Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Identification of potential source regions and long-range transport routes/channels of marine PM2.5 at remote sites in East Asia

Long-range transport (LRT) of air masses in East Asia and their impacts on marine PM2.5 were explored. Situated in the leeward region of East Asia, Taiwan Island marked by its elevated Central Mountain Range (CMR) separates air masses into two distinct air currents. This study aims to investigate the transport of PM2.5 from the north to the leeward region. Six transport routes (A-F) were identified and further classified them into three main channels (i.e. East, West, and South Channels) based on their transport routes and potential sources. Green Island (Site GR) and Hengchun Peninsula (Site HC) exhibited similarities in their transport routes, with Central China, North China, and Korean Peninsula being the major source regions of PM2.5, particularly during the Asian Northeastern Monsoons (ANMs). Dongsha Island (Site DS) was influenced by both Central China and coastal regions of East China, indicating Asian continental outflow (ACO) as the major source of PM2.5. The positive matrix factorization (PMF) analysis of PM2.5 resolved that soil dust, sea salts, biomass burning, ship emissions, and secondary aerosols were the major sources. Northerly Channels (i.e. East and West Channels) were primarily influenced by ship emissions and secondary aerosols, while South Channel was dominated by oceanic spray and soil dust. The results of W-PSCF and W-CWT analysis indicated that three remote sites experienced significant contributions from Central China in the highest PM2.5 concentration range (75-100%). In contrast, PM2.5 in the 0-25% and 25-50% ranges primarily originated from the open seas, with ship emissions being the prominent source. It suggested that northern regions with heavy industrialization and urbanization have impacts on high PM2.5 concentrations, while open seas are the main sources of low PM2.5 concentrations.

Relationship between atmospheric pollution and polycyclic aromatic hydrocarbons in fresh snow during heavy pollution episodes in a cold city, northeast China

Air quality index (AQI) and air pollutants during two typical pollution episodes, and polycyclic aromatic hydrocarbons (PAHs) in fresh snow after each episode in the winter 2019 across Harbin City in northeast China were investigated to explore the co-environmental behaviors. Significantly greater values of AQI and PAHs were found in the more serious atmospheric pollution episode (episode Ⅱ), demonstrating that PAHs in fresh snow is a robust indicator. PM_2.5 was the primary air pollutant in both episodes based on PM_2.5/PM₁₀ ratios, which might be attributed to fine particulate converted from gas-to-particle process. PM_2.5 and 4-ring PAHs significantly positive correlated, indicating that airborne particulate PAHs were co-emitted and co-transported with atmospheric fine particles released from coal combustion and vehicular emission under low temperature and high relative humidity. 3- and 4- rings PAHs were dominant in episode Ⅱ, while 5- and 6- rings PAHs were found the lowest in both episodes. These characteristics reflected that long-range transportation of coal and biomass burning were from the surrounding areas, while vehicle exhausts were mainly from local emissions. Except for the impact of local pollution source emissions, the regional transport could make a greater contribution in a more serious pollution event.

Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China

PURPOSE

In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions).

METHODS

The new seven-channel aethalometer model (AE-33), PM_2.5, and meteorological data were collected in the dry season (October-May) from 2019 to 2020, to quantify BC emissions in urban Shenzhen. Explored the source allocation of BC based on Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model.

RESULTS

We revealed that the mean BC concentration was 2672 ± 1506 ng/m³ in the dry season, with values of 4062 ± 1182 ng/m³, 2519 ± 1568 ng/m³, and 1900 ± 776 ng/m³ in autumn, winter, and spring, respectively. Additionally, we found that fossil fuels have higher contributions to BC concentrations (86.3% to 86.8% in autumn and spring) in the dry season than biomass fuels (16% to 20% in autumn, spring and winter), which is different from Beijing, Nanjing and other large economic zones in China. The diurnal variation in BC and the contribution of fossil fuels indicate that there is a significantly greater increase in BC during peak traffic hours in urban Shenzhen than in other cities. Finally, meteorological parameters and PM_2.5 data provided supporting evidence that BC is sourced mainly from local vehicle emissions and industry-related combustion in the western and northeastern/southeastern parts of the study area.

CONCLUSION

This study showed that the concentration of BC is lower than other regions, and the source allocation is mainly local fossil fuels (vehicle emission, etc.).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-022-00793-3.

Transport routes and potential source areas of PM10 in Kirklareli, Turkey

In this study, the seasonal variation, transport routes, and potential source areas of PM₁₀ in the central district of Kirklareli (Turkey) were investigated. It was determined that PM₁₀ concentrations had the highest seasonal average value in autumn and the lowest seasonal average value in spring. Cumulative distributions of PM₁₀ concentrations data set were examined. In order to determine the air mass source and transport routes, the backward trajectories of the air masses obtained by using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model were run and cluster analysis, which is one of the multivariate statistical analyses, was performed. Cluster analysis results revealed that there are five main clusters affecting the receptor site in all four seasons. By defining the PM₁₀ concentrations data as an input to the potential source contribution function (PSCF) model, the probable locations of potential source areas were identified. It has been observed that there are obvious seasonal differences in the potential source areas of PM₁₀. High PSCF values were observed especially in Greece and the Mediterranean during the winter and especially in Albania and Greece during the spring. While high PSCF values were observed especially in the Anatolian side of Istanbul, Kocaeli, Sakarya, and the Black Sea coasts of these regions during the summer, they were observed especially in İzmir and Balikesir during the autumn.

Identification of Long-Range Transport Pathways and Potential Source Regions of PM2.5 and PM10 at Akedala Station, Central Asia

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 µg/m3 and PM10 concentrations from 30 to 60 µg/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 µg/m3 and PM10 concentrations from 20 to 60 µg/m3 in these seasons.

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Air pollution has attracted increasing attention in recent years. Cluster analysis, scene analysis, and the potential source contribution function (PSCF), based on the backward trajectory model, were used to identify the transport pathways and potential source regions of PM_2.5 and PM₁₀ (particulate matter with an aerodynamic diameter of not more than 2.5 µm and 10 µm) in Changchun in 2018. In addition, the PSCF was slightly improved. The highest average monthly concentrations of PM_2.5 and PM₁₀ appeared in March and April, when they reached 53.9μg/m³ and 120.0 μg/m³, respectively. The main potential source regions of PM_2.5 and PM₁₀ were generally similar: western Jilin Province, northwestern Inner Mongolia, northeastern Liaoning Province, and the Yellow Sea region. The secondary potential source regions were southern Russia, central Mongolia, western Shandong Province, eastern Hebei Province, and eastern Jiangsu Province. The northwest and southwest directions were found to be the two pathways that mainly affect the air quality of Changchun City. Moreover, the northwestern pathway had a larger potential contribution source area than the southwestern pathway. The airflow in the southwest direction came from Liaoning Province, Shandong Province, and the Yellow Sea region. This mainly occurred in summer; its transmission distance was short; it had a relatively higher weight potential source contribution function (WPSCF) value; it can be regarded as a local source; and its representative pollutants were SO₂ (sulfur dioxide), CO (carbon monoxide), and O₃ (ozone). The northwestern pathway passed through Russia, Mongolia, and Inner Mongolia. The transmission distance of this pathway was longer; it had a relatively lower WPSCF value; it can be considered as a natural source to a certain extent; it mainly occurred in autumn and, especially, in winter; and the representative pollutants of this pathway were NO (nitric oxide), NOx (nitrogen oxide), PM_2.5, and PM₁₀.

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM_2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM_2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM_2.5, thus leading to possible estimation biases for PM_2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM_2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM_2.5, capture how land-use magnitude impacts PM_2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM_2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM_2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September-November) and winter (December-February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM_2.5 pollution, referring to the status of regional urbanization and greening construction.