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Which processes drive observed variations of HCHO columns over India?

机译:哪个过程驱动着在印度的Hcho列的变化?

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We interpret HCHO column variations observed by the Ozone Monitoring Instrument (OMI), aboard the NASA Aura satellite, over India during 2014 using the GEOS-Chem atmospheric chemistry and transport model. We use a nested version of the model with a horizontal resolution of approximately 25?km. HCHO columns are related to local emissions of volatile organic compounds (VOCs) with a spatial smearing that increases with the VOC lifetime. Over India, HCHO has biogenic, pyrogenic, and anthropogenic VOC sources. Using a 0-D photochemistry model, we find that isoprene has the largest molar yield of HCHO which is typically realized within a few hours. We also find that forested regions that neighbour major urban conurbations are exposed to high levels of nitrogen oxides. This results in depleted hydroxyl radical concentrations and a delay in the production of HCHO from isoprene oxidation. We find that propene is the only anthropogenic VOC emitted in major Indian cities that produces HCHO at a comparable (but slower) rate to isoprene. The GEOS-Chem model reproduces the broad-scale annual mean HCHO column distribution observed by OMI (r?=?0.6), which is dominated by a distinctive meridional gradient in the northern half of the country, and by localized regions of high columns that coincide with forests. Major discrepancies are noted over the Indo-Gangetic Plain (IGP) and Delhi. We find that the model has more skill at reproducing observations during winter (JF) and pre-monsoon (MAM) months with Pearson correlations r??0.5 but with a positive model bias of ???1×1015?molec?cm?2. During the monsoon season (JJAS) we reproduce only a diffuse version of the observed meridional gradient (r?=?0.4). We find that on a continental scale most of the HCHO column seasonal cycle is explained by monthly variations in surface temperature (r?=?0.9), suggesting a role for biogenic VOCs, in agreement with the 0-D and GEOS-Chem model calculations. We also find that the seasonal cycle during 2014 is not significantly different from the 2008 to 2015 mean seasonal variation. There are two main loci for biomass burning (the states of Punjab and Haryana, and northeastern India), which we find makes a significant contribution (up to 1×1015?molec?cm?2) to observed HCHO columns only during March and April over northeastern India. The slow production of HCHO from propene oxidation results in a smeared hotspot over Delhi that we resolve only on an annual mean timescale by using a temporal oversampling method. Using a linear regression model to relate GEOS-Chem isoprene emissions to HCHO columns we infer seasonal isoprene emissions over two key forest regions from the OMI HCHO column data. We find that the a posteriori emissions are typically lower than the a?priori emissions, with a much stronger reduction of emissions during the monsoon season. We find that this reduction in emissions during monsoon months coincides with a large drop in satellite observations of leaf phenology that recovers in post monsoon months. This may signal a forest-scale response to monsoon conditions.
机译:我们通过Geos-Chemberic化学和运输模型在2014年,在2014年,在印度上,解释了臭氧监测仪(OMI)观察到的HCHO柱变体。我们使用模型的嵌套版本,水平分辨率约为25英里。 HCHO柱与挥发性有机化合物(VOC)的局部排放有关,其空间涂抹随着VOC寿命而增加。在印度,HCHO具有生物生成,热原和人为杂志来源。使用0-D光化学模型,发现异戊二烯具有最大的Hcho摩尔产量,通常在几小时内实现。我们还发现邻居主要城市发生的森林区域暴露于高水平的氮氧化物。这导致亚羟基浓度耗尽和来自异戊二烯氧化的Hcho的延迟。我们发现丙烯是主要印度城市中唯一的人类学VOC,可以可比(但较慢)率与异戊二烯的速率产生HCHO。 Geos-Chem模型再现由OMI(R?= 0.6)观察到的广大年度平均HCho柱分布,该列在该国北半部分的独特子午梯度,以及由局部区域的高列与森林一致。在印度难以平原(IGP)和Delhi上指出了主要差异。我们发现该模型在冬季(JF)和Pearson相关性冬季(JF)和季风前(MAM)的月份中具有更多技能R -?? 0.5但是具有阳性模型偏差?1×1015?分子?2 。在季风季节(JJAS)期间,我们只重现了观察到的子午梯度的漫射版本(r?= 0.4)。我们发现,在大陆规模上,大多数HCho列季节循环由表面温度的每月变化(R?= 0.9)解释,表明生物转向的作用,同意0-D和Geos-Chem模型计算。我们还发现2014年季节性周期与2008年至2015年的季节性变化没有显着差异。生物量燃烧(旁遮普郡和哈里亚纳邦的州和印度东北部)有两种主要轨迹,我们发现这是一个显着的贡献(最多1×1015?分子?CM?2),只在3月和4月期间观察Hcho列在印度东北部。来自丙烯氧化的HCho缓慢产生的HCHO导致德里的涂抹热点,我们仅通过使用时间过采样方法在年度平均时间尺度上解决。利用线性回归模型将Geos-Chem异戊二烯排放与Hcho柱联系起来,我们从OMI Hcho列数据中的两个关键林地区推断出季节性异戊二烯排放。我们发现,后验排放通常低于A的先验排放,在季风季节期间减少了更强的排放。我们发现,季风月份的排放减少恰逢季叶候选的卫星观察中的大幅下降,在季风月份恢复。这可能会向季风条件发出森林规模的反应。

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