On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis.

ABSTRACT: One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM_2.5 and PM₁₀). This paper aims to develop descriptive-analytical solutions for increasing the accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Photometer instruments DUST TRAK and BS-EN-12341 method were used to determine concentration of PM_2.5 and PM₁₀. Sampling was performed on 4 environmental stations of Kerman cement plant in the four seasons. In order to accurate assessment of particulate matter concentration, a new model was proposed to resample cement plant time series data using Pandas in Python. The effect of meteorological parameters including wind speed, relative humidity, air temperature

SUBMITTER: Borhani F 

PROVIDER: S-EPMC9643923 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature