Assessment of daily concentrations of air pollutants over four stations of Andhra Pradesh, India, during 2019-2022

Gogineni, Rajesh; Umakanth, Nandivada; Ramakrishna, Yarlagadda

doi:10.1007/s11600-023-01155-9

Artykuł - szczegóły

Tytuł artykułu

Assessment of daily concentrations of air pollutants over four stations of Andhra Pradesh, India, during 2019-2022

Autorzy

Gogineni Rajesh , Umakanth Nandivada , Ramakrishna Yarlagadda

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01155-9

Warianty tytułu

Języki publikacji

Abstrakty

In recent years, pollution levels have increased to dangerous levels in several Indian cities. These levels are posing a severe threat to human’s health. Using the data from Central Pollution Control Board (CPCB), the current work focuses on highlighting the primary air pollutants in various regions such as Visakhapatnam (VSK), Hyderabad (HYD), Amaravati (AMV), and Tirupati (TPTY). Data from the Zoo Park area were used to study the location of HYD. Sulphur dioxide (SO₂), carbon monoxide (CO), nitrogen dioxide (NO₂), nitrogen oxides (NOX), particulate matter with particles less than 2.5 gm in diameter (PM_2.5), particulate matter with particles less than 10 gm in diameter (PM₁₀), and ozone (O₃) are the air pollutants used for analysis in this work. An attempt was made to research the meteorological factors that contribute to the rising levels of air pollution between 2019 and 2022. Wind speed (WS), temperature (TEMP), relative humidity (RHUM), rainfall (RF), and solar radiation (SR) are the meteorological variables used in the analysis. The prediction of PM_2.5 and PM₁₀ was done using artificial neural network (NN) method. The NN method's outcomes show strong correlation in the forecasting of air pollution across four locations. The VSK station exhibited a high correlation of 91.29%, whereas TPTY station had a low correlation of 82.1%, based on CPCB PM_2.5 observation and NN technique. The VSK station revealed a high correlation of 90.30%, whereas TPTY station had a low correlation of 81.61%, based on CPCB PM₁₀ observation and NN technique.

Słowa kluczowe

particulate matter pollutant analysis neural network

cząstki stałe zanieczyszczenie analiza sieć neuronowa

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 2

Strony

1227--1249

Opis fizyczny

Bibliogr. 52 poz.

Twórcy

autor

Gogineni Rajesh

rgogineni9@gmail.com

Department of ECE, Dhanekula Institute of Engineering and Technology, Vijayawada 521139, India

autor

Umakanth Nandivada

Department of ECE, Kallam Haranadhareddy Institute of Technology, Chowdavaram 522019, India

autor

Ramakrishna Yarlagadda

Department of ECE, Dhanekula Institute of Engineering and Technology, Vijayawada 521139, India

redaktor

Veeraswamy Parise

Department of ECE, Dhanekula Institute of Engineering and Technology, Vijayawada 521139, India

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Typ dokumentu

Bibliografia

Identyfikator YADDA

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