A Surrogate-Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network

2015 ◽  
Vol 93 (7) ◽  
pp. 1176-1187 ◽  
Author(s):  
Suad Al-Adwani ◽  
Ali Elkamel ◽  
Thomas A. Duever ◽  
Kaan Yetilmezsoy ◽  
Sabah Ahmed Abdul-Wahab
Keyword(s):  
Air Quality ◽  
Optimal Design ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Surrogate Based Optimization ◽  
Optimization Methodology

scholarly journals Analysis and optimal design of air quality monitoring networks using a variational approach

Tellus B ◽  
2015 ◽  
Vol 67 (1) ◽  
pp. 25385 ◽  
Author(s):  
Adolfo Henriquez ◽  
Axel Osses ◽  
Laura Gallardo ◽  
Melisa Diaz Resquin
Keyword(s):  
Air Quality ◽  
Optimal Design ◽  
Variational Approach ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

scholarly journals A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

2018 ◽  
Vol 5 (9) ◽  
pp. 180889 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Rongxiao Wang ◽  
Yiping Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Data Driven ◽  
Atmospheric Environment ◽  
Illustrative Case ◽  
Air Quality Monitoring ◽  
Detection Capability ◽  
Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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