Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.

亚利桑那州的居民由于亚利桑那州的干旱气候，长期以来一直在处理引起健康问题的悬浮颗粒物。亚利桑那州容易受到沙尘暴的影响，特别是在季风季节，因为风向和风向异常。在这项研究中，模拟了高分辨率天气研究和预报（WRF）模型以及化学模块（WRF-Chem），以计算颗粒物的时空分布以及亚利桑那州的气候参数。随后，利用普通最小二乘（OLS），空间滞后，空间误差和地理加权回归（GWR）技术，基于影响沙尘暴期间颗粒物形成和扩散的气候指标，开发了预测模型。采用人口普查区域为所选变量创建局部空间平均值。地形高度，温度，风速和植被分数被指定为最显着的变量，而基础状态和摄动压力，行星边界层高度和土壤湿度被作为补充变量。OLS，空间滞后，空间误差和GWR模型的确定系数分别达到0.92、0.93、0.96和0.97的峰值。这些模型可以更好地了解颗粒物的当前分布，并可用于预测未来趋势。行星边界层高度和土壤湿度被作为补充变量。OLS，空间滞后，空间误差和GWR模型的确定系数分别达到0.92、0.93、0.96和0.97的峰值。这些模型可以更好地了解颗粒物的当前分布，并可用于预测未来趋势。行星边界层高度和土壤湿度被作为补充变量。OLS，空间滞后，空间误差和GWR模型的确定系数分别达到0.92、0.93、0.96和0.97的峰值。这些模型可以更好地了解颗粒物的当前分布，并可用于预测未来趋势。