Chemical characterization and sources of submicron aerosols in Lhasa on the Qinghai-Tibet Plateau: Insights from high-resolution mass spectrometry

Insights into anthropogenic impact on atmospheric inorganic aerosols in the largest city of the Tibetan Plateau through multidimensional isotope analysis

Particulate inorganic nitrogen aerosols (PIN) significantly influence air pollution and pose health risks worldwide. Despite extensive observations on ammonium (pNH4+) and nitrate (pNO3-) aerosols in various regions, their key sources and mechanisms in the Tibetan Plateau remain poorly understood. To bridge this gap, this study conducted a sampling campaign in Lhasa, the Tibetan Plateau's largest city, with a focus on analyzing the multiple isotopic signatures (δ15N, ∆17O). These isotopes were integrated into a Bayesian mixing model to quantify the source contributions and oxidation pathways for pNH4+ and pNO3-. Our results showed that traffic was the largest contributor to pNH4+ (31.8 %), followed by livestock (25.4 %), waste (21.8 %), and fertilizer (21.0 %), underscoring the impact of vehicular emissions on urban NH3 levels in Lhasa. For pNO3-, coal combustion emerged as the largest contributor (27.3 %), succeeded by biomass burning (26.3 %), traffic emission (25.3 %), and soil emission (21.1 %). In addition, the ∆17O-based model indicated a dominant role of NO2 + OH (52.9 %) in pNO3- production in Lhasa, which was similar to previous observations. However, it should be noted that the NO3 + volatile organic component (VOC) contributed up to 18.5 % to pNO3- production, which was four times higher than the Tibetan Plateau's background regions. Taken together, the multidimensional isotope analysis performed in this study elucidates the pronounced influence of anthropogenic activities on PIN in the atmospheric environment of Lhasa.

Vertical distribution of VOCs in the boundary layer of the Lhasa valley and its impact on ozone pollution

To investigate the vertical distribution of volatile organic compounds (VOCs) concentrations in the Lhasa valley region, an intensive measurement campaign was first conducted in summer using a tethered balloon. The results showed that the average concentration of surface VOCs was 49.1 ± 30.1 ppbv, alkanes and aromatics were the main components. Notably, a very large discrepancy in VOCs was obtained between the wet (71.6 ± 25.9 ppbv) and dry (25.6 ± 8.0 ppbv) episodes, which was attributed to the atmospheric stability and diffusion capacity. Moreover, the total VOC (TVOCs) concentration declined under fluctuations, but it rapidly increased with height in the afternoon during the wet episode (2.50 ppbv/100 m, R2 = 0.47). According to the PMF results, combustion was the dominant emission source, additionally, the contribution of solvent coating in the wet episode and the background in the dry episode increased with height. Moreover, the O3 concentration increased with height, and the decrease in LNOx-OH could effectively prevent the occurrence of high O3 values. This study indicated that low wind speeds and high humidity levels highly likely cause the accumulation of atmospheric VOCs under static and stable conditions, while the control of high O3 concentrations must still greatly consider summertime NOx emissions in Lhasa.

Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022

The Tibetan Plateau (known as the Earth's Third Pole) has significant impact on climate. Fine particulate matter (PM_2.5) is an important air pollutant in this region and has significant impact on health and climate. To mitigate PM_2.5 air pollution over China, a series of clean air actions has been implemented. However, interannual trends in particulate air pollution and its response to anthropogenic emissions in the Tibetan Plateau are poorly understood. Here, we applied a random forest (RF) algorithm to quantify drivers of PM_2.5 trends in six cities of the Tibetan Plateau from 2015 to 2022. The decreasing trends (-5.31 to -0.73 μg m^-3 a^-1) in PM_2.5 during 2015-2022 were observed in all cities. The RF weather-normalized PM_2.5 trends - which were driven by anthropogenic emissions - were -4.19 to -0.56 μg m^-3 a^-1, resulting in dominant contributions (65 %-83 %) to the observed PM_2.5 trends. Relative to 2015, such anthropogenic emission driver was estimated to contribute -27.12 to -3.16 μg m^-3 to declines in PM_2.5 concentrations in 2022. However, the interannual changes in meteorological conditions only made a small contribution to the trends in PM_2.5 concentrations. Potential source analysis suggested biomass burning from local residential sector and/or long-range transports originated from South Asia could significantly promote PM_2.5 air pollution in this region. Based on health-risk air quality index (HAQI) assessment, the HAQI value was decreased by 15 %-76 % between 2015 and 2022 in these cities, with significant contributions (47 %-93 %) from anthropogenic emission abatements. Indeed, relative contribution of PM_2.5 to the HAQI was decreased from 16 %-30 % to 11 %-18 %, while increasing and significant contribution from ozone was observed, highlighting that further effective mitigation of both PM_2.5 and ozone air pollution could obtain more substantial health benefits in the Tibetan Plateau.