工业环境中的空气污染，特别是镀铬过程中的空气污染，由于有害污染物浓度高，对工人构成重大健康风险。接触六价铬、挥发性有机化合物 (VOC) 和颗粒物等物质可能会导致严重的健康问题，包括呼吸系统问题和肺癌。持续监控和及时干预对于减轻这些风险至关重要。传统的空气质量监测方法往往缺乏实时数据分析和预测能力，限制了其主动解决污染危害的有效性。本文介绍了一种专为镀铬行业设计的实时空气污染监测和预报系统。该系统在物联网 (IoT) 传感器和人工智能方法的支持下，可检测多种空气污染物，包括 NH3、CO、NO2、CH4、CO2、SO2、O3、PM2.5 和 PM10，并提供实时数据污染物浓度水平的时间数据。传感器收集的数据使用 LSTM、随机森林和线性回归模型进行处理，以预测污染水平。 LSTM 模型在温度和湿度预测方面实现了 99% 的变异系数 (R²) 和 0.33 的平均绝对百分比误差 (MAE)。对于 PM2.5，随机森林模型的表现优于其他模型，实现了 84% 的 R² 和 10.11 的 MAE。当预计未来几小时内出现高污染水平时，该系统会启动工厂排气扇使空气循环，从而在问题出现之前采取主动措施改善空气质量。这种创新方法展示了工业环境监测方面的重大进步，能够动态响应污染并改善工业环境中的空气质量。版权所有 © 2024 作者。由爱思唯尔公司出版。保留所有权利。

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.