Evaluation of a new real-time source apportionment system of PM2.5 and its implication on rapid aging of vehicle exhaust.

Accurate identification and rapid analysis of PM2.5 sources and formation mechanisms are essential to mitigate PM2.5 pollution. However, studies were limited in developing a method to apportion sources to the total PM2.5 mass in real-time. In this study, we developed a real-time source apportionment method based on chemical mass balance (CMB) modeling and a mass-closure PM2.5 composition online monitoring system in Shenzhen, China. Results showed that secondary sulfate, secondary organic aerosol (SOA), vehicle emissions and secondary nitrate were the four major PM2.5 sources during autumn 2019 in Shenzhen, together contributed 76 % of PM2.5 mass. The novel method was verified by comparing with other source apportionment methods, including offline filter analysis, aerosol mass spectrometry, and carbon isotopic analysis. The comparison of these methods showed that the new real-time method obtained results generally consistent with the others, and the differences were interpretable and implicative. SOA and vehicle emissions were the major PM2.5 and OA contributors by all methods. Further investigation on the OA sources indicated that vehicle emissions were not only the main source of primary organic aerosol (POA), but also the main contributor to SOA by rapid aging of the exhaust in the atmosphere. Our results demonstrated the great potential of the new real-time source apportionment method for aerosol pollution control and deep understandings on emission sources.

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions.

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Coarse particles compensate for missing daytime sources of nitrous acid and enhance atmospheric oxidation capacity in a coastal atmosphere.

Large missing sources of daytime atmospheric nitrous acid (HONO), a vital source of hydroxyl radicals (OH) through its photolysis, frequently exist in global coastal regions. In this study, ambient HONO and relevant species were measured at a coastal site in the Pearl River Delta (PRD), China, during October 2019. Relatively high concentrations (0.32 ± 0.19 ppbv) and daytime peaks at approximately 13:00 of HONO were observed, and HONO photolysis was found to be the dominant (55.5 %) source of the primary OH production. A budget analysis of HONO based on traditional sources suggested large unknown sources during the daytime (66.4 %), which had a significant correlation with the mass of coarse particles (PM2.5-10) and photolysis frequency (J(NO2)). When incorporating photolysis of the abundant nitrate measured in coarse particles with a reasonable enhancement factor relative to fine particles due to favorable aerosol conditions, the missing daytime sources of HONO could be fully compensated by coarse particles serving as the largest source at this coastal site. Our study revealed great potential of coarse particles as a strong daytime HONO source, which has been ignored before but can efficiently promote NOx recycling and thus significantly enhance atmospheric oxidation capacity.

Comprehensive effects of climate, land use/cover and management practices on runoff and nutrient variations in a rapidly urbanizing watershed.

As non-point source pollution has emerged as a significant global and regional concern, climate change (CC), land use/cover transformation (LUCT), and management practices (MP) play vital roles in addressing nutrient pollution. However, current studies lack comprehensive quantification and consistent conclusions on the response to these factors, especially for management practices. To quantify and elucidate the impact of representative environmental factors on rapidly urbanizing regions, this study focused on the Shenzhen River, which serves as the most typical urbanizing watershed. Using a process-based distributed hydrological model with a factor-controlled simulation method, we identified significant differences in nutrient concentrations and the impacts of climate variability, land use/cover changes, and anthropogenic interventions from 2003 to 2020. Moreover, effective measures greatly improved water quality in the Shenzhen River during study period, as evident from trend and cluster analysis. However, ecological water supplements implemented since 2016 have led to a slight reduction in simulated runoff performance, and CC may amplify the synergistic effects of precipitation and temperature on the river system. While the implemented practices have been effective in reducing total nitrogen (TN) and total phosphorus (TP) loads, strong TN pollution control is still needed in rapidly urbanizing areas due to the results of land use/cover type changes. Our findings emphasize the intricate interplay among CC, LUCT, and MP in shaping water quality and hydrological processes in rapidly urbanizing watersheds, and clarify the independent effects of these factors on nutrients. This study contributes to a better understanding of the complex interactions between multiple factors in watersheds and provides guidance for sustainable watershed management.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods.

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of "Urban Development - Ecological Conservation" and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications.

The Pearl River (PR) is China's second-largest river, playing a crucial role in regulating and supplying water in the southeast. However, for the last decade, the PR has been experiencing water quality deterioration due to population growth, rapid economic development, and diverse human activities, particularly in its delta areas. This study analyzed the characteristics and evolution of eight water quality variables, including pH values (pH), water temperature (WT), dissolved oxygen (DO), five-day biochemical oxygen demand (BOD5), permanganate index (PI), total phosphorus (TP), ammonia nitrogen (NH3N), and fluoride (F-), which were monitored monthly at 16 water quality monitoring stations from January 2009 to August 2019. Overall, annual average BOD5 and F- concentrations met Class I water quality standards, while TP and NH3N conformed to lower standards. The cluster results showed noticeable differences for parameter grouping (DO-organic parameters-nutrient and solutes), seasonal variation (wet and dry), and water quality status (contaminated-remediating-fine). The Water Quality Index (WQI) ranged from 8.3 ("very poor") to 91.7 ("excellent") in the entire basin from 2009 to 2019, and NH3N-DO based WQImins were identified using the All-Subsets Linear Regression method. The fitting results of the Generalized Additive Models displayed that the deviance explained by natural factors ranged from 37.2% to 61.3%, while the socioeconomic explanation exceeded 70%. The WQImin component evolution (2003-2019) of Shenzhen River estuary, the most important part of the PR estuary, agreed with key parameter variations in heterogeneous clusters in the entire basin. Moreover, Shenzhen's water quality remediation applications indicated that reasonable-efficient-powerful efforts and support from governments could accelerate recovery. For the management departments, consistent measures should be strictly enforced, and elaborate regionalized management based on clusters could be attempted to maintain excellent water quality.

Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized.

Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.

Evolution characteristic of atmospheric black carbon particles at a coastal site in the Pearl River Delta, China.

The mixing of black carbon (BC) with secondary materials is a major uncertainty source in assessing its radiative forcing. However, current understanding of the formation and evolution of various BC components is limited, particularly in the Pearl River Delta, China. This study measured submicron BC-associated nonrefractory materials and the total submicron nonrefractory materials using a soot particle aerosol mass spectrometer and a high-resolution time-of-flight aerosol mass spectrometer, respectively, at a coastal site in Shenzhen, China. Two distinct atmospheric conditions were also identified to further explore the distinctive evolution of BC-associated components: polluted period (PP) and clean period (CP). Comparing the components of two particles, we found that more-oxidized organic factor (MO-OOA) prefers to form on BC during PP rather CP. The formation of MO-OOA on BC (MO-OOABC) was affected by both enhanced photochemical processes and nocturnal heterogeneous processes. Enhanced photo-reactivity of BC, photochemistry during the daytime, and heterogeneous reaction at nighttime were potential pathways for MO-OOABC formation during PP. The fresh BC surface was favorable for the formation of MO-OOABC. Our study shows the evolution of BC-associated components under different atmospheric conditions, which should be considered in regional climate models to improve the assessment of the climate effects of BC.

Characterizing formation mechanisms of secondary aerosols on black carbon in a megacity in South China.

Refractory black carbon (rBC) aerosols emitted from incomplete combustion are important climate forcers. Understanding the chemical characteristics and evolution of rBC-related components is particularly crucial to assess rBC environmental impacts. Here, we explored the chemical components of rBC in Shenzhen, China, using a soot-particle aerosol mass spectrometer (SP-AMS). The observations showed that the rBC coating was mainly composed of secondary aerosols with an average mass contribution of 84.7 %. Among them, secondary organic coating occupied ∼57.7 % of the total coating mass. Exploration of the relationship between secondary organic aerosol (SOA) coating and Ox (=NO2 + O3, an indicator of the extent of photochemical processing) showed that SOA coating was generated mainly through photochemical oxidation during the day. Similarly, sulfate coating, with a small mass fraction of 0.9 %, was also dominated by photochemical oxidation. In contrast, nitrate coating responded positively to ambient relative humidity, especially at night, indicating that it was driven by heterogeneous reactions. In addition, the increased ratio of nitrate on rBC to bulk nitrate at night suggested that black carbon surface could facilitate nocturnal nitrate formation.

A novel machine learning method for evaluating the impact of emission sources on ozone formation.

Ambient ozone air pollution is one of the most important environmental challenges in China today, and it is particularly significant to identify pollution sources and formulate control strategies. In present study, we proposed a novel method of positive matrix factorization-SHapley Additive explanation (PMF-SHAP) for evaluating the impact of emission sources on ozone formation, which can quantify the main emission sources of ozone pollution. In this method, we first used the PMF model to identify the source of volatile organic compounds (VOCs), and then quantified various emission sources using a combination of machine learning (ML) models and the SHAP algorithm. The R2 of the optimal ML model in this method was as high as 0.96, indicating that the prediction performance was excellent. Furthermore, we explored the impact of different emission sources on ozone formation, and found that ozone formation in Shenzhen was more affected by VOCs, of which vehicle emission sources may have the greatest impact. Our results suggest that the appropriate combination of traditional models with ML models can well address environmental pollution problems. Moreover, the conclusions obtained based on the PMF-SHAP method were different from the traditional ozone formation potential (OFP) results, providing valuable clues for related mechanism studies.

Ambra1 regulates apoptosis and chemosensitivity in breast cancer cells through the Akt-FoxO1-Bim pathway.

The sensitivity of cells to chemotherapeutic agents has a major effect on disease outcome in breast cancer patients. Unfortunately, there are numerous factors involved in the regulation of chemosensitivity, and the mechanisms need to be further investigated. Autophagy/Beclin 1 regulator 1 (Ambra1) is a key protein in the crosstalk between autophagy and apoptosis. It controls the switch between these two processes, which determines whether cells survive or die. Induction of apoptosis is the primary mechanism by which most chemotherapeutic drugs eliminate cancer cells. Recently, Ambra1 has been shown to modulate paclitaxel-induced apoptosis in breast cancer cells via the Bim/mitochondrial pathway, thereby modifying the sensitivity of cells to paclitaxel. However, how Ambra1 regulates Bim expression remains unclear. Here, we further confirmed that Bim plays an indispensable role in Ambra1's regulation of apoptosis and chemosensitivity in breast cancer cells. Furthermore, Ambra1 was found to regulate Bim expression at the transcriptional level through the Akt-FoxO1 pathway. Therefore, we propose a novel pathway, Ambra1-Akt-FoxO1-Bim, which regulates apoptosis and chemosensitivity in breast cancer cells. Thus, Ambra1 may represent a potential target for breast cancer treatment.

Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China.

During the COVID-19 lockdown, atmospheric PM2.5 in the Pearl River Delta (PRD) showed the highest reduction in China, but the reasons, being a critical question for future air quality policy design, are not yet clear. In this study, we analyzed the relationships among gaseous precursors, secondary aerosols and atmospheric oxidation capacity in Shenzhen, a megacity in the PRD, during the lockdown period in 2020 and the same period in 2021. The comprehensive observational datasets showed large lockdown declines in all primary and secondary pollutants (including O3). We found that, however, the daytime concentrations of secondary aerosols during the lockdown period and normal period were rather similar when the corresponding odd oxygen (Ox≡O3+NO2, an indicator of photochemical processing avoiding the titration effect of O3 by freshly emitted NO) were at similar levels. Therefore, reduced Ox, rather than the large reduction in precursors, was a direct driver to achieve the decline in secondary aerosols. Moreover, Ox was also found to determine the spatial distribution of intercity PM2.5 levels in winter PRD. Thus, an effective strategy for winter PM2.5 mitigation should emphasize on control of winter O3 formation in the PRD and other regions with similar conditions.

Soil dust as a potential bridge from biogenic volatile organic compounds to secondary organic aerosol in a rural environment.

The role of coarse particles has recently been proven to be underestimated in the atmosphere and can strongly influence clouds, ecosystems and climate. However, previous studies on atmospheric chemistry of volatile organic compounds (VOCs) have mostly focused on the products in fine particles, it remains less understood how coarse particles promote secondary organic aerosol (SOA) formation. In this study, we investigated water-soluble compounds of size-segregated aerosol samples (0.056 to >18 µm) collected at a coastal rural site in southern China during late summer and found that oxygenated organic matter was abundant in the coarse mode. Comprehensive source apportionment based on mass spectrum and 14C analysis indicated that different from fossil fuel SOA, biogenic SOA existed more in the coarse mode than in the fine mode. The SOA in the coarse mode showed a unique correlation with biogenic VOCs. 13C and elemental composition strongly suggested a pathway of heterogeneous reactions on coarse particles, which had an abundant low-acidic aqueous environment with soil dust to possibly initiate iron-catalytic oxidation reactions to form SOA. This potential pathway might complement understanding of both formation of biogenic SOA and sink of biogenic VOCs in global biogeochemical cycles, warrantying future relevant studies.

Trends and Challenges Regarding the Source-Specific Health Risk of PM2.5-Bound Metals in a Chinese Megacity from 2014 to 2020.

Identifying the health risk of PM2.5 is essential for urban air pollution control. In 2013, China announced the ever-strict national Air Pollution Prevention and Control Action Plan, and its health benefit should be evaluated to provide reference for future policymaking. In this study, we conducted a seven-year (2014-2020) continuous observation of PM2.5 in Shenzhen, the third largest city in China, which has relatively good air quality. The results showed that the annual mean PM2.5 and total concentration of 21 associated metals dropped from 37.7 to 18.5 µg/m3 and from 2.4 to 1.1 µg/m3, respectively. Combining methods for source apportionment and health risk assessment, we found that the total carcinogenic risk (CR) of five hazardous metals (Cd, Cr, Ni, Co, and Pb) showed a clear decreasing trend. However, the total CR (1.8 × 10-6) in 2020 still exceeded the widely acceptable risk level (i.e., 1 × 10-6), with the primary contributor changing from industrial emissions (61%) to vehicle emissions (63%). Further analysis indicated that the CR of vehicles mainly came from Cr and Ni released by braking and tire wearing and has fluctuated in recent years, highlighting a great challenge of controlling nonexhaust emissions of vehicles (including electric cars) in the future.

Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China.

High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R2 (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.

Biomass-burning emissions could significantly enhance the atmospheric oxidizing capacity in continental air pollution.

Volatile organic compounds (VOCs) are important precursors of photochemical pollution. However, a substantial fraction of VOCs, namely, oxygenated VOCs (OVOCs), have not been sufficiently characterized to evaluate their sources in air pollution in China. In this study, a total of 119 VOCs, including 60 OVOCs in particular, were monitored to provide a more comprehensive picture based on different online measurement techniques, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and online gas chromatography/mass spectrometry (GC/MS), at a receptor site in southeastern China during a photochemically active period. Positive matrix factorization (PMF) and photochemical age-based parameterization were combined to identify and quantify different sources of major VOCs during daytime hours, with the advantage of including VOC decay processes. The results revealed the unexpected role of biomass burning (21%) in terms of ozone (O3) formation potential (OFP) when including the contributions of OVOCs and large contributions (30-32%) of biomass burning to aldehydes, as more OVOCs were measured in this study. We argue that biomass burning could significantly enhance the continental atmospheric oxidizing capacity, in addition to the well-recognized contributions of primary pollutants, which should be seriously considered in photochemical models and air pollution control strategies.

Critical Role of Simultaneous Reduction of Atmospheric Odd Oxygen for Winter Haze Mitigation.

The lockdown due to COVID-19 created a rare opportunity to examine the nonlinear responses of secondary aerosols, which are formed through atmospheric oxidation of gaseous precursors, to intensive precursor emission reductions. Based on unique observational data sets from six supersites in eastern China during 2019-2021, we found that the lockdown caused considerable decreases (32-61%) in different secondary aerosol components in the study region because of similar-degree precursor reductions. However, due to insufficient combustion-related volatile organic compound (VOC) reduction, odd oxygen (Ox = O3 + NO2) concentration, an indicator of the extent of photochemical processing, showed little change and did not promote more decreases in secondary aerosols. We also found that the Chinese provinces and international cities that experienced reduced Ox during the lockdown usually gained a greater simultaneous PM2.5 decrease than other provinces and cities with an increased Ox. Therefore, we argue that strict VOC control in winter, which has been largely ignored so far, is critical in future policies to mitigate winter haze more efficiently by reducing Ox simultaneously.

Cross-regional transport of PM2.5 nitrate in the Pearl River Delta, China: Contributions and mechanisms.

Cross-regional transport potentially contributes to PM2.5 nitrate (pNO3), and this can occur as indirect transport, through which pNO3 precursors are transported to targeted regions, wherein they subsequently react with locally emitted ones to produce pNO3. However, the process has been rarely studied, which limits its comprehensive understanding. We applied the CMAQ model to study the contributions and mechanisms of pNO3 transport during autumn in the Pearl River Delta (PRD), a metropolitan region under the growing influence of cross-regional transport on PM2.5 pollution. Results showed that cross-regional transport contributed to 58% pNO3 monthly in the PRD, and this mostly occurred as indirect transport contributions (accounting for 43% among all contributions). For the first time, we identified the mechanism of indirect pNO3 transport in the PRD, which mainly involves transported O3 and locally emitted NOx reacting to produce pNO3 through N2O5 heterogeneous hydrolysis. pNO3 contributions in different periods and regions indicated differences in the indirect transport contributions to N2O5 heterogeneous hydrolysis under varying O3 availability conditions, which are determined by wind fields and the intensity of NOx emissions. On the regional scale, the pNO3 level is controlled by both transported O3 and local NOx emissions, but pNO3 sensitivity to these two precursors varies among cities. This study demonstrates the notable effect and complex process of cross-regional pNO3 transport in the PRD. Considering the important role of transported O3 for pNO3, O3 reduction within a larger scale is required to achieve PM2.5 pollution control target.

Characterization of submicron aerosol volatility in the regional atmosphere in Southern China.

The volatility of atmospheric aerosols greatly influences the gas-particle partitioning, chemical mechanisms and lifetime of aerosols. Due to the complex composition, the volatility of organic aerosol is one of the major sources of uncertainty in measuring and modeling ambient aerosols. Despite high aerosol loading in the atmosphere in China, especially in winter, few field measurements were conducted targeting the volatility of ambient organic aerosol (OA). With the deployment of a thermodenuder-aerosol mass spectrometer (TD-AMS) system, the volatility of non-refractory submicron aerosols (NR-PM1) were measured on an island near the coastal line for the regional air in wintertime in southern China. NO3- and Cl- showed the highest volatility in the NR-PM1 chemical species, while SO42- showed the least volatility. Organic aerosol showed a moderate volatility, evaporating at a stable rate (0.57% °C-1) at temperatures lower than 150 °C and keeping a stable volatility when its loading increases, which could be an advantage for parameterization of OA in air quality models. Based on both positive matrix factorization and chemical mass balance modeling of OA composition, biomass burning OA was found to be the most volatile factor, followed by hydrocarbon-like OA and more-oxidized oxygenated OA. By summarizing the OA volatility measured in this study and in the literature, we found that the volatilities of different OA factors at different locations do not have a clear relationship with the OA oxidation state, possibly due to the vague understanding of local OA aging mechanisms and mixing states.

Exploration of sources of OVOCs in various atmospheres in southern China.

Oxygenated volatile organic compounds (OVOCs) are critical atmospheric ozone and secondary organic aerosol (SOA) precursors and radical sources, while understanding of OVOC sources in the atmosphere, especially with large anthropogenic emissions, still has large uncertainties. A high-sensitivity proton transfer reaction mass spectrometer (PTR-MS) was deployed in vastly different atmospheres in southern China, including an urban site (SZ-U), a regional site (NA-R), and a background site (NL-B). Four critical OVOCs, i.e., methanol, acetone, methyl ethyl ketone (MEK) and acetaldehyde, five groups of aromatic hydrocarbons, isoprene and acetonitrile were measured with a high time resolution. The featured relative abundance and diurnal variations of the OVOCs indicated that methanol, acetone and MEK had prominent contributions from urban industrial activities, while acetaldehyde was closely related to the photochemical formation at all three sites. The photochemical age-based parameterization method was improved locally and then applied to quantify different sources of daytime OVOCs: anthropogenic secondary and biogenic sources (together 60-73%) were always the dominant source for acetaldehyde in various atmospheres; in addition to a significant background for methanol, acetone and MEK, anthropogenic primary emissions (mostly industrial) were their dominant source at SZ-U (38-73%), while biogenic sources played the key role for them at NL-B (30-43%); biomass burning contributed a small fraction of 5-17% for the four OVOCs at the three sites.