[Characteristics of Elements in PM2.5 and PM10 in Road Dust Fall During Spring in Tianjin]

Chemical characterization and source apportionment of PM2.5 in a Northeastern China city during the epidemic period

To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissions and catering fume sources contributing the most to PM2.5.

Analysis of Spatio-Temporal Heterogeneity and Socioeconomic driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

Sub-type source profiles of fine particles for fugitive dust and accumulative health risks of heavy metals: a case study in a fast-developing city of China

Sub-type source profiles for atmospheric fine particle (PM_2.5) were still scare in China, which limited the accurate source identification of it. Fugitive dust (including road dust, soil dust, resuspended dust, and construction dust, etc.) was one type of the most important contributors to PM_2.5 and its associated toxic metals held potential threaten to human health. The chemical compositions, sources, and health risks of sub-type fugitive dust deserved an investigation for further accurate control of particles and alleviating human health risks. A total of sixty-five fugitive dust samples were collected in Suzhou, a fast-developing city in southern China, including eleven sub-types of road dust (overpass, main street, collector street, and ordinary street), soil dust (farmland and tree lawn), resuspended dust (site types were corresponding to those of road dust), and construction dust (large construction sites). Chemical analysis of water-soluble ions, elements, and carbonaceous components was carried out to establish the sub-type source profiles of PM_2.5 for fugitive dust. Results showed that crustal elements were the most abundant components of fugitive dust, and soil dust was less polluted by anthropogenic activities. High contents of OC and low contents of EC were found in all the eleven types of dust. Equivalent ratios of anions and cations indicated that the fugitive dust was obviously alkaline. The contents of OC and EC in the four types of road dust were higher than those in other types of dust, while there existed differences among the sub-types of road dust. The NO₃^-/SO₄^2- ratios (0.03-0.09) implied that coal-burning and motor vehicle emission co-existed in Suzhou. Coefficient divergence (CD) values of eleven sub-type source profiles showed that there were certain differences among them, which suggested the possibility of sub-type source identification. Cluster analysis indicated the heavy metals in fugitive dust were mainly from crustal materials, metallurgical manufacturing, vehicle emissions, and industrial activities. The enrichment degree of heavy metals for the four types of road dust was also inconsistent. Heavy metals in road dust and soil dust posed a non-carcinogenic risk to children through direct ingestion, and the non-carcinogenic risk of direct intake of heavy metals was much higher than that of respiratory and skin contact. It was found that the accumulative health risks of heavy metals were higher in densely populated areas, traffic intensive areas, and industrial areas through the spatial analysis. This study firstly discussed the chemical compositions of PM_2.5 for eleven sub-types of fugitive dust in a Chinese city and assessed the accumulative health risks of heavy metals, which could be a demonstration for further related researches.