This research delves into the air quality dynamics within Abu Dhabi, UAE, focusing on the analysis and future projection of NO2, PM10, and PM2.5 levels from 2015 through 2023. Through the application of ARIMA models for predictive analysis and ordinary kriging for spatial evaluation, the study meticulously assesses pollutant concentrations across 19 monitoring stations. Advanced data processing methods were employed, utilizing RStudio for statistical forecasting, complemented by the Openair package for the visualization and analysis of spatial data. Findings from this study unveil pronounced spatial distributions and evolving trends in pollutant concentrations, identifying notable pollution hotspots such as Mussafah, Hamdan Street, and Baniyas School, which consistently report higher levels of pollutants. The predictive accuracy of ARIMA models, as demonstrated by MAPE values, affirms their capability to project air quality trends accurately, particularly in identifying zones with increased pollution due to industrial operations and traffic congestion. The analysis highlights the profound impact of urban expansion and vehicle emissions on air quality. Predictions from ARIMA models are shown to be invaluable for guiding environmental strategy and policy making. The detailed examination not only illuminates current air quality conditions but also predicts a rise in pollutant levels in specific locales, signaling a pressing need for targeted environmental policies. Contributing significantly to the field of environmental science, this study proposes a comprehensive approach for evaluating and forecasting air quality in urban areas. It underscores the importance of predictive modeling in crafting and informing environmental policies and measures aimed at pollution reduction. In line with UAE's sustainability ambitions, the research advocates for improved air quality management practices to address pollution effectively and protect public health in Abu Dhabi and cities with similar environmental challenges, highlighting the results' practical implications for environmental policy and urban planning.

中文翻译：

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一种全面的时空方法绘制荒漠地区空气质量分布图和预测


本研究深入研究了阿联酋阿布扎比的空气质量动态，重点是 2015 年至 2023 年 NO2、PM10 和 PM2.5 水平的分析和未来预测。通过应用 ARIMA 模型进行预测分析，并应用普通克里金法进行空间评估，该研究仔细评估了 19 个监测站的污染物浓度。采用先进的数据处理方法，利用 RStudio 进行统计预测，并辅以 Openair 软件包对空间数据进行可视化和分析。这项研究的结果揭示了污染物浓度的明显空间分布和演变趋势，确定了著名的污染热点，如穆萨法、哈姆丹街和巴尼亚斯学校，这些地区的污染物水平一直较高。MAPE 值所证明的 ARIMA 模型的预测准确性肯定了它们准确预测空气质量趋势的能力，尤其是在识别因工业运营和交通拥堵而导致污染增加的区域方面。该分析强调了城市扩张和车辆排放对空气质量的深远影响。ARIMA 模型的预测被证明对于指导环境战略和政策制定非常有价值。详细的检查不仅阐明了当前的空气质量状况，还预测了特定地区的污染物水平上升，这表明迫切需要制定有针对性的环境政策。本研究对环境科学领域做出了重大贡献，提出了一种评估和预测城市地区空气质量的综合方法。它强调了预测建模在制定和告知旨在减少污染的环境政策和措施方面的重要性。 根据阿联酋的可持续发展目标，该研究倡导改进空气质量管理实践，以有效解决污染问题并保护阿布扎比和面临类似环境挑战的城市的公共卫生，并强调了结果对环境政策和城市规划的实际意义。