Spatial and Temporal Distributions of Air Pollutants and Size Distribution of Aerosols over Central and Eastern China

Analysis of spatio-temporal distribution characteristics and socioeconomic drivers of urban air quality in China

Having high spatio-temporal resolution data of pollutants is critical to understand environmental pollution patterns and their mechanisms. Our research employs the hourly average concentration data on the air quality index (AQI) and its six component pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in 336 Chinese cities from 2014 to 2019. We analyze annual, seasonal, monthly, hourly, and spatial variations of different air pollutants and their socioeconomic factors. The results are as follows. (1) Air pollutants' concentration in Chinese cities decreased year by year during 2014-2019. Among the primary pollutants, PM_2.5 dominated pollution days, accounting for 38.46%, followed by PM₁₀. Monthly concentration curves of AQI, PM_2.5, NO₂, SO₂, and CO showed a U-shaped trend from January to December, while that of O₃ presented an inverted U-shaped unimodal pattern. Regarding daily variation, urban air quality tended to be worse around sunrise compared with sunset. (2) Chinese cities' air quality decreased from north to south and from inland to coastal areas. Recently, air quality has improved, and polluted areas have shrunk. The six pollutant types showed different spatial agglomeration characteristics. (3) Industrial pollution emissions were the main source of urban air pollutants. Energy-intensive industries, dominated by coal combustion, had the greatest impact on SO₂ concentration. A "pollution shelter" was established in China because foreign investment introduced more pollution-intensive industries. Thus, China has crossed the Kuznets U-curve inflection point. In addition, population agglomeration contributed the most to PM_2.5 concentration, increasing the PM_2.5 exposure risk and causing disease, and vehicle exhaust aggravated the pollution of NO₂ and CO. The higher China's per capita gross domestic product, the more significant the effect of economic development is on reducing pollutant concentration.

Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment

To control the spread of the 2019 novel coronavirus (COVID-19), China imposed rigorous restrictions, which resulted in great reductions in pollutant emissions. This study examines the characteristics of air pollutants, including PM_2.5 (particles with aerodynamic diameters < 2.5 μm), gas pollutants, water-soluble ions (WSIs), black carbon (BC) and elements, as well as the source apportionment of PM_2.5 in Suzhou before, during and after the Chinese New Year (CNY) holiday of 2020 (when China was under an unprecedented state of lockdown to restrict the COVID-19 outbreak). Compared to those before CNY, PM_2.5, BC, SNA (sulfate, nitrate and ammonium), other ions, elements, and NO₂ and CO mass concentrations decreased by 9.9%-64.0% during CNY. The lockdown policy had strong (weak) effects on the diurnal variations in aerosol chemical compositions (gas pollutants). Compared to those before CNY, source concentrations and contributions of vehicle exhaust during CNY decreased by 72.9% and 21.7%, respectively. In contrast, increased contributions from coal combustion and industry were observed during CNY, which were recorded to be 2.9 and 1.7 times higher than those before CNY, respectively. This study highlights that the lockdown policy that was imposed in Suzhou during CNY not only reduced the mass concentrations of air pollutants but also modified their diurnal variations and the source contributions of PM_2.5, which revealed the complex responses of PM_2.5 sources to the rare, low emissions of anthropogenic pollutants that occurred during the COVID-19 lockdown.

Effect of ship emissions on O3 in the Yangtze River Delta region of China: Analysis of WRF-Chem modeling

The Yangtze River Delta (YRD) region locates on the eastern coast of China, and it has suffered severe O₃ pollutions due to high and mixed emissions of air pollutants. There are 3 different emission sectors for O₃ precursors in the region, including anthropogenic VOC_S and NO_X emissions, ship emissions (mainly NO_X), and biogenic emissions from a large forest (biogenic VOC_S). This unique emission mixture produces complicated chemical processes in studying the O₃ pollutions in the region. This study aims to identify the contribution of the ship emissions to O₃ pollutions, as well as the effect of mixing emissions on O₃ pollutions in YRD. To identify the individual emission effect, the WRF-Chem model is used in this study. The model generally performs well in simulating meteorological parameters and air pollutants against observations in YRD. Sensitive study suggests that the ship emissions have important effects on the O₃ concentrations over ocean and inland, with a maximum increase of 30-50 μg m^-3 occurred mainly in the ship track regions. However, the ship emissions have a very complicated effect on the in-land O₃ concentrations. In the north of Shanghai, the NO_X concentrations are high due to high anthropogenic emissions, and a further increase in NO_X emissions from ship results in depressing O₃ chemical production. In contrast, in the south of Shanghai, there are high biogenic VOC emissions (mainly isoprene) and low NOx concentrations. As a result, the O₃ concentrations are enhanced by 30-50 μg m^-3, due to the mixing between ship and forest emissions. This study suggests that ship emissions play important roles in controlling O₃ pollutions in YRD. Furthermore, the mixing emissions between ship, anthropogenic, and biogenic emissions in YRD produce a complicated O₃ chemical production and need to be carefully considered in controlling strategy of O₃ pollution in the region.

Observation and Source Apportionment of Trace Gases, Water-Soluble Ions and Carbonaceous Aerosol During a Haze Episode in Wuhan

As the new core region of the haze pollution, the terrain effect of sub-basin and water networks over the Twin-Hu Basin (THB) in the Yangtze River Middle-Reach (YRMR) had great impacts on the variations and distributions of air pollutants. In this study, trace gases (NH3, HNO3, and HCl), water-soluble ions (WSIs), organic carbon (OC), and elemental carbon (EC) were measured in PM2.5 from 9 January to 27 January 2018, in Wuhan using monitoring for aerosols and gases (MARGA) and a semi-continuous OC/EC analyzer (Model RT-4). The characteristics of air pollutants during a haze episode were discussed, and the PM2.5 sources were quantitatively analyzed on haze and non-haze days using the principal component analysis/absolute principal component scores (PCA/APCS) model. The average PM2.5 concentration was 122.61 μg·m−3 on haze days, which was 2.20 times greater than it was on non-haze days. The concentrations of secondary water soluble ions (WSIs) including NO3−, SO42−, and NH4+ increased sharply on haze days, which accounted for 91.61% of the total WSIs and were 2.43 times larger than the values on non-haze days. The heterogeneous oxidation reactions of NO2 and SO2 during haze episodes were proven to be the major sources of sulfate and nitrate in PM2.5. On haze days, the concentrations of EC, primary organic carbon (POC), and secondary organic carbon (SOC) were 1.68, 1.69, and 1.34 times larger than those on non-haze days, the CO, HNO3, and NH3 concentrations enhanced and relatively low SO2, O3, and HNO2 levels were observed on haze days. The diurnal variations of different pollutants distinctly varied on haze days. The PM2.5 in Wuhan primarily originated from the secondary formation, combustion, dust, industry, and vehicle exhaust sources. The source contributions of the secondary formation + combustion sources to PM2.5 on haze days were 2.79 times larger than the level on non-haze days. The contribution of the vehicle exhaust + combustion source on haze days were 0.59 times the value on non-haze days. This description is supported by a summary of how pollutant concentrations and patterns vary in the THB compared to the variations in other pollution regions in China, which have been more completely described.