Abstract：
BACKGROUND: Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health.
OBJECTIVE: The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model.
METHODS: Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values.
RESULTS: With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year.
CONCLUSIONS: The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients\' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.
OBJECTIVE: The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model.
METHODS: Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values.
RESULTS: With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year.
CONCLUSIONS: The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients\' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.
摘要：
背景：空气污染对于像印度这样的发展中国家来说是一个重要问题，空气质量指数（AQI）预测有助于提前预测空气质量水平，并允许个人采取预防措施来保护他们的健康。
目的：该研究旨在预测工业区的AQI(SIDCUL，HaridwarCity)使用时间序列回归模型。
方法：从Haridwar市SIDCUL地区的北阿坎德邦污染控制委员会收集了三年的现有AQI数据点（COVID-19后），并试图了解随后12个月的AQI值状况。通过分解过程可以看到趋势和季节性成分。Further,在最终确定最适合预测AQI值的时间序列模型之前,应用增强的Dickey-Fuller检验来检查序列的平稳性.
结果：借助自相关函数（ACF）/部分ACF图，选择了具有最小akaike信息准则（253.143）和平均绝对百分比误差（17.42％）的季节性自回归综合移动平均（ARIMA）（0,1,0）（1,0，0）[12]模型。还预测了下一年该工业区（SIDCUL）的AQI值。
结论：季节性ARIMA(0,1,0)(1,0,0)[12]模型可能有助于预测非平稳时间序列数据集的AQI值。研究表明，SIDCUL地区的空气将受到中度污染，并可能对哮喘患者的健康造成呼吸不适。科学家可能会将此模型应用于该国的其他污染地区，以便公众和政府可以提前采取预防措施。
目的：该研究旨在预测工业区的AQI(SIDCUL，HaridwarCity)使用时间序列回归模型。
方法：从Haridwar市SIDCUL地区的北阿坎德邦污染控制委员会收集了三年的现有AQI数据点（COVID-19后），并试图了解随后12个月的AQI值状况。通过分解过程可以看到趋势和季节性成分。Further,在最终确定最适合预测AQI值的时间序列模型之前,应用增强的Dickey-Fuller检验来检查序列的平稳性.
结果：借助自相关函数（ACF）/部分ACF图，选择了具有最小akaike信息准则（253.143）和平均绝对百分比误差（17.42％）的季节性自回归综合移动平均（ARIMA）（0,1,0）（1,0，0）[12]模型。还预测了下一年该工业区（SIDCUL）的AQI值。
结论：季节性ARIMA(0,1,0)(1,0,0)[12]模型可能有助于预测非平稳时间序列数据集的AQI值。研究表明，SIDCUL地区的空气将受到中度污染，并可能对哮喘患者的健康造成呼吸不适。科学家可能会将此模型应用于该国的其他污染地区，以便公众和政府可以提前采取预防措施。
