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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
Environmental Science and Pollution Research ( IF 5.8 ) Pub Date : 2021-09-17 , DOI: 10.1007/s11356-021-16150-0
Abdulwaheed Tella 1 , Abdul-Lateef Balogun 1
Affiliation  

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia’s air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Graphical abstract



中文翻译:

基于 GIS 的空气质量建模:使用机器学习算法对马来西亚雪兰莪州的 PM10 进行空间预测

快速城市化导致全球空气质量严重恶化,导致住院和过早死亡人数增加。因此,准确预测空气质量对于支持城市可持续性和复原力的缓解规划至关重要。尽管一些研究已经使用机器学习算法 (MLA) 预测了空气污染物,例如颗粒物 (PM),但是关于空气质量指数 (AQI) 的空间危害评估研究却很少。将 PM 纳入 AQI 研究至关重要,因为其易于吸入的微小尺寸会对生态、环境和人类健康产生不利影响。准确及时地预测空气质量指数可以确保充分干预以帮助空气质量管理。所以,本研究通过开发四种机器学习模型,使用直径为 10 μm 或更小的颗粒物 (PM10) 对马来西亚雪兰莪州的空气质量指数进行空间危害评估:极端梯度提升 (XGBoost)、随机森林 (RF) 、K 最近邻 (KNN) 和朴素贝叶斯 (NB)。NDVI、SAVI、BU、LST、Ws、坡度、海拔和道路密度等空间处理数据用于建模。该模型使用 70% 的数据集进行训练,而 30% 用于交叉验证。结果表明,XGBoost 的总体准确率和精度最高,分别为 0.989 和 0.995,其次分别是随机森林 (0.989, 0.993)、K 最近邻 (0.987, 0.984) 和朴素贝叶斯 (0.917, 0.922)。空间空气质量地图是通过将地理信息系统(GIS)与四个与马来西亚的空气污染指数相关联的 MLA 相结合而生成的。这些地图显示雪兰莪州的空气质量令人满意,不会对健康构成威胁。然而,具有最佳性能的两种算法(XGBoost 和 RF)表明大部分空气质量为中等。该研究得出结论,雪兰莪和其他东南亚城市可以采用成功的空气污染管理政策,例如绿色基础设施实践、提高能源效率和限制重型车辆,以防止未来空气质量恶化。这些地图显示雪兰莪州的空气质量令人满意,不会对健康构成威胁。然而,具有最佳性能的两种算法(XGBoost 和 RF)表明大部分空气质量为中等。该研究得出结论,雪兰莪和其他东南亚城市可以采用成功的空气污染管理政策,例如绿色基础设施实践、提高能源效率和限制重型车辆,以防止未来空气质量恶化。这些地图显示雪兰莪州的空气质量令人满意,不会对健康构成威胁。然而,具有最佳性能的两种算法(XGBoost 和 RF)表明大部分空气质量为中等。该研究得出结论,雪兰莪和其他东南亚城市可以采用成功的空气污染管理政策,例如绿色基础设施实践、提高能源效率和限制重型车辆,以防止未来空气质量恶化。

图形概要

更新日期:2021-09-17
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