[Pollution Characteristics, Source, and Health Risk Assessment of Metal Elements in PM2.5 Between Winter and Spring in Zhengzhou].

A total of 18 metal elements in ambient PM2.5 in Zhengzhou were continuously determined using an online heavy metal observation instrument in January and April, 2021, and the changes in element concentrations were analyzed. Metal elements were traced via enrichment factors, positive matrix factorization (PMF), and a characteristic radar chart. The US EPA health risk assessment model was used to assess the health risks of heavy metals, and the backward trajectory method and the concentration-weighted trajectory (CWT) method were used to evaluate the potential source regions of health risks. The results showed that the element concentrations were higher in spring, and the sum of Fe, Ca, Si, and Al concentrations accounted for 89.8% and 87.5% of the total element concentrations in winter and spring, respectively. Cd was enriched significantly, which was related to human activities. The concentrations of Pb, Se, Zn, Ni, Sb, and K in winter and Cr, Ni, Fe, Mn, V, Ba, Ca, K, Si, and Al in spring increased with the increasing pollution level. The results of PMF and the characteristic radar chart showed that the main sources of metal elements in winter and spring were industry, crust, motor vehicles, and mixed combustion, with industry and mixed combustion pollution occurring more often in winter and crust pollution occurring more often in spring. Significant non-carcinogenic risks existed in both winter and spring with more severe health risks in winter, and Mn caused significant non-carcinogenic risks. The health risks in winter were mainly influenced by Zhengzhou and surrounding cities and long-distance transport in the northwest, and the health risks in spring were mainly influenced by Zhengzhou and surrounding cities.

[Pollution Characteristics and Sources of Water-soluble Ions in PM2.5 in Zhengzhou City].

In order to explore the pollution characteristics, seasonal variations, and sources of water-soluble inorganic ions (WSIIs) in PM2.5 in Zhengzhou, PM2.5 samples were seasonally collected from December 2020 to October 2021; then, combining gaseous pollutants (SO2, NO2, and O3) and meteorological parameters (temperature and relative humidity), nine WSIIs (NO3-, NH4+, SO42-, Ca2+, K+, Na+, Mg2+, F-, and Cl-) were analyzed. The results showed that the annual average concentration of the total water-soluble ions (TWSIIs) was (39.34±21.56) µg·m-3for the four seasons, showing obvious seasonal variations with the maximum value in winter and the minimum value in summer. Annual PM2.5 was slightly alkaline in Zhengzhou, and NH4+ most likely existed in the form of NH4NO3 and (NH4)2SO4. The average sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) were 0.35 and 0.19, respectively, indicating that SO42- and NO3- mainly derived from secondary formation. The main potential source regions of WSIIs obtained by the concentration weight trajectory (CWT) model showed temporal and spatial variations. The significant sources of WSIIs based on principal component analysis (PCA) were dust, secondary generation, combustion, and industrial activities, which were obviously influenced by wind direction and speed in Zhengzhou.

[Characteristics and Sources of PM2.5 and Its Light-scattering Properties with Different Chemical Compositions in Urban Area of Beijing].

In recent years, the concentration of PM2.5 in the Beijing urban area has decreased with the increase in the proportion of secondary inorganic ions. In order to explore the characteristics and sources of the light scattering of PM2.5 with different chemical compositions, PM2.5 with its chemical components and scattering coefficient were continuously measured at hourly resolution in the Beijing urban area from December 2020 to November 2021. The components, scattering characteristics, and sources of PM2.5 were analyzed. The results showed that NO3- was the major component of PM2.5 in the Beijing urban area, and the ω(NO3-) and ω(SNA) were 24% and 46% in PM2.5, respectively. PM2.5 could be divided into six types according to mass concentration and component proportion. The occurrence frequency of the good-type was the highest during the study with a similar duration in the four seasons, and the ω(SNA), ω(OM), and ω(FS) were 32%, 32%, and 28% in PM2.5, respectively. The dust(D)-type and the OM(O)-type appeared mainly in spring and summer with the lowest frequency during the study. FS and OM were their major components, and the ω(FS) and ω(OM) were 66% and 46% in PM2.5, respectively. The OM+SO42-(OS)-type, OM+NO3-(ON)-type, and NO3-(N)-type appeared mainly in the afternoon in summer, in the early morning and morning in winter, and at approximately 07:00 every day in spring. Under the condition of low humidity[relative humidity (RH)<40%], the MSE of N-type PM2.5 was the highest (4.3 m2·g-1), and that of D-type PM2.5 was the lowest (2.1 m2·g-1), reflecting the high scattering ability of SNA. The MSE increased with relative humidity. Under the condition of high humidity (RH>80%), the MSE of all types of PM2.5 rose to 1.5 to 1.8 times the values under low humidity. The variation trends of SAE showed that particle size increased with the rising of RH level. Under non-high humidity conditions, the scattering coefficients reconstructed by the revised IMPROVE formula fitted well with the measured values at hourly resolution, the correlation coefficients were between 0.81 and 0.97, and the slopes were between 1.00 and 1.21 except for that of D-type. The N-type fitting result was the best. Under high-humidity conditions, the R and the slopes were from 0.82 to 0.84 and from 0.48 to 0.53, respectively. The annual Bsca was 203.8 Mm-1, and N-type PM2.5 contributed the most, accounting for 53%, in which the large particles of NH4NO3 were the major contributor. Bsca of good-type PM2.5 was 67.2 Mm-1, in which small particles of OM were the major contributor. Bsca was 1.5 times the annual Bsca(dry), whereas the Bsca values of SNA were 1.8 to 2.1 times the Bsca(dry). The peak value of NO3- and RH simultaneously appeared around 07:00, resulting in the maximum Bsca of NH4NO3 at this time. The peak value of SO42- and the Bsca of (NH4)2SO4 mainly appeared at 16:00 and at 04:00, respectively. The diurnal variation curves of OM concentration and Bsca were consistent, and the bimodal peaks appeared at 13:00 and 20:00, respectively. In spring and winter, NO3-, SO42- and OM mainly came from the plains east of the Taihang Mountains, and their potential source regions were not in any particular place in summer and autumn; the main potential source regions of FS were the northwest areas of Beijing in spring and autumn. The flow with high RH across the south and southeast of the north China plain and the eastern rim of Bohai Sea was likely to increase the weighted potential source contribution factor values of Bsca of SNA in this region.

[Characteristics of PM2.5 Pollution in Beijing During the Historical Period of the 2022 Olympic Winter Games].

Based on the dataset derived from January to March between 2015 and 2021 in Beijing, the PM2.5 pollution characteristics and its potential source regions during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games were investigated. From 2015 to 2018, both the number of severely polluted days (daily average ρ(PM2.5)>75 µg·m-3) and the average PM2.5 concentrations during severe pollution episodes decreased significantly in the period of January to March. While, neither variable has changed obviously since 2018. On average, severely polluted days occurred 23 times in each year between 2018 and 2021 during the period of January to March, and the average of ρ(PM2.5) was approximately 120.0 µg·m-3 during such polluted days. From January to March in 2015-2021, the severely polluted event with more than 5 consecutive polluted days occurred 2-3 times in each year, and the severest one lasted 8 d. During the historical period of the Beijing 2022 Olympic Winter Games, severely polluted days took place 2-9 d every year. The large quantities of fireworks during the Spring Festival maybe one of important primary sources of the PM2.5. The number of severely polluted days during the historical period of the Paralympic Winter Games ranged from 1 to 5 d, except for 2021 with 9 d owing to the frequent stagnant weather condition. The PM2.5 chemical composition was dominated by secondary species on severely polluted days during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games. Nitrate accounted for 46% of the measurable chemical components of PM2.5 during severe pollution events in 2020, which was remarkably higher than that during clean days in the same year (11%). The mass fraction of SO42- ranged from 12% to 19% in 2018-2020, indicating that the contribution of sulfate was much less, but cannot be ignored. The main potential source regions of PM2.5 in Beijing during the period concerned in this study were central and western Inner Mongolia, Hebei Province, Tianjin City, Shanxi Province, Shaanxi Province, central and western Shandong Province, and northern Henan Province.

Characteristics, source apportionment and long-range transport of black carbon at a high-altitude urban centre in the Kashmir valley, North-western Himalaya.

Six years of data (2012-2017) at an urban site-Srinagar in the Northwest Himalaya were used to investigate temporal variability, meteorological influences, source apportionment and potential source regions of BC. The daily BC concentration varies from 0.56 to 40.16 µg/m3 with an inter-annual variation of 4.20-7.04 µg/m3 and is higher than majority of the Himalayan urban locations. High mean annual BC concentration (6.06 µg/m3) is attributed to the high BC observations during winter (8.60 µg/m3) and autumn (8.31 µg/m3) with a major contribution from Nov (13.88 µg/m3) to Dec (13.4 µg/m3). A considerable inter-month and inter-seasonal BC variability was observed owing to the large changes in synoptic meteorology. Low BC concentrations were observed in spring and summer (3.14 µg/m3 and 3.21 µg/m3), corresponding to high minimum temperatures (6.6 °C and 15.7 °C), wind speed (2.4 and 1.6 m/s), ventilation coefficient (2262 and 2616 m2/s), precipitation (316.7 mm and 173.3 mm) and low relative humidity (68% and 62%). However, during late autumn and winter, frequent temperature inversions, shallow PBL (173-1042 m), stagnant and dry weather conditions cause BC to accumulate in the valley. Through the observation period, two predominant diurnal BC peaks were observed at ⁓9:00 h (7.75 µg/m3) and ⁓21:00 h (6.67 µg/m3). Morning peak concentration in autumn (11.28 µg/m3) is ⁓2-2.5 times greater than spring (4.32 µg/m3) and summer (5.23 µg/m3), owing to the emission source peaks and diurnal boundary layer height. Diurnal BC concentration during autumn and winter is 65% and 60% higher than spring and summer respectively. During autumn and winter, biomass burning contributes approximately 50% of the BC concentration compared to only 10% during the summer. Air masses transport considerable BC from the Middle East and northern portions of South Asia, especially the Indo-Gangetic Plains, to Srinagar, with serious consequences for climate, human health, and the environment.

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions.

Five-year (2013-2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM10 and PM2.5 concentration was 135 ± 112 µg/m3 and 87 ± 93 µg/m3 respectively with significant intra- and inter-daily variation. The annual PM10 and PM2.5 concentration was 2.0-3.2 and 1.7-2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM10 = 60 µg/m3 and PM2.5 = 40 µg/m3). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM10) more efficiently than fine particles (PM2.5), while as the moderate to high humid conditions (55-95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM10 (>250 µg/m3) and PM2.5 (>150 µg/m3) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

[Changes and Potential Sources of Atmospheric Black Carbon Concentration in Shanghai over the Past 40 Years Based on MERRA-2 Reanalysis Data].

As an important component of atmospheric aerosols, black carbon (BC) has a great influence on the regional and global radiation balance, climate, and human health due to its small particle size, large specific surface area, and radiative forcing potential. Here, the spatio-temporal characteristics of atmospheric BC were investigated based on modern-era retrospective analysis for research and applications version 2 (MERRA-2) reanalysis data and ground observation data during 1980-2019 in Shanghai, a highly urbanized city in mainland China. The influences of local emissions and regional transmission on regional-scale BC concentrations were examined using the M-K trend test, backward trajectory analysis, and the potential source contribution function (PSCF). The results showed that:① MERRA-2 BC and ground observation datasets showed good consistency (R∈[0.68, 0.72]), indicating that MERRA-2 reanalysis data can be used to reveal long-term changes in ground-level atmospheric BC concentrations; ② Atmospheric BC concentrations in Shanghai over the past 40 years can be divided into three stages:a "low value" stage of slow growth[1980-1986, (1.75±0.17) µg·m-3], a relatively stable "median value" stage[1987-1999, (2.18 ±0.07) µg·m-3], and a fluctuating "high value" stage[2000-2019, (3.07±0.31) µg·m-3]. Seasonally, Shanghai's BC concentrations generally show a "U" pattern with low concentrations in summer and high concentrations in winter. As a result of black carbon emissions from marine diesel engines and other engines used for water transportation, a small peak also occurs in July; ③ The diagnostic quality ratio of air pollutants and the bivariate correlation analysis[R(BC-NO2)>R(BC-CO)>R(BC-SO2)] indicated that traffic emissions were the main sources of atmospheric BC in Shanghai, especially by heavy diesel vehicles; ④ The backward trajectory and PSCF analyses found that the air mass of Shanghai in summer was dominated by a clean sea breeze, accounting for 77.18%. In contrast, during the other seasons, more than 50% of the air mass came from the north. The potential source regions of atmospheric BC in Shanghai are mainly distributed in eastern China, expanding outwards and centering on the Yangtze River Delta, and the expansion direction is consistent with the directions of the backward trajectories.

Source apportionment of carbon monoxide over India: a quantitative analysis using MOZART-4.

MOZART-4 chemistry transport model has been used to examine the contribution of carbon monoxide (CO) from different source regions/types by tagging their emissions in model simulations. These simulations are made using tagged tracer approach to estimate the relative contribution of different geographical regions and different emission sources, such as anthropogenic or biomass burning to the CO concentration at the surface, in the planetary boundary layer (PBL), and in the free troposphere (FT) over the Indian sub-continent. The CO budget analyses highlight the significant contribution of the Indian emissions on surface CO and influence of chemical production on the free tropospheric CO concentration. The total CO mixing ratio is estimated as 263 ± 139 parts per billion by volume (ppbv) for surface, 177 ± 71 ppbv for PBL, and 112 ± 14 ppbv for FT. The percentage contributions of primary sources are found to be 80%, 68%, and 53% at the surface, in the PBL, and in the FT, respectively. The sub-regional analysis of India shows that anthropogenic and photochemical processes contribute 41-75% and 15-46% CO, respectively, at the surface. Maximum percentage contribution of anthropogenic CO is observed over Indo-Gangetic Plain and Eastern India (75%). CO contribution from local anthropogenic and biomass burning emissions and transported from other global source regions are analyzed over the Indian region at the surface, in the PBL, and in the FT. The local anthropogenic sources contribute largest to the surface CO over India with 108 ppbv, followed by China with 98 ppbv, Europe with 55 ppbv, North America (NA) with 46 ppbv, and South-east Asia (SEA) and Middle East (ME) with 23 ppbv each. India's PBL (FT) CO is mostly influenced by China's anthropogenic emissions with 12 ppbv (8 ppbv) followed by SEA with 7 ppbv (6 ppbv). Surface biomass burning CO over India (6 ppbv) is much lower than in other regions such as SEA (32 ppbv), Africa (24 ppbv), and South America (11 ppbv). In the PBL (FT), SEA and Africa's BB emissions show major impact on CO over India with 6 ppbv (5 ppbv) and 5 ppbv (4 ppbv), respectively.

Chemical characteristics of trace metals in PM10 and their concentrated weighted trajectory analysis at Central Delhi, India.

Trace metals associated with PM10 aerosols and their variation during day and nighttime as well as during different seasons have been studied for the year 2012. PCA analysis suggested 5 PCs, which accounted for 86.8% cumulative variance. PC1 accounted for 30% with a significant loading of metals of anthropogenic origin, while PC2 showed 28% variance with the loading of metals of crustal origin. These trace metals showed seasonal distinct day and night time characteristics. The concentrations of Cu, Pb, and Cd were found to be higher during nighttime in all the seasons. Only Fe was observed with significantly higher mean concentrations during daytime of all seasons except monsoon. The highest mean values of Cu, Cd, Zn, and Pb during post-monsoon might be attributed to winds advection over the regions of waste/biomass burning and industrial activities in Punjab and Haryana regions. Furthermore, concentration weighted trajectory analysis suggested that metals of crustal origin were contributed by long-range transport while metals of anthropogenic and industrial activities were contributed by regional/local source regions.