scholarly journals Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 654 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Rasa Zalakeviciute ◽  
Angela Maria Diaz-Marquez
Keyword(s):  
Air Pollution ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quality Index ◽  
Urban Park ◽  
Absolute Deviation ◽  
The Mean ◽  
Concentration Measurements ◽  
Pm2.5 Concentration

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5   μ m ) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a -trimmed mean and Winsorized standard error of order a , location and scale estimators based on the Andrew’s wave, biweight location and scale estimators, and estimators based on the bootstrap- t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

scholarly journals Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4648 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Angela Maria Diaz-Marquez ◽  
Rasa Zalakeviciute
Keyword(s):  
Air Pollution ◽  
Location Estimation ◽  
Urban Park ◽  
Absolute Deviation ◽  
Pollution Levels ◽  
Nonparametric Confidence Intervals ◽  
Concentration Measurements ◽  
The City ◽  
Pm2.5 Concentration

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: α-trimmed mean, trimean, and median estimators; and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

15. Quantitative Analysis: Description and Inference

2020 ◽  
pp. 393-421
Author(s):  
Sandra Halperin ◽  
Oliver Heath
Keyword(s):  
Quantitative Analysis ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quantitative Research ◽  
Building Blocks ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Margin Of Error ◽  
Univariate Statistics ◽  
The Mean

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

scholarly journals Analysis of the Information Obtained From PM2.5 Concentration Measurements in an Urban Park

2020 ◽  
Vol 69 (9) ◽  
pp. 6296-6311 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Rasa Zalakeviciute ◽  
Angela Maria Diaz-Marquez
Keyword(s):  
Urban Park ◽  
Concentration Measurements ◽  
Pm2.5 Concentration

scholarly journals Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 192 ◽  
Author(s):  
José M. Ferreira ◽  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
Eftim Zdravevski ◽  
...  
Keyword(s):  
Systematic Review ◽  
Standard Deviation ◽  
Mobile Devices ◽  
Magnetic Sensors ◽  
Daily Activities ◽  
Motion Sensors ◽  
Accurate Identification ◽  
Absolute Deviation ◽  
Environment Recognition ◽  
The Mean

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.

Cleaning of Matched License Plate Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Stephen D. Clark ◽  
S. Grant-Muller ◽  
Haibo Chen
Keyword(s):  
Standard Deviation ◽  
Road Traffic ◽  
Statistical Test ◽  
License Plate ◽  
Mean Absolute Deviation ◽  
Test Results ◽  
Absolute Deviation ◽  
Traffic Conditions ◽  
The Third ◽  
The Mean

Three methods for identifying outlying journey time observations collected as part of a motorway license plate matching exercise are presented. Each method is examined to ensure that it is comprehensible to transport practitioners, is able to correctly classify outliers, and is efficient in its application. The first method is a crude method based on percentiles. The second uses a mean absolute deviation test. The third method is a modification of a traditional z- or t-statistical test. Results from each method and combinations of methods are compared. The preferred method is judged to be the third method alone, which uses the median rather than the mean as its measure of location and the inter-quartile range rather than the standard deviation as its measure of variability. This method is seen to be robust to both the outliers themselves and the presence of incident conditions. The effectiveness of the method is demonstrated under a number of typical and atypical road traffic conditions. In particular, the method is applied to a different section of motorway and is shown to still produce useful results.

Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

2013 ◽  
Vol 49 (4) ◽  
pp. 764-766 ◽  
Author(s):  
Christophe Leys ◽  
Christophe Ley ◽  
Olivier Klein ◽  
Philippe Bernard ◽  
Laurent Licata
Keyword(s):  
Standard Deviation ◽  
Absolute Deviation ◽  
The Mean

scholarly journals Health Risk Associated with Near-Road Ambient Air Concentration of Particulate Matter in Mukono Municipal Council, Uganda

2021 ◽  
Vol 9 (1) ◽  
pp. 127-137
Author(s):  
Edward Kibikyo Mukooza
Keyword(s):  
Air Pollution ◽  
Health Risk ◽  
Ambient Air ◽  
Air Concentration ◽  
Middle Income ◽  
Middle Income Countries ◽  
Premature Deaths ◽  
Increased Risk ◽  
The Mean ◽  
Pm2.5 Concentration

More than 98% of urban centres exceeding 100,000 people in Low and Middle-Income Countries (LMICs), do not meet the WHO air quality limits. Data on air pollution from LMICs is scarce. We measured the mean concentrations of near-road PM2.5 in the period of Aug.-Dec. 2020, described the Mukono Municipality’s near-road populations’ exposure to PM2.5, and assessed the associated health risk. PurpleAir PA-II laser particle counters, measured near-road ambient air PM2.5 concentration in Mukono Municipality during the period of 09/1/20 to 12/04/20. Excel Toolpak was used for data analysis and the health risk assessed with the WHO AirQ+ tool. The mean ambient near–road PM2.5 in Mukono Municipality were 30.97, 33.84 and 47.74 ug/m3for background, near-unpaved and near-paved roads, respectively. Mukono Municipality’s population was exposed to ambient PM2.5 concentrations higher than the WHO annual limit of 10 ug/m3. This level of air pollution is associated with preventable annual premature deaths of up to 133.11 per 100,000 population. Vehicles were assumed to be the predominant source of near-road ambient air PM2.5 pollution. The Municipality’s population was exposed to near-road ambient air PM2.5 exceeding the WHO annual limit by as much as *4.7 for the paved roads, *3.3 for the unpaved roads and *3 for the background. This leads to increased risk of preventable premature deaths in the Municipality.Mukono Municipality could monitor PM2.5; guide developers to placebuildings more than 100 meters away from roadsides and should promotepolicies for newer vehicles on Ugandan roads.

scholarly journals Influence of the Spatial Distribution Pattern of Buildings on the Distribution of PM2.5 Concentration

2021 ◽  
Author(s):  
Zhixuan Zhang ◽  
Baoyan Shan ◽  
Qikai Lin ◽  
Yanqiu Chen ◽  
Xinwei Yu
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Spatial Structure ◽  
Distribution Pattern ◽  
Distribution Patterns ◽  
Spatial Distribution Pattern ◽  
Urban Spatial Structure ◽  
Coverage Ratio ◽  
The Mean ◽  
Pm2.5 Concentration

Abstract The spatial distribution pattern of buildings is an entry point for controlling the diffusion of pollution particles at an urban spatial structure scale. In this study, we adopted ordinary kriging interpolation and other methods to study the spatial distribution pattern of PM2.5 and constructed urban spatial structure indexes based on building distribution patterns to reveal the influence of building spatial distribution patterns on PM2.5 concentration across the study area and at different elevations. The present study suggests that: (1) Topographic elevation is an important factor influencing the distribution of PM2.5; the correlation coefficient reaches −0.761 and exceeds the 0.001 confidence level. As the elevation increases, the urban spatial structure indexes show significant correlations with PM2.5, and the regularity becomes stronger. (2) The PM2.5 concentration is negatively correlated with the mean and standard deviation of the DEM, the mean and maximum absolute building height, the outdoor activity area, and the average distance between adjacent buildings; and is positively correlated with the sum of the building base area, the building coverage ratio, the space area, the building coverage ratio, the space occupation ratio, and the sum of the building volume. These urban spatial structure indexes are important factors affecting PM2.5 concentration and distribution and should be considered in urban planning. (3) Spatio-temporal differences in PM2.5 concentration and distribution were found at different elevation and time ranges. Indexes, such as the average building height, the average building base area, the sum of the building volume, and the standard deviation of building volume experienced significant changes. Higher PM2.5 concentration yielded a more significant influence of urban spatial structure indexes on PM2.5 distribution. More discrete spatial distributions of PM2.5 yielded weaker correlations between PM2.5 concentrations and the urban spatial structure indexes.

scholarly journals Properties of the Standard Deviation that are Rarely Mentioned in Classrooms

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
Mohammad Fraiwan Al-Saleh ◽  
Adil Eltayeb Yousif
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Sample Variance ◽  
Mean Absolute Deviation ◽  
Hidden Information ◽  
Absolute Deviation ◽  
Sample Mean ◽  
The Mean ◽  
Vague Concept ◽  
Chebyshev's Inequality

Unlike the mean, the standard deviation ¾ is a vague concept. In this paper, several properties of ¾ are highlighted. These properties include the minimum and the maximum of ¾, its relationship to the mean absolute deviation and the range of the data, its role in Chebyshev’s inequality and the coefficient of variation. The hidden information in the formula itself is extracted. The confusion about the denominator of the sample variance being n ¡ 1 is also addressed. Some properties of the sample mean and varianceof normal data are carefully explained. Pointing out these and other properties in classrooms may have significant effects on the understanding and the retention of the concept.

scholarly journals EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 747-751
Author(s):  
N. Demir ◽  
M. Kaynarca ◽  
S. Oy
Keyword(s):  
Standard Deviation ◽  
Optical Sensors ◽  
Land Surface ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Sar Image ◽  
Fuzzy Approach ◽  
The Mean

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

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