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.

As one of the biggest cities in North China, Jinan has been suffering heavy air pollution in recent decades. To better characterize the ambient particulate matter in Jinan during heavy pollution periods, we collected daily PM_2.5 (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) filter samples from 15 October 2017 to 31 January 2018 and analyzed their chemical compositions (including inorganic water-soluble ions (WSIs), carbonaceous species, and inorganic elements). The daily average concentration of PM_2.5 was 83.5 μg/m³ during the sampling period. A meteorological analysis revealed that both low wind speed and high relative humidity facilitated the occurrence of high PM_2.5 pollution episodes. A chemical analysis indicated that high concentrations of water-soluble ions, carbonaceous species, and elements were observed during heavy pollution days. The major constituents of PM_2.5 in Jinan were secondary aerosol particles and organic matter based on the results of mass closure. Chemical Mass Balance (CMB) was used to track possible sources and identified that nitrate, sulfate, vehicle exhaust and coal fly ash were the main contributors to PM_2.5 during heavy pollution days in Jinan, accounting for 25.4%, 18.6%, 18.2%, and 13.3%, respectively.

Air pollution is an increasing threat to human health in China. In this study, daily PM₁₀ and PM_2.5 samples were collected simultaneously at the Jinan Environmental Monitoring Station (EMS)in Jinan, China from 15 November 2016 to 15 March 2017. The aim of this work was to improve the understanding of the characteristics and sources of air particles and determine different levels of PM_2.5 pollution and its constituent elements, water-soluble ions and carbonaceous species. Nitrate (NO₃⁻), organic materials (OM) and sulfate (SO₄²⁻) were identified as the three main components of PM_2.5 pollution. With increasing pollution level, the contributions of SO₄²⁻, NO₃⁻ and NH₄⁺ increased at greater rates, unlike that of OM. The proportion of SO₄²⁻ exceeded that of NO₃⁻ and became predominant in severe PM_2.5 pollution (SP; 250 μg m⁻³ ≤ PM_2.5 ≤ 500 μg m⁻³). This work demonstrates that SO₄²⁻ has a dominant role in SP level and, consequently, requires greater research attention. It is demonstrated that relative humidity (RH) enhances the rate of sulfate formation more than that of nitrate. Therefore, under the current Chinese emergency response measures, it is necessary to further reduce emissions of SO₂ and NO₂. Four clusters of backward trajectories identified dominant pollution vectors originating from highly industrialized areas that exacerbate the poor air quality in Jinan. It is, therefore, necessary to undertake regional control measures to reduce pollutant emissions.

Although carbonyl compounds are a key species with atmospheric oxidation capacity, their concentrations and sources have not been sufficiently characterized in various atmospheres, especially in desert areas. In this study, atmospheric carbonyl compounds were measured from 16 May to 15 June 2018 in Tazhong in the central Taklimakan Desert, Xinjiang Uygur Autonomous Region, China. Concentrations, chemical compositions, and sources of carbonyl compounds were investigated and compared with those of different environments worldwide. The average concentration of total carbonyls during the sampling period was 11.79 ± 4.03 ppbv. Formaldehyde, acetaldehyde, and acetone were the most abundant carbonyls, with average concentrations of 6.08 ± 2.37, 1.68 ± 0.78, and 2.52 ± 0.68 ppbv, respectively. Strong correlations between formaldehyde and other carbonyls were found, indicating same or similar sources and sinks. A hybrid single-particle Lagrangian integrated trajectory was used to analyze 72 h back trajectories. The values of C1/C2 (formaldehyde to acetaldehyde, 3.22–4.59) and C2/C3 (acetaldehyde to propionaldehyde, 15.00–17.03) from different directions and distances of the trajectories were consistent with the characteristics of a remote area. Relative to various environments, the carbonyl concentration in the Tazhong desert site was lower than that in urban areas and higher than that in suburban and remote areas, implying contributions from local primary and secondary sources. The obtained data can be used to improve the source and sink estimation of carbonyls at the regional scale.

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.