Contribution Of Vehicle Exhaust Research Articles

Chemical Composition and Source of PM2.5 during Winter Heating Period in Guanzhong Basin

An intensive field campaign was carried out from December 2022 to March 2023 at six different sites across five major cities (Xi’an, Baoji, Xianyang, Weinan, and Hancheng) in the Guanzhong Basin, China, covering most of the heating period there, which is characterized by high PM2.5 pollution levels. During the campaign, the mean PM2.5 concentrations at these sites exceeded the 24 h PM2.5 standard (75 μg m−3), except the site at Hancheng, with mean PM2.5 concentrations of 57.8 ± 32.3 μg m−3. The source apportionment of PM2.5 varied significantly across sites, with vehicle exhaust being the dominant source at urban sites located in Xi’an and Baoji, coal combustion at suburban sites in Hancheng, and comparable contribution from coal combustion and industrial emissions at suburban sites in Xianyang and Weinan. Compared with clean condition, the contribution of vehicle exhaust and secondary inorganic sources (SIs) were largely enhanced during heavy PM2.5 pollution periods, while the contribution from biomass burning (BB) and dust decreased significantly at all sites. Combined with an analysis of meteorological parameters, the study further found that higher contributions of SIs and heavy PM2.5 pollution were generally associated with higher relative humidity (RH). In addition, higher PM2.5 concentrations at suburban sites were related to lower wind speeds, which could be explained by the stagnant condition favoring the accumulation of local emissions as well as the formation of secondary pollutants. In contrast, at urban sites (e.g., Xianyang), higher PM2.5 concentrations were more associated with the strong influence of vehicle exhaust at slightly higher wind speeds.

Characteristics and secondary transformation potential of volatile organic compounds in Wuhan, China

In this study, comprehensive field observations were collected out in urban, suburban and industrial areas of Wuhan, China in winter to compare and analyse the concentration level, source and secondary transformation potential of volatile organic compounds (VOCs). And the CJZ, SLDD and HGQ sites were used to assess the atmospheric VOC pollution in urban, suburban and industrial areas respectively. The total VOC (TVOC) concentrations at the three sites were 25.35 ± 12.66, 28.55 ± 9.19 and 66.81 ± 65.40 ppb, respectively, which shows that pollution in the industrial area is more serious. Vehicle exhaust and industrial emissions are the main sources of VOC emissions in Wuhan. The contributions of vehicle exhaust and industrial emissions to the VOCs are 57.03%, 48.44% and 55.18% at the CJZ, SLDD and HGQ sites, respectively. High potential source contribution function values mainly occur in the east and southwest areas of the sampling sites, indicating that Wuhan has a strong local VOC source. In addition, alkenes and aromatics play a leading role in secondary conversion during selected periods of relatively high O3 concentration. At the same time, the simulation results of the F0AM model fall within the VOC-limited regimes, and the suburban and industrial area are more sensitive to VOCs. The O3 concentration can be effectively reduced when the reduction ratio of AVOCs/NOx is 1.25–1.56, 1.11–1.20 and 1.42–1.61. In general, it is imperative to reduce VOC emissions by region and implement active VOC-species-focused strategies.

Characteristics, sources of volatile organic compounds, and their contributions to secondary air pollution during different periods in Beijing, China

Continuous measurements of volatile organic compounds (VOCs), ozone (O3), fine particulate matter (PM2.5), and related parameters were conducted between April 2020 and March 2021 in Beijing, China, to characterize potential sources of VOCs and their impacts on secondary organic aerosols (SOAs) and O3 levels. The annual average mixing ratio of VOCs was 17.4 ± 10.1 ppbv, with monthly averages ranging from 11.6 to 25.2 ppbv. According to the empirical kinetic modeling approach (EKMA), O3 formation during O3 season was “VOCs-limited”, while it was in a “transition” regime during O3 pollution episodes. In the O3 season, higher ozone formation potential (OFP) of m/p-xylene, o-xylene, toluene, isopentane, and n-butane were evident during O3 pollution episodes, in line with the increasing contributions of solvent usage and coating, as well as gasoline evaporation to OFP obtained through a matrix factorization model (PMF). Aromatics contributed the most to the secondary organic aerosol formation potential (SOAFP). In the non-O3 season, the contribution of vehicle exhaust to SOAFP elevated on hazy days, thereby revealing the importance of traffic-derived VOCs for PM2.5 pollution. Our results indicate that the prior control of different VOC sources should vary by season, thereby facilitating the synergistic control of O3 and PM2.5 in Beijing.

Quantifying the source and formation of nitrate in PM2.5 using dual isotopes combined with Bayesian mixing model: A case study in an inland city of southeast China

Atmospheric nitrate has been attracting increasing attention because it is one of the important components of PM2.5. Understanding the sources and formation mechanism of nitrate in PM2.5 is essential to take effective measures to prevent and control the emission of nitrogen oxides in the atmosphere and reduce the formation of haze. In this study, PM2.5 samples were collected from Ganzhou, an inland city in southeast China, during summer and winter. The concentrations of PM2.5 and NO3− were determined, and the isotopic compositions of NO3− (δ15N–NO3− and δ18O–NO3−) were analyzed in order to quantify the relative contributions of different emission sources and formation pathways of nitrate in PM2.5. The results showed that PM2.5 and NO3− concentrations were lower in summer (39.80 ± 11.10 μg·m−3 and 1.79 ± 0.70 μg·m−3) while higher in winter (69.85 ± 29.58 μg·m−3 and 10.83 ± 9.89 μg·m−3). The values of δ18O–NO3- and δ15N–NO3- ranged from 42.84‰ to 56.80‰ and from −11.17‰ to −2.08‰ in summer, while from 55.86‰ to 78.66‰ and from −10.63‰ to −1.88‰ in winter, respectively. The results of δ15N–NO3− combined with Bayesian isotope mixing model showed that the relative contributions of vehicle exhaust, soil microbial activity, biomass combustion and coal fired power plants were 59.3%, 28.5%, 8.7% and 3.4% in summer, while 65.1%, 20.1%, 10.6% and 4.1% in winter, respectively. The results of δ18O–NO3- combined with Bayesian isotope mixing model showed that the possible relative contributions of pathway 1 (P1) (NO2 + ·OH), pathway 2 (P2) (NO3 + HC) and pathway 3 (P3) (N2O5 + H2O) were 73.5%, 12.4% and 14.1% in summer, while 41.6%, 28.9% and 29.5% in winter, respectively. Moreover, P2 and P3 contributed more when NO3− concentrations were higher, suggesting that P2 and P3 were of significance to the formation of nitrate in PM2.5, especially during winter.

Direct identification of total and missing OH reactivities from light-duty gasoline vehicle exhaust in China based on LP-LIF measurement

Considerable efforts have been devoted to characterising the chemical components of vehicle exhaust. However, these components may not accurately reflect the contribution of vehicle exhaust to atmospheric reactivity because of the presence of species not accounted for (“missing species”) given the limitations of analytical instruments. In this study, we improved the laser photolysis–laser-induced fluorescence (LP–LIF) technique and applied it to directly measure the total OH reactivity (TOR) in exhaust gas from light-duty gasoline vehicles in China. The TOR for China I to VI-a vehicles was 15.6, 16.3, 8.4, 2.6, 1.5, and 1.6 × 104 sec−1, respectively, reflecting a notable drop as emission standards were upgraded. The TOR was comparable between cold and warm starts. The missing OH reactivity (MOR) values for China I to IV vehicles were close to zero with a cold start but were much higher with a warm start. The variations in oxygenated volatile organic compounds (OVOCs) under different emission standards and for the two start conditions were similar to those of the MOR, indicating that OVOCs and the missing species may have similar production processes. Online measurement revealed that the duration of the stable driving stage was the primary factor leading to the production of OVOCs and missing species. Our findings underscore the importance of direct measurement of TOR from vehicle exhaust and highlight the necessity of adding OVOCs and other organic reactive gases in future upgrades of emission standards, such that the vehicular contribution to atmospheric reactivity can be more effectively controlled.

Source Apportionment of VOCs and O3 Production Sensitivity at Coastal and Inland Sites of Southeast China

Urbanization and industrialization levels have a significant impact on the pollution of O3 and its precursors. However, current studies of VOC sources and O3 formation sensitivity in the regions with different urbanization and industrialization levels remained limited. Therefore, offline and online VOC observations were conducted at coastal sites (CS) and inland sites (IS) of southeast China to analyze spatial-temporal characteristics, source apportionment of VOCs based on the positive matrix factorization (PMF) model, and their effects on O3 formation using the observation-based model (OBM). The results showed that the average concentrations of TVOCs at CS and IS were 49.1 ± 14.4 and 28.4 ± 9.6 ppb, respectively, with higher levels in autumn compared to those in spring and summer. Alkene species contributed the largest to the OH radical loss rate (LOH) and ozone formation potential (OFP). The contributions of vehicle exhaust and combustion sources, industrial sources, solvent usage, biogenic sources, and fuel evaporation at CS and IS were 42.2% and 34.5%, 22.4% and 18.2%, 12.7% and 4.5%, 11.7% and 19.4%, and 11.0% and 23.4%, respectively. Meanwhile, vehicle exhaust and combustion sources (31.8%), fuel evaporation (21.3%) were the major contributors to O3 formation in Ningde. The results of sensitivity analysis indicated that O3 formation at CS was mainly VOC control in spring and autumn, and controlled by both VOCs and NOx in summer, but the VOCs is the key factor for the O3 formation at IS, and the emission control of alkenes and aromatics was conductive to decrease O3 levels. The scenario analysis suggested that the 20% reduction of VOC concentrations and 3% reduction of NOx concentrations could realize the 5% reduction of O3 concentrations. This study might enhance the understanding of O3 and its precursors in southeast China with different urbanization levels, as well as the emission reduction strategies.

Size Distributions of Water-Soluble Inorganic Ions in Atmospheric Aerosols During the Meiyu Period in the Yangtze River Delta, China

In order to investigate the chemical composition distributions and pollution characteristics of Total water-soluble inorganic ions (TWSII) in the rain period (Meiyu) in the East Asian summer monsoon season, including the impact of Meiyu on air pollution in the Yangtze River Delta, East China, the gaseous pollutant concentrations, the 9 sizes segregated particles, and water-soluble inorganic ions of aerosols were measured on the north shore of Taihu Lake from June 4 to July 5, 2016. Results show that the mass concentrations of atmospheric particulate matters (PM2.5 and PM10) and main gaseous pollutants (SO2, NO2, CO, and O3) decrease during the Meiyu period, with the largest decline in PM10 and the smallest in CO. TWSII in atmospheric particles are mainly concentrated in fine particles during the Meiyu period. The values of ρ (TWSII) for PM1.1, PM1.1–2.1, and PM2.1–10 before the Meiyu onset are generally greater than those during the Meiyu period. During the first pollution process, the ρ(TWSII) for PM1.1 and PM1.1–2.1 first increase to the peak values, and then decrease during the moderate rainfall period, when the ρ(TWSII) in PM2.1–10 increase to its maximum before the Meiyu onset. The mass concentrations for anions, cations, and total ions at different particle-size sections all exhibit bimodal distributions before and after the Meiyu onset. The mass concentration peaks at a particle size of 1.1–2.1 μm for fine particles, while at 5.8–9.0 μm (before the Meiyu onset) and 9.0–10.0 μm (during the Meiyu period) for coarse particles, respectively. The peak particle size for mass concentration of coarse particles moves toward larger sizes during the Meiyu period. The mass concentrations of SO42− at different particle-size sections show a bimodal distribution before the Meiyu onset and a multi-modal distribution during the Meiyu period. The mass concentrations of NO3− at different particle-size sections show a bimodal distribution before the Meiyu onset and a unimodal distribution during the Meiyu period. The mass concentrations of NH4+ at different particle-size sections present a bimodal distribution before and after the Meiyu onset, with the particle-size for peak concentrations distributing in 1.1–2.1 and 5.8–9.0 μm before the Meiyu onset, and 9.0–10.0 μm during the Meiyu period. The mean value of nitrogen oxidation ratio (NOR) is higher before the Meiyu onset than after, indicating that the secondary conversion of NO2 before the Meiyu onset is enhanced. The sulfur oxidation ratio (SOR) values are greater than NOR values, but the concentrations of NO2 in the same period during the Meiyu period are higher than those of SO2, which indicates that the secondary conversion of SO2 during the Meiyu period on the north bank of Taihu Lake is stronger than that of NO2. During the whole observation, the contribution of stationary sources mainly contributed to the atmospheric particulate matters during the Meiyu period. The contributions of vehicle exhaust and coal combustion to fine particles are more obviously affected by the changes in meteorological conditions during the Meiyu period, and the vehicle emissions contribute more to PM1.1–2.1 than to PM1.1.

Extinction Characteristics of Aerosols and the Contribution of Pollution Sources to Light Extinction During Three Heavy Pollution Episodes in the Winter of 2020 in Tianjin

This study examined high-resolution online monitoring data from January to February 2020 to study the extinction characteristics and sources of heavy pollution episodes during winter in Tianjin. Heavy pollution episodes occurred during this period from January 16 to 18 (episode Ⅰ), from January 24 to 26 (episode Ⅱ), and from February 9 to 10 (episode Ⅲ). The results showed that the concentrations of PM2.5 during the three heavy pollution episodes were (229±52), (219±48), and (161±25) μg·m-3, respectively, with NO3-, SO42-, NH4+, OC, EC, Cl-, and K+ comprising the main species. The values of the scattering coefficient(Bsp550) during the three heavy pollution episodes were (1055.65±250.17), (1054.26±263.22), and (704.44±109.89) Mm-1, respectively, while the absorption coefficient(Bap550) showed much lower values of (52.96±13.15), (39.72±8.21), and (34.50±8.53) Mm-1, respectively. PM2.5 played a major role in atmospheric extinction during heavy pollution episodes. Specifically, nitrate (38.9%-48.8%), sulfate (31.1%-40.7%), and OM (9.9%-21.8%) were the most important extinction components. The contribution of PM2.5 chemical components to the extinction coefficient varied significantly between the three episodes; the percentage of nitrate was higher in episode Ⅰ than in the other two episodes; in episode Ⅱ, the percentage of OM was highest, significantly affected by the discharge of fireworks; in episode Ⅲ, as traffic decreased but coal combustion emissions remained constant, the contribution of nitrate to the extinction coefficient decreased, while that of sulfate increased. Source apportionment of extinction coefficients was performed using PMF model combined with IMPROVE. Various pollution sources contributed to the extinction coefficient, namely: secondary sources (37.1%-42.0%), industrial and coal combustion (22.9%-24.2%), vehicle exhaust (23.9%-27.2%), crustal dust (5.0%-6.4%), and fireworks and biomass burning (3.9%-6.2%). Compared with episode Ⅰ, the contribution of fireworks and biomass burning increased significantly during episode Ⅱ, while the contribution of vehicle exhaust decreased significantly during episode Ⅲ. The contribution of industrial and coal combustion was similar during all three heavy pollution episodes. According to backward analysis, the small-scale and short-distance transmissions from Hebei provinces, as well as the medium and short-distance transmissions from central Inner Mongolia, were the major sources during heavy pollution episodes in the winter in Tianjin City.

Spatiotemporal variation, sources, and secondary transformation potential of volatile organic compounds in Xi'an, China

Abstract. As critical precursors of ozone (O3) and secondary organic aerosols, volatile organic compounds (VOCs) play a vital role in air quality, human health, and climate change. In this study, a campaign of comprehensive field observations and VOC grid sampling was conducted in Xi'an, China, from 20 June to 20 July 2019 to identify the spatiotemporal concentration levels, sources, and secondary transformation potential of VOCs. During the observation period, the average VOC concentrations at the Chanba (CB), Di Huan Suo (DHS), Qinling (QL), and gridded sampling sites were 27.8 ± 8.9, 33.8 ± 10.5, 15.5 ± 5.8, and 29.1 ± 8.4 ppb, respectively. Vehicle exhaust was the primary source of VOC emissions in Xi'an, and the contributions of vehicle exhaust to VOCs at the CB, DHS, and QL sites were 41.3 %, 30.6 %, and 23.6 %–41.4 %, respectively. While industrial emissions were the second-largest source of VOCs in urban areas, contributions from aging sources were high in rural areas. High potential source contribution function values primarily appeared in eastern and southern Xi'an near the sampling site, which indicates that Xi'an exhibits a strong local VOC source. Moreover, alkenes, aromatics, and oxygenated VOCs played a dominant role in secondary transformation, which is a major concern in reducing O3 pollution in Xi'an.

Carbon isotope seasonal characteristics of fine carbonaceous aerosol in Jinzhong City, Shanxi Province, China

Stable carbon isotope signatures were observed in organic carbon (OC) and elemental carbon (EC) fractions of fine carbonaceous aerosol (PM2.5) sampled in spring, summer, and winter (December 2013 to August 2014) in Jinzhong in Shanxi Province. The stable carbon isotope composition of EC (δ13CEC) varied from −25.93‰ to −22.20‰ during the sampling (mean value of −24.35 ± 1.11‰), with depletion in summer (−25.44 ± 0.32‰) and enrichment in winter (−23.04 ± 0.71‰). The contribution of vehicle exhaust was 65% in summer, and the contribution of fossil fuel combustion was 92% in winter based on carbon isotope mass balance model. The stable carbon isotope compositions of OC (δ13COC) over the whole sampling period ranged from −29.75‰ to −22.87‰ (mean value of −25.10 ± 1.47‰), with enrichment in winter (−24.12 ± 0.88‰) and depletion in spring (−26.40 ± 1.50‰). Compared with δ13CEC, the δ13COC in spring is depleted in 13C (Δ13COC-EC = δ13COC − δ13CEC = −1.55‰) because of the formation of secondary OC (SOC) from the photo-oxidation reactions. Summertime δ13COC (−24.52 ± 0.28‰) is more positive than δ13CEC because of the strong photochemical aging. The negative value of Δ13COC-EC in winter may be attributed to a complex set of reasons, including the unfavorable meteorological conditions (e.g., low temperature, low boundary layer height, and frequent temperature inversions), coal combustion, vehicle exhaust, gaseous fuel burning, and SOC formation from photo-oxidation reactions of volatile organic compounds (VOCs) caused by fossil fuel combustion. The seasonal variation of δ13CTC was consistent with that of δ13COC, and the annual average δ13CTC (−24.80 ± 1.12‰) implicated fossil fuels combustion. This study highlights that the isotope signatures of EC can be used for source apportionment, reveals the strong influence of atmospheric processing on the isotope signature of OC (e.g., SOC formation and photochemical aging), and suggests the potential application of isotope technology in air pollution research.

Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment.

To control the spread of the 2019 novel coronavirus (COVID-19), China imposed rigorous restrictions, which resulted in great reductions in pollutant emissions. This study examines the characteristics of air pollutants, including PM2.5 (particles with aerodynamic diameters < 2.5 μm), gas pollutants, water-soluble ions (WSIs), black carbon (BC) and elements, as well as the source apportionment of PM2.5 in Suzhou before, during and after the Chinese New Year (CNY) holiday of 2020 (when China was under an unprecedented state of lockdown to restrict the COVID-19 outbreak). Compared to those before CNY, PM2.5, BC, SNA (sulfate, nitrate and ammonium), other ions, elements, and NO2 and CO mass concentrations decreased by 9.9%–64.0% during CNY. The lockdown policy had strong (weak) effects on the diurnal variations in aerosol chemical compositions (gas pollutants). Compared to those before CNY, source concentrations and contributions of vehicle exhaust during CNY decreased by 72.9% and 21.7%, respectively. In contrast, increased contributions from coal combustion and industry were observed during CNY, which were recorded to be 2.9 and 1.7 times higher than those before CNY, respectively. This study highlights that the lockdown policy that was imposed in Suzhou during CNY not only reduced the mass concentrations of air pollutants but also modified their diurnal variations and the source contributions of PM2.5, which revealed the complex responses of PM2.5 sources to the rare, low emissions of anthropogenic pollutants that occurred during the COVID-19 lockdown.

Characterization and Source Apportionment of PM2.5 Based on the Online Observation in Tianjin

To study the characterization and source apportionment of PM2.5 in Tianjin, based on high-resolution online monitoring data from 2017 to 2019, the concentrations and its chemical compositions and sources of PM2.5 were analyzed. The results showed that the average concentration of PM2.5 was 61 μg ·m-3. The primary chemical compositions of PM2.5 were nitrate, organic carbon (OC), ammonium, sulfate, elemental carbon (EC), and Cl- and their corresponding mass percentages to PM2.5 were 17.7%, 12.6%, 11.5%, 10.7%, 3.4%, and 3.1%, respectively. From 2017 to 2019, the concentrations of PM2.5 and its main chemical compositions exhibited a decreasing trend; the mass ratios of NO3- and NH4+ to PM2.5 exhibited an increasing trend, while the mass ratios of SO42-, OC, and EC to PM2.5 exhibited a decreasing trend; further, the mass ratio of Cl- exhibited a slight increasing trend. The concentrations of K+, Ca2+, and Na+ and their mass percentages to PM2.5 increased. The concentrations of PM2.5 and its primary components were relatively higher during heating season, and relatively lower during non-heating season. High values of SOR and NOR indicated that the secondary transformation of nitrate and sulfate played an important role during summer and autumn, which resulted in higher mass percentages of secondary inorganic ions (NO3-, SO42-, and NH4+) to PM2.5 during summer and autumn. When the PM2.5 concentrations were at excellent levels, the mass ratios of the secondary inorganic ions to PM2.5 were relatively lower, the mass ratios of OC, Ca2+, and Na+ to PM2.5 were relatively higher, and secondary organic carbon (SOC) was high. When the PM2.5 concentrations were between light pollution to heavy pollution levels, as the pollution levels increased, the mass percentages of secondary inorganic ions, OC, EC, and Cl-, and other components (K+, Ca2+, and Na+) showed a significant increasing trend, relatively stable level, slightly increasing trend, and decreasing trend, respectively. When PM2.5 concentrations were between moderate pollution to heavy pollution levels, the influence of vehicle emission increased significantly. The source apportionment of PM2.5 were analyzed using the positive matrix factorization model. The major sources of PM2.5 in Tianjin were secondary source, vehicle exhaust, industrial and coal combustion emissions, and crustal dust. From 2017 to 2019, the contribution of vehicle exhaust increased, and the contribution of secondary source and crustal dust showed a slight increasing trend, while the contribution of industrial and coal combustion emissions decreased. For Tianjin, vehicle exhaust and industrial and coal combustion emissions were the primary sources of PM2.5. The adjustment of industrial and energy structure and management and control of vehicle exhaust are the main directions for air pollution control in Tianjin.

Characterization of Two Heavy Pollution Episodes in Tianjin in 2020

High-resolution online monitoring data from January to February in 2020 was used to study the characterization of two heavy pollution episodes in Tianjin in 2020; the heavy pollution episode that lasted from January 16 to 18, 2020 (referred to as episode Ⅰ) and that from February 9 to 10, 2020 (referred to as episode Ⅱ) were analyzed. The results showed that two heavy pollution episodes were influenced by regional transportation in the early stage and local adverse meteorological conditions in the later stage. During these episodes, the average wind speed was low, the average relative humidity was close to 70%, and relative humidity approached the saturated, the boundary layer heights were below 300 m, and the horizontal and vertical diffusion conditions were poor. Compared to episode Ⅰ, the concentration of pollutants decreased during episode Ⅱ, especially for the concentration of NO2. During the episode Ⅱ, the concentrations of PM2.5 and CO were higher in the north of Tianjin. The chemical component concentrations and their mass ratios to PM2.5 changed significantly in both episodes; the concentrations of secondary inorganic ions (NO3-, SO42-, and NH4+), elemental carbon (EC) and Ca2+were higher in episode Ⅰ, the concentrations of organic carbon (OC) and Cl- slightly increased in episode Ⅱ; and the concentrations of K+were higher in episode Ⅱ. Compared to episode Ⅰ, because of the increase in the combustion sources and significant reductions in the number of vehicles, the mass ratios of SO42-, OC, and K+ to PM2.5 increased while the mass ratios of NO3- and EC to PM2.5 decreased in episode Ⅱ; the mass ratios of NH4+ and Cl- to PM2.5 were relatively higher due to the continuity of the industrial production processes; the mass ratios of Ca2+ to PM2.5 were lower in two heavy pollution episodes because construction activities were halted. Source apportionment of PM2.5 was performed using the positive matrix factorization (PMF) model. In episode Ⅰ, the major sources of PM2.5 in Tianjin were secondary sources, industrial and coal combustion, vehicle exhaust, crustal dust, fireworks and biomass burning, with contributions of 53.8%, 20.2%, 18.6%, 6.3%, and 1.1%, respectively. In episode Ⅱ, the same sources were identified in the PMF analysis with contributions of 48.3%, 28.2%, 8.7%, 2.6%, and 12.2%, respectively. Compared to episode Ⅰ, the contributions of industrial and coal combustion, fireworks and biomass burning increased, and the contributions of secondary sources, vehicle exhaust, and crustal dust decreased in episode Ⅱ; contributions of vehicle exhaust and crustal dust decreased by 53.2% and 58.7%, respectively.

Aromatic compounds in a semi-urban site of western India: Seasonal variability and emission ratios

Ambient concentrations of benzene, toluene, and carbon-monoxide (CO) were measured at a semi-urban site in western India during the year 2015. Monthly mixing ratios of benzene, toluene, and CO were in the ranges of 0.58–1.18, 0.62–1.48, and 218–548 ppbv, respectively. In wintertime, the higher concentrations of aromatic volatile organic compounds (VOCs) and CO were due to shallower boundary layer depths and transport from polluted Indo-Gangetic Plain. The ∆toluene/∆benzene slope of 1.18 ± 0.41 ppb ppb−1 characterizes vehicle exhaust emissions. The higher values of ∆toluene/∆benzene in May coincided with the higher ambient temperatures (average ~ 35 °C) indicating emissions from evaporative sources. The emission ratios of ∆benzene/∆CO (1.82 ± 0.47 ppb ppm−1) and ∆toluene/∆CO (2.2 ± 0.72 ppb ppm−1) in the winter season are comparable to those for several urban regions of the world. The ozone formation potential and propylene-equivalent concentration of aromatics were lower than those of alkanes and alkenes measured during the same study period. Ethane (4.4 ± 3 ppbv), propane (3.4 ± 2 ppbv), and n-Butane (3.9 ± 2 ppbv), ethene (5.8 ± 3 ppbv), and propene (1.6 ± 1 ppbv) were the dominant alkane and alkene compounds measured during the study period. The composition of non-methane volatile organic compounds shows small change with the photochemical age suggesting the contributions of several other sources in addition to vehicular exhaust. The positive matrix factorization analysis suggests relatively high loadings of benzene (~30%), toluene (45%), and CO (32%), respectively from vehicle exhaust. The study highlights major contributions of vehicle exhaust, but emissions from other sources such as biomass burning, industries, evaporative loss, and biogenic are also significant.

Characteristics and Sources Apportionment of Fine Particulate Matter in a Typical Coastal City During the Heating Period

To clarify the pollution characteristics and sources of PM2.5 in Weihai during the heating period, PM2.5 samples from ambient air were collected at three routine air quality monitoring sites from January to March 2018. The OC, EC, water-soluble ions, and elements in PM2.5 were analyzed, and the sources of PM2.5 were identified using the PMF model. The results showed that the average daily mass concentration of PM2.5 was (33.80±22.45) μg·m-3, and the NO3-, NH4+, SO42-, OC, and EC were the main components of PM2.5. As a coastal city, the Cl- ratio was relatively high in PM2.5. Meanwhile, the compositions of PM2.5 were affected by the emission of pollutants with local industrial characteristics. Both NO3-/SO42- and OC/EC showed that mobile sources had a high contribution during the heating period. The acid-base ions in water-soluble ions showed that PM2.5 is weakly alkaline, and NH4+ is excessive. NH4+ mainly existed in the form of NH4NO3 and (NH4)2SO4. During the polluted period, the concentration of secondary pollutants significantly increased, and the mass concentrations of NH4+, NO3-, SO42-, OC, and EC were 4.21, 5.27, 3.23, 2.02, and 1.81 times that of the cleaning period, respectively. The PMF model showed that secondary aerosols were the major source of PM2.5, accounting for 32.4%-36.0% of PM2.5. The contributions of vehicle exhaust, coal combustion, biomass burning, and dust were 15.6%-18.9%, 12.1%-17.8%, 9.0%-10.4%, and 8.6%-11.3%, respectively, while the contributions of process emission (2.1%-8.3%), non-road mobile sources (2.4%-3.7%), and sea salt (3.5%-5.6%) were less.

Assessment of mobile source contributions in El Paso by PMF receptor modeling coupled with wind direction analysis

It is well-known that El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to air pollution, along with other sources including industrial, residential, and cross-border. This study aims at separating unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. The level of contributions from vehicle emissions to air pollution cannot be determined by simply comparing ambient air quality data with traffic levels because of the various other contributors to overall air pollution. To estimate contributions from vehicle emissions, researchers employed advanced multivariate receptor modeling called positive matrix factorization (PMF) to analyze hydrocarbon data consisting of hourly concentrations measured from the Chamizal air pollution monitoring station in El Paso. The analysis of hydrocarbon data collected at the Chamizal site in 2008 showed that approximately 25% of measured Total Non-Methane Hydrocarbons (TNMHC) was apportioned to motor vehicle exhaust. Using wind direction analysis, researchers also showed that the motor vehicle exhaust contributions to hydrocarbons were significantly higher when winds blow from the south (Mexico) than those when winds blow from other directions. The results from this research can be used to improve understanding source apportionment of pollutants measured in El Paso and can also potentially inform transportation planning strategies aimed at reducing emissions across the region.

Emissions of fine particulate nitrated phenols from various on-road vehicles in China

Nitrated phenols are receiving increasing attention due to their adverse impacts on the environment and human health. Previous measurements have revealed the non-ignorable contribution of vehicle exhaust to atmospheric nitrated phenols in urban areas. However, there is a lack of comprehensive understanding of the emission characteristics and the total emission of nitrated phenols from current on-road traffic. This study investigated the emissions from eight passenger vehicles, eight trucks, and two taxis, with fuel types including diesel, gasoline, and compressed natural gas. Exhaust emissions were collected and measured using a mobile measurement system on two testing routes. Twelve nitrated phenols in the collected fine particulate matter were detected using ultrahigh performance liquid chromatography-mass spectrometry. Overall, the emission profiles of fine particulate nitrated phenols varied with vehicle load and fuel type. The 4-nitrophenol and its methyl derivatives were dominant nitrated phenol species emitted by the vehicles with proportions of 38.4%-68.0%, which is significantly different from the proportions of nitrated phenols emitted from biomass burning and coal combustion. The emission factors also exhibited large variations across vehicle type, fuel type, and emission standards, with relatively low values for gasoline vehicles and taxis fueled by compressed natural gas and high values for diesel vehicles. Based on the emission factors of nitrated phenols from different vehicles, the estimated total emission of nitrated phenols from on-road vehicles in China was 58.9 Mg (-86%-85% within 95% confidence interval), with diesel trucks contributing the most substantial fractions. This work highlights the very high level of emissions of nitrated phenols from diesel vehicles and provides an essential basis for atmospheric modeling and effective pollution control.

An Integrated Method for Factor Number Selection of PMF Model: Case Study on Source Apportionment of Ambient Volatile Organic Compounds in Wuhan

The positive matrix factorization (PMF) model is widely used for source apportionment of volatile organic compounds (VOCs). The question about how to select the proper number of factors, however, is rarely studied. In this study, an integrated method to determine the most appropriate number of sources was developed and its application was demonstrated by case study in Wuhan. The concentrations of 103 ambient volatile organic compounds (VOCs) were measured intensively using online gas chromatography/mass spectrometry (GC/MS) during spring 2014 in an urban residential area of Wuhan, China. During the measurement period, the average temperature was approximately 25 °C with very little domestic heating and cooling. The concentrations of the most abundant VOCs (ethane, ethylene, propane, acetylene, n-butane, benzene, and toluene) in Wuhan were comparable to other studies in urban areas in China and other countries. The newly developed integrated method to determine the most appropriate number of sources is in combination of a fixed minimum threshold value for the correlation coefficient, the average weighted correlation coefficient of each species, and the normalized minimum error. Seven sources were identified by using the integrated method, and they were vehicular emissions (45.4%), industrial emissions (22.5%), combustion of coal (14.7%), liquefied petroleum gas (LPG) (9.7%), industrial solvents (4.4%), and pesticides (3.3%) and refrigerants. The orientations of emission sources have been characterized taking into account the frequency of wind directions and contributions of sources in each wind direction for the measurement period. It has been concluded that the vehicle exhaust contribution is greater than 40% distributed in all directions, whereas industrial emissions are mainly attributed to the west southwest and south southwest.

Eighteen-year trends of local and non-local impacts to ambient PM10 in Hong Kong based on chemical speciation and source apportionment

Along with rapid economic development in China in the past twenty years, air pollution has deteriorated significantly and then gradually improved afterwards. Emission changes in response to the economic development and control measures and their impacts to ambient pollution levels are thus of great scientific and policy-making implications. By using the eighteen-year PM10 speciation dataset that have been continuously collected at multiple stations over Hong Kong since 1998, we demonstrated a novel approach to identify long-term trends of local and non-local contributions to ambient PM10 in Hong Kong based on Positive Matrix Factorization source apportionment and backward trajectory cluster analysis. It is found that vehicle exhaust contribution had decreased from 9.9 μg·m−3 in 1998 to 3.4 μg·m−3 in 2015, corresponding to a 66% decrease. However, residual oil contribution increased threefold, which indicated ship emission needs to be primarily addressed in air pollution management in Hong Kong. The mostly non-local sources of secondary sulfate, industry, and coal / biomass burning all showed inverse V-shape trends during the eighteen years, indicating the dual roles of economic development and emission control in shaping the ambient pollution levels. Local contributions dominated for vehicle exhaust and aged sea salt, while non-local ones dominated for secondary sulfate, secondary nitrate, industry, and coal / biomass burning. Although with small fluctuations, non-local contributions consistently accounted for 63–70% of total PM10 during the eighteen years, similar as previous estimates using a different approach. Such a dominant non-local contribution highlights the importance of regional collaboration to reduce PM levels in Hong Kong.

Characterization of Fine Particulate Matter and Associated Health Burden in Nanjing

Particulate matter (PM) air pollution has become a serious environmental problem in Nanjing and poses great health risks to local residents. In this study, characteristics of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) over Nanjing were analyzed using hourly and daily averaged PM2.5 concentrations and meteorological parameters collected from nine national monitoring sites during the period of March 2014 to February 2017. Then, the integrated exposure-response (IER) model was applied to assess premature mortality, years of life lost (YLL) attributable to PM2.5, and mortality benefits due to PM2.5 reductions. The concentrations of PM2.5 varied among hours, seasons and years, which can be explained by differences in emission sources, secondary formations and meteorological conditions. The decreased ratio of PM2.5 to CO suggested that secondary contributions decreased while the relative contributions of vehicle exhaust increased from increased CO data. According to the values of attributable fractions (AF), stroke was the major cause of death, followed by ischemic heart disease (IHD), lung cancer (LC) and chronic obstructive pulmonary disease (COPD). The estimated total deaths in Nanjing due to PM2.5 were 12,055 and 10,771, leading to 98,802 and 87,647 years of life lost in 2014 and 2015, respectively. The elderly and males had higher health risks than youngsters and females. When the PM2.5 concentrations meet the World Health Organization (WHO) Air Quality Guidelines (AQG) of 10 μg/m3, 84% of the premature deaths would be avoided, indicating that the Nanjing government needs to adopt more stringent measure to reduce PM pollution and enhance the health benefits.