Air Quality and Meteorological Dataset from Monitoring Stations in Salvador, Brazil, 2011–2016

This dataset contains hourly air quality and meteorological measurements collected from air quality monitoring stations located in Salvador, Bahia, Brazil, covering the period from 2011 to 2016. The data were obtained from the monitoring network operated by CETREL S.A. and include records from eight monitoring stations: Av. ACM–Detran (ACM), Av. Barros Reis (BR), Paralela–CAB (CAB), Campo Grande (CG), Dique do Tororó (DT), Itaigara (IT), Pirajá (PI), and Rio Vermelho (RV). The dataset was used in the study entitled “Interpretação de Poluentes e Variáveis Meteorológicas por Meio de Modelos Explicáveis de Aprendizado de Máquina”, which investigates atmospheric patterns in Salvador using supervised machine learning and explainable artificial intelligence techniques. The dataset includes pollutant concentrations and meteorological variables represented as hourly averages. The pollutant variables include carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), nitrogen oxides (NO, NO2, and NOX), and particulate matter (MP). The meteorological variables include relative humidity (HUM), air temperature (TEMP), rainfall (RAIN), wind speed (WIND_SPEED), standard deviation of wind direction (STWD), and transformed wind direction components represented by sine and cosine values. The final processed dataset contains 193,569 samples and 16 columns, including timestamp, monitoring station identifier, pollutant variables, meteorological variables, and trigonometric representations of wind direction. The preprocessing steps included removal of missing or inconsistent records, treatment of outliers in pollutant variables using an interquartile range criterion, and transformation of the angular wind direction variable into sine and cosine components to avoid discontinuities associated with circular data. This dataset can support studies on urban air quality, atmospheric pollution, environmental monitoring, machine learning, spatial characterization of monitoring stations, and explainable artificial intelligence applied to environmental data. In the associated article, the dataset was used to train a Random Forest classifier for monitoring station classification and to apply SHAP-based explainability analysis, allowing the identification of relevant pollutant and meteorological variables associated with spatial atmospheric patterns in Salvador. The data are suitable for reproducibility studies, benchmarking of machine learning models, exploratory analysis of pollutant and meteorological relationships, and development of interpretable models for air quality assessment.本数据集收录了巴西巴伊亚州萨尔瓦多市空气质量监测站采集的逐小时空气质量与气象观测数据，采集时段为2011年至2016年。数据来源于CETREL S.A.运营的监测网络，涵盖8个监测站点：ACM大道-交通管理局（Av. ACM–Detran，简称ACM）、巴罗斯雷斯大道（Av. Barros Reis，简称BR）、帕拉莱拉-卡布（Paralela–CAB，简称CAB）、大坎普（Campo Grande，简称CG）、托罗罗水坝（Dique do Tororó，简称DT）、伊泰加拉（Itaigara，简称IT）、皮拉雅（Pirajá，简称PI）以及里约韦尔梅柳（Rio Vermelho，简称RV）。本数据集曾用于题为《基于可解释机器学习模型解析污染物与气象变量》的研究，该研究借助监督式机器学习（supervised machine learning）与可解释人工智能（explainable artificial intelligence）技术，探究萨尔瓦多市的大气变化规律。 数据集包含以小时均值表征的污染物浓度与气象变量。污染物变量涵盖一氧化碳（carbon monoxide, CO）、二氧化硫（sulfur dioxide, SO2）、臭氧（ozone, O3）、氮氧化物（nitrogen oxides, NO、NO2及NOX）与颗粒物（particulate matter, MP）。气象变量包括相对湿度（relative humidity, HUM）、气温（air temperature, TEMP）、降雨量（rainfall, RAIN）、风速（wind speed, WIND_SPEED）、风向标准差（standard deviation of wind direction, STWD），以及经正弦、余弦变换得到的风向分量。最终处理后的数据集包含193569条样本与16个字段，涵盖时间戳、监测站点标识符、污染物变量、气象变量以及风向的三角函数表征值。预处理步骤包括剔除缺失或不一致的记录、采用四分位距（interquartile range）准则处理污染物变量中的异常值，以及将风向角度变量转换为正弦、余弦分量以规避循环数据带来的不连续性问题。 本数据集可支撑城市空气质量、大气污染、环境监测、机器学习、监测站点空间特征刻画，以及面向环境数据的可解释人工智能相关研究。在配套学术文章中，该数据集被用于训练随机森林（Random Forest）分类器以实现监测站点分类，并开展基于SHAP的可解释性分析，从而识别出与萨尔瓦多市空间大气模式相关的关键污染物与气象变量。本数据集适用于可复现性研究、机器学习模型基准测试、污染物与气象变量关系探索性分析，以及面向空气质量评估的可解释模型开发。