Contribution of road traffic emissions to ambient air concentrations of hydrocarbons: the interpretation of monitoring measurements in Switzerland by Principal Component Analysis and road tunnel measurements

2001 ◽  
Vol 27 (1/2/3/4) ◽  
pp. 161 ◽  
Author(s):  
Johannes Staehelin ◽  
Rene Locher ◽  
Sigrid Monkeberg ◽  
Werner A. Stahel
Keyword(s):  
Principal Component Analysis ◽  
Road Traffic ◽  
Principal Component ◽  
Ambient Air ◽  
Component Analysis ◽  
Traffic Emissions ◽  
Road Tunnel ◽  
Road Traffic Emissions ◽  
Air Concentrations

scholarly journals Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

2020 ◽  
Vol 17 (6) ◽  
pp. 2099 ◽  
Author(s):  
Martin Otto Paul Ramacher ◽  
Matthias Karl
Keyword(s):  
Air Pollution ◽  
Population Dynamics ◽  
Urban Areas ◽  
Road Traffic ◽  
Ambient Air ◽  
Traffic Emissions ◽  
Exposure Model ◽  
Population Exposure ◽  
Road Traffic Emissions ◽  
Pollutant Concentrations

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen’s health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from −12% to +7% for NO2 and PM2.5. The developed “dynamic transport approach” is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

scholarly journals Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

2020 ◽  
Vol 17 (20) ◽  
pp. 7598
Author(s):  
Khaled Assi
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Road Traffic ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Road Traffic Crash ◽  
Crash Severity ◽  
Traffic Crash ◽  
Severity Prediction

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment.

scholarly journals Principal Component Analysis of Chlorophyll Content in Tobacco, Bean and Petunia Plants Exposed to Different Tropospheric Ozone Concentrations

2014 ◽  
Vol 12 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Klaudia Borowiak ◽  
Janina Zbierska ◽  
Anna Budka ◽  
Dariusz Kayzer
Keyword(s):  
Principal Component Analysis ◽  
Chlorophyll Content ◽  
Plant Species ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Principal Component ◽  
Ambient Air ◽  
Component Analysis ◽  
Chlorophyll Contents ◽  
Ozone Concentrations

Abstract Three plant species were assessed in this study - ozone-sensitive and -resistant tobacco, ozone-sensitive petunia and bean. Plants were exposed to ambient air conditions for several weeks in two sites differing in tropospheric ozone concentrations in the growing season of 2009. Every week chlorophyll contents were analysed. Cumulative ozone effects on the chlorophyll content in relation to other meteorological parameters were evaluated using principal component analysis, while the relation between certain days of measurements of the plants were analysed using multivariate analysis of variance. Results revealed variability between plant species response. However, some similarities were noted. Positive relations of all chlorophyll forms to cumulative ozone concentration (AOT 40) were found for all the plant species that were examined. The chlorophyll b/a ratio revealed an opposite position to ozone concentration only in the ozone-resistant tobacco cultivar. In all the plant species the highest average chlorophyll content was noted after the 7th day of the experiment. Afterwards, the plants usually revealed various responses. Ozone-sensitive tobacco revealed decrease of chlorophyll content, and after few weeks of decline again an increase was observed. Probably, due to the accommodation for the stress factor. While during first three weeks relatively high levels of chlorophyll contents were noted in ozone-resistant tobacco. Petunia revealed a slow decrease of chlorophyll content and the lowest values at the end of the experiment. A comparison between the plant species revealed the highest level of chlorophyll contents in ozone-resistant tobacco.

scholarly journals Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Milad Jamali-Dolatabad ◽  
Parvin Sarbakhsh ◽  
Homayoun Sadeghi-bazargani
Keyword(s):  
Principal Component Analysis ◽  
Road Traffic ◽  
Principal Component ◽  
Component Analysis ◽  
Optimum Number ◽  
Categorical Principal Component Analysis ◽  
Hidden Patterns ◽  
Fourth Component ◽  
East Azerbaijan ◽  
Medical Relief

Abstract Background Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran. Methods This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index. Results The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions. Conclusion CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate.

scholarly journals Data-driven regionalization for analyzing the spatiotemporal characteristic of air quality in China

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Sheng Li ◽  
Jiangtao Liu ◽  
Chao Wu
Keyword(s):  
Principal Component Analysis ◽  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Principal Component ◽  
Ambient Air ◽  
Component Analysis ◽  
Data Driven ◽  
Regional Collaboration ◽  
The Government

<p><strong>Abstract.</strong> With the development of urbanization and industrialization, the degradation of ambient air quality has become a serious issue that impacts human health and the environment; thus, it has attracted more attention from scholars. Usually, the mass concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with an aerodynamic diameter less than 10&amp;thinsp;&amp;mu;m and 2.5&amp;thinsp;&amp;mu;m (PM10 and PM2.5) are used to evaluate air quality. A commonly used data-driven regionalization framework for studying air quality issues, identifying areas with similar air pollution behavior and locating emission sources involves an incorporation of the principal component analysis (PCA) with cluster analysis (CA) methods. However, the traditional PCA does not consider spatial variations, which is a notable issue in geographic studies. This article focuses on extracting the local principal components (PCs) of air quality indicators based on a geographically weighted principal component analysis (GWPCA), which is superior to the PCA when considering spatial heterogeneity. Then, a spatial cluster analysis (SCA) is used to identify the areas with similar air pollution behavior based on the results of the GWPCA. The results are all visualized and show that the GWPCA has a higher explanatory ability than the traditional PCA. Our modified framework based on the GWPCA and SCA for assessing air quality can effectively guide environmentalists and geographers in evaluating and improving air quality from a new perspective. Furthermore, the visualization results can be used by city planners and the government for monitoring and managing air pollution. Finally, policy suggestions are recommended for mitigating air pollution via regional collaboration.</p>

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