Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(5), С. 114043 - 114043
Опубликована: Сен. 4, 2024
Язык: Английский
Results in Engineering, Год журнала: 2024, Номер 22, С. 102305 - 102305
Опубликована: Май 22, 2024
Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality environmental risks provided by pollutant data crucial for management. The use artificial neural network (ANN) approaches predicting pollutants reviewed this research. These methods are based on several forecast intervals, including hourly, daily, monthly ones. This study shows that ANN techniques contaminants more precisely than traditional methods. It has been discovered input parameters architecture-type algorithms used affect accuracy prediction models. therefore accurate reliable other empirical models because they can handle wide range meteorological parameters. Finally, research gap networks identified. review may inspire researchers to certain extent promote development intelligence prediction.
Язык: Английский
Процитировано
18
Опубликована: Март 12, 2025
Abstract Real-time forecasting of carbon monoxide (CO) concentrations is essential for enabling timely interventions to improve urban air quality. Conventional quality models often require extensive computational resources accurate, multi-scale predictions, limiting their practicality rapid, real-time application. To address this challenge, we introduce the Complex Neural Operator Air Quality (CoNOAir), a machine learning model that forecast CO efficiently. CoNOAir demonstrates superior performance over state-of-the-art models, such as Fourier (FNO), in both short-term (hourly) and extended (72-h) forecasts at national scale. It excels capturing extreme pollution events performs consistently across multiple Indian cities, achieving an R 2 above 0.95 hourly predictions all evaluated locations. equips authorities with effective tool issuing early warnings designing targeted intervention strategies. This work marks step forward dependable, densely populated centres.
Язык: Английский
Процитировано
1
Results in Engineering, Год журнала: 2025, Номер unknown, С. 105388 - 105388
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0
Urban Science, Год журнала: 2024, Номер 8(3), С. 104 - 104
Опубликована: Авг. 1, 2024
Artificial intelligence (AI) has become a transformative force across various disciplines, including urban planning. It unprecedented potential to address complex challenges. An essential task is facilitate informed decision making regarding the integration of constantly evolving AI analytics into planning research and practice. This paper presents review how methods are applied in studies, focusing particularly on carbon neutrality We highlight already being used generate new scientific knowledge interactions between human activities nature. consider conditions which advantages AI-enabled studies can positively influence decision-making outcomes. also importance interdisciplinary collaboration, responsible governance, community engagement guiding data-driven suggest contribute supporting carbon-neutrality goals.
Язык: Английский
Процитировано
3
Atmosphere, Год журнала: 2024, Номер 15(8), С. 917 - 917
Опубликована: Июль 31, 2024
Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and characteristics, particularly focusing on how green cover can mitigate heat air pollution increase temperatures. Recognizing predictive value of LST island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, Phoenix—each characterized by unique climate planning features. investigates atmospheric pollutants (SO2, NO2, CO, O3) Normalized Difference Vegetation Index (NDVI) using regression correlation analyses. The analysis aims elucidate changes in NDVI affect variations temperature. Regression is employed estimate coefficients independent variables quantify their impact LST. Correlation assesses linear relationships variables, providing insights into pairwise associations. also multicollinearity identify potential confounding factors. results reveal significant associations pollutants, NDVI, temperature, contributing our understanding factors influencing dynamics informing change mitigation strategies. observed inconsistencies correlations across cities highlight importance local context studies. Understanding these aid developing tailored policies that consider city characteristics more effective resilience. Furthermore, positive association was consistently obtained LST, indicating increased levels contribute higher temperatures different settings.
Язык: Английский
Процитировано
3
Results in Engineering, Год журнала: 2024, Номер 23, С. 102706 - 102706
Опубликована: Авг. 9, 2024
In this study, an integrated system for remotely monitoring air pollution data was developed. This combines vehicle exhaust emission with driving information. It comprises extraction device (EED), improved On-Board Diagnostic II (OBDII) device, a mobile app, and backend server database. The EED detects gases such as NOx, COx, PM2.5 transmits information to the OBDII through 2.4G communication. gathers CAN 2.0 interface, integrates data, app Bluetooth. forwards database 4G/5G communication storage. Overall, provides platform comprehensive collection, analysis, application of big pertaining sources behavior. Analysis actual experiments involving three vehicles revealed correlation over 80 % between vehicles' revolutions per minute CO2 emissions, indicating that behaviors affect emissions. summary, proposed not only stores relevant in real time but also future analysis behavior
Язык: Английский
Процитировано
0
Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(5), С. 114043 - 114043
Опубликована: Сен. 4, 2024
Язык: Английский
Процитировано
0