Hourly organic tracers-based source apportionment of PM2.5 before and during the Covid-19 lockdown in suburban Shanghai, China: Insights into regional transport influences and response to urban emission reductions.

During the Covid-19 outbreak, strict lockdown measures led to notable reductions in transportation-related emissions and significantly altered atmospheric pollution characteristics in urban and suburban areas. In this work, we compare comprehensive online measurements of PM2.5 major components and organic molecular markers in a suburban location in Shanghai, China before lockdown (Dec. 28, 2019 to Jan. 23, 2020) and during lockdown (Jan. 24 to Feb. 9, 2020). The NOx levels declined sharply by 59% from 44 to 18 ppb during the lockdown, while O3 rose two times higher to 42 ppb. The PM2.5 level dropped from 64 to 49 µg m-3 (-24%). The major components all showed reductions, with the reduction of nitrate most prominent at -58%, followed by organics at -19%, and sulfate at -17%. Positive matrix factorization analysis identifies fourteen source factors, including nine primary sources and five secondary sources. The secondary sources consist of sulfate-rich factor, nitrate-rich factor, and three secondary organic aerosol (SOA) factors, with SOA_I being anthropogenic SOA, SOA_II associated with later generation products of organic oxidation, and SOA_III being biogenic SOA. The combined secondary sources contributed to 69% and 63% (40 and 22 µg m-3) of PM2.5 before and during lockdown, respectively, among which the reductions in the nitrate-rich (-55%) factor was the most prominent. Among primary sources, large reductions (>80%) were observed in contributions from industrial, cooking, and vehicle emissions. Unlike some studies reporting that the restriction during the Covid-19 resulted in enhanced secondary sulfate and SOA formation, we observed decreases in both secondary inorganic and SOA formation despite the overall elevated oxidizing capacity in the suburban site. Our results indicate that the formation change in secondary inorganic and organic compounds in response to substantial reductions in urban primary precursors are different for urban and suburban environments.

Measurement of Black Carbon in Delhi: Evidences of Regional Transport, Meteorology and Local Sources for Pollution Episodes

Measurement of particulate matter (PM) constituent such as black carbon (BC) over urban sites is critically important owing to its adverse health and climate impacts. However, the impacts associated with BC are poorly understood primarily because of the scarcity and uncertainties of measurements of BC. Here, we present BC measurement at an urban site of Delhi using a characterized continuous soot monitoring system (COSMOS) for a year-long period, i.e., from September, 2019 to August, 2020. This measurement period covers events, i.e., period of crop residue burnings from nearby states, festive events, e.g., Diwali and New Year, and first COVID-19 lockdown period. Effects of these events combining with local emissions and meteorological conditions on BC mass concentration (MBC) are investigated to find the possible cause of severe pollution levels in Delhi. Mean MBC for the complete observation period was found to be 5.02 ?? 4.40 ??g m???3. MBC showed significant seasonal as well diurnal variations. Winter season (December to February) is observed to be the most polluted season owing to increased local emissions and non-favorable meteorological conditions. Regional emission from crop burning in nearby states during October and November is the main contributing factor for increased pollution in this postmonsoon season. Furthermore, analysis reveals that cracker burning during festivals can also be considered as contributing factor to high MBC for a short period in post-monsoon and winter seasons. Significant decrease in MBC due to COVID-19 lockdown is also observed. MBC in summer and monsoon are lower as compared to other seasons but are still higher than mean MBC levels in several other urban cities of different countries. Also, the BC data obtained from nearby sites surface black carbon (SBC) are compared against the MBC to evaluate coherency among the different datasets, and discussed in detail.

The Seesaw Pattern of PM2.5 Interannual Anomalies Between Beijing-Tianjin-Hebei and Yangtze River Delta Across Eastern China in Winter

Since 2013, the winter mean fine particulate matter (PM2.5) had been decreased significantly due to stringent emission controls in most of China. Nevertheless, we found a seesaw pattern of PM2.5 interannual anomalies between Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD). Using the multiple linear regression method, meteorology-driven PM2.5 interannual anomalies show that the low (high) PM2.5 relative difference between BTH and YRD (RDB&Y) was associated with the strong (weak) East Asian winter monsoon (EAWM). The strong EAWM transported more air pollutants from BTH to YRD. During the COVID-19 lockdown period, due to the weak EAWM, air pollution still occurred in northern BTH, while the PM2.5 was relatively low in YRD, causing high RDB&Y values. Our results imply that the activity of EAWM and characteristics of regional transport have obvious interannual variations, which is indispensable in evaluating the achievements of PM2.5 quality management between up and downstream regions.

Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM2.5 during the COVID-19 outbreak in Hubei Province, China.

Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM2.5 concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 µ g/m3). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM2.5 concentration to the standard (35 µ g/m3). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM2.5, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016-2020.

Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM2.5 concentration (31 µg/m3) in 2018, decreasing by 12.1% year-on-year, and the hourly PM2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM2.5 in Northeast China has a high degree of autocorrelation and a south-hot-north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H-H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM2.5 pollution trajectories reaching 222.4 µg/m3, and the contribution of daily average PM2.5 concentrations exceeding 60 µg/m3 within Northeast China. Pollution of PM2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.

Mitigated PM2.5 Changes by the Regional Transport During the COVID-19 Lockdown in Shanghai, China.

Intensive observations and WRF-Chem simulations are applied in this study to investigate the adverse impacts of regional transport on the PM2.5 (fine particulate matter; diameter ≤2.5 µm) changes in Shanghai during the Coronavirus Disease 2019 lockdown. As the local atmospheric oxidation capacity was observed to be generally weakened, strong regional transport carried by the frequent westerly winds is suggested to be the main driver of the unexpected pollution episodes, increasing the input of both primary and secondary aerosols. Contributing 40%-80% to the PM2.5, the transport contributed aerosols are simulated to exhibit less decreases (13.2%-21.8%) than the local particles (37.1%-64.8%) in urban Shanghai due to the lockdown, which largely results from the less decreased industrial and residential emissions in surrounding provinces. To reduce the influence of the transport, synergetic emission control, especially synergetic ammonia control, measures are proved to be effective strategies, which need to be considered in future regulations.

Insights into chemical composition, abatement mechanisms and regional transport of atmospheric pollutants in the Yangtze River Delta region, China during the COVID-19 outbreak control period.

To investigate chemical characteristics, abatement mechanisms and regional transport of atmospheric pollutants during the COVID-19 outbreak control period in the Yangtze River Delta (YRD) region, China, the measurements of air pollutants including fine particulate matter (PM2.5) and volatile organic compounds (VOCs) on non-control period (NCP, 24 December 2019-23 January 2020) and control period (CP, 24 January-23 February 2020) were analyzed at the urban Pudong Supersite (PD) and the regional Dianshan Lake Supersite (DSL). Due to the stricter outbreak control, the levels of PM2.5 and VOCs, and the occurrence frequencies of haze-fog episodes decreased substantially from NCP to CP, with average reduction rates of 31.6%, 38.9% and 35.1% at PD, and 34.5%, 50.7% and 37.9% at DSL, respectively. The major source for PM2.5 was secondary sulfate & nitrate in both periods, and the emission control of primary sources such as coal burning and vehicle exhaust decreased the levels of precursors gas sulfur dioxide and nitrogen oxide, which highly contributed to the abatement of PM2.5 from NCP to CP. The higher levels of ozone at both PD and DSL on CP might be due to the weak nitrogen monoxide titration, low relative humidity and high visibility compared with NCP. Vehicle exhaust and fugitive emission from petrochemical industry were the major contributors of ambient VOCs and their decreasing activities mainly accounted for VOCs abatement. Moreover, the high frequency of haze-fog events was closely impacted by medium-scale regional transport within Anhui and Jiangsu provinces. Therefore, the decreasing regional transported air pollutants coincided with the emission control of local sources to cause the abatement of haze-fog events in YRD region on CP. This study could improve the understanding of the change of atmospheric pollutants during the outbreak control period, and provide scientific base for haze-fog pollution control in YRD region, China.

Air pollution episodes during the COVID-19 outbreak in the Beijing-Tianjin-Hebei region of China: An insight into the transport pathways and source distribution.

Although anthropogenic emissions decreased, polluted days still occurred in the Beijing-Tianjin-Hebei (BTH) region during the initial outbreak of the coronavirus disease (COVID-19). Analysis of the characteristics and source distribution of large-scale air pollution episodes during the COVID-19 outbreak (from 23 January to April 8, 2020) in the BTH region is helpful for exploring the efficacy of control measures and policy making. The results indicated that the BTH region suffered two large-scale air pollution episodes (23-28 January and 8-13 February), which were characterized by elevated PM2.5, SO2, NO2, and CO concentrations, while the O3 concentration decreased by 1.5%-33.9% (except in Shijiazhuang, where it increased by 16.6% during the second episode). These large-scale air pollution episodes were dominated by unfavorable meteorological conditions comprising a low wind speed and increased relative humidity. The transport pathways and source distribution were explored using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT), potential source contribution function (PSCF), and concentration weighted trajectory (CWT) models. The air pollution in the BTH region was mainly affected by local emission sources during the first episode, which contributed 51.6%-60.6% of the total trajectories in the BTH region with a PM2.5 concentration ranging from 146.2 µg/m3 to 196.7 µg/m3. The short-distance air masses from the southern and southwestern areas of the BTH region were the main transport pathways of airflow arriving in the BTH region during the second episode. These contributed 51.9%-57.9% of the total trajectories and originated in Hebei, Henan, central Shanxi, and Shaanxi provinces, which were the areas contributing the most to the PM2.5 level and exhibited the highest PSCF and CWT values. Therefore, on the basis of local emission reduction, enhancing regional environmental cooperation and implementing a united prevention and control of air pollution are effective mitigation measures for the BTH region.