A survey on air pollutant PM2.5 prediction using random forest model
Journal Title: Environmental Health Engineering and Management Journal - Year 2023, Vol 10, Issue 2
Abstract
Background: One of the most critical contributors to air pollution is particulate matter (PM2.5) that its acute or chronic exposure causes serious health effects to human. Accurate forecasting of PM2.5 concentration is essential for air pollution control and prevention of health complications. A survey of the available scientific literature on random forest model for PM2.5 prediction is presented here. Methods: The scientific literature is extracted from Science Direct database based on a set of specified search criteria. The input features, data length, and evaluation parameters used in PM2.5 prediction were analyzed in this study. Results: The study shows that majority of the publications are aimed at the daily prediction of outdoor PM2.5. Most publications base their PM2.5 prediction on features aerosol optical depth (AOD) and boundary layer height (BLH). PM10 and NO2 are the main air pollutants employed in the PM2.5 estimation. Majority studies utilized input data lengths covering more than one year, and the effectiveness of prediction models are unaffected by the length of investigation. The coefficient of determination, R2, is the primary evaluation parameter used in all publications. The majority of research study indicated R2 values greater than 0.85, demonstrating the reasonable dependability and efficiency of random forest regression-based PM2.5 prediction models. Conclusion: The study demonstrates that the publications use a variety of meteorological and geological features for PM2.5 estimation, depending on the context of the research as well as data accessibility. The findings demonstrate that it is hard to pinpoint the optimal model in any particular way.
Authors and Affiliations
Sherin Babu , Binu Thomas
Keywords
Related Articles
- EP ID EP718082
- DOI 10.34172/EHEM.2023.18
- Views 54
- Downloads 0
Knowledge, attitude, and practice of nurse aids and service staff about nosocomial infection control: A case study in Iran
Background: Hospital-acquired infection (HAI) or nosocomial infection is a major public health concern. In this study, the status of knowledge, attitude, and practice (KAP) of service staff and nurse aids in reference...
Evaluation of removal efficiency of phenol from synthetic aqueous solutions by Citrullus colocynthis seed ash
Background: Phenol is a prevalent pollutant found in many industrial wastewaters, and it is paid singular attention because of its special features like high toxicity, carcinogenic properties, and vital gathering ability...
Evaluation of the impact of the COVID-19 pandemic on photochemical pollution in urban areas
Background: The effect of confinement due to the coronavirus disease 2019 (COVID-19) pandemic on air pollution has been investigated in several cities. Indeed, the concentration of the main air pollutants have decrease...
Comparison of hybrid regression and multivariate regression in the regional flood frequency analysis: A case study in Khorasan Razavi province
Background: Magnitude, rate and frequency of the stochastic and unexpected events are of great significance and importance in hydrology. Nowadays, for economic planning of the projects, the use of analytical methods of u...
Investigating the performance of urban air quality monitoring station in measuring PM2.5 and PM10: A case study in Tehran, Iran
Background: In recent years, new findings on the relationship between human health and air pollutants have emerged, underscoring the necessity for appropriate site selection of air monitoring stations. Methods: This an...