Model Simulations and Predictions of Hydroxymethanesulfonate (HMS) in the Beijing-Tianjin-Hebei Region, China: Roles of Aqueous Aerosols and Atmospheric Acidity.

Hydroxymethanesulfonate (HMS) has been found to be an abundant organosulfur aerosol compound in the Beijing-Tianjin-Hebei (BTH) region with a measured maximum daily mean concentration of up to 10 µg per cubic meter in winter. However, the production medium of HMS in aerosols is controversial, and it is unknown whether chemical transport models are able to capture the variations of HMS during individual haze events. In this work, we modify the parametrization of HMS chemistry in the nested-grid GEOS-Chem chemical transport model, whose simulations provide a good account of the field measurements during winter haze episodes. We find the contribution of the aqueous aerosol pathway to total HMS is about 36% in winter in Beijing, due primarily to the enhancement effect of the ionic strength on the rate constants of the reaction between dissolved formaldehyde and sulfite. Our simulations suggest that the HMS-to-inorganic sulfate ratio will increase from the baseline of 7% to 13% in the near future, given the ambitious clean air and climate mitigation policies for the BTH region. The more rapid reductions in emissions of SO2 and NOx compared to NH3 alter the atmospheric acidity, which is a critical factor leading to the rising importance of HMS in particulate sulfur species.

Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China.

After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM2.5 emission trends have recovered, the specific changes in different sources of PM2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 µg m-3 h-1) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals.

Catalytic Ozonation of Polluter Benzene from -20 to >50 °C with High Conversion Efficiency and Selectivity on Mullite YMn2O5.

Catalytic decomposition of aromatic polluters at room temperature represents a green route for air purification but is currently challenged by the difficulty of generating reactive oxygen species (ROS) on catalysts. Herein, we develop a mullite catalyst YMn2O5 (YMO) with dual active sites of Mn3+ and Mn4+ and use ozone to produce a highly reactive O* upon YMO. Such a strong oxidant species on YMO shows complete removal of benzene from -20 to >50 °C with a high COx selectivity (>90%) through the generated reactive species O* on the catalyst surface (60 000 mL g-1 h-1). Although the accumulation of water and intermediates gradually lowers the reaction rate after 8 h at 25 °C, a simple treatment by ozone purging or drying in the ambient environment regenerates the catalyst. Importantly, when the temperature increases to 50 °C, the catalytic performance remains 100% conversion without any degradation for 30 h. Experiments and theoretical calculations show that such a superior performance stems from the unique coordination environment, which ensures high generation of ROS and adsorption of aromatics. Mullite's catalytic ozonation degradation of total volatile organic compounds (TVOC) is applied in a home-developed air cleaner, resulting in high efficiency of benzene removal. This work provides insights into the design of catalysts to decompose highly stable organic polluters.

Contributors to reductions of PM2.5-bound heavy metal concentrations and health risks in a Chinese megacity during 2013, 2016 and 2019: An advanced method to quantify source-specific risks from various directions.

PM2.5-bound heavy metals were measured in a Chinese megacity (Tianjin) in 2013, 2016 and 2019, and analyzed by a new RSDA method (source directional apportionment of risks). Through combining the receptor model, cluster analysis of back trajectories, and risk assessment, the RSDA was developed in this work to quantify source-specific risks from each direction. Concentrations of PM2.5 and most species (especially for heavy metals) underwent various reductions, and the incremental lifetime cancer risk (ILCR) and non-cancer risk (HQ) declined by more than 80% from 2013 to 2019. Pb was the highest contributor to the reduction of HMs mass concentration (58.6%), while Cr (85.5% for cancer risk) and As (26.0% for non-cancer risk) were more prominent for the reduction of HM risks. The coal combustion and industrial emissions were vital contributors to the reduction of both PM2.5 mass concentrations (contributed 34.0% and 7.8% to the reduction respectively) and health risks (contributed 36.1% and 25.7% to the cancer risk reduction respectively). Although the percentage mass contribution of traffic emissions increased (7.7% in 2013 and 21.9% in 2019), the associated risks decreased (contributed 26.8% to the cancer risk reduction). Furthermore, the results of RSDA consistently implied that coal combustion, industrial emissions and traffic emissions controls in the northeast/north-northeast, south and southwest of the studied area played important roles in the risk reductions, which mainly due to the risk reduction of air masses from NE/NNE, S and SW, and their strong influence to Tianjin. The RSDA method can quantify the health risks from different sources and directions, and the evaluation of contributors to the reductions of risks in this work would provide a meaningful reference for policy maker to control PM2.5 emissions and protect population health.

Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021.

Deterioration of surface ozone (O3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O3 trend and identify the key meteorological factors affecting O3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O3 time series, we found that variation of O3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O3 pollution since 2015. Since then, the favorable effects of meteorology on O3 pollution tended to be weaker. Temperature was the most important factor affecting O3 level, followed by air humidity in O3 pollution season. In the midday of summer days, O3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O3 forecasting.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: H2O2 Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H2O2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H2O2 pathway. The collaborative effects of atmospheric oxidants and SO2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO2 concentrations in China.

Formaldehyde Decomposition from -20 °C to Room Temperature on a Mn-Mullite YMn2O5 Catalyst.

Large ambient temperature changes (-20->25 °C) bring great challenges to the purification of the indoor pollutant formaldehyde. Within such a large ambient temperature range, we herein report a manganese-based strategy, that is, a mullite catalyst (YMn2O5) + ozone, to efficiently remove the formaldehyde pollution. At -20 °C, the formaldehyde removal efficiency reaches 62% under the condition of 60,000 mL gcat-1 h-1. As the reaction temperature is increased to -5 °C, formaldehyde and ozone are completely converted into CO2, H2O, and O2, respectively. Such a remarkable performance was ascribed to the highly reactive oxygen species generated by ozone on the YMn2O5 surface based on the low temperature-programed desorption measurements. The in situ infrared spectra showed the intermediate product carboxyl group (-COOH) to be the key species. Based on the superior performance, we built a consumable-free air purifier equipped with mullite-coated ceramics. In the simulated indoor condition (25 °C and 30% relative humidity), the equipment can effectively decompose formaldehyde (150 m3 h-1) without producing secondary pollutants, rivaling a commercial removal efficiency. This work provides an air purification route based on the mullite catalyst + ozone to remove formaldehyde in an ambient temperature range (-20->25 °C).

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution.

PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 µg/m3) and 33% (19.7 µg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds.

Fine particulate matter (PM2.5) and ozone (O3) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O3 and secondary organic aerosols (SOA) contributing PM2.5. Herein, we investigated 54 VOCs, O3 and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O3, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O3 was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O3 and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O3 formation; alkanes (Cn, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O3 and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O3 formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O3.

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.

Source-specific health risk assessment of PM2.5-bound heavy metals based on high time-resolved measurement in a Chinese megacity: insights into seasonal and diurnal variations.

The health effects of PM2.5 associated heavy metals have caused wide public concern. To more accurately assess source-specific health risks of PM2.5-bound heavy metals, and to formulate a cost-effective control strategy to health risk reduction, it is necessary to have a better understanding of the temporal variation of source-specific health risks. For this purpose, hourly PM2.5 and associated heavy metals were measured during four seasons in 2018-2019 in a Chinese megacity. A method integrating positive matrix factorization (PMF) with the health risk assessment model was used to quantify the source-specific health risks. Results showed that the total hazard index (HI) of PM2.5-bound heavy metals was 1.35, higher than the safety level, the sum cancer risks (R) of carcinogenic elements (Cr, Co, Ni and As) were 2.8 × 10-5, implying nonnegligible risks. Industrial source 1 (61.3%), which was related with Mn posed the largest non-cancer risk, while coal combustion (36.1%) and industrial source 1 (34.9%) posed most of the cancer risk, and slightly fluctuated with seasons. Health risks of most resolved sources were higher in autumn and winter than in other seasons. In terms of the diurnal variation, they were the lowest in the afternoon. Besides, the health risks of vehicle source had a peak value in rush hours. Different scenarios were simulated to understand the influences of time resolutions and sampling periods on source-specific risk assessment. The results showed the cancer risks of coal combustion and industrial source 1 calculated from the dataset with reduced sampling periods were different from those calculated from the whole dataset. We conclude that source-specific health risks of heavy metals show seasonal and diurnal variations, which suggests that targeted strategies should be adopted on the basis of seasonal and diurnal cycles to protect public health. In addition, a sufficient sampling period is required to generate representative and reliable results for source-specific health risk assessment.

Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown.

Stringent quarantine measures during the Coronavirus Disease 2019 (COVID-19) lockdown period (January 23, 2020 to March 15, 2020) have resulted in a distinct decrease in anthropogenic source emissions in North China Plain compared to the paralleled period of 2019. Particularly, 22.7% decrease in NO2 and 3.0% increase of O3 was observed in Tianjin, nonlinear relationship between O3 generation and NO2 implied that synergetic control of NOx and VOCs is needed. Deteriorating meteorological condition during the COVID-19 lockdown obscured the actual PM2.5 reduction. Fireworks transport in 2020 Spring Festival (SF) triggered regional haze pollution. PM2.5 during the COVID-19 lockdown only reduced by 5.6% in Tianjin. Here we used the dispersion coefficient to normalize the measured PM2.5 (DN-PM2.5), aiming to eliminate the adverse meteorological impact and roughly estimate the actual PM2.5 reduction, which reduced by 17.7% during the COVID-19 lockdown. In terms of PM2.5 chemical composition, significant NO3- increase was observed during the COVID-19 lockdown. However, as a tracer of atmospheric oxidation capacity, odd oxygen (Ox = NO2 + O3) was observed to reduce during the COVID-19 lockdown, whereas relative humidity (RH), specific humidity and aerosol liquid water content (ALWC) were observed with noticeable enhancement. Nitrogen oxidation rate (NOR) was observed to increase at higher specific humidity and ALWC, especially in the haze episode occurred during 2020SF, high air humidity and obvious nitrate generation was observed. Anomalously enhanced air humidity may response for the nitrate increase during the COVID-19 lockdown period.

Size distribution and chemical characteristics of particles from crop residue open burning in North China.

Crop residue open burning is an important emission source of ambient particles in China. This study analyzed the particle emission characteristics of crop residue open burning through combustion experiments with a novel open combustion simulation device using three typical crop straws in north China (corn, wheat, and rice). Particle samples size ranging from 0.006-9.890 µm were collected by an Electrical Low Pressure Impactor plus, a high size-resolution instrument capable of dividing particles into 14 size stages. The size distributions of organic carbon (OC), elemental carbon (EC), water-soluble ions, and elements were analyzed, and source chemical profiles were constructed for PM0.1, PM1, PM2.5, and PM10. The number concentration of particles was concentrated in the Aiken nuclei mode (0.006-0.054 µm), accounting for 75% of the total number, whereas the mass concentration was concentrated in the accumulation mode (0.054-0.949 µm), accounting for 85.43% of the mass loading. OC, EC, Cl-, and K(include total K and water-soluble K) were the major chemical components of the particles, whose mass percentage distributions differed from those of other components. These five main components exhibited a bell-shaped size distribution in the 0.006-9.890 µm range, whereas the other components exhibited a U-shaped distribution. Among the chemical profiles for PM0.1-PM10, OC was the most important component at 10-30%, followed by EC at 2%-8%. The proportions of K+, Cl-, and K varied substantially in different experimental groups, ranging from 0-15%, and K+ and Cl- were significantly correlated (r = 0.878, α = 0.000).

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources.

The submicron particulate matter (PM1) and fine particulate matter (PM2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM1 and PM2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM1 and PM2.5 was based on their concentrations, compositions, and characteristic ratios (PM1/PM2.5, AE/CE, NO3-/SO42-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K+, V/Cu, and V/Ni). The average concentrations of PM1 and PM2.5 were 32.4 µg/m3 and 53.3 µg/m3, and PM1 constituted 63% of PM2.5 on average. The source apportionment of PM1 and PM2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM1 and PM2.5, and the natural sources were more evident in PM2.5. This work significantly contributes to the chemical characterization and source apportionment of PM1 and PM2.5 in near-port cities influenced by the diverse sources.

Using High-Temporal-Resolution Ambient Data to Investigate Gas-Particle Partitioning of Ammonium over Different Seasons.

Ammonium is one of the dominant inorganic water-soluble ions in fine particulate matter (PM2.5). In this study, source apportionment and thermodynamic equilibrium models were used to analyze the relationship between pH and the partitioning of ammonium (ε(NH4+)) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH4+) from the ambient hourly aerosol dataset when the theoretical ε(NO3-)* (an index identified in this work) was within specific ranges. A Boltzmann function was then used to fit the Reversed-S curve. For the summer data set, when ε(NO3-)* was between 0.7 and 0.8, the fitted R2 was 0.88. Through thermodynamic analysis, we found that the values of k[H+]2 (k = 3.08 × 104 L2 mol-2) and ε(NO3-)* can influence the pH-ε(NH4+) curve. Under certain situations, the values of k[H+]2 and ε(NO3-)* are similar to each other, and ε(NH4+) is sensitive to pH, suggesting that ε(NO3-)* plays an important role in affecting the ε(NH4+). During summer, winter, and spring seasons, when the relative humidity was greater than 0.36 and ε(NO3-)* was between 0.8 and 0.95, there was an obvious Reversed-S curve, with R2 = 0.60. The theoretical k[H+]2 and ε(NO3-)* developed in this work can be used to analyze the gas-particle partitioning of ammonia-ammonium and nitrate-nitric acid in the ambient atmosphere. Also, it is the first time that we created the joint source-NH3/HNO3 maps to integrate sources, aerosol pH and liquid water content, and ions (altogether in one map), which can provide useful information for designing effective strategies to control particulate matter pollution.

Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak.

Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM2.5 source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic.

High-Resolution Data Sets Unravel the Effects of Sources and Meteorological Conditions on Nitrate and Its Gas-Particle Partitioning.

Nitrate is one of the most abundant inorganic water-soluble ions in fine particulate matter (PM2.5). However, the formation mechanism of nitrate in the ambient atmosphere, especially the impacts of its semivolatility and the various existing forms of nitrogen, remain under-investigated. In this study, hourly ambient observations of speciated PM2.5 components (NO3-, SO42-, etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( ITL) is developed and combined with pH to explore the sensitive regions of "S-curve". Controlling the emissions of anions (SO42-, Cl-), cations (Ca2+, Mg2+, etc.) and gases (NO x, NH3, SO2, etc.) will change pH, potentially reducing or increasing SN. The findings of this work provide an effective approach for exploring the formation mechanisms of nitrate under different influencing factors (sources, pH, and IRL).

Detailed Analysis of Estimated pH, Activity Coefficients, and Ion Concentrations between the Three Aerosol Thermodynamic Models.

In this work, we utilize a rich set of simulated and ground-based observational data in Tianjin, China to examine and compare the differences in aerosol acidity and composition predicted by three popular thermodynamic equilibrium models: ISORROPIA II, the Extended Aerosol Inorganics Model vision IV (E-AIM IV), and the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients model (AIOMFAC). The species used to estimate aerosol acidity for both simulated and ambient data were NH4+, Na+, SO42-, NO3-, and Cl-. For simulated data, there is good agreement between ISORROPIA II and E-AIM IV predicted acidity in the forward and metastable mode, resulting from the hydrogen ion activity coefficient (γ(H+)) and the molality (m(H+)) showing opposite trends. While almost all other inorganic species concentrations are found to be similar among the three models, such is not the case for the bisulfate ion (HSO4-), which is linked to m(H+). We find that differences in predicted bisulfate between the three models primarily result from differences in the treatment of the HSO4- ↔ H+ + SO42- reaction for highly acidic conditions. This difference in bisulfate is responsible for much of the difference in estimated pH for the ambient data (average pH of 3.5 for ISORROPIA II and 3.0 for E-AIM IV).

Sources-specific carcinogenicity and mutagenicity of PM2.5-bound PAHs in Beijing, China: Variations of contributions under diverse anthropogenic activities.

To study source-specific carcinogenicity and mutagenicity of polycyclic aromatic hydrocarbons (PAHs) under diverse anthropogenic activities, PM2.5-bound PAHs were detected in Beijing in four periods. PAHs in Asia-Pacific Economic Cooperation meeting (APEC) was much lower than that in after-APEC period. The highest PAHs concentration was in heating period (303 ng/m3). Sources were quantified by Positive Matrix Factorization (PMF). In heating period, due to high emissions, weak diffusion, low degradation and evaporation, high contributions of all sources were observed, and both absolute and relative contributions of coal combustion increased. Changed contributions in during-APEC and after-APEC periods implied effectiveness of reinforced emission control, especially for coal combustion and vehicles. Furthermore, variations of sources-specific carcinogenicity and mutagenicity were investigated. In non-heating period, contributions of gasoline exhaust (38.4% TEQ: Toxic Equivalent Quantity, 33.7% MEQ: Mutagenic Equivalent Quantity) and diesel exhaust (53.8% TEQ, 57.9% MEQ) dominated both carcinogenic and mutagenic risks. Coal combustion sharply increased in heating period, attributing 27.5% TEQ and 21.7% MEQ. In during-APEC period, all contributions to carcinogenicity and mutagenicity were lower than those in after-APEC period, but "others" linked with regional transport contributed increased fractions (above 20%). Sources-specific carcinogenicity and mutagenicity under diverse anthropogenic activities, especially for APEC meeting with reinforced control, gave a new insight into assessment of control measures based on health risks.

Comparative study of PM10-bound heavy metals and PAHs during six years in a Chinese megacity: Compositions, sources, and source-specific risks.

To comparatively analyze source-specific risks of atmospheric particulate matter (PM), PM10-bound polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) were synchronously detected in a megacity (Chengdu, China) from 2009 to 2016. Non-cancer risk (assessed by hazard quotient, HQ) of PAHs and HMs was within the acceptable level, while cancer risk (assessed by incremental life cancer risk (ILCR), R) of PAHs and HMs were 1.01 × 10-4 and 9.40 × 10-5 in DP and WP, which showed low risk. HMs dominated cancer (92.12%) and non-cancer (99.99%) risks. An advanced method named as joint source-specific risk assessment of HMs and PAHs (HP-SRA model) was developed to assess comprehensive source-specific risks. Gasoline combustion (contributed 9.6% of PM10, 0.3% of HQ and 10.0% of R), diesel combustion (6.2% of PM10, 0.2% of HQ and 10.7% of R), coal combustion (17.5% of PM10, 1.8% of HQ and 13.4% of R), industrial source (9.1% of PM10, 80.7% of HQ and 35.0% of R), crustal dust (28.1% of PM10, 9.0% of HQ and 1.6% of R), nitrate (7.5% of PM10, 1.1% of HQ and 6.2% of R) and sulphate & secondary organic carbon & adsorption (SSA, 19.6% of PM10, 6.9% of HQ and 23.1% of R) were identified as main sources. For cancer risk, industrial sources and SSA posed the highest proportion. Higher levels of Co and Ni generated from industrial sources and Cr (Ⅵ), Cd and Ni absorbed in the SSA can result in high-risk contributions. Thus, controlling HMs levels in industrial emissions is essential to protecting human health.