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Abbass RA, Kumar P, El-Gendy A. Fine particulate matter exposure in four transport modes of Greater Cairo. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 791:148104. [PMID: 34126484 DOI: 10.1016/j.scitotenv.2021.148104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/18/2021] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
The number of daily commuters in Greater Cairo has exceeded 15 million nevertheless personal exposure studies in transport microenvironments are limited. The aim of this study is to quantify PM2.5 exposure during peak hours in four transport modes of Greater Cairo - car (windows-open, windows-closed with recirculation and AC-on), microbus (windows-open), cycling and walking - and understand its underlying drivers. Data was collected using a pDR-1500 monitor and analysed to capture concentration variations, spatial variability, exposure doses, commuting costs versus inhaled doses, health burden and economic losses. Car with recirculation resulted in the least average PM2.5 concentrations (32 ± 6 μg/m3), followed by walking (77 ± 35 μg/m3), car with windows-open (82 ± 32 μg/m3), microbus with windows-open (96 ± 29 μg/m3) and cycling (100 ± 28 μg/m3). Evening hours observed average PM2.5 concentrations by 26-58% lesser than morning. Spatial variability analysis showed that 75th-90th percentile PM2.5 concentrations coincided with congested spots. Cycling and walking lanes are rare hence commuters are exposed to surges in PM2.5 concentrations when passing near construction and solid waste burning sites. Cycling and walking also resulted in inhaling 40-times and 32-times higher PM2.5 dose per kilometre than for car with recirculation. Commuting by microbus cost (with windows-open) ~45% of car cost (with recirculation) but it resulted in 4-times higher inhaled PM2.5 dose. As expected due to the lowest PM2.5 exposure concentrations, health burden resulting from car travel (with recirculation) caused the least death rates of 0.07 (95% CI 0.07-0.08) prematures deaths per 100,000 commuters/year while microbus with windows-open resulted in the highest death rates; 0.52 (95% CI 0.49-0.56). Microbus deaths represent 57% of national economic losses due to PM2.5 exposure amongst the four transport modes. This study provides real-time exposure data and analyses its implications on commuter health as a first step in informed decision-making and better urban planning.
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Affiliation(s)
- Rana Alaa Abbass
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland.
| | - Ahmed El-Gendy
- Department of Construction Engineering, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt
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Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, Syed Zakaria SMM, Kanagaraj E, Abdull Sukor AS, Elham MF. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. SENSORS 2021; 21:s21154956. [PMID: 34372192 PMCID: PMC8348785 DOI: 10.3390/s21154956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 11/30/2022]
Abstract
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.
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Affiliation(s)
- Chew Cheik Goh
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (C.C.G.); (N.R.); (S.M.M.S.Z.); (E.K.)
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
| | - Latifah Munirah Kamarudin
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (C.C.G.); (N.R.); (S.M.M.S.Z.); (E.K.)
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
- Correspondence:
| | - Ammar Zakaria
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia
| | - Hiromitsu Nishizaki
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan; (H.N.); (X.M.)
| | - Nuraminah Ramli
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (C.C.G.); (N.R.); (S.M.M.S.Z.); (E.K.)
| | - Xiaoyang Mao
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan; (H.N.); (X.M.)
| | - Syed Muhammad Mamduh Syed Zakaria
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (C.C.G.); (N.R.); (S.M.M.S.Z.); (E.K.)
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
| | - Ericson Kanagaraj
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (C.C.G.); (N.R.); (S.M.M.S.Z.); (E.K.)
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
| | - Abdul Syafiq Abdull Sukor
- Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia; (A.Z.); (A.S.A.S.)
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia
| | - Md. Fauzan Elham
- Selangor Industrial Corporation Sdn Bhd, Seksyen 14, Shah Alam 40000, Malaysia;
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