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1.
Ecotoxicol Environ Saf ; 260: 115091, 2023 Jul 15.
Article in English | MEDLINE | ID: mdl-37267779

ABSTRACT

Air quality index (AQI) and air pollutants during two typical pollution episodes, and polycyclic aromatic hydrocarbons (PAHs) in fresh snow after each episode in the winter 2019 across Harbin City in northeast China were investigated to explore the co-environmental behaviors. Significantly greater values of AQI and PAHs were found in the more serious atmospheric pollution episode (episode Ⅱ), demonstrating that PAHs in fresh snow is a robust indicator. PM2.5 was the primary air pollutant in both episodes based on PM2.5/PM10 ratios, which might be attributed to fine particulate converted from gas-to-particle process. PM2.5 and 4-ring PAHs significantly positive correlated, indicating that airborne particulate PAHs were co-emitted and co-transported with atmospheric fine particles released from coal combustion and vehicular emission under low temperature and high relative humidity. 3- and 4- rings PAHs were dominant in episode Ⅱ, while 5- and 6- rings PAHs were found the lowest in both episodes. These characteristics reflected that long-range transportation of coal and biomass burning were from the surrounding areas, while vehicle exhausts were mainly from local emissions. Except for the impact of local pollution source emissions, the regional transport could make a greater contribution in a more serious pollution event.


Subject(s)
Air Pollutants , Air Pollution , Polycyclic Aromatic Hydrocarbons , Particulate Matter/analysis , Polycyclic Aromatic Hydrocarbons/analysis , Snow , Environmental Monitoring , Air Pollutants/analysis , China , Vehicle Emissions/analysis , Seasons , Coal/analysis , Dust , Air Pollution/analysis
2.
Chemosphere ; 331: 138785, 2023 Aug.
Article in English | MEDLINE | ID: mdl-37121285

ABSTRACT

Spatiotemporal variation of PM2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM2.5, and meteorological and socioeconomic impacts of PM2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM2.5 concentration in 2020 was 32.73 µg/m3 existing a U-shaped variation pattern, which has decreased by 6.38 µg/m3 compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM2.5 concentrations. The lowest PM2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM2.5 concentrations, with precipitation has the greatest impact on PM2.5, which are beneficial for reducing PM2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM2.5.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Humans , Air Pollutants/analysis , Particulate Matter/analysis , COVID-19/epidemiology , Air Pollution/analysis , China/epidemiology , Seasons , Socioeconomic Factors , Environmental Monitoring/methods , Cities
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