Spatiotemporal distribution prediction for PM2.5 based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China.

IF 8 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Journal of Environmental Management Pub Date : 2025-01-01 Epub Date: 2024-12-18 DOI:10.1016/j.jenvman.2024.123682
Shiqi Zhao, Hong Lin, Hongjun Wang, Gege Liu, Xiaoning Wang, Kailun Du, Ge Ren
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引用次数: 0

Abstract

The increasing demand for air pollution control has driven the application of low-cost sensors (LCS) in air quality monitoring, enabling higher observation density and improved air quality predictions. However, the inherent limitations in data quality from LCS necessitate the development of effective methodologies to optimize their application. This study established a hybrid framework to enhance the accuracy of spatiotemporal predictions of PM2.5, standard instrument measurements were employed as reference data for the remote calibration of LCS. To account for local emission characteristics, the calibration model was trained using statistical values from LCS during periods of reduced anthropogenic emissions. This calibration approach significantly improved data quality, increasing R2 values of LCS data from 0.60 to 0.85. Subsequently, an advanced predictive model, STXGBoost, was developed, combining Kriging interpolation technology with high-density LCS data to integrate temporal trends and geographic spatial correlations. The STXGBoost model effectively captured the spatiotemporal variability of PM2.5 data, producing accurate and high spatiotemporal resolution PM2.5 prediction maps, with R2 values of 0.96, 0.92, and 0.89 for 1-h, 4-h, and 48-h predictions, respectively. These findings demonstrate the feasibility of generating high-resolution urban air pollution maps by integrating high-density ground monitoring data with advanced computational approaches. This framework provides valuable support for precise management and informed decision-making in urban atmospheric environments.

基于 STXGBoost 模型和高密度监测传感器的中国郑州高新区 PM2.5 时空分布预测。
日益增长的空气污染控制需求推动了低成本传感器(LCS)在空气质量监测中的应用,实现了更高的观测密度和改进的空气质量预测。然而,LCS在数据质量方面的固有限制需要开发有效的方法来优化其应用。为了提高PM2.5时空预测的准确性,本研究建立了一个混合框架,采用标准仪器测量数据作为LCS远程校准的参考数据。为了考虑当地的排放特征,校正模型是使用LCS在减少人为排放期间的统计值进行训练的。这种校准方法显著提高了数据质量,LCS数据的R2值从0.60提高到0.85。随后,开发了先进的预测模型STXGBoost,将Kriging插值技术与高密度LCS数据相结合,整合时间趋势和地理空间相关性。STXGBoost模型有效捕获了PM2.5数据的时空变动性,生成了准确、高时空分辨率的PM2.5预测图,预测1 h、4 h和48 h的R2分别为0.96、0.92和0.89。这些发现表明,通过将高密度地面监测数据与先进的计算方法相结合,生成高分辨率城市空气污染地图是可行的。该框架为城市大气环境的精确管理和知情决策提供了宝贵的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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