Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Department of Environmental Science and Engineering, Tunghai University, Taichung, Taiwan.
Environ Res. 2024 Mar 1;244:117906. doi: 10.1016/j.envres.2023.117906. Epub 2023 Dec 13.
Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM by the LCS network. The calibrated PM were shown to agree with reference PM measured by the BAM-1020 with R of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM concentrations in the urban area. Results showed that the highest average PM concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM hotspots and source identification, aiding decision-makers in reducing atmospheric PM concentrations and formulating regional air pollution control strategies.
低成本传感器（LCS）网络被广泛用于提高城市地区空气污染物浓度时空分布的分辨率。然而，对于微环境中的空气污染来源的研究，尤其是在工业和混合用途住宅区，仍需要进一步完成。本研究基于 6 个月的 LCS 网络数据集，调查了城市地区 PM 的时空分布和来源贡献。人工神经网络（ANN）用于校准 LCS 网络测量的 PM。校准后的 PM 与 BAM-1020 测量的参考 PM 之间的 R 为 0.85，MNE 为 30.91%，RMSE 为 3.73μg/m，符合热点识别和个人暴露研究目的的标准。进一步采用克里金方法建立了城市地区 PM 浓度的时空分布。结果表明，由于季风和地形的影响，秋冬季 PM 浓度最高。从日变化的角度来看，由于交通排放和工业生产，白天 PM 浓度最高。基于目前基于 ANN 的微环境源贡献评估模型，确定寺庙、炸鸡店、购物和住宅区的交通排放以及机械制造和精密金属加工等工业活动是 PM 的来源。本研究提出的数值算法与 LCS 网络相结合，是 PM 热点和源识别的实用框架，有助于决策者降低大气 PM 浓度并制定区域空气污染控制策略。
