Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Jiae, Xiang; Li, Ruopu; Wang, Guangxing; Qie, Guangping; Wang, Qing; Xu, Li-Hua; Zhang, Maozhen; Mengping, Tang

doi:10.1007/s11270-017-3421-6

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…In recent decades, with the rapid population growth, economic development, industrialization and urbanization, ecological environment is facing considerable pressure, such as the decline of vegetation coverage [1][2][3], soil pollution [4], air pollution [5,6], urban heat island effect [7,8], ecological environment damage and the reduction of animal and plant habitats [9,10]. A series of ecological environment changes have attracted wide attention from government and academic.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multi-Source Remote Sensing Data for Spatial-Temporal Change Monitoring of Ecological Environment in the Yellow River Delta

Liu¹,

Yin²,

Liu³

2022

Pol. J. Environ. Stud.

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Rapid and timely evaluation of ecological environment change is of great significance to the sustainable development of the region. Owing to climate change and more anthropological intervention, ecological environment is deteriorating in the Yellow River Delta High-Efficiency Ecological Economic Zone (YRDHEEZ). To evaluate ecological environment change of the YRDHEEZ, a comprehensive evaluation index (CEI) was built in this investigation, which integrated vegetation cover (VC), land surface temperature (LST) and fine particulate matter (PM2.5) concentration. The experimental results demonstrated that significant regional differences existed in the ecological environment of the YRDHEEZ from 2000 to 2018. The ecological environmental changes in most districts and counties are complex and diverse. More specifically, regions with improvement, moderate improvement and the unchanged ecological environment accounted for more than 70 % of the study area, which suggested that the ecological environment has improved. In order to further verify the validity of the CEI, a field survey was performed, and it found that the result of field survey corresponded with the CEI evaluation, which demonstrated the potential of multi-source remote sensing data to quickly monitor the change of ecological environment quality in the YRDHEEZ.

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Section: Introductionmentioning

confidence: 99%

Integration of Multi-Source Remote Sensing Data for Spatial-Temporal Change Monitoring of Ecological Environment in the Yellow River Delta

Liu¹,

Yin²,

Liu³

2022

Pol. J. Environ. Stud.

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“…For example, Fatehifar et al used a multiple cell model to evaluate the releasing of pollutants from Tabriz oil refinery stacks by considering a variable location of stacks and wind blowing angle [20]. Xiang et al [21] proposed statistical models combined with a linear regression model and a non-linear logistic one to model the PM 2.5 concentration. Assuming fixed atmospheric parameters, Regland et al introduced a two-dimensional approach for modeling pollutant emissions from surface emission sources [22].…”

Section: Introductionmentioning

confidence: 99%

A 3D mathematical evaluation of the emission into the air of reactive BTEX compounds: A new approach for mechanism reduction

Eslami

Fatehifar

Kaynejad

2021

Environmental Engineering Research

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BTEX species are abundant volatile organic compounds that are classified as main pollutants by several environmental protection agencies. In this study, a new 3D air pollution dispersion model with the capability of taking into account the BTEX's reactions was developed and evaluated. The Toxchem model is employed to estimate the amount of emitted BTEX from several area sources, followed by simulating the transport of the reactive species through a 3D model. Also, reduced mechanisms were developed, based on Master Chemical Mechanism (MCM), for the simulation of the atmospheric chemistry of BTEX. The application of the mechanism reduction method yielded a mechanism of 43 species and 45 reactions. Based on findings, the deviation of the reduced mechanism from the whole mechanism was < 4% throughout the simulation. In addition, the result showed that, during a 12 h period of simulation, the effect of the atmospheric chemical reaction on reducing the final concentration of benzene, toluene, ethylbenzene, m-xylene, o-xylene, and p-xylene was about 0.3, 1, 1.4, 5, 3.1, and 3.2%, respectively. Lastly, it was indicated that the estimated emission rates by Toxchem and simulated concentrations by the dispersion model were in good agreement with the reported experimental values.

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Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods

Chen

Wang

et al. 2022

IJGI

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In urban environmental management and public health evaluation efforts, there is an urgent need for fine-grained urban air quality monitoring. However, the high price and sparse distribution of air quality monitoring equipment make it difficult to develop effective and comprehensive fine-scale monitoring at the city scale. This has also led to air quality estimation methods based on incomplete monitoring data, which lack the ability to detect urban air quality differences within a neighborhood. To address this problem, this study proposes a refined urban air quality estimation method that fuses multisource spatio-temporal data. Based on the fact that urban air quality is easily affected by social activities, this method integrates meteorological data with urban social activity data to form a comprehensive environmental data set. It uses the spatio-temporal feature extraction model to extract the multi-source spatio-temporal features of the comprehensive environmental data set. Finally, the improved cascade forest algorithm is used to fit the relationship between the multisource spatio-temporal features and the air quality index (AQI) to construct an air quality estimation model, and the model is used to estimate the hourly PM2.5 index in Beijing on a 1 km × 1 km grid. The results show that the estimation model has excellent performance, and its goodness-of-fit (R2) and root mean square error (RMSE) reach 0.961 and 17.47, respectively. This method effectively achieves the assessment of urban air quality differences within a neighborhood and provides a new strategy for preventing information fragmentation and improving the effectiveness of information representation in the data fusion process.

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Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Cited by 3 publications

References 41 publications

Integration of Multi-Source Remote Sensing Data for Spatial-Temporal Change Monitoring of Ecological Environment in the Yellow River Delta

Integration of Multi-Source Remote Sensing Data for Spatial-Temporal Change Monitoring of Ecological Environment in the Yellow River Delta

A 3D mathematical evaluation of the emission into the air of reactive BTEX compounds: A new approach for mechanism reduction

Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods

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