Fine particulate matter is one of the major air pollutants in urban areas, which adversely affects people's health and is considered as a serious threat to the society. Effective prediction of this pollutant can provide information for sensitive people to avoid or reduce outdoor activities and on the other hand can help regulators for efficient decision-making related to precautionary measures. This paper develops a novel hybrid algorithm for multi-step-ahead prediction of PM2.5 in which two challenges of forecasting in real applications has been taken into account. First, the challenge of employing decomposition techniques in practice is addressed. Different real-time approaches have been explored and compared in decomposition-based and noise-removal-based frameworks. Also, a real-time approach which combines feature selection and noise-removal-based technique is implemented in the framework of the proposed algorithm. The second challenge is the lack of access to real values of meteorological parameters, as influential predictors, in practical cases and use of forecasted values instead. To address the uncertainty in model inputs, Monte Carlo simulation is employed in the framework of the proposed algorithm. Probabilistic forecasts of the proposed algorithm output a distribution of the predicted values for a given time-point which can be used to calculate prediction intervals and probability of exceeding PM2.5 warning threshold. These results provide valuable information for decision-makers and regulators to take precautions and put control measures in place. According to the results, if a public alert is issued on days with at least 50% probability of exceeding PM2.5 warning level, it is observed that in 76.88% of days the algorithm indicates a correct warning for 1-day-ahead prediction which decreases to 55.00%, 41.25% and 37.50% for 2-days-ahead to 4-days-ahead forecasts.

细颗粒物是城市地区的主要空气污染物之一，它对人们的健康产生不利影响，被认为是对社会的严重威胁。对这种污染物的有效预测可以为敏感人群提供信息，避免或减少户外活动，另一方面，可以帮助监管机构进行与预防措施有关的有效决策。本文开发了一种新颖的混合算法，用于PM2.5的多步超前预测，其中考虑了实际应用中的两个预测挑战。首先，解决了在实践中采用分解技术的挑战。在基于分解和基于噪声消除的框架中​​，已经探索并比较了不同的实时方法。也，在该算法的框架内，实现了一种结合特征选择和噪声消除技术的实时方法。第二个挑战是在实际情况下无法获得作为影响预测因子的气象参数实际值，而是使用预测值。为了解决模型输入中的不确定性，在所提出算法的框架中采用了蒙特卡洛模拟。所提出算法的概率预测输出给定时间点的预测值分布，该分布可用于计算预测间隔和超过PM2.5警告阈值的概率。这些结果为决策者和监管者采取预防措施并采取控制措施提供了有价值的信息。根据结果​​，