Daily Estimation of Fine Particulate Matter Mass Concentration Through Satellite Based Aerosol Optical Depth

Karimian, Hamed; Li, Q.; Li, C. C.; Fan, Jiachen; Jin, Longyang; Gong, Chongshui; Mo, Yuqin; Hou, Jianxia; Ahmad, Aasim

doi:10.5194/isprs-annals-iv-4-w2-175-2017

Cited by 12 publications

(4 citation statements)

References 37 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PM 2.5 consists of toxic and hazardous substances that are highly active, leading to its long residence time in the atmosphere and far transportation distance. As a result, exposure to high concentrations of PM 2.5 has been found to exacerbate the development of cardiovascular and respiratory diseases 1 , 2 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

Mohammadi,

Teiri,

Hajizadeh

et al. 2024

Sci Rep

View full text Add to dashboard Cite

With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

Mohammadi,

Teiri,

Hajizadeh

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…There are well-known SARS, MERS, Ebola, Zika and recent COVID-19. As monitoring and mitigation of environmental pollutants have been one of the major concerns in recent years ( Karimian et al, 2017 ; Wu et al, 2018 ; Hojjati-Najafabadi et al, 2023 ), one of the ultimate usage of epidemiological studies could be to control and mitigate the environmental influence of different disease through considering various factors and scenarios ( Mansoorianfar et al, 2022 ; Vasseghian et al, 2023 ). Some of the epidemiological studies provide basic dataset for further deep understanding of a phenomenon and its health effect ( Chen et al, 2022 ; Karimian et al, 2023 ), while others investigate the transmission of an infection and they ways to control it ( Hu et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal transmission of infectious particles in environment: A case study of Covid-19

Karimian¹,

Fan

et al. 2023

Chemosphere

View full text Add to dashboard Cite

“…The accuracy of NWP models highly depends on the initial conditions [2][3][4][5]. Therefore, it is essential that these models incorporate observations of high quality and that have the proper spatial distribution density.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CAMEL over the Taklimakan Desert Using Field Observations

Han

et al. 2023

Land

View full text Add to dashboard Cite

Infrared (IR) land surface emissivity (LSE) plays an important role in numerical weather prediction (NWP) models through the satellite radiance assimilation. However, due to the large uncertainties in LSE over the desert, many land-surface sensitive channels of satellite IR sensors are not assimilated. This calls for further assessments of the quality of satellite-retrieved LSE in these desert regions. A set of LSE observations were made from field experiments conducted on 16–18 October 2013 along a south/north desert road in the Taklimakan Desert (TD), China. The observed LSEs (EOBS) are thus used in this study as the reference values to evaluate the quality of Combined ASTER MODIS Emissivity over Land (CAMEL) data. Analysis of these data shows four main results. First, the CAMEL datasets appear to sufficiently capture the spatial variations in LSE from the oasis to the hinterland of the TD (this is especially the case in the quartz reststrahlen band). From site 1 at the southern edge of the Taklimakan Desert to site 10 at the northern edge, the measured LSE and the corresponding CAMEL observation in the quartz reststrahlen band first decrease and reach their minimum around sites 4–6 in the hinterland of the Taklimakan Desert. Then, the LSE increases gradually and finally reaches its maximum at site 10, which has a clay ground surface, showing that the LSE is higher at the edges of the desert and lower in the center. Second, the CAMEL values at 11.3 μm have a zonal distribution characterized by a northeast–southwest strike, though such an artifact might have been introduced by ASTER LSE data during the merging process that created the CAMEL dataset. Third, the unrealistic variation of the original EOBS can be filtered out with useful signals, as identified by the first six principal components of the PCA conducted on the laboratory-measured hyperspectral emissivity spectra (ELAB). Fourth, the CAMEL results correlate well with the measured LSE at the 10 observation sites, with the observed LSE being slightly smaller than the CAMEL values in general.

show abstract

Daily Estimation of Fine Particulate Matter Mass Concentration Through Satellite Based Aerosol Optical Depth

Cited by 12 publications

References 37 publications

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

Spatiotemporal transmission of infectious particles in environment: A case study of Covid-19

Evaluation of CAMEL over the Taklimakan Desert Using Field Observations

Contact Info

Product

Resources

About