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Abstract Traditional atmospheric chemistry posits that sulfur dioxide (SO2) can be oxidized to sulfate (SO4 2–) through aqueous-phase reactions in clouds and gas-phase oxidation. Despite adequate knowledge of traditional mechanisms, several studies have highlighted the potential for SO2 oxidation within aerosol water. Given the widespread presence of tropospheric aerosols, SO4 2− production through aqueous-phase oxidation in aerosol water could have a pervasive global impact. Here, we quantify the potential contributions of aerosol aqueous pathways to global sulfate formation based on the GEOS-Chem simulations and subsequent theoretical calculations. Hydrogen peroxide (H2O2) oxidation significantly influences continental regions both horizontally and vertically. Over the past two decades, shifts in the formation pathways within typical cities reveal an intriguing trend: despite reductions in SO2 emissions, the increased atmospheric oxidation capacities, like rising H2O2 levels, prevent a steady decline in SO4 2− concentrations. Abating oxidants would facilitate the benefit of SO2 reduction and the positive feedback in sulfate mitigation.

Although most source apportionments of VOCs use mixing ratios, about 23 % of published studies use mass concentrations. Thus, systematically exploring the changes in VOC source apportioned results caused by metric differences is important to assess the differences in key precursor apportionment results given the observed increases in O3 pollution situation. Different monitoring instruments measured hourly VOC volumetric concentrations in three typical Chinese cities (i.e., Qingdao, Shijiazhuang, and Zhumadian). Converting volumetric to mass concentrations under standard and/or actual temperature–pressure conditions, VOC values with different metrics were obtained. The impacts of different metrics on the source apportionments were then investigated. Compared to the positive matrix factorization of the volumetric data (VC-PMF), the VOC species concentrations with low relative molecular mass (RMM) in the factor profiles substantially decreased in mass data analyses (MC-PMF). However, those species with high RMM substantially increased. There were no substantial differences in the apportioned source contributions based on standard and actual condition mass concentrations. However, the high-low rankings of percent contributions apportioned using the volumetric and mass data produced substantial differences. Compared with the VC-PMF results, the percent contributions of sources dominated by species with low RMM (e.g., natural gas usage and mixed sources containing natural gas usage) apportioned by MC-PMF decreased, while those of sources that emitted high RMM species (e.g., solvent usage and mixed sources containing solvent usage) increased. Source apportionments based on the volumetric concentration data had more practical significance compared to the mass concentration data results for control strategy development since the mass data analyses created issues.

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.

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3−)). The mechanism between ε(NO3−) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3−). Here we introduce a supervised machine learning approach—the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42−, and temperature as pivotal drivers for ε(NO3−). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

This study leveraged 2019 online data of particulate matter (PM2.5) and volatile organic compounds (VOCs) in Tianjin to analyze atmospheric pollution characteristics. PM2.5 was found to be primarily composed of water-soluble ions, with nitrates as the dominant component, while VOCs were predominantly alkanes, followed by alkenes and aromatic hydrocarbons, with notable concentrations of propane, ethane, ethylene, toluene, and benzene. The receptor model identified six major sources of PM2.5 and seven major sources of VOCs. The secondary source is the main contribution source, while motor vehicles and coal burning are important primary contribution sources in PM2.5. And, industrial processes and natural gas volatilization were considered major contributors for VOCs. A health risk assessment indicated negligible non-carcinogenic risks but potential carcinogenic risks from trace metals As and Cr, and benzene within VOCs, underscoring the necessity for focused public health measures. A risk attribution analysis attributed As and Cr in PM to coal combustion and vehicular emissions. Benzene in VOCs primarily originates from fuel evaporation, and industrial and vehicular emissions. These findings underscore the potential for reducing health risks from PM and VOCs through enhanced regulation of emissions in coal, industry, and transportation. Such strategies are vital for advancing air quality management and safeguarding public health.

Particulate matter (PM) from residential combustion is an existential threat to human health. Emission factors (EFs) of multiple potential toxic components (PTCs) in size-resolved PM and gas from eight residential fuel combustion were measured, and size distribution, gas/particle partitioning and health risks of the PTCs were investigated. Average EFs from clean coal and anthracite coal were PTEs (sum of EFs of 11 Potential Toxic Elements, 6.62 mg/kg fuels) > PAHs (sum of 22 Polycyclic Aromatic Hydrocarbons, 1.12 mg/kg) > OPAHs (sum of 5 Oxygenated Polycyclic Aromatic Hydrocarbons, 0.45 mg/kg) > PAEs (sum of 6 Phthalate Esters, 0.11 mg/kg) > NPAHs (sum of 14 Nitropolycyclic Aromatic Hydrocarbons, 16.84 μg/kg) > OPEs (sum of 7 Organophosphate Esters, 7.57 μg/kg) > PCBs (sum of 6 Polychorinated Biphenyls, 0.07 μg/kg), which were 2–3 and 1–2 orders of magnitude lower than the EFs of PTCs (except PTEs) from bituminous coal and biomass. Most PAHs, OPAHs and NPAHs, which may mainly originate from chemical reactions, showed similar size distributions and averagely 85 % concentrated in PM1. PTEs, PAEs, OPEs and PCBs generated from the release from raw fuels may have a higher proportion, so their size distributions were more complex and varied with combustion temperature, volatility of compounds, binding mode of the raw fuels, and so on. In addition, clean coal and high-quality anthracite coal could reduce the health risks from the potential organic toxic components, but also reveal the stumbling block of PTEs in risk control.

Abstract Ground-level ozone affects Earth’s climate and human health. The formation of ozone is a complex process, depending on both atmospheric chemical processes and meteorological factors. In this study, machine learning coupled with a structure mining analysis was applied to investigate the ozone formation mechanism in Tianjin, China. The results showed isoprene has the greatest individual impact on local ozone generation, which suggests the biogenic emission of vegetation contribute significantly to native ozone pollution. The interaction between isoprene and nitrogen oxides is the strongest among precursors, with an obvious antagonistic effect between them. Reducing active volatile organic compounds is more effective for mitigating ozone pollution. Visualized network diagram also clearly illustrated the impacts of multiple drivers on ozone formation: isoprene, temperature and nitrogen oxides were the key drivers among all the influencing factors, other drivers (such as relative humidity) could assist the key drivers to collaboratively enhance or suppress ozone formation.

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.

Exploring coordinated pathways that can promote not only the sustainable development of the industrial economy but also air quality is of great significance for the prevention and control of air pollution in China. Currently, the joint development pathways of the industrial economy-environment nexus remain unclear and poorly evaluated. In this study, we proposed a comprehensive performance evaluation combining objective and subjective weighting to identify industrial enterprises' economic-environment nexus benefits. It would be one of the most important steps to explore the coordinated pathways. Based on data envelopment analysis (DEA), the proposed method integrated with the index integration was used to evaluate the comprehensive performances of 41 industrial sectors in China's 13th five-year plan (2016–2020). Evaluation results showed that the comprehensive performances of the economy-environment nexus of the industrial sectors varied significantly, with the five-year average comprehensive technical efficiency (TE) of 0.11–1. Overall, the best two performances were realized by the industries of equipment manufacturing and living consumption, whereas the worst one belonged to the industry of bulk raw materials, with average comprehensive TE values of 0.50, 0.43, and 0.19, respectively. The results of the quantitative evaluation were consistent with those of the qualitative analysis in terms of the developmental status of the industrial sectors. According to the analyses of pure technical efficiency and scale effect, the proposed method identified the industrial sectors with the highest developmental value and with the highest need to control air pollution. Compared with those of the original DEA model, the results of the proposed method showed pronounced differences in terms of the performances of industrial sectors with high energy consumption and high particulate matter (PM) emissions and with low energy consumption and low PM emissions. The proposed evaluation method combining the weighting was suitable for identifying the comprehensive performance of the industrial economy-environment nexus and provides the basis for the prevention and control of air pollution.

Abstract Tropospheric ozone (O3) affects Earth’s climate and human health. Volatile organic compounds (VOCs), major contributors to O3 formation, are of particular interest. Generally, the measured concentrations of VOCs (M-VOCs) and O3 show nonlinear or even opposite time serial-trend. We attributed the phenomenon to survivor bias: lack of insight of the photochemically consumed VOCs (C-VOCs) which emitted from sources to ambient and devote to forming O3, while excessive concern on the measured VOCs (M-VOCs) at observation site. Both observational and model results provide evidence that C-VOCs are the key to O3 formation. We proposed an improved model to quantify the source contributions of C-VOCs (biogenic emissions, gasoline evaporation, industry, etc.) and their impacts on the formation of O3, successfully avoiding the misidentification of dominant VOCs sources originated from the survivor bias in observational data. The survivor bias found in this study highlights that focusing of M-VOCs directly is insufficient and demonstrates the necessity of capture the sources of C-VOCs which contribute to O3 formation.

Photochemical losses of ambient volatile organic compounds (VOCs) substantially affect source apportionment analysis. Hourly speciated VOC data measured from April to August 2020 in Tianjin, China were used to analyze the photochemical losses of VOC species and assess the impacts of photochemical losses on source apportionment by comparing the positive matrix factorization (PMF) results based on observed and initial concentration data (OC-PMF and IC-PMF). The initial concentrations of the VOC species were estimated using a photochemical age-based parameterization method. The results suggest that the average photochemical loss of total VOCs (TVOCs) during the ozone pollution period was 2.4 times higher than that during the non-ozone pollution period. The photochemical loss of alkenes was more significant than that of the other VOC species. Temperature has an important effect on photochemical losses, and different VOC species have different sensitivities to temperature; high photochemical losses mainly occurred at temperatures between 25 °C and 35 °C. Photochemical losses reduced the concentrations of highly reactive species in the OC-PMF factor profile. Compared with the IC-PMF results, the OC-PMF contributions of biogenic emissions and polymer production-related industrial sources were underestimated by 73 % and 50 %, respectively, likely due to the oxidation of isoprene and propene, respectively. The contribution of diesel and gasoline evaporation was underestimated by 39 %, which was likely due to the loss of m,p-xylene. Additionally, the contributions of liquefied petroleum gas, vehicle emissions, natural gas, and oil refinery were underestimated by 31 %, 29 %, 23 %, and 13 %, respectively. When the O3 concentrations were higher than 140 μg m−3 or the temperatures were higher than 30 °C, the photochemical losses from most sources increased substantially. Additionally, solar radiation produced different photochemical losses for different source types.

Ground-level ozone (O3) pollution has received widespread attention because its rising trend and adverse ecological impacts. However, the extremely strong photochemical reactions of its precursor volatile organic compounds (VOCs) increase the difficulty of reducing VOCs emissions to alleviate O3. Here, we carried out a one-year comprehensive observation in two representative cities, Tianjin (TJ, Northern China) and Guangzhou (GZ, Southern China). By revealing the concentration characteristics of three different types of VOCs, i.e., initial VOCs without photochemical reaction (In-VOCs), consumed VOCs (C-VOCs), and measured VOCs after the reaction (M-VOCs), we elucidated the important role of C-VOCs in the formation of O3. Although the overall trends were similar in both cities, the average concentration level of VOCs in GZ was 8.2 ppbv higher than that in TJ, and the photochemical loss of VOCs was greater by 2.2 ppbv. In addition, various drivers affecting O3 generation from C-VOCs were specifically explored, and it was found that most alkenes of TJ were key substances for rapid O3 formation compared to aromatics of GZ. Meanwhile, favorable meteorological conditions such as high temperature (T > 31 °C in TJ, and T > 33 °C in GZ), low relative humidity (56% in TJ and 45% in GZ), and stable atmospheric environment (proper pressure and gentle wind speed) also contribute to the generation of O3. More importantly, we combined chemical kinetics and receptor model to quantify the three-type VOCs source contributions and assess the potential impact of C-VOCs sources on O3 production, thus proposing environmental abatement technologies corresponding to the three types of VOCs. The differences in the comparison results of the three-type VOCs highlight the need to reduce O3 pollution from C-VOCs sources, which provides insights for future clean air policies development.

Highly time-resolved data for volatile organic compounds (VOCs) can now be monitored. Source analyses of such high time-resolved concentrations provides key information for controlling VOC emissions. This work reviewed the literature on VOCs source analyses published from 2015 to 2021, and assesses the state-of-the-art and the existing issues with these studies. Gas chromatography system and direct-inlet mass spectrometry are the main monitoring tools. Quality control (QC) of the monitoring process is critical prior to analysis. QC includes inspection and replacement of instrument consumables, calibration curve corrections, and reviewing the data. Approximately 54% published papers lacked details on the quantitative evaluation of the effectiveness of QC measures. Among the reviewed works, the number of monitored species varied from 5 to 119, and fraction of papers with more than 90 monitored species increased yearly. US EPA PMF v5.0 was the most commonly used (∼86%) for VOC source analyses. However, conventional source apportionment directly uses the measured VOCs and may be problematic given the impacts of dispersion and photochemical losses, uncertainty setting of VOCs data, factor resolution, and factor identification. Excluding species with high-reactivity or estimation of corrected concentrations were often applied to reduce the influence of photochemical reactions on the results. However, most reports did not specify the selection criteria or the specific error fraction values in the uncertainty estimation. Model diagnostic indexes were used in 99% of the reports for PMF analysis to determine the factor resolution. Due to lack of known local source profiles, factor identification was mainly achieved using marker species and characteristic species ratios. However, multiple sources had high-collinearity and the same species were often used to identify different sources. Vehicle emissions and fuel evaporation were the primary contributors to VOCs around the world. Contribution of coal combustion in China was substantially higher than in other countries.

Accurate measurements of PM2.5 related heavy metals (HMs) and some components are critical to better understanding the sources and health risks of PM2.5. HMs and other components in PM2.5 were simultaneously measured using online and offline filter-based methods in a Chinese megacity. Online Mn, Pb, Cu, and Zn concentrations exhibited good correspondence with offline results (R2 ≥ 0.7, relative biases = −3%–22%). Some differences were observed for Cr, As, Co, and V. The assessment of online and offline probabilistic health risks indicated that non-cancer and total cancer risks were higher than the acceptable limits. Different analytical methods of As may result in different uncertainties in risk analysis. The positive matrix factorization model (PMF) was used to perform online and offline source apportionment. Five sources (coal combustion, vehicle source, industrial source, secondary source, and resuspended dust) were identified. Lower online percentage contribution of resuspended dust (8%) might relate to the lack of Si and Al measurements. The different rate contributions of vehicle source were potentially linked to the different time resolutions of the online and offline data. The assessment of online and offline source-specific health risks revealed that industrial source and coal combustion were the most important sources. Online and offline source-specific risks were not significantly different (P > 0.05). This contrastive study not only helps decision makers to manage health risk more effectively, but also provides insights into the applicability of online and offline measurements for quantifying source-specific risks.

Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-Ox normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future.

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.

The ambient particulate matter (PM) pollution adversely influences the human health and natural environment. The size distribution of ambient PM determines the physiochemical and optical properties of ambient aerosol, whereas it reflects the variability in local and regional PM emission sources and formation mechanisms. In the present work, the size distribution and characteristics of the carbonaceous, ionic, elemental, and saccharide species were categorically investigated for the fraction-wise PM in Tianjin during 2018 early summer. The average concentrations were 32.4, 20.9, and 49.3 μg/m3 for the PM1, PM1–2.5, and PM2.5–10, respectively. The coarse PM2.5–10 accounted for most of the PM10 mass (47%), followed by the fine PM1 (33%) and intermodal PM1–2.5 (20%). The carbonaceous and ionic species exhibited bimodal distribution and were distributed mostly to the fine size fraction and then to the coarse size fraction. The elemental species exhibited unimodal distribution and were distributed mostly to the coarse size fraction. The specific saccharide species indicated the significant contribution of biomass burning and primary biogenic emissions. The bimodal mass size distribution of levoglucosan indicated the significant biomass burning contributions to the fine and coarse size fractions. The unimodal glucose, fructose, and arabitol distribution and the bimodal mannitol distribution indicated the dominant primary biogenic contributions to the coarse size fraction. The PM1/PM10, PM1–2.5/PM10, PM2.5–10/PM10, OC/EC, SOC/OC, AE/CE, NO3–/SO42–, K+/EC, and levoglucosan/K+ ratios were used to investigate the characteristics of the ambient size-fractionated PM. The anthropogenic sources (combustion processes, traffic emissions, and secondary particles, etc.) contributed mostly to the fine PM1 and intermodal PM1–2.5 fractions, whereas the natural sources (primary biogenic, marine salt, and mineral dust, etc.) contributed mostly to the coarse PM2.5–10 fraction. This work is a significant addition to the multi-size ambient PM’s size distribution and characterization studies.

Synchronous heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in inhalable particulate matter (PM10) were measured during 2009–2012 and 2015–2016 in a Chinese megacity (Chengdu) to understand the variations in source-specific health risks during haze episodes. Samples were divided into four mass concentration levels: PM10 ≤ 150 μg m−3 (L1), 150 μg m−3 350 μg m−3 (L4). The percentages of some HMs and PAHs (accounting for PM10) decreased from L1 to L4, indicating that they exhibited lower growth rates than other species during heavy pollution. The combined cancer risk (R) for HMs and PAHs was higher at L1 and L4, and the combined non-cancer risk (HQ) was significantly high at L4. The HMs and PAHs combined source-specific risk apportion (HP-SRA) model was employed to quantify the source-specific risks. The relative contributions of (i) diesel and gasoline vehicles to the R, and (ii) crustal dust to the HQ increased during heavy pollution (L3 and L4). The relative contribution of industrial source declined from 81% (L1) to 60% (L4) for the HQ, and from 49% (L1) to 36% (L4) for the R, implying that the control of industrial emissions during heavy pollution events could alleviate risk growth as a co-benefit of controlling PM mass concentration. However, the risks associated with industrial emissions should also be considered during ‘clean’ days.

Fine particle (PM2.5) mass concentration and relative humidity (RH) are the primary factors influencing atmospheric visibility. There are some studies focused on the complex, nonlinear relationships among visibility, PM2.5 concentration, and RH. However, the relative contribution of the two factors to visibility degradation, especially for different aerosol types, is difficult to quantify. In this study, the normalized forward sensitivity index method for identifying the dominant factors of visibility was used on the basis of the sensitivity of visibility to PM2.5 and RH changes. The visibility variation per unit of PM2.5 or RH was parameterized by derivation of the visibility multivariate function. The method was verified and evaluated based on 4453 valid hour data records in Tianjin, and visibility was identified as being in the RH-sensitive regime when RH was above 75%. In addition, the influence of aerosol chemical compositions on sensitivity of visibility to PM2.5 and RH changes was discussed by analyzing the characteristics of extinction components ((NH4)2SO4, NH4NO3, organic matter, and elemental carbon) measured in Tianjin, 2015. The result showed that the fitting equation of visibility, PM2.5, and RH, separately for different aerosol types, further improved the accuracy of the parameterization scheme for visibility in most cases.

Ambient PM2.5-bound ions, OC, EC, heavy metals (HMs), 18 polycyclic aromatic hydrocarbons (PAHs), 7 hopanes, and 29 n-alkanes were detected at Tuoji Island (TI), the only marine background atmospheric monitoring station in North China. The annual PM2.5 average concentration was 47 ± 31 μg m−3, and the average concentrations of the compositions in PM2.5 were higher in cold seasons than in warm seasons. The cancer and non-cancer risks of HMs and PAHs in cold seasons were also higher than in warm seasons. BaP, Ni, and As dominated the ∑HQ (hazard quotient) in cold seasons, while the non-carcinogenic risk in warm seasons was mainly dominated by Ni, Mn, and As. The ILCR (incremental lifetime cancer risk) values associated with Cr and As were higher in the cold season, while ILCR-Ni values were higher in the warm season. The backward trajectory was calculated to identify the potential directions of air mass at TI. Through the diagnostic ratios of organic and inorganic tracers, the sources of particulate matter in different directions were judged. It was found that ship emissions and sea salt were the main sources from marine directions, while coal combustion, vehicles emissions, industrial process, and secondary aerosols were the main source categories for inland directions. In addition, potential HM and PAH risks from inland and marine directions were explored. The non-cancerous effects of TI were mainly affected by inland transport, especially from the southeast, northwest, and west-northwest. The cancerous effects of TI were mainly simultaneously affected by the inland direction and marine direction of transport.

Tropospheric ozone is the only normal pollutant with a continuously increasing annual average concentration worldwide. In this study, data were monitored at the Nankai University Air Quality Research Supersite (NKAQRS) (38.99° N, 117.33° E) between 1 April, and 31 August from 2018 to 2020, 33 O3 episodes from 2018 to 2020 were analyzed to reveal the characteristics of O3, VOCs and OFP during O3 episodes and to evaluate the driving factors. The O3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. Ethane, acetylene, cyclopentane, and methylcyclopentane were the major types in 2020, while 1-hexene was the main component in 2019. The main ozone-contributing species in 2020 were propene cyclopentane methylcyclopentane and ethylene. Alkenes were important contributors to ozone formation. Using generalized additive models (GAMs), the explanatory variables in the study are divided into environmental and meteorological factors, and 16 impact factors are selected as explanatory variables. We found that the influence of these meteorological factors on O3 pollution was nonlinear and impacted by the interaction between variables. O3 episodes were mainly driven by meteorological and precursor (NO) factors in 2018, while meteorological conditions (T), followed by precursor (NO2) were the driving factors in 2019 and 2020, suggesting that O3 episodes were mainly driven by meteorological conditions.

Aerosol pH is commonly used to characterize the acidity of aqueous aerosols and is of significant scientific interest due to its close relationship with atmospheric processes. Estimation of ambient aerosol pH usually relies on the thermodynamic modeling approach. In the existing chemical transport model and field observation studies, the temporal resolution of the input chemical and meteorological data into thermodynamic models varied substantially ranging from less than an hour to a year because of the inconsistency in the resolution of the original data and the aggregation of time-series data in some studies. Furthermore, the average value of aerosol pH has been represented by diverse metrics of central tendency in existing studies. This study attempts to evaluate the potential discrepancies in the calculated average aerosol pH arising from differences in both averaging metrics and temporal resolutions based on the ISORROPIA-II thermodynamic model and the example datasets prepared by the GEOS-Chem chemical transport model simulation. Overall, we find that the variation in the temporal resolutions of input data may lead to a change of up to more than two units in the average pH, and that the averaging metrics calculated based on the pH value of individual samples may be about two units higher than the averaging metrics calculated based on the activity of hydrogen ions. Accordingly, we recommend that the chosen averaging metrics and temporal resolutions should be stated clearly in future studies to ensure comparability of the average aerosol pH between models and/or observations. [ABSTRACT FROM AUTHOR]