scholarly journals Regional forecasting of fine particulate matter concentrations: A novel hybrid model based on principal component regression and EOF

2021 ◽  
Author(s):  
Xianghua Wu ◽  
Kang Xie ◽  
Jane Liu ◽  
Duanyang Liu ◽  
Jieqin Zhou ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Hybrid Model ◽  
Fine Particulate Matter ◽  
Principal Component Regression ◽  
Principal Component ◽  
Model Based ◽  
Fine Particulate

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

2009 ◽  
Vol 6 (5) ◽  
pp. 398 ◽  
Author(s):  
Adrian J. Friend ◽  
Godwin A. Ayoko
Keyword(s):  
Decision Making ◽  
Particulate Matter ◽  
Meteorological Data ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Alternative Methods ◽  
Preference Ranking ◽  
Receptor Models ◽  
Fine Particulate ◽  
Atmospheric Concentrations

Environmental context. There are serious global concerns about the environmental and health effects of atmospheric air pollutants. However, estimates of pollutants from measurements made in the proximity of a source do not always represent the ultimate atmospheric concentrations. Therefore alternative methods of attributing pollutants to sources, and estimating their contributions to atmospheric concentrations, as demonstrated in the current work, will become an increasingly important area of environmental research. Abstract. This paper reports the application of multicriteria decision making techniques, Preference Ranking Organisation Methods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and receptor models, principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorisation (PMF), to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. Whereas PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulfate, sea salt and soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

The Mortality Impact of Fine Particulate Matter in China

2020 ◽  
Author(s):  
Yazhen Gong ◽  
Shanjun Li ◽  
Nicholas Sanders ◽  
Guang Shi
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Fine Particulate

Size-fractionated Electrochemical Quantification for Compact Monitoring of Fine Particulate Matter

2021 ◽  
pp. 106386
Author(s):  
Heyu Yin ◽  
Sina Parsnejad ◽  
Ehsan Ashoori ◽  
Hao Wan ◽  
Wen Li ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Fine Particulate

SIZE SEGREGATED CHARACTERIZATION OF FINE PARTICULATE MATTER IN LEIPZIG AND MELPITZ AEROSOL

2001 ◽  
Vol 32 ◽  
pp. 353-354
Author(s):  
E. BRÜGGEMANN ◽  
T. GNAUK ◽  
K. MULLER ◽  
H. HERRMANN
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Fine Particulate

scholarly journals Association of prenatal Exposure to Fine Particulate matter Pollution with Childhood eczema

Allergy ◽  
2021 ◽  
Author(s):  
Tsung‐Chieh Yao ◽  
Hsin‐Yi Huang ◽  
Wen‐Chi Pan ◽  
Chao‐Yi Wu ◽  
Shun‐Yu Tsai ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Prenatal Exposure ◽  
Fine Particulate Matter ◽  
Fine Particulate ◽  
Particulate Matter Pollution
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