Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi-110016, India.
Sci Total Environ. 2011 Nov 15;409(24):5517-23. doi: 10.1016/j.scitotenv.2011.08.069. Epub 2011 Oct 1.
As the impact of air pollutants on human health through ambient air address much attention in recent years, the air quality forecasting in terms of air pollution parameters becomes an important topic in environmental science. The Air Quality Index (AQI) can be estimated through a formula, based on comprehensive assessment of concentration of air pollutants, which can be used by government agencies to characterize the status of air quality at a given location. The present study aims to develop forecasting model for predicting daily AQI, which can be used as a basis of decision making processes. Firstly, the AQI has been estimated through a method used by US Environmental Protection Agency (USEPA) for different criteria pollutants as Respirable Suspended Particulate Matter (RSPM), Sulfur dioxide (SO2), Nitrogen dioxide (NO2) and Suspended Particulate Matter (SPM). However, the sub-index and breakpoint concentrations in the formula are made according to Indian National Ambient Air Quality Standard. Secondly, the daily AQI for each season is forecasted through three statistical models namely time series auto regressive integrated moving average (ARIMA) (model 1), principal component regression (PCR) (model 2) and combination of both (model 3) in Delhi. The performance of all three models are evaluated with the help of observed concentrations of pollutants, which reflects that model 3 agrees well with observed values, as compared to the values of model 1 and model 2. The same is supported by the statistical parameters also. The significance of meteorological parameters of model 3 has been assessed through principal component analysis (PCA), which indicates that daily rainfall, station level pressure, daily mean temperature, wind direction index are maximum explained in summer, monsoon, post-monsoon and winter respectively. Further, the variation of AQI during the weekends (holidays) and weekdays are found negligible. Therefore all the days of week are accounted same in the models.
近年来，由于大气污染物对人体健康的影响受到了广泛关注，因此空气质量预测（特别是针对空气污染参数）已成为环境科学领域的一个重要课题。空气质量指数 (AQI) 可以通过一个公式进行估算，该公式基于对空气污染物浓度的综合评估，政府机构可以用其来描述特定地点的空气质量状况。本研究旨在开发一种预测每日 AQI 的模型，为决策过程提供依据。首先，采用美国环境保护署（USEPA）为不同标准污染物（可吸入悬浮颗粒物 (RSPM)、二氧化硫 (SO2)、二氧化氮 (NO2) 和悬浮颗粒物 (SPM)）制定的方法估算 AQI。然而，公式中的子指数和断点浓度是根据印度国家空气质量标准制定的。其次，通过时间序列自回归综合移动平均 (ARIMA)（模型 1）、主成分回归 (PCR)（模型 2）和两者的组合（模型 3）三种统计模型，对德里每个季节的每日 AQI 进行预测。借助污染物的观测浓度对所有三种模型的性能进行评估，结果表明模型 3 的预测值与观测值吻合度最高，优于模型 1 和模型 2 的预测值。统计参数也支持这一结果。通过主成分分析 (PCA) 评估了模型 3 中气象参数的显著性，结果表明，在夏季、季风期、季风后期和冬季，每日降雨量、台站气压、日平均温度和风向指数对模型的解释程度最大。此外，周末（节假日）和工作日期间的 AQI 变化可以忽略不计。因此，在模型中所有工作日的权重相同。
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