Air pollution is a crucial concern which impacts human health, agricultural harvests, forestry, animals as well as the environment. Indigenous environmental or health organizations regularly make daily air contamination predictions for public awareness and for use in making decisions concerning reduction methods in addition to the management air quality. Predictions are customarily based on statistical associations between meteorological conditions and ambient air contamination concentrations. Multiple linear regression (MLR) models, using similar input and output parameters, allowing a comparative study of the two methods are extensively utilized. This work aims to develop the ANN models for forecasting concentrations of PM₁₀, NO₂, and O₃ in Nicosia and to compare the predictive ability of the MLR (linear method) and Artificial Neural Network (ANN) (non-linear method) models. Previous day's pollutant concentration, atmospheric pressure, wind speed, relative humidity, and temperature data from 2012 to 2015 were used as input parameters or independent variables, while observed pollutant concentration for each pollutant was used as output or dependent variable. The reliability and strength of the models were evaluated via the root mean square error, mean absolute error, and the Pearson correlation coefficient. Study results indicated that MLR did better than ANN except in a few cases. However, the Backpropagation (BP) models of all three pollutants developed in this research were found to agree with other studies in the literature proving that the BPANN models built in this study can be used for the prediction of NO₂, O₃, and PM₁₀.

空气污染是影响人类健康，农业收成，林业，动物以及环境的至关重要的问题。土著环境或卫生组织定期做出每日空气污染预测，以提高公众意识，并用于管理空气质量以外的有关减少方法的决策。通常根据气象条件与周围空气污染浓度之间的统计关联来进行预测。使用相似的输入和输出参数的多元线性回归（MLR）模型，允许对这两种方法进行比较研究。这项工作旨在开发用于预测PM ₁₀，NO ₂和O ₃浓度的ANN模型并比较了MLR（线性方法）和人工神经网络（ANN）（非线性方法）模型的预测能力。前一天的污染物浓度，大气压力，风速，相对湿度和温度数据（2012年至2015年）用作输入参数或自变量，而观察到的每种污染物的污染物浓度用作输出或因变量。通过均方根误差，平均绝对误差和Pearson相关系数评估模型的可靠性和强度。研究结果表明，除少数情况外，MLR的效果优于ANN。然而，₂，O ₃和PM ₁₀。