Dynamic Modeling Under Temperature Variations for Sustainable Air Quality Solutions: PM2.5 and Negative Ion Interactions DOI Open Access
Paola M. Ortiz-Grisales,

Leidy Gutiérrez-León,

Eduardo Alexander Duque Grisales

и другие.

Sustainability, Год журнала: 2024, Номер 17(1), С. 70 - 70

Опубликована: Дек. 26, 2024

Air pollution caused by fine particles known as PM2.5 is a significant health concern worldwide, contributing to illnesses like asthma, heart disease, and lung cancer. To address this issue, study focused on improving air purification systems using negative ions, which can attach these harmful help remove them from the air. This paper developed novel mathematical model based linear differential equations how interact with making it easier design more effective systems. The proposed was validated in small, controlled space, common urban pollutants such cigarette smoke, incense, coal, gasoline. These tests were conducted at different temperatures under two levels of ion generation. results showed that system could over 99% five minutes when low or moderate. However, higher temperatures, system’s performance dropped significantly. research goes beyond earlier studies examining temperature affects process, had not been fully explored before. Furthermore, approach aligns global sustainability goals promoting public health, reducing healthcare costs, providing scalable solutions for sustainable living.

Язык: Английский

Air pollution status and attributable health effects across the state of West Bengal, India, during 2016–2021 DOI

Buddhadev Ghosh,

Harish Chandra Barman,

Sayoni Ghosh

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(2)

Опубликована: Янв. 17, 2024

Язык: Английский

Процитировано

8
Navigating the Challenges of Rainfall Variability: Precipitation Forecasting using Coalesce Model DOI Creative Commons
Suraj Kumar Bhagat

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

1
PM2.5 concentration prediction system combining fuzzy information granulation and multi-model ensemble learning DOI

Yamei Chen,

Jianzhou Wang,

Runze Li

и другие.

Journal of Environmental Sciences, Год журнала: 2024, Номер unknown

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

3
Integrating D–S evidence theory and multiple deep learning frameworks for time series prediction of air quality DOI Creative Commons
Siling Feng,

Le Tang,

Mengxing Huang

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 18, 2025

Abstract Accurate prediction of air quality time series data is helpful to identify and warn pollution events in advance. Although the current models have made some progress improving accuracy prediction, due impact specific pollutants or complex meteorological conditions, these still problems low accuracy, robustness generalization ability univariate prediction. In order solve problems, this study proposes a framework that integrates D–S evidence theory variety deep learning models. The three representative cities with climate characteristics China are obtained five indicators on collected. preprocessed divided by length form short-term, medium-term long-term input data, MLP, RNN, CNN, LSTM, BI-LSTM GRU established respectively. By comparing performance six models, most suitable selected predict short, medium Taking results reliability as bodies theory, fusion model based established. For MAE, RMSE MAPE model, best result increases 7.42%, 4.25% 12.82% compared sub optimal architecture. This shows integrating algorithms provides an effective method accurately level urban areas.

Язык: Английский

Процитировано

0
Assessing particulate matter (PM2.5) concentrations and variability across Maharashtra using satellite data and machine learning techniques DOI Creative Commons

Ganesh Machhindra Kunjir,

Suvarna Tikle, Sandipan Das

и другие.

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0
Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA Model) in Hunan Province, China DOI Open Access
Wenyuan Gao,

Tongjue Xiao,

Lin Zou

и другие.

Sustainability, Год журнала: 2024, Номер 16(19), С. 8471 - 8471

Опубликована: Сен. 29, 2024

Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, autoregressive integrated moving average model (ARIMA) is introduced evaluate and predict current status quality China, constructed ARIMA has an excellent prediction effect Province. The following conclusions are obtained through analysis based model: (1) shows a year-on-year improvement trend; (2) method reliable effective can accurately analyze concentrations air pollutants (PM2.5, PM10, SO2, CO) quality, results show that outdoor will improve gradually each year 2024 2028; (3) this study contributes better understanding ambient during 2016–2023 provides good forecasting for period 2024–2028.

Язык: Английский

Процитировано

3
Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors DOI
Atilla Mutlu, Gülşen Aydın Keskin,

İhsan Çıldır

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(8)

Опубликована: Июль 24, 2024

Язык: Английский

Процитировано

1
Predicting long-term air pollutant concentrations through deep learning-based integration of heterogeneous urban data DOI
Chao Chen, Hui Liu,

Chengming Yu

и другие.

Atmospheric Pollution Research, Год журнала: 2024, Номер 15(11), С. 102282 - 102282

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

1
Dynamic Modeling Under Temperature Variations for Sustainable Air Quality Solutions: PM2.5 and Negative Ion Interactions DOI Open Access
Paola M. Ortiz-Grisales,

Leidy Gutiérrez-León,

Eduardo Alexander Duque Grisales

и другие.

Sustainability, Год журнала: 2024, Номер 17(1), С. 70 - 70

Опубликована: Дек. 26, 2024

Air pollution caused by fine particles known as PM2.5 is a significant health concern worldwide, contributing to illnesses like asthma, heart disease, and lung cancer. To address this issue, study focused on improving air purification systems using negative ions, which can attach these harmful help remove them from the air. This paper developed novel mathematical model based linear differential equations how interact with making it easier design more effective systems. The proposed was validated in small, controlled space, common urban pollutants such cigarette smoke, incense, coal, gasoline. These tests were conducted at different temperatures under two levels of ion generation. results showed that system could over 99% five minutes when low or moderate. However, higher temperatures, system’s performance dropped significantly. research goes beyond earlier studies examining temperature affects process, had not been fully explored before. Furthermore, approach aligns global sustainability goals promoting public health, reducing healthcare costs, providing scalable solutions for sustainable living.

Язык: Английский

Процитировано

0