Remote Sensing Analysis of Smog-Inducing Aerosol Optical Depth: An Integrated Approach for Air Pollution Mitigation DOI Creative Commons
Shazia Pervaiz, Kanwal Javid,

Filza Zafar Khan

et al.

Environment and Natural Resources Journal, Journal Year: 2024, Volume and Issue: 22(5), P. 1 - 12

Published: Aug. 26, 2024

Aerosol aggravation poses a significant challenge in the administrative Lahore Division of Punjab, Pakistan and contributes greatly to persistent issue smog. Since 2017, division has experienced recurrent episodes smog pollution, most notably months October November. In present study, aerosol optical depth (AOD) been analyzed alongside three metrological parameters: temperature, humidity rainfall. These were tracked November 2018, 2020, 2022 using remote sensing data satellite imaging. Additionally, anthropogenic emissions from automobile exhaust, industries stubble burning derived secondary sources. Ultimately, study helped piece together complex environmental picture The results showed that AOD levels not only increased during this time, they significantly influenced by full range variables such as low high relative humidity, lack rainfall variety human activities. It was found trucks, tractors buses among worst contributors, industry burning. Therefore, suggests multi sectoral plans mitigate combat menace, promoting sustainability Division. A set recommendation is included, divided into categories: industry, transport agriculture. are focused on technology, control systems, disposal, incentives, green solutions more. At all levels, commitment, collaboration, coordination absolutely vital.

Language: Английский

An integrated feature selection and machine learning framework for PM10 concentration prediction DOI
Elham Kalantari, Hamid Gholami, Hossein Malakooti

et al.

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102456 - 102456

Published: Feb. 1, 2025

Language: Английский

Citations

0
Application of machine learning models for PM2.5 prediction in bengaluru using precursor air pollutants and meteorological data DOI
Gourav Suthar,

Saurabh Singh

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(3)

Published: March 1, 2025

Language: Английский

Citations

0
Identifying the determinants of natural, anthropogenic factors and precursors on PM1 pollution in urban agglomerations in China: Insights from optimal parameter-based geographic detector and robust geographic weighted regression models DOI
Ping Zhang, Yong Wang, Wenjie Ma

et al.

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121817 - 121817

Published: May 1, 2025

Language: Английский

Citations

0
A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers DOI
Hao Cui, Jian Li,

Yutong Sun

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 926, P. 171910 - 171910

Published: March 24, 2024

Language: Английский

Citations

2
Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India DOI

Saurabh Singh,

Gourav Suthar

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 22, 2024

Language: Английский

Citations

2
Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives DOI Creative Commons
Hasnain Iftikhar, Moiz Qureshi, Justyna Żywiołek

et al.

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 10, 2024

Particulate matter with a diameter of 2.5 microns or less ( PM2.5 ) is significant type air pollution that affects human health due to its ability persist in the atmosphere and penetrate respiratory system. Accurate forecasting particulate crucial for healthcare sector any country. To achieve this, current work, new time series ensemble approach proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, theta) nonlinear (nonparametric neural network autoregressive) models. Three models are also developed, each employing distinct weighting strategies: equal distribution weight among all single (ESME), assignment training average accuracy errors (ESMT), validation mean measures (ESMV). This technique was applied daily id="m3">PM2.5 concentration data from 1 January 2019, 31 May 2023, Pakistan’s main cities, including Lahore, Karachi, Peshawar, Islamabad, forecast short-term id="m4">PM2.5 concentrations. When compared other models, best model (ESMV) demonstrated ranging 3.60% 25.79% 0.81%–13.52% 1.08%–7.06% 1.09%–12.11% Peshawar. These results indicate more efficient accurate id="m5">PM2.5 than existing Furthermore, using model, made next 15 days (June June 2023). The showed highest id="m6">PM2.5 value (236.00 id="m7">μg/m3 observed 8 2023. Other displayed higher poor quality throughout days. Conversely, Karachi experienced moderate id="m8">PM2.5 levels between 50 id="m9">μg/m3 80 id="m10">μg/m3 . In id="m11">PM2.5 were consistently unhealthy, peak (153.00 id="m12">μg/m3 9 experience can assist environmental monitoring organizations implementing cost-effective planning minimize pollution.

Language: Английский

Citations

1
Remote Sensing Analysis of Smog-Inducing Aerosol Optical Depth: An Integrated Approach for Air Pollution Mitigation DOI Creative Commons
Shazia Pervaiz, Kanwal Javid,

Filza Zafar Khan

et al.

Environment and Natural Resources Journal, Journal Year: 2024, Volume and Issue: 22(5), P. 1 - 12

Published: Aug. 26, 2024

Aerosol aggravation poses a significant challenge in the administrative Lahore Division of Punjab, Pakistan and contributes greatly to persistent issue smog. Since 2017, division has experienced recurrent episodes smog pollution, most notably months October November. In present study, aerosol optical depth (AOD) been analyzed alongside three metrological parameters: temperature, humidity rainfall. These were tracked November 2018, 2020, 2022 using remote sensing data satellite imaging. Additionally, anthropogenic emissions from automobile exhaust, industries stubble burning derived secondary sources. Ultimately, study helped piece together complex environmental picture The results showed that AOD levels not only increased during this time, they significantly influenced by full range variables such as low high relative humidity, lack rainfall variety human activities. It was found trucks, tractors buses among worst contributors, industry burning. Therefore, suggests multi sectoral plans mitigate combat menace, promoting sustainability Division. A set recommendation is included, divided into categories: industry, transport agriculture. are focused on technology, control systems, disposal, incentives, green solutions more. At all levels, commitment, collaboration, coordination absolutely vital.

Language: Английский

Citations

0