The effects of climate change on the dust phenomenon was simulated in this study using an artificial neural network (ANN) until 2050. Hourly Particulate matter concentrations and daily meteorological data were analyzed from 2007 to 2018 and 1988 to 2018, respectively, in Zanjan city. The outputs of HadGM2-ES (Hadley Centre Global Environmental Model, version 2- Earth System) models of atmospheric circulation were used to generate future climatic patterns under two scenarios of Representative Concentration Pathway (RCP_2.6 and RCP_8.5). The Long Ashton Research Station Weather Generator (LARS-WG 6.0) software was utilized for statistical downscaling and production of climate-related datasets in artificial high-resolution time series. The observed climatic variables, including maximum and minimum temperature and precipitation, were determined as predictors in the artificial neural network. The highest surge of PM₁₀ levels was in May and July, and the lowest increase of PM₁₀ was observed in December with a monthly average of 84.85 and 50.54 μg/m³, respectively. The highest amount of PM₁₀ was estimated for the year 2043, with a concentration of 74.26 μg/m³. Minimum and maximum temperature and wind speed had a significant relationship with PM₁₀ concentrations; further, this pollutant level increased by boosting each atmospheric variable. The minimum and maximum temperatures in both scenarios were rising till 2050, and the highest temperature growth was obtained under the worst situation of RCP_8.5.

在这项研究中，使用人工神经网络（ANN）模拟了气候变化对粉尘现象的影响，直到2050年。分别对赞詹市2007年至2018年和1988年至2018年的小时颗粒物浓度和每日气象数据进行了分析。HadGM2-ES（哈德利中心全球环境模型，版本2-地球系统）大气环流模型的输出用于在两种代表性浓度路径（RCP _2.6和RCP _8.5）的情况下生成未来的气候模式）。Long Ashton研究站天气生成器（LARS-WG 6.0）软件用于在人工高分辨率时间序列中进行统计缩减和生产与气候相关的数据集。确定的观测到的气候变量（包括最高和最低温度和降水）被确定为人工神经网络中的预测因子。PM的最高浪涌₁₀水平是在五月份和7月和PM的最低增加₁₀中12月观察到的84.85和50.54微克/ m的平均每月³分别。估计2043年的最高PM ₁₀含量为74.26μg/ m ³。最低和最高温度和风速与PM ₁₀浓度有显着关系。此外，通过增加每个大气变量来增加该污染物水平。两种情况下的最低和最高温度都一直上升到2050年，在RCP _8.5的最坏情况下获得最高的温度增长。