Long-Term Trends of Air Pollution in Thailand and Effects on Health

Mueller, Will; Steinle, Susanne; Loh, Miranda; Vardoulakis, Sotiris; Precha, Nopadol; Kliengchuay, Wissanupong; Sahanavin, Narut; Sillaparassamee, Ratthaphol; Nakhapakorn, Kanchana; Tantrakarnapa, Kraichat; Cherrie, John W.

doi:10.1289/isesisee.2018.p02.0960

Cited by 1 publication

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“…According to Kota et al (2018), the India-Ganges region has the highest concentrations of PM2.5, NO2, and SO2 in northern and eastern India. Statistical methods like correlation, regression models, Fourier transform are the usual means of studying the relationship between particulate matters (PM2.5, PM10) and gaseous air pollutants(SO2, CO, Ozone, NOx)(e.g., Giri et al,2008;Tecer et al, 2008;Kassomenos et al,2014;Li et al, 2017;Mueller et al, 2018).However, for nonlinear and nonstationary time series, such techniques may not be suitable for such techniques and the PM and gaseous air pollutants often possess these characteristics. For the analysis of such complex non-stationary series, wavelet transforms are proven to be one of the appropriate tools (Das et al 2020;Yeditha et al 2022;Sreedevi et al 2022).There are certain studies which focussed on investigating the relationships of meteorological factors on PMs (Anusasananan2019; Barlik 2021).…”

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confidence: 99%

Analyzing the Effect of Air Pollutants on Particulate Matter Concentrations of the Tropical coastal city of Thiruvananthapuram, India by Wavelet Coherence

Aiswarya,

Resmi,

Rahsha

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The main environmental risk factor that need regular monitoring and analysis for efficient air quality management is particulate matter (PM). This paper analyzes the effect of gaseous air pollutants (SO2, CO, Ozone, NOx) on PM concentration in the coastal city of Thiruvananthapuram, India using Wavelet coherence (WTC). The study was conducted using the data from Plammoodu station of the city for the period of 2018 to 2021. The teleconnections of air pollutants with PMs are analysed using WTC for the data of different time spells of 2018-2021, 2018 and 2020. Results showed that among the air pollutants, Ozone (AWC of 0.4713 & PoSC of 23.0234 for PM10, AWC of 0.4814 & PoSC of 26.2261 for PM2.5) have most dominant influence on irrespective of the particulate matter type and time spells. The secondary influencing factors were found to be different with respect to the time spells chosen, indicating the dynamics of local variables on PM concentrations of the city.

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