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首页> 外文期刊>Atmospheric chemistry and physics >Using TROPOspheric Monitoring Instrument (TROPOMI) measurements and Weather Research and Forecasting (WRF) CO modelling to understand the contribution of meteorology and emissions to an extreme air pollution event in India
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Using TROPOspheric Monitoring Instrument (TROPOMI) measurements and Weather Research and Forecasting (WRF) CO modelling to understand the contribution of meteorology and emissions to an extreme air pollution event in India

机译：使用对流层监测仪器（Tropomi）测量和天气研究和预测（WRF）CO建模，了解气象学和排放对印度极端空气污染事件的贡献

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Several ambient air quality records corroborate the severe and persistent degradation of air quality over northern India during the winter months, with evidence of a continued, increasing trend of pollution across the Indo-Gangetic Plain (IGP) over the past decade. A combination of atmospheric dynamics and uncertain emissions, including the post-monsoon agricultural stubble burning, make it challenging to resolve the role of each individual factor. Here we demonstrate the potential use of an atmospheric transport model, the Weather Research and Forecasting model coupled with chemistry (WRF–Chem) to identify and quantify the role of transport mechanisms and emissions on the occurrence of the pollution events. The investigation is based on the use of carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite and the surface measurement network, as well as the WRF–Chem simulations, to investigate the factors contributing to CO enhancement over India during November 2018. We show that the simulated column-averaged dry air mole fraction (XCO) is largely consistent with TROPOMI observations, with a spatial correlation coefficient of 0.87. The surface-level CO concentrations show larger sensitivities to boundary layer dynamics, wind speed, and diverging source regions, leading to a complex concentration pattern and reducing the observation-model agreement with a correlation coefficient ranging from 0.41 to 0.60 for measurement locations across the IGP. We find that daily satellite observations can provide a first-order inference of the CO transport pathways during the enhanced burning period, and this transport pattern is reproduced well in the model. By using the observations and employing the model at a comparable resolution, we confirm the significant role of atmospheric dynamics and residential, industrial, and commercial emissions in the production of the exorbitant level of air pollutants in northern India. We find that biomass burning plays only a minimal role in both column and surface enhancements of CO, except for the state of Punjab during the high pollution episodes. While the model reproduces observations reasonably well, a better understanding of the factors controlling the model uncertainties is essential for relating the observed concentrations to the underlying emissions. Overall, our study emphasizes the importance of undertaking rigorous policy measures, mainly focusing on reducing residential, commercial, and industrial emissions in addition to actions already underway in the agricultural sectors.
机译：几个环境空气质量记录证实了冬季北部印度北部空气质量的严重和持续退化，有证据表明，过去十年的陷入困难平原（IGP）的持续越来越大的污染趋势。大气动力学和不确定排放的组合，包括季风农业茬燃烧，使得解决每个单独因素的作用挑战。在这里，我们展示了与化学（WRF-Chem）联系的大气运输模型，天气研究和预测模型的潜在使用，以确定和量化运输机制和排放对污染事件发生的作用。该调查基于从哨子-5前体卫星和地表测量网络以及WRF-CHEMI模拟的船上的对流层监测仪（Tropomi）的一氧化碳（CO）观察的使用，以及WRF-Chem模拟，以研究有贡献的因素在2018年11月期间对印度的共同提升。我们表明模拟的柱平均的干燥空气摩尔分数（XCO）主要与Tropomi观察一致，空间相关系数为0.87。表面级CO浓度向边界层动态，风速和发散源区显示出更大的敏感性，导致复杂的浓度模式并将观察模型协议减少到跨越IGP的测量位置的0.41至0.60的相关系数。 。我们发现日常卫星观测可以在增强的燃烧时段期间提供CO运输途径的一阶推断，并且在模型中再现该传输模式。通过使用观察和采用可比解的模式，我们证实了大气动力学和住宅，工业和商业排放在印度北部空气污染物水平的生产中的重大作用。除了高污染发作期间，除了旁遮普事件中的旁遮普州之外，生物量燃烧在CO的柱和表面增强中仅发挥着最小的作用。虽然该模型相当良好地再现观察，但更好地理解控制模型不确定性的因素对于将观察到的浓度与潜在的排放相关，而是必不可少的。总体而言，我们的研究强调了承担严格的政策措施的重要性，主要关注减少住宅，商业和工业排放以及农业部门已经进行的行动。

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