Xueyan Zhao

To investigative the causes and potential sources of fine particulate matter (PM2.5) pollution during winter haze pollution in typical industrial areas in northwest China, PM2.5 samples were collected during a winter extreme pollution event (from January 15th to January 29th, 2016). The daily average PM2.5 concentrations were ~210 μg m-3 and peak daily concentrations were ~496 μg m-3 in the Kuitun-Dushanzi-Wusu (K-D-W region) of Xinjiang Uygur Autonomous Region, China. Eighty-eight samples (including 44 quartz and 44 Teflon samples for PM2.5) were assessed for water-soluble ions (WSIIs), organic/elemental carbon (OC/EC) and inorganic elements. The results showed that the percentage of carbonaceous compounds decreased with more severe pollution levels, and the OC and SOC decreased more rapidly than EC. The sum of 39 inorganic element concentrations (8.28% ± 3.59%) was lower than that of water-soluble ions (63.26% ± 8.78%) and carbonaceous compounds (10.95% ± 3.22%). SO42- is the component with the highest percentage, and the percentage of SO42- increases continuously in severe pollution, indicating that secondary transformation of SO42- was more significant during polluted periods. The increased pollution, combined with high relative humidity (RH) increased the liquid water content (LWC), which in turn promoted heterogeneous reactions. The Positive Matrix Factorization (PMF) analysis shows that secondary particulate matter (47%), coal combustion (19%), fugitive dust (14%), industrial sources (10%) and vehicular emissions (10%) are identified as the major emission sources during winter in the K-D-W region. Potential areas in the K-D-W region are distributed in the southeast direction of the 8th Division.

Urban and suburban PM_2.5 samples were collected simultaneously during selected periods representing each season in 2019 in Zibo, China. Samples were analysed for water-soluble inorganic ions, carbon components, and elements. A chemical mass balance model and health risk assessment model were used to investigate the source contributions to PM_2.5 and the human health risks posed by various pollution sources via the inhalation pathway. Almost 50% of the PM_2.5 samples exceeded the secondary standard of China’s air quality concentration limit (75 µg/m³, 24 h). Water-soluble inorganic ions were the main component of PM_2.5 in Zibo, accounting for 50 ± 8% and 56 ± 11% of PM_2.5 at the urban and suburban sites, respectively. OC and OC/EC decreased significantly in the past few years due to enhanced energy restructuring. Pearson correlation analysis showed that traffic emissions were the main source of heavy metals. The Cr(VI) concentrations were 1.53 and 1.92 ng/m³ for urban and suburban sites, respectively, exceeding the national ambient air quality standards limit of 0.025 ng/m³. Secondary inorganic aerosols, traffic emissions, and secondary organic aerosols were the dominant contributors to PM_2.5 in Zibo, with the total contributions from these three sources accounting for approximately 80% of PM_2.5 and the remaining 20% attributed to traffic emissions. The non-carcinogenic risks from crustal dust for children were 2.23 and 1.15 in urban and suburban areas, respectively, exceeding the safe limit of 1.0 in both locations, as was the case for adults in urban areas. Meanwhile, the carcinogenic risks were all below the safe limit, with the non-carcinogenic and carcinogenic risks from traffic emissions being just below the limits. Strict control of precursor emissions, such as SO₂, NOx, and VOCs, is a good way to reduce PM_2.5 pollution resulting from secondary aerosols. Traffic control, limiting or preventing outdoor activities, and wearing masks during haze episodes may be also helpful in reducing PM_2.5 pollution and its non-carcinogenic and carcinogenic health impacts in Zibo.

Rapid economic expansion and urbanisation have seriously affected the atmospheric environmental quality of the Central Liaoning Urban Agglomeration (CLUA). This study aimed to establish a detailed vehicle emission inventory of the CLUA with a 3 km × 3 km gridded spatiotemporal distribution. A top-down methodology using vehicle kilometres travelled annually, emission factors, and activity data of each city was established. Carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO₂), ammonia (NH₃), volatile organic compounds (VOCs), particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5), particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀), Black Carbon (BC), and organic carbon (OC) emissions were 291.0, 221.8, 3.6, 2.2, 42.8, 9.3, 10.3, 5.2, and 1.6 Gg in 2018, respectively. The contribution of diesel heavy-duty trucks to NOx, SO₂, PM_2.5, PM₁₀, BC, and OC emissions was greater than 54.5%, the largest contribution of all vehicles. Gasoline small passenger vehicles were the primary contributor to CO, VOC, and NH₃ emissions, contributing 37.3%, 39.5%, and 75.3% of total emissions, respectively. For emission standards, Pre-China 1 vehicles were the largest contributor to CO and VOC emissions and China 3 vehicles contributed the largest amount of NOx, SO₂, PM_2.5, PM₁₀, BC, and OC emissions. The spatial distribution of pollutants showed “obvious lines” and grids with high emissions were concentrated in expressways, national highways, and provincial highways. The temporal variation showed morning–evening peaks during diurnal variations, which was consistent with resident behaviour. This work can help us understand vehicular emission characteristics of the CLUA and provide basic data for air quality modelling. Future research should investigate traffic flow by vehicle types and emission factors at a local level, which will be helpful for transport management planning.

The accumulation of tailings from gold mining and smelting may result in PTE pollution. We investigated PTE contamination from a large amalgamated gold mine tailings pond in Pinggu County, Beijing. In November 2017, 30 soil samples were collected around the tailings pond. The concentrations and pollution degree of PTEs in the samples and the sources of Sb, As, Cd, Cu, Pb, Zn and Hg were analyzed. The average concentration of these elements in soil samples near the tailings pond (16.24, 28.29, 0.99, 171.04, 263.25, 99.73, 0.72 mg/kg, respectively) were higher than their corresponding standard values and background values of the study area. The geoaccumulation index showed that the pollution degree of As, Pb and Hg was moderate, while Sb and Cu present non-pollution to moderate pollution. The average EF values of the elements were Sb (38.31), As (4.23), Cd (0.71), Cu (3.68), Pb (21.24), Zn (0.82) and Hg (5.29), respectively. The environmental risk assessment developed throughout the PERI method indicated that Sb, As, Hg and Pb were the main pollutants in the study area. The three quantitative risk indicators (RI, Igeo and EF) were positively correlated, and all of them indicated that PTEs had significant pollution to the local area. Thus, Sb, As, Pb, Cu, and Hg pollution should be highly concerning. Multivariate statistical analysis shows that the pollution of PTEs was mainly caused by the accumulation of tailings ponds after gold mining and smelting. The research result is of great significance for the prevention and control of soil pollution of PTEs near the tailings pond.

To better understand the source and health risk of metal elements in PM_2.5, a field study was conducted from May to December 2018 in the central region of the Liaoning province, China, including the cities of Shenyang, Anshan, Fushun, Benxi, Yingkou, Liaoyang, and Tieling. 24 metal elements (Na, K, V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sn, Sb, Pb, Bi, Al, Sr, Mg, Ti, Ca, Fe, Ba, and Si) in PM_2.5 were measured by ICP-MS and ICP-OES. They presented obvious seasonal variations, with the highest levels in winter and lowest in summer for all seven cities. The sum of 24 elements were ranged from to in these cities. The element mass concentration ratio was the highest in Yingkou in the spring (26.15%), and the lowest in Tieling in winter (3.63%). The highest values of elements in PM_2.5 were mostly found in Anshan and Fushun among the studied cities. Positive matrix factorization (PMF) modelling revealed that coal combustion, industry, traffic emission, soil dust, biomass burning, and road dust were the main sources of measured elements in all cities except for Yingkou. In Yingkou, the primary sources were identified as coal combustion, metal smelting, traffic emission, soil dust, and sea salt. Health risk assessment suggested that Mn had non-carcinogenic risks for both adults and children. As for Cr, As, and Cd, there was carcinogenic risks for adults and children in most cities. This study provides a clearer understanding of the regional pollution status of industrial urban agglomeration.