Assessment of meteorological factors and air pollution impact on cardiovascular mortality using random forest analysis 2017 to 2020 DOI Creative Commons

Yousef Dowlatabadi,

Zahra Khajeh, Mitra Mohammadi

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Air pollution, a global health hazard, significantly impacts mortality, cardiovascular health, mental well-being, and overall human health. This study aimed to investigate the impact of air pollution meteorological factors on mortality rates in Mashhad City, northeastern Iran 2017–2020. We utilized Random Forest (RF) model this study. gathered daily data (pressure, humidity, temperature, solar radiation) from 2017 2020, pollutant levels (PM2.5, PM10, SO2, NO2, CO), Health System Registration (Sina). The RF was then applied Excel Python analyze interplay between these variables. we found that time, pressure, temperature impacted mortality. Among pollutants, NO2 SO2 were most influential. Overall, had greater than pollutants. Furthermore, discovered increased with higher colder seasons, temperatures. CO, PM2.5 rates. These findings highlight importance understanding relationship diseases, climatic factors, pollution. Environmental like climate change play significant role Therefore, it is vital for individuals, especially those heart conditions, pay attention weather alerts.

Language: Английский

AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition DOI Creative Commons
Mughair Aslam Bhatti,

Zhiyao Song,

Uzair Aslam Bhatti

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: March 21, 2024

Abstract The integration of multi-source sensors based AIoT (Artificial Intelligence Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields sustainable smart environmental design, urban development, pollution control. This study focuses on enhancing prediction emission, with a special emphasis pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) long short-term memory (LSTM) have shown promise predicting trends time series data. However, challenges persist due to unpredictability data scarcity long-term historical for training. To address these challenges, this introduces AIoT-enhanced EEMD-CEEMDAN-GCN model. innovative approach involves decomposing input signal using EEMD (Ensemble Empirical Mode Decomposition) CEEMDAN (Complete Ensemble Decomposition Adaptive Noise) extract intrinsic mode functions. These functions are then processed through GCN (Graph Convolutional Network) model, enabling precise trends. model’s effectiveness validated datasets from four provinces China, demonstrating its superiority over various models (GCN, EMD-GCN) decomposition (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy better fitting, outperforming other key metrics such as MAE (Mean Absolute Error), MSE Squared MAPE Percentage R 2 (Coefficient Determination). implementation model allows decision-makers more accurately anticipate changes quality, particularly concerning carbon emissions. facilitates effective planning mitigation measures, improvement public health, optimization resource allocation. Moreover, adeptly addresses complexities data, contributing significantly enhanced monitoring management strategies context development conservation.

Language: Английский

Citations

12
Electric Vehicle (EV) Review: Bibliometric Analysis of Electric Vehicle Trend, Policy, Lithium-Ion Battery, Battery Management, Charging Infrastructure, Smart Charging, and Electric Vehicle-to-Everything (V2X) DOI Creative Commons
Ibham Veza,

Mohd Syaifuddin,

Muhammad Idris

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(15), P. 3786 - 3786

Published: July 31, 2024

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and negative impacts of internal combustion engine (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, charging infrastructure vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis Scopus database investigate development technology. specifically focuses on analyzing trends, policy implications, lithium-ion batteries, battery management systems, infrastructure, smart technologies, V2X. Through this detailed discussion, we aim provide better understanding holistic technology inspire further research in vehicles. covers period from 1990 2022. underscores interplay technology, focusing developments possibility V2X In addition, suggests synchronization international policy, advancement promotion use systems. emphasizes that expansion EVs sustainable mobility relies comprehensive strategy encompasses infrastructure. recommends fostering collaboration between different sectors drive innovation advancements

Language: Английский

Citations

11
The impact of air pollution on career changes among Chinese workers DOI Creative Commons
Jinhuang Chen, Xuewen Long

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

The influence exerted by air pollution on interregional workforce migration has garnered considerable attention in ecological economics over time; however, relatively scant consideration been given to its effects occupational transition dynamics. This study presents an empirical examination of the job changes among working population and seeks understand underlying causal mechanisms. By merging detailed micro-level survey data with regional Fine particulate matter (PM2.5) from Chinese counties spanning years 1997 2015, we have constructed extensive database support our analysis. revealed that a significant negative impact likelihood career switching. Mechanistic analysis indicates wage compensation, as well declines health status, serve primary pathways through which exerts influence. To reduce welfare loss caused pollution, it is crucial prioritize benefits conditions labor force.

Language: Английский

Citations

1
PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework DOI Open Access
Mei-Hsin Chen,

Yao-Chung Chen,

Tien‐Yin Chou

et al.

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(5), P. 4077 - 4077

Published: Feb. 24, 2023

Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid still some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and regression ability random forest (RF) propose a novel CNN-RF ensemble framework for concentration modeling. The observational data from 13 monitoring stations in Kaohsiung 2021 were selected model training testing. First, CNN was implemented extract key meteorological pollution data. Subsequently, RF algorithm employed train with five input factors, namely extracted features spatiotemporal including day year, hour day, latitude, longitude. Independent observations two evaluate models. findings demonstrated that proposed had better modeling capability compared independent models: average improvements root mean square error (RMSE) absolute (MAE) ranged 8.10% 11.11%, respectively. In addition, has fewer excess residuals at thresholds 10 μg/m3, 20 30 μg/m3. results revealed is stable, reliable, accurate method can generate superior methods. could be valuable reference readers may inspire researchers develop even more effective air research important implications research, analysis, estimation, learning.

Language: Английский

Citations

20
Unveiling Pollutants in Sonipat District, Haryana: Exploring Seasonal, Spatial and Meteorological Patterns DOI

Diksha Rana,

Maya Kumari, Varun Narayan Mishra

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 135, P. 103678 - 103678

Published: July 24, 2024

Language: Английский

Citations

5
Remote Sensing for Reducing Spatial Uncertainty in Air Pollution Measurements in Indo-Pacific Region DOI Open Access
Umesh Chandra Kulshrestha

Current World Environment, Journal Year: 2025, Volume and Issue: 19(3), P. 1041 - 1046

Published: Jan. 10, 2025

Language: Английский

Citations

0
Advancing Air Quality Monitoring Systems Towards Sustainable Green Development: Insight for Metropolitan Cities in Indonesia DOI Creative Commons
Hunggul Yudono Setio Hadi Nugroho, Tyas Mutiara Basuki,

Pratiwi Pratiwi

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100649 - 100649

Published: Feb. 1, 2025

Language: Английский

Citations

0
Air Quality and Public Health: Assessing Pollution Processes and Their Mitigation DOI

Ayobami Omozemoje Aigberua,

Kurotimipa Frank Ovuru

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 25 - 52

Published: Jan. 1, 2025

Language: Английский

Citations

0
Implementation of Random Forest Algorithm for Air Quality Classification: A Case Study of DKI Jakarta's Air Quality Index DOI
Mochammad Junus,

Vidorova Nurcahyani,

Rachmad Saptono

et al.

Published: April 7, 2025

Air quality monitoring and classification in urban environments present significant challenges for environmental management public health policy. This study implements an optimized Random Forest (RF) algorithm to classify air levels DKI Jakarta, Indonesia, using the Quality Index (AQI) data from 2021. The analysis incorporates six key pollutants: PM10, PM2.5, NO2, SO2, CO, O3, with collected Environmental Management Agency of Jakarta. RF model was developed 5000 decision trees parameters (mtry=2) evaluated through stratified sampling a 70:30 train-test split. achieved exceptional accuracy 99.09% low Out-of-Bag (OOB) error rate 2.35%. Feature importance revealed that particulate matter (PM2.5 PM10) were most influential factors, collectively accounting 78.70% model's decision-making process. high performance metrics across all categories (Good, Moderate, Unhealthy) demonstrate reliability tasks. research provides insights into policymaking, presenting framework adaptable other settings. findings highlight crucial role assessment suggest targeted strategies pollution control.

Language: Английский

Citations

0
The influence of environmental, social, and governance disclosure on market reaction: evidence from emerging markets DOI Creative Commons
Iskandar Itan,

Sylvia Sylvia,

Sheila Septiany

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: April 29, 2025

Language: Английский

Citations

0