4.5 Article

Role of meteorology in seasonality of air pollution in megacity Delhi, India

期刊

ENVIRONMENTAL MONITORING AND ASSESSMENT
卷 184, 期 5, 页码 3199-3211

出版社

SPRINGER
DOI: 10.1007/s10661-011-2182-8

关键词

Air quality in Delhi; Particulates pollution; Winter highs; Mixing layer height; Role of meteorology

资金

  1. Max Planck Society, Munich
  2. Max Planck Institute for Chemistry, Mainz, Germany, through the Max Planck Partner Group for Megacities and Global Change established at Indian Institute of Technology Roorkee, India

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The winters in megacity Delhi are harsh, smoggy, foggy, and highly polluted. The pollution levels are approximately two to three times those monitored in the summer months, and the severity is felt not only in the health department but also in the transportation department, with regular delays at airport operations and series of minor and major accidents across the road corridors. The impacts felt across the city are both manmade (due to the fuel burning) and natural (due to the meteorological setting), and it is hard to distinguish their respective proportions. Over the last decade, the city has gained from timely interventions to control pollution, and yet, the pollution levels are as bad as the previous year, especially for the fine particulates, the most harmful of the criteria pollutants, with a daily 2009 average of 80 to 100 mu g/m(3). In this paper, the role of meteorology is studied using a Lagrangian model called Atmospheric Transport Modeling System in tracer mode to better understand the seasonality of pollution in Delhi. A clear conclusion is that irrespective of constant emissions over each month, the estimated tracer concentrations are invariably 40% to 80% higher in the winter months (November, December, and January) and 10% to 60% lower in the summer months (May, June, and July), when compared to annual average for that year. Along with monitoring and source apportionment studies, this paper presents a way to communicate complex physical characteristics of atmospheric modeling in simplistic manner and to further elaborate linkages between local meteorology and pollution.

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