Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference.

Heating is a major source of air pollution. To improve air quality, a range of clean heating policies were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating policies 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.

Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning.

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.

Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities.

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.

Chemistry of Atmospheric Fine Particles During the COVID-19 Pandemic in a Megacity of Eastern China.

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.

Variation in Concentration and Sources of Black Carbon in a Megacity of China During the COVID-19 Pandemic.

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.

Traffic-related metrics and adverse birth outcomes: A systematic review and meta-analysis.

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.

PM2.5-bound silicon-containing secondary organic aerosols (Si-SOA) in Beijing ambient air.

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.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK.

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.

Health risks of inhaled selected toxic elements during the haze episodes in Shijiazhuang, China: Insight into critical risk sources.

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.

Potential health risks of inhaled toxic elements and risk sources during different COVID-19 lockdown stages in Linfen, China.

Levels of toxic elements in ambient PM2.5 were measured from 29 October 2019 to 30 March 2020 in Linfen, China, to assess the health risks they posed and to identify critical risk sources during different periods of the COVID-19 lockdown and haze episodes using positive matrix factorization (PMF) and a health-risk assessment model. The mean PM2.5 concentration during the study period was 145 µg/m3, and the 10 investigated toxic elements accounted for 0.31% of the PM2.5 mass. The total non-cancer risk (HI) and total cancer risk (TCR) of the selected toxic elements exceed the US EPA limits for children and adults. The HI for children was 2.3 times that for adults for all periods, which is likely due to the high inhalation rate per unit body weight for children. While the TCR for adults was 1.7 times that of children, which is mainly attributed to potential longer exposure duration for adults. The HI and TCR of the toxic elements during full lockdown were reduced by 66% and 58%, respectively, compared to their pre-lockdown levels. The HI and TCR were primarily attributable to Mn and As, respectively. Health risks during haze episodes were significantly higher than the average levels during COVID-19 lockdowns, though the HI and TCR of the selected toxic elements during full-lockdown haze episodes were 68% and 17% lower, respectively, than were the levels during pre-lockdown haze episodes. During the study period, fugitive dust and steel-related smelting were the highest contributors to HI and TCR, respectively, and decreased in these emission sources contributed the most to the lower health risks observed during the full lockdown. There, the control of these sources is critical to effectively reduce public health risks.

Changes in nitrate accumulation mechanisms as PM2.5 levels increase on the North China Plain: A perspective from the dual isotopic compositions of nitrate.

Nitrate (NO3-) 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 NO3-concentration, δ18O-NO3- and δ15N-NO3- values in PM2.5 were analyzed. NO3- concentrations increased as PM2.5 levels increased during both polluted and non-polluted days over the entire year. δ18O-NO3- 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-NO3- 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-NO3-. These results suggested that nitrate formation pathways changed from POH to PN2O5 and PNO3 pathways when PM2.5 < 100 µg/m3, but that PN2O5 and PNO3 dominated nitrate production when PM2.5 > 100 µg/m3. Higher δ15N-NO3- 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 NO3-. These results provide information on the dual isotopic compositions of nitrate to understand nitrate formation pathways under different PM2.5 levels.

Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF.

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.

Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns.

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 (O x = 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.

Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning.

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.

Chemical characteristics and sources of ambient PM2.5 in a harbor area: Quantification of health risks to workers from source-specific selected toxic elements.

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.

Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China.

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.

Multi-scale volatile organic compound (VOC) source apportionment in Tianjin, China, using a receptor model coupled with 1-hr resolution data.

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.

Vehicular non-exhaust particulate emissions in Chinese megacities: Source profiles, real-world emission factors, and inventories.

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.

[Pollution Characteristics and Emission Factors of VOCs from Vehicle Emissions in the Tianjin Tunnel].

The pollution characteristics and emission factors (EFs) of the volatile organic compounds (VOCs) of vehicles were investigated using the tunnel test method on weekdays and weekends in the Wujinglu Tunnel in Tianjin, China. Gas samples in the tunnel were collected with 3.2 L stainless steel canisters and 99 VOCs species were analyzed by gas chromatography-mass spectrometry (GC-MS). The concentration levels, variation characteristics, and EFs of the VOCs were analyzed. The ozone formation potentials (OFPs) and secondary organic aerosol formation potentials (SOAFPs) of the VOCs in the tunnel were calculated. Moreover, a comparison of the study results with current literature was conducted. The total concentrations of VOCs at the inlet and midpoint are (190.85±51.15) µg·m-3 and (257.44±62.02) µg·m-3, respectively. The total EFs are (45.12±10.97) mg·(km·veh)-1 and the EFs for alkanes, alkenes, alkynes, aromatics, halocarbons, and oxygenated volatile organic compounds (OVOCs) are (22.79±7.15), (5.04±1.20), (0.78±0.34), (9.86±2.81), (0.26±0.17), and (6.25±2.27) mg·(km·veh)-1, respectively. They are notably smaller than the values obtained in a previous test in 2009. Isopentane, toluene, ethylene, methyl tert-butyl ether (MTBE), and ethane were the top five species among the VOC EFs. The ratios of methyl tert-butyl ether/benzene (MTBE/B) and methyl tert-butyl ether/toluene (MTBE/T) are 1.07 and 0.77, respectively. This implies that the contribution of evaporative emissions from vehicles to VOCs emissions cannot be ignored. The OFPs and SOAFPs in the tunnel are (145.50±37.85) and (43.87±12.75) mg·(km·veh)-1, respectively. Compared with the test in 2009, the OFPs and SOAFPs are 94.23% and 90.88% smaller, respectively. The sharp decrease of the OFPs and SOAFPs is closely related to stricter emission standards and the upgrade of oil products.

[Association Between Fine Particulate Matter and Asthma Hospital Outpatient Visits in Hangzhou].

To study the short-term effects of air pollution on asthma visits and differences in susceptibility to various groups of people, data for asthma visits from January 1, 2013 to December 31, 2015 were obtained from a Hangzhou hospital. Considering the nonlinear relationships among concentration of air pollutants, respiratory hospital outpatient visits and meteorological factors, Generalized Additive Models (GAM) and stratification analysis were used to explore the lag effects and differences in people stratifications. The natural cubic spline function was used for smoothing the average temperature, the average relative humidity and the long-term trend, using dummy variables to control the effect of the day of the week and of holidays. Correlation of PM2.5, NO2 and SO2 daily mean concentrations were significant (under 0.01) in Spearman correlation analysis, while the correlations of daily mean temperature and 3 pollutants were significantly negative. The lag effects of PM2.5 concentration on outpatient visits of asthma peaked at 3-5 days. The relative risk of asthma reached maximum at lag day 5 as 1.0056 (95% CI:1.0021-1.0091), with per 10 µg·m-3 increment of PM2.5 concentration. The relative risk of asthma outpatient visits of all groups of patients were statistically significant (P<0.05). The relative risk of asthma outpatient visits of males and young and middle-aged group were statistically significant at lag days 3-5, and for females and the elderly, were statistically significant at lag day 5. With the introduction of the effects of NO2, the relative risk of asthma outpatient visits increased at lag 5 day in co-pollutant models. The authors concluded that the increase of PM2.5 may be related to the increase of asthma hospital outpatient visits within 3-5 days in Hangzhou, and the effects on male group and elderly group were more definite.