Journal of Earth System Science ( IF 1.9 ) Pub Date : 2020-09-19 , DOI: 10.1007/s12040-020-01453-6 Theertha Kariyathan , Dhanyalekshmi Pillai , Eldho Elias , Thara Anna Mathew
Abstract
Delhi, the capital city of India witnesses severe degradation of air quality and rapid enhancement of trace gases during winter. Still it is unclear about the relative role of the meteorological conditions and the post-monsoon agricultural stubble burning on the occurrence of these events. To overcome this, we examine the use of applying high-resolution transport model to establish the link between atmospheric concentrations and upstream surface fluxes. This study reports the implementation of a Lagrangian approach and demonstrates its capability in deriving the upwind influences over Delhi. We simulate stochastic back trajectories over Delhi by implementing stochastic time-inverted Lagrangian transport (STILT) model, driven by the meteorological fields from the European Centre for Medium Range Weather Forecasts (ECMWF) model. During the post-monsoon, when mixing layer height is shallow, we find high near-field influence. The variations in footprint simulations with receptor heights show the effect of mixing layer dynamics on the air-parcels. By using atemporal emission fields, we find a considerable impact of meteorological conditions during November that contributes to the enhancements of trace gases. Together with strong emissions (anthropogenic and biomass burning), these enhancements can be several orders higher compared to other seasons. Through the receptor-oriented STILT implementation over India, we envision a wide range of applications spanning from air quality to climate change. An advantage of this implementation is that it allows the use of pre-calculated footprints in simulating any trace gas species and particulate matter, making it computationally less demanding than running an ensemble of full atmospheric transport model.
Research highlights
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Our study elaborates a method to estimate the near-field influences on a region of interest or receptor location (Delhi), which is pivotal in devising mitigation strategies to curb the increasing pollution events (one of the country's greatest concerns).For this, we implement STILT that uses ECMWF meteorological data to generate simulations of realistic atmospheric trajectories and footprints to the Indian subcontinent domain. From this data, the influence matrix is derived.
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We demonstrate the usefulness of the STILT modeling framework by deriving air-parcel trajectories and footprint over Delhi, which shows a higher influence of Haryana and Punjab region as upwind location to Delhi during the pre-monsoon and post-monsoon season.
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We highlight the importance of proper accounting of vertical mixing in the atmospheric model by simulating footprint and trajectory measurements at different heights. This shows that during postmonsoon when PBL height is shallow there is a higher influence function thus choking Delhi in the winter.
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Our analysis shows the usefulness of pre-calculated footprints in simulating any trace gas species and particulate matter concentration thus saving a lot of computational costs incurred. This is illustrated by generating concentration signals using EDGAR global inventory for CO and CO2 emissions from biomass burning and CO2, CO, N2O and CH4 from anthropogenic emissions. We observed concentration enhancement in agreement with the diurnal PBL dynamics (higher in lower layers (20m) compared to higher layers (500m)).
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We envision extending the STILT network to have wider insights on regional and local fluxes of CO2, CO, and CH4. The study can be improvised by utilizing hourly-varying emission fluxes and also by accounting for other sources of emissions.
中文翻译：
关于印度德里上风农业和人为排放对温室气体浓度和空气质量的影响：随机拉格朗日足迹法
摘要
在印度首都德里，冬季空气质量严重下降，微量气体迅速增加。尚不清楚气象条件和季风后农业发茬燃烧在这些事件发生上的相对作用。为了克服这个问题，我们研究了使用高分辨率传输模型建立大气浓度与上游表面通量之间的联系的方法。这项研究报告了拉格朗日方法的实施，并证明了其对德里产生上风影响的能力。我们通过实施随机时间倒置的拉格朗日运输（STILT）模型（在欧洲中程天气预报中心（ECMWF）模型的气象领域的驱动下），来模拟德里上空的随机反向轨迹。在季风后期间，当混合层高度较浅时，我们发现较高的近场影响。足迹模拟随受体高度的变化表明混合层动力学对空气包裹的影响。通过使用时间发射场，我们发现11月份的气象条件产生了相当大的影响，这有助于增加痕量气体。再加上大量排放（人为燃烧和生物质燃烧），与其他季节相比，这些改进可能要高几个数量级。通过在印度实施面向受体的STILT，我们设想了从空气质量到气候变化的广泛应用。这种实现方式的优势在于，它可以在模拟任何痕量气体物种和颗粒物时使用预先计算的足迹，
研究重点
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我们的研究精心设计了一种方法来估计对感兴趣区域或受体位置（Delhi）的近场影响，这对于设计缓解策略以遏制日益严重的污染事件（该国最大的担忧之一）至关重要。实施使用ECMWF气象数据的STILT，以生成对印度次大陆区域真实大气轨迹和足迹的模拟。从该数据得出影响矩阵。
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我们通过推导德里的空运轨迹和足迹来证明STILT建模框架的有用性，这表明在季风前和季风后季节，哈里亚纳邦和旁遮普地区作为上风向德里的影响较大。
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我们通过模拟不同高度的足迹和轨迹测量结果，突出说明了在大气模型中正确考虑垂直混合的重要性。这表明在季风后PBL高度较浅时，影响函数较高，从而使冬季的德里窒息。
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我们的分析显示了预先计算的足迹在模拟任何痕量气体种类和颗粒物浓度方面的有用性，从而节省了大量的计算成本。使用EDGAR全球清单生成浓度信号来说明这一点，该信号用于生物质燃烧产生的CO和CO2排放以及人为排放产生的CO 2，CO，N 2 O和CH 4。我们观察到浓度增加与昼夜PBL动力学一致（较低层（20m）较高，较高层（500m）较高）。
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我们设想扩展STILT网络，以更广泛地了解CO 2，CO和CH 4的区域和局部通量。可以通过利用随时间变化的排放通量并考虑其他排放源来简化研究。
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