Contributions of Traffic and Industrial Emission Reductions to the Air Quality Improvement after the Lockdown of Wuhan and Neighboring Cities Due to COVID-19

Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

Spatial and Temporal Distribution Characteristics and Source Apportionment of VOCs in Lianyungang City in 2018

From April to September 2018, five sampling sites were selected in Lianyungang City for volatile organic compounds (VOCs) analysis, including two sampling sites in the urban area (Lianyungang City Environmental Monitoring Supersite and Mine Design Institute), one sampling site in the industrial area (Deyuan Pharmaceutical Factory), and two sampling sites from the suburb (Hugou Management Office and YuehaiLou). The results showed that the mean VOCs concentration followed this pattern: industrial area (36.06 ± 12.2 µg m−3) > urban area (33.47 ± 13.0 µg m−3) > suburban area (27.68 ± 9.8 µg m−3). The seasonal variation of the VOCs trend in the urban and suburban areas was relatively consistent, which was different from that in industrial areas. The concentration levels of VOCs components in urban and industrial areas were relatively close, which were significantly higher than that in suburban areas. The possible sources and relative importance of VOCs in Lianyungang City atmosphere were measured by the characteristic ratio of toluene/benzene (T/B), ethane/acetylene (E/E) and isopentane/TVOCs. The contribution of traffic sources to the VOCs in Lianyungang City was significant (T/B ~ 2), and there were obvious aging phenomena in the five sampling sites (E/E > 4). The ratio of isopentane/TVOCs in the contribution of gasoline volatilization sources in urban and suburban areas was significantly bigger than that in industrial areas. According to the maximum incremental reactivity (MIR) method, aromatics (40.32–58.09%) contributed the most to ozone formation potential (OFP) at the five sampling sites. The top 10 OFP species showed that controlling n-hexane and aromatics, such as benzene, toluene, xylene, and trimethylbenzene in Lianyungang City can effectively control ozone generation. Nineteen typical VOCs components were selected and the sources of VOCs from five sampling points were analyzed by the principal component analysis (PCA) model. The sources of VOCs in different areas in Lianyungang were relatively consistent. Five sources were analyzed at the two sampling sites in the urban area: industrial emission + plants, vehicle exhaust, fuel evaporation, combustion and industrial raw materials. Four sources were analyzed in the industrial area: industrial emission + plants, vehicle exhaust, fuel evaporation and combustion. Five sources were analyzed at the two sampling sites in the suburban area: industrial emission + plants, vehicle exhaust, fuel evaporation, combustion and solvent usage.

Measurement report: Spatiotemporal and policy-related variations of PM_2.5 compositions and sources during 2015–2019 at multisite of a Chinese megacity

A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a fast-developing Chinese megacity as the studied area to investigate the spatiotemporal and policy-related variations of PM2.5 compositions and sources based on a long-term observation at multisite. A total of 836 samples were collected at 19 sites in wintertime of 2015–2019. According to the specific characteristics, 19 sampling sites were assigned into three layers. Layer 1 was the most urbanized area referred to the core zone of Chengdu, layer 2 was located in the outside circle of layer 1, and layer 3 belonged to the outer-most zone with the lowest urbanization level. The averaged PM2.5 concentrations for five years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121µg m−3). And for each year, the spatial clustering of chemical compositions at sampling sites was generally consistent with the classification of layers. PM2.5 compositions for layer 3 in 2019 were found to be similar to that for other layers two or three years ago, implying that the urbanization levels had a strong effect on air quality. During the sampled period, a decreasing trend was observed for the annual averaged PM2.5 concentrations, especially at sampling sites in layer 1, which was caused by the more strict control policies implemented in layer 1. The SO42−/NO3− mass ratio at most sites exceeded 1 in 2015 but dropped less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified with the average contributions of 15.5 % (traffic emission), 19.7 % (coal and biomass combustion), 8.8 % (industrial emission), 39.7 % (secondary particles) and 16.2 % (resuspended dust), respectively. From 2015 to 2019, dramatical decline was observed in the average percentage contributions of coal and biomass combustion, but traffic emission source showed an increasing trend. For spatial variations, coal and biomass combustion and industrial emission showed the stronger distribution patterns. High contributions of resuspended dust were occurred at sites with intensive construction activities such as subway and airport constructions. Combining the PMF results, we developed the source weighted potential source contribution function (SWPSCF) method for source localization, this new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was well-evaluated.

Source apportionment of PM_2.5 in Shanghai based on hourly organic molecular markers and other source tracers

Identification of various emission sources and quantification of their contributions comprise an essential step in formulating scientifically sound pollution control strategies. Most previous studies have been based on traditional offline filter analysis of aerosol major components (usually inorganic ions, elemental carbon – EC, organic carbon – OC, and elements). In this study, source apportionment of PM2.5 using a positive matrix factorization (PMF) model was conducted for urban Shanghai in the Yangtze River Delta region, China, utilizing a large suite of molecular and elemental tracers, together with water-soluble inorganic ions, OC, and EC from measurements conducted at two sites from 9 November to 3 December 2018. The PMF analysis with inclusion of molecular makers (i.e., MM-PMF) identified 11 pollution sources, including 3 secondary-source factors (i.e., secondary sulfate; secondary nitrate; and secondary organic aerosol, SOA, factors) and 8 primary sources (i.e., vehicle exhaust, industrial emission and tire wear, industrial emission II, residual oil combustion, dust, coal combustion, biomass burning, and cooking). The secondary sources contributed 62.5 % of the campaign-average PM2.5 mass, with the secondary nitrate factor being the leading contributor. Cooking was a minor contributor (2.8 %) to PM2.5 mass while a significant contributor (11.4 %) to the OC mass. Traditional PMF analysis relying on major components alone (PMFt) was unable to resolve three organics-dominated sources (i.e., biomass burning, cooking, and SOA source factors). Utilizing organic tracers, the MM-PMF analysis determined that these three sources combined accounted for 24.4 % of the total PM2.5 mass. In PMFt, this significant portion of PM mass was apportioned to other sources and thereby was notably biasing the source apportionment outcome. Backward trajectory and episodic analysis were performed on the MM-PMF-resolved source factors to examine the variations in source origins and composition. It was shown that under all episodes, secondary nitrate and the SOA factor were two major source contributors to the PM2.5 pollution. Our work has demonstrated that comprehensive hourly data of molecular markers and other source tracers, coupled with MM-PMF, enables examination of detailed pollution source characteristics, especially organics-dominated sources, at a timescale suitable for monitoring episodic evolution and with finer source breakdown.