired power sector is estimated to have the smallest emission uncertainties for all species except BC and OC. Due to poorer source classifications and a wider range of estimated emission factors, considerable uncertainties of NOx and PM emissions from cement production and boiler combustion in other industries are found. The probability distributions of emission factors for biomass burning, the largest source of BC and OC, are fitted based on very limited domestic field measurements, and special caution should thus be taken interpreting these emission uncertainties. Although Monte Carlo simulation yields narrowed estimates of uncertainties compared to previous bottom-up emission studies, the results are not always consistent with those derived from satellite observations. The results thus represent an incremental research advance; while the analysis provides current estimates of uncertainty to researchers investigating Chinese and global atmospheric transport and chemistry, it also identifies specific needs in data collection and analysis to improve on them. Strengthened quantification of emissions of the included species and other, closely associated ones notably CO_2, generated largely by the same processes and thus subject to many of the same parameter uncertainties is essential not only for science but for the design of policies to redress critical atmospheric environmental hazards at local, regional, and global scales.
机译:使用蒙特卡洛模拟法首次对中国人为排放的SO_2,NO_x和不同大小等级和含碳物质的颗粒物(PM)的排放量进行了全国性的自下而上的清点。清单由七个主要部门构成:燃煤电力,水泥,钢铁,其他行业(锅炉燃烧),其他行业(非燃烧过程),交通运输和住宅。对于与排放因子或活动水平计算相关的每个参数,不确定性(表示为概率分布)可以使用国内现场测试的结果进行统计拟合,或者在缺少这些不确定性时根据国外或其他国内数据进行估算。 2005年中国SO_2,NO_x,总PM,PM_(10),PM_(2.5),黑碳(BC)和有机碳(OC)排放的不确定性(即中心估计的95%置信区间)为估计分别为14%〜13%,13%〜37%,11%〜38%,14%〜45%,17%〜54%,25%〜136%和40%〜121%。活动水平的变化(例如,能源消耗或工业生产)不是排放不确定性的主要来源。由于源类型的分类狭窄,样本量大且数据质量相对较高,据估计,除BC和OC外,燃煤发电部门的所有排放不确定性均最小。由于较差的来源分类和更广泛的估计排放因子,在其他行业中,水泥生产和锅炉燃烧产生的NOx和PM排放存在很大的不确定性。基于非常有限的国内实地测量,拟合了生物质燃烧(BC和OC的最大来源)的排放因子的概率分布,因此在解释这些排放不确定性时应格外谨慎。尽管与以前的自下而上的排放研究相比,蒙特卡洛模拟得出的不确定性的估计范围有所缩小,但结果并不总是与从卫星观测中得出的结果一致。因此,结果代表了渐进的研究进展。该分析为研究中国和全球大气传输和化学的研究人员提供了当前的不确定性估计,同时也确定了数据收集和分析中的特殊需求以改进这些需求。加强对包括物种和其他与之密切相关的物种,尤其是CO_2的排放的量化,这些排放主要由相同的过程产生,因此受到许多相同的参数不确定性的影响,这不仅对科学而且对于设计纠正关键性大气环境的政策都是至关重要的在地方,区域和全球范围内的危害。

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