Motor Vehicle Sources Research Articles

Characteristics, Sources Analysis, and Environmental Impacts of VOCs Based on Photochemical Loss in Summer in Zhengzhou

To clarify the pollution characteristics and sources of atmospheric VOCs in Zhengzhou City in the summer, multi-site offline sampling and laboratory analyses of atmospheric VOCs in Zhengzhou were carried out in August 2022. The observed and initial VOC volume fraction levels, OFP, SOAFP, and sources were compared. During the study period, the average values of three-site observation and initial φ（VOCs） during the study period were （31.83 ±13.51）×10-9 and （35.92 ±15.30）×10-9,respectively. Olefins （52.5 %） and aromatic hydrocarbons （29.7 %） were the components with a higher photochemical loss rate, and the spatial variations of the observed TVOCs concentration at each site were： Zhengzhou University （ZZU） > Gangli Reservoir （GLR） > Jingkaiqu （JKQ）, and the concentrations of alkanes and OVOCs at each site were higher. Olefins and aromatic hydrocarbons were the components that contributed greatly to the formation of O3 and SOA. Motor vehicle sources, solvent-use sources, and industrial sources were the main contributing sources of atmospheric VOCs in Zhengzhou. Compared with the source analysis results based on the initial concentration, the contribution rates of motor vehicle sources, industrial sources, and solvent use sources were relatively high, and the contribution rates of combustion sources, plant sources, and oil and gas volatilization sources were relatively low.

Pollution Characteristics，Source Analysis，and Activity Analysis of Atmospheric VOCs During Winter and Summer Pollution in Zhengzhou

In order to study the pollution characteristics of volatile organic compounds （VOCs）， continuous monitoring of VOCs in two pollution processes was conducted in June and December 2021 in Zhengzhou. Combined with meteorological conditions， the pollution characteristics， source contributions， and reactivity of VOCs in winter and summer were compared and analyzed. The results showed that the volume fraction of atmospheric VOCs in two episodes were （27.92±12.68）×10-9 and （24.30±5.93）×10-9， respectively. The volume fraction of atmospheric VOCs in the haze pollution process in winter was larger than that in the ozone pollution process in summer. The analysis results of winter sources were as follows： industrial source （27.0%）， motor vehicle source （22.5%）， combustion source （20.1%）， solvent use source （16.3%）， and oil and gas volatilization source （14.1%）. The analysis results of summer sources were as follows： motor vehicle source （24.8%）， industrial source （24.1%）， solvent source （17.4%）， oil and gas volatilization source （14.2%）， combustion source （11.2%）， and plant source （8.4%）. The results of the smog production model showed that the proportion of days in the synergistic control zone of VOCs during the two pollution processes in summer （66.7%） was smaller than that in winter （100.0%）. The secondary reaction activity results showed that the average ·OH loss rate （L·OH） values in winter and summer were 4.12 s-1 and 4.75 s-1， respectively. The average ozone formation potential （OFP） values in summer were 108.36 μg·m-3. The olefins were dominant in the top ten species due to L·OH and OFP contributions in summer. The total SOAFP values in winter in Zhengzhou were 54.38 μg·m-3. Among the top ten species contributing to SOAFP in winter， nine were aromatic hydrocarbons.

Exploration and comparison of the relationship between PAHs and ROS in PM2.5 emitted from multiple anthropogenic sources in the Guanzhong Plain, China

Anthropogenic emissions have emerged as an important source of urban atmospheric PM2.5, exacerbating air pollution and the associated health implications. This study analyses PM2.5, originating from major anthropogenic sources (industries, motor vehicles, and solid-fuel combustion for domestic applications) in the Guanzhong Plain in China, along with the parent- (p-), alkylated- (a-), and oxygenated- (o-) polycyclic aromatic hydrocarbons (PAHs) and reactive oxygen species (ROS) levels in PM2.5. Industrial emissions are mainly characterised by high abundances of benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), and benz[a]fluoranthene (BaF). The 4-ring p-PAHs, such as fluoranthene (FLA), pyrene (PYR), benzo[a]anthracene (BaA), and chrysene (CHR) proportions and the diagnostic ratios of indeno[1,2,3-cd]pyrene (IcdP)/[IcdP + benzo[ghi]perylene (BghiP)] and 1-acenaphthenone (1ACO)/[1ACO + 9-fluorenone (9FO)] in motor vehicle emission PM2.5 were higher than the other sources. Household solid fuel combustion features high proportions of methylnaphthalene (M-NAP), i.e., 2 M-NAP and 1 M-NAP and 3-ring p-PAHs. Acenaphthylene (ACY), acenaphthene (ACE), anthracene (ANT), 1,4-chrysenequinone (1,4CHRQ), and reactive oxygen species (ROS) were positively correlated among the three anthropogenic sources. Moreover, the correlations between other PAHs and ROS varied significantly among the three sources. As mixed and compound organic pollutants, 2- and 3-ring p-PAHs were more positively correlated with the ROS activity of household solid fuel combustion sources compared with industrial and motor vehicle sources. Based on the relative contribution of these three sources to PAHs in PM2.5, we estimated the cancer risks of males and females in the Guanzhong area to be 2.95 × 10−6 and 2.87 × 10−6, respectively, exceeding the safety threshold of 1 × 10−6. This study provides a basic dataset for conducting a refined source apportionment of PM2.5 and a scientific basis for further understanding the relationship between PM2.5, PAHs, and ROS in northern China.

Characteristics and Source Apportionment of VOCs Initial Mixing Ratio in Beijing During Summer

To clarify the characteristics and source apportionment of the VOCs initial mixing ratio in Beijing in summer, continuous monitoring of VOCs was conducted in the Beijing urban area from May to August 2022, and the initial mixing ratio was calculated using the photochemical ratio method. The results showed that:① during the study period, initial φ(TVOCs) in the Beijing urban area were (30.0 ±11.5)×10-9, in which the proportion of VOCs and alkanes containing oxygen reached 34.2% and 33.2%, respectively. The species with high volume fractions were low carbon substances such as acetone, ethane, acetaldehyde, and propane. ② The initial TVOCs mixing ratio in Beijing showed a slightly unimodal trend, reaching the peak at 11:00 and slightly decreasing in the afternoon. ③ Isoprene, acetaldehyde, n-butanal, and ethylene were the major contributors to the generation of O3, whereas toluene, isoprene, m-paraxylene, and ethylbenzene were the major contributors to the generation of secondary organic aerosols. ④ Based on the initial mixing ratio of PMF analysis, it was found that aging background and secondary sources (30%) contributed the most to VOCs in Beijing, and motor vehicle sources (25%) were the main primary human sources. In addition, solvent and fuel volatile sources contributed 16%, combustion sources contributed 11%, industrial process sources contributed 9%, and natural sources contributed 9%. ⑤ The anthropogenic sources of Beijing were mainly from the eastern and southern regions, whereas the natural sources were from the western and northwestern regions. This research showed that vehicle emissions should be further reduced, and regional joint prevention and control to reduce VOCs in the whole region is an effective means to control VOCs in Beijing.

Seasonal Distribution and Source Apportionment of Chemical Compositions in PM2.5 in Nanchang, Inland Area of East China

PM2.5 was sampled in four seasons of 2021 in Nanchang. Organic carbon (OC), elemental carbon (EC), and water-soluble ions were the main chemical compositions in PM2.5. The annual average of OC/PM2.5 and EC/PM2.5 was 17.1% and 2.1%, respectively, while nine water-soluble ions were 56.7%. The order of each ion percentage in PM2.5 was NO3− > SO42− > K+ > Na+ > NH4+ > Cl− > NO2− > Ca2+ > Mg2+. The OC/EC (6.54, 13.17, 8.95, 7.99) and Char-EC/Soot-EC (0.88, 0.64, 1.32, 3.74) indicated that the carbon aerosols mainly originated from coal combustion, biomass combustion, and motor-vehicle emissions. High concentrations of Cl− and Ca2+ in spring were associated with dust sources. A good correlation between Na+, SO42−, and NO3− suggests the formation of Na2SO4 and NaNO3. The results of PM2.5 source apportionment by positive matrix factorisation (PMF) showed five main sources: motor-vehicle sources (18–33%), secondary sources (16–36%), coal combustion sources (16–30%), biomass-combustion sources (10–28%), and dust sources (5–7%). Backward trajectory clustering analysis showed PM2.5 in spring and autumn were more influenced by medium distance and local air but mainly influenced by local sources in winter.

Apportionment of Chemical Components and Sources of PM2.5 in Shihezi City of Xinjiang, China

In order to reveal the pollution characteristics and sources of PM2.5, in this study, we collected PM2.5 filter membrane samples in Shihezi continuously from December 2020 to January 2021, and analyzed 10 kinds of water-soluble inorganic ions (WSIIs) and 24 inorganic elements (IEs), as well as organic carbon (OC) and elemental carbon (EC). The sources and transport paths of PM2.5 were also analyzed via PMF modeling and backward trajectory clustering analysis. The results show that, in winter, Shihezi can have a mean PM2.5 mass concentration as high as 164.69 ± 76.48 μg/m3, and the PM2.5 mass concentration on polluted days is 3.3 times that of clean days. Water-soluble inorganic ions (WSIIs), total carbon (TC), and inorganic elements (IEs) make up the percentage of PM2.5 mass concentration by 64.9%, 9.3%, and 2.6%, respectively. SO42−, NO3−, and NH4+ (SIAs) are the major WSIIs, accounting for 91.0% of the WSII concentration. The heavier the pollution, the more SIAs contribute to PM2.5. The OC mean mass concentration is 14.04 ± 5.32 μg/m3. As the winter pollution becomes worse and worse, the value of OC/PM2.5 decreases constantly while that of SOC/OC follows an opposite trend. During the process of heavy pollution in Shihezi, the secondary transformation of total carbon cannot be ignored. The positive definite matrix factorization (PMF) model result suggests that the main pollution origins of PM2.5 in Shihezi City comprise secondary sources, coal-burning sources, motor vehicle sources, industrial sources, and flying dust sources. The backward trajectory clustering analysis denotes that the winter pollution in Shihezi mainly comes from the local pollutants in Manas and the short-distance transport of pollutants from the Urumqi-Changji Region.

Characteristics and Source Apportionment of PM2.5 in the Core Area of Ili River Valley in Spring

To explore the characteristics and sources of PM2.5 in the core area of Ili River Valley in spring, a total of 140 PM2.5 samples were collected at six sampling sites during April 20-29, 2021, and 51 chemical components including inorganic elements, water-soluble ions, and carbon components were analyzed. The results showed that ρ(PM2.5) was at a low level during sampling, ranging from 9 μg·m-3 to 35 μg·m-3. Si, Ca, Al, Na, Mg, Fe, and K were the most abundant elements, accounting for 12% of PM2.5, indicating that PM2.5 was affected by the dust sources in spring. The spatial distribution characteristics of elements depended on the surrounding environments of the sampling sites. The new government area was affected by coal-fired sources, so the value of As concentration was high. Yining Municipal Bureau and the Second Water Plant were greatly affected by motor vehicle sources, so the values of Sb and Sn concentration were higher. The enrichment factor results showed that Zn, Ni, Cr, Pb, Cu, and As were mainly emitted from fossil fuel combustion and motor vehicles. The concentration of water-soluble ions accounted for 33.2% of PM2.5. Among them, ρ(SO42-), ρ(NO3-), ρ(Ca2+), and ρ(NH4+) were (2.48±0.57), (1.22±0.75), (1.18±0.49), and (0.98±0.45) μg·m-3, respectively. The higher Ca2+ concentration also reflected the contribution of dust sources. The ratio of n(NO3-)/n(SO42-) was between 0.63 and 0.85, which indicated that the influence of stationary sources was more important than that of mobile sources. Both Yining Municipal Bureau and the Second Water Plant were affected by motor vehicle exhaust; therefore, their n(NO3-)/n(SO42-) ratios were high. Yining County was in a residential area, and therefore its n(NO3-)/n(SO42-) ratio was lower. The average ρ(OC) and ρ(EC) in PM2.5 were 5.12 μg·m-3(4.67-6.25 μg·m-3) and 0.75 μg·m-3(0.51-0.97 μg·m-3), respectively. Yining Municipal Bureau was significantly affected by motor vehicle exhaust from both sides, so the values of OC and EC concentration were slightly higher than those in other sampling sites. The SOC concentration was calculated by the minimum ratio method, and the results showed that the values of SOC concentration in the New Government Area, the Second Water Plant, and Yining Ecological Environment Bureau were higher than those in other sampling sites. The results of the CMB model showed that PM2.5 in this area mainly came from the contribution of secondary particulate matter and dust sources, which accounted for 33.3% and 17.5%, respectively. Secondary organic carbon (16.2%) was the main contribution source of secondary particulate matter.

Socioeconomic and racial disparities in source-apportioned PM2.5 levels across urban areas in the contiguous US, 2010

Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n = 25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.

High contribution of vehicular exhaust and coal combustion to PM2.5-bound Pb pollution in an industrial city in North China: An insight from isotope

Although industrial emissions are the major pollution source of PM2.5-bound Pb, which affects air quality in cities, their influence has gradually decreased with the enhancement of industrial control in recent years. Thus, at present, the source of PM2.5-bound Pb is unclear, especially in an industrial city in North China. In this study, by incorporating the stable Pb isotopic compositions into a Bayesian isotope mixing model (MixSIAR), source apportionment of PM2.5-bound Pb was conducted during autumn and winter in 2017, and summer in 2018 in Changzhi. The daily average concentrations of PM2.5 and PM2.5-bound Pb during the study period were lower than the second-grade limit value of China (75 μg m−3) and the Chinese National Ambient Air Quality Standard (1 μg m−3), respectively. The pollution sources of PM2.5-bound Pb were fully distinguishable using Pb isotopes. In Changzhi, the 206Pb/207Pb ratio in soil (average: 1.0891) and exhaust dust from gasoline (average: 1.0832) and diesel (average: 1.0814) showed low levels of Pb isotope signature in Changzhi, whereas the rest of the source exhibited Pb isotope signature levels. The source apportionment results revealed that in an industrial city with many steel and coking plants, the primary source of PM2.5-bound Pb were motor vehicle emission (29.7%) and coal combustion (26.9%), rather than industrial emission (26.5%). Moreover, seasonal variations in PM2.5 of Pb sub-sources were observed: soil dust (21.7% and 18.4%) and diesel motor vehicles emission (16.4% and 15.6%) in summer and autumn; domestic coal combustion (17.1%) and coal-fired power plant emission (15.5%) in winter. The carcinogenic risk from motor vehicle emission in autumn was higher than that from industrial and coal sources in winter, and higher than that from motor vehicle sources in summer. This finding is a critical evidence of an anomalistic enrichment of PM2.5-bound Pb in an industrial city. These results obtained through Pb isotopic composition and MixSIAR model also improved the accuracy of source apportionment of atmospheric PM2.5-bound Pb, representing a high scientific significance and reference value for Pb pollution control in North China.

Characterization and Formation Mechanism of Water-soluble Inorganic Ions in PM2.5 and PM10 in Summer in the Urban Agglomeration of the Ili River Valley

The simultaneous observation and analysis of atmospheric particles on a regional scale is an important approach to developing control strategies for air pollution. To study the spatial distribution characteristics of particulate matter and water-soluble inorganic ions in the Ili Valley Urban agglomeration, PM2.5 and PM10 samples were synchronously collected from July 19 to July 29, 2021 in Yining City and the surrounding three counties, and then nine types of water-soluble inorganic ions (WSIIs) were analyzed. The spatial distribution characteristics, existence form of WSIIs, and influencing factors were discussed in depth. The results showed that the average ρ(PM2.5) and ρ(PM10) in the Ili River Valley urban agglomeration in summer were 23 μg·m-3 and 59 μg·m-3, respectively. The emission of local industrial and mobile sources in Yining City was higher than that of the surrounding three counties, resulting in the highest ρ(PM2.5) in the region (25 μg·m-3). Due to the influence of dust sources and topography, the ρ(PM10) in Yining county was the highest in the region (63 μg·m-3). Huocheng county is located upwind of the region, and these favorable diffusion conditions resulted in the lowest ρ(PM2.5) and ρ(PM10) (20 μg·m-3 and 49 μg·m-3, respectively). The concentrations of WSIIs in PM2.5 and PM10 ranged from 28.2%-29.9% and 16.0%-20.2%, respectively. The four main ions (SO42-, NO3-, NH4+, and Ca2+) accounted for approximately 90% of WSIIs mass concentrations. The concentration order of the four main ions in PM2.5 was SO42->Ca2+>NH4+>NO3- and SO42->Ca2+>NO3->NH4+ in PM10. The results of correlation analysis showed that the similar SO42- concentrations in the four cities were mainly caused by regional transport. Ca2+ was the highest-concentration ion in PM10 of Yining City and Qapqal Xibe Autonomous county, and the proportion of Ca2+ was significantly higher than that in most cities in China, which reflected that the cities in the core area of the Ili Valley were greatly affected by the dust sources. The ratios of n(NO3-)/n(SO42-) in PM2.5 and PM10 were 0.78 and 0.76, respectively, indicating that the influence of stationary sources was greater than that of mobile sources. The ratio of n(NO3-)/n(SO42-) in Yining City>Huocheng county>Yining county>Qapqal Xibe Autonomous county, which was consistent with the motor vehicle populations of the four cities, reflecting that Yining City was affected by motor vehicle sources more than the surrounding three counties. The secondary components mainly existed in the form of (NH4)2SO4, NH4HSO4, and NH4NO3. There was excess ammonia after the reaction between NH4+ and SO42- in each city. NH4NO3 mainly existed in Yining City, which was mainly related to high NO2 in Yining City. The NOR of the four cities were 0.03-0.10 and 0.03-0.16 in PM2.5 and PM10, respectively, and the secondary transformation of NO3- was weak due to the influence of high temperatures in summer. The SOR were 0.21-0.41 and 0.23-0.44, respectively. The SOR of Qapqal Xibe Autonomous county was the highest due to the relatively high humidity, whereas the SOR of Huocheng county was higher than that of the three sites in Yining City due to the influence of regional transportation. The formation mechanisms showed that SO42- in Qapqal Xibe Autonomous county and Yining City were mainly produced by the heterogeneous reaction, and in Yining county it was mainly formed via the homogeneous reaction. However, the formation mechanism in Huocheng county was complex and was affected by both homogeneous and heterogeneous reactions.

Characteristics and Source Analysis of VOCs Pollution During the Period of Ozone Exceeding the Standard in Zibo City

In recent years, ozone pollution has been growing increasingly serious in the urban areas of China. Volatile organic compounds (VOCs) are important precursors of O3 formation, which is of great significance to studying the main characteristics and sources of VOCs for controlling O3 pollution. In this study, we conducted online VOCs observation in Zibo City from May to September in 2019, monitoring 56 species in total. During the observation, the over-standard rate of ozone was up to 67.8%, the average of ρ(VOCs) was 140.71 μg·m-3, and the concentration of VOCs in the ozone over standard days was 1.04 times that on the non-standard days. The rank of VOC classes was aromatic hydrocarbons>alkanes>alkenes>alkynes. Among them, 1,3,5-tritoluene, o-ethyltoluene, 1-butene, and n-hexane achieved high emission in the exceeding O3 and non-exceeding days. Aromatic hydrocarbon and alkenes contributed more to the potential of ozone formation. According to the PMF source analysis results, VOCs sources in the urban area mainly included motor vehicle sources, fixed combustion sources, solvent sources, process sources, and natural plant sources, among which motor vehicle sources were the most important source of VOCs in the urban area. In addition, motor vehicle sources accounted for 32.3%, and fixed combustion sources accounted for 24.2% on days when ozone exceeded the standard, which increased by 3.3% and 6.9%, respectively, compared with those on days when ozone did not exceed the standard. However, the proportion of solvent sources and process sources decreased by 5.1% when ozone exceeded the standard compared with that on a non-standard day.

Concentration, Source, and Health Risk Assessment of PM1 Heavy Metals in Typical Pollution Processes in Zhengzhou

Heavy metal elements in particulate matter can cause adverse effects on human health, and the smaller the particle size, the greater the harm. A total of 16 heavy metal elements (Al, Si, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Ba, Pb, and Cd) in PM1 were continuously determined by an online heavy metal observation instrument in Zhengzhou city from January 7 to 25, 2021. The results showed that ρ(K) concentration was the highest during the observation period (0.62 μg·m-3). According to pollutant concentration and meteorological characteristics, the observation period was divided into clean days, dust days, and haze days. The contribution of heavy metal pollution characteristics and health risk assessment in atmospheric PM1 was different under different pollution processes. The US EPA health risk assessment method was used to assess the health risks of heavy metals, and the enrichment factor method and positive matrix factorization (PMF) were used to analyze the sources of heavy metals. The influence of the transmission was evaluated by using the concentration-weighted trajectory (CWT) method and the backward trajectory method. The results show that the enrichment factors of Zn, As, Se, Pb, and Cd were more than 100 under different pollution processes, which were greatly affected by human activities. During the sampling period, the main sources of heavy metals were industrial sources, coal/biomass sources, motor vehicle sources, and dust sources. The results of the health risk assessment were substituted into PMF analysis, and it was found that industrial sources were the main contributing sources of carcinogenic and non-carcinogenic health risks during cleaning days, dust days, and haze days, and the carcinogenic risk of heavy metal elements in PM1 in this region for adults exceeded that for children. CWT and backward trajectory methods revealed that regional transmission was one of the main factors affecting local health risks.

Significant Contribution of C3 - Type Forest Plants’ Burning to Airborne PM2.5 Pollutions in Chiang Mai Province, Northern Thailand

Abstract This study aimed to identify the contributing sources of PM2.5 in Chiang Mai Province during February to April 2016. We therefore investigated the chemical compositions of PM2.5 at two different sites. An urban site is in Chiang Mai University (CMU) while a rural site is in Mae Chaem (MC) District where frequent intensive biomass burning was reported. Thirty pair samples of 24-h PM2.5 were analyzed for organic carbon (OC), elemental carbon (EC), levoglucosan and stable carbon isotope (δ13C). The mean concentrations (Mean ± SD) of PM2.5, OC and EC at the CMU vs MC sites were not significant different (P >0.05) including 44.5 ± 32.1 vs 40.5 ± 21.2 µg/m3; 14.9 ± 12.5 vs 14.8 ± 10.0 µg/m3; and 1.80 ± 1.60 vs 1.62 ± 0.80 µg/m3, respectively. Levoglucosan concentrations, a tracer of biomass burning from both sites were not significant different (P >0.05) and the mean ± SD concentrations at CMU vs MC sites were 0.46 ± 0.56 µg/m3 vs 0.55 ± 0.67 µg/m3, respectively. Meanwhile, the mean values of δ13C in total carbon (TC) at CMU vs MC sites were -27.9 ± 0.68 vs -27.6 ± 0.60‰, respectively which major data (n = 48, 85.4%) fell within the ranged of C3-type plants and minor data (n = 48, 14.6%) in C3-type plants and motor vehicle sources. This finding corresponds to the vast biomass burning area from satellite data. Forest plants in northern Thailand, Chiang Mai particular are mostly mixed deciduous forest i.e. C3-type plants which falling leaves in dry season and easily causing fire. The results of this study therefore strongly suggest that the burning of C3-type forest plants attribute to airborne PM2.5 pollutants in Chiang Mai Province. Keywords: Air pollution, Biomass burning, C3-type plants, Levoglucosan, Stable carbon isotope

Characteristics and Source Apportionment of Ambient VOCs in Spring in Liuzhou

The objective was to investigate the characteristics and sources of ambient volatile organic compounds (VOCs) in the karst region in southwestern China. We monitored atmospheric VOCs in Liuzhou by the GC955 VOCs Online Monitoring System and analyzed the pollution characteristics, ozone formation potential (OFP), aerosol formation potential (AFP), and the positive matrix factorization (PMF) model in March 2019. The results show that ① 50 kinds of VOC components were detected during the supervised period, with an average daily concentration of 25.52×10-9 mol·mol-1, which was composed of alkanes (56.08%), alkenes (19.63%), alkynes (14.25%), and aromatics (10.04%), respectively. ② The concentration of VOCs was lower during the day and higher at night, with the highest value at 23:00. The VOC concentration was low in daytime and high at night. The peak value of VOCs with regard to diurnal variation was correlated with the time of morning and the evening traffic peak and may be influenced by various factors. ③ The contribution of alkenes, aromatics, and alkanes to OFP was 44.30%, 33.03%, and 19.96%, respectively. This indicates that the control of aromatic and olefin should prioritize alkanes. In addition, Liuzhou city is in the VOC sensitive area of O3 generation, and the reduction of VOCs had a controlling effect on O3 generation. ④ The contribution of aromatic hydrocarbons to AFP was up to 95.27%. Therefore, the improvement and control of the processes in motor vehicle exhaust emissions, solvent use, and the automobile industry and the chemical industry could effectively suppress ozone and haze pollution. ⑤ The emission sources of VOCs in spring were mainly industrial emission sources (28.34%), motor vehicle sources (25.47%), combustion sources (24.37%), solvent sources (13.28%), and plant emission sources (8.54%), respectively. This indicates that the control of industrial emission sources, motor vehicle sources, and combustion sources is the main way to control VOC pollution in Liuzhou City. Meanwhile, the olefin and aromatic hydrocarbons emitted by these emission sources should be mainly considered.

Seasonal Chemical Composition Characteristics and Source Apportionment of PM2.5 in Zhengzhou

The aim of this study was to fully understand the pollution characteristics and sources of PM2.5 in Zhengzhou, and to investigate the differences in four seasons and between urban and suburban areas. At the Zhengzhou environmental monitoring center (urban areas) and Zhengzhou University (suburban areas), 1284 environmental PM2.5 samples were collected in the four seasons of 2018. The concentrations of nine kinds of inorganic water-soluble ions, organic carbon, elemental carbon and 27 kinds of elements, were measured by ion chromatography, carbon analyzer, and X-ray fluorescence spectrometry, respectively. Enrichment factors (EF), index of geoaccumulation (Igeo), potential ecological risk index (RI), chemical mass balance model (CMB), backward trajectory, and potential source contribution function were the methods used to study the chemical component characteristics and source differences of PM2.5 in different seasons in the urban and suburban areas of Zhengzhou. The results showed that the annual average PM2.5 concentration at the Zhengzhou environmental monitoring center and Zhengzhou University sites reached (59.7±24.0) μg·m-3 and (74.7±13.5) μg·m-3, respectively. The PM2.5 concentration at the suburban point was higher than at the urban point with the exception of winter, and the seasonal mean concentration decreased in the order of winter > autumn > spring > summer. Compared with the urban areas, the suburban areas were more affected by crustal substances in spring, and the concentrations of all components were higher in summer and autumn than the urban areas. Nevertheless, urban areas were more affected by coal burning sources and motor vehicle sources in winter. The component analysis results showed that the influences of soil dust and building dust were greater in the suburbs in spring than in the urban areas. In autumn, the suburbs were more affected by biomass sources than the urban areas, while the urban areas were more affected by building dust than were the suburbs. The concentrations of Cu, As, Zn, Pb, and Sb were strongly influenced by anthropogenic sources, and the enrichments of Zn, Cu, As, and Pb in urban areas were greater than in the suburbs. In addition, Zn, Cu, As, and Pb exhibited potential ecological risks. The outcomes of the CMB model showed that dust sources, secondary sulfate, secondary nitrate, and coal burning sources contributed significantly to PM2.5 concentrations in spring, summer, autumn and winter, respectively. The contributions of secondary pollution sources (secondary organic aerosol, secondary sulfate, and secondary nitrate) and motor vehicle sources to urban areas were higher than to suburban areas, and the influences of biomass sources in autumn and winter were significantly higher than in spring and summer and urban areas. The backward trajectory results indicated that the local PM2.5 concentration was affected by distant transmission from the northwest except in summer, was affected by neighboring provinces in the east in four seasons, and was affected by transmission from the south, with the exception of winter. Furthermore, the consequences of potential sources demonstrated that the local PM2.5 concentration was mainly affected by the potential areas in Henan province and its boundary with neighboring provinces.

Different roles of nitrate and sulfate in air pollution episodes in the North China Plain

Different chemical substances make different contributions to particles with different diameters. It is important to determine which species play important roles to control air pollution. In this study, the concentrations of water-soluble inorganic ions, elemental carbon, organic carbon and metal elements in PM1 (particulate matter with a diameter less than 1 μm) and PM2.5 (particulate matter with a diameter less than 2.5 μm) in the Shijiazhuang suburban area were measured by synchronous sampling for one month in the summer of 2016. The results showed that the chemical components of particulate matter are mainly SO42−, NO3−, NH4+ and organic matter (OM), accounting for 14%, 8%, 11%, and 11%, respectively, in PM1 and 13%, 8%, 10%, and 9%, respectively, in PM2.5. PM1 and PM2.5 have similar sources, including secondary nitrate, secondary sulfate, industrial sources, motor vehicle sources, dust, biomass combustion and coal burning, which contribute 29%, 30%, 10%, 9%, 8%, 8% and 6%, respectively, to PM1 and 29%, 33%, 12%, 13%, 8%, 2%, and 4%, respectively, to PM2.5. Based on the analysis of a typical pollution episode, the concentrations of PM1 and PM2.5 increase synchronously in the explosive growth stage, with nitrate acting as the key chemical component. In the most polluted period, the concentration of PM2.5 continues to increase because of the increase in sulfate, while that of PM1 is almost constant. The results show that the prevention and control of PM1 and PM2.5 should be different in different pollution stages.

Ethylene source attribution modeling in the Greater Houston area

Ethylene is a ubiquitous industrial gas that is emitted by a wide variety of biogenic, biomass burning, and anthropogenic sources, so the opportunity for human exposure is widespread. We believe that a comprehensive exposure assessment of ethylene from emissions sources has not specifically been evaluated so this study was conducted to estimate the relative contribution of different sources on atmospheric concentrations in the Greater Houston area.Source attribution modeling shows that when averaged over the full modeling domain, inclusive of industrial, urban, rural and water areas, ethylene concentrations are dominated by natural source emissions (fires and biogenic) with an annual mean of 69% of total ethylene concentration. Of the anthropogenic sources, which account for the remaining 31% of the annual ethylene concentration, the largest contribution was from area/non-road sources (annual mean of 16%) with industrial source (annual mean of 11%) and on-road motor vehicle (annual mean of 4%) sources showing relatively lower concentration contributions.

Analysis of Chemical Composition and Pollution Source of the Fine Particulate Matter by the SPAMS in the Four Seasons in Nanning

To study the chemical composition and pollution sources of the fine particulate matter during the four seasons in Nanning, single particle aerosol mass spectrometry was performed to characterize the fine particulate matter in Nanning during the four seasons. The correlation (R2) between the fine particulate matter number concentration and the mass concentration of the fine particulate matter obtained using SPAMS was all above 0.75 in the observation period. The particle number concentration could reflect the atmospheric pollution situation to some degree. The average mass spectrogram of the fine particulate matter reflected that secondary pollutants were more in winter and spring in Nanning. The Art-2a classification method was used to classify the chemical composition of the fine particulate matter. The results showed that the number concentration of the chemical composition and sources of pollution had differences in the four seasons. Chemical composition could reflect pollution sources. The elevated elemental carbon was positively with increasing coal combustion in winter. The elevated organic carbon was positively correlated with motor vehicle source in autumn. Rich potassium particles, levoglucosan and substance were positively correlated with biomass burning source and dust in summer. The rich sodium particles and heavy metals were higher in spring. During the process of increasing pollution, the contributions of biomass and coal combustion sources were large.

The 2013 severe haze over the Southern Hebei, China: PM2.5 composition and source apportionment

PM2.5 samples were collected and analyzed for the first time in Handan City, which was listed in the top 4 polluted cities in China, during December 2012 to January 2013 when the record–breaking severe haze pollution happened. Positive Matrix Factorization method (PMF) was applied to understand major sources to the severe haze pollution over this city. The daily average concentration of PM2.5 was 160.1μg m–3, which was 2.1 times of the National Ambient Air Quality Standards of China (Class II, Annual Average Level) for daily average PM2.5 of 75μg m–3. SO42– was the most abundant ion (15.4%), followed by NH4+ and NO3–. They accounted for 39.5% of PM2.5. Eight factors were identified by positive matrix factorization (PMF) model. The major sources were coal combustion source (25.9%), secondary source (21.8%), industry source (16.2%), Ba, Mn and Zn source (12.7%), motor vehicle source (7.7%), road dust source (10.9%), K+, As and V source (6.3%) and fuel oil combustion source (2.5%).The mean value of extinction coefficient (Bext) was 682.1Mm–1 and the largest contributor to Bext was ammonium sulfate with the mean value of 221.0Mm–1, accounted for 32.4% of the Bext.

Identification of haze-creating sources from fine particulate matter in Dhaka aerosol using carbon fractions

Fine particulate matter (PM2.5) samples were simultaneously collected on Teflon and quartz filters between February 2010 and February 2011 at an urban monitoring site (CAMS2) in Dhaka, Bangladesh. The samples were collected using AirMetrics MiniVol samplers. The samples on Teflon filters were analyzed for their elemental composition by PIXE and PESA. Particulate carbon on quartz filters was analyzed using the IMPROVE thermal optical reflectance (TOR) method that divides carbon into four organic carbons (OC), pyrolized organic carbon (OP), and three elemental carbon (EC) fractions. The data were analyzed by positive matrix factorization using the PMF2 program. Initially, only total OC and total EC were included in the analysis and five sources, including road dust, sea salt and Zn, soil dust, motor vehicles, and brick kilns, were obtained. In the second analysis, the eight carbon fractions (OC1, OC2, OC3, OC4, OP, EC1, EC2, EC3) were included in order to ascertain whether additional source information could be extracted from the data. In this case, it is possible to identify more sources than with only total OC and EC. The motor vehicle source was separated into gasoline and diesel emissions and a fugitive Pb source was identified. Brick kilns contribute 7.9 μg/m3 and 6.0 μg/m3 of OC and EC, respectively, to the fine particulate matter based on the two results. From the estimated mass extinction coefficients and the apportioned source contributions, soil dust, brick kiln, diesel, gasoline, and the Pb sources were found to contribute most strongly to visibility degradation, particularly in the winter. Implications: Fine particle concentrations in Dhaka, Bangladesh, are very high and cause significant degradation of urban visibility. This work shows that using carbon fraction data from the IMPROVE OC/EC protocol provides improved source apportionment. Soil dust, brick kiln, diesel, gasoline, and the Pb sources contribute strongly to haze, particularly in the winter.