Contribution Of Mobile Sources Research Articles

Apportionment of PM2.5 Sources across Sites and Time Periods: An Application and Update for Detroit, Michigan

Identifying sources of air pollutants is essential for informing actions to reduce emissions, exposures, and adverse health impacts. This study updates and extends apportionments of particulate matter (PM2.5) in Detroit, MI, USA, an area with extensive industrial, vehicular, and construction activity interspersed among vulnerable communities. We demonstrate an approach that uses positive matrix factorization models with combined spatially and temporally diverse datasets to assess source contributions, trend seasonal levels, and examine pandemic-related effects. The approach consolidates measurements from 2016 to 2021 collected at three sites. Most PM2.5 was due to mobile sources, secondary sulfate, and secondary nitrate; smaller contributions arose from soil/dust, ferrous and non-ferrous metals, and road salt sources. Several sources varied significantly by season and site. Pandemic-related changes were generally modest. Results of the consolidated models were more consistent with respect to trends and known sources, and the larger sample size should improve representativeness and stability. Compared to earlier apportionments, contributions of secondary sulfate and nitrate were lower, and mobile sources now represent the dominant PM2.5 contributor. We show the growing contribution of mobile sources, the need to update apportionments performed just 5–10 years ago, and that apportionments at a single site may not apply elsewhere in the same urban area, especially for local sources.

Characteristics and Sources of Water-Soluble Inorganic Ions in PM2.5 in Urban Nanjing, China

In this study, the water-soluble inorganic ions (WSIIs) composition of fine particulate matter (PM2.5) was measured in the northern Nanjing city from 2015 to 2021. NH4+, NO3− and SO42− concentrations dominated in total WSIIs (Na+, NH4+, K+, Mg2+, Ca2+, Cl−, NO3− and SO42−), accounting for 87.8%. The nitrate with highest average concentration among all ions was 11.0 μg·m−3. Total WSIIs concentrations were higher in winter and lower in summer, with the highest levels in December (45.6 μg·m−3) and the lowest levels in August (15.1 μg·m−3). NO3−/SO42− was higher than 1, indicating the important contribution of mobile sources. The aerosols exhibited a weak acidic by the molar ratio of water-soluble anions and cations. Positive matrix factorization (PMF) analysis results showed that secondary nitrate and sulfate were the major pollution sources in December 2016 and 2020. The contribution of secondary nitrate in 2020 increased by 47.6% compared to 2016, while that of secondary sulfate decreased by 42.4%. The potential source contribution results demonstrated that for secondary aerosol concentrations, the contribution of regional transport from north of Anhui increased, while the contribution of local emissions decreased. The results from this study could contribute to the better prevention and control of regional air pollution in the future.

Mobile Sources Are Still an Important Source of Secondary Organic Aerosol and Fine Particulate Matter in the Los Angeles Region.

Secondary organic aerosol (SOA) is a significant component of atmospheric fine particulate matter. Mobile sources have historically been a major source of SOA precursors in urban environments, but decades of regulations have reduced their emissions. Less regulated sources, such as volatile chemical products (VCPs), are of growing importance. We analyzed ambient and emissions data to assess the contribution of mobile sources to SOA formation in Los Angeles during the period of 2009-2019. During this period, air quality in the Los Angeles region has improved, but organic aerosol (OA) concentrations did not decrease as much as primary pollutants. This appears to be largely due to SOA, whose mass fraction in OA increased over this period. In 2010, about half of the freshly formed SOA measured in Pasadena, CA appears to be formed from hydrocarbon (non-oxygenated) precursors. Chemical mass balance analysis indicates that these hydrocarbon SOA precursors (including intermediate volatility organic compounds) can largely be explained by emissions from mobile sources in 2010. Our analysis indicates that continued reduction in emissions from mobile sources should lead to additional significant decreases in atmospheric SOA and PM2.5 mass in the Los Angeles region.

Characteristics of PM2.5 and PM10 pollution in the urban agglomeration of Central Liaoning

Based on the concentration data, the pollution sources emission inventory of January 2019 to March 2020, and the source analysis data of 2018 in urban agglomeration of central Liaoning, HYSPLIT model, CWT (Concentration Weighted Trajectory) and SCWT (Source Concentration Weighted Trajectory) method were used to analyze the characteristics of particulate pollution. The results showed that seasonal concentration followed winter > spring > autumn > summer, and the primary pollution was the main source. Pollution in spring and winter were both mainly affected by Russia. In summer, there were few polluted air masses. In autumn, local emission was the main source of pollution. CWT showed that contribution of local emission was higher, with sporadic contributions from Russia, Inner Mongolia, Jilin, and Beijing-Tianjin-Hebei area. SCWT showed that road mobile sources and dust sources were concentrated in the Shenhai Expressway, the densely populated and the main developed areas of Shenyang, Anshan and Tieling. Areas with high contribution of non-road mobile sources were located in Liaoyang, Anshan, and Yingkou port area. Industrial processing sources were concentrated in Anshan, Liaoyang, and Benxi. Fixed combustion sources were distributed in the main urbanized areas.

Variations in traffic-related water-soluble inorganic ions in PM2.5 in Kanazawa, Japan, after the implementation of a new vehicle emission regulation

In this study, a three-year sampling campaign was conducted at a roadside air pollution monitoring station in Kanazawa, Japan. A total of 7 water-soluble inorganic ions (WSIIs) in ambient particulate matter (PM2.5) was determined via ion chromatography. After the implementation of a new vehicle emission regulation enacted in 2016 in Japan, the annual average concentration of the total WSIIs decreased, from 5.32 ± 3.27 μg/m3 in 2017 to 3.70 ± 2.43 μg/m3 in 2018. This decrease is mainly attributed to the reduction in the three major species of sulfate (SO42−), ammonium (NH4+), and nitrate (NO3−). The reduction in NO3− with decreasing nitrogen oxide (NOx) emphasizes the role of the new regulation. The reduction in NOx might also indirectly lead to a decrease in SO42− or directly lead to a decrease in NH4+. Through a comparison to other WSIIs emission sources, a similar composition was found to that of other traffic-related WSIIs, but the considered WSIIs are clearly different from those emitted by coal and biomass combustion sources. Via further comparison of the distribution of the [NO3−]/[SO42−] ratio values from different sources, we observed that the ratio values are close to those of WSIIs emitted by coal combustion sources, which indicates that this ratio might not be a suitable indicator for the identification of the contribution of mobile and stationary sources in Japanese cities. Overall, this study affirms the effectiveness of the new regulation in terms of WSIIs emission reduction and provides basic WSIIs data pertaining to the roadside environment in Kanazawa for the first time.

Characteristics of Particulate Matter at Different Pollution Levels in Chengdu, Southwest of China

Air pollution is becoming increasingly serious along with social and economic development in the southwest of China. The distribution characteristics of particle matter (PM) were studied in Chengdu from 2016 to 2017, and the changes of PM bearing water-soluble ions and heavy metals and the distribution of secondary ions were analyzed during the haze episode. The results showed that at different pollution levels, heavy metals were more likely to be enriched in fine particles and may be used as a tracer of primary pollution sources. The water-soluble ions in PM2.5 were mainly Sulfate-Nitrate-Ammonium (SNA) accounting for 43.02%, 24.23%, 23.50%, respectively. SO42−, NO3−, NH4+ in PM10 accounted for 34.56%, 27.43%, 19.18%, respectively. It was mainly SO42− in PM at Clean levels (PM2.5 = 0~75 μg/m3, PM10 = 0~150 μg/m3), and mainly NH4+ and NO3− at Light-Medium levels (PM2.5 = 75~150 μg/m3, PM10 = 150~350 μg/m3). At Heavy levels (PM2.5 = 150~250 μg/m3, PM10 = 350~420 μg/m3), it is mainly SO42− in PM2.5, and mainly NH4+ and NO3− in PM10. The contribution of mobile sources to the formation of haze in the study area was significant. SNA had significant contributions to the PM during the haze episode, and more attention should be paid to them in order to improve air quality.

Characterization and source analysis of water-soluble inorganic ionic species in PM2.5 during a wintertime particle pollution episode in Nanjing, China

Water-soluble inorganic ions (WSIIs) are major components of PM2.5, and play a prominent role in atmospheric acidification. In January 2019, a large-scale persistent particle pollution episode occurred in the Yangtze River Delta (YRD) region of China. High time resolution on-line monitoring of WSIIs in PM2.5 were conducted at the Atmospheric Environment Vertical Observation Station of Nanjing University by using In-situ Gas and Aerosol Compositions Monitor. From January 1 to 13, the mean concentration of WSIIs was 76.7 ± 48.2 μg·m−3. WSIIs accounted for 64.2% of PM2.5 concentration. Sulfate, nitrate and ammonium (SNA) were the major components of WSIIs, and accounted for 25.5%, 45.6% and 26.3%, respectively. The values of NO3−/SO42− increased from 1.37 to 2.86, indicating that the contribution of mobile sources to fine particle pollution was significantly enhanced than that of stationary sources. The main factors affecting the values of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) were gaseous precursor concentrations, relative humidity and air temperature. The values of SOR and NOR exceeded 0.1, implying the secondary transformation processes played critical roles during this pollution episode. To investigate the potential source areas of WSIIs in PM2.5, the Principal Component Analysis - Multiple Linear Regression (PCA-MLR) analysis and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used. By using PCA-MLR, it was found that the main pollution sources in Nanjing were secondary transformation (58%), manmade dust (26%) and biomass combustion (16%). Based on the backward trajectory calculation, the potential source contribution function (PSCF) analysis and the concentration weighted trajectory (CWT) analysis, it was revealed that the air pollutants mainly came from the local sources in and around Nanjing. With the aggravation of pollution, the contribution of regional transport to PM2.5 concentrations decreased and the contribution of local emissions increased. Among the main components of WSIIs in Nanjing, NH4+ was mainly caused by agricultural activities, and generally came from Jiangsu, eastern Henan, southern Shandong, and northeastern Anhui. SO42− was mainly derived from coal burning in the nearby industrial areas or from the north through the long-distance transmission. NO3− mainly concentrated in the local areas, which was greatly affected by human activities. The weighted concentration weighted trajectory (WCWT) peak values of NH4+ and SO42− were about 10 μg·m−3, while that of NO3− was more than 20 μg·m−3, which further indicated that the local emission of NO3− was the main potential source. The findings can contribute to understand the behavior of WSIIs and their roles in the wintertime haze event, reveal potential source areas for the corresponding components, and enlighten beneficial suggestions for particle pollution prevention and control in high polluted areas.

VOCs Emission Inventory and Variation Characteristics of Artificial Sources in Hubei Province in the Yangtze River Economic Belt

In this study, a 2018 anthropogenic volatile organic compounds (VOCs) emission inventory in Hubei Province was conducted using the emission factor method based on activity levels of five sources. The emission characteristics and variation trends of process sources from 2009 to 2018 were further analyzed. Total anthropogenic VOCs emissions were 6.52×105 tons in Hubei Province, accounting for about 6.41% of the country's total omissions. The contributions of fossil combustion sources, process sources, solvent sources, mobile sources, and waste disposal sources were 3.26%, 76.39%, 4.54%, 14.72%, and 1.09%, respectively. Process sources involving 45 sub-categories of nine industries accounted for a significant proportion of VOCs emissions, with Wuhan and Yichang recording the highest VOCs emission levels. The VOCs emissions intensity of each city and state were analyzed based the level of economic activity and territorial area. Tianmen and Shennongjia had higher VOCs emissions per unit of GDP, while Wuhan, Ezhou, and Tianmen had higher VOCs emissions per unit area. Regarding process source contributions, VOCs emissions increased progressively to 2.45×105 tons in 2009 and then stabilized between 2015 and 2017 with maximum emissions of 7.01×105 tons. In 2018, VOCs emissions decreased to 4.98×105 tons. This trend was similar to national anthropogenic emissions. Two industrial sectors, namely chemical raw materials and rubber and plastics, were the main driving force with contributions of 33.85%-51.55% and 7.07%-38.13%, respectively. Among them, the production of chemicals and active pesticide and pharmaceutical ingredients played an important role in contributing to VOCs emissions, while emissions during foam plastics production varied greatly, increasing sharply to more than 2.00×104 tons in 2015-2017. Under the guidance of the relevant national and local policies, emissions from key industries were significantly reduced in Hubei Province.

Multi-type Air Pollutant Emission Inventory of Non-road Mobile Sources in China for the Period 1990-2017

With the increasingly strict supervision of road mobile sources and the ongoing progress in research, the relative contribution of non-road mobile sources to the total emissions has grown in prominence. Therefore, accurately estimating the emissions from non-road mobile sources is essential to developing effective policies for improving air quality. Hence, this study established an inventory of nine air pollutants, viz., CO, NOx, HC, PM2.5, PM10, BC, OC, VOCs, and SO2, emitted by four types of non-road mobile sources, viz., construction machinery, agricultural machinery, vessels, and diesel locomotives, in China for the period 1990–2017. These emissions increased continually from 1990 till 2014, after which they decreased before increasing again during 2017. With the exception of SO2, which primarily originated from vessels, agricultural machinery accounted for the largest proportion of emissions for all of the pollutants, contributing 67.7% (1472.5 Gg) and 46.1% (2170.4 Gg) of the total CO and NOx from non-road mobile sources, respectively. Additionally, the spatial distribution of the investigated pollutants revealed high concentrations on the North China Plain and in the Southeast China coastal area. Our findings demonstrate the necessity of improving the registration system for non-road mobile sources as well as the urgency of obtaining accurate emission factors for them by further researching their emission characteristics.

Contribution of mobile sources to secondary formation of carbonyl compounds

ABSTRACT In the 2014 National Air Toxics Assessment (NATA), the carbonyl compounds formaldehyde and acetaldehyde were identified as key cancer risk drivers and acrolein was identified as one of the three air toxics that drive most of the noncancer risk. In this assessment, averaged across the Continental United States, about 75% of ambient formaldehyde and acetaldehyde, and about 18% of acrolein, is formed secondarily. This study was conducted to estimate the potential contribution to these secondarily formed carbonyl compounds from mobile sources. To develop such estimates, we conducted several CMAQ runs, where emissions are set to zero for different mobile source sectors, to determine their potential contribution. Although zeroing out emissions from an individual sector can offer only a rough approximation of how the sector might contribute to overall secondary concentrations, our results suggest that across the U. S., mobile sources contribute about 6–18% to secondary formaldehyde, 0–10% to secondary acetaldehyde, and 0–70% to secondary acrolein, depending on location. Implications: Photochemical modeling of carbonyl compounds was conducted with emissions set to zero for various mobile source sectors to determine their contribution to secondary concentrations. Results indicated mobile sources contributed to total and secondary concentrations of formaldehyde, acetaldehyde, and acrolein in many locations across the U.S. with acrolein the dominant contributor in some locations. However, biogenic sources dominated secondary formaldehyde and acetaldehyde, and fires dominated secondary acrolein.

Differential analysis of FA-NNC, PCA-MLR, and PMF methods applied in source apportionment of PAHs in street dust.

Many source apportionment models have been applied to identify pollution sources, and differences often exist in the diagnostic results. The reasons causing these differences have not been fully elucidated. In this study, three receptor models, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and factor analysis-nonnegative constraints (FA-NNC), were compared and applied for the analysis of 16 EPA priority polycyclic aromatic hydrocarbons (PAHs) adsorbed in street dust samples from Harbin City (China). The differences in the results were caused by different calculation approaches, including matrix decomposition, variable grouping extraction, and nonnegative constraints, especially between PCA-MLR and the other two models. PCA-MLR has no nonnegative constraints, making PCA-MLR less similar to the real world than the other two. Both PMF and FA-NNC have a nonnegative constraint process, which may be the main reason why their results were much more similar to each other than to those of PCA-MLR. PCA-MLR distinguishes variables into several groups that have the greatest variances from each other, whereas the other two methods find similarities among variables and extract them. In the case study of Harbin City, the contributions of mobile and industrial sources ranged from 47 to 69%, and the contributions of coal and other sources ranged from 30 to 52%. The recognized types of pollution sources were generally equivalent, but the proportional contributions were different. PCA-MLR performed best in calculating contributions, whereas PMF and FA-NNC were better in terms of source diagnosis.

Pollution Characteristics of Water-soluble Inorganic Ions in Chengdu in Summer and Winter

The water-soluble inorganic ions (WSIIs) in PM2.5 and gaseous precursors of Chengdu were continuously observed by a gas and aerosol collector combined with ion chromatography (GAC-IC) in the summer and winter of 2017, and both their pollution characteristics and a typical pollution process in winter were analyzed. It was found that the concentration of PM2.5 in winter (100.2 μg·m-3) was significantly higher than that in summer (34.0 μg·m-3). WSIIs were important components of PM2.5 and their total contributions to PM2.5 were 52.9% and 53.3% in summer and winter, respectively. Secondary ions (SNA) accounted for 73.2% and 87.6% of WSIIs in summer and winter, respectively. SO42- and NO3- dominated the SNA in summer and winter, and the contributions to SNA were 37.7% and 59.7%, respectively. The NO3-/SO42- ratio (2.7) in winter was significantly higher than that in summer (0.8), reflecting the important contribution of mobile sources (especially motor vehicles) to PM2.5 in this season. The diurnal variation of SNA in the two seasons was obviously different due to the differences in sources and meteorological conditions. In winter, with the aggravation of pollution, the concentrations of WSIIs and gaseous precursors increased significantly, and NO3- was the key component in causing heavy pollution. Backward trajectory analysis revealed that the air masses in the two seasons in Chengdu differed significantly from each other. The WSIIs in summer and winter were dominated by SO42- and NO3-, respectively. The short-distance and low-altitude transmission from the east and south of Chengdu contributed significantly to PM2.5 pollution in Chengdu.

Seasonal Variation of Water-soluble Ions in PM2.5 in Xi'an

To explore the seasonal variations and sources of water-soluble ions, PM2.5 samples were collected from 2017 to 2018. Water-soluble ions including SO42-, NO3-, Cl-, F-, Na+, Mg2+, NH4+, K+, and Ca2+ were determined via ion chromatography. Furthermore, the existing form of NH4+, nitrogen oxidation rate (NOR), sulfur oxidation rate (SOR), and [NO3-]/[SO42-] ratio were explored. The results showed that dust, coal combustion, biomass burning, and secondary aerosols were the dominant contributors to water-soluble ions. Ca2+, SO42-, NH4+, and NO3- were the main water-soluble ions in PM2.5 in Xi'an. Correlation analysis results showed that NH4+ could not completely neutralize SO42- in spring; unneutralized SO42- could be mainly combined with K+ and Ca2+. NH4+ mainly existed in the form of ① NH4HSO4 and (NH4)2SO4 in summer; ② NH4HSO4 and NH4NO3 in autumn; and ③ (NH4)2SO4 and NH4NO3 in winter. The yearly mean values of SOR and NOR were 0.35 and 0.16, respectively, indicating a high secondary aerosol transformation rate during the study period. The [NO3-]/[SO42-] ratio showed Xi'an was mainly affected by stationary sources in spring and summer, while the contribution of mobile sources in autumn and winter was greater than stationary sources.

A high spatiotemporal resolution anthropogenic VOC emission inventory for Qingdao City in 2016 and its ozone formation potential analysis

Accurate and gridded emission inventories are crucial for better air quality modeling and air pollution policy making. As the economic center of the Shandong province and part of the first batch of open coastal cities in China, Qingdao has been frequently plagued by volatile organic compound (VOC) pollution along with its rapid economic development and urbanization in recent years. In this study, a high spatiotemporal resolution VOC emission inventory for Qingdao was established for the year 2016 using updated source-specific emission factors and the latest activity data for different sources. The anthropogenic sources considered in this study are classified into seven major sources and 46 subcategories. The results indicated that a total of 151.5 kt of VOCs were emitted in Qingdao in 2016. The three largest emission sources, industrial process, on-road mobile, and solvent use sources accounted for 49.9% 20.1%, and 14.1% of the total VOC emissions, respectively. Spatially, the Huangdao District (57.9 kt), the Chengyang District (15.4 kt), and Pingdu City (15.1 kt) were the top three emitters of VOCs. The high concentration of VOCs was mainly distributed in the central areas of Qingdao city and the emissions distribution was highly consistent with its road network. The maximum monthly VOC emissions occurred in June (14.4 kt) and the minimum monthly VOC emissions occurred mainly in February (9.5 kt). The uncertainties in the emission inventory were quantified using a Monte Carlo simulation provided by the Oracle Crystal Ball software. There exist relatively high uncertainties in the on-road mobile (-84.79%, 252.07%), biomass burning (-65.38%, 134.77%) and industrial process (-36.08%, 133.62%) sources. For the complicated species and different reaction rates of ozone formation, the control of the amount of VOCs emitted and the highly reactive VOCs are equally important. Based on the emissions of individual VOC species and the corresponding maximum incremental reactivity (MIR), the ozone formation potential (OFP) profiles of Qingdao City were constructed. The results indicated that alkenes/alkynes and aromatics contributed the most to the OFP and that m/p-xylene, ethylene, propylene, formaldehyde, toluene, trans-2-butene, cis-2-butene, 1-butene, o-xylene, and 1-pentene were the top 10 individual VOC species contributing to the OFP. The contribution of industrial process and on-road mobile sources accounted for 37.5% and 24.5% of the total OFP in Qingdao City, respectively. Both the large VOC emission and OFP indicate that the focus of VOC emission control should be on industrial process and on-road mobile sources in Qingdao City.

Simulation of organic aerosol formation during the CalNex study: updated mobile emissions and secondary organic aerosol parameterization for intermediate-volatility organic compounds.

We describe simulations using an updated version of the Community Multiscale Air Quality model version 5.3 (CMAQ v5.3) to investigate the contribution of intermediate-volatility organic compounds (IVOCs) to secondary organic aerosol (SOA) formation in southern California during the CalNex study. We first derive a model-ready parameterization for SOA formation from IVOC emissions from mobile sources. To account for SOA formation from both diesel and gasoline sources, the parameterization has six lumped precursor species that resolve both volatility and molecular structure (aromatic versus aliphatic). We also implement new mobile-source emission profiles that quantify all IVOCs based on direct measurements. The profiles have been released in SPECIATE 5.0. By incorporating both comprehensive mobile-source emission profiles for semivolatile organic compounds (SVOCs) and IVOCs and experimentally constrained SOA yields, this CMAQ configuration best represents the contribution of mobile sources to urban and regional ambient organic aerosol (OA). In the Los Angeles region, gasoline sources emit 4 times more non-methane organic gases (NMOGs) than diesel sources, but diesel emits roughly 3 times more IVOCs on an absolute basis. The revised model predicts all mobile sources (including on- and off-road gasoline, aircraft, and on- and off-road diesel) contribute ~ 1 μgm-3 to the daily peak SOA concentration in Pasadena. This represents a ~ 70% increase in predicted daily peak SOA formation compared to the base version of CMAQ. Therefore, IVOCs in mobile-source emissions contribute almost as much SOA as traditional precursors such as single-ring aromatics. However, accounting for these emissions in CMAQ does not reproduce measurements of either ambient SOA or IVOCs. To investigate the potential contribution of other IVOC sources, we performed two exploratory simulations with varying amounts of IVOC emissions from nonmobile sources. To close the mass balance of primary hydrocarbon IVOCs, IVOCs would need to account for 12% of NMOG emissions from nonmobile sources (or equivalently 30.7 t d-1 in the Los Angeles-Pasadena region), a value that is well within the reported range of IVOC content from volatile chemical products. To close the SOA mass balance and also explain the mildly oxygenated IVOCs in Pasadena, an additional 14.8% of nonmobile-source NMOG emissions would need to be IVOCs (assuming SOA yields from the mobile IVOCs apply to nonmobile IVOCs). However, an IVOC-to-NMOG ratio of 26.8% (or equivalently 68.5 t d-1 in the Los Angeles-Pasadena region) for nonmobile sources is likely unrealistically high. Our results highlight the important contribution of IVOCs to SOA production in the Los Angeles region but underscore that other uncertainties must be addressed (multigenerational aging, aqueous chemistry and vapor wall losses) to close the SOA mass balance. This research also highlights the effectiveness of regulations to reduce mobile-source emissions, which have in turn increased the relative importance of other sources, such as volatile chemical products.

Long term trends of chemical constituents and source contributions of PM2.5 in Seoul

PM2.5 was measured and analyzed between 2014 and 2015 in Seoul, and its sources were identified with a positive matrix factorization (PMF) to characterize chemical constituents and sources of the measured PM2.5. To verify policy interventions in reducing PM2.5 levels in Korea, the results were compared with previously published results from 2003 to 2007 at the same study site. A total of 215 PM2.5 samples were collected and analyzed for 24 species, i.e., carbonaceous species (OCEC), ionic species (NO3−, SO42−, and NH4+), and 19 element species in this study. The average PM2.5 mass concentration during the sampling period was 42.6±23.3 μg m−3. The seasonal average mass concentration of PM2.5 was the highest during winter (49.9±20.6 μg m−3), followed by spring (45.2±25.3 μg m−3), fall (34.4±19.3 μg m−3), and summer (28.4±12.5 μg m−3). Nine sources were identified and quantified using the PMF model: secondary nitrate (19.0%), secondary sulfate (20.2%), mobile (23.3%), biomass burning (12.1%), soil (8.3%), roadway emissions (3.1%), aged sea salt (1.0%), coal combustion (4.1%), and oil combustion (9.0%). The PM2.5 levels and chemical constituents during this study were lower than those during the previous study from 2003 to 2007. Particularly, concentrations of mobile related chemicals (OC, EC, and nitrate) and mobile source contributions consistently decreased from 2003 to 2015, indicating that the mobile emission reduction policy is improving PM2.5 levels in the region. The comparison between the two periods allows trends in chemical constituents and the sources of PM2.5 in Seoul to be understood.

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.

MOBILE POLLUTION SOURCES EMISSION FACTORS IN THE TASKS OF AIR QUALITY MANAGEMENT OF LARGE CITIES

Purpose. Increasing the traffic intensity in large cities requires the implementation of plans to improve the air quality in accordance with the Procedure for the implementation of state monitoring in the field of atmospheric air protection. To develop and justify the measures to reduce air pollution and negative impact on the environment and public health in decision-making information systems, it is necessary to process large amounts of available heterogeneous information and use mathematical decision-making models. The paper proposes a mathematical decision-making model for evaluating the effectiveness of air quality management plans in cities with high emissions of mobile pollution sources. Methodology. For air quality management problems in cities, a methodology is used for constructing mathematical models of decision-making under emission parameters uncertainty due to incomplete data on vehicles` emissions and their distribution over the city. The structure of data flows in the information system is considered in accordance with the requirements of modern environmental decision support systems, during which the management bodies have the opportunity to take into account different social and economic criteria. Findings. Analysis of national statistics showed an increase in the contribution of mobile sources to the structure of urban air pollution. Information technologies and optimization models are considered that make it possible to quickly assess the impact of vehicles and their traffic on atmospheric air quality in cities and make strategic decisions on planning measures to improve it. Originality. The structure of an information system and a decision-making model for air quality management are proposed based on the multi-criteria optimization of emission parameters using the construction of “source – receptor” matrix in the network area for modelling air pollution of a city’s territory with motor vehicle emissions. Practical value. The model could be used at the stage of designing municipal environmental monitoring systems and developing plans for improving atmospheric air quality in urban agglomerations.

Black carbon and PM2.5 monitoring campaign on the roadside and residential urban background sites in the city of Tehran

Fine particulate matter characterized as PM2.5 is the most important criteria air pollutant in the city of Tehran. Tehran is one of the most polluted cities of the Middle East based on annual mean PM2.5 concentrations. Tehran emission inventory shows the large contribution of mobile sources to the total particles. PM2.5 source apportionment studies show large fraction of black carbon (BC) in the total mass of PM2.5, especially during the cold seasons. BC is the product of incomplete combustion that is mainly derived from diesel engines and rich-burned gasoline carburetor engines on scooters and light-duty vehicles. The present study shows the results of a large experimental campaign in which BC concentration data along with PM2.5 mass concentration data were collected at various sites in the city of Tehran for a period of a year. The effect of heavy-duty diesel vehicle traffic on fresh BC and on the mass fraction of BC to PM2.5 was observed. Nearly 17% of total mass of PM2.5 was identified as BC. During the night hours the BC/PM2.5 mass ratio was increased to more than 4 times than that of day hours due to heavy-duty diesel traffic restrictions during the day. More detailed analyses based on absorption characteristics of BC showed that by increasing heavy diesel vehicle traffic during the night hours, more fresh BC was collected compared to that of aged BC collected during the day time. A city wide temporary ban of heavy-duty diesel traffic during an air pollution episode resulted in a sudden reduction of BC concentration in Tehran air, while the total mass of PM2.5 remained constant. This shows the direct contribution of diesel fleet to BC. It shows the necessity of introducing more accurate and relevant pollutant indicators such as BC mass instead of total PM2.5 mass for better and more effective policies.

Characteristics of PM2.5 and its chemical constituents in Beijing, Seoul, and Nagasaki

Ambient fine particulate matter (PM2.5) samples were collected from September 2013 to May 2015 in three cities in East Asian countries (Beijing, China; Seoul, South Korea; and Nagasaki, Japan) in order to analyze the spatiotemporal trends of PM2.5 chemical constituents including organic matter (OM), elemental carbon (EC), water-soluble inorganic ions (NO3−, SO42−, and NH4+), and trace elements. The average PM2.5 mass concentration were 125 ± 6.80 μg m−3, 44.6 ± 0.84 μg m−3, and 17.4 ± 0.37 μg m−3 in Beijing, Seoul, and Nagasaki, respectively. Higher carbonaceous concentrations were observed during winter in Beijing and Seoul, while higher concentrations were found during spring in Nagasaki. The highest seasonal averages of organic carbon (OC) to EC ratios were found during spring in Beijing, winter in Seoul, and fall in Nagasaki. The concentrations of secondary OC and its ratio to OC were high during fall and winter. For ion species, NO3− was dominant in Beijing and Seoul, while SO42− was dominant in Nagasaki. Increased contributions of mobile sources in Beijing and Seoul were observed, with higher NO3−/SO42− ratios than those in Nagasaki. Three groups of air masses were found for the three cities using cluster analyses based on 72-h backward trajectories. The cluster from the Bohai economic zone had the highest concentration of PM2.5 for Beijing. For Seoul, a cluster that originated from the Yellow Sea near an industrial area in Liaoning Province and passed through a highly polluted industrial area in southwestern Seoul had high PM2.5 concentrations. A long-range transported cluster that originated in and crossed through heavily industrialized areas in China and South Korea for Nagasaki had higher ion species concentrations. The results of this study are useful to identify the current levels of PM2.5 and its chemical properties to establish a control plan for PM2.5 for Northeast Asia, including China, South Korea, and Japan.