Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously affect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classification models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM_2.5 (aerodynamics size up to 2.5 μ) and PM_10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six different locations of the Muzaffarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifiers. The support vector machine (SVM) kernels, ensemble classifiers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fine Gaussian and cubic SVM as well as with the cubic and weighted KNN.

中文翻译：

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提取质量浓度时间序列特征以对室内和室外大气颗粒进行分类

颗粒物（PMs）被认为是在室外和室内环境中通常与空气质量差相关的空气污染物之一。这些颗粒的组成，分布和大小会严重影响人体健康，导致心血管健康问题，肺功能障碍，呼吸系统问题，慢性阻塞性肺疾病和肺癌。通过分析大气颗粒物的质量浓度时间序列数据而建立的分类模型可用于预测空气质量和发布警告以保护公众健康。在这项研究中，室内和室外颗粒物的质量浓度时间序列数据关系到PM _2.5（空气动力学尺寸最大为2.5μ）和PM _10.0使用Haz-Dust EPAM-5000从穆扎法拉巴德市Azad克什米尔的六个不同地点采集到的空气动力学数据（最大空气动力学尺寸为10.0μ）。线性和非线性方法被用来提取室内和室外大气颗粒物的质量浓度时间序列特征。这些功能作为强大的机器学习分类器的输入。支持向量机（SVM）内核，集成分类器，决策树和K近邻（KNN）用于对室内和室外颗粒物时间序列进行分类。根据曲线下面积（AUC），准确性，真阴性率，真阳性率，阴性预测值和阳性预测值来评估性能。使用三次和粗高斯SVM以及余弦和三次KNN可获得最高的准确度（95.8％），而使用精细高斯和三次SVM以及三次加权KNN可获得最高的AUC，即1.00。