Modeling PM2.5 concentration in tehran using satellite-based Aerosol optical depth (AOD) and machine learning: Assessing input contributions and prediction accuracy

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-04-01 DOI:10.1016/j.rsase.2025.101549

Zahra Amiri , Maryam Zare Shahne

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引用次数: 0

Abstract

The adverse effects of PM_2.5 on human health and the environment necessitate precise and continuous monitoring of this pollutant. Satellite remote sensing technology provides an effective and cost-efficient alternative to ground-based measurements. However, accurately estimating ground-based PM_2.5 concentrations using Aerosol Optical Depth (AOD) is challenging due to the influence of various parameters and atmospheric conditions on the AOD-PM_2.5 relationship. In this study, Aerosol Optical Depth (AOD) data were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at a spatial resolution of 1 km, covering a ten-year period from 2012 to 2023. The objective was to estimate PM₂.₅ concentrations for six ground-based monitoring stations in Tehran, Iran. These estimated concentrations were compared with daily measurements from the Tehran Air Quality Control Company air pollution monitoring stations for the same period. To determine the most significant conditioning factors in the modeling process and their impacts, the genetic algorithm optimization method and the Recursive Feature Elimination (RFE) technique were employed. The results indicated that, in addition to the AOD parameter, meteorological parameters such as wind speed, wind direction, temperature, precipitation, normalized difference vegetation index (NDVI), and visibility (VIS) could enhance model accuracy. Predictions were made using three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). The findings revealed that the RF method was the most accurate, achieving accuracy in the range of 94–98 % for predicting PM_2.5 concentrations for all the studied stations. This study's results can significantly aid policymakers and researchers in utilizing satellite data for air pollution monitoring and management.

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利用基于卫星的气溶胶光学深度（AOD）和机器学习模拟德黑兰PM2.5浓度：评估输入贡献和预测精度

PM2.5对人类健康和环境的不利影响，需要对这种污染物进行精确和持续的监测。卫星遥感技术为地面测量提供了一种有效和具有成本效益的替代方法。然而，由于各种参数和大气条件对AOD-PM2.5关系的影响，利用AOD（气溶胶光学深度）准确估算地面PM2.5浓度具有挑战性。在本研究中，气溶胶光学深度（AOD）数据来自中分辨率成像光谱仪（MODIS）传感器，空间分辨率为1 km，覆盖2012 - 2023年10年。目的是估算伊朗德黑兰六个地面监测站的PM2.5浓度。这些估计的浓度与同期德黑兰空气质量控制公司空气污染监测站的每日测量值进行了比较。为了确定建模过程中最重要的调节因素及其影响，采用了遗传算法优化方法和递归特征消除（RFE）技术。结果表明，除AOD参数外，风速、风向、气温、降水、归一化植被指数（NDVI）、能见度（VIS）等气象参数均能提高模型精度。预测使用三种机器学习算法：随机森林（RF），支持向量机（SVM）和高斯过程回归（GPR）。研究结果表明，RF方法是最准确的，在预测所有研究站点的PM2.5浓度时，准确率在94 - 98%之间。这项研究的结果可以极大地帮助决策者和研究人员利用卫星数据进行空气污染监测和管理。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems