Search

Icelandic dust can impact the radiative budget in high-latitude regions directly by affecting light absorption and scattering and indirectly by changing the surface albedo after dust deposition. This tends to produce a positive radiative forcing. However, the limited knowledge of the spectral optical properties of Icelandic dust prevents an accurate assessment of these radiative effects. Here, the spectral single scattering albedo (SSA) and the complex refractive index (m=n-ik) of Icelandic dust from five major emission hotspots were retrieved between 370–950 nm using online measurements of size distribution and spectral absorption (βabs) and scattering (βsca) coefficients of particles suspended in a large-scale atmospheric simulation chamber. The SSA(λ) estimated from the measured βabs and βsca increased from 0.90–0.94 at 370 nm to 0.94–0.96 at 950 nm in Icelandic dust from the different hotspots, which falls within the range of mineral dust from northern Africa and eastern Asia. The spectral complex refractive index was retrieved by minimizing the differences between the measured βabs and βsca and those computed using the Mie theory for spherical and internally homogeneous particles, using the size distribution data as input. The real part of the complex refractive index (n(λ)) was found to be 1.60–1.61 in the different samples and be independent of wavelength. The imaginary part (k(λ)) was almost constant with wavelength and was found to be around 0.004 at 370 nm and 0.002–0.003 at 950 nm. The estimated complex refractive index was close to the initial estimates based on the mineralogical composition, also suggesting that the high magnetite content observed in Icelandic dust may contribute to its high absorption capacity in the shortwave part of the spectrum. The k(λ) values retrieved for Icelandic dust are at the upper end of the reported range for low-latitude dust (e.g., from the Sahel). Furthermore, Icelandic dust tends to be more absorbing towards the near-infrared. In Icelandic dust, k(λ) between 660–950 nm was 2–8 times higher than most of the dust samples sourced in northern Africa and eastern Asia. This suggests that Icelandic dust may have a stronger positive direct radiative forcing on climate that has not been accounted for in climate predictions.

Calcium is known to be enriched in sea spray aerosols (SSA), but its controlling factors and individual mixing states remain ambiguous. Here, we investigate the impact of various environmental factors on the water-soluble calcium (Ca2+) distribution in SSA through R/V Xuelong cruise observations over the Ross Sea, Antarctica, from December 2017 to February 2018. We observed enhanced Ca2+ enrichment in aerosol samples at lower temperatures (< -3.5 °C), lower wind speeds (< 7 m s-1) and in the presence of sea ice. Further individual particle mass spectral analysis indicated that considerable fractions of calcium in SSA likely bind with organic matter (a single-particle type, OC-Ca), which may be neglected in current water-soluble estimation of Ca2+ enrichment. Also, this is the first time that a calcium-dominated single-particle type has been observed in the Antarctic atmosphere. We suggest that a broader focus on individual OC-Ca and its subsequent environmental behavior should be included in future Antarctic atmospheric modeling. Given the context of global warming and sea ice retreat, an understanding of the mechanisms of calcium enrichment and mixing state of individual particles involved is valuable for further recognizing the aerosol-cloud-climate interactions in the Antarctica summer.

16 pages, 6 figures, 1 table, supplement https://doi.org/10.5194/acp-23-2183-2023-supplement.-- Code and data availability: The code and data used to produce all non-illustrative figures are available from the corresponding authors under reasonable request., The Arctic is a rapidly changing ecosystem, with complex ice–ocean–atmosphere feedbacks. An important process is new particle formation (NPF), from gas-phase precursors, which provides a climate forcing effect. NPF has been studied comprehensively at different sites in the Arctic, ranging from those in the High Arctic and those at Svalbard to those in the continental Arctic, but no harmonised analysis has been performed on all sites simultaneously, with no calculations of key NPF parameters available for some sites. Here, we analyse the formation and growth of new particles from six long-term ground-based stations in the Arctic (Alert, Villum, Tiksi, Zeppelin Mountain, Gruvebadet, and Utqiaġvik). Our analysis of particle formation and growth rates in addition to back-trajectory analysis shows a summertime maxima in the frequency of NPF and particle formation rate at all sites, although the mean frequency and particle formation rates themselves vary greatly between sites, with the highest at Svalbard and lowest in the High Arctic. The summertime growth rate, condensational sinks, and vapour source rates show a slight bias towards the southernmost sites, with vapour source rates varying by around an order of magnitude between the northernmost and southernmost sites. Air masses back-trajectories during NPF at these northernmost sites are associated with large areas of sea ice and snow, whereas events at Svalbard are associated with more sea ice and ocean regions. Events at the southernmost sites are associated with large areas of land and sea ice. These results emphasise how understanding the geographical variation in surface type across the Arctic is key to understanding secondary aerosol sources and providing a harmonised analysis of NPF across the Arctic, This research has been supported by the Ministerio de Economía y Competitividad (grant nos. CTM2017-89117-R, CGL2013-49020-R, and RYC-2012-11922), the Natural Environment Research Council (grant no. NE/S00579X/1), and the National Centre for Atmospheric Science funded by the Natural Environment Research Council. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Heating is a major source of air pollution. To improve air quality, a range of clean heating polices were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating polices on air quality in China using a novel, observation-based causal inference approach. During 2015-2021, winter heating causally increased annual PM2.5, daily maximum 8-h average O3, and SO2 by 4.6, 2.5, and 2.3 μg m-3, respectively. From 2015 to 2021, the impacts of winter heating on PM2.5 in Beijing and surrounding cities (i.e., “2 + 26” cities) decreased by 5.9 μg m-3 (41.3%), whereas that in other northern cities only decreased by 1.2 μg m-3 (12.9%). This demonstrates the effectiveness of stricter clean heating policies on PM2.5 in “2 + 26” cities. Overall, clean heating policies caused the annual PM2.5 in mainland China to reduce by 1.9 μg m-3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.

The Arctic is a rapidly changing ecosystem, with complex ice-ocean-atmosphere feedbacks. An important process is new particle formation (NPF) from gas phase precursors, which provide a climate forcing effect. NPF has been studied comprehensively at different sites in the Arctic ranging from those in the high Arctic, those at Svalbard, and those in the continental Arctic, but no harmonized analysis has been performed on all sites simultaneously, with no calculations of key NPF parameters available for some sites. Here, we analyse the formation and growth of new particles from six long-term ground-based stations in the Arctic (Alert, Villum, Tiksi, Mt. Zeppelin, Gruvebadet. & Utqiagvik). Our analysis of particle formation and growth rates, as well as back trajectory analysis shows summertime maxima in frequency of NPF and particle formation rate at all sites, although the mean frequency and particle formation rates themselves vary greatly between sites, highest at Svalbard, and lowest in the high Arctic. Growth rate, condensational sinks and vapour source rates show a slight bias towards the southernmost sites, with vapour source rates varying by around an order of magnitude between the northernmost and southernmost sites. Air masse back trajectories during NPF at these northernmost sites are associated with large areas of sea ice and snow, whereas events at Svalbard are associated with more sea ice and ocean regions. Events at the southernmost sites are associated with large areas of land, and sea ice. These results emphasize how understanding the geographical variation in surface type across the Arctic is key to understanding secondary aerosol sources, and provide a harmonised analysis of NPF across the Arctic.

10 pages, 3 figures, 1 table, supporting information https://doi.org/10.1021/acs.est.1c07796, Atmospheric aerosols are important drivers of Arctic climate change through aerosol–cloud–climate interactions. However, large uncertainties remain on the sources and processes controlling particle numbers in both fine and coarse modes. Here, we applied a receptor model and an explainable machine learning technique to understand the sources and drivers of particle numbers from 10 nm to 20 μm in Svalbard. Nucleation, biogenic, secondary, anthropogenic, mineral dust, sea salt and blowing snow aerosols and their major environmental drivers were identified. Our results show that the monthly variations in particles are highly size/source dependent and regulated by meteorology. Secondary and nucleation aerosols are the largest contributors to potential cloud condensation nuclei (CCN, particle number with a diameter larger than 40 nm as a proxy) in the Arctic. Nonlinear responses to temperature were found for biogenic, local dust particles and potential CCN, highlighting the importance of melting sea ice and snow. These results indicate that the aerosol factors will respond to rapid Arctic warming differently and in a nonlinear fashion, This research was supported by the Natural Environment Research Council (grant no. NE/S00579X/1) and endorsed by the Surface Ocean-Lower Atmosphere Study (SOLAS), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national ‘lockdown’ periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting., Graphical abstract Image 1

Volatile methyl siloxanes (VMS) have been widely used in personal care products and industrial applications, and are an important component of VOCs (volatile organic compounds) indoors. They have sufficiently long lifetimes to undergo long-range transport and to form secondary aerosols through atmospheric oxidation. To investigate these silicon-containing secondary organic aerosols (Si-SOA), we collected PM

Responding to the 2020 COVID‐19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO2, O3, and PM2.5, respectively., Key Points The Chinese Spring Festival led to a 14.1% decrease in NO2, 6.6% increase in O3 and a mixed effect on PM2.5 across China in 2015–2019The 2020 lockdown resulted in −15.4%, −17.0%, −14.5%, −7.6%, −9.7%, and +24.6% changes for NO2, SO2, CO, PM10, PM2.5, and O3, respectivelyFireworks emissions contributed to haze in many cities such as an additional 29 μg m−3 PM2.5 in Beijing on the first lunar day of 2020

Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown in Tianjin, China. The data also included gaseous pollutants and hourly PM2.5 compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emissions, photonucleation, coal combustion, residential heating and festival emissions. The DN-PMF enhanced the diel patterns of photonucleation and the two traffic factors by enlarging the differences between daytime peak values and nighttime concentrations. The municipal incinerator plant, traffic emissions, and coal combustion have cleaner and more clearly defined directionalities after dispersion normalization. Thus, dispersion normalized PMF is capable of enhancing the source emission patterns. After the COVID-19 lockdown began, PNC of traffic nucleation and traffic emissions decreased by 41% and 44%, respectively, while photonucleation produced more particles likely due to the reduction in the condensation sink. The significant changes in source emissions indicate a substantially reduced traffic volume after the implement of lockdown measures., Graphical abstract Unlabelled Image, Highlights • A dispersion normalized PMF was applied to PNCs dataset. • DN-PMF enhanced the diel emission pattern and directionality of sources. • Traffic emissions decreased by more than 40% after the COVID-19 outbreak.

Volatile organic compounds (VOCs) are largely affected by ambient temperature. However, responses of source-specific VOCs to ambient temperature are poorly understood. Based on one-year (2016.4–2017.3) observations for 57 VOC species (average: 33.4 ± 26 ppbv), and on the use of a k-means cluster technique and positive matrix factorization model, temperature dependence of VOCs and their potential sources were studied. The VOC reactivity was dominated by alkenes, though alkanes contributed the most to the VOCs both by volume and by carbon atoms. The cluster analysis suggested that real-world VOCs were influenced directly and indirectly by temperature. The responses of the VOCs in terms of volume and carbon atoms to temperature exhibited V-shape patterns, and the inflection points occurred when temperatures were around 15∼25∘C. Nevertheless, the VOC reactivity generally increased with increasing temperature, suggesting that temperature could be used as a proxy for VOC reactivity. The source apportionment study revealed that vehicle emissions (44.8%) contributed the most to ambient VOCs, and followed by natural gas (NG)/liquefied petroleum gas (LPG)/Combustion (24.9%), petrochemical industry (9.6%), other industrial process (8.4%), solvent use (7.4%), and biogenic emission (4.9%). Solvent use and biogenic emissions were the greatest contributors to VOC reactivity. Unexpectedly high propane/ethylene ratios were observed at low temperatures. This might be associated with domestic heating-related emissions from NG/LPG sources because their contributions generally increased with the decreasing temperature. By comparison with ∼25 ∘C, the VOC concentrations and ozone formation potentials contributed by vehicles were respectively 7- and 2.6- times greater at ∼0 ∘C (cold engine starts dominance), and were 50.4% and 57.5% greater at ∼35 ∘C (gasoline evaporation dominance). Local emission controls for VOCs were beneficial for alleviating atmospheric secondary pollution because the potential source-areas of VOCs were mostly distributed in the geographically flat North China Plain. However, alkanes-dominated VOC sources (e.g., vehicle emission, NG/LPG) could experience long-distance air transport bacause of their relatively long atmospheric lifetime. The results highlight that the temperature dependences of VOCs depend on the emission sources, and this is of great value in understanding the linkage between meteorology and air quality.

The size distribution of a particle population has proven to be of great value in source apportionment studies. Previous studies have reported that particle size distributions in ambient urban air vary with location and time, indicating that the ambient particles are affected by known sources. However, the size distributions of individual chemical species emitted from primary sources have received relatively little attention. This study presents a preliminary real-world investigation of the size distributions of particles emissions from coal-fired power plants, residential coal combustion, biomass burning, vehicles, soil dust and steelworks based on a high size-resolution measurement using an Electrical Low Pressure Impactor. The size profiles of individual chemical elements are found to have a high degree of source specificity, which assists source classification. We subsequently employed a chemical mass balance approach termed size-resolved CMB (SR-CMB), which is based on the use of both physical and chemical information on the high size-resolution measurements of particulate matter. By applying SR-CMB on the measured ambient data, source contributions to ambient particles were successfully obtained when copper (Cu) was chosen as the fitting species. The approach described in this paper enhances the capability of the CMB model and provides a useful tool to validate results of traditional receptor models when a high size-resolution measurement is available. This effort also highlights that the size profiles of sources is of potential concern in the situations when the chemically collinear sources exist.

Sulfate is a major component of atmospheric fine particulate matter in the troposphere. Globally, observed sulfate concentrations are generally high in summer and low in winter. Current air quality models fail to reproduce high winter sulfate concentrations observed during substantial pollution episodes in northern China. From ambient measurement data, it was reported that contrary to current belief, sulfate, that is normally considered as a secondary species, was actually largely primary. The present study presents direct evidence of primary sulfate emissions from residential coal combustion (RCC) by testing particulate matter (PM) emissions from briquettes and chunk coal burned in a household stove. Sulfate accounted for 16.6 ± 7.7% and 29.2 ± 8.7% of PM2.5 mass emissions from this stove burning coal briquettes and chunk coal, respectively. Using the 2014 emissions inventory, 1215 tons of PM10 sulfate were estimated to have been emitted from RCC in Xi'an, a megacity that continually experiences severe PM pollution in northern China. The RCC contribution was also estimated using a constrained positive matrix factorization in which RCC accounted for averages of 26% and 32% of ambient PM2.5 mass during the heating season at urban and rural sites in Xi'an, respectively. The winter contributions were higher than that during non-heating season. PM2.5 emissions from RCC during heating season were 1.5–4.0 times those during non-heating season for the urban sites, and 2.5 to 6.8 times those for the rural sites. Thus, severe PM pollution in rural areas resulted from intense household coal combustion for space heating. Primary sulfate from RCC was responsible for a large fraction of ambient sulfate during heating season, on average contributing 38.9% and 49.2% of ambient sulfate mass in PM2.5 at urban sites and rural sites, respectively. These results highlight the fact that RCC is an important primary source of atmospheric sulfate during the heating season, and provides information that will improve air quality models and support development of more effective abatement strategies for atmospheric particles.

In Xi'an, a city that frequently experiences serious PM pollution in northern China, 1476 PM10 and 1464 PM2.5 valid daily filter samples were collected at six sites from December 2014 to November 2015 and analyzed for 29 species. The annual mean PM10 and PM2.5 concentrations were 149.4 ± 93.1, 108.0 ± 70.9 μg/m3, respectively. Organic carbon (OC) is the predominant PM2.5 component while crustal material predominated in PM10. Sulfate concentrations, which was the largest component in Xi'an PM in previous studies, were lower than nitrate. Winter sulfate, OC, and elemental carbon (EC) have decreased since 2003, while nitrate remained constant in recent years and the ratio of NO3−/SO42− increased from 0.4 in 2006 to 1.3 in 2014. This result suggests that the motor vehicle contribution to PM has increased relative to coal-fired power plant emissions over the past decade. The mass fractions of crustal material, sulfate, and EC in PM2.5 decreased as the PM2.5 concentrations increased from “clean” days (

19 pages, 8 figures,1 table, 1 appendix.-- Data availability: The APS data can be accessed from https://doi.org/10.5281/zenodo.3961473 (Traversi et al., 2020). The absorption coefficient data are available upon request from Gilardoni et al. (2020). Data supporting this publication can be accessed upon request from the corresponding authors, Understanding aerosol–cloud–climate interactions in the Arctic is key to predicting the climate in this rapidly changing region. Whilst many studies have focused on submicrometer aerosol (diameter less than 1 µm), relatively little is known about the supermicrometer aerosol (diameter above 1 µm). Here, we present a cluster analysis of multiyear (2015–2019) aerodynamic volume size distributions, with diameter ranging from 0.5 to 20 µm, measured continuously at the Gruvebadet Observatory in the Svalbard archipelago. Together with aerosol chemical composition data from several online and offline measurements, we apportioned the occurrence of the coarse-mode aerosols during the study period (mainly from March to October) to anthropogenic (two sources, 27 %) and natural (three sources, 73 %) origins. Specifically, two clusters are related to Arctic haze with high levels of black carbon, sulfate and accumulation mode (0.1–1 µm) aerosol. The first cluster (9 %) is attributed to ammonium sulfate-rich Arctic haze particles, whereas the second one (18 %) is attributed to larger-mode aerosol mixed with sea salt. The three natural aerosol clusters were open-ocean sea spray aerosol (34 %), mineral dust (7 %) and an unidentified source of sea spray-related aerosol (32 %). The results suggest that sea-spray-related aerosol in polar regions may be more complex than previously thought due to short- and long-distance origins and mixtures with Arctic haze, biogenic and likely blowing snow aerosols. Studying supermicrometer natural aerosol in the Arctic is imperative for understanding the impacts of changing natural processes on Arctic aeroso, This research has been supported by the Natural Environment Research Council (grant no. NE/S00579X/1). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Air pollution in megacities represents one of the greatest environmental challenges. Our observed results show that the dramatic NOx decrease (77%) led to significant O3 increases (a factor of 2) during the COVID‐19 lockdown in megacity Hangzhou, China. Model simulations further demonstrate large increases of daytime OH and HO2 radicals and nighttime NO3 radical, which can promote the gas‐phase reaction and nocturnal multiphase chemistry. Therefore, enhanced NO3 − and SO4 2− formation was observed during the COVID‐19 lockdown because of the enhanced oxidizing capacity. The PM2.5 decrease was only partially offset by enhanced aerosol formation with its reduction reaching 50%. In particular, NO3 − decreased largely by 68%. PM2.5 chemical analysis reveals that vehicular emissions mainly contributed to PM2.5 under normal conditions in Hangzhou. Whereas, stationary sources dominated the residual PM2.5 during the COVID‐19 lockdown. This study provides evidence that large reductions in vehicular emissions can effectively mitigate air pollution in megacities., Key Points Air pollutants PM10, PM2.5, NOx, CO, and SO2 decreased whereas O3 increased during the COVID‐19 lockdown in megacity HangzhouEnhanced NO3 − and SO4 2− formation caused by the enhanced oxidizing capacity partially offset the decrease of PM2.5 in megacity HangzhouThe contribution of vehicular emissions to PM2.5 was over stationary sources under normal conditions in megacity Hangzhou

True air quality improvements during the COVID-19 lockdowns in global cities are more limited than we thought., The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (Ox = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

PM2.5 in Shijiazhuang was collected from October 15, 2018 to January 31, 2019, and selected toxic elements were measured. Five typical haze episodes were chosen to analyze the health risks and critical risk sources. Toxic elements during the haze episodes accounted for 0.33% of PM2.5 mass. Non-cancer risk of toxic elements for children was 1.8 times higher than that for adults during the haze episodes, while cancer risk for adults was 2.5 times higher than that for children; cancer and non-cancer risks were primarily attributable to As and Mn, respectively. Health risks of toxic elements increased during the growth and stable periods of haze episodes. Non-cancer and cancer risks of toxic elements during the haze stable periods were higher than other haze stages, and higher for children than for adults during the stable period. Mn was the largest contributor to non-cancer risk during different haze stages, while As was the largest contributor to cancer risk. Crustal dust, vehicle emissions, and industrial emissions were critical sources of cancer risk during the clean-air periods; while vehicle emissions, coal combustion, and crustal dust were key sources of cancer risk during the haze episodes. Cancer risks of crustal dust and vehicle emissions during the haze episodes were 2.0 and 1.7 times higher than those in the clean-air periods. Non-cancer risks from emission sources were not found during different periods. Cancer risks of biomass burning and coal combustion increased rapidly during the haze growth period, while that of coal combustion decreased sharply during the dissipation period. Vehicle emissions, crustal dust, and coal combustion were significant cancer risk sources during different haze stages, cancer risk of each source was the highest during the stable period. Southern Hebei, Northern and central Shaanxi were potential risk regions that affected the health of both adults and children in Shijiazhuang.

Volatile methyl siloxanes (VMS) have been widely used in personal care products and industrial applications, and are an important component of VOCs (volatile organic compounds) indoors. They have sufficiently long lifetimes to undergo long-range transport and to form secondary aerosols through atmospheric oxidation. To investigate these silicon-containing secondary organic aerosols (Si-SOA), we collected PM2.5 samples during 8th-21st August 2018 (summer) and 3rd-23rd January 2019 (winter) at an urban site of Beijing. As the oxidation of VMS mainly results in hydrophilic polar semi-volatile and non-volatile oxidation products, the differences between total water-soluble Si and total water-soluble inorganic Si were used to estimate water-soluble organic Si, considered to be secondary organic Si (SO–Si). The average concentrations of SO–Si during the summer and winter campaigns were 4.6 ± 3.7 and 13.2 ± 8.6 ng m−3, accounting for approximately 80.1 ± 10.1% and 80.2 ± 8.7% of the total water-soluble Si, and 1.2 ± 1.2% and 5.0 ± 6.9% of total Si in PM2.5, respectively. The estimated Si-SOA concentrations were 12.7 ± 10.2 ng m−3 and 36.6 ± 23.9 ng m−3 on average in summer and winter, which accounted for 0.06 ± 0.07% and 0.16 ± 0.22% of PM2.5 mass, but increased to 0.26% and 0.92% on certain days. We found that net solar radiation is positively correlated with SO–Si levels in the summer but not in winter, suggesting seasonally different formation mechanisms.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the Coronavirus Disease 2019 (COVID‐19) pandemic led to a major reduction in human activity during the past 30 years. Here, the concentration of BC in the urban, urban‐industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multiwavelength Aethalometer to estimate the impact of the COVID‐19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 to 1.29 μg/m3 following the COVID‐19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou., Key Points BC concentrations during the COVID‐lockdown were reduced by 44% in a megacity of ChinaBC from fossil fuels has the largest reduction in urban area during the COVID‐lockdownVehicles and industrial activities are the major contributors of BC in the megacity, followed by and biomass burning

Samples of ambient PM

Vehicular non-exhaust emissions account for a significant share of atmospheric particulate matter (PM) pollution, but few studies have successfully quantified the contribution of non-exhaust emissions via real-world measurements. Here, we conduct a comprehensive study combining tunnel measurements, laboratory dynamometer and resuspension experiments, and chemical mass balance modeling to obtain source profiles, real-world emission factors (EFs), and inventories of vehicular non-exhaust PM emissions in Chinese megacities. The average vehicular PM2.5 and PM10 EFs measured in the four tunnels in four megacities (i.e., Beijing, Tianjin, Zhengzhou, and Qingdao) range from 8.8 to 16.0 mg km−1 veh−1 and from 37.4 to 63.9 mg km−1 veh−1, respectively. A two-step source apportionment is performed with the information of key tracers and localized profiles of each exhaust and non-exhaust source. Results show that the reconstructed PM10 emissions embody 51–64% soil and cement dust, 26–40% tailpipe exhaust, 7–9% tire wear, and 1–3% brake wear, while PM2.5 emissions are mainly composed of 59–80% tailpipe exhaust, 11–31% soil and cement dust, 4–10% tire wear, and 1–5% brake wear. Fleet composition, road gradient, and pavement roughness are essential factors in determining on-road non-exhaust emissions. Based on the EFs and the results of source apportionment, we estimate that the road dust, tire wear, and brake wear emit 8.1, 2.5, and 0.8 Gg year−1 PM2.5 in China, respectively. Our study highlights the importance of non-exhaust emissions in China, which is essential to assess their impacts on air quality, human health, and climate and formulating effective controlling measures.

With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM2.5 during the heating period was 96 μg/m3. Organic carbon (OC) and NO3- were found to be the dominant species of PM2.5 during the study. A high correlation was detected between elemental carbon (EC) and NO3– on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM2.5. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model.

The multi-scale chemical characteristics and source apportionment of volatile organic compounds (VOCs) were analysed in Tianjin, China, using 1-hr resolution VOC-species data between November 1, 2018 and March 15, 2019. The average total VOC (TVOC) concentration was 30.6 ppbv during the heating season. The alkanes accounted for highest proportion of the TVOC, while the alkenes were the predominant species forming ozone, especially ethylene. Compared to the clean period, the concentration of acetylene during the haze events showed highest increase rate, followed by the ethane; and the concentrations and proportions of alkanes and alkenes were highest during the growth stage (GS) of haze events. The multi-scale apportionment results suggested petrochemical industry and solvent usage (PI/SU, 31.2%), vehicle emissions and liquefied petroleum gas (VE/LPG, 20.5%), and combustion emissions (CE, 19.1%) were the main VOC sources during the heating season. Compared to the clean period, the contributions of PI/SU, VE/LPG, CE, and refinery emissions notably increased during the haze events, while that of gasoline evaporation decreased. The contributions of PI/SU and RPI showed significantly increase during the GS of haze events, whereas most sources decreased during the dissipation stage of haze events. Diurnal-variations in source contributions during the haze events were clearer than the clean period, and the contributions of PI/SU, VE/LPG, and CE during the haze events were markedly higher at night. These findings provide valuable information to inform effective VOC control and prevention measures with specific relevance for the control of ozone pollution in Tianjin.

The chemical species in PM

Potential health benefits from improved ambient air quality during the COVID-19 shutdown have been recently reported and discussed. Despite the shutdown measures being in place, northern China still suffered severe haze episodes (HE) that are not yet fully understood, particularly how the source emissions changed. Thus, the meteorological conditions and source emissions in processing five HEs occurred in Beijing-Tianjin-Hebei area were investigated by analyzing a comprehensive real-time measurement dataset including air quality data, particle physics, optical properties, chemistry, aerosol lidar remote sensing, and meteorology. Three HEs recorded before the shutdown began were related to accumulated primary pollutants and secondary aerosol formation under unfavorable dispersion conditions. The common "business as usual" emissions from local primary sources in this highly polluted area exceeded the wintertime atmospheric diffusive capacity to disperse them. Thus, an intensive haze formed under these adverse meteorological conditions such as in the first HE, with coal combustion to be the predominant source. Positive responses to the shutdown measures were demonstrated by reduced contributions from traffic and dust during the final two HEs that overlapped the Spring and Lantern Festivals, respectively. Local meteorological dispersion during the Spring Festival was the poorest among the five HEs. Increased residential burning plus fireworks emissions contributed to the elevated PM2.5 with the potential of enhancing the HEs. Our results highlight that reductions from shutdown measures alone do not prevent the occurrence of HEs. To further reduce air pollution and thus improve public health, abatement strategies with an emphasis on residential burning are needed.

To investigate the interannual variations of particulate matter (PM) pollution in winter, this paper examines the pollution characteristics of PM with aerodynamic diameters of less than 2.5 and 10 μm (i.e., PM2.5 and PM10), and their relationship to meteorological conditions over the Beijing municipality, Tianjin municipality, and Hebei Province—an area called Jing–Jin–Ji (JJJ, hereinafter)—in December 2013–16. The meteorological conditions during this period are also analyzed. The regional average concentrations of PM2.5 (PM10) over the JJJ area during this period were 148.6 (236.4), 100.1 (166.4), 140.5 (204.5), and 141.7 (203.1) μg m–3, respectively. The high occurrence frequencies of cold air outbreaks, a strong Siberian high, high wind speeds and boundary layer height, and low temperature and relative humidity, were direct meteorological causes of the low PM concentration in December 2014. A combined analysis of PM pollution and meteorological conditions implied that control measures have resulted in an effective improvement in air quality. Using the same emissions inventory in December 2013–16, a modeling analysis showed emissions of PM2.5 to decrease by 12.7%, 8.6%, and 8.3% in December 2014, 2015, and 2016, respectively, each compared with the previous year, over the JJJ area.

In recent years, China has experienced severe and persistent air pollution associated with rapid urbanization and climate change. Three years' time series (January 2014 to December 2016) concentrations data of air pollutants including particulate matter (PM2.5 and PM10) and gaseous pollutants (SO2, NO2, CO, and O3) from over 1300 national air quality monitoring sites were studied to understand the severity of China's air pollution. In 2014 (2015, 2016), annual population-weighted-average (PWA) values in China were 65.8 (55.0, 50.7) μg m−3 for PM2.5, 107.8 (91.1, 85.7) μg m−3 for PM10, 54.8 (56.2, 57.2) μg m−3 for O3_8 h, 39.6 (33.3, 33.4) μg m−3 for NO2, 34.1 (26, 21.9) μg m−3 for SO2, 1.2 (1.1, 1.1) mg m−3 for CO, and 0.60 (0.59, 0.58) for PM2.5/PM10, respectively. In 2014 (2015, 2016), 7% (14%, 19%), 17% (27%, 34%), 51% (67%, 70%) and 88% (97%, 98%) of the population in China lived in areas that meet the level of annual PM2.5, PM10, NO2, and SO2 standard metrics from Chinese Ambient Air Quality Standards-Grade II. The annual PWA concentrations of PM2.5, PM10, O3_8 h, NO2, SO2, CO in the Northern China are about 40.4%, 58.9%, 5.9%, 24.6%, 96.7%, and 38.1% higher than those in Southern China, respectively. Though the air quality has been improving recent years, PM2.5 pollution in wintertime is worsening, especially in the Northern China. The complex air pollution caused by PM and O3 (the third frequent major pollutant) is an emerging problem that threatens the public health, especially in Chinese mega-city clusters. NOx controls were more beneficial than SO2 controls for improvement of annual PM air quality in the northern China, central, and southwest regions. Future epidemiologic studies are urgently required to estimate the health impacts associated with multi-pollutants exposure, and revise more scientific air quality index standards.

In China, over 1.3 billion people have high health risks associated with exposure to ambient fine particulate matter (PM2.5) that exceeds the World Health Organization (WHO) Air Quality Guidelines (AQG). The PM2.5 mass concentrations from 1382 national air quality monitoring stations in 367 cities, between January 2014 and December 2016, were analyzed to estimate the health burden attributable to ambient PM2.5 across China. The integrated exposure-response model was applied to estimate the relative risks of disease-specific mortality. Disease-specific mortality baselines in province-level administrative units were adjusted by the national mortality baseline to better reveal the spatial inequality of the health burden associated with PM2.5. Our study suggested that PM2.5 in 2015 contributed as much as 40.3% to total stroke deaths, 33.1% to acute lower respiratory infection (ALRI

Nitrate ( NO 3 − ) has become recognized as the most important water-soluble ion in fine particulate (PM2.5), and has been proposed as a driving factor for regional haze formation. However, nitrate formation mechanisms are still poorly understood. In this study, PM2.5 samples were collected from September 2017 to August 2018 in Shijiazhuang, a city located on the North China Plain, and NO 3 − concentration, δ18O- NO 3 − and δ15N- NO 3 − values in PM2.5 were analyzed. NO 3 − concentrations increased as PM2.5 levels increased during both polluted and non-polluted days over the entire year. δ18O- NO 3 − values during cold months (63.5–103‰) were higher than those during warm months (50.3–85.4‰), these results suggested that the nitrate formation pathways shifted from the NO2 + OH (POH) in warm months to the N2O5 + H2O (PN2O5) and NO3 + VOCs (PNO3) pathways in cold months. Especially during cold months, δ18O- NO 3 − values increased from 65.2–79.9‰ to 80.7–96.2‰ when PM2.5 increased from ∼25 to >100 μg/m3, but when PM2.5 > 100 μg/m3, there were relatively small variations in δ18O- NO 3 − . These results suggested that nitrate formation pathways changed from POH to PN2O5 and PNO3 pathways when PM2.5 100 μg/m3. Higher δ15N- NO 3 − values in warm months (−11.8–13.8‰) than in cold months (−0.7–22.6‰) may be attributed to differences in NOx emission sources and nitrogen isotopic fractionation among NOx and NO 3 − . These results provide information on the dual isotopic compositions of nitrate to understand nitrate formation pathways under different PM2.5 levels.

Samples of ambient PM2.5 were collected in the Qingdao harbor area between 21 March and May 25, 2016, and analyzed to investigate the compositions and sources of PM2.5 and to assess source-specific selected toxic element health risks to workers via a combination of positive matrix factorization (PMF) and health risk (HR) assessment models. The mean concentration of PM2.5 in harbor area was 48 μg m−3 with organic matter (OM) dominating its mass. Zn and V concentrations were significantly higher than the other selected toxic elements. The hazard index (HI) and cancer risk (Ri) of all selected toxic elements were lower than the United States Environmental Protection Agency (USEPA) limits. There were no non-cancer and cancer risks for workers in harbor area. The contributions from industrial emissions (IE), ship emissions (SE), vehicle emissions (VE), and crustal dust and coal combustion (CDCC) to selected toxic elements were 39.0%, 12.8%, 24.0%, and 23.0%, respectively. The HI values of selected toxic elements from IE, CDCC, SE, and VE were 1.85 × 10−1, 7.08 × 10−2, 6.36 × 10−2, and 3.37 × 10−2, respectively; these are lower than the USEPA limits. The total cancer risk (Rt) value from selected toxic elements in CDCC was 2.04 × 10−7, followed by IE (6.40 × 10−8), SE (2.26 × 10−8), and VE (2.18 × 10−8). CDCC and IE were the likely sources of cancer risk in harbor area. The Bo Sea and coast were identified as the likely source areas for health risks from IE via potential source contribution function (PSCF) analysis based on the results of PMF-HR modelling. Although the source-specific health risks were below the recommended limit values, this work illustrates how toxic species in PM2.5 health risks can be associated with sources such that control measures could be undertaken if the risks warranted it.

Given the inconsistency of epidemiologic evidence for associations between maternal exposures to traffic-related metrics and adverse birth outcomes, this manuscript aims to provide clarity on this topic. Pooled meta-estimates were calculated using random-effects analyses. Subgroup analyses were conducted by study area, study design, and Newcastle-Ottawa quality score (NOS). Funnel plots and Egger's test were conducted to evaluate the publication bias, and Fail-safe Numbers (Fail-safe N) were measured to evaluate the robustness of models. From the initial 740 studies (last search, July 11, 2019), 26 studies were included in our analysis. The pooled odds ratio for the change in small for gestational age associated with per 500 m decrease in the distance to roads was 1.016 (95% CI: 1.004, 1.029). Subgroup analyses revealed significant positive associations between term low birth weight and traffic density in higher-quality literatures with higher NOS [1.060 (95% CI: 1.002, 1.121)], cohort studies [1.020 (95% CI: 1.006, 1.033)], and studies in North America [1.018 (95% CI: 1.005, 1.131)]. The buffer of traffic density made no difference in the effect size. Traffic density seemed to be a better indicator of traffic pollution than the distance to roads.

The chemical species in PM2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM2.5 was 124 μg/m3 during the heating period, and NO3− and organic carbon (OC) were the dominant species. The concentrations and percentages of NO3−, SO42−, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.

Gasoline vehicles (GVs) emissions generally dominate ambient volatile organic compounds (VOCs) in urban areas, while VOC emissions from liquefied natural gas (LNG)-fueled vehicles play an increasingly important role in urban air quality, due to fuel transition from gasoline/diesel to alternative fuels. Here, an extensive dataset of VOC samples collected in three urban tunnels in China was used to explore real-world emission characteristics of ninety-nine VOC species from both GVs and LNG-fueled vehicles. The fleets in the Beijing and Tianjin tunnels comprised > 94% GVs whereas the fleet in the Nanjing tunnel comprised both GVs ( ∼ 87%) and LNG-buses ( ∼ 13%). The VOC emission factors (EFs) in the Beijing tunnel and Tianjin tunnel were highly correlated, implying that they can be applied as existing emission datasets from GVs with aim of distinguishing emissions from LNG-fueled vehicles and GVs in the Nanjing tunnel. For fleet emissions, the average VOC EFs have declined substantially over the last decade; the relative compositions of benzene, toluene, and ethylbenzene were quite stable despite differences in fleet composition. Ethylene, isopentane, ethane, and toluene; and ethane and propane were enriched in VOC emissions ( v ∕ v ) from GVs and LNG-fueled vehicles, respectively. Methyl t e r t -butyl ether, 2,2,4-trimethylpentane, 2,3,4-trimethylpentane, 3-methylpentane, and methylcyclopentane were potential VOC tracers for GVs. Ethane, propane, and 2,3-dimethylbutane were key tracers that distinguished LNG-fueled vehicles from GVs. Propane, isobutane, and n-butane were key VOC tracers that distinguished liquefied petroleum gas-fueled vehicles from GVs. Alkanes dominated fleet emissions both by mass and by volume. However, aromatics and alkenes (mainly ethylene and propylene) dominated VOC reactivity from gasoline- and LNG-fueled vehicles, respectively. Our study highlights that the wide discrepancy in fleet VOC emissions could be attributed to fleet compositions.

With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM2.5 during the heating period was 96 μg/m3. Organic carbon (OC) and NO3− were found to be the dominant species of PM2.5 during the study. A high correlation was detected between elemental carbon (EC) and NO3− on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM2.5. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model.

Vehicular emission is a key contributor to ambient volatile organic compounds (VOCs) and NOx in Chinese megacities. However, the information of real-world emission factors (EFs) for a typical urban fleet is still limited, hindering the development of a more reliable emission inventory in China. Based on a more-than-two-week (August 8–24, 2017) tunnel test in urban Tianjin in northern China, and on the use of a statistical regression model, the Positive Matrix Factorization (PMF) receptor model, and the Calculate Emissions from Road Transport (COPERT) IV model, characteristics of vehicular VOCs-NOx-CO emissions were analyzed systematically. The fleet-average EFs (pollutant: downslope, upslope, and overall in mg km−1 veh−1) were estimated respectively as follows: (NO: 61.92 ± 72.46, 158.58 ± 73.48, 97.52 ± 69.84), (NO2: 16.52 ± 11.49, 23.98 ± 20.14, 15.86 ± 9.38), (NOx: 79.45 ± 78.43, 181.22 ± 88.29, 116.56 ± 77.61), and (CO: 269.96 ± 342.38, 577.76 ± 382.22, 344.67 ± 250.01). The EFs of NO-NO2-NOx and CO from heavy-duty vehicles (or diesel vehicles) were differentiated from light-duty vehicles (or gasoline vehicles). The ratios (v / v) of NO2 to NOx in the primary vehicular exhaust were approximately 0.18 ± 0.09, 0.10 ± 0.22 and 0.10 ± 0.05 for downslope, upslope, and the entire tunnel, respectively. The fleet-average EF of the 99-target non-methane VOCs (NMVOCs) was 40.56 ± 12.18 mg km−1 veh−1, lower than the previous studies in China. The BTEX (benzene, toluene, ethylbenzene, p-xylene, m-xylene and o-xylene) levels decreased by approximately 79 % when emission standards increased from China I to China V. The source profiles of NMVOCs from the tailpipe and evaporative emissions were resolved by the PMF model. The evaporative emissions accounted for nearly one-half of the total vehicular VOC emissions, indicating that evaporative and tailpipe emissions contributed equally to VOC emissions. The relative contributions of evaporative NMVOC emissions to total vehicular NMVOC emissions are temperature-dependent with the average increasing ratio of 7.55 % °C−1. The primary emission ratio (ER, m / m) of VOCs / NOx was approximately 2.04, suggesting that vehicular NOx and VOCs can be co-emitted with a proper ER. According to the vehicular ERs of VOCs / NOx in Tianjin (2000–2016) and China (2010–2030), as even more stringent emission standards are implemented in the future, the O3 chemical regimes were likely to be VOCs-limited (i.e., 8 : 1 threshold) for cities or regions where VOCs and NOx emissions are dominated by vehicular exhaust. Our study enriched the database on the fleet-average emission factors of on-road vehicles for emission inventory, air quality modeling, and health effects studies, provided implications for following O3 control in China from the view of primary emission, and highlighted the importance of further control of evaporative emissions.

The relative importance of contributions of gasoline vehicles (GVs) and diesel vehicles (DVs), heavy-duty diesel vehicles (HDDVs) and non-HDDVs to on-road vehicle emissions remains unclear. Vehicle emission factors (EFs), including fine particulate matter (PM2.5), NO-NO2-NOx, and carbon monoxide (CO), were measured (August 4-18, 2017) in an urban tunnel in Tianjin, northern China. The average EFs (mg km-1 veh-1) of the fleet were as follows: 9.21 (95% confidence interval: 1.60, 23.07) for PM2.5, 62.08 (21.21, 138.25) for NO, 20.42 (0.79, 45.48) for NO2, 83.72 (26.29, 162.87) for NOx, and 284.54 (18.22, 564.67) for CO. The fleet-average EFs exhibited diurnal variations, due to diurnal variations in the proportion of HDDVs in the fleet, though the hourly proportion of HDDVs never exceeded 10% during the study period. The reconstructed average EFs for on-road vehicle emissions of PM2.5, NO, NO2, and NOx, and CO were approximately 2.2, 1.7, 1.5, 2.0, and 1.6 times as much as those in the tunnel, respectively, due to the higher HDDV fractions in the whole city than those in the tunnel. The EFs of PM2.5, NO, NO2, and NOx, and CO from each HDDV were approximately 75, 81, 24, 65, and 33 times of those from each non-HDDV, respectively. HDDVs were responsible for approximately 81.92%, 83.02%, 59.79%, 79.79%, and 66.77% of the total PM2.5, NO, NO2, and NOx, and CO emissions from on-road vehicles in Tianjin, respectively. DVs, especially HDDVs, are major sources of on-road PM2.5, NO-NO2-NOx, and CO emissions in northern China. The contribution of HDDVs to fleet emissions calculated by the EFs from Chinese 'on-road vehicle emission inventory guidebook' were underestimated, as compared to our results. The EFs from on-road vehicles should be updated due to the rapid progression of vehicle technology combined with emission standards in China. The management and control of HDDV emissions have become urgent to reduction of on-road vehicle emissions.

In Xi'an, a city that frequently experiences serious PM pollution in northern China, 1476 PM