scholarly journals Robust Estimation of Carbon Monoxide Measurements

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4958 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Confidence Intervals ◽  
Research Work ◽  
Random Variable ◽  
Air Quality Monitoring ◽  
Central Tendency ◽  
Nonparametric Bootstrap ◽  
Co Concentration ◽  
Robust Statistical Methods

This paper presents a robust analysis of carbon monoxide (CO) concentration measurements conducted at the Belisario air-quality monitoring station (Quito, Ecuador). For the analysis, the data collected from 1 January 2008 to 31 December 2019 were considered. Additionally, each of the twelve years analyzed was considered as a random variable, and robust location and scale estimators were used to estimate the central tendency and dispersion of the data. Furthermore, classic, nonparametric, bootstrap, and robust confidence intervals were used to group the variables into categories. Then, differences between categories were quantified using confidence intervals and it was shown that the trend of CO concentration at the Belisario station in the last twelve years is downward. The latter was proven with the precision provided by both nonparametric and robust statistical methods. The results of the research work robustly proved that the CO concentration at Belisario station in the last twelve years is not considered a health risk, according to the criteria established by the Quito Air Quality Index.

scholarly journals The Contributions To The Study Of Carbon Monoxide Pollution Due To Car Traffic In A Densely Populated Area

2015 ◽  
Vol 66 (1) ◽  
pp. 11-16
Author(s):  
Borza Sorin
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Research Work ◽  
Motor Vehicles ◽  
Human Society ◽  
Air Quality Monitoring ◽  
Populated Area ◽  
Car Traffic ◽  
Research Findings ◽  
Gis Software

Abstract Air quality monitoring is the most important environmental factor to be considered because it is the fastest way that helps pollutant transport into the environment. The development of human society has led to a negative anthropogenic and technogenic impact on air quality, resulting into a significant series of adverse effects on human health, flora, fauna and ecosystems in general. In this paper it is presentd the research work performed to monitor carbon monoxide emissions from motor vehicles in traffic, in a densely populated area in Sibiu. Also, in the paper it is described, the research findings conducted in accordance with national and European legislation. In our research we used GIS software, Geomedia Professional.

Prediction of hourly indoor Carbon Monoxide concentrations in semiarid regions using Regression and feedforward backpropagation as a hybrid model

2018 ◽  
Vol 5 (1) ◽  
pp. 717-739
Keyword(s):  
Principal Component Analysis ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Natural Ventilation ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Co Concentration

Abstract Accurate site-specific forecasting of indoor hourly carbon monoxide (CO) concentrations in school microenvironments is a key issue in air quality research nowadays due to its impact on children’s health. This paper investigated the improvement prediction of multiple linear regression (MLR) and feed forward back propagation (FFBP) by combining them with principal component analysis (PCA) for predicting indoor CO concentration in Gaza Strip, Palestine. Measurements were carried in 12 schools from October 2012 to May 2013 (one academic year). The results suggested that the selected models are effective forecasting tools and hence can be applicable for short-term forecasting of indoor CO level. The predicted indoor CO concentration values agree strongly well with the measured data with high coefficients of determination (R2) 0.869, 0.870, 0.885 and 0.915 for MLR, PCA-MLR, FFBP and PCA-FFBP, respectively. Overall, results showed that PCA models combined with MLR and PCA with FFBP improved MLR and FFBP models of predicting indoor CO concentration, with reduced errors by as much as 7.14% (PCA-MLR) and 56.6% (PCA-FFBP). Moreover, PCA improved the accuracy of the FFBP model by as much as by 3.3%. Keywords: Natural Ventilation; Children; Indoor Air Quality; Feed forward back propagation; Principal component analysis.

scholarly journals Understanding Spatial Variability of Air Quality in Sydney: Part 1—A Suburban Balcony Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 181 ◽  
Author(s):  
Jack Simmons ◽  
Clare Paton-Walsh ◽  
Frances Phillips ◽  
Travis Naylor ◽  
Élise-Andrée Guérette ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Monitoring Network ◽  
Ambient Air ◽  
Companion Paper ◽  
Quality Monitoring ◽  
Urban Landscapes ◽  
Good Representation ◽  
Air Quality Monitoring ◽  
Oxides Of Nitrogen

There is increasing awareness in Australia of the health impacts of poor air quality. A common public concern raised at a number of “roadshow” events as part of the federally funded Clean Air and Urban Landscapes Hub (CAUL) project was whether or not the air quality monitoring network around Sydney was sampling air representative of typical suburban settings. In order to investigate this concern, ambient air quality measurements were made on the roof of a two-storey building in the Sydney suburb of Auburn, to simulate a typical suburban balcony site. Measurements were also taken at a busy roadside and these are discussed in a companion paper (Part 2). Measurements made at the balcony site were compared to data from three proximate regulatory air quality monitoring stations: Chullora, Liverpool and Prospect. During the 16-month measurement campaign, observations of carbon monoxide, oxides of nitrogen, ozone and particulate matter less than 2.5-µm diameter at the simulated urban balcony site were comparable to those at the closest permanent air quality stations. Despite the Auburn site experiencing 10% higher average carbon monoxide amounts than any of the permanent air quality monitoring sites, the oxides of nitrogen were within the range of the permanent sites and the pollutants of greatest concern within Sydney (PM2.5 and ozone) were both lowest at Auburn. Similar diurnal and seasonal cycles were observed between all sites, suggesting common pollutant sources and mechanisms. Therefore, it is concluded that the existing air quality network provides a good representation of typical pollution levels at the Auburn “balcony” site.

scholarly journals A Study Regarding Carbon Monoxid Pollution in Cluj-Napoca City

2011 ◽  
Vol 68 (2) ◽  
Author(s):  
Ilarie IVAN ◽  
Teodora DEAC
Keyword(s):  
European Union ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Environmental Protection ◽  
Air Pollutants ◽  
Large City ◽  
Large Cities ◽  
City Hall ◽  
Co Concentration ◽  
Union Project

The European adhesion established the necessity of pollution decrease and an increase in air quality in our country, especially in large cities. According to this necessity, The Regional Agency of Environmental Protection from Cluj-Napoca, within a European Union project, benefited by five stations for air quality measured in Cluj region. One on the main air pollutants in large city is carbon monoxide. Thus in this paper, a study regarding carbon monoxide pollution in Cluj-Napoca, has been realized. CO measurements were carried out in January, February and March 2011, daily at 14 o’clock, in the CJ1-Traffic Station from Cluj-Napoca. The study followed the maximum and minimum values of CO and the weekly evolution of CO concentration. According to the results a series of recommendations to the management of Cluj-Napoca City Hall has been elaborated.

scholarly journals Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5831 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Bootstrap Method ◽  
Central Tendency ◽  
Concentration Measurements ◽  
Inference Techniques ◽  
The Right ◽  
The City ◽  
Robust Statistical Methods

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

scholarly journals Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 277
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Vicente González-Posadas ◽  
José Luis Jiménez-Martín ◽  
Iván Menes Camejo
Keyword(s):  
Air Quality ◽  
Confidence Intervals ◽  
Tropospheric Ozone ◽  
Air Quality Monitoring ◽  
Robust Methods ◽  
Time Period ◽  
Measurement Results ◽  
Robust Techniques ◽  
The City ◽  
Better Than

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

Indoor and outdoor air quality monitoring in hemp house — Case study

2011 ◽  
Vol 6 (3) ◽  
pp. 63-72 ◽  
Author(s):  
Jarmila Rimbalová ◽  
Silvia Vilčeková ◽  
Adriana Eštoková
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Quality ◽  
Indoor And Outdoor
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