Chelani Asha, Gautam Sneha
Air Pollution Control Division, CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440020, India.
Karunya Institute of Technology and Sciences, Coimbatore - 641114, Tamil Nadu, India.
Geosci Front. 2022 Nov;13(6):101284. doi: 10.1016/j.gsf.2021.101284. Epub 2021 Aug 12.
The influence of reduction in emissions on the inherent temporal characteristics of PM and NO concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24-April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM and NO concentration in urban cities, causing low concentrations. It is observed that the original PM and NO concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM and NO concentrations. Hurst exponent of the PM and NO concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.
通过在封锁期间(2020年3月24日至4月20日)以及前两年(即2018年和2019年)的相应时间段,使用去趋势波动分析(DFA)计算赫斯特指数,评估了印度六个城市排放减少对PM和NO浓度时间序列固有时间特征的影响。分析表明了城市中封锁措施对PM和NO浓度的预期影响,导致浓度降低。据观察,原始的PM和NO浓度时间序列具有持续性,但通过在实际时间序列上拟合一阶自回归过程并减去它来对时间序列进行滤波,会显著改变持续性特征。这表明PM和NO浓度中存在线性相关性。封锁期间以及前两年的PM和NO浓度的赫斯特指数表明,即使在停止排放后,短期空气污染物浓度(APC)时间序列的固有时间特征也不会改变。三个时间段内气象变化也没有改变。这一发现有助于开发预测模型,以用于未来改善各城市空气质量的政策决策。