Biomass burning in Peninsular Southeast Asia intensifies meteorological drought in Southwest China.

Biomass burning in Peninsular Southeast Asia (BB-PSEA) affects the climate in downwind regions, especially precipitation (PRE) in southern China. However, the impact of BB-PSEA on the meteorological drought in Southwest China (SWC), where closes to PSEA and often occurs seasonal drought, have not been clear yet. We selected a severe drought event in SWC from January to April 2010 and conducted sensitivity simulations using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) to evaluate the impact of BB-PSEA on the meteorological drought in SWC. Comparisons with observations revealed that the model performed well in simulating the spatiotemporal evolution of the drought in SWC. BB-PSEA increased the drought severity by 0.01-0.75 levels, enlarged drought areas by about 10 %, and prolonged the drought duration mainly by one month in SWC. The impact of BB-PSEA on the drought in SWC in March/April was almost tenfold that in January/February, due to the higher emissions of BB-PSEA in March/April. The mechanism that BB-PSEA influenced drought predominantly involved the reduction of PRE, potential evapotranspiration (PET), and moisture fluxes in SWC. BB-PSEA aerosols warmed the air at 600-800 hPa and cooled the air near the surface in SWC, which stabilized the atmosphere and suppressed PRE and reduced PET in SWC. BB-PSEA aerosols also increased the sea surface temperature in South China Sea and the geopotential heights in the north of the Bay of Bengal, where the moisture sources of SWC originated from. This perturbation reduced the moisture fluxes across the west and south boundaries of SWC, resulting in the reduction of the water vapor content and PRE in SWC. Through elucidating the impact of BB-PSEA on the drought in SWC, this study clarified how BB-PSEA affected the climate in the downwind region and provided new understanding for drought prediction in SWC.

Correction: Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements.

[This corrects the article DOI: 10.1371/journal.pone.0287187.].

Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements.

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.