Abstract

In an era of rapidly changing climate, investigating the impacts of climate parameters on major air pollutants such as Particulate matter (PM₁₀) is imperative to mitigate its adverse effect. This study utilizes Geographic Information System (GIS), a multivariate regression model (MVR) and Pearson correlation analysis to examine the inter-relationship between PM₁₀ and major climate parameters such as temperature, wind speed, and humidity. Although the application of MVR for predicting PM10 has been examined in previous studies, however, the spatial modelling and prediction of this air pollutant is limited. Accurate spatial assessment of pollutants’ hazard susceptibility in relation to climate change can accelerate mitigation initiatives. Thus, to understand the behavior, seasonal pattern, and trend of PM₁₀ concentration which is vital for good air quality, GIS is essential for enhanced visualization and interpretation of the predicted occurrence of the pollutant. The acquired data were randomly divided into 80% and 20% for training and validation of the MVR model, respectively while GIS was used to model the spatial distribution of the predicted ambient PM10 concentration, highlighting the hotspots of future PM₁₀ hazard. A positive correlation index was obtained between PM₁₀ with temperature and wind speed. However, humidity showed a negative correlation. The regression model showed high predictive performance of R² = 0.298, RMSE = 12.737, and MAE of 10.343, with the highest PM₁₀ concentration correlated with the warming event in the southwest monsoon. Temperature, wind speed, and humidity were identified as the most critical variables influencing PM₁₀ concentration in the study area, in descending order of importance. This study’s outcome provides valuable spatio-temporal information on future climate change impact on PM₁₀ in the study area with the potential to support effective air quality management.

摘要

在气候迅速变化的时代，必须研究气候参数对主要空气污染物（例如颗粒物（PM ₁₀））的影响，以减轻其不利影响。这项研究利用地理信息系统（GIS），多元回归模型（MVR）和Pearson相关分析来检验_10号纸机之间的相互关系以及主要的气候参数，例如温度，风速和湿度。尽管在先前的研究中已经研究了MVR在预测PM10中的应用，但是，这种空气污染物的空间建模和预测是有限的。对与气候变化有关的污染物危害敏感性进行准确的空间评估可以加快缓解措施。因此，要了解PM ₁₀的行为，季节性模式和趋势浓度对保证良好的空气质量至关重要，而GIS对于增强可视化和解释预计的污染物发生至关重要。分别将获得的数据随机分为80％和20％分别用于MVR模型的训练和验证，而GIS用于建模预测的PM10浓度预测值的空间分布，突出了未来PM ₁₀危害的热点。在PM ₁₀与温度和风速之间获得正相关指数。然而，湿度显示出负相关。回归模型显示R ² = 0.298，RMSE = 12.737和MAE为10.343的高预测性能，其中PM ₁₀最高浓度与西南季风的变暖事件有关。温度，风速和湿度被确定为是研究区域中影响PM ₁₀浓度的最关键变量，其重要性从高到低依次排列。这项研究的结果提供了有关未来气候变化对研究区域PM _10的影响的宝贵时空信息，有可能支持有效的空气质量管理。