Abstract

Particulate matter (PM_2.5) concentrations are a serious human health concern and global models are the common methods for PM_2.5 particle estimation disregarding the local changes and factors. In this study, a polynomial model for PM_2.5 particles prediction was proposed to examine the correlations among PM_2.5, PM₁₀, and meteorological parameters. The study was carried out in the north of Iraq including two provinces; Kirkuk and Sulaymaniyah. The data gathered from different sources. Two datasets have been used, collected during July 2019 and February 2020. To test our methodology, the model was applied on a small subset of the study area (5.6 km²) inside the Kirkuk province. Datasets (observation and ground truth) were utilized to examine the model. Based on the July 2019 dataset, the mean local R² values were estimated at 0.98 and 0.97 in the north part of Iraq, and inside the Kirkuk province (the small subset), respectively. While based on the February 2020 dataset, the mean local R² values were estimated at 0.98 inside the Kirkuk province. High values of prediction accuracies were obtained by 82% and 96% in July and February, respectively. Moreover, our findings highlighted that the health impacts and air quality varied from moderate to unhealthy in the region.

摘要

颗粒物 (PM _2.5 ) 浓度是一个严重的人类健康问题，全球模型是不考虑局部变化和因素的PM _2.5颗粒估计的常用方法。在这项研究中，提出了用于 PM _2.5粒子预测的多项式模型来检查 PM _2.5、PM ₁₀和气象参数之间的相关性。该研究在伊拉克北部包括两个省进行；基尔库克和苏莱曼尼亚。从不同来源收集的数据。使用了两个数据集，分别于 2019 年 7 月和 2020 年 2 月收集。为了测试我们的方法，该模型应用于研究区域的一个小子集（5.6 km ²) 在基尔库克省内。数据集（观察和地面实况）用于检查模型。根据 2019 年 7 月的数据集，伊拉克北部和基尔库克省（小子集）内部的平均局部 R ²值估计分别为 0.98 和 0.97。虽然基于 2020 年 2 月的数据集，基尔库克省内的平均本地 R ²值估计为 0.98。7 月和 2 月的预测准确率分别为 82% 和 96%。此外，我们的研究结果强调，该地区的健康影响和空气质量从中等到不健康不等。