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1.
Huan Jing Ke Xue ; 45(3): 1349-1360, 2024 Mar 08.
Artigo em Chinês | MEDLINE | ID: mdl-38471851

RESUMO

Pollution variation, source characteristics, and meteorological effects of water-soluble inorganic ions (WSIIs) in PM2.5 were analyzed in Xinxiang city, Henan Province. PM2.5 samples and their chemical components were monitored online by using URG-9000 in four seasons:winter (January, 2022), spring (April, 2022), summer (July, 2022), and fall (October, 2022). The results showed that the TWSIIs had the same seasonal fluctuations as PM2.5. The average seasonal concentrations of WSIIs ranged from 19.62-72.15 µg·m-3, accounting for more than 60% of PM2.5, demonstrating that WSIIs were the major components of PM2.5. The annual concentration value of NO3-/SO42- was 2.11, which showed an increasing trend, suggesting predominantly mobile sources for secondary inorganic aerosols (SNA). Further, the molar concentration value [NH4+]/[NO3-] was 1.95, demonstrating that agriculture emissions were the dominant contributors to atmospheric nitrogen. Furthermore, the backward trajectory analysis showed that the concentrations of Ca2+ and Mg2+ were higher when the northeasterly wind prevailed and the wind speed was high. High values of SOR and NOR were correlated with low temperatures and high relative humidity (T < 8℃, RH > 60%), demonstrating that more gaseous precursors were converted into sulfate and nitrate. At high temperatures (T > 24℃), there was no apparent high NOR value like that for SOR, mainly due to the decomposition of NH4NO3 at high temperatures. Finally, backward trajectories associated with the PMF-resolved results were used to explore the regional transport characteristics. The results illustrated that dust sources in the study areas were mainly influenced by air trajectories originating from the northwest regions, whereas secondary sulfate, secondary nitrate, and biomass sources contributed more to WSIIs when wind speed and altitude air masses were low in the area surrounding the observation site.

2.
Huan Jing Ke Xue ; 42(9): 4140-4150, 2021 Sep 08.
Artigo em Chinês | MEDLINE | ID: mdl-34414712

RESUMO

This study analyzed the seasonal variation, sources, and source-specific health risks of PM2.5-bound metals in Xinxiang city, Henan province. A total of 112 daily PM2.5 samples were collected over four consecutive seasons during 2019-2020. In total, 19 elements were identified using inductively coupled plasma atomic emission spectroscopy (ICP-AES). The annual concentrations of PM2.5 and 11 heavy metals were calculated to be (66.25±35.73) µg·m-3 and (1.32±0.84) µg·m-3, respectively. Strong seasonal variations were observed in PM2.5 concentrations and the concentrations of associated metal elements, with the lowest concentrations all occurring in summer. The highest concentrations of dust-related elements (e.g., Al, Ca, Fe, Mg,and Ti) were recorded in spring, differing significantly from other elements, which all exhibited the highest mass concentrations in winter. The results apportioned from positive matrix factorization (PMF) and potential source contribution function (PSCF) models showed that the major sources of PM2.5-bound elements were Ni-and Co-related emissions (5.8%), motor vehicles (13.7%), Cd-related emissions(5.1%), combustion emissions (18.2%), and dust (57.3%). Health risk models showed that there were no obvious non-carcinogenic risks associated with these metals, because their hazard quotient (HQ) values were all below 1. Lifetime carcinogenic risks of the five apportioned sources were all higher than the acceptable level (1×10-6). Of these five sources, combustion emissions were the largest contributors to cancer risk (8.74×10-6, 36.9%) and non-cancer risk (0.60, 25.6%). This study suggests that control strategies to mitigate exposure risk in Xinxiang should emphasize reducing the sources of combustion emissions.


Assuntos
Metais Pesados , Material Particulado , Clima , Monitoramento Ambiental , Humanos , Metais Pesados/análise , Material Particulado/efeitos adversos , Material Particulado/análise , Estações do Ano
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