Research Progress on Physical-Chemical Characteristics, Influencing Factors, and Emission Inventory Estimation of Soluble Iron in Anthropogenic Atmospheric Aerosols
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摘要:
大气气溶胶中可溶性铁对海洋初级生产力、气候变化、大气二次污染和人体健康都具有重要影响.准确认知可溶性铁的来源,构建高精度可溶性铁的排放清单,是改进生物地球化学模式、模拟铁沉降量和海洋初级生产力、提升大气化学传输模式模拟二次气溶胶精度,以及揭示影响人体内可溶性铁的来源及其定量贡献的重要前提.本文总结分析了气溶胶中铁及可溶性铁的来源解析、气溶胶中铁的可溶性影响因素及作用机制,以及燃烧源排放气溶胶中铁的样品采集和测试方法研究进展,着重对燃烧源排放可溶性铁的清单构建方法和存在问题进行归纳总结,以期为精准评估铁的气候、环境和健康效应提供基础数据和理论支撑.
Abstract:Soluble iron in atmospheric aerosols substantially influences marine primary productivity, climate change, secondary atmospheric pollution, and human health. Identifying sources and developing high-accuracy emission inventories of soluble iron are fundamental for refining biogeochemical models to simulate iron deposition fluxes and marine productivity, improving atmospheric chemistry transport models for secondary aerosol simulations and quantifying the sources and contributions of soluble iron affecting human health. This study systematically reviews the source apportionment of iron and soluble iron in atmospheric aerosols, the key factors and underlying mechanisms governing iron solubility, and recent methodological advances in sampling and characterization of combustion-derived iron-containing aerosols. With particular focus on emission inventory development for combustion-related soluble iron, we critically examine current methodologies and identify persistent challenges. It is expected the summarization here provides a basic dataset and theoretical frameworks for accurate assessment of iron's climatic, environmental and health impacts.
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图 2 不同研究气溶胶中δ56Fe的值(Wang et al., 2022b)
城市气溶胶研究点位为美国凤凰城(Majestic et al., 2009)、法国敦刻尔克(Flament et al., 2008)和日本广岛(Kurisu et al., 2016);海洋气溶胶研究点位为西北太平洋(Kurisu et al., 2016, 2021)、赤道西太平洋(Labatut et al., 2014)和北大西洋(Conway et al., 2019;Waeles et al., 2007;Mead et al., 2013). 对于图中的平均δ56Fe数据,测量精度以1倍标准差(1SD)表示
Fig. 2. δ56Fe values from studies in different regions (Wang et al., 2022b)
表 1 不同类型气溶胶中铁的可溶性及各类源对可溶性铁的贡献率(%)
Table 1. Iron solubility in various types of aerosols and mass contributions of different sources to soluble Iron (%)
实验地点 采样时间 样品采集和粒径 不同类型气溶胶中铁的可溶性 源对于可溶性铁的贡献率 参考文献 美国亚特兰大 - 燃煤飞灰和矿物粉尘,通过气溶胶发生器和滤膜采样,获取2.5 μm样品;通过滤膜采集小于2.5 μm的环境样品和生物质燃烧烟气样品 矿物粉尘和煤飞灰,小于1%;生物质燃烧和移动源46%~75%;夏季环境样品9%;冬季环境样品22% - Oakes et al., 2012 我国山西(煤飞灰1、煤飞灰2、黄土)、内蒙古(煤飞灰3)、辽宁(油飞灰)、山东(玉米秸秆)、上海(稻草) - 分子筛,干筛,选出小于50 μm的灰样 燃油飞灰28%;玉米秸秆燃烧95%;稻草秸秆燃烧81%;燃煤飞灰约35%;中国黄土1.4% - Fu et al., 2012 非洲(沙尘)、中国(黄土)、阿拉斯加(冰川粉尘)、意大利(燃油飞灰) 2008/11至2009/3 分子筛,干筛,选出10~50 µm的灰样 非洲沙尘 < 1%;中国黄土2%;冰川粉尘2%~3%;燃油飞灰77%~81% - Schroth et al., 2009 澳大利亚南部的亚南极区和极地锋区 2007/1/21至2/19 大气气溶胶样品采集,通过54 µm孔径的聚酯筛 0.2%~2.5%受生物质燃烧影响的气溶胶为7.7% - Bowie et al., 2009 西北太平洋 2015/3/30至5/5 采集大气气溶胶滤膜样品,小于等于2.5 μm 自然尘埃1%;工业燃煤8%;生物质燃烧18%;船舶排放65%;人为源总体10.3% 船舶排放27.5%;工业燃煤34%;生物质燃烧16%;自然尘埃3.7%~20.3% Zhang et al., 2024b 中国青岛 2022/6至2023/4 采集大气气溶胶滤膜样品,小于1 μm和大于1 μm 细粒子:溶解度中位数分别为0.24%、2.05%、0.64%和0.46%(春夏秋冬四季);粗粒子:溶解度中位数分别为0.15%、1.55%、0.51%和0.20% 海洋源0.8%~5.8%;二次源13.5%~85.5%;燃烧源5.4%~52.3%;工业源8.3%~28.4% Chen et al., 2024 中国杭州 2018/11至2020/1 采集大气气溶胶滤膜样品,小于等于2.5 μm 雾天(6.7±3.0)%;霾天(4.8±1.9)%;沙尘天(2.1±0.7)%;晴天(1.9±1.0)%;雨天(0.9±0.5)% 工业排放44.5%~72.4%;二次源3.1%~16.5%;交通排放5.8%~18.9%;煤炭燃烧2.9%~4.4%;自然尘埃3.1% Zhu et al., 2022 中国青岛 2018/11/9至12/1 采集大气气溶胶滤膜样品,小于等于10 μm 老化沙尘1.9%~3.6%;新鲜沙尘0.1%~0.3%;垃圾焚烧1.4%~11.3%;二次源5.8%;工业排放16.2%;船舶排放10.3% 老化沙尘44%~33.2%;新鲜沙尘1.9%~8.9%;二次源4.2%~10%;燃烧源15.5%~78.4% Sun et al., 2024 注:“-”代表没有数据. 表 2 飞灰浸出实验的样品选择、实验条件设置和铁溶解度
Table 2. Selection of fly ash particles, leaching experiment condition settings and Fe solubility (%)
名称 来源 样品粒径 酸溶液配制 浸出时间 铁溶解度 参考文献 佛得角黄土(黄土) 矿物尘 撒哈拉气溶胶颗粒的相似物,粒径2~20 μm 20 mg固体样品,H2SO4水溶液,调整pH=4.7 45、60、90和120 min 0.04% Desboeufs et al., 2005 亚利桑那州粉尘 矿物尘 空气悬浮沙尘,粒径0.6~12 μm 1.5% 波切维尔飞灰(PFA) 重油排放 静电除尘器捕集,成分为石墨碳、方石英和莫来石,粒径 < 100 μm 35.7% 维特里飞灰(VFA) 燃煤排放 铝硅酸盐(石英石和莫来石)组成,粒径2~100 μm 0.2% 城市颗粒物 美国国家标准与技术研究院提供 污泥和粉煤灰混合物,粒径在30 nm~10 μm 3.0% 三种飞灰(SRMs 2689, 2690和2691);亚利桑那州精细测试粉尘 燃煤飞灰 粒径1~50 μm 用H2SO4将溶液酸化至pH为1±0.1或2±0.1 0~50 h 随着时间增加,溶解性从~1%增加到~17% Chen et al., 2012 重油燃烧飞灰、生物质燃烧颗粒、燃煤飞灰、中国黄土 燃烧源颗粒物 研磨过筛,粒径 < 50 μm 用HCl和NaOH调节pH=2或pH=7.0 0~12 h 光照条件下:8.3%~41.2%;暗条件下:2.9%~74.1% Fu et al., 2012 非洲灰尘、中国黄土、阿拉斯加冰川粉尘、库库拉纳冰川和马塔努斯卡冰川粉尘、燃油飞灰 冰川风化产物、干旱土壤、石油燃烧产物 样品干筛,粒径10~50 µm 20%的痕量金属级HCl,在pH为2的去离子水中调节2天(用Optima级HNO3酸化) 1周 0.04%、0.54%、3.2%和1.9%、81% Schroth et al., 2009 伊利石、高岭石、蒙脱石、其他黏土、长石、铁(氢)氧化物 粘土、长石和铁(氢)氧化物等矿物 / 溶解于250 mL用硝酸(NormatonTM)酸化至pH=2的去离子水中;或调至pH=4.7 / 0.003%~5.250% Journet et al., 2008 中国粉煤灰、美国粉煤灰、欧洲城市垃圾燃烧飞灰、亚利桑那试验尘土、中国洛川黄土和新疆尘土 粉煤灰样品、城市垃圾粉煤灰样品、沙漠粉尘样品 / 5 mmol/L的醋酸钠缓冲液(pH=4.3)和去离子水(pH=5.9) 120 min 0.04%~1.98% Li et al., 2022 表 3 文献中可溶性铁清单构建方法和排放量对比
Table 3. Methods for establishing emission inventories of soluble iron in aerosols in literature
研究尺度 研究年份 研究方法 空间分辨率 可溶性铁排放量(Tg/a) 参考文献 全球 2001 1. Fe的排放量:(1)工业燃烧源Fe的排放量=燃料消耗数据×排放占比×技术普及率;a. 燃料消耗数据,排放占比和技术普及率来源于国际能源署;b. 粒径为PM < 1(细颗粒),PM > 1(粗颗粒);(2)生物质燃烧Fe的排放量=生物质燃烧排放BC的量×Fe与BC比值;a. 生物质燃烧排放BC和气溶胶的量(卫星数据和陆地碳模型);b. PM > 1:Fe/BC为1.4;PM < 1:Fe/BC为0.02(相关性分析的斜率).2. 可溶性Fe的排放量: (1)可溶性Fe排放量=赤铁矿中Fe的摩尔数×赤铁矿溶解速率;(2)可溶性Fe排放量=燃烧源Fe的排放量×Fe的溶解度(4%). 1.9°×1.9° 0.018~0.89 Luo et al., 2008 全球 2001 1. Fe的排放量=PM排放量×Fe的占比;(1)PM排放量;(2)Fe在PM中的占比. 2.可溶性Fe的排放量=Fe的排放量×Fe溶解度(4%). 2.0°×2.5° / Ito and Feng, 2010 全球 2001 1. Fe的排放量:(1)PM排放量;(2)Fe的排放因子=PM的排放因子×不同粒径的颗粒物占比×细颗粒物的Fe含量×控制措施;PM < 1或PM < 2.5 μm为细颗粒;PM > 2.5 μm为粗颗粒.2. 可溶性Fe排放量=Fe排放量×Fe溶解度;溶解度:石油燃烧为79%,煤炭燃烧和生物质燃烧分别为11%和18%. 2.0°×2.5° 细颗粒和粗颗粒物中:燃油:0.009 5和0.002 9;燃煤:0.006 8和0.048; 生物质:0.025和0.102 Ito et al., 2013 全球 1990~2007 1. 飞灰中Fe的排放量:(1)燃料消耗数据(PKU-FUEL-2007);(2)燃料中Fe含量;(3)Fe在残渣及飞灰中的分配比值或获取相似元素的分配比值代替;(4)文献中报道的PM1、PM1~10、PM10的相对排放占比,不同燃料的完全燃烧度,燃烧后的控制措施和对不同粒径的去除效率. 2. 可溶性Fe排放量=Fe排放量×Fe溶解度;溶解度:燃煤飞灰为22.5%,生物质燃烧为18%,燃油飞灰为79%. 0.1°×0.1° PM1中:0.023 09 Wang et al., 2015 全球 2000 1. Fe排放量:(1)燃烧源Fe排放量和粒径分布作为输入数据模拟;(2)粒径为0.001 ~10 μm.2. 可溶性Fe排放量=Fe排放量×Fe溶解度溶解度:化石燃料人为源为7.2%(情景1)和7.7%(情景2),生物质燃烧为11%. 1.9°×2.5° 化石燃烧源:0.012;生物质燃烧源:0.039 Matsui et al., 2018 全球 2010 1. 不同粒径气溶胶中Fe的排放量:(1)燃料消耗量(国际能源署和联合国数据库);(2)文献中PM排放因子;(3)PM中不同粒径颗粒物的分布,假设50%为PM1,50%为PM1~10;(4)文献中颗粒物控制技术及减排效率;(5)文献中不同粒径PM中铁的占比. 2.可溶性Fe排放量=Fe排放量×Fe溶解度;溶解度:人为燃烧源(如煤和石油燃烧)为4%. 1°×1° PM10中:0.020~0.440 Rathod et al., 2020 -
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