首都国际机场移动源大气污染物排放清单研究

根据收集到的首都国际机场飞行区活动水平数据,采用适合估算各类移动源污染物排放量的方法和排放因子,建立了2013年首都国际机场移动源排放清单。结果表明,首都国际机场2013年移动源NO_x、CO、HC、SO2和PM_(2.5)排放总量为6 287.1 t、3 596.1 t、364.2t、373.4 t和185.0 t,分别占北京市各污染物总体排放的3.4%、0.3%、0.1%、0.4%和0.2%。其中非道路移动源是各污染物排放的最大贡献源,NO_x、CO、HC、SO2和PM_(2.5)排放量的90.7%、86.7%、79.4%、97.4%和81.3%来源于飞机,中型窄体客机及大型宽体客机贡献突出。相较而言,道路移动源排放比例较低,对HC、CO、PM_(2.5)和NO_x各污染物的贡献率为9.1%、8.6%、6.7%和4.4%。通过标准LTO循环方法估算飞机逐月排放,对LTO循环次数与各污染物排放量进行拟合,发现飞机排放的HC、CO、NO_x、SO2和LTO循环次数之间呈现较为明显的正相关关系,从而提出一种本地化的基于LTO循环次数估算飞机污染气体排放量的简单方法。此外,减少滑行时间可有效降低飞机在LTO循环过程中的污染物排放。     

基于AIS数据的长江口船舶排放清单研究

为了研究繁忙水域的船舶排放清单,基于船舶自识别系统(Automatic Identification System,AIS)的数据建立了针对不同船型的船舶排放计算模型。先根据AIS数据中包含的船舶尺度数据计算各类船型的发动机功率,然后运用基于AIS数据的模型计算船舶排放清单及排放分担率,最后对船舶排放的空间分布进行分析。以2010年长江口水域的船舶交通流数据为基础,计算该水域的船舶排放清单,结果表明:1)在各类船舶废气排放物中,CO2排放量最多,NOx和SOx次之,N2O最少,结果合理可信;2)各类船型的排放分担率分别为5.36%(客船)、6.59%(散货船)、51.47%(集装箱船)、15.95%(油船)、5.37%(渔船)、15.27%(其他船型);3)船舶排放聚集区主要是长江口的南、北槽航段及其附近的码头水域。     

Air quality impact of carbon monoxide emission from diesel engine electric power generators

An emissions inventory and the AERMOD View dispersion model were used to estimate the concentrations and the potential effects of carbon monoxide (CO) from diesel engine electric power generators operated by and providing electricity to a textile factory in Nigeria on its host air shed. The CO emissions from simultaneous operations of all of the electric power generators in the factory resulted in: 1‐hr average CO emissions of 4.2 to 54.5 micrograms per cubic meters (μg/m³) and 24‐hr average CO emissions of 0.3 to 20.9 μg/m³. The estimated 1‐hr averaging period maximum ground‐level concentrations of CO were deposited within the factory, while the 24‐hr maximum ground‐level concentrations are estimated at a distance 90 meters (m) from the factory in a southeast direction. The ground‐level concentrations of CO emanating from the textile factory are within the stipulated ambient air quality standards.     

1994～2006年中国人为源大气氨排放时空分布

大气中的氨对酸沉降、区域细粒子、水体富营养化等重要环节问题都有直接或间接的影响.对我国人为源大气氨排放进行估算可以为酸沉降和区域细粒子污染控制对策的制定提供依据.利用排放因子法,基于牲畜养殖、化肥施用、化工生产、人体呼吸排汗和排泄等部门的排放因子和分省活动水平,建立了1994～2006年我国分省分部门的大气氨排放清单,分析了其历史变化趋势和地理分布特征.2006年,全国大气氨排放量从1994年的11.06Mt增长到16.07Mt.其中,牲畜养殖、化肥施用、化工生产、人呼吸排汗和排泄的氨排放分别从1994年的4.47、5.94、0.09、0.59Mt增长到2006年的6.61、8.68、0.14、0.65Mt.牲畜养殖和化肥施用是最主要的氨排放源,分别贡献了2006年氨排放总量的40.79%和53.53%.2006年,全国平均的氨排放强度为1.67t·km-2,但是全国大气氨排放量的地理分布很不均衡,河南、山东、四川、河北、江苏等省的排放量分别为11.0%、9.7%、6.9%、6.7%、6.6%,共占全国总排放量的40.82%.     

长三角地区秸秆焚烧污染物排放清单及其在重霾污染天气模拟中的应用

根据2008年长三角地区江苏、安徽、浙江3省各地级市及上海市水稻、小麦、玉米、油菜4种农作物的年产量,结合谷草比、秸秆焚烧比例及排放因子建立了长三角地区秸秆焚烧大气污染物排放清单.结果表明：长三角地区秸秆焚烧产生的PM10、PM2.5、SO2、NOx、CO、EC、OC分别为36.8×104、14.4×104、1.5×104、9.2×104、20.8×104、2.6×104、12.2×104t.秸秆焚烧污染物排放量较大的区域主要集中在江苏中北部和安徽北部.在区域大气环境模拟系统RegAEMS中考虑秸秆焚烧源的影响,针对2008年10月底江苏一次重霾污染天气事件进行模拟,发现考虑秸秆焚烧源后模拟结果有较大的改善.秸秆焚烧可以导致区域PM10、CO浓度上升30%以上,黑碳和有机物的消光贡献明显增强.区域输送研究表明,苏中地区、外省秸秆焚烧排放源对此次重霾污染的贡献分别达到32.4%、33.3%.     

江苏省温室气体排放清单基础研究

随着气候问题日益严峻,温室气体排放清单编制已经成为温室气体研究的一项重要的基础工作。本文概述了中国温室气体清单编制的发展情况,并重点介绍了江苏省能源部门温室气体排放清单编制基础工作,包括江苏省温室气体排放清单编制计算方法和步骤,江苏省温室气体排放清单及数据来源等。江苏省能源部门温室气体清单编制主要统计二氧化碳、甲烷、氧化亚氮三种最主要的温室气体,将为国家及其他省份相关研究提供参考。     

机动车车载尾气检测技术研究综述

车载尾气检测技术的出现和发展为机动车尾气的相关研究提供了一种较为准确、便捷、实时反映城市路网排放的测试方法。本文详细介绍了车载尾气检测技术的发展历程和原理,综述了该技术在行驶周期开发、尾气排放特性和规律研究、建立机动车尾气排放数据库和建立城市路网排放清单等领域的应用现状,并通过比较该技术在国内、外的应用差距,提出了在绿色交通领域充分利用该技术进行机动车污染评价与控制的合理建议。     

Emission Inventories of Carbon-containing Greenhouse Gases in China and Technological Measures for Their Abatement

Abstract

The report summarizes surveys on carbon inventories and initiatives on sustainable carbon cycling taken by the Research Center for Eco-Environmental Sciences, where the authors work/worked. The first part of the report, which appeared in the preceding issue of this journal, deals with the concept of sustainable carbon cycling, the historic evolution of carbon cycling processes in China, carbon pool enhancement, value addition, carbon sequestration and carbon balance. This very paper, as the second part of the report, covers the results of carbon dynamics modeling, emission inventories of various carbon-containing greenhouse gases and their potential abatement measures.     

疫情背景下的福建省高速公路机动车污染物排放清单

机动车排放污染物已经成为大气污染的重要来源.基于福建省高速公路交通流量数据,采用自下而上的计算方法建立了2020年1—7月福建省高速公路机动车高分辨率污染物排放清单.结果表明,受疫情影响,福建省高速公路月均车流量和污染物排放量呈先下降后上升的变化趋势,4月污染物排放量达到最低,5月污染物排放量又迅速恢复到疫情前的排放水平,其中,疫情中期污染物CO、HC、NO_x、PM_2.5和PM₁₀排放较疫情后期分别减少了90.68%、89.06%、92.58%、89.58%和89.63%.在整个研究期内,不同城市高速公路机动车污染物排放的分担率有所不同,泉州、福州和漳州的高速公路机动车排放分担率较高;从车型来看,小型客车和轻型货车是CO和HC的主要贡献车型,NO_x和PM主要来自重型货车和轻型货车;从燃料类型来看,汽油车是CO和HC的主要贡献源,柴油车则对NO_x和PM贡献突出;从排放标准来看,国三和国四车对各项污染物的贡献率最大.各项污染物空间分布一致,排放高值区位于东部沿海地区路段,西部内陆的...     

Variation of spatio-temporal distribution of on-road vehicle emissions based on real-time RFID data

High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution in the main urban area of Chongqing, based on real-time traffic data from 820 RFID detectors covering 454 roads, and the differences in spatio-temporal emission characteristics between inner and outer districts were analysed. The result showed that the daily vehicular emission intensities of CO, hydrocarbons, PM_2.5, PM₁₀, and NOx were 30.24, 3.83, 0.18, 0.20, and 8.65 kg/km per day, respectively, in the study area during 2018. The pollutants emission intensities in inner district were higher than those in outer district. Light passenger cars (LPCs) were the main contributors of all-day CO emissions in the inner and outer districts, from which the contributors of NOx emissions were different. Diesel and natural gas buses were major contributors of daytime NOx emissions in inner districts, accounting for 40.40%, but buses and heavy duty trucks (HDTs) were major contributors in outer districts. At nighttime, due to the lifting of truck restrictions and suspension of buses, HDTs become the main NOx contributor in both inner and outer districts, and its three NOx emission peak hours were found, which are different to the peak hours of total NOx emission by all vehicles. Unlike most other cities, bridges and connecting channels are always emission hotspots due to long-time traffic congestion. This knowledge will help fully understand vehicular emissions characteristics and is useful for policymakers to design precise prevention and control measures.