An Effective AQI Estimation Using Sensor Data and Stacking Mechanism

2021 ◽  
Author(s):  
Dat Q. Duong ◽  
Quang M. Le ◽  
Tan-Loc Nguyen-Tai ◽  
Hien D. Nguyen ◽  
Minh-Son Dao ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Research Topic ◽  
Sensor Data ◽  
Weather Data ◽  
Quality Estimation ◽  
Data Set ◽  
Adverse Health Effects ◽  
Overall Performance ◽  
Selection Of

Accurately assessing the air quality index (AQI) values and levels has become an attractive research topic during the last decades. It is a crucial aspect when studying the possible adverse health effects associated with current air quality conditions. This paper aims to utilize machine learning and an appropriate selection of attributes for the air quality estimation problem using various features, including sensor data (humidity, temperature), timestamp features, location features, and public weather data. We evaluated the performance of different learning models and features to study the problem using the data set “MNR-HCM II”. The experimental results show that adopting TLPW features with Stacking generalization yields higher overall performance than other techniques and features in RMSE, accuracy, and F1-score.

Selection of Climatic Design Points for Gas Turbine Power Augmentation

2011 ◽  
Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Optimal Solution ◽  
Climatic Conditions ◽  
Weather Data ◽  
Climatic Data ◽  
Data Set ◽  
Suggested Approach ◽  
Design Point ◽  
Impact Performance ◽  
Selection Of

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling or mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of ASHRAE data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as TMY, EWD, and IWS. This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance and economics of the installation. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.

scholarly journals A Short-Term Air Quality Control for PM10 Levels

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1409
Author(s):  
Claudio Carnevale ◽  
Elena De Angelis ◽  
Franco Luis Tagliani ◽  
Enrico Turrini ◽  
Marialuisa Volta
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Control Variable ◽  
Air Quality Index ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Forecasting Model ◽  
Short Term ◽  
Air Quality Control ◽  
Selection Of

In this work, the implementation and test of an integrated assessment model (IAM) to aid governments to define their short term plans (STP) is presented. The methodology is based on a receding horizon approach where the forecasting model gives information about a selected air quality index up to 3 days in advance once the emission of the involved pollutants (control variable) are known. The methodology is fully general with respect to the model used for the forecast and the air quality index; nevertheless, the selection of these models must take into account the peculiarities of the pollutants to be controlled. This system has been tested for particulate matter (PM10) control over a domain located in Northern Italy including the highly polluted area of Brescia. The results show that the control system can be a valuable asset to aid local authorities in the selection of suitable air quality plans.

Selection of Climatic Design Points for Gas Turbine Power Augmentation

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Optimal Solution ◽  
Climatic Conditions ◽  
Weather Data ◽  
Climatic Data ◽  
Data Set ◽  
Suggested Approach ◽  
Design Point ◽  
Impact Performance ◽  
Selection Of

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling and mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of American Society of Heating and Refrigeration Engineers (ASHRAE) data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as typical meteorological year (TMY), engineering weather data (EWD), and integrated weather surface (IWS). This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.

scholarly journals A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

2020 ◽  
Vol 10 (13) ◽  
pp. 4587 ◽  
Author(s):  
Edoardo Arnaudo ◽  
Alessandro Farasin ◽  
Claudio Rossi
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Linear Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Learning Approaches ◽  
Short Term ◽  
Quality Estimation ◽  
Memory Network ◽  
Long Short Term Memory

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

scholarly journals Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fang Zhao ◽  
Ziyi Liang ◽  
Qiyan Zhang ◽  
Dewen Seng ◽  
Xiyuan Chen
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Weather Data ◽  
Short Term ◽  
Concentration Data ◽  
Data Set ◽  
Term Memory ◽  
Proposed Model

Accurate monitoring of air quality can no longer meet people’s needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models.

scholarly journals Air Quality Index: A Case of One-Day Monitoring in 253 Nigerian Urban and Suburban Towns

2021 ◽  
Author(s):  
Francis Olawale Abulude ◽  
I. A. Abulude ◽  
S. D. Oluwagbayide ◽  
S. D. Afolayan ◽  
D Ishaku
Keyword(s):  
Air Quality ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Air Quality Index ◽  
Data Set ◽  
The Public ◽  
Ogun State ◽  
Border Town ◽  
Pollution Impacts

Abstract Government departments use the air quality index (AQI) to inform the public about how unhealthy the air is now or may become in the future. As the AQI increases, so do the health threats. It is a daily air quality index that is used to report on air quality. In addition, a measure of how air pollution impacts one's health over a limited period of time. The AQI was created to assist people in understanding how local air quality affects their health. Therefore the aim of the study was to assess one-day air quality of 253 towns in Nigeria, thereby determining the health threat in these towns. The data was collected from the Tutiempo Network's regular data set by the EPA Environmental Protection Agency. Data on all of the major pollutants (O3, PM2.5, PM10, CO, NO, SO2) was collected and statistical analysis was performed. Kura (Kano State), a town in northern Nigeria, recorded the highest level of 184, while Idiroko, a border town (Nigeria-Benin Republic) in Ogun State, had the least value of 41. Kura was portrayed as unhealthy, while Idiroko was portrayed as healthy, implying that Idiroko air poses little to no danger, while Kura air showed that certain people of the general public, as well as members of sensitive groups, could encounter more severe health effects.

scholarly journals A short review on different techniques used for site selection of air quality monitoring stations

2021 ◽  
Vol 2 (1) ◽  
pp. 27-30
Author(s):  
Hemanth Kollati ◽  
◽  
Animesh Debnath ◽  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Site Selection ◽  
Quality Index ◽  
Air Quality Index ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Air Quality Monitoring Stations ◽  
Selection Of ◽  
Monitoring Stations

Air pollution has become a serious concern across the world in the last few decades. In specific cities, the air quality index value had changed from very unhealthy to a hazardous level of health concern. Air pollution has a serious impact on daily lives in those cities. Monitoring of air pollution is becoming necessary these days. Air quality monitoring stations are installed to get the air pollution data, which indicates in the air quality index (AQI) value. In order to contain a proper air quality index (AQI) value, it is essential to locate the air quality monitoring stations in the appropriate place of the study area. Several techniques were being used for site selection of air quality monitoring stations for the last few decades. In this short review, all such techniques have been studied systematically, and comprehensive analysis has been reported for further use by the scientific community and policymakers. In this study, the methods used in the site selection of air quality monitoring stations were categorized into four groups. (1) Multi-Criteria Decision Making (MCDM) techniques; (2) Geographical Information System (GIS); (3) hybrid techniques; and (4) miscellaneous. In the site selection of air quality monitoring stations, the decision-makers should consider various parameters based on the study area. While considering various parameters, the problem solving becomes complex. At this point, MCDM techniques, GIS, and Hybrid techniques are found to be helpful tools for the decision-makers.

scholarly journals Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time

2020 ◽  
Vol 15 (10) ◽  
pp. 1040a7
Author(s):  
Nico Schulte ◽  
Xiang Li ◽  
Jo Kay Ghosh ◽  
Philip M Fine ◽  
Scott A Epstein
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Real Time ◽  
Quality Index ◽  
Air Quality Index ◽  
Sensor Data ◽  
Index Mapping

scholarly journals PENERAPAN DATA MINING UNTUK PRAKIRAAN CUACA DI KOTA MALANG MENGGUNAKAN ALGORITMA ITERATIVE DICHOTOMISER TREE (ID3)

JOUTICA ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Broto Poernomo T.P ◽  
Rina Dewi Indah Sari
Keyword(s):  
Data Mining ◽  
Forecast Model ◽  
Weather Data ◽  
Data Set ◽  
Weather Forecasts ◽  
Id3 Algorithm ◽  
Application Data ◽  
Many Sources ◽  
Selection Of

In the preparation of weather forecasts information there are several obstacles, involving many sources of data and weather forecasts relying on the ability of the forerunner, so that the interpretations produced may differ between forecasters because of their own experience. Differences in interpretation can confuse users and potentially reduce the quality of information submitted. Based on this problem the author intends to study the forecast model with data mining using ID3 algorithm to obtain the appropriate model so as to facilitate the process of weather analysis and forecast. In building the application, data obtained from BMKG website is addressed dataonline.bmkg.go.id and the data taken is datacuaca on January 1, 2012 through November 30, 2015 for a total of 2414 data and 9 attributes. After the selection of attributes (only weather related attributes) and removing damaged data (incomplete data and outliers), the data is reduced to 1790 and attributes reduced to 6 pieces. In the testing process performed with 179 data (10% of the data set) random, it is known that there are 112 databases corresponding to the actual weather data. So it can be concluded that the accuracy of 73.74%.

A Comparison of Four Adolescent Language Tests

1987 ◽  
Vol 18 (3) ◽  
pp. 250-266 ◽  
Author(s):  
R. Jane Lieberman ◽  
Ann Marie C. Heffron ◽  
Stephanie J. West ◽  
Edward C. Hutchinson ◽  
Thomas W. Swem
Keyword(s):  
Test Performance ◽  
Sixth Grade ◽  
Significant Discrepancy ◽  
Language Impaired ◽  
Language Functions ◽  
Language Tests ◽  
Significant Difference ◽  
Sixth Grade Students ◽  
Overall Performance ◽  
Selection Of

Four recently developed adolescent language tests, the Fullerton Test for Adolescents (FLTA), the Test of Adolescent Language (TOAL), the Clinical Evaluation of Language Functions (CELF), and the Screening Test of Adolescent Language (STAL), were compared to determine: (a) whether they measured the same language skills (content) in the same way (procedures); and (b) whether students performed similarly on each of the tests. First, respective manuals were reviewed to compare selection of subtest content areas and subtest procedures. Then, each of the tests was administered according to standardized procedures to 30 unselected sixth-grade students. Despite apparent differences in test content and procedures, there was no significant difference in students' performance on three of the four tests, and correlations among test performance were moderate to high. A comparison of the pass/fail rates for overall performance on the tests, however, revealed a significant discrepancy between the proportions of students identified in need of further evaluation on the STAL (20%) and the proportion diagnosed as language impaired on the three diagnostic tests (60-73%). Clinical implications are discussed.

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