Machine Learning for Markov Modeling of COVID-19 Dynamics Concerning Air Quality Index, PM-2.5, NO2, PM-10, and O3 DOI Creative Commons
Izaz Ullah Khan,

Mehran Ullah,

Seema Tripathi

et al.

International Journal of Computational Methods and Experimental Measurements, Journal Year: 2024, Volume and Issue: 12(2), P. 121 - 134

Published: June 30, 2024

In this research Python machine learning module sklearn has been utilized to solve the Markov model.Markov modelling of COVID-19 dynamics with air quality index (AQI), PM-2.5, NO2, PM-10, and O3, respectively.Data Chhattisgarh state India analyzed in two phases.In phase-1 time duration is from March 15, 2020, May 01, for phase-2 it Jun Jul 2020.It noticed that initially change AQI 103 84.83 changed disease dynamics, first cases reported.In next fortnights April are same, later 63.83, but no reported 2020.In phase 1, a cyclic trend observed changes concerning PM-2.5.The trends respectively O3 different.COVID-19 reports negative correlation AQI, PM-10.Moreover, positive O3.This proves lockdown ban on transport activities improved not O3.

Language: Английский

Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: a review DOI

Zhanxia Du,

Hanbing Li, Sha Chen

et al.

Atmospheric Environment, Journal Year: 2025, Volume and Issue: unknown, P. 121054 - 121054

Published: Jan. 1, 2025

Language: Английский

Citations

0
AI-based prediction of the improvement in air quality induced by emergency measures DOI

Pavithra Pari,

Tasneem Abbasi, S. A. Abbasi

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119716 - 119716

Published: Dec. 7, 2023

Language: Английский

Citations

4
Impact of urban spatial factors on NO2 concentration based on different socio-economic restriction scenarios in U.S. cities DOI
Muhammad Waqas, Majid Nazeer, Man Sing Wong

et al.

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 316, P. 120191 - 120191

Published: Nov. 3, 2023

Language: Английский

Citations

2
ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity DOI Creative Commons
David Santiago Hernández-Medina, Carlos Alfonso Zafra Mejía, Hugo Alexánder Rondón Quintana

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(6), P. 683 - 683

Published: June 2, 2024

The COVID-19 pandemic precipitated a unique period of social isolation, presenting an unprecedented opportunity to scrutinize the influence human activities on urban air quality. This study employs ARIMA models explore impact isolation measures PM10 and PM2.5 concentrations in high-altitude Latin American megacity (Bogota, Colombia). Three scenarios were examined: strict (5 months), sectorized (1 flexible (2 months). Our findings indicate that exert more pronounced effect short-term simulated (PM10: −47.3%; PM2.5: −54%) compared long-term effects −29.4%; −28.3%). suggest tend diminish persistence over time, both short long term. In term, appear augment variation concentrations, with substantial increase observed for PM2.5. Conversely, these seem reduce variations PM indicating stable behavior is less susceptible abrupt peaks. differences reduction between 23.8% 12.8%, respectively. research provides valuable insights into potential strategic improve quality environments.

Language: Английский

Citations

0
Machine Learning for Markov Modeling of COVID-19 Dynamics Concerning Air Quality Index, PM-2.5, NO2, PM-10, and O3 DOI Creative Commons
Izaz Ullah Khan,

Mehran Ullah,

Seema Tripathi

et al.

International Journal of Computational Methods and Experimental Measurements, Journal Year: 2024, Volume and Issue: 12(2), P. 121 - 134

Published: June 30, 2024

In this research Python machine learning module sklearn has been utilized to solve the Markov model.Markov modelling of COVID-19 dynamics with air quality index (AQI), PM-2.5, NO2, PM-10, and O3, respectively.Data Chhattisgarh state India analyzed in two phases.In phase-1 time duration is from March 15, 2020, May 01, for phase-2 it Jun Jul 2020.It noticed that initially change AQI 103 84.83 changed disease dynamics, first cases reported.In next fortnights April are same, later 63.83, but no reported 2020.In phase 1, a cyclic trend observed changes concerning PM-2.5.The trends respectively O3 different.COVID-19 reports negative correlation AQI, PM-10.Moreover, positive O3.This proves lockdown ban on transport activities improved not O3.

Language: Английский

Citations

0